2023-01-11T21:14:25.0839617Z Requested labels: linux.2xlarge
2023-01-11T21:14:25.0839718Z Job defined at: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/tags/ciflow/trunk/91627
2023-01-11T21:14:25.0839878Z Reusable workflow chain:
2023-01-11T21:14:25.0839917Z pytorch/pytorch/.github/workflows/trunk.yml@refs/tags/ciflow/trunk/91627 (8419ddda87c8a47eacc63b54bc7ec98c1f27c26e)
2023-01-11T21:14:25.0839960Z -> pytorch/pytorch/.github/workflows/_linux-test.yml@refs/tags/ciflow/trunk/91627 (8419ddda87c8a47eacc63b54bc7ec98c1f27c26e)
2023-01-11T21:14:25.0839985Z Waiting for a runner to pick up this job...
2023-01-11T21:14:25.7232456Z Job is about to start running on the runner: i-06cf3037fd0558aaa (organization)
2023-01-11T21:14:29.9740659Z Current runner version: '2.300.2'
2023-01-11T21:14:29.9746339Z Runner name: 'i-06cf3037fd0558aaa'
2023-01-11T21:14:29.9746845Z Runner group name: 'Default'
2023-01-11T21:14:29.9747402Z Machine name: 'ip-10-0-1-101'
2023-01-11T21:14:29.9749439Z ##[group]GITHUB_TOKEN Permissions
2023-01-11T21:14:29.9750073Z Actions: write
2023-01-11T21:14:29.9750398Z Checks: write
2023-01-11T21:14:29.9750655Z Contents: write
2023-01-11T21:14:29.9750999Z Deployments: write
2023-01-11T21:14:29.9751310Z Discussions: write
2023-01-11T21:14:29.9751574Z Issues: write
2023-01-11T21:14:29.9751892Z Metadata: read
2023-01-11T21:14:29.9752220Z Packages: write
2023-01-11T21:14:29.9752475Z Pages: write
2023-01-11T21:14:29.9752790Z PullRequests: write
2023-01-11T21:14:29.9753138Z RepositoryProjects: write
2023-01-11T21:14:29.9753435Z SecurityEvents: write
2023-01-11T21:14:29.9753767Z Statuses: write
2023-01-11T21:14:29.9754071Z ##[endgroup]
2023-01-11T21:14:29.9757088Z Secret source: Actions
2023-01-11T21:14:29.9757879Z Prepare workflow directory
2023-01-11T21:14:30.6234895Z Prepare all required actions
2023-01-11T21:14:30.6390045Z Getting action download info
2023-01-11T21:14:30.9235374Z Download action repository 'pytorch/test-infra@main' (SHA:2c225610d00fb13c04fcd60389d3e4d8326167c3)
2023-01-11T21:14:31.2003414Z Download action repository 'pytorch/pytorch@master' (SHA:c5836153f5332ca83d5cacde38f2829a4d54793e)
2023-01-11T21:14:33.9077603Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2023-01-11T21:14:34.1571349Z Getting action download info
2023-01-11T21:14:34.4625699Z Download action repository 'malfet/checkout@silent-checkout' (SHA:c7b8fef48edfe1bca0044a44b1f7f7c4318a3076)
2023-01-11T21:14:34.6589171Z Getting action download info
2023-01-11T21:14:34.8244636Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2023-01-11T21:14:34.9565709Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml
2023-01-11T21:14:34.9567314Z ##[group] Inputs
2023-01-11T21:14:34.9567610Z   build-environment: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:14:34.9568716Z   test-matrix: { include: [
  { config: "default", shard: 1, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "default", shard: 2, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "default", shard: 3, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "default", shard: 4, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "functorch", shard: 1, num_shards: 1, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "nogpu_AVX512", shard: 1, num_shards: 1, runner: "linux.2xlarge" },
  { config: "nogpu_NO_AVX2", shard: 1, num_shards: 1, runner: "linux.2xlarge" },
  { config: "jit_legacy", shard: 1, num_shards: 1, runner: "linux.4xlarge.nvidia.gpu" },
  { config: "distributed", shard: 1, num_shards: 3, runner: "linux.8xlarge.nvidia.gpu" },
  { config: "distributed", shard: 2, num_shards: 3, runner: "linux.8xlarge.nvidia.gpu" },
  { config: "distributed", shard: 3, num_shards: 3, runner: "linux.8xlarge.nvidia.gpu" },
]}

2023-01-11T21:14:34.9570177Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:14:34.9570545Z   sync-tag: 
2023-01-11T21:14:34.9571275Z   timeout-minutes: 240
2023-01-11T21:14:34.9571451Z   use-gha: 
2023-01-11T21:14:34.9571636Z ##[endgroup]
2023-01-11T21:14:34.9572117Z Complete job name: linux-bionic-cuda11.7-py3.10-gcc7 / test (nogpu_AVX512, 1, 1, linux.2xlarge)
2023-01-11T21:14:35.0235628Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2023-01-11T21:14:35.0235909Z with:
2023-01-11T21:14:35.0236372Z   github-secret: ***
2023-01-11T21:14:35.0236702Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2023-01-11T21:14:35.0237042Z   activate-with-label: false
2023-01-11T21:14:35.0237332Z   label: with-ssh
2023-01-11T21:14:35.0237523Z   remove-existing-keys: true
2023-01-11T21:14:35.0237695Z env:
2023-01-11T21:14:35.0237871Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:14:35.0238058Z ##[endgroup]
2023-01-11T21:14:35.0982178Z ciflow reference detected, attempting to extract PR number
2023-01-11T21:14:35.5039575Z Grabbing public ssh keys from https://github.com/pytorch-bot[bot].keys
2023-01-11T21:14:35.5818835Z No SSH keys found for user pytorch-bot[bot]
2023-01-11T21:14:35.5819177Z Grabbing public ssh keys from https://github.com/LucaLumetti.keys
2023-01-11T21:14:35.6971231Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh
2023-01-11T21:14:35.6983859Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys
2023-01-11T21:14:35.7006641Z Login using: ssh ec2-user@ec2-3-239-182-156.compute-1.amazonaws.com
2023-01-11T21:14:35.7007313Z All testing is done inside the container, to start an interactive session run:
2023-01-11T21:14:35.7007753Z   docker exec -it $(docker container ps --format '{{.ID}}') bash
2023-01-11T21:14:35.7212707Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@master
2023-01-11T21:14:35.7212986Z with:
2023-01-11T21:14:35.7213176Z   submodules: recursive
2023-01-11T21:14:35.7213389Z   fetch-depth: 0
2023-01-11T21:14:35.7213581Z env:
2023-01-11T21:14:35.7213772Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:14:35.7213985Z ##[endgroup]
2023-01-11T21:14:35.7414198Z ##[group]Run retry () {
2023-01-11T21:14:35.7414463Z retry () {
2023-01-11T21:14:35.7414721Z   $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
2023-01-11T21:14:35.7414951Z }
2023-01-11T21:14:35.7415168Z echo "${GITHUB_WORKSPACE}"
2023-01-11T21:14:35.7415419Z if [ -z "${NO_SUDO}" ]; then
2023-01-11T21:14:35.7415664Z   retry sudo rm -rf "${GITHUB_WORKSPACE}"
2023-01-11T21:14:35.7415899Z else
2023-01-11T21:14:35.7416129Z   retry rm -rf "${GITHUB_WORKSPACE}"
2023-01-11T21:14:35.7416353Z fi
2023-01-11T21:14:35.7416600Z mkdir "${GITHUB_WORKSPACE}"
2023-01-11T21:14:35.7431833Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:14:35.7432083Z env:
2023-01-11T21:14:35.7432297Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:14:35.7432512Z   NO_SUDO: 
2023-01-11T21:14:35.7432699Z ##[endgroup]
2023-01-11T21:14:35.7523233Z /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:14:38.1135558Z ##[group]Run malfet/checkout@silent-checkout
2023-01-11T21:14:38.1135767Z with:
2023-01-11T21:14:38.1135969Z   ref: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:14:38.1136168Z   fetch-depth: 0
2023-01-11T21:14:38.1136345Z   submodules: recursive
2023-01-11T21:14:38.1136529Z   quiet-checkout: true
2023-01-11T21:14:38.1136713Z   repository: pytorch/pytorch
2023-01-11T21:14:38.1137035Z   token: ***
2023-01-11T21:14:38.1137206Z   ssh-strict: true
2023-01-11T21:14:38.1137397Z   persist-credentials: true
2023-01-11T21:14:38.1137579Z   clean: true
2023-01-11T21:14:38.1137749Z   lfs: false
2023-01-11T21:14:38.1137928Z   set-safe-directory: true
2023-01-11T21:14:38.1138093Z env:
2023-01-11T21:14:38.1138264Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:14:38.1138445Z ##[endgroup]
2023-01-11T21:14:38.2199742Z Syncing repository: pytorch/pytorch
2023-01-11T21:14:38.2201068Z ##[group]Getting Git version info
2023-01-11T21:14:38.2201479Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2023-01-11T21:14:38.2201949Z [command]/usr/bin/git version
2023-01-11T21:14:38.2202221Z git version 2.38.1
2023-01-11T21:14:38.2203413Z ##[endgroup]
2023-01-11T21:14:38.2217338Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/16626393-b543-4033-8ad5-53b59506932d' before making global git config changes
2023-01-11T21:14:38.2218006Z Adding repository directory to the temporary git global config as a safe directory
2023-01-11T21:14:38.2222014Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:14:38.2258507Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2023-01-11T21:14:38.2263012Z ##[group]Initializing the repository
2023-01-11T21:14:38.2265863Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:14:38.2397396Z hint: Using 'master' as the name for the initial branch. This default branch name
2023-01-11T21:14:38.2398017Z hint: is subject to change. To configure the initial branch name to use in all
2023-01-11T21:14:38.2398578Z hint: of your new repositories, which will suppress this warning, call:
2023-01-11T21:14:38.2398936Z hint: 
2023-01-11T21:14:38.2399387Z hint: 	git config --global init.defaultBranch <name>
2023-01-11T21:14:38.2399717Z hint: 
2023-01-11T21:14:38.2400215Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2023-01-11T21:14:38.2400736Z hint: 'development'. The just-created branch can be renamed via this command:
2023-01-11T21:14:38.2400981Z hint: 
2023-01-11T21:14:38.2401279Z hint: 	git branch -m <name>
2023-01-11T21:14:38.2401657Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/
2023-01-11T21:14:38.2406817Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch
2023-01-11T21:14:38.2434294Z ##[endgroup]
2023-01-11T21:14:38.2434678Z ##[group]Disabling automatic garbage collection
2023-01-11T21:14:38.2437599Z [command]/usr/bin/git config --local gc.auto 0
2023-01-11T21:14:38.2462271Z ##[endgroup]
2023-01-11T21:14:38.2462619Z ##[group]Setting up auth
2023-01-11T21:14:38.2468932Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2023-01-11T21:14:38.2496239Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :
2023-01-11T21:14:38.2726261Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2023-01-11T21:14:38.2750414Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :
2023-01-11T21:14:38.2976188Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2023-01-11T21:14:38.3012919Z ##[endgroup]
2023-01-11T21:14:38.3013294Z ##[group]Fetching the repository
2023-01-11T21:14:38.3019373Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --quiet --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2023-01-11T21:15:32.8994586Z [command]/usr/bin/git rev-parse --verify --quiet 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e^{object}
2023-01-11T21:15:32.9019767Z 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:15:32.9023750Z ##[endgroup]
2023-01-11T21:15:32.9024203Z ##[group]Determining the checkout info
2023-01-11T21:15:32.9024958Z ##[endgroup]
2023-01-11T21:15:32.9025529Z ##[group]Checking out the ref
2023-01-11T21:15:32.9029729Z [command]/usr/bin/git checkout --quiet --force 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:15:34.1924225Z ##[endgroup]
2023-01-11T21:15:34.1924632Z ##[group]Setting up auth for fetching submodules
2023-01-11T21:15:34.1930073Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2023-01-11T21:15:34.1974073Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2023-01-11T21:15:34.2001564Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2023-01-11T21:15:34.2027499Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2023-01-11T21:15:34.2052593Z ##[endgroup]
2023-01-11T21:15:34.2053028Z ##[group]Fetching submodules
2023-01-11T21:15:34.2056625Z [command]/usr/bin/git submodule sync --recursive
2023-01-11T21:15:34.2302530Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2023-01-11T21:15:34.2539415Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni'
2023-01-11T21:15:34.2540414Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16'
2023-01-11T21:15:34.2542650Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv'
2023-01-11T21:15:34.2544977Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK'
2023-01-11T21:15:34.2547316Z Submodule 'third_party/QNNPACK' (https://github.com/pytorch/QNNPACK) registered for path 'third_party/QNNPACK'
2023-01-11T21:15:34.2549888Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator'
2023-01-11T21:15:34.2552411Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK'
2023-01-11T21:15:34.2554989Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark'
2023-01-11T21:15:34.2557696Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo'
2023-01-11T21:15:34.2560700Z Submodule 'third_party/cub' (https://github.com/NVlabs/cub.git) registered for path 'third_party/cub'
2023-01-11T21:15:34.2563645Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend'
2023-01-11T21:15:34.2566650Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass'
2023-01-11T21:15:34.2570217Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen'
2023-01-11T21:15:34.2573229Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm'
2023-01-11T21:15:34.2576597Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers'
2023-01-11T21:15:34.2580000Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'
2023-01-11T21:15:34.2583499Z Submodule 'third_party/foxi' (https://github.com/houseroad/foxi.git) registered for path 'third_party/foxi'
2023-01-11T21:15:34.2587325Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp'
2023-01-11T21:15:34.2590966Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo'
2023-01-11T21:15:34.2594946Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest'
2023-01-11T21:15:34.2598862Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep'
2023-01-11T21:15:34.2602977Z Submodule 'third_party/ios-cmake' (https://github.com/Yangqing/ios-cmake.git) registered for path 'third_party/ios-cmake'
2023-01-11T21:15:34.2607091Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi'
2023-01-11T21:15:34.2611569Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto'
2023-01-11T21:15:34.2615889Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl'
2023-01-11T21:15:34.2620357Z Submodule 'third_party/neon2sse' (https://github.com/intel/ARM_NEON_2_x86_SSE.git) registered for path 'third_party/neon2sse'
2023-01-11T21:15:34.2624996Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann'
2023-01-11T21:15:34.2629623Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx'
2023-01-11T21:15:34.2635242Z Submodule 'third_party/onnx-tensorrt' (https://github.com/onnx/onnx-tensorrt) registered for path 'third_party/onnx-tensorrt'
2023-01-11T21:15:34.2639354Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft'
2023-01-11T21:15:34.2644435Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf'
2023-01-11T21:15:34.2650186Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd'
2023-01-11T21:15:34.2654986Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool'
2023-01-11T21:15:34.2660453Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11'
2023-01-11T21:15:34.2666071Z Submodule 'third_party/python-enum' (https://github.com/PeachPy/enum34.git) registered for path 'third_party/python-enum'
2023-01-11T21:15:34.2671432Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy'
2023-01-11T21:15:34.2676951Z Submodule 'third_party/python-six' (https://github.com/benjaminp/six.git) registered for path 'third_party/python-six'
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2023-01-11T21:16:57.4096373Z Submodule path 'third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark': checked out 'e776aa0275e293707b6a0901e0e8d8a8a3679508'
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2023-01-11T21:17:05.5023649Z ##[endgroup]
2023-01-11T21:17:05.5025023Z ##[group]Persisting credentials for submodules
2023-01-11T21:17:05.5030146Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :
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2023-01-11T21:17:05.7482438Z [command]/usr/bin/git submodule foreach --recursive git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url
2023-01-11T21:17:05.7735267Z Entering 'android/libs/fbjni'
2023-01-11T21:17:05.7768432Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
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2023-01-11T21:17:05.7817850Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
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2023-01-11T21:17:05.7869873Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
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2023-01-11T21:17:05.7921471Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
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2023-01-11T21:17:05.7971415Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/QNNPACK/config	remote.origin.url
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2023-01-11T21:17:05.8020769Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
2023-01-11T21:17:05.8034609Z Entering 'third_party/XNNPACK'
2023-01-11T21:17:05.8065438Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
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2023-01-11T21:17:05.8120519Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
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2023-01-11T21:17:05.8167761Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
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2023-01-11T21:17:05.8213536Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cub/config	remote.origin.url
2023-01-11T21:17:05.8228126Z Entering 'third_party/cudnn_frontend'
2023-01-11T21:17:05.8258695Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
2023-01-11T21:17:05.8277483Z Entering 'third_party/cutlass'
2023-01-11T21:17:05.8309485Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
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2023-01-11T21:17:05.8359369Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
2023-01-11T21:17:05.8376772Z Entering 'third_party/fbgemm'
2023-01-11T21:17:05.8411208Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2023-01-11T21:17:05.8425212Z Entering 'third_party/fbgemm/third_party/asmjit'
2023-01-11T21:17:05.8455944Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
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2023-01-11T21:17:05.8503239Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
2023-01-11T21:17:05.8517577Z Entering 'third_party/fbgemm/third_party/googletest'
2023-01-11T21:17:05.8548404Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
2023-01-11T21:17:05.8561923Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2023-01-11T21:17:05.8593183Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
2023-01-11T21:17:05.8607722Z Entering 'third_party/flatbuffers'
2023-01-11T21:17:05.8638168Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2023-01-11T21:17:05.8653667Z Entering 'third_party/fmt'
2023-01-11T21:17:05.8687157Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
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2023-01-11T21:17:05.8732790Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/foxi/config	remote.origin.url
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2023-01-11T21:17:05.8777874Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
2023-01-11T21:17:05.8792139Z Entering 'third_party/gloo'
2023-01-11T21:17:05.8826124Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
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2023-01-11T21:17:05.8873770Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
2023-01-11T21:17:05.8890470Z Entering 'third_party/ideep'
2023-01-11T21:17:05.8925002Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
2023-01-11T21:17:05.8938767Z Entering 'third_party/ideep/mkl-dnn'
2023-01-11T21:17:05.8972631Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
2023-01-11T21:17:05.8988583Z Entering 'third_party/ideep/mkl-dnn/third_party/oneDNN'
2023-01-11T21:17:05.9023463Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/modules/third_party/oneDNN/config	remote.origin.url
2023-01-11T21:17:05.9047898Z Entering 'third_party/ios-cmake'
2023-01-11T21:17:05.9083111Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ios-cmake/config	remote.origin.url
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2023-01-11T21:17:05.9132975Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
2023-01-11T21:17:05.9150708Z Entering 'third_party/kineto'
2023-01-11T21:17:05.9185352Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config	remote.origin.url
2023-01-11T21:17:05.9199782Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2023-01-11T21:17:05.9232909Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config	remote.origin.url
2023-01-11T21:17:05.9247204Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2023-01-11T21:17:05.9282275Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config	remote.origin.url
2023-01-11T21:17:05.9297224Z Entering 'third_party/nccl/nccl'
2023-01-11T21:17:05.9328108Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config	remote.origin.url
2023-01-11T21:17:05.9342377Z Entering 'third_party/neon2sse'
2023-01-11T21:17:05.9372942Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/neon2sse/config	remote.origin.url
2023-01-11T21:17:05.9387086Z Entering 'third_party/nlohmann'
2023-01-11T21:17:05.9419600Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config	remote.origin.url
2023-01-11T21:17:05.9434234Z Entering 'third_party/onnx'
2023-01-11T21:17:05.9465013Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config	remote.origin.url
2023-01-11T21:17:05.9489662Z Entering 'third_party/onnx/third_party/benchmark'
2023-01-11T21:17:05.9519753Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/benchmark/config	remote.origin.url
2023-01-11T21:17:05.9534423Z Entering 'third_party/onnx/third_party/pybind11'
2023-01-11T21:17:05.9564264Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T21:17:05.9580285Z Entering 'third_party/onnx-tensorrt'
2023-01-11T21:17:05.9610308Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/config	remote.origin.url
2023-01-11T21:17:05.9623387Z Entering 'third_party/onnx-tensorrt/third_party/onnx'
2023-01-11T21:17:05.9657631Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/config	remote.origin.url
2023-01-11T21:17:05.9676152Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark'
2023-01-11T21:17:05.9706688Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/benchmark/config	remote.origin.url
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2023-01-11T21:17:05.9755984Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T21:17:05.9768529Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang'
2023-01-11T21:17:05.9803616Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/pybind11/modules/tools/clang/config	remote.origin.url
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2023-01-11T21:17:05.9855780Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config	remote.origin.url
2023-01-11T21:17:05.9872919Z Entering 'third_party/protobuf'
2023-01-11T21:17:05.9908630Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config	remote.origin.url
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2023-01-11T21:17:05.9960189Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config	remote.origin.url
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2023-01-11T21:17:06.0012462Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config	remote.origin.url
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2023-01-11T21:17:06.0067398Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config	remote.origin.url
2023-01-11T21:17:06.0080722Z Entering 'third_party/pthreadpool'
2023-01-11T21:17:06.0114380Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config	remote.origin.url
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2023-01-11T21:17:06.0167438Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T21:17:06.0186107Z Entering 'third_party/python-enum'
2023-01-11T21:17:06.0221200Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-enum/config	remote.origin.url
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2023-01-11T21:17:06.0269591Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config	remote.origin.url
2023-01-11T21:17:06.0283645Z Entering 'third_party/python-six'
2023-01-11T21:17:06.0320461Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-six/config	remote.origin.url
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2023-01-11T21:17:06.0371751Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config	remote.origin.url
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2023-01-11T21:17:06.0424487Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tbb/config	remote.origin.url
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2023-01-11T21:17:06.0479188Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config	remote.origin.url
2023-01-11T21:17:06.0493881Z Entering 'third_party/tensorpipe/third_party/googletest'
2023-01-11T21:17:06.0527580Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config	remote.origin.url
2023-01-11T21:17:06.0545286Z Entering 'third_party/tensorpipe/third_party/libnop'
2023-01-11T21:17:06.0578999Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config	remote.origin.url
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2023-01-11T21:17:06.0630759Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config	remote.origin.url
2023-01-11T21:17:06.0680741Z Entering 'third_party/tensorpipe/third_party/pybind11'
2023-01-11T21:17:06.0711650Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T21:17:06.0724558Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2023-01-11T21:17:06.0760359Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config	remote.origin.url
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2023-01-11T21:17:06.0806528Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/zstd/config	remote.origin.url
2023-01-11T21:17:06.1773916Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:'
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2023-01-11T21:17:06.6747337Z ##[endgroup]
2023-01-11T21:17:06.6783325Z [command]/usr/bin/git log -1 --format='%H'
2023-01-11T21:17:06.6807739Z '8419ddda87c8a47eacc63b54bc7ec98c1f27c26e'
2023-01-11T21:17:06.6919505Z Prepare all required actions
2023-01-11T21:17:06.6943458Z ##[group]Run ./.github/actions/setup-linux
2023-01-11T21:17:06.6943661Z env:
2023-01-11T21:17:06.6943839Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:06.6944321Z ##[endgroup]
2023-01-11T21:17:06.6957309Z ##[group]Run set -euo pipefail
2023-01-11T21:17:06.6957539Z set -euo pipefail
2023-01-11T21:17:06.6957745Z function get_ec2_metadata() {
2023-01-11T21:17:06.6957975Z   # Pulled from instance metadata endpoint for EC2
2023-01-11T21:17:06.6958330Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2023-01-11T21:17:06.6958626Z   category=$1
2023-01-11T21:17:06.6958870Z   curl -fsSL "http://169.254.169.254/latest/meta-data/${category}"
2023-01-11T21:17:06.6959081Z }
2023-01-11T21:17:06.6959278Z echo "ami-id: $(get_ec2_metadata ami-id)"
2023-01-11T21:17:06.6959564Z echo "instance-id: $(get_ec2_metadata instance-id)"
2023-01-11T21:17:06.6959833Z echo "instance-type: $(get_ec2_metadata instance-type)"
2023-01-11T21:17:06.6960081Z echo "system info $(uname -a)"
2023-01-11T21:17:06.6971620Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:06.6971826Z env:
2023-01-11T21:17:06.6971998Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:06.6972185Z ##[endgroup]
2023-01-11T21:17:06.7046348Z ami-id: ami-096198a0bccc6bad4
2023-01-11T21:17:06.7094293Z instance-id: i-06cf3037fd0558aaa
2023-01-11T21:17:06.7140032Z instance-type: c5.2xlarge
2023-01-11T21:17:06.7145913Z system info Linux ip-10-0-1-101.ec2.internal 4.14.252-195.483.amzn2.x86_64 #1 SMP Mon Nov 1 20:58:46 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
2023-01-11T21:17:06.7160796Z ##[group]Run if systemctl is-active --quiet docker; then
2023-01-11T21:17:06.7161101Z if systemctl is-active --quiet docker; then
2023-01-11T21:17:06.7161380Z     echo "Docker daemon is running...";
2023-01-11T21:17:06.7161628Z else
2023-01-11T21:17:06.7161883Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2023-01-11T21:17:06.7162145Z fi
2023-01-11T21:17:06.7172917Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:06.7173156Z env:
2023-01-11T21:17:06.7173365Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:06.7173588Z ##[endgroup]
2023-01-11T21:17:06.7212317Z Docker daemon is running...
2023-01-11T21:17:06.7227177Z ##[group]Run AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
2023-01-11T21:17:06.7227556Z AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
2023-01-11T21:17:06.7227872Z retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T21:17:06.7228278Z retry aws ecr get-login*** "$AWS_DEFAULT_REGION" | docker login --username AWS \
2023-01-11T21:17:06.7228663Z     --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"
2023-01-11T21:17:06.7238859Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:06.7239112Z env:
2023-01-11T21:17:06.7239319Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:06.7239534Z   AWS_RETRY_MODE: standard
2023-01-11T21:17:06.7239752Z   AWS_MAX_ATTEMPTS: 5
2023-01-11T21:17:06.7239978Z   AWS_DEFAULT_REGION: us-east-1
2023-01-11T21:17:06.7240185Z ##[endgroup]
2023-01-11T21:17:07.9088740Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2023-01-11T21:17:07.9089486Z Configure a credential helper to remove this warning. See
2023-01-11T21:17:07.9090095Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2023-01-11T21:17:07.9090419Z 
2023-01-11T21:17:07.9090530Z Login Succeeded
2023-01-11T21:17:07.9117381Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T21:17:07.9117680Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T21:17:07.9118017Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T21:17:07.9128920Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:07.9129320Z env:
2023-01-11T21:17:07.9129497Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:07.9129682Z ##[endgroup]
2023-01-11T21:17:07.9203519Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2023-01-11T21:17:07.9203765Z with:
2023-01-11T21:17:07.9204130Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:17:07.9204472Z env:
2023-01-11T21:17:07.9204642Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:07.9204825Z ##[endgroup]
2023-01-11T21:17:07.9219727Z ##[group]Run retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T21:17:07.9219989Z retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T21:17:07.9220252Z # ignore output since only exit code is used for conditional
2023-01-11T21:17:07.9220537Z # only pull docker image if it's not available locally
2023-01-11T21:17:07.9220833Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2023-01-11T21:17:07.9221123Z   retry docker pull "${DOCKER_IMAGE}"
2023-01-11T21:17:07.9221317Z fi
2023-01-11T21:17:07.9231363Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:17:07.9231579Z env:
2023-01-11T21:17:07.9231750Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:17:07.9232129Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:17:07.9232492Z ##[endgroup]
2023-01-11T21:17:08.1384751Z fd224c2e6c79d7fdec6408da598bf52bc5b201dd: Pulling from pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7
2023-01-11T21:17:08.1393848Z fb668870d8a7: Pulling fs layer
2023-01-11T21:17:08.1394239Z 4542784317be: Pulling fs layer
2023-01-11T21:17:08.1394494Z e0bec5df5af5: Pulling fs layer
2023-01-11T21:17:08.1394680Z 4053f75740ab: Pulling fs layer
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2023-01-11T21:17:08.1398843Z 4cd507bccac2: Pulling fs layer
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2023-01-11T21:17:08.1403393Z f9f75aaba8d7: Pulling fs layer
2023-01-11T21:17:08.1403718Z 4053f75740ab: Waiting
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2023-01-11T21:17:08.1407623Z a08ab4e0594b: Waiting
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2023-01-11T21:17:08.1412041Z eedfbaa04e4f: Pulling fs layer
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2023-01-11T21:17:08.1412844Z c1a92fad2c2c: Pulling fs layer
2023-01-11T21:17:08.1413207Z bc41eab7f454: Waiting
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2023-01-11T21:17:08.1414119Z a11b4b5fd784: Waiting
2023-01-11T21:17:08.1414442Z b8f759fd0191: Waiting
2023-01-11T21:17:08.1414821Z 1a2fd7b216d7: Pulling fs layer
2023-01-11T21:17:08.1415218Z 765839304d2e: Pulling fs layer
2023-01-11T21:17:08.1415607Z f410dcc9d0be: Waiting
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2023-01-11T21:17:08.1421424Z 1a2fd7b216d7: Waiting
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2023-01-11T21:17:08.1425410Z 6d83ca3dedf3: Waiting
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2023-01-11T21:17:08.1428882Z bdf297d7f88c: Waiting
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2023-01-11T21:19:45.9000666Z Digest: sha256:0da23f4faf0ce20770149c4a783e08eaa91c07112511dc5511c77937c66edb24
2023-01-11T21:19:45.9137185Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:19:45.9270125Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:19:45.9567119Z ##[group]Run python3 -m pip install psutil==5.9.1
2023-01-11T21:19:45.9567400Z python3 -m pip install psutil==5.9.1
2023-01-11T21:19:45.9567628Z python3 -m pip install pynvml==11.4.1
2023-01-11T21:19:45.9567891Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2023-01-11T21:19:45.9568174Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2023-01-11T21:19:46.4979977Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:19:46.4980206Z env:
2023-01-11T21:19:46.4980393Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:19:46.4980569Z ##[endgroup]
2023-01-11T21:19:49.0347611Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T21:19:49.0610580Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.7/site-packages (5.9.1)
2023-01-11T21:19:49.4922677Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T21:19:49.5098847Z Requirement already satisfied: pynvml==11.4.1 in /home/ec2-user/.local/lib/python3.7/site-packages (11.4.1)
2023-01-11T21:19:49.7164573Z Prepare all required actions
2023-01-11T21:19:49.7164829Z Getting action download info
2023-01-11T21:19:49.9358896Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:4a8bfae15cc25cc0785c1603ee87a9da8fd442ea)
2023-01-11T21:19:50.1008251Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a)
2023-01-11T21:19:50.3353572Z ##[group]Run ./.github/actions/download-build-artifacts
2023-01-11T21:19:50.3353784Z with:
2023-01-11T21:19:50.3353990Z   name: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:19:50.3354196Z env:
2023-01-11T21:19:50.3354356Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:19:50.3354550Z ##[endgroup]
2023-01-11T21:19:50.3375859Z ##[group]Run seemethere/download-artifact-s3@v4
2023-01-11T21:19:50.3376075Z with:
2023-01-11T21:19:50.3376279Z   name: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:19:50.3376494Z   s3-bucket: gha-artifacts
2023-01-11T21:19:50.3376686Z   region: us-east-1
2023-01-11T21:19:50.3376887Z env:
2023-01-11T21:19:50.3377043Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:19:50.3377226Z ##[endgroup]
2023-01-11T21:19:50.7751620Z Found 1 objects with prefix pytorch/pytorch/3896346758/linux-bionic-cuda11.7-py3.10-gcc7/
2023-01-11T21:19:50.7752500Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2023-01-11T21:20:04.6391708Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2023-01-11T21:20:04.6392116Z 
2023-01-11T21:20:04.6415398Z ##[warning]The `set-output` command is deprecated and will be disabled soon. Please upgrade to using Environment Files. For more information see: https://github.blog/changelog/2022-10-11-github-actions-deprecating-save-state-and-set-output-commands/
2023-01-11T21:20:04.6427701Z Artifact download has finished successfully
2023-01-11T21:20:04.6599163Z ##[group]Run unzip -o artifacts.zip
2023-01-11T21:20:04.6599381Z unzip -o artifacts.zip
2023-01-11T21:20:04.6610949Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:20:04.6611170Z env:
2023-01-11T21:20:04.6611331Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:20:04.6611518Z ##[endgroup]
2023-01-11T21:20:04.6678797Z Archive:  artifacts.zip
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2023-01-11T21:20:13.3836986Z ##[group]Run df -H
2023-01-11T21:20:13.3837166Z df -H
2023-01-11T21:20:13.3848356Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:20:13.3848648Z env:
2023-01-11T21:20:13.3848828Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:20:13.3849017Z ##[endgroup]
2023-01-11T21:20:13.4208717Z Filesystem      Size  Used Avail Use% Mounted on
2023-01-11T21:20:13.4209286Z devtmpfs        8.2G     0  8.2G   0% /dev
2023-01-11T21:20:13.4212564Z tmpfs           8.2G   13M  8.2G   1% /dev/shm
2023-01-11T21:20:13.4212984Z tmpfs           8.2G  431k  8.2G   1% /run
2023-01-11T21:20:13.4213362Z tmpfs           8.2G     0  8.2G   0% /sys/fs/cgroup
2023-01-11T21:20:13.4213647Z /dev/nvme0n1p1  162G   26G  136G  16% /
2023-01-11T21:20:13.4247331Z ##[group]Run .github/scripts/parse_ref.py
2023-01-11T21:20:13.4247573Z .github/scripts/parse_ref.py
2023-01-11T21:20:13.4257795Z shell: /usr/bin/bash -e {0}
2023-01-11T21:20:13.4257979Z env:
2023-01-11T21:20:13.4258142Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:20:13.4258327Z ##[endgroup]
2023-01-11T21:20:13.4883314Z ##[group]Run set -x
2023-01-11T21:20:13.4883585Z set -x
2023-01-11T21:20:13.4883756Z 
2023-01-11T21:20:13.4883952Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2023-01-11T21:20:13.4884214Z   TEST_COMMAND=.jenkins/pytorch/multigpu-test.sh
2023-01-11T21:20:13.4884477Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2023-01-11T21:20:13.4884892Z   TEST_COMMAND=.jenkins/onnx/test.sh
2023-01-11T21:20:13.4885123Z else
2023-01-11T21:20:13.4885329Z   TEST_COMMAND=.jenkins/pytorch/test.sh
2023-01-11T21:20:13.4885577Z fi
2023-01-11T21:20:13.4885846Z 
2023-01-11T21:20:13.4886093Z COMMIT_MESSAGES=$(git cherry -v "origin/${GIT_DEFAULT_BRANCH:-master}")
2023-01-11T21:20:13.4886379Z 
2023-01-11T21:20:13.4886650Z # sanitize the input commit message and PR body here:
2023-01-11T21:20:13.4886917Z #
2023-01-11T21:20:13.4887276Z # trim all new lines from commit messages + PR_BODY to avoid issues with batch environment
2023-01-11T21:20:13.4887734Z # variable copying. see https://github.com/pytorch/pytorch/pull/80043#issuecomment-1167796028
2023-01-11T21:20:13.4888053Z COMMIT_MESSAGES="${COMMIT_MESSAGES//[$'\n\r']}"
2023-01-11T21:20:13.4888319Z PR_BODY="${PR_BODY//[$'\n\r']}"
2023-01-11T21:20:13.4888549Z 
2023-01-11T21:20:13.4888845Z # then trim all special characters like single and double quotes to avoid unescaped inputs to
2023-01-11T21:20:13.4889282Z # wreak havoc internally
2023-01-11T21:20:13.4889555Z export COMMIT_MESSAGES="${COMMIT_MESSAGES//[\'\"]}"
2023-01-11T21:20:13.4889837Z export PR_BODY="${PR_BODY//[\'\"]}"
2023-01-11T21:20:13.4890089Z 
2023-01-11T21:20:13.4890332Z # detached container should get cleaned up by teardown_ec2_linux
2023-01-11T21:20:13.4890682Z # TODO: Stop building test binaries as part of the build phase
2023-01-11T21:20:13.4890990Z # Used for GPU_FLAG since that doesn't play nice
2023-01-11T21:20:13.4891236Z # shellcheck disable=SC2086,SC2090
2023-01-11T21:20:13.4891533Z container_name=$(docker run \
2023-01-11T21:20:13.4891781Z   ${GPU_FLAG:-} \
2023-01-11T21:20:13.4891995Z   -e BUILD_ENVIRONMENT \
2023-01-11T21:20:13.4892250Z   -e PR_NUMBER \
2023-01-11T21:20:13.4892526Z   -e GITHUB_ACTIONS \
2023-01-11T21:20:13.4892772Z   -e BASE_SHA \
2023-01-11T21:20:13.4892980Z   -e BRANCH \
2023-01-11T21:20:13.4893211Z   -e SHA1 \
2023-01-11T21:20:13.4893460Z   -e AWS_DEFAULT_REGION \
2023-01-11T21:20:13.4893690Z   -e IN_WHEEL_TEST \
2023-01-11T21:20:13.4893937Z   -e SHARD_NUMBER \
2023-01-11T21:20:13.4914515Z   -e TEST_CONFIG \
2023-01-11T21:20:13.4914724Z   -e NUM_TEST_SHARDS \
2023-01-11T21:20:13.4914912Z   -e PR_BODY \
2023-01-11T21:20:13.4915112Z   -e COMMIT_MESSAGES \
2023-01-11T21:20:13.4915312Z   -e CONTINUE_THROUGH_ERROR \
2023-01-11T21:20:13.4915644Z   -e PYTORCH_RETRY_TEST_CASES \
2023-01-11T21:20:13.4915855Z   -e PYTORCH_OVERRIDE_FLAKY_SIGNAL \
2023-01-11T21:20:13.4916058Z   -e PR_LABELS \
2023-01-11T21:20:13.4916265Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2023-01-11T21:20:13.4916468Z   -e SCCACHE_BUCKET \
2023-01-11T21:20:13.4916671Z   -e SCCACHE_S3_KEY_PREFIX \
2023-01-11T21:20:13.4916870Z   -e XLA_CUDA \
2023-01-11T21:20:13.4917067Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2023-01-11T21:20:13.4917307Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2023-01-11T21:20:13.4917549Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2023-01-11T21:20:13.4917794Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2023-01-11T21:20:13.4918031Z   --ulimit stack=10485760:83886080 \
2023-01-11T21:20:13.4918316Z   --security-opt seccomp=unconfined \
2023-01-11T21:20:13.4918542Z   --cap-add=SYS_PTRACE \
2023-01-11T21:20:13.4918722Z   --ipc=host \
2023-01-11T21:20:13.4918914Z   --shm-size="${SHM_SIZE}" \
2023-01-11T21:20:13.4919100Z   --tty \
2023-01-11T21:20:13.4919261Z   --detach \
2023-01-11T21:20:13.4919462Z   --name="${container_name}" \
2023-01-11T21:20:13.4919664Z   --user jenkins \
2023-01-11T21:20:13.4919886Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2023-01-11T21:20:13.4920143Z   -w /var/lib/jenkins/workspace \
2023-01-11T21:20:13.4920349Z   "${DOCKER_IMAGE}"
2023-01-11T21:20:13.4920512Z )
2023-01-11T21:20:13.4920733Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2023-01-11T21:20:13.4921067Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2023-01-11T21:20:13.4931392Z shell: /usr/bin/bash -e {0}
2023-01-11T21:20:13.4931567Z env:
2023-01-11T21:20:13.4931742Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:20:13.4932000Z   BUILD_ENVIRONMENT: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:20:13.4932219Z   PR_NUMBER: 
2023-01-11T21:20:13.4932379Z   BRANCH: 
2023-01-11T21:20:13.4932586Z   SHA1: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:13.4932825Z   BASE_SHA: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:13.4933054Z   PYTORCH_RETRY_TEST_CASES: 1
2023-01-11T21:20:13.4933263Z   PYTORCH_OVERRIDE_FLAKY_SIGNAL: 1
2023-01-11T21:20:13.4933454Z   TEST_CONFIG: nogpu_AVX512
2023-01-11T21:20:13.4933639Z   SHARD_NUMBER: 1
2023-01-11T21:20:13.4933812Z   NUM_TEST_SHARDS: 1
2023-01-11T21:20:13.4933970Z   PR_BODY: 
2023-01-11T21:20:13.4934151Z   CONTINUE_THROUGH_ERROR: False
2023-01-11T21:20:13.4934591Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2023-01-11T21:20:13.4934842Z   SCCACHE_S3_KEY_PREFIX: trunk
2023-01-11T21:20:13.4935028Z   SHM_SIZE: 2g
2023-01-11T21:20:13.4935407Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:20:13.4935773Z   XLA_CUDA: 
2023-01-11T21:20:13.4936025Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2023-01-11T21:20:13.4936306Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2023-01-11T21:20:13.4936528Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2023-01-11T21:20:13.4936709Z ##[endgroup]
2023-01-11T21:20:13.4959598Z + [[ nogpu_AVX512 == \m\u\l\t\i\g\p\u ]]
2023-01-11T21:20:13.4960312Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *onnx* ]]
2023-01-11T21:20:13.4960613Z + TEST_COMMAND=.jenkins/pytorch/test.sh
2023-01-11T21:20:13.4962579Z ++ git cherry -v origin/master
2023-01-11T21:20:16.7402923Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into '\''input'\''
2023-01-11T21:20:16.7403366Z + 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch
2023-01-11T21:20:16.7404127Z + 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e '\''other'\'' instead of '\''output'\'' in documentation'
2023-01-11T21:20:16.7405385Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into '\''input'\''+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e '\''other'\'' instead of '\''output'\'' in documentation'
2023-01-11T21:20:16.7406202Z + PR_BODY=
2023-01-11T21:20:16.7406880Z + export 'COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation'
2023-01-11T21:20:16.7407846Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation'
2023-01-11T21:20:16.7408266Z + export PR_BODY=
2023-01-11T21:20:16.7408431Z + PR_BODY=
2023-01-11T21:20:16.7414420Z +++ nproc --ignore=2
2023-01-11T21:20:16.7490024Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e PR_BODY -e COMMIT_MESSAGES -e CONTINUE_THROUGH_ERROR -e PYTORCH_RETRY_TEST_CASES -e PYTORCH_OVERRIDE_FLAKY_SIGNAL -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS --env-file=/tmp/github_env_3896346758 --ulimit stack=10485760:83886080 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=2g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T21:20:24.4003411Z + container_name=ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T21:20:24.4004119Z + echo DOCKER_CONTAINER_ID=ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T21:20:24.4007767Z ++ echo dist/torch-2.0.0a0+git8419ddd-cp310-cp310-linux_x86_64.whl
2023-01-11T21:20:24.4010064Z + docker exec -t ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4 sh -c 'pip install dist/torch-2.0.0a0+git8419ddd-cp310-cp310-linux_x86_64.whl[opt-einsum] && .jenkins/pytorch/test.sh'
2023-01-11T21:20:24.9747361Z Processing ./dist/torch-2.0.0a0+git8419ddd-cp310-cp310-linux_x86_64.whl
2023-01-11T21:20:25.6914512Z Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.0.0a0+git8419ddd) (1.11.1)
2023-01-11T21:20:25.6916879Z Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.0.0a0+git8419ddd) (2.6.3)
2023-01-11T21:20:25.6919995Z Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch==2.0.0a0+git8419ddd) (4.4.0)
2023-01-11T21:20:25.6931510Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.0.0a0+git8419ddd) (3.3.0)
2023-01-11T21:20:25.6983041Z Requirement already satisfied: numpy>=1.7 in /opt/conda/lib/python3.10/site-packages (from opt-einsum>=3.3->torch==2.0.0a0+git8419ddd) (1.21.2)
2023-01-11T21:20:25.7125010Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.0.0a0+git8419ddd) (1.2.1)
2023-01-11T21:20:26.3169415Z Installing collected packages: torch
2023-01-11T21:20:33.1661136Z Successfully installed torch-2.0.0a0+git8419ddd
2023-01-11T21:20:33.2959121Z + echo 'Environment variables:'
2023-01-11T21:20:33.2959382Z Environment variables:
2023-01-11T21:20:33.2959563Z + env
2023-01-11T21:20:33.2982196Z SHARD_NUMBER=1
2023-01-11T21:20:33.2982735Z NV_LIBCUBLAS_DEV_VERSION=11.10.1.25-1
2023-01-11T21:20:33.2983234Z NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-7
2023-01-11T21:20:33.2983499Z LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
2023-01-11T21:20:33.2983874Z NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.13.4-1+cuda11.7
2023-01-11T21:20:33.2984095Z UCC_HOME=/usr
2023-01-11T21:20:33.2984553Z BUILD_ENVIRONMENT=linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:20:33.2985023Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2023-01-11T21:20:33.2985485Z NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-7=11.7.3.21-1
2023-01-11T21:20:33.2985845Z INSTALLED_DB=yes
2023-01-11T21:20:33.2986144Z HOSTNAME=ecb438e252c9
2023-01-11T21:20:33.2986459Z GITHUB_REF_NAME=ciflow/trunk/91627
2023-01-11T21:20:33.2986876Z GITHUB_API_URL=https://api.github.com
2023-01-11T21:20:33.2993294Z GITHUB_REPOSITORY_OWNER_ID=21003710
2023-01-11T21:20:33.2993571Z OPENSSL_DIR=/opt/openssl
2023-01-11T21:20:33.2993970Z UCC_COMMIT=1c7a7127186e7836f73aafbd7697bbc274a77eee
2023-01-11T21:20:33.2994526Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.2994878Z CUDA_PATH=/usr/local/cuda
2023-01-11T21:20:33.2995253Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2023-01-11T21:20:33.2995593Z GITHUB_RUN_ATTEMPT=1
2023-01-11T21:20:33.2995787Z TEST_CONFIG=nogpu_AVX512
2023-01-11T21:20:33.2995993Z NV_LIBNPP_VERSION=11.7.3.21-1
2023-01-11T21:20:33.2996286Z NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-7=11.7.50-1
2023-01-11T21:20:33.2996551Z GITHUB_REPOSITORY_OWNER=pytorch
2023-01-11T21:20:33.2996735Z GITHUB_ACTIONS=true
2023-01-11T21:20:33.2996925Z NVIDIA_VISIBLE_DEVICES=all
2023-01-11T21:20:33.2997139Z NV_NVPROF_VERSION=11.7.50-1
2023-01-11T21:20:33.2997353Z NV_LIBCUSPARSE_VERSION=11.7.3.50-1
2023-01-11T21:20:33.2997665Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/trunk.yml@refs/tags/ciflow/trunk/91627
2023-01-11T21:20:33.2997955Z NVIDIA_PRODUCT_NAME=CUDA
2023-01-11T21:20:33.2998121Z CI=true
2023-01-11T21:20:33.2998304Z PYTORCH_OVERRIDE_FLAKY_SIGNAL=1
2023-01-11T21:20:33.2998603Z NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-7=11.10.1.25-1
2023-01-11T21:20:33.2998801Z BRANCH=
2023-01-11T21:20:33.2998962Z GITHUB_HEAD_REF=
2023-01-11T21:20:33.2999190Z UCX_COMMIT=31e74cac7bee0ef66bef2af72e7d86d9c282e5ab
2023-01-11T21:20:33.2999450Z GITHUB_ACTOR=pytorch-bot[bot]
2023-01-11T21:20:33.2999686Z CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
2023-01-11T21:20:33.2999945Z GITHUB_ACTION_REF=
2023-01-11T21:20:33.3000129Z NCCL_VERSION=2.13.4-1
2023-01-11T21:20:33.3000312Z GITHUB_ACTION=__self
2023-01-11T21:20:33.3000504Z GITHUB_REF_PROTECTED=false
2023-01-11T21:20:33.3000827Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2023-01-11T21:20:33.3001112Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2023-01-11T21:20:33.3001795Z ***
2023-01-11T21:20:33.3001965Z INSTALLED_VISION=yes
2023-01-11T21:20:33.3002132Z NVARCH=x86_64
2023-01-11T21:20:33.3002352Z NV_LIBCUSPARSE_DEV_VERSION=11.7.3.50-1
2023-01-11T21:20:33.3002558Z HOME=/var/lib/jenkins
2023-01-11T21:20:33.3002950Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3003254Z CARGO_NET_GIT_FETCH_WITH_CLI=true
2023-01-11T21:20:33.3003454Z NVIDIA_CUDA_END_OF_LIFE=1
2023-01-11T21:20:33.3003685Z GITHUB_ACTION_REPOSITORY=
2023-01-11T21:20:33.3003874Z GITHUB_REF_TYPE=tag
2023-01-11T21:20:33.3004093Z NV_LIBNCCL_PACKAGE_VERSION=2.13.4-1
2023-01-11T21:20:33.3004284Z GITHUB_RETENTION_DAYS=90
2023-01-11T21:20:33.3004569Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2023-01-11T21:20:33.3004870Z NV_LIBNCCL_PACKAGE=libnccl2=2.13.4-1+cuda11.7
2023-01-11T21:20:33.3005268Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3005566Z DEBIAN_FRONTEND=noninteractive
2023-01-11T21:20:33.3005831Z NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
2023-01-11T21:20:33.3006055Z GITHUB_REF=refs/tags/ciflow/trunk/91627
2023-01-11T21:20:33.3006357Z NV_CUDA_LIB_VERSION=11.7.0-1
2023-01-11T21:20:33.3006591Z GITHUB_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:33.3006815Z INSTALLED_PROTOBUF=yes
2023-01-11T21:20:33.3006999Z GITHUB_REPOSITORY_ID=65600975
2023-01-11T21:20:33.3009841Z GITHUB_RUN_ID=3896346758
2023-01-11T21:20:33.3010436Z NV_LIBNPP_PACKAGE=libnpp-11-7=11.7.3.21-1
2023-01-11T21:20:33.3010848Z NV_LIBNCCL_PACKAGE_NAME=libnccl2
2023-01-11T21:20:33.3011248Z LIBRARY_PATH=/usr/local/cuda/lib64/stubs
2023-01-11T21:20:33.3011639Z NV_NVTX_VERSION=11.7.50-1
2023-01-11T21:20:33.3011989Z CONTINUE_THROUGH_ERROR=False
2023-01-11T21:20:33.3049767Z GITHUB_SERVER_URL=https://github.com
2023-01-11T21:20:33.3050105Z MAX_JOBS=6
2023-01-11T21:20:33.3050443Z GITHUB_ACTOR_ID=54816060
2023-01-11T21:20:33.3050842Z NV_LIBCUBLAS_VERSION=11.10.1.25-1
2023-01-11T21:20:33.3051515Z NV_LIBCUBLAS_PACKAGE=libcublas-11-7=11.10.1.25-1
2023-01-11T21:20:33.3051981Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2023-01-11T21:20:33.3052232Z UCX_HOME=/usr
2023-01-11T21:20:33.3052414Z PYTORCH_RETRY_TEST_CASES=1
2023-01-11T21:20:33.3052658Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2023-01-11T21:20:33.3052912Z BASE_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:33.3053167Z NV_CUDA_CUDART_DEV_VERSION=11.7.60-1
2023-01-11T21:20:33.3053360Z PR_BODY=
2023-01-11T21:20:33.3053512Z GITHUB_BASE_REF=
2023-01-11T21:20:33.3053680Z TERM=xterm
2023-01-11T21:20:33.3053835Z XLA_CUDA=
2023-01-11T21:20:33.3054016Z NV_NVML_DEV_VERSION=11.7.50-1
2023-01-11T21:20:33.3054213Z TORCH_CUDA_ARCH_LIST=Maxwell
2023-01-11T21:20:33.3054401Z CUDA_VERSION=11.7.0
2023-01-11T21:20:33.3054639Z NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-7
2023-01-11T21:20:33.3054856Z OPENSSL_ROOT_DIR=/opt/openssl
2023-01-11T21:20:33.3055268Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3055540Z GITHUB_JOB=test
2023-01-11T21:20:33.3055728Z SCCACHE_S3_KEY_PREFIX=trunk
2023-01-11T21:20:33.3056190Z COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation
2023-01-11T21:20:33.3056705Z NVIDIA_DRIVER_CAPABILITIES=compute,utility
2023-01-11T21:20:33.3056916Z NUM_TEST_SHARDS=1
2023-01-11T21:20:33.3057075Z PR_NUMBER=
2023-01-11T21:20:33.3057465Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3057742Z SHLVL=1
2023-01-11T21:20:33.3057982Z NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-7
2023-01-11T21:20:33.3058221Z GITHUB_REPOSITORY=pytorch/pytorch
2023-01-11T21:20:33.3059115Z NVIDIA_REQUIRE_CUDA=cuda>=11.7 brand=tesla,driver>=450,driver<451 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511
2023-01-11T21:20:33.3059964Z NV_LIBNPP_DEV_VERSION=11.7.3.21-1
2023-01-11T21:20:33.3060195Z SHA1=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:33.3060402Z GITHUB_EVENT_NAME=push
2023-01-11T21:20:33.3060620Z NV_CUDA_CUDART_VERSION=11.7.60-1
2023-01-11T21:20:33.3060879Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2023-01-11T21:20:33.3061088Z GITHUB_RUN_NUMBER=22986
2023-01-11T21:20:33.3061345Z GITHUB_WORKFLOW=trunk
2023-01-11T21:20:33.3061650Z PATH=/opt/cache/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T21:20:33.3061982Z NV_LIBNCCL_DEV_PACKAGE_VERSION=2.13.4-1
2023-01-11T21:20:33.3062227Z GITHUB_WORKFLOW_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:33.3062575Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:20:33.3062883Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot]
2023-01-11T21:20:33.3063072Z _=/usr/bin/env
2023-01-11T21:20:33.3063355Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2023-01-11T21:20:33.3146448Z + TORCH_INSTALL_DIR=/opt/conda/lib/python3.10/site-packages/torch
2023-01-11T21:20:33.3147121Z + TORCH_BIN_DIR=/opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T21:20:33.3147834Z + TORCH_LIB_DIR=/opt/conda/lib/python3.10/site-packages/torch/lib
2023-01-11T21:20:33.3148439Z + TORCH_TEST_DIR=/opt/conda/lib/python3.10/site-packages/torch/test
2023-01-11T21:20:33.3148827Z + BUILD_DIR=build
2023-01-11T21:20:33.3149114Z + BUILD_RENAMED_DIR=build_renamed
2023-01-11T21:20:33.3149427Z + BUILD_BIN_DIR=build/bin
2023-01-11T21:20:33.3149707Z + export VALGRIND=ON
2023-01-11T21:20:33.3149873Z + VALGRIND=ON
2023-01-11T21:20:33.3150070Z + export TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T21:20:33.3150284Z + TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T21:20:33.3150581Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *clang9* ]]
2023-01-11T21:20:33.3150907Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *bazel* ]]
2023-01-11T21:20:33.3152397Z ++ realpath build/custom_test_artifacts
2023-01-11T21:20:33.3190012Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2023-01-11T21:20:33.3192627Z ++ dirname .jenkins/pytorch/test.sh
2023-01-11T21:20:33.3198308Z + source .jenkins/pytorch/common.sh
2023-01-11T21:20:33.3206806Z +++ dirname .jenkins/pytorch/common.sh
2023-01-11T21:20:33.3215662Z ++ source .jenkins/pytorch/common_utils.sh
2023-01-11T21:20:33.3223003Z +++ declare -f -t trap_add
2023-01-11T21:20:33.3227952Z ++ set -ex
2023-01-11T21:20:33.3228589Z ++ [[ linux-bionic-cuda11.7-py3.10-gcc7 == *rocm* ]]
2023-01-11T21:20:33.3228940Z ++ BUILD_TEST_LIBTORCH=0
2023-01-11T21:20:33.3229360Z + echo 'Environment variables'
2023-01-11T21:20:33.3229724Z Environment variables
2023-01-11T21:20:33.3229940Z + env
2023-01-11T21:20:33.3236050Z SHARD_NUMBER=1
2023-01-11T21:20:33.3236481Z NV_LIBCUBLAS_DEV_VERSION=11.10.1.25-1
2023-01-11T21:20:33.3236924Z NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-7
2023-01-11T21:20:33.3237329Z LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
2023-01-11T21:20:33.3237831Z NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.13.4-1+cuda11.7
2023-01-11T21:20:33.3238166Z UCC_HOME=/usr
2023-01-11T21:20:33.3238883Z BUILD_ENVIRONMENT=linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T21:20:33.3239301Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2023-01-11T21:20:33.3239784Z NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-7=11.7.3.21-1
2023-01-11T21:20:33.3240168Z INSTALLED_DB=yes
2023-01-11T21:20:33.3240482Z HOSTNAME=ecb438e252c9
2023-01-11T21:20:33.3240775Z GITHUB_REF_NAME=ciflow/trunk/91627
2023-01-11T21:20:33.3241015Z GITHUB_API_URL=https://api.github.com
2023-01-11T21:20:33.3241240Z GITHUB_REPOSITORY_OWNER_ID=21003710
2023-01-11T21:20:33.3241560Z OPENSSL_DIR=/opt/openssl
2023-01-11T21:20:33.3241790Z UCC_COMMIT=1c7a7127186e7836f73aafbd7697bbc274a77eee
2023-01-11T21:20:33.3242276Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3242588Z CUDA_PATH=/usr/local/cuda
2023-01-11T21:20:33.3242956Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2023-01-11T21:20:33.3243235Z GITHUB_RUN_ATTEMPT=1
2023-01-11T21:20:33.3243414Z TEST_CONFIG=nogpu_AVX512
2023-01-11T21:20:33.3243633Z NV_LIBNPP_VERSION=11.7.3.21-1
2023-01-11T21:20:33.3243913Z NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-7=11.7.50-1
2023-01-11T21:20:33.3244259Z GITHUB_REPOSITORY_OWNER=pytorch
2023-01-11T21:20:33.3244457Z GITHUB_ACTIONS=true
2023-01-11T21:20:33.3244646Z NVIDIA_VISIBLE_DEVICES=all
2023-01-11T21:20:33.3244850Z NV_NVPROF_VERSION=11.7.50-1
2023-01-11T21:20:33.3245078Z NV_LIBCUSPARSE_VERSION=11.7.3.50-1
2023-01-11T21:20:33.3245366Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/trunk.yml@refs/tags/ciflow/trunk/91627
2023-01-11T21:20:33.3245620Z NVIDIA_PRODUCT_NAME=CUDA
2023-01-11T21:20:33.3245800Z CI=true
2023-01-11T21:20:33.3245983Z PYTORCH_OVERRIDE_FLAKY_SIGNAL=1
2023-01-11T21:20:33.3246267Z NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-7=11.10.1.25-1
2023-01-11T21:20:33.3246480Z BRANCH=
2023-01-11T21:20:33.3246641Z GITHUB_HEAD_REF=
2023-01-11T21:20:33.3246858Z UCX_COMMIT=31e74cac7bee0ef66bef2af72e7d86d9c282e5ab
2023-01-11T21:20:33.3247132Z GITHUB_ACTOR=pytorch-bot[bot]
2023-01-11T21:20:33.3247414Z CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
2023-01-11T21:20:33.3247618Z GITHUB_ACTION_REF=
2023-01-11T21:20:33.3247817Z NCCL_VERSION=2.13.4-1
2023-01-11T21:20:33.3248003Z GITHUB_ACTION=__self
2023-01-11T21:20:33.3248166Z VALGRIND=ON
2023-01-11T21:20:33.3248349Z GITHUB_REF_PROTECTED=false
2023-01-11T21:20:33.3248687Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2023-01-11T21:20:33.3248969Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2023-01-11T21:20:33.3249401Z ***
2023-01-11T21:20:33.3249567Z INSTALLED_VISION=yes
2023-01-11T21:20:33.3249745Z NVARCH=x86_64
2023-01-11T21:20:33.3249948Z NV_LIBCUSPARSE_DEV_VERSION=11.7.3.50-1
2023-01-11T21:20:33.3250151Z HOME=/var/lib/jenkins
2023-01-11T21:20:33.3250549Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3250843Z CARGO_NET_GIT_FETCH_WITH_CLI=true
2023-01-11T21:20:33.3251044Z NVIDIA_CUDA_END_OF_LIFE=1
2023-01-11T21:20:33.3251240Z GITHUB_ACTION_REPOSITORY=
2023-01-11T21:20:33.3251419Z GITHUB_REF_TYPE=tag
2023-01-11T21:20:33.3251636Z NV_LIBNCCL_PACKAGE_VERSION=2.13.4-1
2023-01-11T21:20:33.3251841Z GITHUB_RETENTION_DAYS=90
2023-01-11T21:20:33.3252109Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2023-01-11T21:20:33.3252528Z NV_LIBNCCL_PACKAGE=libnccl2=2.13.4-1+cuda11.7
2023-01-11T21:20:33.3252943Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3253241Z DEBIAN_FRONTEND=noninteractive
2023-01-11T21:20:33.3253482Z NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
2023-01-11T21:20:33.3253704Z GITHUB_REF=refs/tags/ciflow/trunk/91627
2023-01-11T21:20:33.3253933Z NV_CUDA_LIB_VERSION=11.7.0-1
2023-01-11T21:20:33.3254155Z GITHUB_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:33.3254381Z INSTALLED_PROTOBUF=yes
2023-01-11T21:20:33.3254577Z GITHUB_REPOSITORY_ID=65600975
2023-01-11T21:20:33.3254758Z GITHUB_RUN_ID=3896346758
2023-01-11T21:20:33.3255009Z NV_LIBNPP_PACKAGE=libnpp-11-7=11.7.3.21-1
2023-01-11T21:20:33.3255229Z NV_LIBNCCL_PACKAGE_NAME=libnccl2
2023-01-11T21:20:33.3255504Z LIBRARY_PATH=/usr/local/cuda/lib64/stubs
2023-01-11T21:20:33.3255730Z NV_NVTX_VERSION=11.7.50-1
2023-01-11T21:20:33.3255924Z CONTINUE_THROUGH_ERROR=False
2023-01-11T21:20:33.3256133Z GITHUB_SERVER_URL=https://github.com
2023-01-11T21:20:33.3256329Z MAX_JOBS=6
2023-01-11T21:20:33.3256502Z GITHUB_ACTOR_ID=54816060
2023-01-11T21:20:33.3256702Z NV_LIBCUBLAS_VERSION=11.10.1.25-1
2023-01-11T21:20:33.3256978Z NV_LIBCUBLAS_PACKAGE=libcublas-11-7=11.10.1.25-1
2023-01-11T21:20:33.3257339Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2023-01-11T21:20:33.3257579Z UCX_HOME=/usr
2023-01-11T21:20:33.3257765Z PYTORCH_RETRY_TEST_CASES=1
2023-01-11T21:20:33.3258002Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2023-01-11T21:20:33.3258264Z BASE_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:33.3258506Z NV_CUDA_CUDART_DEV_VERSION=11.7.60-1
2023-01-11T21:20:33.3258697Z PR_BODY=
2023-01-11T21:20:33.3258862Z GITHUB_BASE_REF=
2023-01-11T21:20:33.3259016Z TERM=xterm
2023-01-11T21:20:33.3259196Z TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T21:20:33.3259451Z XLA_CUDA=
2023-01-11T21:20:33.3259633Z NV_NVML_DEV_VERSION=11.7.50-1
2023-01-11T21:20:33.3259828Z TORCH_CUDA_ARCH_LIST=Maxwell
2023-01-11T21:20:33.3260015Z CUDA_VERSION=11.7.0
2023-01-11T21:20:33.3260251Z NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-7
2023-01-11T21:20:33.3260485Z OPENSSL_ROOT_DIR=/opt/openssl
2023-01-11T21:20:33.3260888Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3261159Z GITHUB_JOB=test
2023-01-11T21:20:33.3261342Z SCCACHE_S3_KEY_PREFIX=trunk
2023-01-11T21:20:33.3261840Z COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation
2023-01-11T21:20:33.3262304Z NVIDIA_DRIVER_CAPABILITIES=compute,utility
2023-01-11T21:20:33.3262500Z NUM_TEST_SHARDS=1
2023-01-11T21:20:33.3262672Z PR_NUMBER=
2023-01-11T21:20:33.3263135Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_e8404cc3-23e5-4de6-aa65-12036809af7f
2023-01-11T21:20:33.3263398Z SHLVL=1
2023-01-11T21:20:33.3263648Z NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-7
2023-01-11T21:20:33.3263972Z GITHUB_REPOSITORY=pytorch/pytorch
2023-01-11T21:20:33.3264867Z NVIDIA_REQUIRE_CUDA=cuda>=11.7 brand=tesla,driver>=450,driver<451 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511
2023-01-11T21:20:33.3265715Z NV_LIBNPP_DEV_VERSION=11.7.3.21-1
2023-01-11T21:20:33.3265933Z SHA1=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:33.3266149Z GITHUB_EVENT_NAME=push
2023-01-11T21:20:33.3266399Z NV_CUDA_CUDART_VERSION=11.7.60-1
2023-01-11T21:20:33.3266642Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2023-01-11T21:20:33.3266851Z GITHUB_RUN_NUMBER=22986
2023-01-11T21:20:33.3267040Z GITHUB_WORKFLOW=trunk
2023-01-11T21:20:33.3267403Z PATH=/opt/cache/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T21:20:33.3267732Z NV_LIBNCCL_DEV_PACKAGE_VERSION=2.13.4-1
2023-01-11T21:20:33.3267992Z GITHUB_WORKFLOW_SHA=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:20:33.3268344Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T21:20:33.3268644Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot]
2023-01-11T21:20:33.3268844Z _=/usr/bin/env
2023-01-11T21:20:33.3269050Z + echo 'Testing pytorch'
2023-01-11T21:20:33.3269221Z Testing pytorch
2023-01-11T21:20:33.3269414Z + export LANG=C.UTF-8
2023-01-11T21:20:33.3269603Z + LANG=C.UTF-8
2023-01-11T21:20:33.3345814Z + PR_NUMBER=
2023-01-11T21:20:33.3346156Z + [[ nogpu_AVX512 == \d\e\f\a\u\l\t ]]
2023-01-11T21:20:33.3346555Z + [[ nogpu_AVX512 == \d\i\s\t\r\i\b\u\t\e\d ]]
2023-01-11T21:20:33.3346903Z + [[ nogpu_AVX512 == \s\l\o\w ]]
2023-01-11T21:20:33.3347508Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *slow-gradcheck* ]]
2023-01-11T21:20:33.3348127Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *cuda* ]]
2023-01-11T21:20:33.3348558Z + export PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
2023-01-11T21:20:33.3348789Z + PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
2023-01-11T21:20:33.3349001Z + [[ nogpu_AVX512 == *crossref* ]]
2023-01-11T21:20:33.3349201Z + [[ nogpu_AVX512 == *dynamo* ]]
2023-01-11T21:20:33.3349507Z + [[ nogpu_AVX512 == *inductor* ]]
2023-01-11T21:20:33.3349793Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *rocm* ]]
2023-01-11T21:20:33.3350117Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *-bazel-* ]]
2023-01-11T21:20:33.3350384Z + pip_install --user ninja==1.10.2
2023-01-11T21:20:33.3350671Z + pip install --progress-bar off --user ninja==1.10.2
2023-01-11T21:20:33.7369008Z Collecting ninja==1.10.2
2023-01-11T21:20:33.7533094Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2023-01-11T21:20:34.3237393Z Installing collected packages: ninja
2023-01-11T21:20:34.3309211Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2023-01-11T21:20:34.3309902Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2023-01-11T21:20:34.3361382Z Successfully installed ninja-1.10.2
2023-01-11T21:20:34.3967340Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T21:20:34.3968196Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T21:20:34.3969226Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *asan* ]]
2023-01-11T21:20:34.3969827Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-tsan* ]]
2023-01-11T21:20:34.3970282Z + [[ nogpu_AVX512 == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2023-01-11T21:20:34.3970685Z + [[ nogpu_AVX512 == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2023-01-11T21:20:34.3971076Z + export ATEN_CPU_CAPABILITY=avx2
2023-01-11T21:20:34.3971448Z + ATEN_CPU_CAPABILITY=avx2
2023-01-11T21:20:34.3977984Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *tbb* ]]
2023-01-11T21:20:34.3989895Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *libtorch* ]]
2023-01-11T21:20:34.3990503Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-bazel-* ]]
2023-01-11T21:20:34.3990836Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-tsan* ]]
2023-01-11T21:20:34.3992591Z + cd test
2023-01-11T21:20:34.4002036Z + python -c 'import torch; print(torch.__config__.show())'
2023-01-11T21:20:35.7897726Z PyTorch built with:
2023-01-11T21:20:35.7898313Z   - GCC 7.5
2023-01-11T21:20:35.7898699Z   - C++ Version: 201703
2023-01-11T21:20:35.7899297Z   - Intel(R) oneAPI Math Kernel Library Version 2022.0-Product Build 20211112 for Intel(R) 64 architecture applications
2023-01-11T21:20:35.7899748Z   - Intel(R) MKL-DNN v2.7.2 (Git Hash fbec3e25a559ee252022ae066817b204e106a6ba)
2023-01-11T21:20:35.7900058Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2023-01-11T21:20:35.7900326Z   - LAPACK is enabled (usually provided by MKL)
2023-01-11T21:20:35.7900566Z   - NNPACK is enabled
2023-01-11T21:20:35.7900796Z   - CPU capability usage: AVX2
2023-01-11T21:20:35.7903320Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Werror -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, FORCE_FALLBACK_CUDA_MPI=1, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=ON, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 
2023-01-11T21:20:35.7905387Z 
2023-01-11T21:20:36.3966881Z + cd test
2023-01-11T21:20:36.3967345Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2023-01-11T21:20:37.5180880Z ATen/Parallel:
2023-01-11T21:20:37.5192999Z 	at::get_num_threads() : 4
2023-01-11T21:20:37.5193267Z 	at::get_num_interop_threads() : 4
2023-01-11T21:20:37.5193514Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2023-01-11T21:20:37.5193720Z 	omp_get_max_threads() : 4
2023-01-11T21:20:37.5194215Z Intel(R) oneAPI Math Kernel Library Version 2022.0-Product Build 20211112 for Intel(R) 64 architecture applications
2023-01-11T21:20:37.5194507Z 	mkl_get_max_threads() : 4
2023-01-11T21:20:37.5195078Z Intel(R) MKL-DNN v2.7.2 (Git Hash fbec3e25a559ee252022ae066817b204e106a6ba)
2023-01-11T21:20:37.5195339Z std::thread::hardware_concurrency() : 8
2023-01-11T21:20:37.5195567Z Environment variables:
2023-01-11T21:20:37.5195763Z 	OMP_NUM_THREADS : [not set]
2023-01-11T21:20:37.5195948Z 	MKL_NUM_THREADS : [not set]
2023-01-11T21:20:37.5196151Z ATen parallel backend: OpenMP
2023-01-11T21:20:37.5196277Z 
2023-01-11T21:20:37.7282612Z + [[ nogpu_AVX512 == *backward* ]]
2023-01-11T21:20:37.7282973Z + [[ nogpu_AVX512 == *xla* ]]
2023-01-11T21:20:37.7283327Z + [[ nogpu_AVX512 == \j\i\t\_\l\e\g\a\c\y ]]
2023-01-11T21:20:37.7283999Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *libtorch* ]]
2023-01-11T21:20:37.7284421Z + [[ nogpu_AVX512 == distributed ]]
2023-01-11T21:20:37.7284733Z + [[ nogpu_AVX512 == deploy ]]
2023-01-11T21:20:37.7285203Z + [[ nogpu_AVX512 == *inductor_distributed* ]]
2023-01-11T21:20:37.7285604Z + [[ nogpu_AVX512 == *dynamo* ]]
2023-01-11T21:20:37.7285944Z + [[ nogpu_AVX512 == *dynamo* ]]
2023-01-11T21:20:37.7286326Z + [[ nogpu_AVX512 == *inductor_huggingface* ]]
2023-01-11T21:20:37.7286723Z + [[ nogpu_AVX512 == *inductor_timm* ]]
2023-01-11T21:20:37.7287119Z + [[ nogpu_AVX512 == *inductor_torchbench* ]]
2023-01-11T21:20:37.7287525Z + [[ nogpu_AVX512 == *inductor* ]]
2023-01-11T21:20:37.7287825Z + [[ 1 == 1 ]]
2023-01-11T21:20:37.7288172Z + [[ 1 -gt 1 ]]
2023-01-11T21:20:37.7288438Z + [[ 1 == 2 ]]
2023-01-11T21:20:37.7288776Z + [[ 1 -gt 2 ]]
2023-01-11T21:20:37.7289426Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *vulkan* ]]
2023-01-11T21:20:37.7290000Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-bazel-* ]]
2023-01-11T21:20:37.7290410Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-mobile-lightweight-dispatch* ]]
2023-01-11T21:20:37.7290769Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *-tsan* ]]
2023-01-11T21:20:37.7290986Z + [[ nogpu_AVX512 = docs_test ]]
2023-01-11T21:20:37.7291186Z + [[ nogpu_AVX512 == *functorch* ]]
2023-01-11T21:20:37.7291380Z + install_torchvision
2023-01-11T21:20:37.7291542Z + local commit
2023-01-11T21:20:37.7291733Z ++ get_pinned_commit vision
2023-01-11T21:20:37.7291944Z ++ cat .github/ci_commit_pins/vision.txt
2023-01-11T21:20:37.7324807Z + commit=32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:37.7325435Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:37.7326010Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:38.0606999Z Collecting git+https://github.com/pytorch/vision.git@32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:38.0611622Z   Cloning https://github.com/pytorch/vision.git (to revision 32d254bbfcf14975f846765775584e61ef25a5bc) to /tmp/pip-req-build-qe4b0fk6
2023-01-11T21:20:38.0781214Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-qe4b0fk6
2023-01-11T21:20:40.2055327Z   Running command git rev-parse -q --verify 'sha^32d254bbfcf14975f846765775584e61ef25a5bc'
2023-01-11T21:20:40.2074636Z   Running command git fetch -q https://github.com/pytorch/vision.git 32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:41.1480830Z   Running command git checkout -q 32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:41.6720364Z   Resolved https://github.com/pytorch/vision.git to commit 32d254bbfcf14975f846765775584e61ef25a5bc
2023-01-11T21:20:43.5625835Z   Preparing metadata (setup.py) ... [?25l- done
2023-01-11T21:20:43.5680711Z [?25hRequirement already satisfied: typing_extensions in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (4.4.0)
2023-01-11T21:20:43.5683945Z Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (1.21.2)
2023-01-11T21:20:43.5686875Z Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (2.28.1)
2023-01-11T21:20:43.5692013Z Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (2.0.0a0+git8419ddd)
2023-01-11T21:20:43.5697477Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.15.0a0+32d254b) (9.3.0)
2023-01-11T21:20:43.5856358Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.15.0a0+32d254b) (1.26.13)
2023-01-11T21:20:43.5861401Z Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.15.0a0+32d254b) (2022.12.7)
2023-01-11T21:20:43.5866905Z Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.15.0a0+32d254b) (3.4)
2023-01-11T21:20:43.5874121Z Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.15.0a0+32d254b) (2.0.4)
2023-01-11T21:20:43.5914151Z Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->torchvision==0.15.0a0+32d254b) (1.11.1)
2023-01-11T21:20:43.5917106Z Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->torchvision==0.15.0a0+32d254b) (2.6.3)
2023-01-11T21:20:43.6205728Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->torchvision==0.15.0a0+32d254b) (1.2.1)
2023-01-11T21:20:43.6257201Z Building wheels for collected packages: torchvision
2023-01-11T21:21:37.1997538Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done
2023-01-11T21:21:37.2029351Z [?25h  Created wheel for torchvision: filename=torchvision-0.15.0a0+32d254b-cp310-cp310-linux_x86_64.whl size=1003186 sha256=6b656ed13309f08ee75611e0bd07879c65bc80f264e06dcc0d6675bfe8c82a1b
2023-01-11T21:21:37.2030578Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/ca/33/ae/1f7c8972d058d079236e7ca0a30b53b050afb405820b9ed787
2023-01-11T21:21:37.2065182Z Successfully built torchvision
2023-01-11T21:21:37.7250527Z Installing collected packages: torchvision
2023-01-11T21:21:38.0985044Z Successfully installed torchvision-0.15.0a0+32d254b
2023-01-11T21:21:38.1960371Z + install_triton
2023-01-11T21:21:38.1960588Z + local commit
2023-01-11T21:21:38.1960781Z + [[ nogpu_AVX512 == *rocm* ]]
2023-01-11T21:21:38.1963179Z ++ get_pinned_commit triton
2023-01-11T21:21:38.1963406Z ++ cat .github/ci_commit_pins/triton.txt
2023-01-11T21:21:38.1982540Z + commit=0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T21:21:38.1983190Z + pip_install --user git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T21:21:38.1983828Z + pip install --progress-bar off --user git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T21:21:38.5276785Z Collecting git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T21:21:38.5280887Z   Cloning https://github.com/openai/triton (to revision 0d7e7532279e45672555e344646f5c19c3972331) to /tmp/pip-req-build-6py_bze3
2023-01-11T21:21:38.5297135Z   Running command git clone --filter=blob:none --quiet https://github.com/openai/triton /tmp/pip-req-build-6py_bze3
2023-01-11T21:21:39.1822034Z   Running command git rev-parse -q --verify 'sha^0d7e7532279e45672555e344646f5c19c3972331'
2023-01-11T21:21:39.1839489Z   Running command git fetch -q https://github.com/openai/triton 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T21:21:39.5039224Z   Running command git checkout -q 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T21:21:39.8464567Z   Resolved https://github.com/openai/triton to commit 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T21:21:39.8465760Z   Running command git submodule update --init --recursive -q
2023-01-11T21:21:40.3222229Z   Preparing metadata (setup.py) ... [?25l- done
2023-01-11T21:21:40.4638072Z [?25hCollecting cmake
2023-01-11T21:21:40.4891355Z   Downloading cmake-3.25.0-py2.py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.7 MB)
2023-01-11T21:21:40.8240079Z Collecting filelock
2023-01-11T21:21:40.8494674Z   Downloading filelock-3.9.0-py3-none-any.whl (9.7 kB)
2023-01-11T21:21:40.8528948Z Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from triton==2.0.0) (2.0.0a0+git8419ddd)
2023-01-11T21:21:40.8707011Z Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->triton==2.0.0) (2.6.3)
2023-01-11T21:21:40.8710339Z Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch->triton==2.0.0) (4.4.0)
2023-01-11T21:21:40.8713331Z Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->triton==2.0.0) (1.11.1)
2023-01-11T21:21:40.8860140Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->triton==2.0.0) (1.2.1)
2023-01-11T21:21:40.8909050Z Building wheels for collected packages: triton
2023-01-11T21:22:21.2331968Z   Building wheel for triton (setup.py) ... [?25l- \ | / - \ | / - \ | / - done
2023-01-11T21:22:21.2701831Z [?25h  Created wheel for triton: filename=triton-2.0.0-cp310-cp310-linux_x86_64.whl size=15377935 sha256=1afa21e5959155ed2bd815b9f7982252c103b5a50cf26a592ee40063a2f21ae8
2023-01-11T21:22:21.2702917Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/3f/1d/23/1c2bc47d618a44f9c949aea4b7e355e737a1f1ed208f009295
2023-01-11T21:22:21.2717807Z Successfully built triton
2023-01-11T21:22:21.8507861Z Installing collected packages: cmake, filelock, triton
2023-01-11T21:22:23.0320302Z Successfully installed cmake-3.25.0 filelock-3.9.0 triton-2.0.0
2023-01-11T21:22:23.1233529Z + pip_install --user jinja2
2023-01-11T21:22:23.1233890Z + pip install --progress-bar off --user jinja2
2023-01-11T21:22:23.5230779Z Collecting jinja2
2023-01-11T21:22:23.5387229Z   Downloading Jinja2-3.1.2-py3-none-any.whl (133 kB)
2023-01-11T21:22:23.5709266Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2) (2.1.1)
2023-01-11T21:22:24.1487726Z Installing collected packages: jinja2
2023-01-11T21:22:24.2841110Z Successfully installed jinja2-3.1.2
2023-01-11T21:22:24.3460470Z + install_monkeytype
2023-01-11T21:22:24.3460837Z + pip_install MonkeyType
2023-01-11T21:22:24.3461364Z + pip install --progress-bar off MonkeyType
2023-01-11T21:22:24.7258305Z Collecting MonkeyType
2023-01-11T21:22:24.7414800Z   Downloading MonkeyType-22.2.0-py3-none-any.whl (37 kB)
2023-01-11T21:22:24.7669068Z Requirement already satisfied: mypy-extensions in /opt/conda/lib/python3.10/site-packages (from MonkeyType) (0.4.3)
2023-01-11T21:22:24.8397710Z Collecting libcst>=0.3.7
2023-01-11T21:22:24.8451338Z   Downloading libcst-0.4.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB)
2023-01-11T21:22:24.9685045Z Collecting typing-inspect>=0.4.0
2023-01-11T21:22:24.9718611Z   Downloading typing_inspect-0.8.0-py3-none-any.whl (8.7 kB)
2023-01-11T21:22:24.9970916Z Requirement already satisfied: pyyaml>=5.2 in /opt/conda/lib/python3.10/site-packages (from libcst>=0.3.7->MonkeyType) (6.0)
2023-01-11T21:22:24.9976698Z Requirement already satisfied: typing-extensions>=3.7.4.2 in /opt/conda/lib/python3.10/site-packages (from libcst>=0.3.7->MonkeyType) (4.4.0)
2023-01-11T21:22:25.6049331Z Installing collected packages: typing-inspect, libcst, MonkeyType
2023-01-11T21:22:26.4096834Z Successfully installed MonkeyType-22.2.0 libcst-0.4.9 typing-inspect-0.8.0
2023-01-11T21:22:26.4770315Z + test_python
2023-01-11T21:22:26.4770833Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --verbose
2023-01-11T21:22:28.1110248Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:28.6766886Z Ignoring disabled issues:  []
2023-01-11T21:22:29.4861351Z /var/lib/jenkins/workspace/test/run_test.py:1169: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
2023-01-11T21:22:29.4861904Z   if torch.version.cuda is not None and LooseVersion(torch.version.cuda) >= "11.6":
2023-01-11T21:22:29.4900017Z Selected tests:
2023-01-11T21:22:29.4900208Z  backends/xeon/test_launch
2023-01-11T21:22:29.4901471Z  benchmark_utils/test_benchmark_utils
2023-01-11T21:22:29.4901805Z  distributions/test_distributions
2023-01-11T21:22:29.4902171Z  dynamo/test_aot_autograd
2023-01-11T21:22:29.4902535Z  dynamo/test_aot_cudagraphs
2023-01-11T21:22:29.4902851Z  dynamo/test_comptime
2023-01-11T21:22:29.4903112Z  dynamo/test_dynamic_shapes
2023-01-11T21:22:29.4903394Z  dynamo/test_export
2023-01-11T21:22:29.4905948Z  dynamo/test_export_mutations
2023-01-11T21:22:29.4906335Z  dynamo/test_functions
2023-01-11T21:22:29.4906578Z  dynamo/test_global
2023-01-11T21:22:29.4906775Z  dynamo/test_global_declaration
2023-01-11T21:22:29.4906958Z  dynamo/test_minifier
2023-01-11T21:22:29.4907147Z  dynamo/test_misc
2023-01-11T21:22:29.4907339Z  dynamo/test_model_output
2023-01-11T21:22:29.4907657Z  dynamo/test_modules
2023-01-11T21:22:29.4907948Z  dynamo/test_nops
2023-01-11T21:22:29.4908245Z  dynamo/test_optimizations
2023-01-11T21:22:29.4908548Z  dynamo/test_optimizers
2023-01-11T21:22:29.4908865Z  dynamo/test_python_autograd
2023-01-11T21:22:29.4909191Z  dynamo/test_recompile_ux
2023-01-11T21:22:29.4909511Z  dynamo/test_replay_record
2023-01-11T21:22:29.4909877Z  dynamo/test_repros
2023-01-11T21:22:29.4910272Z  dynamo/test_skip_non_tensor
2023-01-11T21:22:29.4910659Z  dynamo/test_subgraphs
2023-01-11T21:22:29.4911081Z  dynamo/test_torchxla_integration
2023-01-11T21:22:29.4911537Z  dynamo/test_torchxla_num_output
2023-01-11T21:22:29.4911947Z  dynamo/test_torchxla_util
2023-01-11T21:22:29.4912342Z  dynamo/test_unspec
2023-01-11T21:22:29.4912749Z  dynamo/test_verify_correctness
2023-01-11T21:22:29.4913142Z  inductor/test_minifier
2023-01-11T21:22:29.4913523Z  inductor/test_perf
2023-01-11T21:22:29.4913911Z  inductor/test_smoke
2023-01-11T21:22:29.4914299Z  inductor/test_torchinductor
2023-01-11T21:22:29.4945841Z  inductor/test_torchinductor_opinfo
2023-01-11T21:22:29.4946246Z  lazy/test_bindings
2023-01-11T21:22:29.4946545Z  lazy/test_debug_util
2023-01-11T21:22:29.4946859Z  lazy/test_extract_compiled_graph
2023-01-11T21:22:29.4947129Z  lazy/test_meta_kernel
2023-01-11T21:22:29.4947312Z  lazy/test_reuse_ir
2023-01-11T21:22:29.4947497Z  lazy/test_step_closures
2023-01-11T21:22:29.4947671Z  lazy/test_ts_opinfo
2023-01-11T21:22:29.4947946Z  nn/test_convolution
2023-01-11T21:22:29.4948240Z  nn/test_dropout
2023-01-11T21:22:29.4948532Z  nn/test_embedding
2023-01-11T21:22:29.4948847Z  nn/test_init
2023-01-11T21:22:29.4949115Z  nn/test_lazy_modules
2023-01-11T21:22:29.4949296Z  nn/test_module_hooks
2023-01-11T21:22:29.4949486Z  nn/test_multihead_attention
2023-01-11T21:22:29.4949681Z  nn/test_packed_sequence
2023-01-11T21:22:29.4949873Z  nn/test_parametrization
2023-01-11T21:22:29.4950053Z  nn/test_pooling
2023-01-11T21:22:29.4950227Z  nn/test_pruning
2023-01-11T21:22:29.4950409Z  profiler/test_memory_profiler
2023-01-11T21:22:29.4950721Z  profiler/test_profiler
2023-01-11T21:22:29.4951064Z  profiler/test_profiler_tree
2023-01-11T21:22:29.4951241Z  test_ao_sparsity
2023-01-11T21:22:29.4951420Z  test_autocast
2023-01-11T21:22:29.4951670Z  test_autograd
2023-01-11T21:22:29.4951917Z  test_binary_ufuncs
2023-01-11T21:22:29.4952201Z  test_bundled_inputs
2023-01-11T21:22:29.4952386Z  test_comparison_utils
2023-01-11T21:22:29.4952547Z  test_complex
2023-01-11T21:22:29.4952721Z  test_cpp_api_parity
2023-01-11T21:22:29.4952912Z  test_cpp_extensions_aot_ninja
2023-01-11T21:22:29.4953105Z  test_cpp_extensions_aot_no_ninja
2023-01-11T21:22:29.4953308Z  test_cpp_extensions_jit
2023-01-11T21:22:29.4953551Z  test_cpp_extensions_open_device_registration
2023-01-11T21:22:29.4953785Z  test_cuda
2023-01-11T21:22:29.4953947Z  test_cuda_nvml_based_avail
2023-01-11T21:22:29.4954153Z  test_cuda_primary_ctx
2023-01-11T21:22:29.4954419Z  test_cuda_sanitizer
2023-01-11T21:22:29.4954586Z  test_cuda_trace
2023-01-11T21:22:29.4954761Z  test_dataloader
2023-01-11T21:22:29.4954939Z  test_datapipe
2023-01-11T21:22:29.4955185Z  test_decomp
2023-01-11T21:22:29.4955438Z  test_deploy
2023-01-11T21:22:29.4955665Z  test_dispatch
2023-01-11T21:22:29.4955908Z  test_dlpack
2023-01-11T21:22:29.4956180Z  test_dynamic_shapes
2023-01-11T21:22:29.4956474Z  test_expanded_weights
2023-01-11T21:22:29.4956693Z  test_fake_tensor
2023-01-11T21:22:29.4956929Z  test_foreach
2023-01-11T21:22:29.4957262Z  test_function_schema
2023-01-11T21:22:29.4957617Z  test_functional_autograd_benchmark
2023-01-11T21:22:29.4957931Z  test_functional_optim
2023-01-11T21:22:29.4958122Z  test_functionalization
2023-01-11T21:22:29.4958285Z  test_futures
2023-01-11T21:22:29.4958508Z  test_fx
2023-01-11T21:22:29.4958822Z  test_fx_experimental
2023-01-11T21:22:29.4959127Z  test_fx_passes
2023-01-11T21:22:29.4959417Z  test_fx_reinplace_pass
2023-01-11T21:22:29.4959679Z  test_hub
2023-01-11T21:22:29.4959955Z  test_import_stats
2023-01-11T21:22:29.4960208Z  test_indexing
2023-01-11T21:22:29.4960462Z  test_itt
2023-01-11T21:22:29.4960725Z  test_jit
2023-01-11T21:22:29.4961016Z  test_jit_autocast
2023-01-11T21:22:29.4961327Z  test_jit_cuda_fuser
2023-01-11T21:22:29.4961618Z  test_jit_disabled
2023-01-11T21:22:29.4961923Z  test_jit_fuser_te
2023-01-11T21:22:29.4962235Z  test_jit_llga_fuser
2023-01-11T21:22:29.4962502Z  test_jiterator
2023-01-11T21:22:29.4962675Z  test_legacy_vmap
2023-01-11T21:22:29.4962847Z  test_license
2023-01-11T21:22:29.4962997Z  test_linalg
2023-01-11T21:22:29.4963158Z  test_logging
2023-01-11T21:22:29.4963321Z  test_masked
2023-01-11T21:22:29.4963477Z  test_maskedtensor
2023-01-11T21:22:29.4963650Z  test_matmul_cuda
2023-01-11T21:22:29.4963841Z  test_meta
2023-01-11T21:22:29.4964005Z  test_mkl_verbose
2023-01-11T21:22:29.4964158Z  test_mkldnn
2023-01-11T21:22:29.4964325Z  test_mkldnn_fusion
2023-01-11T21:22:29.4964497Z  test_mkldnn_verbose
2023-01-11T21:22:29.4964669Z  test_mobile_optimizer
2023-01-11T21:22:29.4964851Z  test_model_dump
2023-01-11T21:22:29.4965026Z  test_module_init
2023-01-11T21:22:29.4965186Z  test_modules
2023-01-11T21:22:29.4965346Z  test_monitor
2023-01-11T21:22:29.4965518Z  test_multiprocessing
2023-01-11T21:22:29.4965704Z  test_multiprocessing_spawn
2023-01-11T21:22:29.4965894Z  test_namedtensor
2023-01-11T21:22:29.4966080Z  test_namedtuple_return_api
2023-01-11T21:22:29.4966258Z  test_native_functions
2023-01-11T21:22:29.4966435Z  test_native_mha
2023-01-11T21:22:29.4966606Z  test_nestedtensor
2023-01-11T21:22:29.4966760Z  test_nn
2023-01-11T21:22:29.4966930Z  test_numba_integration
2023-01-11T21:22:29.4967114Z  test_numpy_interop
2023-01-11T21:22:29.4967279Z  test_nvfuser_dynamo
2023-01-11T21:22:29.4967459Z  test_nvfuser_frontend
2023-01-11T21:22:29.4967637Z  test_openmp
2023-01-11T21:22:29.4967784Z  test_ops
2023-01-11T21:22:29.4967953Z  test_ops_fwd_gradients
2023-01-11T21:22:29.4968134Z  test_ops_gradients
2023-01-11T21:22:29.4968291Z  test_ops_jit
2023-01-11T21:22:29.4968457Z  test_optim
2023-01-11T21:22:29.4968620Z  test_overrides
2023-01-11T21:22:29.4968772Z  test_package
2023-01-11T21:22:29.4969227Z  test_per_overload_api
2023-01-11T21:22:29.4969411Z  test_prims
2023-01-11T21:22:29.4969567Z  test_proxy_tensor
2023-01-11T21:22:29.4969743Z  test_pruning_op
2023-01-11T21:22:29.4969921Z  test_public_bindings
2023-01-11T21:22:29.4970094Z  test_python_dispatch
2023-01-11T21:22:29.4970273Z  test_pytree
2023-01-11T21:22:29.4970447Z  test_quantization
2023-01-11T21:22:29.4970607Z  test_reductions
2023-01-11T21:22:29.4970792Z  test_scatter_gather_ops
2023-01-11T21:22:29.4970976Z  test_schema_check
2023-01-11T21:22:29.4971138Z  test_serialization
2023-01-11T21:22:29.4971341Z  test_set_default_mobile_cpu_allocator
2023-01-11T21:22:29.4971540Z  test_shape_ops
2023-01-11T21:22:29.4971701Z  test_show_pickle
2023-01-11T21:22:29.4971927Z  test_sort_and_select
2023-01-11T21:22:29.4972100Z  test_sparse
2023-01-11T21:22:29.4972254Z  test_sparse_csr
2023-01-11T21:22:29.4972502Z  test_spectral_ops
2023-01-11T21:22:29.4972679Z  test_stateless
2023-01-11T21:22:29.4972835Z  test_subclass
2023-01-11T21:22:29.4973020Z  test_tensor_creation_ops
2023-01-11T21:22:29.4973207Z  test_tensorboard
2023-01-11T21:22:29.4973371Z  test_tensorexpr
2023-01-11T21:22:29.4973554Z  test_tensorexpr_pybind
2023-01-11T21:22:29.4973735Z  test_testing
2023-01-11T21:22:29.4973885Z  test_torch
2023-01-11T21:22:29.4974055Z  test_transformers
2023-01-11T21:22:29.4974229Z  test_type_hints
2023-01-11T21:22:29.4974386Z  test_type_info
2023-01-11T21:22:29.4974559Z  test_type_promotion
2023-01-11T21:22:29.4974740Z  test_unary_ufuncs
2023-01-11T21:22:29.4974893Z  test_utils
2023-01-11T21:22:29.4975055Z  test_view_ops
2023-01-11T21:22:29.4975219Z  test_vulkan
2023-01-11T21:22:29.4975365Z  test_weak
2023-01-11T21:22:29.4975539Z  test_xnnpack_integration
2023-01-11T21:22:29.4975714Z  doctests
2023-01-11T21:22:29.7676071Z Prioritized test from test file changes.
2023-01-11T21:22:29.7676424Z reordering tests for PR:
2023-01-11T21:22:29.7677932Z prioritized: ['dynamo/test_export', 'dynamo/test_misc', 'dynamo/test_optimizations', 'dynamo/test_repros', 'dynamo/test_torchxla_integration', 'dynamo/test_unspec', 'inductor/test_torchinductor', 'inductor/test_torchinductor_opinfo', 'profiler/test_profiler_tree', 'test_ao_sparsity', 'test_autograd', 'test_cpp_extensions_jit', 'test_cuda', 'test_fake_tensor', 'test_foreach', 'test_function_schema', 'test_jit', 'test_masked', 'test_meta', 'test_nn', 'test_overrides', 'test_proxy_tensor', 'test_public_bindings', 'test_python_dispatch', 'test_scatter_gather_ops', 'test_sort_and_select', 'test_sparse', 'test_sparse_csr', 'test_stateless', 'test_testing', 'test_torch', 'test_transformers', 'test_utils']
2023-01-11T21:22:29.7684432Z the rest: ['backends/xeon/test_launch', 'benchmark_utils/test_benchmark_utils', 'distributions/test_distributions', 'dynamo/test_aot_autograd', 'dynamo/test_aot_cudagraphs', 'dynamo/test_comptime', 'dynamo/test_dynamic_shapes', 'dynamo/test_export_mutations', 'dynamo/test_functions', 'dynamo/test_global', 'dynamo/test_global_declaration', 'dynamo/test_minifier', 'dynamo/test_model_output', 'dynamo/test_modules', 'dynamo/test_nops', 'dynamo/test_optimizers', 'dynamo/test_python_autograd', 'dynamo/test_recompile_ux', 'dynamo/test_replay_record', 'dynamo/test_skip_non_tensor', 'dynamo/test_subgraphs', 'dynamo/test_torchxla_num_output', 'dynamo/test_torchxla_util', 'dynamo/test_verify_correctness', 'inductor/test_minifier', 'inductor/test_perf', 'inductor/test_smoke', 'lazy/test_bindings', 'lazy/test_debug_util', 'lazy/test_extract_compiled_graph', 'lazy/test_meta_kernel', 'lazy/test_reuse_ir', 'lazy/test_step_closures', 'lazy/test_ts_opinfo', 'nn/test_convolution', 'nn/test_dropout', 'nn/test_embedding', 'nn/test_init', 'nn/test_lazy_modules', 'nn/test_module_hooks', 'nn/test_multihead_attention', 'nn/test_packed_sequence', 'nn/test_parametrization', 'nn/test_pooling', 'nn/test_pruning', 'profiler/test_memory_profiler', 'profiler/test_profiler', 'test_autocast', 'test_binary_ufuncs', 'test_bundled_inputs', 'test_comparison_utils', 'test_complex', 'test_cpp_api_parity', 'test_cpp_extensions_aot_ninja', 'test_cpp_extensions_aot_no_ninja', 'test_cpp_extensions_open_device_registration', 'test_cuda_nvml_based_avail', 'test_cuda_primary_ctx', 'test_cuda_sanitizer', 'test_cuda_trace', 'test_dataloader', 'test_datapipe', 'test_decomp', 'test_deploy', 'test_dispatch', 'test_dlpack', 'test_dynamic_shapes', 'test_expanded_weights', 'test_functional_autograd_benchmark', 'test_functional_optim', 'test_functionalization', 'test_futures', 'test_fx', 'test_fx_experimental', 'test_fx_passes', 'test_fx_reinplace_pass', 'test_hub', 'test_import_stats', 'test_indexing', 'test_itt', 'test_jit_autocast', 'test_jit_cuda_fuser', 'test_jit_disabled', 'test_jit_fuser_te', 'test_jit_llga_fuser', 'test_jiterator', 'test_legacy_vmap', 'test_license', 'test_linalg', 'test_logging', 'test_maskedtensor', 'test_matmul_cuda', 'test_mkl_verbose', 'test_mkldnn', 'test_mkldnn_fusion', 'test_mkldnn_verbose', 'test_mobile_optimizer', 'test_model_dump', 'test_module_init', 'test_modules', 'test_monitor', 'test_multiprocessing', 'test_multiprocessing_spawn', 'test_namedtensor', 'test_namedtuple_return_api', 'test_native_functions', 'test_native_mha', 'test_nestedtensor', 'test_numba_integration', 'test_numpy_interop', 'test_nvfuser_dynamo', 'test_nvfuser_frontend', 'test_openmp', 'test_ops', 'test_ops_fwd_gradients', 'test_ops_gradients', 'test_ops_jit', 'test_optim', 'test_package', 'test_per_overload_api', 'test_prims', 'test_pruning_op', 'test_pytree', 'test_quantization', 'test_reductions', 'test_schema_check', 'test_serialization', 'test_set_default_mobile_cpu_allocator', 'test_shape_ops', 'test_show_pickle', 'test_spectral_ops', 'test_subclass', 'test_tensor_creation_ops', 'test_tensorboard', 'test_tensorexpr', 'test_tensorexpr_pybind', 'test_type_hints', 'test_type_info', 'test_type_promotion', 'test_unary_ufuncs', 'test_view_ops', 'test_vulkan', 'test_weak', 'test_xnnpack_integration', 'doctests']
2023-01-11T21:22:29.7687897Z 
2023-01-11T21:22:29.7688341Z Downloading https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json
2023-01-11T21:22:29.7910237Z Downloading https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2023-01-11T21:22:29.8044575Z parallel (file granularity) tests:
2023-01-11T21:22:29.8047712Z  dynamo/test_export
2023-01-11T21:22:29.8048130Z  dynamo/test_misc
2023-01-11T21:22:29.8048476Z  dynamo/test_optimizations
2023-01-11T21:22:29.8048737Z  dynamo/test_repros
2023-01-11T21:22:29.8048940Z  dynamo/test_torchxla_integration
2023-01-11T21:22:29.8049294Z  dynamo/test_unspec
2023-01-11T21:22:29.8049474Z  inductor/test_torchinductor
2023-01-11T21:22:29.8049685Z  profiler/test_profiler_tree
2023-01-11T21:22:29.8049879Z  test_ao_sparsity
2023-01-11T21:22:29.8050037Z  test_foreach
2023-01-11T21:22:29.8050220Z  test_function_schema
2023-01-11T21:22:29.8050393Z  test_jit
2023-01-11T21:22:29.8050540Z  test_masked
2023-01-11T21:22:29.8050705Z  test_meta
2023-01-11T21:22:29.8050871Z  test_proxy_tensor
2023-01-11T21:22:29.8051038Z  test_public_bindings
2023-01-11T21:22:29.8051220Z  test_python_dispatch
2023-01-11T21:22:29.8051407Z  test_scatter_gather_ops
2023-01-11T21:22:29.8051580Z  test_sort_and_select
2023-01-11T21:22:29.8051752Z  test_sparse
2023-01-11T21:22:29.8051918Z  test_stateless
2023-01-11T21:22:29.8052070Z  test_testing
2023-01-11T21:22:29.8052238Z  test_transformers
2023-01-11T21:22:29.8052407Z  test_utils
2023-01-11T21:22:29.8052571Z  backends/xeon/test_launch
2023-01-11T21:22:29.8052779Z  benchmark_utils/test_benchmark_utils
2023-01-11T21:22:29.8052986Z  dynamo/test_aot_autograd
2023-01-11T21:22:29.8053166Z  dynamo/test_aot_cudagraphs
2023-01-11T21:22:29.8053356Z  dynamo/test_comptime
2023-01-11T21:22:29.8053544Z  dynamo/test_dynamic_shapes
2023-01-11T21:22:29.8053734Z  dynamo/test_export_mutations
2023-01-11T21:22:29.8053926Z  dynamo/test_functions
2023-01-11T21:22:29.8054105Z  dynamo/test_global
2023-01-11T21:22:29.8054282Z  dynamo/test_global_declaration
2023-01-11T21:22:29.8054594Z  dynamo/test_minifier
2023-01-11T21:22:29.8054783Z  dynamo/test_model_output
2023-01-11T21:22:29.8054956Z  dynamo/test_modules
2023-01-11T21:22:29.8055135Z  dynamo/test_nops
2023-01-11T21:22:29.8055318Z  dynamo/test_optimizers
2023-01-11T21:22:29.8055500Z  dynamo/test_python_autograd
2023-01-11T21:22:29.8055698Z  dynamo/test_recompile_ux
2023-01-11T21:22:29.8055896Z  dynamo/test_replay_record
2023-01-11T21:22:29.8056082Z  dynamo/test_skip_non_tensor
2023-01-11T21:22:29.8056273Z  dynamo/test_subgraphs
2023-01-11T21:22:29.8056473Z  dynamo/test_torchxla_num_output
2023-01-11T21:22:29.8056663Z  dynamo/test_torchxla_util
2023-01-11T21:22:29.8056864Z  dynamo/test_verify_correctness
2023-01-11T21:22:29.8057058Z  inductor/test_minifier
2023-01-11T21:22:29.8057230Z  inductor/test_perf
2023-01-11T21:22:29.8057462Z  inductor/test_smoke
2023-01-11T21:22:29.8057643Z  lazy/test_bindings
2023-01-11T21:22:29.8057807Z  lazy/test_debug_util
2023-01-11T21:22:29.8058009Z  lazy/test_extract_compiled_graph
2023-01-11T21:22:29.8058205Z  lazy/test_meta_kernel
2023-01-11T21:22:29.8058371Z  lazy/test_reuse_ir
2023-01-11T21:22:29.8058553Z  lazy/test_step_closures
2023-01-11T21:22:29.8058737Z  lazy/test_ts_opinfo
2023-01-11T21:22:29.8058899Z  nn/test_dropout
2023-01-11T21:22:29.8059073Z  nn/test_embedding
2023-01-11T21:22:29.8059244Z  nn/test_init
2023-01-11T21:22:29.8059401Z  nn/test_lazy_modules
2023-01-11T21:22:29.8059581Z  nn/test_module_hooks
2023-01-11T21:22:29.8059769Z  nn/test_multihead_attention
2023-01-11T21:22:29.8059962Z  nn/test_packed_sequence
2023-01-11T21:22:29.8060139Z  nn/test_parametrization
2023-01-11T21:22:29.8060320Z  nn/test_pruning
2023-01-11T21:22:29.8060509Z  profiler/test_memory_profiler
2023-01-11T21:22:29.8060694Z  profiler/test_profiler
2023-01-11T21:22:29.8060872Z  test_autocast
2023-01-11T21:22:29.8061044Z  test_binary_ufuncs
2023-01-11T21:22:29.8061209Z  test_bundled_inputs
2023-01-11T21:22:29.8061393Z  test_comparison_utils
2023-01-11T21:22:29.8061571Z  test_complex
2023-01-11T21:22:29.8061727Z  test_cuda_sanitizer
2023-01-11T21:22:29.8061903Z  test_dataloader
2023-01-11T21:22:29.8062072Z  test_datapipe
2023-01-11T21:22:29.8062222Z  test_decomp
2023-01-11T21:22:29.8062384Z  test_deploy
2023-01-11T21:22:29.8062544Z  test_dlpack
2023-01-11T21:22:29.8062698Z  test_dynamic_shapes
2023-01-11T21:22:29.8062880Z  test_expanded_weights
2023-01-11T21:22:29.8063084Z  test_functional_autograd_benchmark
2023-01-11T21:22:29.8063271Z  test_functional_optim
2023-01-11T21:22:29.8063457Z  test_functionalization
2023-01-11T21:22:29.8063710Z  test_futures
2023-01-11T21:22:29.8063872Z  test_fx_experimental
2023-01-11T21:22:29.8064054Z  test_fx_passes
2023-01-11T21:22:29.8064236Z  test_fx_reinplace_pass
2023-01-11T21:22:29.8064398Z  test_hub
2023-01-11T21:22:29.8064564Z  test_import_stats
2023-01-11T21:22:29.8064732Z  test_itt
2023-01-11T21:22:29.8064884Z  test_jit_autocast
2023-01-11T21:22:29.8065060Z  test_jit_fuser_te
2023-01-11T21:22:29.8065236Z  test_jit_llga_fuser
2023-01-11T21:22:29.8065404Z  test_jiterator
2023-01-11T21:22:29.8065581Z  test_legacy_vmap
2023-01-11T21:22:29.8065751Z  test_license
2023-01-11T21:22:29.8065900Z  test_logging
2023-01-11T21:22:29.8066067Z  test_maskedtensor
2023-01-11T21:22:29.8066237Z  test_matmul_cuda
2023-01-11T21:22:29.8066393Z  test_mkl_verbose
2023-01-11T21:22:29.8066557Z  test_mkldnn
2023-01-11T21:22:29.8066724Z  test_mkldnn_fusion
2023-01-11T21:22:29.8066955Z  test_mkldnn_verbose
2023-01-11T21:22:29.8067143Z  test_model_dump
2023-01-11T21:22:29.8067313Z  test_module_init
2023-01-11T21:22:29.8067466Z  test_monitor
2023-01-11T21:22:29.8067633Z  test_namedtensor
2023-01-11T21:22:29.8067809Z  test_native_functions
2023-01-11T21:22:29.8067973Z  test_native_mha
2023-01-11T21:22:29.8068145Z  test_nestedtensor
2023-01-11T21:22:29.8068327Z  test_numba_integration
2023-01-11T21:22:29.8068497Z  test_numpy_interop
2023-01-11T21:22:29.8068680Z  test_nvfuser_dynamo
2023-01-11T21:22:29.8068860Z  test_nvfuser_frontend
2023-01-11T21:22:29.8069019Z  test_openmp
2023-01-11T21:22:29.8069238Z  test_optim
2023-01-11T21:22:29.8069402Z  test_package
2023-01-11T21:22:29.8069564Z  test_per_overload_api
2023-01-11T21:22:29.8069746Z  test_pruning_op
2023-01-11T21:22:29.8069917Z  test_pytree
2023-01-11T21:22:29.8070073Z  test_quantization
2023-01-11T21:22:29.8070249Z  test_schema_check
2023-01-11T21:22:29.8070426Z  test_serialization
2023-01-11T21:22:29.8070618Z  test_set_default_mobile_cpu_allocator
2023-01-11T21:22:29.8070818Z  test_shape_ops
2023-01-11T21:22:29.8070985Z  test_subclass
2023-01-11T21:22:29.8071173Z  test_tensorboard
2023-01-11T21:22:29.8071418Z  test_tensorexpr_pybind
2023-01-11T21:22:29.8071600Z  test_type_hints
2023-01-11T21:22:29.8071757Z  test_type_info
2023-01-11T21:22:29.8071929Z  test_type_promotion
2023-01-11T21:22:29.8072109Z  test_unary_ufuncs
2023-01-11T21:22:29.8072268Z  test_view_ops
2023-01-11T21:22:29.8072497Z  test_vulkan
2023-01-11T21:22:29.8072661Z  test_weak
2023-01-11T21:22:29.8072823Z  test_xnnpack_integration
2023-01-11T21:22:29.8073029Z serial (file granularity) tests:
2023-01-11T21:22:29.8073248Z  inductor/test_torchinductor_opinfo
2023-01-11T21:22:29.8073433Z  test_autograd
2023-01-11T21:22:29.8073616Z  test_cpp_extensions_jit
2023-01-11T21:22:29.8073794Z  test_cuda
2023-01-11T21:22:29.8073950Z  test_fake_tensor
2023-01-11T21:22:29.8074115Z  test_nn
2023-01-11T21:22:29.8074280Z  test_overrides
2023-01-11T21:22:29.8074436Z  test_sparse_csr
2023-01-11T21:22:29.8074603Z  test_torch
2023-01-11T21:22:29.8074794Z  distributions/test_distributions
2023-01-11T21:22:29.8074979Z  nn/test_convolution
2023-01-11T21:22:29.8075155Z  nn/test_pooling
2023-01-11T21:22:29.8075332Z  test_cpp_api_parity
2023-01-11T21:22:29.8075512Z  test_cpp_extensions_aot_ninja
2023-01-11T21:22:29.8075721Z  test_cpp_extensions_aot_no_ninja
2023-01-11T21:22:29.8076020Z  test_cpp_extensions_open_device_registration
2023-01-11T21:22:29.8076230Z  test_cuda_nvml_based_avail
2023-01-11T21:22:29.8076420Z  test_cuda_primary_ctx
2023-01-11T21:22:29.8076597Z  test_cuda_trace
2023-01-11T21:22:29.8076756Z  test_dispatch
2023-01-11T21:22:29.8076918Z  test_fx
2023-01-11T21:22:29.8077076Z  test_indexing
2023-01-11T21:22:29.8077232Z  test_jit_cuda_fuser
2023-01-11T21:22:29.8077408Z  test_jit_disabled
2023-01-11T21:22:29.8077578Z  test_linalg
2023-01-11T21:22:29.8077739Z  test_mobile_optimizer
2023-01-11T21:22:29.8077911Z  test_modules
2023-01-11T21:22:29.8078085Z  test_multiprocessing
2023-01-11T21:22:29.8078267Z  test_multiprocessing_spawn
2023-01-11T21:22:29.8078470Z  test_namedtuple_return_api
2023-01-11T21:22:29.8078648Z  test_ops
2023-01-11T21:22:29.8078801Z  test_ops_fwd_gradients
2023-01-11T21:22:29.8078984Z  test_ops_gradients
2023-01-11T21:22:29.8079156Z  test_ops_jit
2023-01-11T21:22:29.8079305Z  test_prims
2023-01-11T21:22:29.8079470Z  test_reductions
2023-01-11T21:22:29.8079641Z  test_show_pickle
2023-01-11T21:22:29.8079806Z  test_spectral_ops
2023-01-11T21:22:29.8079992Z  test_tensor_creation_ops
2023-01-11T21:22:29.8080177Z  test_tensorexpr
2023-01-11T21:22:29.8080328Z  doctests
2023-01-11T21:22:31.4281152Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:31.4344725Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:31.4923119Z Ignoring disabled issues:  []
2023-01-11T21:22:31.4990126Z Ignoring disabled issues:  []
2023-01-11T21:22:31.5055423Z Running dynamo/test_export ... [2023-01-11 21:22:31.505334]
2023-01-11T21:22:31.5057507Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_export.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:31.505561]
2023-01-11T21:22:31.5120940Z Running dynamo/test_misc ... [2023-01-11 21:22:31.511887]
2023-01-11T21:22:31.5123151Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_misc.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:31.512129]
2023-01-11T21:22:36.4437638Z 
2023-01-11T21:22:36.4438435Z Expand the folded group to see the log file of dynamo/test_export
2023-01-11T21:22:36.4444148Z ##[group]PRINTING LOG FILE of dynamo/test_export (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_oad0law8)
2023-01-11T21:22:36.4444568Z 
2023-01-11T21:22:36.4444717Z Running tests...
2023-01-11T21:22:36.4445108Z ----------------------------------------------------------------------
2023-01-11T21:22:36.4445498Z Test results will be stored in test-reports/python-unittest/dynamo.test_export
2023-01-11T21:22:36.4445806Z   test_dict_return (__main__.ExportTests) ... ok (0.925s)
2023-01-11T21:22:36.4446281Z   test_dict_return_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:36.4446695Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:36.4446926Z ok (0.038s)
2023-01-11T21:22:36.4447350Z   test_dupes (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4447601Z ok (0.008s)
2023-01-11T21:22:36.4447954Z   test_dupes_2 (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4448328Z ok (0.008s)
2023-01-11T21:22:36.4448729Z   test_dupes_2_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4449002Z ok (0.020s)
2023-01-11T21:22:36.4449574Z   test_dupes_and_bypass (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4449845Z ok (0.009s)
2023-01-11T21:22:36.4450244Z   test_dupes_and_bypass_reorder_with_non_tensor_arg (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4450538Z ok (0.091s)
2023-01-11T21:22:36.4450976Z   test_dupes_and_bypass_reorder_with_non_tensor_arg_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4451264Z ok (0.022s)
2023-01-11T21:22:36.4451658Z   test_dupes_and_bypass_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4451941Z ok (0.021s)
2023-01-11T21:22:36.4452325Z   test_dupes_and_bypass_with_non_tensor_arg (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4452605Z ok (0.010s)
2023-01-11T21:22:36.4453025Z   test_dupes_and_bypass_with_non_tensor_arg_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4453324Z ok (0.021s)
2023-01-11T21:22:36.4453710Z   test_dupes_and_bypass_with_non_tensor_output (__main__.ExportTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:36.4453993Z ok (0.013s)
2023-01-11T21:22:36.4454421Z   test_dupes_and_bypass_with_non_tensor_output_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:36.4454725Z ok (0.013s)
2023-01-11T21:22:36.4455089Z   test_dupes_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4455361Z ok (0.020s)
2023-01-11T21:22:36.4455620Z   test_export (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:36.4455951Z stats [('calls_captured', 80), ('fusions_possible', 78), ('unique_graphs', 2)]
2023-01-11T21:22:36.4456178Z ok (0.118s)
2023-01-11T21:22:36.4456581Z   test_export_compare_optimize_with_make_fx (__main__.ExportTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:36.4456848Z ok (0.266s)
2023-01-11T21:22:36.4457214Z   test_export_decomp (__main__.ExportTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:36.4457478Z ok (0.071s)
2023-01-11T21:22:36.4457712Z   test_export_decomp_asserts_bad_args (__main__.ExportTests) ... ok (0.001s)
2023-01-11T21:22:36.4458086Z   test_export_decomp_asserts_bad_args_mode (__main__.ExportTests) ... ok (0.001s)
2023-01-11T21:22:36.4458532Z   test_export_graph_bypass (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4458804Z ok (0.011s)
2023-01-11T21:22:36.4459189Z   test_export_graph_bypass_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4459467Z ok (0.022s)
2023-01-11T21:22:36.4459862Z   test_export_graph_with_complex_reorder (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:36.4460142Z ok (0.015s)
2023-01-11T21:22:36.4460593Z   test_export_graph_with_complex_reorder_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:36.4460888Z ok (0.040s)
2023-01-11T21:22:36.4461268Z   test_export_graph_with_list (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4461525Z ok (0.013s)
2023-01-11T21:22:36.4461925Z   test_export_graph_with_list_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4462205Z ok (0.024s)
2023-01-11T21:22:36.4462567Z   test_export_meta_val (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:36.4462816Z ok (0.092s)
2023-01-11T21:22:36.4463193Z   test_export_mismatched_out (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4463464Z ok (0.009s)
2023-01-11T21:22:36.4463924Z   test_export_mismatched_out_2 (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4464200Z ok (0.009s)
2023-01-11T21:22:36.4464710Z   test_export_mismatched_out_2_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4464989Z ok (0.021s)
2023-01-11T21:22:36.4465432Z   test_export_mismatched_out_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4465713Z ok (0.020s)
2023-01-11T21:22:36.4466030Z   test_export_shape_control_flow_1 (__main__.ExportTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:36.4466383Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:36.4466606Z ok (0.052s)
2023-01-11T21:22:36.4466834Z   test_export_with_aten_graph (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:36.4467179Z stats [('calls_captured', 80), ('fusions_possible', 78), ('unique_graphs', 2)]
2023-01-11T21:22:36.4467403Z ok (0.367s)
2023-01-11T21:22:36.4467800Z   test_export_with_constant_dict_values (__main__.ExportTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:36.4468081Z ok (0.011s)
2023-01-11T21:22:36.4468466Z   test_export_with_constant_free_function (__main__.ExportTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:36.4468748Z ok (0.016s)
2023-01-11T21:22:36.4469167Z   test_export_with_constant_free_function_and_class_method (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:36.4469445Z ok (0.013s)
2023-01-11T21:22:36.4469878Z   test_export_with_constant_free_function_and_class_method_multiarg (__main__.ExportTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:36.4470244Z ok (0.015s)
2023-01-11T21:22:36.4470761Z   test_export_with_constant_free_function_and_class_method_multiarg_diff (__main__.ExportTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:36.4471150Z ok (0.008s)
2023-01-11T21:22:36.4471675Z   test_export_with_constant_list_nonzero (__main__.ExportTests) ... stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:36.4471969Z ok (0.018s)
2023-01-11T21:22:36.4472444Z   test_export_with_constant_list_nonzero_free_function (__main__.ExportTests) ... stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:36.4472795Z ok (0.019s)
2023-01-11T21:22:36.4473209Z   test_export_with_constant_method_on_module (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:36.4473576Z ok (0.023s)
2023-01-11T21:22:36.4474084Z   test_export_with_constant_method_on_module_invoke_twice (__main__.ExportTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:36.4474439Z ok (0.027s)
2023-01-11T21:22:36.4474856Z   test_export_with_constant_none_control_flow (__main__.ExportTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:36.4475193Z ok (0.011s)
2023-01-11T21:22:36.4475684Z   test_export_with_constant_none_control_flow_free_func (__main__.ExportTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:36.4476008Z ok (0.010s)
2023-01-11T21:22:36.4476469Z   test_export_with_constant_not_none_control_flow (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:36.4476805Z ok (0.013s)
2023-01-11T21:22:36.4477296Z   test_export_with_constant_not_none_control_flow_free_func (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:36.4477606Z ok (0.009s)
2023-01-11T21:22:36.4478081Z   test_export_with_constant_not_none_control_flow_pos (__main__.ExportTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:36.4478426Z ok (0.008s)
2023-01-11T21:22:36.4478836Z   test_export_with_constant_not_return_const (__main__.ExportTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:36.4479188Z ok (0.006s)
2023-01-11T21:22:36.4479653Z   test_export_with_constant_tuple_nonzero (__main__.ExportTests) ... stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:36.4479989Z ok (0.018s)
2023-01-11T21:22:36.4480232Z   test_export_with_module_layer (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:36.4480642Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:36.4480949Z ok (0.026s)
2023-01-11T21:22:36.4481341Z   test_export_with_stack_trace (__main__.ExportTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:36.4481668Z ok (0.104s)
2023-01-11T21:22:36.4481938Z   test_func_return (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:36.4482301Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:36.4482609Z ok (0.014s)
2023-01-11T21:22:36.4482897Z   test_func_return_with_aten_graph (__main__.ExportTests) ... inline_call []
2023-01-11T21:22:36.4483310Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:36.4483552Z ok (0.047s)
2023-01-11T21:22:36.4484007Z   test_input_container_type (__main__.ExportTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:36.4484332Z ok (0.065s)
2023-01-11T21:22:36.4484709Z   test_list_unpack (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4485033Z ok (0.013s)
2023-01-11T21:22:36.4485473Z   test_list_unpack_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:36.4485779Z ok (0.024s)
2023-01-11T21:22:36.4486254Z   test_zeroes_in_and_out_different_shape_on_test (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:36.4486645Z ok (0.014s)
2023-01-11T21:22:36.4487129Z   test_zeroes_in_and_out_different_shape_on_test_with_aten_graph (__main__.ExportTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:36.4487445Z ok (0.038s)
2023-01-11T21:22:36.4487919Z   test_zeroes_in_new_shape_scalar_out (__main__.ExportTests) ... stats [('calls_captured', 16), ('fusions_possible', 14), ('unique_graphs', 2)]
2023-01-11T21:22:36.4488298Z ok (0.021s)
2023-01-11T21:22:36.4488732Z   test_zeroes_in_new_shape_scalar_out_permute (__main__.ExportTests) ... stats [('calls_captured', 22), ('fusions_possible', 20), ('unique_graphs', 2)]
2023-01-11T21:22:36.4489213Z ok (0.026s)
2023-01-11T21:22:36.4489866Z   test_zeroes_in_new_shape_scalar_out_permute_dupe_and_bypass (__main__.ExportTests) ... stats [('calls_captured', 22), ('fusions_possible', 20), ('unique_graphs', 2)]
2023-01-11T21:22:36.4528602Z ok (0.026s)
2023-01-11T21:22:36.4528787Z 
2023-01-11T21:22:36.4529223Z ----------------------------------------------------------------------
2023-01-11T21:22:36.4529462Z Ran 60 tests in 3.016s
2023-01-11T21:22:36.4529571Z 
2023-01-11T21:22:36.4529624Z OK
2023-01-11T21:22:36.4529710Z 
2023-01-11T21:22:36.4529794Z Generating XML reports...
2023-01-11T21:22:36.4530197Z Generated XML report: test-reports/python-unittest/dynamo.test_export/TEST-ExportTests-20230111212233.xml
2023-01-11T21:22:36.4530416Z 
2023-01-11T21:22:36.4530691Z ##[endgroup]
2023-01-11T21:22:36.4531075Z FINISHED PRINTING LOG FILE of dynamo/test_export (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_oad0law8)
2023-01-11T21:22:36.4531292Z 
2023-01-11T21:22:37.8427489Z 
2023-01-11T21:22:37.8427963Z Expand the folded group to see the log file of dynamo/test_misc
2023-01-11T21:22:37.8429017Z ##[group]PRINTING LOG FILE of dynamo/test_misc (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_misc_yb8r8erq)
2023-01-11T21:22:37.8429378Z 
2023-01-11T21:22:37.8429501Z Running tests...
2023-01-11T21:22:37.8430506Z ----------------------------------------------------------------------
2023-01-11T21:22:37.8431055Z Test results will be stored in test-reports/python-unittest/dynamo.test_misc
2023-01-11T21:22:37.8431484Z   test_allow_in_graph (__main__.MiscTests) ... ok (0.925s)
2023-01-11T21:22:37.8431996Z   test_autocast (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8432547Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:37.8433125Z skip: requires cuda (0.001s)
2023-01-11T21:22:37.8434042Z   test_autocast_cpu (__main__.MiscTests) ... stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:37.8434679Z ok (0.030s)
2023-01-11T21:22:37.8435231Z   test_autocast_device (__main__.MiscTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:37.8435980Z   test_autocast_float64 (__main__.MiscTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:37.8439062Z   test_autograd_function_equivalence (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8445725Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:22:37.8446170Z ok (0.100s)
2023-01-11T21:22:37.8446922Z   test_autograd_profiler (__main__.MiscTests) ... STAGE:2023-01-11 21:22:34 1287:1287 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:37.8447836Z STAGE:2023-01-11 21:22:34 1287:1287 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:37.8448658Z STAGE:2023-01-11 21:22:34 1287:1287 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:37.8449301Z [2023-01-11 21:22:34,096] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:22:37.8449618Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8449801Z unimplemented []
2023-01-11T21:22:37.8450039Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:22:37.8450544Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:37.8450765Z ok (0.018s)
2023-01-11T21:22:37.8451389Z   test_autograd_profiler_enabled (__main__.MiscTests) ... STAGE:2023-01-11 21:22:34 1287:1287 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:37.8451876Z STAGE:2023-01-11 21:22:34 1287:1287 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:37.8452321Z STAGE:2023-01-11 21:22:34 1287:1287 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:37.8452624Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:37.8452815Z unimplemented []
2023-01-11T21:22:37.8453121Z graph_break [('torch.autograd._profiler_enabled not supported yet', 1)]
2023-01-11T21:22:37.8453527Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:37.8453763Z ok (0.011s)
2023-01-11T21:22:37.8454128Z   test_boolarg (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:22:37.8454391Z ok (0.011s)
2023-01-11T21:22:37.8454599Z   test_build_tuple_unpack (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8454950Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:37.8455177Z ok (0.013s)
2023-01-11T21:22:37.8455488Z   test_builder_for_class_with_metaclass (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8455862Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8456090Z ok (0.005s)
2023-01-11T21:22:37.8456449Z   test_builtin_isinstance (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8456717Z ok (0.011s)
2023-01-11T21:22:37.8456964Z   test_builtin_subclasses_as_method_on_class_type (__main__.MiscTests) ... ok (0.002s)
2023-01-11T21:22:37.8457273Z   test_builtin_subclasses_as_method_on_var (__main__.MiscTests) ... ok (0.004s)
2023-01-11T21:22:37.8457573Z   test_call_parent_non_class_methods_from_child (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8457943Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8458172Z ok (0.008s)
2023-01-11T21:22:37.8458524Z   test_callpacked (__main__.MiscTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:37.8458790Z ok (0.010s)
2023-01-11T21:22:37.8459082Z   test_cell_output1 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8459419Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8459646Z ok (0.005s)
2023-01-11T21:22:37.8459937Z   test_cell_output2 (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8460172Z unimplemented []
2023-01-11T21:22:37.8460547Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:22:37.8460968Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8461190Z ok (0.009s)
2023-01-11T21:22:37.8461901Z   test_change_backends (__main__.MiscTests) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T21:22:37.8462514Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T21:22:37.8462872Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:22:37.8463152Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8463321Z ok (0.057s)
2023-01-11T21:22:37.8463684Z   test_cond (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8463909Z inline_call []
2023-01-11T21:22:37.8464200Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8464475Z ok (0.013s)
2023-01-11T21:22:37.8464689Z   test_cond_export (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8465033Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8465245Z ok (0.016s)
2023-01-11T21:22:37.8465472Z   test_cond_export_single_arg (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8465823Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8466036Z ok (0.009s)
2023-01-11T21:22:37.8466324Z   test_cond_nested (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8466550Z inline_call []
2023-01-11T21:22:37.8466836Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8467095Z ok (0.015s)
2023-01-11T21:22:37.8467330Z   test_cond_side_effects (__main__.MiscTests) ... expected failure (0.001s)
2023-01-11T21:22:37.8467691Z   test_config_getattr_default (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:37.8468062Z stats [('calls_captured', 21), ('fusions_possible', 18), ('unique_graphs', 3)]
2023-01-11T21:22:37.8468291Z ok (0.034s)
2023-01-11T21:22:37.8468588Z   test_config_log_level (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8468931Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8469159Z ok (0.006s)
2023-01-11T21:22:37.8469444Z   test_config_obj (__main__.MiscTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:37.8469777Z stats [('calls_captured', 8), ('fusions_possible', 4), ('unique_graphs', 4)]
2023-01-11T21:22:37.8470003Z ok (0.021s)
2023-01-11T21:22:37.8470235Z   test_const_dict_variable_python_type (__main__.MiscTests) ... ok (0.001s)
2023-01-11T21:22:37.8470831Z   test_cross_entropy_loss_fancy_ctor (__main__.MiscTests) ... /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:22:37.8471240Z   warnings.warn(warning.format(ret))
2023-01-11T21:22:37.8471438Z ok (0.002s)
2023-01-11T21:22:37.8471667Z   test_cross_entropy_loss_simple_ctor (__main__.MiscTests) ... ok (0.001s)
2023-01-11T21:22:37.8472014Z   test_dataclass_fields (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8472246Z inline_call []
2023-01-11T21:22:37.8472550Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:37.8472765Z ok (0.034s)
2023-01-11T21:22:37.8473075Z   test_dict_mutation_side_effect (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8473438Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8473650Z ok (0.005s)
2023-01-11T21:22:37.8473991Z   test_dict_reconstruct_keeps_original_order (__main__.MiscTests) ... frames [('total', 13), ('ok', 12)]
2023-01-11T21:22:37.8474462Z unimplemented [("Guard setup for uninitialized class <class 'torch.nn.modules.container.ModuleDict'>", 1)]
2023-01-11T21:22:37.8475395Z graph_break [('UnspecializedNNModuleVariable missing add_module', 3), ('construct nn.Module: ReLU', 1), ('call_function in skip_files /opt/conda/lib/python3.10/collections/__init__.py', 1), ('construct nn.Module: ModuleDict', 1), ('Patched init cannot be inlined.', 1), ('construct nn.Module: Linear', 1), ('construct nn.Module: Sigmoid', 1), ('call_method ConstDictVariable() update [TupleVariable()] {}', 1)]
2023-01-11T21:22:37.8476063Z inline_call [('inline __setitem__', 2), ('Patched init cannot be inlined.', 1)]
2023-01-11T21:22:37.8476282Z ok (0.036s)
2023-01-11T21:22:37.8476567Z   test_dictcomp (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8476792Z inline_call []
2023-01-11T21:22:37.8477079Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8477307Z ok (0.007s)
2023-01-11T21:22:37.8477554Z   test_disable_flag (__main__.MiscTests) ... ok (0.002s)
2023-01-11T21:22:37.8477799Z   test_disable_optimize (__main__.MiscTests) ... ok (0.002s)
2023-01-11T21:22:37.8478150Z   test_disallow_in_graph (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8478389Z unimplemented []
2023-01-11T21:22:37.8478758Z graph_break [('call_function UserDefinedObjectVariable(sub) [TensorVariable(), ConstantVariable(int)] {}', 1)]
2023-01-11T21:22:37.8479171Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:37.8479396Z ok (0.011s)
2023-01-11T21:22:37.8479619Z   test_dunder_methods (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8479948Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:37.8480179Z ok (0.020s)
2023-01-11T21:22:37.8480435Z   test_duplicate_graph_break_warning (__main__.MiscTests) ... break
2023-01-11T21:22:37.8480644Z break
2023-01-11T21:22:37.8480855Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:22:37.8481193Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:22:37.8481438Z unimplemented []
2023-01-11T21:22:37.8481767Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 4)]
2023-01-11T21:22:37.8482143Z stats [('calls_captured', 6), ('unique_graphs', 4), ('fusions_possible', 2)]
2023-01-11T21:22:37.8482371Z ok (0.026s)
2023-01-11T21:22:37.8482589Z   test_dynamo_min_operator_with_shape (__main__.MiscTests) ... ok (0.002s)
2023-01-11T21:22:37.8482937Z   test_empty_list (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8483283Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:37.8483496Z ok (0.008s)
2023-01-11T21:22:37.8483875Z   test_enum_no_graphbreaks (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:37.8484146Z ok (0.010s)
2023-01-11T21:22:37.8484442Z   test_error_on_nested_fx_trace (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8484807Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8485032Z ok (0.005s)
2023-01-11T21:22:37.8485365Z   test_fold (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8485622Z ok (0.004s)
2023-01-11T21:22:37.8486014Z   test_frozenset_torch_func_contains (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:37.8486291Z ok (0.009s)
2023-01-11T21:22:37.8486650Z   test_function_annotation (__main__.MiscTests) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:37.8486917Z ok (0.008s)
2023-01-11T21:22:37.8487270Z   test_generate_tensor_from_list_of_numpy_primitive_type (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8487524Z unimplemented []
2023-01-11T21:22:37.8487748Z graph_break [('numpy', 1)]
2023-01-11T21:22:37.8488057Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8488268Z ok (0.007s)
2023-01-11T21:22:37.8488493Z   test_get_device (__main__.MiscTests) ... skip: requires cuda (0.000s)
2023-01-11T21:22:37.8488829Z   test_grad (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8489245Z unimplemented []
2023-01-11T21:22:37.8489494Z graph_break [('Tensor.backward', 1)]
2023-01-11T21:22:37.8489822Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:37.8490050Z ok (0.012s)
2023-01-11T21:22:37.8490330Z   test_grad_mode_guard (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8490565Z unimplemented []
2023-01-11T21:22:37.8490801Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:22:37.8491113Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:37.8491339Z ok (0.011s)
2023-01-11T21:22:37.8491705Z   test_graph_break (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:37.8491923Z unimplemented []
2023-01-11T21:22:37.8492297Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 2)]
2023-01-11T21:22:37.8492698Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:22:37.8492927Z ok (0.015s)
2023-01-11T21:22:37.8493284Z   test_guard_failure_fn (__main__.MiscTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:37.8493548Z ok (0.012s)
2023-01-11T21:22:37.8493916Z   test_guard_failure_fn2 (__main__.MiscTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:37.8494167Z ok (0.010s)
2023-01-11T21:22:37.8494566Z   test_id_of_nn_module (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:37.8494834Z ok (0.010s)
2023-01-11T21:22:37.8495186Z   test_if_cond_nn_mod (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:37.8495446Z ok (0.010s)
2023-01-11T21:22:37.8495738Z   test_inference_mode (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8496089Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8496302Z ok (0.005s)
2023-01-11T21:22:37.8496603Z   test_inline_dict_mutation (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8496836Z inline_call []
2023-01-11T21:22:37.8497121Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8497347Z ok (0.016s)
2023-01-11T21:22:37.8497674Z   test_inline_func_jump_on_tensor_condition (__main__.MiscTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:37.8498001Z inline_call [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:37.8498222Z unimplemented []
2023-01-11T21:22:37.8498488Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:37.8498818Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:22:37.8499043Z ok (0.012s)
2023-01-11T21:22:37.8499347Z   test_inline_list_mutation (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8499579Z inline_call []
2023-01-11T21:22:37.8499864Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8500091Z ok (0.015s)
2023-01-11T21:22:37.8500443Z   test_inplace (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8500689Z ok (0.011s)
2023-01-11T21:22:37.8501067Z   test_inplace_param_update (__main__.MiscTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:37.8501337Z ok (0.007s)
2023-01-11T21:22:37.8501617Z   test_is_compiling (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8501976Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8502199Z ok (0.009s)
2023-01-11T21:22:37.8502565Z   test_is_floating_point (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8502814Z ok (0.006s)
2023-01-11T21:22:37.8503180Z   test_is_floating_point2 (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8503443Z ok (0.006s)
2023-01-11T21:22:37.8503897Z   test_is_tensor (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8504154Z ok (0.006s)
2023-01-11T21:22:37.8504454Z   test_is_tensor2 (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8504791Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:37.8505015Z ok (0.012s)
2023-01-11T21:22:37.8505374Z   test_is_tensor_like (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:37.8505657Z ok (0.012s)
2023-01-11T21:22:37.8505948Z   test_is_tensor_like2 (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:37.8506178Z unimplemented []
2023-01-11T21:22:37.8506486Z graph_break [('call_function args: UserDefinedObjectVariable(MyTensor) ', 1)]
2023-01-11T21:22:37.8506861Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:37.8507084Z ok (0.015s)
2023-01-11T21:22:37.8507429Z   test_item (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8507668Z ok (0.016s)
2023-01-11T21:22:37.8508028Z   test_item_changes (__main__.MiscTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:37.8508313Z ok (0.031s)
2023-01-11T21:22:37.8508679Z   test_item_changes_new_shape (__main__.MiscTests) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:37.8508945Z ok (0.031s)
2023-01-11T21:22:37.8509158Z   test_large_reduction_list (__main__.MiscTests) ... ok (0.011s)
2023-01-11T21:22:37.8509405Z   test_linetable_writer (__main__.MiscTests) ... ok (0.001s)
2023-01-11T21:22:37.8509758Z   test_list_append_return_none (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8510115Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8510341Z ok (0.005s)
2023-01-11T21:22:37.8510606Z   test_list_mul (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8510817Z ok (0.002s)
2023-01-11T21:22:37.8511097Z   test_listcomp (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8511302Z inline_call []
2023-01-11T21:22:37.8511598Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:37.8511819Z ok (0.009s)
2023-01-11T21:22:37.8512044Z   test_lnotab_writer (__main__.MiscTests) ... skip: use lnotab when python < 3.10 (0.000s)
2023-01-11T21:22:37.8512475Z   test_manual_seed (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8512731Z ok (0.007s)
2023-01-11T21:22:37.8513081Z   test_matmul1 (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8513319Z ok (0.004s)
2023-01-11T21:22:37.8513532Z   test_module_complex_iter (__main__.MiscTests) ... ok (0.008s)
2023-01-11T21:22:37.8513873Z   test_module_deepcopy (__main__.MiscTests) ... frames [('total', 6), ('ok', 6)]
2023-01-11T21:22:37.8514092Z unimplemented []
2023-01-11T21:22:37.8514408Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 2)]
2023-01-11T21:22:37.8514649Z inline_call []
2023-01-11T21:22:37.8514934Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:37.8515157Z ok (0.045s)
2023-01-11T21:22:37.8515371Z   test_named_parameters (__main__.MiscTests) ... ok (0.019s)
2023-01-11T21:22:37.8515690Z   test_namedtuple1 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8516036Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8516260Z ok (0.006s)
2023-01-11T21:22:37.8516545Z   test_namedtuple2 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8516874Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8517095Z ok (0.007s)
2023-01-11T21:22:37.8517379Z   test_namedtuple3 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8517710Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8517930Z ok (0.005s)
2023-01-11T21:22:37.8518203Z   test_nan (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8518526Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8518775Z ok (0.005s)
2023-01-11T21:22:37.8518989Z   test_nested_closure (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8519316Z stats [('calls_captured', 9), ('fusions_possible', 7), ('unique_graphs', 2)]
2023-01-11T21:22:37.8519537Z ok (0.032s)
2023-01-11T21:22:37.8519761Z   test_nested_closure_mutation (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8520110Z stats [('calls_captured', 11), ('fusions_possible', 9), ('unique_graphs', 2)]
2023-01-11T21:22:37.8520319Z ok (0.024s)
2023-01-11T21:22:37.8520808Z   test_nested_disable_decorator (__main__.MiscTests) ... [2023-01-11 21:22:35,066] torch._dynamo.convert_frame: [ERROR] WON'T CONVERT fn3 /var/lib/jenkins/workspace/test/dynamo/test_misc.py line 1197 
2023-01-11T21:22:37.8521133Z due to: 
2023-01-11T21:22:37.8521305Z Traceback (most recent call last):
2023-01-11T21:22:37.8521704Z   File "/opt/conda/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 67, in unimplemented
2023-01-11T21:22:37.8521981Z     raise Unsupported(msg)
2023-01-11T21:22:37.8522336Z torch._dynamo.exc.Unsupported: call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7fb1e4b9c280>
2023-01-11T21:22:37.8522583Z 
2023-01-11T21:22:37.8522653Z from user code:
2023-01-11T21:22:37.8522894Z    File "/var/lib/jenkins/workspace/test/dynamo/test_misc.py", line 1199, in fn3
2023-01-11T21:22:37.8523125Z     return fn2(x)
2023-01-11T21:22:37.8523353Z   File "/var/lib/jenkins/workspace/test/dynamo/test_misc.py", line 1192, in fn2
2023-01-11T21:22:37.8523594Z     x = fn1(x)  # graph break
2023-01-11T21:22:37.8523709Z 
2023-01-11T21:22:37.8523836Z Set torch._dynamo.config.verbose=True for more information
2023-01-11T21:22:37.8523995Z 
2023-01-11T21:22:37.8524000Z 
2023-01-11T21:22:37.8524121Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8524298Z unimplemented []
2023-01-11T21:22:37.8524729Z graph_break [('call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7fb1e4b9c280>', 1)]
2023-01-11T21:22:37.8525162Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:37.8525627Z inline_call [('call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7fb1e4b9c280>', 1)]
2023-01-11T21:22:37.8525924Z ok (0.013s)
2023-01-11T21:22:37.8526290Z   test_nested_optimize (__main__.MiscTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:37.8526553Z ok (0.014s)
2023-01-11T21:22:37.8526767Z   test_nested_optimize_decorator (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8527120Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:37.8527347Z ok (0.008s)
2023-01-11T21:22:37.8527707Z   test_nested_optimize_run (__main__.MiscTests) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:37.8527970Z ok (0.012s)
2023-01-11T21:22:37.8528345Z   test_nn_functional_reduction (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8528602Z ok (0.006s)
2023-01-11T21:22:37.8528825Z   test_nn_sequential_invocation (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8529326Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8529552Z ok (0.022s)
2023-01-11T21:22:37.8529787Z   test_nn_sequential_invocation_reposition_indices (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8530158Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8530382Z ok (0.017s)
2023-01-11T21:22:37.8530679Z   test_no_error_on_nested_fx_trace (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8531045Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8531265Z ok (0.005s)
2023-01-11T21:22:37.8531607Z   test_no_grad (__main__.MiscTests) ... stats [('calls_captured', 40), ('fusions_possible', 32), ('unique_graphs', 8)]
2023-01-11T21:22:37.8531923Z ok (0.050s)
2023-01-11T21:22:37.8532221Z   test_not_dynamic_scope (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8532438Z inline_call []
2023-01-11T21:22:37.8532732Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8532955Z ok (0.005s)
2023-01-11T21:22:37.8533299Z   test_numel (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8533538Z ok (0.006s)
2023-01-11T21:22:37.8533833Z   test_numpy_int_constant (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8534183Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8534444Z ok (0.006s)
2023-01-11T21:22:37.8534760Z   test_numpy_variable_isinstance (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8535127Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8535339Z ok (0.004s)
2023-01-11T21:22:37.8535559Z   test_object_classmethod (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8535900Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8536120Z ok (0.010s)
2023-01-11T21:22:37.8536328Z   test_object_staticmethod (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8536676Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8536898Z ok (0.010s)
2023-01-11T21:22:37.8537260Z   test_onnx_shape_as_tensor (__main__.MiscTests) ... stats [('calls_captured', 15), ('fusions_possible', 10), ('unique_graphs', 5)]
2023-01-11T21:22:37.8537529Z ok (0.069s)
2023-01-11T21:22:37.8537897Z   test_optimize_on_module (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8538146Z ok (0.005s)
2023-01-11T21:22:37.8538491Z   test_pair (__main__.MiscTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:37.8538746Z ok (0.019s)
2023-01-11T21:22:37.8539031Z   test_python_slice (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8539237Z ok (0.006s)
2023-01-11T21:22:37.8539524Z   test_raise_on_backend_error (__main__.MiscTests) ... frames [('total', 1)]
2023-01-11T21:22:37.8539840Z stats [('calls_captured', 3), ('fusions_possible', 2)]
2023-01-11T21:22:37.8540031Z ok (0.005s)
2023-01-11T21:22:37.8540306Z   test_raises (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:37.8540529Z unimplemented []
2023-01-11T21:22:37.8540828Z graph_break [('call_function BuiltinVariable(str) [TensorVariable()] {}', 1)]
2023-01-11T21:22:37.8541191Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8541411Z ok (0.009s)
2023-01-11T21:22:37.8541615Z   test_rand (__main__.MiscTests) ... skip: requires cuda (0.000s)
2023-01-11T21:22:37.8541879Z   test_range_input (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8542214Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8542434Z ok (0.007s)
2023-01-11T21:22:37.8542735Z   test_recursive_inline_list_mutation (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8542975Z inline_call []
2023-01-11T21:22:37.8543267Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:37.8543477Z ok (0.014s)
2023-01-11T21:22:37.8543863Z   test_release_input_memory (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8544220Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8544431Z ok (0.003s)
2023-01-11T21:22:37.8544733Z   test_release_module_memory (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8545087Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8545342Z ok (0.008s)
2023-01-11T21:22:37.8545742Z   test_repro_graph_breaks_in__get_item_by_idx (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8546023Z ok (0.009s)
2023-01-11T21:22:37.8546320Z   test_restore_graphstate (__main__.MiscTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:37.8546631Z inline_call [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:37.8546845Z unimplemented []
2023-01-11T21:22:37.8547105Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:37.8547429Z stats [('calls_captured', 6), ('unique_graphs', 4), ('fusions_possible', 2)]
2023-01-11T21:22:37.8547652Z ok (0.019s)
2023-01-11T21:22:37.8548070Z   test_restore_graphstate_internals (__main__.MiscTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8548335Z ok (0.007s)
2023-01-11T21:22:37.8548638Z   test_return_nested_function (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8548997Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 2)]
2023-01-11T21:22:37.8549219Z ok (0.014s)
2023-01-11T21:22:37.8549414Z   test_sample_input (__main__.MiscTests) ... ok (1.241s)
2023-01-11T21:22:37.8549757Z   test_setattr_mutation1 (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8550199Z unimplemented [('call_method UserDefinedObjectVariable(member_descriptor) __mul__ [ConstantVariable(int)] {}', 1)]
2023-01-11T21:22:37.8550718Z graph_break [("isinstance called on UserDefinedClass UserDefinedObjectVariable(member_descriptor) <class 'torch.Tensor'>", 1)]
2023-01-11T21:22:37.8551066Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8551384Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:22:37.8551596Z ok (0.018s)
2023-01-11T21:22:37.8551891Z   test_setattr_mutation2 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8552119Z inline_call []
2023-01-11T21:22:37.8552413Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:37.8552625Z ok (0.014s)
2023-01-11T21:22:37.8552919Z   test_setattr_mutation3 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8553143Z inline_call []
2023-01-11T21:22:37.8553423Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:37.8553643Z ok (0.014s)
2023-01-11T21:22:37.8553931Z   test_shape_unpack (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8554265Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8554488Z ok (0.004s)
2023-01-11T21:22:37.8554818Z   test_side_effects_codegen_update_mutated (__main__.MiscTests) ... frames [('total', 6), ('ok', 6)]
2023-01-11T21:22:37.8555060Z unimplemented []
2023-01-11T21:22:37.8555291Z graph_break [('Tensor.item', 4)]
2023-01-11T21:22:37.8555607Z stats [('calls_captured', 8), ('fusions_possible', 4), ('unique_graphs', 4)]
2023-01-11T21:22:37.8555828Z ok (0.119s)
2023-01-11T21:22:37.8556099Z   test_size_input (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8556442Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:22:37.8556668Z ok (0.008s)
2023-01-11T21:22:37.8557013Z   test_slice_input (__main__.MiscTests) ... stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:22:37.8557268Z ok (0.031s)
2023-01-11T21:22:37.8557575Z   test_tensor_build_list_unpack (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8557922Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8558144Z ok (0.012s)
2023-01-11T21:22:37.8558500Z   test_tensor_data (__main__.MiscTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8558756Z ok (0.013s)
2023-01-11T21:22:37.8559066Z   test_tensor_dict1 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8559411Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8559630Z ok (0.006s)
2023-01-11T21:22:37.8559902Z   test_tensor_dict2 (__main__.MiscTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:37.8560246Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:22:37.8560468Z ok (0.032s)
2023-01-11T21:22:37.8560771Z   test_tensor_dot_grad_no_graph_break (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8561016Z unimplemented []
2023-01-11T21:22:37.8561251Z graph_break [('Tensor.backward', 1)]
2023-01-11T21:22:37.8561563Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:37.8561814Z ok (0.014s)
2023-01-11T21:22:37.8562120Z   test_tensor_is_contiguous (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8562478Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:22:37.8562687Z ok (0.069s)
2023-01-11T21:22:37.8562984Z   test_tensor_item_capture (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8563338Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8563546Z ok (0.006s)
2023-01-11T21:22:37.8563847Z   test_tensor_item_no_capture (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8564084Z unimplemented []
2023-01-11T21:22:37.8564299Z graph_break [('Tensor.item', 1)]
2023-01-11T21:22:37.8564612Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8564833Z ok (0.006s)
2023-01-11T21:22:37.8565184Z   test_tensor_layout (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8565444Z ok (0.010s)
2023-01-11T21:22:37.8565734Z   test_tensor_types (__main__.MiscTests) ... frames [('total', 10), ('ok', 10)]
2023-01-11T21:22:37.8566091Z stats [('calls_captured', 10), ('unique_graphs', 10), ('fusions_possible', 0)]
2023-01-11T21:22:37.8566302Z ok (0.078s)
2023-01-11T21:22:37.8566667Z   test_top_package_import (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8566932Z ok (0.005s)
2023-01-11T21:22:37.8567296Z   test_torch_cuda_is_available (__main__.MiscTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8567567Z ok (0.005s)
2023-01-11T21:22:37.8567808Z   test_torch_cudnn_is_acceptable (__main__.MiscTests) ... skip: requires cuda (0.000s)
2023-01-11T21:22:37.8568125Z   test_torch_cudnn_is_acceptable_bad_inputs (__main__.MiscTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:37.8568533Z   test_torch_nn_parameter_isinstance (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8568896Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8569265Z ok (0.011s)
2023-01-11T21:22:37.8570024Z   test_torch_profiler (__main__.MiscTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91868 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:22:37.8571077Z   test_torch_seed (__main__.MiscTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91867 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:22:37.8572113Z   test_torch_size (__main__.MiscTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91866 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:22:37.8572805Z   test_type_copy (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8573153Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:37.8573366Z ok (0.013s)
2023-01-11T21:22:37.8573679Z   test_typing_variable_isinstance (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8574043Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8574268Z ok (0.005s)
2023-01-11T21:22:37.8574606Z   test_unpack4 (__main__.MiscTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:37.8574901Z ok (0.015s)
2023-01-11T21:22:37.8575250Z   test_unpack5 (__main__.MiscTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:37.8575490Z ok (0.014s)
2023-01-11T21:22:37.8575827Z   test_update_locals_and_stack_uses_shared_cache (__main__.MiscTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:37.8576082Z inline_call []
2023-01-11T21:22:37.8576243Z unimplemented []
2023-01-11T21:22:37.8576569Z graph_break [('call_method ListVariable() extend [ListIteratorVariable()] {}', 1)]
2023-01-11T21:22:37.8576811Z ok (0.007s)
2023-01-11T21:22:37.8577173Z   test_user_defined_class_name (__main__.MiscTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8577442Z ok (0.008s)
2023-01-11T21:22:37.8577691Z   test_user_function_variable_supports_enum_argument (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8577929Z ok (0.004s)
2023-01-11T21:22:37.8578171Z   test_user_function_variable_supports_function_argument (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8578547Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8578772Z ok (0.005s)
2023-01-11T21:22:37.8579017Z   test_user_function_variable_supports_type_abcmeta_argument (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8579398Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:37.8579618Z ok (0.005s)
2023-01-11T21:22:37.8579893Z   test_user_getattr1 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8580113Z inline_call []
2023-01-11T21:22:37.8580406Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8580626Z ok (0.006s)
2023-01-11T21:22:37.8580899Z   test_user_getattr2 (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8581119Z inline_call []
2023-01-11T21:22:37.8581413Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8581619Z ok (0.008s)
2023-01-11T21:22:37.8581905Z   test_user_property (__main__.MiscTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:37.8582129Z inline_call []
2023-01-11T21:22:37.8582411Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8582629Z ok (0.006s)
2023-01-11T21:22:37.8582846Z   test_usr_cls_classmethod (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8583174Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8583393Z ok (0.007s)
2023-01-11T21:22:37.8583684Z   test_usr_cls_staticmethod (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8584031Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:37.8584237Z ok (0.007s)
2023-01-11T21:22:37.8584439Z   test_version_ci (__main__.MiscTests) ... ok (0.000s)
2023-01-11T21:22:37.8584708Z   test_write_to_closures_in_inlining (__main__.MiscTests) ... inline_call []
2023-01-11T21:22:37.8585047Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:37.8585311Z ok (0.009s)
2023-01-11T21:22:37.8585512Z   test_jit_save (__main__.TestTracer) ... ok (0.042s)
2023-01-11T21:22:37.8585653Z 
2023-01-11T21:22:37.8585842Z ----------------------------------------------------------------------
2023-01-11T21:22:37.8586085Z Ran 161 tests in 4.287s
2023-01-11T21:22:37.8586198Z 
2023-01-11T21:22:37.8586292Z OK (skipped=11, expected failures=1)
2023-01-11T21:22:37.8586422Z 
2023-01-11T21:22:37.8586505Z Generating XML reports...
2023-01-11T21:22:37.8586884Z Generated XML report: test-reports/python-unittest/dynamo.test_misc/TEST-MiscTests-20230111212233.xml
2023-01-11T21:22:37.8587372Z Generated XML report: test-reports/python-unittest/dynamo.test_misc/TEST-TestTracer-20230111212233.xml
2023-01-11T21:22:37.8587584Z 
2023-01-11T21:22:37.8587918Z ##[endgroup]
2023-01-11T21:22:37.8588337Z FINISHED PRINTING LOG FILE of dynamo/test_misc (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_misc_yb8r8erq)
2023-01-11T21:22:37.8588557Z 
2023-01-11T21:22:38.2813624Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:38.3465810Z Ignoring disabled issues:  []
2023-01-11T21:22:38.3594468Z Running dynamo/test_optimizations ... [2023-01-11 21:22:38.359190]
2023-01-11T21:22:38.3596671Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_optimizations.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:38.359411]
2023-01-11T21:22:39.6925905Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:39.7577385Z Ignoring disabled issues:  []
2023-01-11T21:22:39.7707719Z Running dynamo/test_repros ... [2023-01-11 21:22:39.770483]
2023-01-11T21:22:39.7709116Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_repros.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:39.770719]
2023-01-11T21:22:40.7870158Z 
2023-01-11T21:22:40.7870708Z Expand the folded group to see the log file of dynamo/test_optimizations
2023-01-11T21:22:40.7872051Z ##[group]PRINTING LOG FILE of dynamo/test_optimizations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizations_9o69xgef)
2023-01-11T21:22:40.7872575Z 
2023-01-11T21:22:40.7872722Z Running tests...
2023-01-11T21:22:40.7873417Z ----------------------------------------------------------------------
2023-01-11T21:22:40.7875582Z Test results will be stored in test-reports/python-unittest/dynamo.test_optimizations
2023-01-11T21:22:40.7876173Z   test_inplace_normalize (__main__.NormalizeIRTests) ... ok (0.301s)
2023-01-11T21:22:40.7876713Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.7877309Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.7877707Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.7878117Z   test_example_inputs (__main__.TestOptimizations) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.7878397Z ok (0.090s)
2023-01-11T21:22:40.7878806Z   test_example_inputs_runtime_use (__main__.TestOptimizations) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.7879079Z ok (0.007s)
2023-01-11T21:22:40.7879298Z   test_has_mutation (__main__.TestOptimizations) ... ok (0.016s)
2023-01-11T21:22:40.7879689Z   test_has_mutation_factory (__main__.TestOptimizations) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.7880048Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:40.7880275Z ok (0.015s)
2023-01-11T21:22:40.7880602Z   test_inplacifier (__main__.TestOptimizations) ... optimizations [('out', 1), ('inplace', 1)]
2023-01-11T21:22:40.7880846Z ok (0.015s)
2023-01-11T21:22:40.7881069Z   test_ipex_bf16 (__main__.TestOptimizations) ... skip: requires ipex (0.001s)
2023-01-11T21:22:40.7881369Z   test_ipex_fp32 (__main__.TestOptimizations) ... skip: requires ipex (0.000s)
2023-01-11T21:22:40.7881745Z   test_log_conv_args (__main__.TestOptimizations) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:40.7882093Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:40.7882521Z ok (0.149s)
2023-01-11T21:22:40.7882622Z 
2023-01-11T21:22:40.7882827Z ----------------------------------------------------------------------
2023-01-11T21:22:40.7883059Z Ran 9 tests in 0.594s
2023-01-11T21:22:40.7883176Z 
2023-01-11T21:22:40.7883250Z OK (skipped=2)
2023-01-11T21:22:40.7883357Z 
2023-01-11T21:22:40.7883443Z Generating XML reports...
2023-01-11T21:22:40.7883877Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizations/TEST-NormalizeIRTests-20230111212239.xml
2023-01-11T21:22:40.7884417Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizations/TEST-TestOptimizations-20230111212239.xml
2023-01-11T21:22:40.7884665Z 
2023-01-11T21:22:40.7884929Z ##[endgroup]
2023-01-11T21:22:40.7885435Z FINISHED PRINTING LOG FILE of dynamo/test_optimizations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizations_9o69xgef)
2023-01-11T21:22:40.7885678Z 
2023-01-11T21:22:42.6029917Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:42.6676770Z Ignoring disabled issues:  []
2023-01-11T21:22:42.6809258Z Running dynamo/test_torchxla_integration ... [2023-01-11 21:22:42.680596]
2023-01-11T21:22:42.6810794Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_torchxla_integration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:42.680862]
2023-01-11T21:22:44.3854413Z 
2023-01-11T21:22:44.3854927Z Expand the folded group to see the log file of dynamo/test_torchxla_integration
2023-01-11T21:22:44.3856153Z ##[group]PRINTING LOG FILE of dynamo/test_torchxla_integration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_integration_ey256jfo)
2023-01-11T21:22:44.3856662Z 
2023-01-11T21:22:44.3856792Z Running tests...
2023-01-11T21:22:44.3857471Z ----------------------------------------------------------------------
2023-01-11T21:22:44.3858216Z Test results will be stored in test-reports/python-unittest/dynamo.test_torchxla_integration
2023-01-11T21:22:44.3858945Z   test_basic (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T21:22:44.3859449Z   test_inplace_update (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T21:22:44.3860035Z   test_linear (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T21:22:44.3860470Z   test_matmul (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T21:22:44.3860705Z 
2023-01-11T21:22:44.3860908Z ----------------------------------------------------------------------
2023-01-11T21:22:44.3861153Z Ran 4 tests in 0.003s
2023-01-11T21:22:44.3861265Z 
2023-01-11T21:22:44.3861336Z OK (skipped=4)
2023-01-11T21:22:44.3861430Z 
2023-01-11T21:22:44.3861516Z Generating XML reports...
2023-01-11T21:22:44.3861981Z Generated XML report: test-reports/python-unittest/dynamo.test_torchxla_integration/TEST-TorchXLAReuseGraphTest-20230111212244.xml
2023-01-11T21:22:44.3862246Z 
2023-01-11T21:22:44.3862479Z ##[endgroup]
2023-01-11T21:22:44.3862912Z FINISHED PRINTING LOG FILE of dynamo/test_torchxla_integration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_integration_ey256jfo)
2023-01-11T21:22:44.3863160Z 
2023-01-11T21:22:46.4220196Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:46.5043392Z Ignoring disabled issues:  []
2023-01-11T21:22:46.5174327Z Running dynamo/test_unspec ... [2023-01-11 21:22:46.517189]
2023-01-11T21:22:46.5176527Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_unspec.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:46.517438]
2023-01-11T21:22:47.9085371Z 
2023-01-11T21:22:47.9086230Z Expand the folded group to see the log file of dynamo/test_repros
2023-01-11T21:22:47.9087445Z ##[group]PRINTING LOG FILE of dynamo/test_repros (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_repros_z7ucuz6t)
2023-01-11T21:22:47.9087801Z 
2023-01-11T21:22:47.9087885Z Running tests...
2023-01-11T21:22:47.9088704Z ----------------------------------------------------------------------
2023-01-11T21:22:47.9089573Z Test results will be stored in test-reports/python-unittest/dynamo.test_repros
2023-01-11T21:22:47.9090043Z   test_Size (__main__.ReproTests) ... ok (0.316s)
2023-01-11T21:22:47.9090610Z   test_abc_setattr (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9091240Z inline_call [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:47.9091510Z unimplemented []
2023-01-11T21:22:47.9091968Z graph_break [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:47.9092360Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:47.9092633Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:47.9092820Z unimplemented []
2023-01-11T21:22:47.9093531Z graph_break [('setattr(UserDefinedObjectVariable) <function ReproTests.test_abc_setattr.<locals>.Derived.__setattr__ at 0x7fcf7bbf2290>', 1)]
2023-01-11T21:22:47.9094050Z inline_call []
2023-01-11T21:22:47.9094532Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9094928Z ok (0.010s)
2023-01-11T21:22:47.9095448Z   test_avoid_dupe_specialization (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9096060Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:47.9096531Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9096835Z ok (0.045s)
2023-01-11T21:22:47.9097181Z   test_batch_norm_act (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9097774Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:47.9098188Z ok (0.100s)
2023-01-11T21:22:47.9098808Z   test_batchnorm_e2e (__main__.ReproTests) ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:47.9099361Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9099589Z inline_call []
2023-01-11T21:22:47.9099875Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:47.9100154Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9100338Z ok (1.255s)
2023-01-11T21:22:47.9100638Z   test_bigbird_unsqueeze_inplace (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9101002Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:47.9101287Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9101473Z ok (0.034s)
2023-01-11T21:22:47.9101670Z   test_boxes_len (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9102006Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9102231Z ok (0.009s)
2023-01-11T21:22:47.9102434Z   test_chunk_reformer_ff (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9102772Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:47.9102992Z ok (0.065s)
2023-01-11T21:22:47.9103336Z   test_class_member (__main__.ReproTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:47.9103670Z ok (0.014s)
2023-01-11T21:22:47.9103993Z   test_convert_boxes_to_pooler_format (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:47.9104222Z inline_call []
2023-01-11T21:22:47.9104399Z unimplemented []
2023-01-11T21:22:47.9104669Z graph_break [('dynamic shapes: repeat_interleave', 2)]
2023-01-11T21:22:47.9105013Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:22:47.9105221Z ok (0.091s)
2023-01-11T21:22:47.9105451Z   test_create_rand_mask_from_inputs (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9105893Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:22:47.9106099Z ok (0.135s)
2023-01-11T21:22:47.9106302Z   test_dict_iter (__main__.ReproTests) ... ok (0.005s)
2023-01-11T21:22:47.9106637Z   test_dict_list_values (__main__.ReproTests) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:47.9106858Z unimplemented []
2023-01-11T21:22:47.9107304Z graph_break [('call_function in skip_files Builtin count', 2), ('call_function BuiltinVariable(zip) [UserDefinedObjectVariable(count), ListVariable()] {}', 2)]
2023-01-11T21:22:47.9107620Z ok (0.017s)
2023-01-11T21:22:47.9107913Z   test_do_paste_mask (__main__.ReproTests) ... frames [('total', 12), ('ok', 11)]
2023-01-11T21:22:47.9108223Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:47.9108610Z graph_break [('dynamic shapes: arange', 6), ('Dynamic slicing not supported', 4)]
2023-01-11T21:22:47.9109057Z stats [('calls_captured', 159), ('fusions_possible', 148), ('unique_graphs', 11)]
2023-01-11T21:22:47.9109285Z ok (1.497s)
2023-01-11T21:22:47.9109669Z   test_dynamic_shapes_right_side (__main__.ReproTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:47.9109932Z ok (0.082s)
2023-01-11T21:22:47.9110286Z   test_ellipsis (__main__.ReproTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:47.9110541Z ok (0.018s)
2023-01-11T21:22:47.9110815Z   test_exec_import (__main__.ReproTests) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:47.9111182Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:47.9111430Z unimplemented []
2023-01-11T21:22:47.9111736Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:47.9111975Z ok (0.004s)
2023-01-11T21:22:47.9112283Z   test_exec_wildcard_import (__main__.ReproTests) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:47.9112661Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:47.9112896Z unimplemented []
2023-01-11T21:22:47.9113216Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:47.9113584Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:47.9113794Z ok (0.016s)
2023-01-11T21:22:47.9114095Z   test_for_loop_graph_break (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9114326Z inline_call []
2023-01-11T21:22:47.9114688Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:47.9115088Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9115311Z ok (0.011s)
2023-01-11T21:22:47.9115625Z   test_for_loop_graph_break_before (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9115856Z unimplemented []
2023-01-11T21:22:47.9116226Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:47.9116495Z inline_call []
2023-01-11T21:22:47.9116780Z stats [('calls_captured', 100), ('fusions_possible', 99), ('unique_graphs', 1)]
2023-01-11T21:22:47.9117005Z ok (0.131s)
2023-01-11T21:22:47.9117226Z   test_get_parameter_dtype (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9117559Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:47.9117780Z ok (0.014s)
2023-01-11T21:22:47.9118029Z   test_grad_mode_carrying_correct_state_after_graph_break (__main__.ReproTests) ... Break
2023-01-11T21:22:47.9118308Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:47.9118495Z unimplemented []
2023-01-11T21:22:47.9118824Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:47.9119241Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:47.9119539Z ok (0.011s)
2023-01-11T21:22:47.9119851Z   test_guard_fail_nested_tuple (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9120209Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:47.9120417Z ok (0.021s)
2023-01-11T21:22:47.9120724Z   test_guard_fail_tensor_bool (__main__.ReproTests) ... frames [('total', 12), ('ok', 12)]
2023-01-11T21:22:47.9121092Z unimplemented [('FOR_ITER UserDefinedObjectVariable(product)', 1)]
2023-01-11T21:22:47.9121696Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_guard_fail_tensor_bool.<locals>.fn.<locals>.get_expected at 0x7fd00e6a93f0>', 5), ('data dependent operator: aten.allclose.default', 5)]
2023-01-11T21:22:47.9122195Z stats [('calls_captured', 5), ('unique_graphs', 5), ('fusions_possible', 0)]
2023-01-11T21:22:47.9122450Z ok (0.078s)
2023-01-11T21:22:47.9122678Z   test_guard_ordering_shape_fail (__main__.ReproTests) ... ok (0.002s)
2023-01-11T21:22:47.9122943Z   test_hf_model_output (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9123281Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:22:47.9123502Z ok (0.053s)
2023-01-11T21:22:47.9123703Z   test_hf_t5_forward (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9124043Z stats [('calls_captured', 11), ('fusions_possible', 10), ('unique_graphs', 1)]
2023-01-11T21:22:47.9124265Z ok (0.575s)
2023-01-11T21:22:47.9124562Z   test_indexing_with_list (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:47.9124836Z inline_call [('Tensor.numpy', 1)]
2023-01-11T21:22:47.9125032Z unimplemented []
2023-01-11T21:22:47.9125260Z graph_break [('Tensor.numpy', 1)]
2023-01-11T21:22:47.9125681Z stats [('unique_graphs', 2), ('calls_captured', 0), ('fusions_possible', -2)]
2023-01-11T21:22:47.9125909Z ok (0.023s)
2023-01-11T21:22:47.9126129Z   test_is_symbolic_tracing (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9126463Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9126685Z ok (0.005s)
2023-01-11T21:22:47.9126899Z   test_isinstance_dtype (__main__.ReproTests) ... ok (0.003s)
2023-01-11T21:22:47.9127497Z   test_isinstance_storage (__main__.ReproTests) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:22:47.9128083Z   bools = torch.BoolStorage.from_buffer(f, "big")
2023-01-11T21:22:47.9128342Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:22:47.9128528Z unimplemented []
2023-01-11T21:22:47.9129008Z graph_break [('call_function BuiltinVariable(bytearray) [ListVariable()] {}', 1), ('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:22:47.9129646Z inline_call [('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:22:47.9130150Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9130412Z expected failure (0.011s)
2023-01-11T21:22:47.9130675Z   test_issue1466_size_aot_autograd (__main__.ReproTests) ... arf
2023-01-11T21:22:47.9130883Z arf
2023-01-11T21:22:47.9131088Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:47.9131264Z unimplemented []
2023-01-11T21:22:47.9131588Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:47.9131956Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:47.9132242Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9132413Z ok (0.032s)
2023-01-11T21:22:47.9132622Z   test_issue175 (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9132955Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:22:47.9133244Z ok (0.127s)
2023-01-11T21:22:47.9133611Z   test_longformer_chunk (__main__.ReproTests) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:47.9133876Z ok (0.132s)
2023-01-11T21:22:47.9134094Z   test_maml_item_capture (__main__.ReproTests) ... expected failure (0.001s)
2023-01-11T21:22:47.9134461Z   test_maml_no_item_capture (__main__.ReproTests) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:47.9134820Z inline_call [('inlining disallowed: <function grad at 0x7fcf852f5240>', 1)]
2023-01-11T21:22:47.9135051Z unimplemented []
2023-01-11T21:22:47.9135479Z graph_break [('Tensor.item', 2), ('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 1), ('inlining disallowed: <function grad at 0x7fcf852f5240>', 1)]
2023-01-11T21:22:47.9135959Z stats [('calls_captured', 29), ('fusions_possible', 25), ('unique_graphs', 4)]
2023-01-11T21:22:47.9136185Z ok (0.148s)
2023-01-11T21:22:47.9136528Z   test_modules (__main__.ReproTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:47.9136782Z ok (0.023s)
2023-01-11T21:22:47.9137077Z   test_multi_dot_import (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:47.9137509Z inline_call [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:22:47.9137796Z unimplemented []
2023-01-11T21:22:47.9138183Z graph_break [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:22:47.9138585Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9138798Z ok (0.011s)
2023-01-11T21:22:47.9139036Z   test_multi_import (__main__.ReproTests) ... skip: requires detectron2 (0.000s)
2023-01-11T21:22:47.9139471Z   test_named_buffers (__main__.ReproTests) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:47.9139722Z ok (0.011s)
2023-01-11T21:22:47.9140012Z   test_nn_parameter (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9140400Z inline_call [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:47.9140661Z unimplemented []
2023-01-11T21:22:47.9141000Z graph_break [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:47.9141505Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:47.9141730Z ok (0.014s)
2023-01-11T21:22:47.9141944Z   test_norm_dtype (__main__.ReproTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:47.9142346Z   test_not_rewrite_assert (__main__.ReproTests) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:47.9142605Z ok (0.005s)
2023-01-11T21:22:47.9142919Z   test_not_rewrite_assert_for_other_errors (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9143294Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:47.9143582Z ok (0.008s)
2023-01-11T21:22:47.9143869Z   test_numpy_list (__main__.ReproTests) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:47.9144082Z unimplemented []
2023-01-11T21:22:47.9144499Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_numpy_list.<locals>.rand_gen at 0x7fd00e4da440>', 1)]
2023-01-11T21:22:47.9144806Z expected failure (0.021s)
2023-01-11T21:22:47.9145113Z   test_optimized_deepcopy (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9145469Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9145687Z ok (0.012s)
2023-01-11T21:22:47.9145961Z   test_primtorch (__main__.ReproTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:47.9146386Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:47.9146716Z unimplemented []
2023-01-11T21:22:47.9147096Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:47.9147358Z ok (0.004s)
2023-01-11T21:22:47.9147813Z   test_primtorch_no_graph_break (__main__.ReproTests) ... inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:47.9148148Z expected failure (0.003s)
2023-01-11T21:22:47.9148446Z   test_recursive_map (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9148671Z inline_call []
2023-01-11T21:22:47.9148963Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:47.9149171Z ok (0.013s)
2023-01-11T21:22:47.9149416Z   test_reformer_eval (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9149754Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:22:47.9149979Z ok (0.043s)
2023-01-11T21:22:47.9150189Z   test_reformer_min_chunk_len (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9150539Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:47.9150761Z ok (0.022s)
2023-01-11T21:22:47.9150965Z   test_reformer_remove_unused_args (__main__.ReproTests) ... foo
2023-01-11T21:22:47.9151175Z foo
2023-01-11T21:22:47.9151376Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:47.9151697Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:47.9151948Z unimplemented []
2023-01-11T21:22:47.9152272Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:22:47.9152631Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:47.9152914Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9153099Z ok (0.027s)
2023-01-11T21:22:47.9153303Z   test_reformer_sorting (__main__.ReproTests) ... inline_call []
2023-01-11T21:22:47.9153647Z stats [('calls_captured', 14), ('fusions_possible', 13), ('unique_graphs', 1)]
2023-01-11T21:22:47.9153867Z ok (0.049s)
2023-01-11T21:22:47.9154156Z   test_reformer_train (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:47.9154590Z inline_call [('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:22:47.9154883Z unimplemented []
2023-01-11T21:22:47.9155351Z graph_break [('autograd.Function with requires_grad', 1), ('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:22:47.9155796Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:22:47.9156023Z ok (0.090s)
2023-01-11T21:22:47.9156315Z   test_reinplacing (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9156666Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:47.9156938Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9157120Z ok (0.232s)
2023-01-11T21:22:47.9157484Z   test_relative_import (__main__.ReproTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:47.9157734Z ok (0.014s)
2023-01-11T21:22:47.9158121Z   test_relative_import_no_modulename (__main__.ReproTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:47.9158397Z ok (0.006s)
2023-01-11T21:22:47.9158753Z   test_rewrite_assert_noop (__main__.ReproTests) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:22:47.9159016Z ok (0.021s)
2023-01-11T21:22:47.9159380Z   test_rewrite_assert_with_fstring_msg (__main__.ReproTests) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:47.9159647Z ok (0.005s)
2023-01-11T21:22:47.9160010Z   test_rewrite_assert_with_msg (__main__.ReproTests) ... stats [('calls_captured', 18), ('fusions_possible', 15), ('unique_graphs', 3)]
2023-01-11T21:22:47.9160317Z ok (0.023s)
2023-01-11T21:22:47.9160701Z   test_rewrite_assert_without_msg (__main__.ReproTests) ... stats [('calls_captured', 12), ('fusions_possible', 10), ('unique_graphs', 2)]
2023-01-11T21:22:47.9160960Z ok (0.016s)
2023-01-11T21:22:47.9161244Z   test_rng_state (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:47.9161632Z unimplemented [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 1)]
2023-01-11T21:22:47.9162048Z graph_break [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 2)]
2023-01-11T21:22:47.9162413Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:47.9162639Z ok (0.028s)
2023-01-11T21:22:47.9163032Z   test_seq_append_list (__main__.ReproTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:47.9163284Z ok (0.023s)
2023-01-11T21:22:47.9163977Z   test_sigmoid_out (__main__.ReproTests) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:47.9164666Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:22:47.9165569Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:22:47.9166153Z   warnings.warn(msg)
2023-01-11T21:22:47.9166814Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:47.9167445Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:22:47.9167665Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:47.9167972Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:22:47.9168202Z ok (0.032s)
2023-01-11T21:22:47.9168571Z   test_slice_into_list_mutable (__main__.ReproTests) ... stats [('calls_captured', 30), ('fusions_possible', 29), ('unique_graphs', 1)]
2023-01-11T21:22:47.9168840Z ok (0.082s)
2023-01-11T21:22:47.9169343Z   test_slicing_dynamic_shape (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9169682Z unimplemented [('Dynamic slicing not supported', 2)]
2023-01-11T21:22:47.9170009Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:47.9170233Z ok (0.020s)
2023-01-11T21:22:47.9170550Z   test_slicing_dynamic_shape_setitem (__main__.ReproTests) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:47.9170874Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:47.9171174Z graph_break [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:47.9171508Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9171783Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9171968Z ok (0.013s)
2023-01-11T21:22:47.9172740Z   test_sort_out (__main__.ReproTests) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:47.9173497Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:47.9174559Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:47.9175210Z   return node.target(*args, **kwargs)
2023-01-11T21:22:47.9175869Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:47.9176520Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:47.9177205Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:47.9177849Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:47.9178100Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:47.9178396Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:22:47.9178626Z ok (0.040s)
2023-01-11T21:22:47.9178930Z   test_specialized_stride (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9179278Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9179503Z ok (0.010s)
2023-01-11T21:22:47.9179808Z   test_swin_base_tensor_attr (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9180155Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:47.9180379Z ok (0.015s)
2023-01-11T21:22:47.9180686Z   test_tensor_isinstance_tuple (__main__.ReproTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:47.9181047Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9181255Z ok (0.009s)
2023-01-11T21:22:47.9181543Z   test_tokenization (__main__.ReproTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:47.9181936Z inline_call [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:22:47.9182179Z unimplemented []
2023-01-11T21:22:47.9182522Z graph_break [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:22:47.9182852Z ok (0.008s)
2023-01-11T21:22:47.9183217Z   test_torch_ops_aten (__main__.ReproTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9183562Z ok (0.010s)
2023-01-11T21:22:47.9183990Z   test_vdd_duplicate_error (__main__.ReproTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:47.9184260Z ok (0.012s)
2023-01-11T21:22:47.9184554Z   test_while_loop_graph_break (__main__.ReproTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:47.9184788Z inline_call []
2023-01-11T21:22:47.9185159Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:47.9185549Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:47.9185772Z ok (0.007s)
2023-01-11T21:22:47.9185995Z   test_with_on_graph_break_inst (__main__.ReproTests) ... Hello world
2023-01-11T21:22:47.9186201Z Hello world
2023-01-11T21:22:47.9186413Z frames [('total', 6), ('ok', 6)]
2023-01-11T21:22:47.9186780Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:47.9187032Z unimplemented []
2023-01-11T21:22:47.9187384Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2), ('Tensor.backward', 1)]
2023-01-11T21:22:47.9187772Z stats [('calls_captured', 11), ('fusions_possible', 7), ('unique_graphs', 4)]
2023-01-11T21:22:47.9187993Z ok (0.026s)
2023-01-11T21:22:47.9188083Z 
2023-01-11T21:22:47.9188283Z ----------------------------------------------------------------------
2023-01-11T21:22:47.9188522Z Ran 76 tests in 6.102s
2023-01-11T21:22:47.9188634Z 
2023-01-11T21:22:47.9188727Z OK (skipped=2, expected failures=4)
2023-01-11T21:22:47.9188854Z 
2023-01-11T21:22:47.9188925Z Generating XML reports...
2023-01-11T21:22:47.9189316Z Generated XML report: test-reports/python-unittest/dynamo.test_repros/TEST-ReproTests-20230111212241.xml
2023-01-11T21:22:47.9189538Z 
2023-01-11T21:22:47.9189826Z ##[endgroup]
2023-01-11T21:22:47.9190204Z FINISHED PRINTING LOG FILE of dynamo/test_repros (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_repros_z7ucuz6t)
2023-01-11T21:22:47.9190427Z 
2023-01-11T21:22:49.7532998Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:49.8190643Z Ignoring disabled issues:  []
2023-01-11T21:22:49.8324937Z Running inductor/test_torchinductor ... [2023-01-11 21:22:49.832246]
2023-01-11T21:22:49.8327340Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:49.832483]
2023-01-11T21:22:55.3909997Z 
2023-01-11T21:22:55.3910909Z Expand the folded group to see the log file of dynamo/test_unspec
2023-01-11T21:22:55.3911968Z ##[group]PRINTING LOG FILE of dynamo/test_unspec (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_unspec_m1lweub2)
2023-01-11T21:22:55.3912319Z 
2023-01-11T21:22:55.3912426Z Running tests...
2023-01-11T21:22:55.3913171Z ----------------------------------------------------------------------
2023-01-11T21:22:55.3913737Z Test results will be stored in test-reports/python-unittest/dynamo.test_unspec
2023-01-11T21:22:55.3914229Z   test_access_by_keys_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.335s)
2023-01-11T21:22:55.3914738Z   test_basicmodule1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3915292Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:55.3916134Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.3916686Z ok (0.018s)
2023-01-11T21:22:55.3917700Z   test_basicmodule2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.3918391Z ok (0.013s)
2023-01-11T21:22:55.3919048Z   test_call_fn_with_non_const_inputs_safe_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3920044Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.3920565Z ok (0.023s)
2023-01-11T21:22:55.3921574Z   test_cfgmod_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:55.3922550Z ok (0.026s)
2023-01-11T21:22:55.3923566Z   test_children_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.3924242Z ok (0.019s)
2023-01-11T21:22:55.3925248Z   test_constloop_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:55.3925969Z ok (0.026s)
2023-01-11T21:22:55.3926549Z   test_densenet_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3927449Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:55.3927976Z ok (0.016s)
2023-01-11T21:22:55.3928703Z   test_enumvalues_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3929877Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:55.3930434Z ok (0.016s)
2023-01-11T21:22:55.3931441Z   test_fnmember_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3932118Z ok (0.012s)
2023-01-11T21:22:55.3933114Z   test_fnmembercmp1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3933822Z ok (0.012s)
2023-01-11T21:22:55.3934839Z   test_fnmembercmp2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3935554Z ok (0.012s)
2023-01-11T21:22:55.3936179Z   test_forward_directly_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3937107Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.3937635Z ok (0.019s)
2023-01-11T21:22:55.3938226Z   test_generation_tag_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.002s)
2023-01-11T21:22:55.3939423Z   test_hasattr_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3940104Z ok (0.009s)
2023-01-11T21:22:55.3941030Z   test_intarg_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.3941725Z ok (0.012s)
2023-01-11T21:22:55.3942716Z   test_iseval1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3943398Z ok (0.011s)
2023-01-11T21:22:55.3944492Z   test_iseval2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3945192Z ok (0.011s)
2023-01-11T21:22:55.3946182Z   test_isnonelayer_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.3946925Z ok (0.010s)
2023-01-11T21:22:55.3947931Z   test_istraining1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3948630Z ok (0.011s)
2023-01-11T21:22:55.3949651Z   test_istraining2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3950344Z ok (0.011s)
2023-01-11T21:22:55.3951324Z   test_layerlist_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.3952008Z ok (0.016s)
2023-01-11T21:22:55.3953565Z   test_lazy_module_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:22:55.3955161Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:22:55.3956837Z [2023-01-11 21:22:48,747] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7fa838121fc0> [ShapeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:22:55.3958716Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:22:55.3960022Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:22:55.3961487Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:22:55.3962715Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:22:55.3964376Z [2023-01-11 21:22:48,823] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7fa838121fc0> [ShapeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:22:55.3966252Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:22:55.3967457Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:22:55.3968153Z frames [('total', 16), ('ok', 14)]
2023-01-11T21:22:55.3970173Z inline_call [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2), ('call_function UserDefinedObjectVariable(_infer_parameters) [NNModuleVariable(), TupleVariable()] {}', 1), ('call_function UserDefinedObjectVariable(_infer_parameters) [UnspecializedNNModuleVariable(LazyModule), TupleVariable()] {}', 1)]
2023-01-11T21:22:55.3971905Z unimplemented [("Guard setup for uninitialized class <class 'test_modules.LazyModule'>", 2)]
2023-01-11T21:22:55.3972881Z graph_break [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2)]
2023-01-11T21:22:55.3973778Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.3974326Z ok (0.237s)
2023-01-11T21:22:55.3974975Z   test_module_attribute_precedence_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3975916Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.3976477Z ok (0.011s)
2023-01-11T21:22:55.3977104Z   test_module_class_method_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3978072Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:55.3978599Z ok (0.028s)
2023-01-11T21:22:55.3979516Z   test_module_forward_has_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 2)]
2023-01-11T21:22:55.3980165Z inline_call []
2023-01-11T21:22:55.3980837Z unimplemented [('reconstruct: ConstantVariable(dict)', 2)]
2023-01-11T21:22:55.3981964Z graph_break [('call_function BuiltinVariable(dict) [ListIteratorVariable()] {}', 1), ('call_method NNModuleVariable() buffers [] {}', 1)]
2023-01-11T21:22:55.3982967Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:55.3983585Z ok (0.073s)
2023-01-11T21:22:55.3984608Z   test_module_name_string_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.3985342Z ok (0.014s)
2023-01-11T21:22:55.3985926Z   test_module_property_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3986955Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.3987478Z ok (0.006s)
2023-01-11T21:22:55.3988069Z   test_module_static_method_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3988992Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:55.3989518Z ok (0.027s)
2023-01-11T21:22:55.3990491Z   test_moduledict_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.3991161Z ok (0.010s)
2023-01-11T21:22:55.3992224Z   test_modulelist_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 40), ('fusions_possible', 39), ('unique_graphs', 1)]
2023-01-11T21:22:55.3992898Z ok (0.105s)
2023-01-11T21:22:55.3993459Z   test_modulemethod1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3994400Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:55.3994954Z ok (0.028s)
2023-01-11T21:22:55.3995539Z   test_modulemethod2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.3996459Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:22:55.3997013Z ok (0.027s)
2023-01-11T21:22:55.3998059Z   test_nn_moduledict_contains_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:22:55.3998926Z frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:55.3999583Z inline_call [('Patched init cannot be inlined.', 1)]
2023-01-11T21:22:55.4000741Z unimplemented [("Guard setup for uninitialized class <class 'test_modules.NNModuleTests.test_nn_moduledict_contains.<locals>.M'>", 1)]
2023-01-11T21:22:55.4001582Z graph_break [('Patched init cannot be inlined.', 1)]
2023-01-11T21:22:55.4001872Z ok (0.018s)
2023-01-11T21:22:55.4002221Z   test_parameters1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4002776Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4003109Z ok (0.009s)
2023-01-11T21:22:55.4003477Z   test_parameters2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4004077Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4004407Z ok (0.009s)
2023-01-11T21:22:55.4005051Z   test_parameters3_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:55.4005474Z ok (0.022s)
2023-01-11T21:22:55.4006101Z   test_self_mutating1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:22:55.4006546Z ok (0.037s)
2023-01-11T21:22:55.4007147Z   test_seq_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4007540Z ok (0.018s)
2023-01-11T21:22:55.4007934Z   test_simple_torch_function_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4008535Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4008881Z ok (0.013s)
2023-01-11T21:22:55.4009647Z   test_stringmember_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4010082Z ok (0.012s)
2023-01-11T21:22:55.4010458Z   test_submodules1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4011036Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:55.4011369Z ok (0.024s)
2023-01-11T21:22:55.4011846Z   test_submodules2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4012391Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:22:55.4012697Z ok (0.024s)
2023-01-11T21:22:55.4013058Z   test_super1_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4013642Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4013955Z ok (0.015s)
2023-01-11T21:22:55.4014316Z   test_super2_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4014859Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.4015176Z ok (0.014s)
2023-01-11T21:22:55.4015660Z   test_super_class_method_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4016238Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4016590Z ok (0.007s)
2023-01-11T21:22:55.4017190Z   test_tensorlist_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4017611Z ok (0.011s)
2023-01-11T21:22:55.4018259Z   test_torch_function_with_closure_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4018710Z ok (0.008s)
2023-01-11T21:22:55.4019241Z   test_unsupportedmethod_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:55.4019972Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:22:55.4020411Z unimplemented []
2023-01-11T21:22:55.4020994Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:22:55.4021633Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:22:55.4021985Z ok (0.028s)
2023-01-11T21:22:55.4022508Z   test_unsupportedmodule_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:55.4023205Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:22:55.4023681Z unimplemented []
2023-01-11T21:22:55.4024214Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:22:55.4024832Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:22:55.4025184Z ok (0.029s)
2023-01-11T21:22:55.4025582Z   test_viamodulecall_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4026148Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4026501Z ok (0.014s)
2023-01-11T21:22:55.4027015Z   test_Size_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4027641Z inline_call [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:55.4028026Z unimplemented []
2023-01-11T21:22:55.4028579Z graph_break [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:55.4029161Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4029492Z ok (0.013s)
2023-01-11T21:22:55.4030018Z   test_abc_setattr_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:55.4030420Z unimplemented []
2023-01-11T21:22:55.4031050Z graph_break [('setattr(UserDefinedObjectVariable) <function ReproTests.test_abc_setattr.<locals>.Derived.__setattr__ at 0x7fa82e5efa30>', 1)]
2023-01-11T21:22:55.4031517Z inline_call []
2023-01-11T21:22:55.4031957Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4032364Z ok (0.008s)
2023-01-11T21:22:55.4032883Z   test_avoid_dupe_specialization_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4033474Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:55.4033914Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4034186Z ok (0.042s)
2023-01-11T21:22:55.4034735Z   test_batch_norm_act_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:55.4035357Z inline_call [('data dependent operator: aten._local_scalar_dense.default', 2)]
2023-01-11T21:22:55.4035718Z unimplemented []
2023-01-11T21:22:55.4036212Z graph_break [('data dependent operator: aten._local_scalar_dense.default', 2)]
2023-01-11T21:22:55.4036856Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4037195Z ok (0.030s)
2023-01-11T21:22:55.4037819Z   test_batchnorm_e2e_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:55.4038342Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4038615Z inline_call []
2023-01-11T21:22:55.4039048Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.4039487Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4039753Z ok (0.895s)
2023-01-11T21:22:55.4040315Z   test_bigbird_unsqueeze_inplace_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4040904Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4041367Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4041626Z ok (0.033s)
2023-01-11T21:22:55.4042033Z   test_boxes_len_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4042595Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4042915Z ok (0.009s)
2023-01-11T21:22:55.4043284Z   test_chunk_reformer_ff_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4043857Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4044193Z ok (0.062s)
2023-01-11T21:22:55.4044797Z   test_class_member_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4045203Z ok (0.010s)
2023-01-11T21:22:55.4045757Z   test_convert_boxes_to_pooler_format_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:55.4046136Z inline_call []
2023-01-11T21:22:55.4046392Z unimplemented []
2023-01-11T21:22:55.4046799Z graph_break [('dynamic shapes: repeat_interleave', 2)]
2023-01-11T21:22:55.4047329Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:22:55.4047679Z ok (0.057s)
2023-01-11T21:22:55.4048065Z   test_create_rand_mask_from_inputs_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4048630Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:22:55.4048949Z ok (0.129s)
2023-01-11T21:22:55.4049445Z   test_dict_iter_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.005s)
2023-01-11T21:22:55.4050091Z   test_dict_list_values_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:55.4050458Z unimplemented []
2023-01-11T21:22:55.4051152Z graph_break [('call_function in skip_files Builtin count', 2), ('call_function BuiltinVariable(zip) [UserDefinedObjectVariable(count), ListVariable()] {}', 2)]
2023-01-11T21:22:55.4051625Z ok (0.017s)
2023-01-11T21:22:55.4052173Z   test_do_paste_mask_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 13), ('ok', 12)]
2023-01-11T21:22:55.4052817Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:55.4053361Z graph_break [('dynamic shapes: arange', 7), ('Dynamic slicing not supported', 4)]
2023-01-11T21:22:55.4053914Z stats [('calls_captured', 131), ('fusions_possible', 119), ('unique_graphs', 12)]
2023-01-11T21:22:55.4054291Z ok (1.443s)
2023-01-11T21:22:55.4055050Z   test_dynamic_shapes_right_side_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4055537Z ok (0.075s)
2023-01-11T21:22:55.4056110Z   test_ellipsis_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.4056540Z ok (0.017s)
2023-01-11T21:22:55.4057189Z   test_exec_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:55.4057873Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:55.4058278Z unimplemented []
2023-01-11T21:22:55.4058788Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:55.4059161Z ok (0.004s)
2023-01-11T21:22:55.4059703Z   test_exec_wildcard_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:55.4060344Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:55.4060733Z unimplemented []
2023-01-11T21:22:55.4061234Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:55.4061806Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:55.4062149Z ok (0.016s)
2023-01-11T21:22:55.4062727Z   test_for_loop_graph_break_before_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4063135Z unimplemented []
2023-01-11T21:22:55.4063791Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:55.4064226Z inline_call []
2023-01-11T21:22:55.4064662Z stats [('calls_captured', 100), ('fusions_possible', 99), ('unique_graphs', 1)]
2023-01-11T21:22:55.4065017Z ok (0.131s)
2023-01-11T21:22:55.4065575Z   test_for_loop_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4065962Z inline_call []
2023-01-11T21:22:55.4066490Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:55.4067111Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4067453Z ok (0.011s)
2023-01-11T21:22:55.4067852Z   test_get_parameter_dtype_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4068432Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4068786Z ok (0.017s)
2023-01-11T21:22:55.4069205Z   test_grad_mode_carrying_correct_state_after_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... Break
2023-01-11T21:22:55.4069684Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:55.4069977Z unimplemented []
2023-01-11T21:22:55.4070487Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:55.4071037Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:22:55.4071388Z ok (0.011s)
2023-01-11T21:22:55.4071946Z   test_guard_fail_nested_tuple_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4072520Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:55.4072847Z ok (0.020s)
2023-01-11T21:22:55.4073400Z   test_guard_fail_tensor_bool_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 12), ('ok', 12)]
2023-01-11T21:22:55.4074101Z unimplemented [('FOR_ITER UserDefinedObjectVariable(product)', 1)]
2023-01-11T21:22:55.4075276Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_guard_fail_tensor_bool.<locals>.fn.<locals>.get_expected at 0x7fa8c124ba30>', 5), ('data dependent operator: aten.allclose.default', 5)]
2023-01-11T21:22:55.4076035Z stats [('calls_captured', 5), ('unique_graphs', 5), ('fusions_possible', 0)]
2023-01-11T21:22:55.4076361Z ok (0.084s)
2023-01-11T21:22:55.4076739Z   test_guard_ordering_shape_fail_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.001s)
2023-01-11T21:22:55.4077244Z   test_hf_model_output_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4077803Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:22:55.4078133Z ok (0.054s)
2023-01-11T21:22:55.4078619Z   test_hf_t5_forward_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4079188Z stats [('calls_captured', 11), ('fusions_possible', 10), ('unique_graphs', 1)]
2023-01-11T21:22:55.4079524Z ok (0.453s)
2023-01-11T21:22:55.4080068Z   test_indexing_with_list_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:55.4080549Z inline_call [('Tensor.numpy', 1)]
2023-01-11T21:22:55.4080836Z unimplemented []
2023-01-11T21:22:55.4081193Z graph_break [('Tensor.numpy', 1)]
2023-01-11T21:22:55.4081678Z stats [('unique_graphs', 2), ('calls_captured', 0), ('fusions_possible', -2)]
2023-01-11T21:22:55.4082001Z ok (0.023s)
2023-01-11T21:22:55.4082386Z   test_is_symbolic_tracing_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4082965Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4083293Z ok (0.006s)
2023-01-11T21:22:55.4083677Z   test_isinstance_dtype_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... ok (0.003s)
2023-01-11T21:22:55.4084689Z   test_isinstance_storage_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:22:55.4085640Z   bools = torch.BoolStorage.from_buffer(f, "big")
2023-01-11T21:22:55.4086055Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:22:55.4086338Z unimplemented []
2023-01-11T21:22:55.4087112Z graph_break [('call_function BuiltinVariable(bytearray) [ListVariable()] {}', 1), ('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:22:55.4087934Z inline_call [('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:22:55.4088545Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4088922Z expected failure (0.012s)
2023-01-11T21:22:55.4089469Z   test_issue1466_size_aot_autograd_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... arf
2023-01-11T21:22:55.4089820Z arf
2023-01-11T21:22:55.4090137Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:55.4090417Z unimplemented []
2023-01-11T21:22:55.4090917Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:55.4091506Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:55.4091956Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4092241Z ok (0.031s)
2023-01-11T21:22:55.4092606Z   test_issue175_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4093175Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:22:55.4093523Z ok (0.125s)
2023-01-11T21:22:55.4094167Z   test_longformer_chunk_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:55.4094744Z ok (0.126s)
2023-01-11T21:22:55.4095141Z   test_maml_item_capture_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... expected failure (0.002s)
2023-01-11T21:22:55.4095817Z   test_maml_no_item_capture_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:22:55.4096414Z inline_call [('inlining disallowed: <function grad at 0x7fa83806e050>', 1)]
2023-01-11T21:22:55.4096772Z unimplemented []
2023-01-11T21:22:55.4097453Z graph_break [('Tensor.item', 2), ('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 1), ('inlining disallowed: <function grad at 0x7fa83806e050>', 1)]
2023-01-11T21:22:55.4098191Z stats [('calls_captured', 29), ('fusions_possible', 25), ('unique_graphs', 4)]
2023-01-11T21:22:55.4098546Z ok (0.146s)
2023-01-11T21:22:55.4099160Z   test_modules_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:55.4099590Z ok (0.021s)
2023-01-11T21:22:55.4100141Z   test_multi_dot_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:55.4100879Z inline_call [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:22:55.4101292Z unimplemented []
2023-01-11T21:22:55.4101855Z graph_break [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:22:55.4102464Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4102804Z ok (0.011s)
2023-01-11T21:22:55.4103186Z   test_multi_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... skip: requires detectron2 (0.000s)
2023-01-11T21:22:55.4104093Z   test_named_buffers_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:22:55.4104530Z ok (0.010s)
2023-01-11T21:22:55.4105053Z   test_nn_parameter_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4105651Z inline_call [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:55.4106026Z unimplemented []
2023-01-11T21:22:55.4106529Z graph_break [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:22:55.4107087Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4107433Z ok (0.013s)
2023-01-11T21:22:55.4107825Z   test_norm_dtype_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... skip: requires cuda (0.001s)
2023-01-11T21:22:55.4108527Z   test_not_rewrite_assert_for_other_errors_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4109129Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.4109457Z ok (0.007s)
2023-01-11T21:22:55.4110078Z   test_not_rewrite_assert_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:55.4110479Z ok (0.005s)
2023-01-11T21:22:55.4110994Z   test_numpy_list_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:55.4111390Z unimplemented []
2023-01-11T21:22:55.4112018Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_numpy_list.<locals>.rand_gen at 0x7fa8c113b9a0>', 1)]
2023-01-11T21:22:55.4112460Z expected failure (0.020s)
2023-01-11T21:22:55.4113050Z   test_optimized_deepcopy_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4113607Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4114008Z ok (0.012s)
2023-01-11T21:22:55.4114778Z   test_primtorch_no_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:55.4115299Z expected failure (0.003s)
2023-01-11T21:22:55.4115832Z   test_primtorch_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:55.4116492Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:55.4116918Z unimplemented []
2023-01-11T21:22:55.4117502Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:22:55.4117856Z ok (0.004s)
2023-01-11T21:22:55.4118456Z   test_recursive_map_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4118843Z inline_call []
2023-01-11T21:22:55.4119290Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4119630Z ok (0.012s)
2023-01-11T21:22:55.4120005Z   test_reformer_eval_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4120568Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:22:55.4120881Z ok (0.042s)
2023-01-11T21:22:55.4121263Z   test_reformer_min_chunk_len_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4121831Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.4122153Z ok (0.015s)
2023-01-11T21:22:55.4122527Z   test_reformer_remove_unused_args_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... foo
2023-01-11T21:22:55.4122882Z foo
2023-01-11T21:22:55.4123195Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:55.4123696Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:55.4124079Z unimplemented []
2023-01-11T21:22:55.4124542Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:22:55.4125075Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:55.4125523Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4125793Z ok (0.024s)
2023-01-11T21:22:55.4126147Z   test_reformer_sorting_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... inline_call []
2023-01-11T21:22:55.4126696Z stats [('calls_captured', 14), ('fusions_possible', 13), ('unique_graphs', 1)]
2023-01-11T21:22:55.4127023Z ok (0.036s)
2023-01-11T21:22:55.4127527Z   test_reformer_train_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:55.4128243Z inline_call [('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:22:55.4128674Z unimplemented []
2023-01-11T21:22:55.4129482Z graph_break [('autograd.Function with requires_grad', 1), ('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:22:55.4130156Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:22:55.4130503Z ok (0.059s)
2023-01-11T21:22:55.4131041Z   test_reinplacing_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4131607Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4132039Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4132326Z ok (0.245s)
2023-01-11T21:22:55.4132984Z   test_relative_import_no_modulename_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4133448Z ok (0.007s)
2023-01-11T21:22:55.4134084Z   test_relative_import_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4134634Z ok (0.006s)
2023-01-11T21:22:55.4135250Z   test_rewrite_assert_noop_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:22:55.4135694Z ok (0.020s)
2023-01-11T21:22:55.4136319Z   test_rewrite_assert_with_fstring_msg_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:22:55.4136740Z ok (0.004s)
2023-01-11T21:22:55.4155023Z   test_rewrite_assert_with_msg_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 18), ('fusions_possible', 15), ('unique_graphs', 3)]
2023-01-11T21:22:55.4155603Z ok (0.022s)
2023-01-11T21:22:55.4156446Z   test_rewrite_assert_without_msg_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 12), ('fusions_possible', 10), ('unique_graphs', 2)]
2023-01-11T21:22:55.4156910Z ok (0.015s)
2023-01-11T21:22:55.4157475Z   test_rng_state_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:55.4158155Z unimplemented [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 1)]
2023-01-11T21:22:55.4158779Z graph_break [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 2)]
2023-01-11T21:22:55.4159316Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:22:55.4159649Z ok (0.028s)
2023-01-11T21:22:55.4160275Z   test_seq_append_list_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:55.4160692Z ok (0.022s)
2023-01-11T21:22:55.4161753Z   test_sigmoid_out_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:55.4162818Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:22:55.4164240Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:22:55.4165130Z   warnings.warn(msg)
2023-01-11T21:22:55.4166121Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:55.4167080Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:22:55.4167451Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:55.4167922Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:22:55.4168254Z ok (0.032s)
2023-01-11T21:22:55.4168893Z   test_slice_into_list_mutable_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 30), ('fusions_possible', 29), ('unique_graphs', 1)]
2023-01-11T21:22:55.4169507Z ok (0.080s)
2023-01-11T21:22:55.4170093Z   test_slicing_dynamic_shape_setitem_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:22:55.4170774Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:55.4171239Z graph_break [('Dynamic slicing not supported', 1)]
2023-01-11T21:22:55.4171744Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4172182Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4172443Z ok (0.012s)
2023-01-11T21:22:55.4172982Z   test_slicing_dynamic_shape_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4173544Z unimplemented [('Dynamic slicing not supported', 2)]
2023-01-11T21:22:55.4174042Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:55.4174389Z ok (0.018s)
2023-01-11T21:22:55.4175546Z   test_sort_out_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:55.4176634Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:55.4178149Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:55.4179122Z   return node.target(*args, **kwargs)
2023-01-11T21:22:55.4180141Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:55.4181143Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:55.4182216Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:22:55.4183245Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:22:55.4183749Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:22:55.4184213Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:22:55.4184563Z ok (0.040s)
2023-01-11T21:22:55.4185124Z   test_specialized_stride_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4185702Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4186046Z ok (0.010s)
2023-01-11T21:22:55.4186616Z   test_swin_base_tensor_attr_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4187211Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:22:55.4187607Z ok (0.013s)
2023-01-11T21:22:55.4188137Z   test_tensor_isinstance_tuple_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4188755Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4189077Z ok (0.009s)
2023-01-11T21:22:55.4189601Z   test_tokenization_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:55.4190293Z inline_call [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:22:55.4190656Z unimplemented []
2023-01-11T21:22:55.4191195Z graph_break [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:22:55.4191583Z ok (0.008s)
2023-01-11T21:22:55.4192292Z   test_torch_ops_aten_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4192724Z ok (0.010s)
2023-01-11T21:22:55.4193379Z   test_vdd_duplicate_error_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:22:55.4193827Z ok (0.013s)
2023-01-11T21:22:55.4194350Z   test_while_loop_graph_break_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:22:55.4194748Z inline_call []
2023-01-11T21:22:55.4195338Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:22:55.4195943Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4196267Z ok (0.007s)
2023-01-11T21:22:55.4196662Z   test_with_on_graph_break_inst_unspec (__main__.make_unspec_cls.<locals>.UnspecTest) ... Hello world
2023-01-11T21:22:55.4197030Z Hello world
2023-01-11T21:22:55.4197339Z frames [('total', 6), ('ok', 6)]
2023-01-11T21:22:55.4197853Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:22:55.4198234Z unimplemented []
2023-01-11T21:22:55.4198765Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2), ('Tensor.backward', 1)]
2023-01-11T21:22:55.4199331Z stats [('calls_captured', 11), ('fusions_possible', 7), ('unique_graphs', 4)]
2023-01-11T21:22:55.4199654Z ok (0.026s)
2023-01-11T21:22:55.4200042Z   test_builtin_functions_on_cuda (__main__.UnspecTests) ... skip: requires cuda (0.001s)
2023-01-11T21:22:55.4200586Z   test_builtin_getitem (__main__.UnspecTests) ... frames [('total', 1)]
2023-01-11T21:22:55.4200936Z expected failure (0.008s)
2023-01-11T21:22:55.4201414Z   test_builtin_max_min (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4201921Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:22:55.4202254Z ok (0.009s)
2023-01-11T21:22:55.4202758Z   test_feed_random_values_into_graph_only (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4203300Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:22:55.4203637Z ok (0.013s)
2023-01-11T21:22:55.4204170Z   test_multiple_consecutive_random_calls_before_graph (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4204750Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4205068Z ok (0.028s)
2023-01-11T21:22:55.4205521Z   test_no_recompilations (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4206063Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4206387Z ok (0.007s)
2023-01-11T21:22:55.4206850Z   test_numpy_correctness (__main__.UnspecTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:22:55.4207362Z unimplemented [('reconstruct: ConstantVariable(float64)', 1)]
2023-01-11T21:22:55.4207905Z graph_break [('Tensor.numpy', 2), ('numpy', 2)]
2023-01-11T21:22:55.4208396Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4208735Z ok (0.024s)
2023-01-11T21:22:55.4209352Z   test_random_call_with_while_loop (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4209910Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:22:55.4210256Z ok (0.007s)
2023-01-11T21:22:55.4210764Z   test_random_values_with_graph_break (__main__.UnspecTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:22:55.4211133Z unimplemented []
2023-01-11T21:22:55.4211479Z graph_break [('Tensor.item', 2)]
2023-01-11T21:22:55.4211938Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:22:55.4212264Z ok (0.020s)
2023-01-11T21:22:55.4212842Z   test_unspec_float_precision (__main__.UnspecTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:22:55.4213427Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:22:55.4213781Z ok (0.105s)
2023-01-11T21:22:55.4213937Z 
2023-01-11T21:22:55.4214256Z ----------------------------------------------------------------------
2023-01-11T21:22:55.4214622Z Ran 137 tests in 6.810s
2023-01-11T21:22:55.4214809Z 
2023-01-11T21:22:55.4214973Z OK (skipped=3, expected failures=5)
2023-01-11T21:22:55.4215166Z 
2023-01-11T21:22:55.4215293Z Generating XML reports...
2023-01-11T21:22:55.4215959Z Generated XML report: test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecNNModuleTests-20230111212248.xml
2023-01-11T21:22:55.4216749Z Generated XML report: test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecReproTests-20230111212248.xml
2023-01-11T21:22:55.4217492Z Generated XML report: test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecTests-20230111212248.xml
2023-01-11T21:22:55.4217834Z 
2023-01-11T21:22:55.4218282Z ##[endgroup]
2023-01-11T21:22:55.4218878Z FINISHED PRINTING LOG FILE of dynamo/test_unspec (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_unspec_m1lweub2)
2023-01-11T21:22:55.4219212Z 
2023-01-11T21:22:57.2187026Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:22:57.2874875Z Ignoring disabled issues:  []
2023-01-11T21:22:57.3007109Z Running profiler/test_profiler_tree ... [2023-01-11 21:22:57.300422]
2023-01-11T21:22:57.3008840Z Executing ['/opt/conda/bin/python', '-bb', 'profiler/test_profiler_tree.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:57.300670]
2023-01-11T21:22:59.2716450Z 
2023-01-11T21:22:59.2717019Z Expand the folded group to see the log file of profiler/test_profiler_tree
2023-01-11T21:22:59.2718383Z ##[group]PRINTING LOG FILE of profiler/test_profiler_tree (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_tree_g35ii7qi)
2023-01-11T21:22:59.2718777Z 
2023-01-11T21:22:59.2718882Z Running tests...
2023-01-11T21:22:59.2719480Z ----------------------------------------------------------------------
2023-01-11T21:22:59.2720164Z Test results will be stored in test-reports/python-unittest/profiler.test_profiler_tree
2023-01-11T21:22:59.2721799Z   test_profiler_experimental_tree (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82499 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.227s)
2023-01-11T21:22:59.2723042Z   test_profiler_experimental_tree_cuda (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.001s)
2023-01-11T21:22:59.2723829Z   test_profiler_experimental_tree_cuda_detailed (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.001s)
2023-01-11T21:22:59.2724655Z   test_profiler_experimental_tree_cuda_with_stream (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.001s)
2023-01-11T21:22:59.2726693Z   test_profiler_experimental_tree_with_memory (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82501 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:22:59.2728044Z   test_profiler_experimental_tree_with_memory_and_stack (__main__.TestProfilerTree) ... STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2728562Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2729293Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2729879Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2730319Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2730761Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2731182Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2731610Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2732050Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2732312Z ok (0.014s)
2023-01-11T21:22:59.2733121Z   test_profiler_experimental_tree_with_record_function (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/83246 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:22:59.2733972Z   test_profiler_experimental_tree_with_stack_and_modules (__main__.TestProfilerTree) ... STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2734485Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2734923Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2735340Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2735762Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2736200Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2736625Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2737040Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2737473Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2737730Z ok (0.013s)
2023-01-11T21:22:59.2738228Z   test_profiler_experimental_tree_with_stack_and_torch_dispatch (__main__.TestProfilerTree) ... STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2738734Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2739207Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2739904Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2740632Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2741111Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2741541Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2741964Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2742385Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2742642Z ok (0.006s)
2023-01-11T21:22:59.2743173Z   test_profiler_experimental_tree_with_stack_and_torch_function (__main__.TestProfilerTree) ... STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2743767Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2744194Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2744621Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2745046Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2745471Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2745898Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:22:59.2746320Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:22:59.2746762Z STAGE:2023-01-11 21:22:58 1526:1526 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:22:59.2747003Z ok (0.005s)
2023-01-11T21:22:59.2747108Z 
2023-01-11T21:22:59.2747310Z ----------------------------------------------------------------------
2023-01-11T21:22:59.2747553Z Ran 10 tests in 0.269s
2023-01-11T21:22:59.2747666Z 
2023-01-11T21:22:59.2747725Z OK (skipped=6)
2023-01-11T21:22:59.2747830Z 
2023-01-11T21:22:59.2747913Z Generating XML reports...
2023-01-11T21:22:59.2748341Z Generated XML report: test-reports/python-unittest/profiler.test_profiler_tree/TEST-TestProfilerTree-20230111212258.xml
2023-01-11T21:22:59.2748584Z 
2023-01-11T21:22:59.2748850Z ##[endgroup]
2023-01-11T21:22:59.2749277Z FINISHED PRINTING LOG FILE of profiler/test_profiler_tree (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_tree_g35ii7qi)
2023-01-11T21:22:59.2749516Z 
2023-01-11T21:23:01.1558698Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:01.2200867Z Ignoring disabled issues:  []
2023-01-11T21:23:01.2333182Z Running test_ao_sparsity ... [2023-01-11 21:23:01.233051]
2023-01-11T21:23:01.2336173Z Executing ['/opt/conda/bin/python', '-bb', 'test_ao_sparsity.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:01.233305]
2023-01-11T21:23:09.3628811Z 
2023-01-11T21:23:09.3629344Z Expand the folded group to see the log file of test_ao_sparsity
2023-01-11T21:23:09.3630406Z ##[group]PRINTING LOG FILE of test_ao_sparsity (/var/lib/jenkins/workspace/test/test-reports/test_ao_sparsity_kknsuqq4)
2023-01-11T21:23:09.3630838Z 
2023-01-11T21:23:09.3630949Z Running tests...
2023-01-11T21:23:09.3631594Z ----------------------------------------------------------------------
2023-01-11T21:23:09.3632279Z Test results will be stored in test-reports/python-unittest/test_ao_sparsity
2023-01-11T21:23:09.3632949Z   test_activation_sparsifier (ao.sparsity.test_activation_sparsifier.TestActivationSparsifier)
2023-01-11T21:23:09.3633645Z Simulates the workflow of the activation sparsifier, starting from object creation ... ok (0.287s)
2023-01-11T21:23:09.3634290Z   test_constructor (ao.sparsity.test_data_scheduler.TestBaseDataScheduler)
2023-01-11T21:23:09.3634883Z Checks if the warning is thrown if the scheduler step is called ... ok (0.002s)
2023-01-11T21:23:09.3635688Z   test_order_of_steps (ao.sparsity.test_data_scheduler.TestBaseDataScheduler) ... ok (0.007s)
2023-01-11T21:23:09.3636365Z   test_state_dict (ao.sparsity.test_data_scheduler.TestBaseDataScheduler) ... ok (0.003s)
2023-01-11T21:23:09.3637027Z   test_step (ao.sparsity.test_data_scheduler.TestBaseDataScheduler) ... ok (0.008s)
2023-01-11T21:23:09.3638461Z   test_nn_embeddings (ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier) ... /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py:104: UserWarning: Replacing existing data of the same name. - Did you mean a different name?
2023-01-11T21:23:09.3639612Z   warnings.warn("Replacing existing data of the same name. - Did you mean a different name?")
2023-01-11T21:23:09.3640116Z ok (0.020s)
2023-01-11T21:23:09.3640617Z   test_nn_parameters (ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier) ... ok (0.015s)
2023-01-11T21:23:09.3641308Z   test_tensors (ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier) ... ok (0.015s)
2023-01-11T21:23:09.3641962Z   test_constructor (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:09.3642605Z   test_mask_squash (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.001s)
2023-01-11T21:23:09.3643272Z   test_mask_squash_with_params1 (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:09.3643953Z   test_mask_squash_with_params2 (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:09.3644615Z   test_mask_squash_with_params3 (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:09.3645273Z   test_prepare_config (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.002s)
2023-01-11T21:23:09.3645913Z   test_state_dict (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.003s)
2023-01-11T21:23:09.3646522Z   test_step (ao.sparsity.test_sparsifier.TestBaseSparsifier) ... ok (0.001s)
2023-01-11T21:23:09.3647198Z   test_complex_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier)
2023-01-11T21:23:09.3647834Z Test fusion for models that contain Conv2d & Linear modules. ... ok (0.087s)
2023-01-11T21:23:09.3648496Z   test_constructor (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.004s)
2023-01-11T21:23:09.3649454Z   test_prepare_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.027s)
2023-01-11T21:23:09.3650147Z   test_prepare_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.012s)
2023-01-11T21:23:09.3650788Z   test_prune_conv2d_activation_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.146s)
2023-01-11T21:23:09.3651458Z   test_prune_conv2d_bias_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.088s)
2023-01-11T21:23:09.3652138Z   test_prune_conv2d_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.049s)
2023-01-11T21:23:09.3652924Z   test_prune_conv2d_padding_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.287s)
2023-01-11T21:23:09.3653616Z   test_prune_conv2d_pool_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.046s)
2023-01-11T21:23:09.3654326Z   test_prune_linear_activation_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.060s)
2023-01-11T21:23:09.3655035Z   test_prune_linear_bias_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.060s)
2023-01-11T21:23:09.3655688Z   test_prune_linear_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier)
2023-01-11T21:23:09.3656351Z test pruning linear-> linear modules ... ok (0.058s)
2023-01-11T21:23:09.3656883Z   test_step_conv2d (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.027s)
2023-01-11T21:23:09.3657725Z   test_step_linear (ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier) ... ok (0.009s)
2023-01-11T21:23:09.3659066Z   test_convert_without_squash_mask (ao.sparsity.test_composability.TestComposability) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
2023-01-11T21:23:09.3659825Z   warnings.warn(
2023-01-11T21:23:09.3660081Z ok (0.281s)
2023-01-11T21:23:09.3660490Z   test_fusion_before_s_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.282s)
2023-01-11T21:23:09.3661056Z   test_q_prep_before_s_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.277s)
2023-01-11T21:23:09.3661737Z   test_qat_prep_before_s_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.012s)
2023-01-11T21:23:09.3662325Z   test_s_prep_before_fusion (ao.sparsity.test_composability.TestComposability) ... ok (0.277s)
2023-01-11T21:23:09.3662869Z   test_s_prep_before_q_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.280s)
2023-01-11T21:23:09.3663499Z   test_s_prep_before_qat_prep (ao.sparsity.test_composability.TestComposability) ... ok (0.014s)
2023-01-11T21:23:09.3664044Z   test_constructor (ao.sparsity.test_scheduler.TestCubicScheduler) ... ok (0.003s)
2023-01-11T21:23:09.3665300Z   test_step (ao.sparsity.test_scheduler.TestCubicScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/scheduler/base_scheduler.py:125: UserWarning: Detected call of `scheduler.step()` before `sparsifier.step()`. You have to make sure you run the sparsifier.step() BEFORE any calls to the scheduer.step().
2023-01-11T21:23:09.3666181Z   warnings.warn("Detected call of `scheduler.step()` before `sparsifier.step()`. "
2023-01-11T21:23:09.3666540Z ok (0.002s)
2023-01-11T21:23:09.3666934Z   test_jit_trace (ao.sparsity.test_parametrization.TestFakeSparsity) ... ok (0.094s)
2023-01-11T21:23:09.3667472Z   test_masking_logic (ao.sparsity.test_parametrization.TestFakeSparsity) ... ok (0.002s)
2023-01-11T21:23:09.3668051Z   test_state_dict_preserved (ao.sparsity.test_parametrization.TestFakeSparsity) ... ok (0.006s)
2023-01-11T21:23:09.3668636Z   test_weights_parametrized (ao.sparsity.test_parametrization.TestFakeSparsity) ... ok (0.002s)
2023-01-11T21:23:09.3669209Z   test_q_prep_fx_before_s_prep (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:09.3669866Z This test checks that the ordering of prepare_fx -> sparse prepare -> convert_fx ... ok (0.314s)
2023-01-11T21:23:09.3670407Z   test_q_prep_fx_s_prep_ref_conv (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:09.3671093Z This checks that the ordering: prepare_fx -> sparse prepare -> convert_to_reference_fx ... ok (0.289s)
2023-01-11T21:23:09.3671619Z   test_s_prep_before_q_prep_fx (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:09.3672293Z This test checks that the ordering of sparse prepare -> prepare_fx -> convert_fx ... ok (0.302s)
2023-01-11T21:23:09.3672854Z   test_s_prep_before_qat_prep_fx (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:09.3673572Z This test checks that the ordering of sparse prepare -> prepare_qat_fx -> convert_fx ... ok (0.030s)
2023-01-11T21:23:09.3674145Z   test_s_prep_q_prep_fx_ref (ao.sparsity.test_composability.TestFxComposability)
2023-01-11T21:23:09.3674898Z This checks that the ordering: sparse prepare -> prepare_fx -> convert_to_reference_fx ... ok (0.296s)
2023-01-11T21:23:09.3675533Z   test_constructor (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.002s)
2023-01-11T21:23:09.3676143Z   test_mask_squash (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.003s)
2023-01-11T21:23:09.3676717Z   test_prepare (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.001s)
2023-01-11T21:23:09.3677286Z   test_sparsity_levels (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.459s)
2023-01-11T21:23:09.3678138Z   test_step (ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier) ... ok (0.278s)
2023-01-11T21:23:09.3678725Z   test_nn_embeddings (ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers) ... ok (0.309s)
2023-01-11T21:23:09.3679314Z   test_nn_parameters (ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers) ... ok (0.216s)
2023-01-11T21:23:09.3679916Z   test_tensors (ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers) ... ok (0.199s)
2023-01-11T21:23:09.3680474Z   test_ptq_quantize_first (ao.sparsity.test_data_sparsifier.TestQuantizationUtils)
2023-01-11T21:23:09.3681023Z The expectation is post_training_sparse_quantize function ... ok (0.019s)
2023-01-11T21:23:09.3681575Z   test_ptq_sparsify_first (ao.sparsity.test_data_sparsifier.TestQuantizationUtils)
2023-01-11T21:23:09.3682160Z The expectation is post_training_sparse_quantize function ... ok (0.008s)
2023-01-11T21:23:09.3683003Z   test_sparse_qlinear (ao.sparsity.test_kernels.TestQuantizedSparseKernels) ... 2023-01-11 21:23:08,351 - root - INFO - static sparse qlinear is only available in fbgemm
2023-01-11T21:23:09.3683774Z 2023-01-11 21:23:08,393 - root - INFO - static sparse qlinear is only available in fbgemm
2023-01-11T21:23:09.3684379Z 2023-01-11 21:23:08,393 - root - INFO - dynamic sparse qlinear is only available in qnnpack
2023-01-11T21:23:09.3684950Z 2023-01-11 21:23:08,397 - root - INFO - dynamic sparse qlinear is only available in qnnpack
2023-01-11T21:23:09.3685287Z ok (0.103s)
2023-01-11T21:23:09.3686292Z   test_sparse_qlinear (ao.sparsity.test_kernels.TestQuantizedSparseLayers) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:23:09.3687036Z   warnings.warn(
2023-01-11T21:23:09.3687587Z [W qlinear_dynamic.cpp:247] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function operator())
2023-01-11T21:23:09.3688553Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:302: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:23:09.3689217Z   warnings.warn(
2023-01-11T21:23:09.3689475Z ok (0.095s)
2023-01-11T21:23:09.3689870Z   test_sparse_qlinear_serdes (ao.sparsity.test_kernels.TestQuantizedSparseLayers) ... ok (0.121s)
2023-01-11T21:23:09.3690403Z   test_constructor (ao.sparsity.test_scheduler.TestScheduler) ... ok (0.001s)
2023-01-11T21:23:09.3690892Z   test_lambda_scheduler (ao.sparsity.test_scheduler.TestScheduler) ... ok (0.001s)
2023-01-11T21:23:09.3691361Z   test_order_of_steps (ao.sparsity.test_scheduler.TestScheduler)
2023-01-11T21:23:09.3691792Z Checks if the warning is thrown if the scheduler step is called ... ok (0.005s)
2023-01-11T21:23:09.3692277Z   test_step (ao.sparsity.test_scheduler.TestScheduler) ... ok (0.001s)
2023-01-11T21:23:09.3692772Z   test_fqn_to_module (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:09.3693198Z Tests that fqn_to_module operates as inverse ... ok (0.005s)
2023-01-11T21:23:09.3693657Z   test_fqn_to_module_fail (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:09.3694186Z Tests that fqn_to_module returns None when it tries to ... ok (0.002s)
2023-01-11T21:23:09.3694673Z   test_fqn_to_module_for_tensors (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:09.3695189Z Tests that fqn_to_module works for tensors, actually all parameters ... ok (0.005s)
2023-01-11T21:23:09.3695779Z   test_get_arg_info_from_tensor_fqn (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:09.3696352Z Tests that get_arg_info_from_tensor_fqn works for all parameters of the model. ... ok (0.005s)
2023-01-11T21:23:09.3696865Z   test_get_arg_info_from_tensor_fqn_fail (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:09.3697377Z Tests that get_arg_info_from_tensor_fqn works as expected for invalid tensor_fqn ... ok (0.003s)
2023-01-11T21:23:09.3697989Z   test_module_to_fqn (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:09.3698471Z Tests that module_to_fqn works as expected when compared to known good ... ok (0.003s)
2023-01-11T21:23:09.3698953Z   test_module_to_fqn_fail (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:09.3699559Z Tests that module_to_fqn returns None when an fqn that doesn't ... ok (0.002s)
2023-01-11T21:23:09.3700061Z   test_module_to_fqn_root (ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions)
2023-01-11T21:23:09.3700684Z Tests that module_to_fqn returns '' when model and target module are the same ... ok (0.002s)
2023-01-11T21:23:09.3701299Z   test_constructor (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.001s)
2023-01-11T21:23:09.3701883Z   test_mask_squash (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.002s)
2023-01-11T21:23:09.3702452Z   test_prepare (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.001s)
2023-01-11T21:23:09.3703080Z   test_sparsity_levels (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.030s)
2023-01-11T21:23:09.3703744Z   test_step (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.268s)
2023-01-11T21:23:09.3704307Z   test_step_2_of_4 (ao.sparsity.test_sparsifier.TestWeightNormSparsifier) ... ok (0.014s)
2023-01-11T21:23:09.3704611Z 
2023-01-11T21:23:09.3704915Z ----------------------------------------------------------------------
2023-01-11T21:23:09.3705240Z Ran 79 tests in 6.267s
2023-01-11T21:23:09.3705401Z 
2023-01-11T21:23:09.3705487Z OK
2023-01-11T21:23:09.3705613Z 
2023-01-11T21:23:09.3705726Z Generating XML reports...
2023-01-11T21:23:09.3706466Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_activation_sparsifier.TestActivationSparsifier-20230111212302.xml
2023-01-11T21:23:09.3707460Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_scheduler.TestBaseDataScheduler-20230111212302.xml
2023-01-11T21:23:09.3708450Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier-20230111212302.xml
2023-01-11T21:23:09.3709457Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestBaseSparsifier-20230111212302.xml
2023-01-11T21:23:09.3710540Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier-20230111212302.xml
2023-01-11T21:23:09.3711660Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_composability.TestComposability-20230111212302.xml
2023-01-11T21:23:09.3712656Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_scheduler.TestCubicScheduler-20230111212302.xml
2023-01-11T21:23:09.3713592Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_parametrization.TestFakeSparsity-20230111212302.xml
2023-01-11T21:23:09.3714521Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_composability.TestFxComposability-20230111212302.xml
2023-01-11T21:23:09.3715498Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier-20230111212302.xml
2023-01-11T21:23:09.3716502Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers-20230111212302.xml
2023-01-11T21:23:09.3717570Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestQuantizationUtils-20230111212302.xml
2023-01-11T21:23:09.3718644Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_kernels.TestQuantizedSparseKernels-20230111212302.xml
2023-01-11T21:23:09.3719737Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_kernels.TestQuantizedSparseLayers-20230111212302.xml
2023-01-11T21:23:09.3720656Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_scheduler.TestScheduler-20230111212302.xml
2023-01-11T21:23:09.3721649Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions-20230111212302.xml
2023-01-11T21:23:09.3722616Z Generated XML report: test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestWeightNormSparsifier-20230111212302.xml
2023-01-11T21:23:09.3723018Z 
2023-01-11T21:23:09.3723422Z ##[endgroup]
2023-01-11T21:23:09.3724041Z FINISHED PRINTING LOG FILE of test_ao_sparsity (/var/lib/jenkins/workspace/test/test-reports/test_ao_sparsity_kknsuqq4)
2023-01-11T21:23:09.3724351Z 
2023-01-11T21:23:11.2319201Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:11.2961417Z Ignoring disabled issues:  []
2023-01-11T21:23:11.3094490Z Running test_foreach ... [2023-01-11 21:23:11.309179]
2023-01-11T21:23:11.3096303Z Executing ['/opt/conda/bin/python', '-bb', 'test_foreach.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:11.309425]
2023-01-11T21:23:13.7584904Z 
2023-01-11T21:23:13.7585642Z Expand the folded group to see the log file of test_foreach
2023-01-11T21:23:13.7586903Z ##[group]PRINTING LOG FILE of test_foreach (/var/lib/jenkins/workspace/test/test-reports/test_foreach_tdwt3cj8)
2023-01-11T21:23:13.7587323Z 
2023-01-11T21:23:13.7587450Z Running tests...
2023-01-11T21:23:13.7588099Z ----------------------------------------------------------------------
2023-01-11T21:23:13.7588387Z 
2023-01-11T21:23:13.7588743Z ----------------------------------------------------------------------
2023-01-11T21:23:13.7589181Z Ran 0 tests in 0.000s
2023-01-11T21:23:13.7589384Z 
2023-01-11T21:23:13.7589485Z OK
2023-01-11T21:23:13.7589654Z 
2023-01-11T21:23:13.7589788Z Generating XML reports...
2023-01-11T21:23:13.7590375Z Test results will be stored in test-reports/python-unittest/test_foreach
2023-01-11T21:23:13.7590691Z 
2023-01-11T21:23:13.7592310Z ##[endgroup]
2023-01-11T21:23:13.7593001Z FINISHED PRINTING LOG FILE of test_foreach (/var/lib/jenkins/workspace/test/test-reports/test_foreach_tdwt3cj8)
2023-01-11T21:23:13.7593387Z 
2023-01-11T21:23:15.5520473Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:15.6166576Z Ignoring disabled issues:  []
2023-01-11T21:23:15.6298728Z Running test_function_schema ... [2023-01-11 21:23:15.629667]
2023-01-11T21:23:15.6301340Z Executing ['/opt/conda/bin/python', '-bb', 'test_function_schema.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:15.629933]
2023-01-11T21:23:17.6781891Z 
2023-01-11T21:23:17.6782403Z Expand the folded group to see the log file of test_function_schema
2023-01-11T21:23:17.6783451Z ##[group]PRINTING LOG FILE of test_function_schema (/var/lib/jenkins/workspace/test/test-reports/test_function_schema_kh66fvrw)
2023-01-11T21:23:17.6783719Z 
2023-01-11T21:23:17.6783795Z Running tests...
2023-01-11T21:23:17.6784204Z ----------------------------------------------------------------------
2023-01-11T21:23:17.6784581Z Test results will be stored in test-reports/python-unittest/test_function_schema
2023-01-11T21:23:17.6784915Z   test_backward_compatible_arguments (__main__.TestFunctionSchema) ... ok (0.218s)
2023-01-11T21:23:17.6785240Z   test_backward_compatible_outputs (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6785546Z   test_backward_compatible_structure (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6785886Z   test_backward_compatible_with_smart_serialization (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6786238Z   test_forward_compatible_arguments_real_use_case (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6786575Z   test_forward_compatible_arguments_with_out (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6787056Z   test_forward_compatible_arguments_without_out (__main__.TestFunctionSchema) ... ok (0.002s)
2023-01-11T21:23:17.6787359Z   test_out_schema (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6787629Z   test_schema_error (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6787909Z   test_serialize_and_deserialize (__main__.TestFunctionSchema) ... ok (0.213s)
2023-01-11T21:23:17.6788234Z   test_string_optional_parameter_default_value (__main__.TestFunctionSchema) ... ok (0.002s)
2023-01-11T21:23:17.6788572Z   test_tensor_list_alias_annotation_properly_parsed (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6788912Z   test_tensor_option_arguments_properly_parsed (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T21:23:17.6789086Z 
2023-01-11T21:23:17.6789349Z ----------------------------------------------------------------------
2023-01-11T21:23:17.6789596Z Ran 13 tests in 0.442s
2023-01-11T21:23:17.6789712Z 
2023-01-11T21:23:17.6789772Z OK
2023-01-11T21:23:17.6789861Z 
2023-01-11T21:23:17.6789933Z Generating XML reports...
2023-01-11T21:23:17.6790371Z Generated XML report: test-reports/python-unittest/test_function_schema/TEST-TestFunctionSchema-20230111212316.xml
2023-01-11T21:23:17.6790613Z 
2023-01-11T21:23:17.6790840Z ##[endgroup]
2023-01-11T21:23:17.6791220Z FINISHED PRINTING LOG FILE of test_function_schema (/var/lib/jenkins/workspace/test/test-reports/test_function_schema_kh66fvrw)
2023-01-11T21:23:17.6791441Z 
2023-01-11T21:23:19.4850345Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:19.5488754Z Ignoring disabled issues:  []
2023-01-11T21:23:19.5620775Z Running test_jit ... [2023-01-11 21:23:19.561813]
2023-01-11T21:23:19.5622698Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:19.562070]
2023-01-11T21:25:29.6895124Z 
2023-01-11T21:25:29.6895618Z Expand the folded group to see the log file of test_jit
2023-01-11T21:25:29.6896622Z ##[group]PRINTING LOG FILE of test_jit (/var/lib/jenkins/workspace/test/test-reports/test_jit_lpu6xhd1)
2023-01-11T21:25:29.6899185Z CUDA not available, skipping tests
2023-01-11T21:25:29.6899610Z 
2023-01-11T21:25:29.6899905Z Running tests...
2023-01-11T21:25:29.6900578Z ----------------------------------------------------------------------
2023-01-11T21:25:29.6901235Z Test results will be stored in test-reports/python-unittest/test_jit
2023-01-11T21:25:29.6901845Z   test_becomes_wildcard_annotations (jit.test_alias_analysis.TestAliasAnalysis) ... ok (0.002s)
2023-01-11T21:25:29.6902500Z   test_nested_list_construct_not_wildcard (jit.test_alias_analysis.TestAliasAnalysis) ... ok (0.010s)
2023-01-11T21:25:29.6903130Z   test_recursive_calls (jit.test_alias_analysis.TestAliasAnalysis) ... ok (0.014s)
2023-01-11T21:25:29.6905037Z   test_async_future_type_python (jit.test_async.TestAsync) ... ok (0.001s)
2023-01-11T21:25:29.6905587Z   test_async_grad_guard_no_grad (jit.test_async.TestAsync) ... ok (0.043s)
2023-01-11T21:25:29.6906181Z   test_async_grad_guard_with_grad (jit.test_async.TestAsync) ... ok (0.006s)
2023-01-11T21:25:29.6906680Z   test_async_kwargs (jit.test_async.TestAsync) ... ok (0.195s)
2023-01-11T21:25:29.6907161Z   test_async_parsing (jit.test_async.TestAsync) ... ok (0.008s)
2023-01-11T21:25:29.6907621Z   test_async_python (jit.test_async.TestAsync) ... ok (0.003s)
2023-01-11T21:25:29.6908040Z   test_async_script (jit.test_async.TestAsync) ... ok (0.006s)
2023-01-11T21:25:29.6908464Z   test_async_script_capture (jit.test_async.TestAsync) ... ok (0.008s)
2023-01-11T21:25:29.6908916Z   test_async_script_error (jit.test_async.TestAsync) ... ok (0.032s)
2023-01-11T21:25:29.6909418Z   test_async_script_multi_forks (jit.test_async.TestAsync) ... ok (0.014s)
2023-01-11T21:25:29.6909933Z   test_async_script_multi_waits (jit.test_async.TestAsync) ... ok (0.006s)
2023-01-11T21:25:29.6910456Z   test_async_script_nested (jit.test_async.TestAsync) ... ok (0.009s)
2023-01-11T21:25:29.6911240Z   test_async_script_no_script_mod (jit.test_async.TestAsync) ... ok (0.001s)
2023-01-11T21:25:29.6912028Z   test_async_script_trace (jit.test_async.TestAsync) ... ok (0.032s)
2023-01-11T21:25:29.6912495Z   test_future_subtyping (jit.test_async.TestAsync)
2023-01-11T21:25:29.6912939Z Test that futures subtype each other properly. ... ok (0.009s)
2023-01-11T21:25:29.6913424Z   test_no_future_subtype_message (jit.test_async.TestAsync) ... ok (0.001s)
2023-01-11T21:25:29.6913915Z   test_trace_fork_wait (jit.test_async.TestAsync) ... ok (0.010s)
2023-01-11T21:25:29.6914380Z   test_trace_fork_wait_inline (jit.test_async.TestAsync) ... ok (0.007s)
2023-01-11T21:25:29.6914901Z   test_trace_fork_wait_leaking (jit.test_async.TestAsync) ... ok (0.002s)
2023-01-11T21:25:29.6915609Z   test_trace_fork_wait_list_modulecalls (jit.test_async.TestAsync) ... ok (0.024s)
2023-01-11T21:25:29.6916195Z   test_trace_modulecalls_with_different_output_types (jit.test_async.TestAsync) ... ok (0.015s)
2023-01-11T21:25:29.6916742Z   test_aten_pow_zero_negative_exponent (jit.test_aten_pow.TestAtenPow)
2023-01-11T21:25:29.6917191Z 1. Testing a = int, b = int ... ok (0.021s)
2023-01-11T21:25:29.6917687Z   test_autodiff_requires_grad_nograd (jit.test_autodiff.TestAutodiffJit) ... ok (0.083s)
2023-01-11T21:25:29.6918267Z   test_requires_grad_outputs (jit.test_autodiff.TestAutodiffJit) ... ok (0.050s)
2023-01-11T21:25:29.6918864Z   test_requires_grad_outputs_profiled_twice (jit.test_autodiff.TestAutodiffJit) ... ok (0.115s)
2023-01-11T21:25:29.6919501Z   test_requires_grad_outputs_side_effects (jit.test_autodiff.TestAutodiffJit) ... ok (0.115s)
2023-01-11T21:25:29.6920137Z   test_undefined_tensor_lists (jit.test_autodiff.TestAutodiffJit) ... ok (0.115s)
2023-01-11T21:25:29.6920811Z   test_aliased_outputs (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.003s)
2023-01-11T21:25:29.6921527Z   test_bias_as_arg (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.363s)
2023-01-11T21:25:29.6922246Z   test_bias_as_module_attr (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.304s)
2023-01-11T21:25:29.6922970Z   test_chunk_constant_script_ad (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.004s)
2023-01-11T21:25:29.6923698Z   test_constructed_bias (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.022s)
2023-01-11T21:25:29.6924444Z   test_diff_graph_inline_threshold (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:29.6925378Z   test_differentiable_graph_ops_requires_grad (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... skip: disable until we property handle tensor lists with undefined gradients (0.001s)
2023-01-11T21:25:29.6926170Z   test_does_not_create_cycles (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:29.6926880Z   test_does_not_merge_unrelated (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.005s)
2023-01-11T21:25:29.6927552Z   test_has_profiled_info_aliasing_outputs (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.001s)
2023-01-11T21:25:29.6928207Z   test_merge_respects_aliasing (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.009s)
2023-01-11T21:25:29.6928914Z   test_merges_dense (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:29.6929786Z   test_merges_down (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:29.6930377Z   test_merges_up (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:29.6931034Z   test_merges_without_cycles (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.005s)
2023-01-11T21:25:29.6932044Z   test_prune_grad (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... skip: Simple Executor doesn't support gradients (0.001s)
2023-01-11T21:25:29.6932962Z   test_requires_grad_for_tensor_list (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.005s)
2023-01-11T21:25:29.6933713Z   test_respects_lexical_scoping (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.006s)
2023-01-11T21:25:29.6934453Z   test_simple_merge (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.004s)
2023-01-11T21:25:29.6935113Z   test_simple_no_merge (jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing) ... ok (0.005s)
2023-01-11T21:25:29.6936254Z   test_errors (jit.test_backends.TestBackends) ... [W backend_detail.cpp:393] Warning: Backend [test_backend_unavailable] is not available. Execution of this Module is still possible by saving and loading on a device where the backend is available. (function codegen_backend_module)
2023-01-11T21:25:29.6937070Z ok (0.136s)
2023-01-11T21:25:29.6937842Z   test_execution (jit.test_backends.TestBackends) ... [W backend_detail.cpp:393] Warning: Backend [test_backend_unavailable] is not available. Execution of this Module is still possible by saving and loading on a device where the backend is available. (function codegen_backend_module)
2023-01-11T21:25:29.6938613Z ok (0.127s)
2023-01-11T21:25:29.6939397Z   test_save_load (jit.test_backends.TestBackends) ... [W backend_detail.cpp:393] Warning: Backend [test_backend_unavailable] is not available. Execution of this Module is still possible by saving and loading on a device where the backend is available. (function codegen_backend_module)
2023-01-11T21:25:29.6940103Z ok (0.230s)
2023-01-11T21:25:29.6940527Z   test_errors (jit.test_backends.TestBackendsWithCompiler) ... ok (0.044s)
2023-01-11T21:25:29.6941101Z   test_execution (jit.test_backends.TestBackendsWithCompiler) ... ok (0.065s)
2023-01-11T21:25:29.6941607Z   test_batch_mm_no_mutation (jit.test_batch_mm.TestBatchMM) ... ok (0.005s)
2023-01-11T21:25:29.6942120Z   test_batch_mm_permitted_mutation (jit.test_batch_mm.TestBatchMM) ... ok (0.006s)
2023-01-11T21:25:29.6942693Z   test_batch_mm_prohibited_mutation (jit.test_batch_mm.TestBatchMM) ... ok (0.006s)
2023-01-11T21:25:29.6943285Z   test_batch_mm_prohibited_mutation_if_node (jit.test_batch_mm.TestBatchMM) ... ok (0.007s)
2023-01-11T21:25:29.6943922Z   test_batch_mm_prohibited_mutation_multiple_adds (jit.test_batch_mm.TestBatchMM) ... ok (0.008s)
2023-01-11T21:25:29.6944525Z   test_batch_mm_side_permitted_mutation (jit.test_batch_mm.TestBatchMM) ... ok (0.007s)
2023-01-11T21:25:29.6946682Z   test_batch_mm_side_prohibited_mutation_common_side (jit.test_batch_mm.TestBatchMM) ... ok (0.009s)
2023-01-11T21:25:29.6947332Z   test_batch_mm_side_prohibited_mutation_uncommon_side (jit.test_batch_mm.TestBatchMM) ... ok (0.009s)
2023-01-11T21:25:29.6947863Z   test_del (jit.test_builtins.TestBuiltins) ... ok (0.005s)
2023-01-11T21:25:29.6948374Z   test_del_multiple_operands (jit.test_builtins.TestBuiltins) ... ok (0.008s)
2023-01-11T21:25:29.6948862Z   test_has_attr (jit.test_builtins.TestBuiltins) ... ok (0.017s)
2023-01-11T21:25:29.6949374Z   test_has_attr_invalid_args (jit.test_builtins.TestBuiltins) ... ok (0.008s)
2023-01-11T21:25:29.6949897Z   test_cast_overloads (jit.test_class_type.TestClassType) ... ok (0.036s)
2023-01-11T21:25:29.6950374Z   test_class_attribute_wrong_type (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6950886Z Test that the error message displayed when convering a class type ... ok (0.019s)
2023-01-11T21:25:29.6951420Z   test_class_constant (jit.test_class_type.TestClassType) ... ok (0.031s)
2023-01-11T21:25:29.6951993Z   test_class_constructs_itself (jit.test_class_type.TestClassType) ... ok (0.016s)
2023-01-11T21:25:29.6952526Z   test_class_inheritance (jit.test_class_type.TestClassType) ... ok (0.112s)
2023-01-11T21:25:29.6953052Z   test_class_inheritance_implicit (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6953543Z Test that inheritance is detected in ... ok (0.030s)
2023-01-11T21:25:29.6953984Z   test_class_sorting (jit.test_class_type.TestClassType) ... ok (0.080s)
2023-01-11T21:25:29.6954622Z   test_class_specialization (jit.test_class_type.TestClassType) ... ok (0.074s)
2023-01-11T21:25:29.6955158Z   test_class_type_as_param (jit.test_class_type.TestClassType) ... ok (0.016s)
2023-01-11T21:25:29.6955658Z   test_classmethod (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6956118Z Test classmethods on class types. ... ok (0.021s)
2023-01-11T21:25:29.6956586Z   test_conditional_set_attr (jit.test_class_type.TestClassType) ... ok (0.107s)
2023-01-11T21:25:29.6957094Z   test_custom_delete (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6957537Z Test that del can be called on an instance of a class that ... ok (0.041s)
2023-01-11T21:25:29.6958015Z   test_default_args (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6958634Z Test that methods on class types can have default arguments. ... ok (0.089s)
2023-01-11T21:25:29.6959111Z   test_get_attr (jit.test_class_type.TestClassType) ... ok (0.012s)
2023-01-11T21:25:29.6959628Z   test_get_attr_not_initialized (jit.test_class_type.TestClassType) ... ok (0.010s)
2023-01-11T21:25:29.6960153Z   test_get_with_method (jit.test_class_type.TestClassType) ... ok (0.001s)
2023-01-11T21:25:29.6960696Z   test_imported_classes (jit.test_class_type.TestClassType) ... ok (0.014s)
2023-01-11T21:25:29.6961188Z   test_in (jit.test_class_type.TestClassType) ... ok (0.013s)
2023-01-11T21:25:29.6961672Z   test_init_compiled_first (jit.test_class_type.TestClassType) ... ok (0.014s)
2023-01-11T21:25:29.6962209Z   test_interface (jit.test_class_type.TestClassType) ... ok (0.237s)
2023-01-11T21:25:29.6962724Z   test_optional_type_promotion (jit.test_class_type.TestClassType) ... ok (0.028s)
2023-01-11T21:25:29.6963254Z   test_out_of_order_methods (jit.test_class_type.TestClassType) ... ok (0.117s)
2023-01-11T21:25:29.6963770Z   test_overloaded_fn (jit.test_class_type.TestClassType) ... ok (0.137s)
2023-01-11T21:25:29.6964232Z   test_properties (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6964775Z Test that a scripted class can make use of the @property decorator. ... ok (0.065s)
2023-01-11T21:25:29.6965356Z   test_py_class_to_ivalue_missing_attribute (jit.test_class_type.TestClassType) ... ok (0.018s)
2023-01-11T21:25:29.6965896Z   test_python_interop (jit.test_class_type.TestClassType) ... ok (0.013s)
2023-01-11T21:25:29.6966375Z   test_recursive_class (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6966894Z Recursive class types not yet supported. We should give a good error message. ... ok (0.118s)
2023-01-11T21:25:29.6967470Z   test_recursive_script_builtin_type_resolution (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6968299Z Test resolution of built-in torch types(e.g. torch.Tensor, torch.device) when a class is recursively compiled. ... ok (0.050s)
2023-01-11T21:25:29.6968951Z   test_recursive_script_module_builtin_type_resolution (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6969955Z Test resolution of built-in torch types(e.g. torch.Tensor, torch.device) when a class is recursively compiled ... ok (0.026s)
2023-01-11T21:25:29.6970557Z   test_recursive_scripting (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6971075Z Test that class types are recursively scripted when an Python instance of one ... ok (0.023s)
2023-01-11T21:25:29.6971613Z   test_recursive_scripting_failed (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6972128Z Test that class types module attributes that fail to script ... ok (0.041s)
2023-01-11T21:25:29.6972626Z   test_reference_semantics (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6973153Z Test that modifications made to a class instance in TorchScript ... ok (0.014s)
2023-01-11T21:25:29.6973717Z   test_save_load_with_classes (jit.test_class_type.TestClassType) ... ok (0.016s)
2023-01-11T21:25:29.6974277Z   test_save_load_with_classes_nested (jit.test_class_type.TestClassType) ... ok (0.142s)
2023-01-11T21:25:29.6974833Z   test_save_load_with_classes_returned (jit.test_class_type.TestClassType) ... ok (0.016s)
2023-01-11T21:25:29.6975510Z   test_schema_human_readable (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6976154Z Make sure that the schema is human readable, ie the mode parameter should read "nearest" instead of being displayed in octal ... ok (0.205s)
2023-01-11T21:25:29.6976811Z   test_self_referential_method (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6977330Z Test that a scripted class can have a method that refers to the class itself ... ok (0.024s)
2023-01-11T21:25:29.6977862Z   test_set_attr_in_method (jit.test_class_type.TestClassType) ... ok (0.013s)
2023-01-11T21:25:29.6978376Z   test_set_attr_non_initialized (jit.test_class_type.TestClassType) ... ok (0.010s)
2023-01-11T21:25:29.6978915Z   test_set_attr_type_mismatch (jit.test_class_type.TestClassType) ... ok (0.010s)
2023-01-11T21:25:29.6979539Z   test_staticmethod (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6979975Z Test static methods on class types. ... ok (0.029s)
2023-01-11T21:25:29.6980403Z   test_type_annotation (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6980935Z Test that annotating container attributes with types works correctly ... ok (0.120s)
2023-01-11T21:25:29.6981503Z   test_type_annotations (jit.test_class_type.TestClassType) ... ok (0.011s)
2023-01-11T21:25:29.6982060Z   test_unresolved_class_attributes (jit.test_class_type.TestClassType) ... ok (0.020s)
2023-01-11T21:25:29.6982538Z   test_unused_method (jit.test_class_type.TestClassType)
2023-01-11T21:25:29.6982955Z Test unused methods on scripted classes. ... ok (0.023s)
2023-01-11T21:25:29.6983507Z   test_binary_op_complex_tensor (jit.test_complex.TestComplex) ... ok (0.016s)
2023-01-11T21:25:29.6984006Z   test_comparison_ops (jit.test_complex.TestComplex) ... ok (0.012s)
2023-01-11T21:25:29.6984469Z   test_complex_constants_and_ops (jit.test_complex.TestComplex) ... ok (0.430s)
2023-01-11T21:25:29.6984957Z   test_complex_constructor (jit.test_complex.TestComplex) ... ok (0.045s)
2023-01-11T21:25:29.6985477Z   test_complex_list_sum (jit.test_complex.TestComplex) ... ok (0.003s)
2023-01-11T21:25:29.6985993Z   test_complex_parse (jit.test_complex.TestComplex) ... ok (0.006s)
2023-01-11T21:25:29.6986521Z   test_complexdict (jit.test_complex.TestComplex) ... ok (0.004s)
2023-01-11T21:25:29.6987023Z   test_complexlist (jit.test_complex.TestComplex) ... ok (0.003s)
2023-01-11T21:25:29.6987503Z   test_div (jit.test_complex.TestComplex) ... ok (0.003s)
2023-01-11T21:25:29.6987963Z   test_infj_nanj_pickle (jit.test_complex.TestComplex) ... ok (0.007s)
2023-01-11T21:25:29.6988404Z   test_pickle (jit.test_complex.TestComplex) ... ok (0.006s)
2023-01-11T21:25:29.6988828Z   test_script (jit.test_complex.TestComplex) ... ok (0.002s)
2023-01-11T21:25:29.6989319Z   test_tensor_attributes (jit.test_complex.TestComplex) ... ok (0.006s)
2023-01-11T21:25:29.6989886Z   test_torch_complex_constructor_with_tensor (jit.test_complex.TestComplex) ... ok (0.036s)
2023-01-11T21:25:29.6990509Z   test_calling_scripted_custom_op (jit.test_custom_operators.TestCustomOperators) ... ok (0.003s)
2023-01-11T21:25:29.6991145Z   test_calling_traced_custom_op (jit.test_custom_operators.TestCustomOperators) ... ok (0.005s)
2023-01-11T21:25:29.6991742Z   test_default_arguments_are_used (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6992328Z   test_dynamic_op_registry (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6992902Z   test_generic_list (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6993457Z   test_passing_and_returning_lists (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6994110Z   test_passing_one_positional_but_not_the_second (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6994804Z   test_passing_too_few_args (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6995429Z   test_passing_too_many_args (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6996194Z   test_passing_unknown_kwargs (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6996873Z   test_script_graph_contains_custom_op (jit.test_custom_operators.TestCustomOperators) ... ok (0.002s)
2023-01-11T21:25:29.6997743Z   test_script_graph_for_custom_ops_matches_traced_graph (jit.test_custom_operators.TestCustomOperators) ... skip: Need to figure out default dtype differences between fbcode and oss (0.000s)
2023-01-11T21:25:29.6998564Z   test_simply_calling_an_operator (jit.test_custom_operators.TestCustomOperators) ... ok (0.001s)
2023-01-11T21:25:29.6999208Z   test_where_no_scalar (jit.test_custom_operators.TestCustomOperators) ... ok (0.003s)
2023-01-11T21:25:29.6999767Z   test_setattr_no_aliasdb (jit.test_dce.TestDCE) ... ok (0.004s)
2023-01-11T21:25:29.7000289Z   test_setattr_removed (jit.test_dce.TestDCE) ... ok (0.007s)
2023-01-11T21:25:29.7001105Z   test_python_submodule_script (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:25:29.7001984Z   test_shared_module (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:25:29.7002834Z   test_tensor_sharing (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:25:29.7003725Z   test_tensor_sharing_with_forward (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:25:29.7004604Z   test_traced_module (jit.test_data_parallel.TestDataParallel) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:25:29.7005236Z   test__post_init__ (jit.test_dataclasses.TestDataclasses) ... ok (3.241s)
2023-01-11T21:25:29.7005807Z   test_comparators (jit.test_dataclasses.TestDataclasses) ... ok (4.747s)
2023-01-11T21:25:29.7006357Z   test_custom__eq__ (jit.test_dataclasses.TestDataclasses) ... ok (0.011s)
2023-01-11T21:25:29.7006942Z   test_default_factories (jit.test_dataclasses.TestDataclasses) ... ok (0.003s)
2023-01-11T21:25:29.7007520Z   test_init_vars (jit.test_dataclasses.TestDataclasses) ... ok (0.079s)
2023-01-11T21:25:29.7008070Z   test_no_source (jit.test_dataclasses.TestDataclasses) ... ok (0.017s)
2023-01-11T21:25:29.7008679Z   test_use_unregistered_dataclass_raises (jit.test_dataclasses.TestDataclasses) ... ok (0.001s)
2023-01-11T21:25:29.7009443Z   test_custom_device_op (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.008s)
2023-01-11T21:25:29.7010063Z   test_device_apply (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.002s)
2023-01-11T21:25:29.7010642Z   test_device_arg (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:29.7011263Z   test_device_if_propagation (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:29.7011884Z   test_if_loop_mix (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.004s)
2023-01-11T21:25:29.7012642Z   test_loop_device_change (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:29.7013257Z   test_loop_simple (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:29.7013860Z   test_mobilenet (jit.test_device_analysis.TestDeviceAnalysis) ... ok (3.552s)
2023-01-11T21:25:29.7014460Z   test_nested_loops (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.005s)
2023-01-11T21:25:29.7015038Z   test_set_dtype (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:29.7015624Z   test_simple (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.005s)
2023-01-11T21:25:29.7016209Z   test_tensor_as_fns (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.010s)
2023-01-11T21:25:29.7016795Z   test_while_change (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.003s)
2023-01-11T21:25:29.7017389Z   test_zerodim_cpu (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.012s)
2023-01-11T21:25:29.7018013Z   test_zerodim_gpu (jit.test_device_analysis.TestDeviceAnalysis) ... skip: No CUDA (0.000s)
2023-01-11T21:25:29.7018652Z   test_zerodim_no_device (jit.test_device_analysis.TestDeviceAnalysis) ... ok (0.024s)
2023-01-11T21:25:29.7019308Z   test_aug_assign (jit.test_list_dict.TestDict) ... ok (0.019s)
2023-01-11T21:25:29.7019780Z   test_basic (jit.test_list_dict.TestDict) ... ok (0.021s)
2023-01-11T21:25:29.7020237Z   test_clear (jit.test_list_dict.TestDict) ... ok (0.004s)
2023-01-11T21:25:29.7020683Z   test_copy (jit.test_list_dict.TestDict) ... ok (0.005s)
2023-01-11T21:25:29.7021138Z   test_del (jit.test_list_dict.TestDict) ... ok (0.008s)
2023-01-11T21:25:29.7021628Z   test_dict_bool_conversion (jit.test_list_dict.TestDict) ... ok (0.024s)
2023-01-11T21:25:29.7022163Z   test_dict_preserves_order (jit.test_list_dict.TestDict) ... ok (0.074s)
2023-01-11T21:25:29.7022662Z   test_dict_to_python (jit.test_list_dict.TestDict) ... ok (0.023s)
2023-01-11T21:25:29.7023132Z   test_dict_variance (jit.test_list_dict.TestDict)
2023-01-11T21:25:29.7023793Z `Dict[T1, _]` is not a subtype of `Dict[T2, _]`, even if `T1` is ... ok (0.012s)
2023-01-11T21:25:29.7024253Z   test_get (jit.test_list_dict.TestDict) ... ok (0.009s)
2023-01-11T21:25:29.7024725Z   test_get_boolkey (jit.test_list_dict.TestDict) ... ok (0.009s)
2023-01-11T21:25:29.7025200Z   test_items (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:29.7025649Z   test_key_type (jit.test_list_dict.TestDict) ... ok (0.001s)
2023-01-11T21:25:29.7026111Z   test_keys (jit.test_list_dict.TestDict) ... ok (0.005s)
2023-01-11T21:25:29.7026567Z   test_len (jit.test_list_dict.TestDict) ... ok (0.004s)
2023-01-11T21:25:29.7027016Z   test_loop (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:29.7027474Z   test_membership (jit.test_list_dict.TestDict) ... ok (0.008s)
2023-01-11T21:25:29.7027968Z   test_mutability (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:29.7028484Z   test_optional_dict_construct (jit.test_list_dict.TestDict) ... ok (0.012s)
2023-01-11T21:25:29.7028994Z   test_ordered_dict (jit.test_list_dict.TestDict) ... ok (0.013s)
2023-01-11T21:25:29.7029464Z   test_pop (jit.test_list_dict.TestDict) ... ok (0.011s)
2023-01-11T21:25:29.7029935Z   test_popitem (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:29.7030399Z   test_setdefault (jit.test_list_dict.TestDict) ... ok (0.008s)
2023-01-11T21:25:29.7030923Z   test_type_annotation_missing_contained_type (jit.test_list_dict.TestDict)
2023-01-11T21:25:29.7031480Z Test that the use of a Dict type annotation without contained ... ok (0.002s)
2023-01-11T21:25:29.7031987Z   test_update (jit.test_list_dict.TestDict) ... ok (0.011s)
2023-01-11T21:25:29.7032470Z   test_update_existing_key (jit.test_list_dict.TestDict) ... ok (0.005s)
2023-01-11T21:25:29.7032964Z   test_values (jit.test_list_dict.TestDict) ... ok (0.003s)
2023-01-11T21:25:29.7033425Z   test_view (jit.test_list_dict.TestDict) ... ok (0.065s)
2023-01-11T21:25:29.7033943Z   test_binary_scalar (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.009s)
2023-01-11T21:25:29.7035431Z   test_binary_tensors (jit.test_dtype_analysis.TestDtypeAnalysis) ... /var/lib/jenkins/workspace/test/jit/test_dtype_analysis.py:165: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:25:29.7036387Z   rand_tensor = torch.rand(shape, dtype=dtype)
2023-01-11T21:25:29.7036741Z ok (0.081s)
2023-01-11T21:25:29.7037169Z   test_combined (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.009s)
2023-01-11T21:25:29.7037770Z   test_conv_no_mixed_args (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.016s)
2023-01-11T21:25:29.7038373Z   test_custom_rules (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.017s)
2023-01-11T21:25:29.7038932Z   test_unary (jit.test_dtype_analysis.TestDtypeAnalysis) ... ok (0.035s)
2023-01-11T21:25:29.7039480Z   test_closed_over_enum_constant (jit.test_enum.TestEnum) ... ok (0.010s)
2023-01-11T21:25:29.7039984Z   test_enum_as_const (jit.test_enum.TestEnum) ... ok (0.007s)
2023-01-11T21:25:29.7040479Z   test_enum_as_module_attribute (jit.test_enum.TestEnum) ... ok (0.009s)
2023-01-11T21:25:29.7041039Z   test_enum_comp (jit.test_enum.TestEnum) ... ok (0.007s)
2023-01-11T21:25:29.7041524Z   test_enum_comp_diff_classes (jit.test_enum.TestEnum) ... ok (0.010s)
2023-01-11T21:25:29.7042035Z   test_enum_explicit_script (jit.test_enum.TestEnum) ... ok (0.001s)
2023-01-11T21:25:29.7042506Z   test_enum_iterate (jit.test_enum.TestEnum) ... ok (0.010s)
2023-01-11T21:25:29.7042983Z   test_enum_ivalue_type (jit.test_enum.TestEnum) ... ok (0.007s)
2023-01-11T21:25:29.7043469Z   test_enum_module_return (jit.test_enum.TestEnum) ... ok (0.009s)
2023-01-11T21:25:29.7043936Z   test_enum_name (jit.test_enum.TestEnum) ... ok (0.007s)
2023-01-11T21:25:29.7044383Z   test_enum_return (jit.test_enum.TestEnum) ... ok (0.008s)
2023-01-11T21:25:29.7044841Z   test_enum_value (jit.test_enum.TestEnum) ... ok (0.007s)
2023-01-11T21:25:29.7045390Z   test_enum_value_types (jit.test_enum.TestEnum) ... ok (0.023s)
2023-01-11T21:25:29.7045910Z   test_heterogenous_value_type_enum_error (jit.test_enum.TestEnum) ... ok (0.005s)
2023-01-11T21:25:29.7046451Z   test_non_existent_enum_value (jit.test_enum.TestEnum) ... ok (0.006s)
2023-01-11T21:25:29.7046984Z   test_string_enum_as_module_attribute (jit.test_enum.TestEnum) ... ok (0.009s)
2023-01-11T21:25:29.7047566Z   test_freeze_interface_swapping_two_methods (jit.test_freezing.TestFreezing) ... ok (0.199s)
2023-01-11T21:25:29.7048218Z   test_freeze_interface_within_object (jit.test_freezing.TestFreezing) ... expected failure (0.039s)
2023-01-11T21:25:29.7048819Z   test_freeze_module (jit.test_freezing.TestFreezing) ... ok (0.014s)
2023-01-11T21:25:29.7049494Z   test_freeze_module_detach_gradient (jit.test_freezing.TestFreezing) ... ok (0.012s)
2023-01-11T21:25:29.7050076Z   test_freeze_module_in_training_mode (jit.test_freezing.TestFreezing) ... ok (0.241s)
2023-01-11T21:25:29.7050669Z   test_freeze_module_inlining (jit.test_freezing.TestFreezing) ... ok (0.038s)
2023-01-11T21:25:29.7051240Z   test_freeze_module_no_forward (jit.test_freezing.TestFreezing) ... ok (0.010s)
2023-01-11T21:25:29.7051800Z   test_freeze_module_return_self (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7052383Z   test_freeze_module_return_sub_module (jit.test_freezing.TestFreezing) ... ok (0.013s)
2023-01-11T21:25:29.7052976Z   test_freeze_module_with_aliased_attr (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7053576Z   test_freeze_module_with_aliased_attr2 (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7054160Z   test_freeze_module_with_aliased_attr3 (jit.test_freezing.TestFreezing) ... ok (0.005s)
2023-01-11T21:25:29.7054773Z   test_freeze_module_with_aliased_tensor_attr (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7055398Z   test_freeze_module_with_aliased_tensor_attr2 (jit.test_freezing.TestFreezing) ... ok (0.009s)
2023-01-11T21:25:29.7056028Z   test_freeze_module_with_aliased_tensor_attr3 (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7056634Z   test_freeze_module_with_aliased_tensor_attr4 (jit.test_freezing.TestFreezing) ... ok (0.008s)
2023-01-11T21:25:29.7057249Z   test_freeze_module_with_call_method (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7057828Z   test_freeze_module_with_fork (jit.test_freezing.TestFreezing) ... ok (0.012s)
2023-01-11T21:25:29.7058377Z   test_freeze_module_with_fork2 (jit.test_freezing.TestFreezing) ... ok (0.009s)
2023-01-11T21:25:29.7058994Z   test_freeze_module_with_fork_calling_module_method (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:29.7059631Z   test_freeze_module_with_helperfunction (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:29.7060254Z   test_freeze_module_with_inplace_mutable (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7060831Z   test_freeze_module_with_list (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7061416Z   test_freeze_module_with_mutable_dict (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:29.7062123Z   test_freeze_module_with_mutable_list (jit.test_freezing.TestFreezing) ... ok (0.004s)
2023-01-11T21:25:29.7062715Z   test_freeze_module_with_mutable_tensor (jit.test_freezing.TestFreezing) ... ok (0.005s)
2023-01-11T21:25:29.7063319Z   test_freeze_module_with_nested_fork (jit.test_freezing.TestFreezing) ... ok (0.021s)
2023-01-11T21:25:29.7064013Z   test_freeze_module_with_nestedaliasing (jit.test_freezing.TestFreezing) ... ok (0.022s)
2023-01-11T21:25:29.7064656Z   test_freeze_module_with_nestedaliasingscalar (jit.test_freezing.TestFreezing) ... ok (0.019s)
2023-01-11T21:25:29.7065290Z   test_freeze_module_with_non_static_module_container_index (jit.test_freezing.TestFreezing)
2023-01-11T21:25:29.7066054Z Test that Modules containing non-static ModuleDict or ModuleList ... ok (0.174s)
2023-01-11T21:25:29.7066751Z   test_freeze_module_with_overlapping_attrs (jit.test_freezing.TestFreezing) ... ok (0.009s)
2023-01-11T21:25:29.7067380Z   test_freeze_module_with_preserve_sub_module (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:29.7068046Z   test_freeze_module_with_preserve_sub_module_and_mutation (jit.test_freezing.TestFreezing) ... ok (0.013s)
2023-01-11T21:25:29.7068699Z   test_freeze_module_with_sharedclasstype (jit.test_freezing.TestFreezing) ... ok (0.023s)
2023-01-11T21:25:29.7069306Z   test_freeze_module_with_submodule (jit.test_freezing.TestFreezing) ... ok (0.014s)
2023-01-11T21:25:29.7069872Z   test_freeze_module_with_tensor (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:29.7070443Z   test_freeze_module_with_tuple (jit.test_freezing.TestFreezing) ... ok (0.007s)
2023-01-11T21:25:29.7071064Z   test_freeze_module_with_tupleoutput_submodule (jit.test_freezing.TestFreezing) ... ok (0.009s)
2023-01-11T21:25:29.7071706Z   test_freeze_module_with_user_preserved_attr (jit.test_freezing.TestFreezing) ... ok (0.005s)
2023-01-11T21:25:29.7072375Z   test_freeze_module_with_user_preserved_attribute_on_submodule (jit.test_freezing.TestFreezing) ... ok (0.011s)
2023-01-11T21:25:29.7073101Z   test_freeze_module_with_user_preserved_attribute_on_unused_submodule (jit.test_freezing.TestFreezing) ... ok (0.008s)
2023-01-11T21:25:29.7073790Z   test_freeze_module_with_user_preserved_method (jit.test_freezing.TestFreezing) ... ok (0.008s)
2023-01-11T21:25:29.7074420Z   test_freeze_module_with_user_preserved_method2 (jit.test_freezing.TestFreezing) ... ok (0.006s)
2023-01-11T21:25:29.7075092Z   test_freeze_module_with_user_preserved_method_on_submodule (jit.test_freezing.TestFreezing) ... ok (0.010s)
2023-01-11T21:25:29.7075702Z   test_freeze_no_forward (jit.test_freezing.TestFreezing) ... ok (0.010s)
2023-01-11T21:25:29.7076284Z   test_freeze_non_interface_module_swap (jit.test_freezing.TestFreezing) ... ok (0.014s)
2023-01-11T21:25:29.7076876Z   test_freeze_non_module_class_getattr (jit.test_freezing.TestFreezing) ... ok (0.043s)
2023-01-11T21:25:29.7077478Z   test_freeze_recursive_interfaces (jit.test_freezing.TestFreezing) ... ok (0.189s)
2023-01-11T21:25:29.7078087Z   test_freeze_recursive_interfaces_same_name (jit.test_freezing.TestFreezing) ... ok (0.084s)
2023-01-11T21:25:29.7078727Z   test_freeze_recursive_interfaces_with_reassignment (jit.test_freezing.TestFreezing) ... ok (0.195s)
2023-01-11T21:25:29.7079358Z   test_freeze_with_interface_mutable (jit.test_freezing.TestFreezing) ... ok (0.155s)
2023-01-11T21:25:29.7079962Z   test_freeze_with_swapping_interfaces (jit.test_freezing.TestFreezing) ... ok (0.051s)
2023-01-11T21:25:29.7080559Z   test_module_getattr_indirection (jit.test_freezing.TestFreezing) ... ok (0.039s)
2023-01-11T21:25:29.7081148Z   test_module_with_shared_type_instances (jit.test_freezing.TestFreezing) ... ok (0.084s)
2023-01-11T21:25:29.7081753Z   test_dictionary_as_example_inputs_for_jit_trace (__main__.TestFrontend) ... ok (0.072s)
2023-01-11T21:25:29.7082289Z   test_instancing_error (__main__.TestFrontend) ... ok (0.372s)
2023-01-11T21:25:29.7082892Z   test_collapse_adjacent_conversions (jit.test_freezing.TestFrozenOptimizations) ... ok (0.015s)
2023-01-11T21:25:29.7083636Z   test_conv_add_folding (jit.test_freezing.TestFrozenOptimizations) ... ok (1.365s)
2023-01-11T21:25:29.7084267Z   test_conv_bn_folding (jit.test_freezing.TestFrozenOptimizations) ... ok (0.383s)
2023-01-11T21:25:29.7085037Z   test_conv_bn_folding_autocast_scenario_cuda (jit.test_freezing.TestFrozenOptimizations) ... skip: Optimization currently only run for GPU (0.002s)
2023-01-11T21:25:29.7085794Z   test_conv_bn_folding_not_forward (jit.test_freezing.TestFrozenOptimizations) ... ok (0.017s)
2023-01-11T21:25:29.7086441Z   test_conv_hardswish (jit.test_freezing.TestFrozenOptimizations) ... ok (2.777s)
2023-01-11T21:25:29.7087066Z   test_conv_mul_add_bn (jit.test_freezing.TestFrozenOptimizations) ... ok (0.055s)
2023-01-11T21:25:29.7087691Z   test_conv_to_mkldnn (jit.test_freezing.TestFrozenOptimizations) ... ok (0.041s)
2023-01-11T21:25:29.7088428Z   test_conv_to_mkldnn_no_mkldnn (jit.test_freezing.TestFrozenOptimizations) ... skip: Testing no mkldnn (0.000s)
2023-01-11T21:25:29.7089265Z   test_freeze_conv_relu_fusion (jit.test_freezing.TestFrozenOptimizations) ... skip: requires CUDNN (0.001s)
2023-01-11T21:25:29.7090021Z   test_freeze_conv_relu_fusion_not_forward (jit.test_freezing.TestFrozenOptimizations) ... skip: requires CUDNN (0.001s)
2023-01-11T21:25:29.7090706Z   test_freeze_mkdlnn (jit.test_freezing.TestFrozenOptimizations) ... ok (0.006s)
2023-01-11T21:25:29.7091326Z   test_freeze_remove_dropout (jit.test_freezing.TestFrozenOptimizations) ... ok (0.005s)
2023-01-11T21:25:29.7091997Z   test_freeze_remove_feature_dropout (jit.test_freezing.TestFrozenOptimizations) ... ok (0.006s)
2023-01-11T21:25:29.7092666Z   test_hardswish_hardsigmoid (jit.test_freezing.TestFrozenOptimizations) ... ok (0.011s)
2023-01-11T21:25:29.7093324Z   test_incompatible_perf_formats (jit.test_freezing.TestFrozenOptimizations) ... ok (0.184s)
2023-01-11T21:25:29.7093985Z   test_linear_bn_folding (jit.test_freezing.TestFrozenOptimizations) ... ok (0.999s)
2023-01-11T21:25:29.7094762Z   test_linear_bn_folding_autocast_scenario_cuda (jit.test_freezing.TestFrozenOptimizations) ... skip: Optimization currently only run for GPU (0.002s)
2023-01-11T21:25:29.7095610Z   test_linear_concat (jit.test_freezing.TestFrozenOptimizations) ... skip: Optimization currently only run for GPU (0.001s)
2023-01-11T21:25:29.7096290Z   test_linear_concat_complex (jit.test_freezing.TestFrozenOptimizations)
2023-01-11T21:25:29.7096984Z Testing that the interleaving of multiple optimizations does not ... skip: Optimization currently only run for GPU (0.001s)
2023-01-11T21:25:29.7097680Z   test_linear_concat_different_input (jit.test_freezing.TestFrozenOptimizations)
2023-01-11T21:25:29.7098384Z There should be no change to the graph due to the optimization pass ... skip: Optimization currently only run for GPU (0.001s)
2023-01-11T21:25:29.7099167Z   test_linear_multiple_blocks (jit.test_freezing.TestFrozenOptimizations) ... skip: Optimization currently only run for GPU (0.001s)
2023-01-11T21:25:29.7099923Z   test_linear_non_constant_weight (jit.test_freezing.TestFrozenOptimizations) ... ok (0.004s)
2023-01-11T21:25:29.7100586Z   test_linear_transpose (jit.test_freezing.TestFrozenOptimizations) ... ok (0.006s)
2023-01-11T21:25:29.7101205Z   test_maxpool_mkldnn (jit.test_freezing.TestFrozenOptimizations) ... ok (0.207s)
2023-01-11T21:25:29.7101864Z   test_mkldnn_fuser_broadcasting (jit.test_freezing.TestFrozenOptimizations) ... ok (0.023s)
2023-01-11T21:25:29.7102531Z   test_mkldnn_inplace_removal (jit.test_freezing.TestFrozenOptimizations) ... ok (0.012s)
2023-01-11T21:25:29.7103219Z   test_numel_less_than_size_with_padding (jit.test_freezing.TestFrozenOptimizations) ... ok (0.016s)
2023-01-11T21:25:29.7103955Z   test_optimize_freeze_module (jit.test_freezing.TestFrozenOptimizations) ... ok (0.018s)
2023-01-11T21:25:29.7104613Z   test_pool2d_batchnorm (jit.test_freezing.TestFrozenOptimizations) ... ok (0.337s)
2023-01-11T21:25:29.7105256Z   test_pool3d_batchnorm (jit.test_freezing.TestFrozenOptimizations) ... ok (1.033s)
2023-01-11T21:25:29.7105985Z   test_remove_detach (jit.test_freezing.TestFrozenOptimizations) ... ok (0.004s)
2023-01-11T21:25:29.7106626Z   test_remove_detach_not_applied (jit.test_freezing.TestFrozenOptimizations) ... ok (0.003s)
2023-01-11T21:25:29.7107269Z   test_scalar_mul (jit.test_freezing.TestFrozenOptimizations) ... ok (0.050s)
2023-01-11T21:25:29.7107893Z   test_subgraph_creation (jit.test_functional_blocks.TestFunctionalBlocks) ... ok (0.054s)
2023-01-11T21:25:29.7108612Z   test_check_no_type_promotion (jit.test_convert_activation.TestFunctionalToInplaceActivation) ... ok (0.238s)
2023-01-11T21:25:29.7109449Z   test_functional_to_inplace_activation (jit.test_convert_activation.TestFunctionalToInplaceActivation) ... ok (0.059s)
2023-01-11T21:25:29.7110294Z   test_no_functional_to_inplace (jit.test_convert_activation.TestFunctionalToInplaceActivation) ... ok (0.009s)
2023-01-11T21:25:29.7111185Z   test_resnet18_correctness (jit.test_convert_activation.TestFunctionalToInplaceActivation) ... ok (3.242s)
2023-01-11T21:25:29.7111876Z   test_getattr_with_default (jit.test_attr.TestGetDefaultAttr) ... ok (0.007s)
2023-01-11T21:25:29.7112492Z   test_fuse_linear (jit.test_graph_rewrite_passes.TestGraphRewritePasses) ... ok (0.029s)
2023-01-11T21:25:29.7113038Z   test_hash_bool (jit.test_hash.TestHash) ... ok (0.007s)
2023-01-11T21:25:29.7113490Z   test_hash_device (jit.test_hash.TestHash) ... ok (0.006s)
2023-01-11T21:25:29.7113956Z   test_hash_float (jit.test_hash.TestHash) ... ok (0.008s)
2023-01-11T21:25:29.7114414Z   test_hash_int (jit.test_hash.TestHash) ... ok (0.007s)
2023-01-11T21:25:29.7114869Z   test_hash_none (jit.test_hash.TestHash) ... ok (0.003s)
2023-01-11T21:25:29.7115310Z   test_hash_string (jit.test_hash.TestHash) ... ok (0.005s)
2023-01-11T21:25:29.7115744Z   test_hash_tensor (jit.test_hash.TestHash)
2023-01-11T21:25:29.7116163Z Tensors should hash by identity ... ok (0.005s)
2023-01-11T21:25:29.7116601Z   test_hash_tuple (jit.test_hash.TestHash) ... ok (0.006s)
2023-01-11T21:25:29.7117116Z   test_hash_tuple_nested_unhashable_type (jit.test_hash.TestHash) ... ok (0.003s)
2023-01-11T21:25:29.7117661Z   test_forward_tuple_input (jit.test_hooks.TestHooks) ... ok (0.020s)
2023-01-11T21:25:29.7118179Z   test_hook_compilation_hint (jit.test_hooks.TestHooks) ... skip:  (0.000s)
2023-01-11T21:25:29.7118718Z   test_hook_hook_name_collision (jit.test_hooks.TestHooks) ... ok (0.007s)
2023-01-11T21:25:29.7119254Z   test_hook_method_name_collision (jit.test_hooks.TestHooks) ... ok (0.007s)
2023-01-11T21:25:29.7119814Z   test_module_direct_forward_invocation (jit.test_hooks.TestHooks) ... ok (0.012s)
2023-01-11T21:25:29.7120365Z   test_module_forward_multiple_inputs (jit.test_hooks.TestHooks) ... ok (0.022s)
2023-01-11T21:25:29.7120923Z   test_module_forward_single_input (jit.test_hooks.TestHooks) ... ok (0.019s)
2023-01-11T21:25:29.7121474Z   test_module_hook_return_nothing (jit.test_hooks.TestHooks) ... ok (0.019s)
2023-01-11T21:25:29.7122027Z   test_module_multiple_hooks_multiple_inputs (jit.test_hooks.TestHooks) ... ok (0.031s)
2023-01-11T21:25:29.7122615Z   test_module_multiple_hooks_single_input (jit.test_hooks.TestHooks) ... ok (0.028s)
2023-01-11T21:25:29.7123161Z   test_module_no_forward_input (jit.test_hooks.TestHooks) ... ok (0.015s)
2023-01-11T21:25:29.7123698Z   test_module_same_hook_repeated (jit.test_hooks.TestHooks) ... ok (0.021s)
2023-01-11T21:25:29.7124242Z   test_submodule_called_directly_with_hooks (jit.test_hooks.TestHooks) ... ok (0.014s)
2023-01-11T21:25:29.7124819Z   test_submodule_direct_forward_invocation (jit.test_hooks.TestHooks) ... ok (0.020s)
2023-01-11T21:25:29.7125403Z   test_submodule_forward_multiple_inputs (jit.test_hooks.TestHooks) ... ok (0.024s)
2023-01-11T21:25:29.7125956Z   test_submodule_forward_single_input (jit.test_hooks.TestHooks) ... ok (0.021s)
2023-01-11T21:25:29.7126561Z   test_submodule_forward_single_input_return_not_tupled (jit.test_hooks.TestHooks) ... ok (0.021s)
2023-01-11T21:25:29.7127155Z   test_submodule_hook_return_nothing (jit.test_hooks.TestHooks) ... ok (0.021s)
2023-01-11T21:25:29.7127806Z   test_submodule_multiple_hooks_multiple_inputs (jit.test_hooks.TestHooks) ... ok (0.035s)
2023-01-11T21:25:29.7128381Z   test_submodule_multiple_hooks_single_input (jit.test_hooks.TestHooks) ... ok (0.030s)
2023-01-11T21:25:29.7128947Z   test_submodule_no_forward_input (jit.test_hooks.TestHooks) ... ok (0.017s)
2023-01-11T21:25:29.7129587Z   test_submodule_same_hook_repeated (jit.test_hooks.TestHooks) ... ok (0.024s)
2023-01-11T21:25:29.7130110Z   test_wrong_hook_signatures (jit.test_hooks.TestHooks) ... ok (0.036s)
2023-01-11T21:25:29.7130637Z   test_wrong_pre_hook_signatures (jit.test_hooks.TestHooks) ... ok (0.042s)
2023-01-11T21:25:29.7131210Z   test_add_out_ignorable_args (jit.test_ignorable_args.TestIgnorableArgs) ... ok (0.003s)
2023-01-11T21:25:29.7131882Z   test_slice_ignorable_args_for_slice (jit.test_ignorable_args.TestIgnorableArgs) ... ok (0.004s)
2023-01-11T21:25:29.7132543Z   test_with_ignore_context_manager_with_inp_out (jit.test_ignore_context_manager.TestIgnoreContextManager) ... ok (0.013s)
2023-01-11T21:25:29.7133273Z   test_with_ignore_context_manager_with_just_inp (jit.test_ignore_context_manager.TestIgnoreContextManager) ... ok (0.004s)
2023-01-11T21:25:29.7133857Z   test_with_ignore_context_manager_with_just_out (jit.test_ignore_context_manager.TestIgnoreContextManager) ... ok (0.005s)
2023-01-11T21:25:29.7134517Z   test_inplace_to_functional_activation (jit.test_convert_activation.TestInplaceToFunctionalActivation) ... ok (0.040s)
2023-01-11T21:25:29.7135218Z   test_resnet18_correctness (jit.test_convert_activation.TestInplaceToFunctionalActivation) ... ok (3.369s)
2023-01-11T21:25:29.7135840Z   test_bool (jit.test_isinstance.TestIsinstance) ... ok (0.007s)
2023-01-11T21:25:29.7136351Z   test_dict (jit.test_isinstance.TestIsinstance) ... ok (0.006s)
2023-01-11T21:25:29.7136807Z   test_dict_nested (jit.test_isinstance.TestIsinstance) ... ok (0.006s)
2023-01-11T21:25:29.7137351Z   test_dict_no_contained_type (jit.test_isinstance.TestIsinstance) ... ok (0.002s)
2023-01-11T21:25:29.7137873Z   test_dict_tensor (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:29.7138425Z   test_empty_container_special_cases (jit.test_isinstance.TestIsinstance) ... ok (0.008s)
2023-01-11T21:25:29.7138994Z   test_empty_container_throws_warning_in_eager (jit.test_isinstance.TestIsinstance) ... ok (0.001s)
2023-01-11T21:25:29.7139533Z   test_float (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:29.7139996Z   test_if_else (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:29.7140446Z   test_in_if (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:29.7140909Z   test_in_while_loop (jit.test_isinstance.TestIsinstance) ... ok (0.007s)
2023-01-11T21:25:29.7141413Z   test_int (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:29.7141843Z   test_list (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:29.7142307Z   test_list_nested (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:29.7142815Z   test_list_no_contained_type (jit.test_isinstance.TestIsinstance) ... ok (0.001s)
2023-01-11T21:25:29.7143452Z   test_list_tensor (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:29.7143932Z   test_list_tensor_type_true (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:29.7144534Z   test_nontuple_container_rhs_throws_in_eager (jit.test_isinstance.TestIsinstance) ... ok (0.001s)
2023-01-11T21:25:29.7145133Z   test_optional (jit.test_isinstance.TestIsinstance) ... ok (0.006s)
2023-01-11T21:25:29.7145680Z   test_optional_nested (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:29.7146270Z   test_optional_no_contained_type (jit.test_isinstance.TestIsinstance) ... ok (0.002s)
2023-01-11T21:25:29.7146860Z   test_optional_none (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:29.7147428Z   test_tensor_type_false (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:29.7147904Z   test_tuple (jit.test_isinstance.TestIsinstance) ... ok (0.005s)
2023-01-11T21:25:29.7148443Z   test_tuple_nested (jit.test_isinstance.TestIsinstance) ... ok (0.007s)
2023-01-11T21:25:29.7148914Z   test_tuple_no_contained_type (jit.test_isinstance.TestIsinstance) ... ok (0.001s)
2023-01-11T21:25:29.7149380Z   test_tuple_rhs (jit.test_isinstance.TestIsinstance) ... ok (0.007s)
2023-01-11T21:25:29.7149816Z   test_tuple_tensor (jit.test_isinstance.TestIsinstance) ... ok (0.004s)
2023-01-11T21:25:29.7150268Z   test_type_refinement (jit.test_isinstance.TestIsinstance) ... ok (0.016s)
2023-01-11T21:25:29.7150673Z   test_ModuleList (__main__.TestJit) ... ok (0.053s)
2023-01-11T21:25:29.7150997Z   test_Sequential (__main__.TestJit) ... ok (0.023s)
2023-01-11T21:25:29.7151327Z   test_T_mT_H_mH (__main__.TestJit) ... ok (0.020s)
2023-01-11T21:25:29.7151726Z   test_add_relu_fusion (__main__.TestJit) ... ok (0.025s)
2023-01-11T21:25:29.7152061Z   test_arg_configurations (__main__.TestJit)
2023-01-11T21:25:29.7152527Z Different arg configurations should trigger different traces ... skip: Need to be adjusted to Graph Executor (0.001s)
2023-01-11T21:25:29.7152953Z   test_attrs (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7153261Z   test_batchnorm (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7153625Z   test_big (__main__.TestJit) ... skip: Requires a lot of RAM (0.001s)
2023-01-11T21:25:29.7153990Z   test_conj_transpose (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7154356Z   test_constant_insertion (__main__.TestJit) ... ok (2.533s)
2023-01-11T21:25:29.7154760Z   test_constant_prop_aliasing_type (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:29.7155179Z   test_constant_prop_exception (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:29.7155595Z   test_constant_prop_if_constant (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:29.7156010Z   test_constant_prop_if_inline (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7156451Z   test_constant_prop_loop_constant (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7156889Z   test_constant_prop_nested (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7157289Z   test_constant_prop_none (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:29.7157693Z   test_constant_prop_print (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7158116Z   test_constant_prop_rand (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7158562Z   test_constant_prop_remove_output (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7158997Z   test_constant_prop_simple (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7159417Z   test_constants_pkl (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7159809Z   test_cpp (__main__.TestJit) ... ok (0.036s)
2023-01-11T21:25:29.7160156Z   test_cse (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:29.7160575Z   test_cse_not_introduce_aliasing (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:29.7161023Z   test_cu_escaped_number (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:29.7161453Z   test_cuda_export_restore (__main__.TestJit) ... skip: requires CUDA (0.001s)
2023-01-11T21:25:29.7161905Z   test_debug_flush_compilation_cache (__main__.TestJit) ... ok (0.013s)
2023-01-11T21:25:29.7162307Z   test_decompose_addmm (__main__.TestJit) ... ok (0.036s)
2023-01-11T21:25:29.7162799Z   test_device_not_equal (__main__.TestJit) ... skip: requires CUDA (0.001s)
2023-01-11T21:25:29.7163238Z   test_diff_subgraph_clones_constants (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7163708Z   test_disabled (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7164079Z   test_dropout (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:29.7164535Z   test_dropout_cuda (__main__.TestJit) ... skip: test_dropout_cuda require CUDA (0.001s)
2023-01-11T21:25:29.7165400Z   test_dropout_func_requires_grad (__main__.TestJit) ... STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:29.7166163Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:29.7166979Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:29.7167738Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:29.7168465Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:29.7169388Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:29.7170110Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:29.7170823Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:29.7171738Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:29.7172470Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:29.7173195Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:29.7174017Z STAGE:2023-01-11 21:24:00 1919:1919 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:29.7174447Z ok (0.100s)
2023-01-11T21:25:29.7174906Z   test_dropout_module_requires_grad (__main__.TestJit) ... skip: Testing differentiable graph (0.001s)
2023-01-11T21:25:29.7175366Z   test_einsum (__main__.TestJit) ... ok (0.024s)
2023-01-11T21:25:29.7175774Z   test_element_size (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7176211Z   test_expand_fold_quant_inputs (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7176662Z   test_expand_quantlint (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7177226Z   test_export_batchnorm (__main__.TestJit) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:29.7177746Z   test_export_dropout (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:29.7178155Z   test_export_lstm (__main__.TestJit) ... ok (0.037s)
2023-01-11T21:25:29.7178553Z   test_export_opnames (__main__.TestJit) ... ok (0.011s)
2023-01-11T21:25:29.7178984Z   test_export_rnn (__main__.TestJit) ... ok (0.065s)
2023-01-11T21:25:29.7179486Z   test_flags (__main__.TestJit) ... skip: Need to instrument GraphExecutors a bit more (0.001s)
2023-01-11T21:25:29.7179987Z   test_function_default_values (__main__.TestJit) ... ok (0.036s)
2023-01-11T21:25:29.7180452Z   test_hide_source_ranges_context_manager (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7180893Z   test_import_method (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7181305Z   test_inferred_as_tensor (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:29.7181728Z   test_layout (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7182167Z   test_matrix_conj_transpose (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7182604Z   test_matrix_transpose (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7183021Z   test_module_default_values (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7183536Z   test_mutable_default_values (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7184041Z   test_native_dropout_corner_case (__main__.TestJit) ... skip: test requires CUDA (0.001s)
2023-01-11T21:25:29.7184506Z   test_nn_conv (__main__.TestJit) ... ok (0.226s)
2023-01-11T21:25:29.7184914Z   test_nn_lp_pool1d (__main__.TestJit) ... ok (0.099s)
2023-01-11T21:25:29.7185317Z   test_nn_lp_pool2d (__main__.TestJit) ... ok (0.135s)
2023-01-11T21:25:29.7185699Z   test_nn_padding (__main__.TestJit) ... ok (0.257s)
2023-01-11T21:25:29.7186111Z   test_nn_padding_functional (__main__.TestJit) ... ok (0.022s)
2023-01-11T21:25:29.7186556Z   test_no_erroneous_warnings (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7187124Z   test_non_ascii_string (__main__.TestJit) ... skip: temporarily disable the test for fwd compatibility (0.001s)
2023-01-11T21:25:29.7187631Z   test_numel (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:29.7188182Z   test_pattern_based_module_rewrite (__main__.TestJit) ... ok (0.026s)
2023-01-11T21:25:29.7188642Z   test_pattern_based_rewrite (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:29.7189112Z   test_pattern_based_rewrite_with_source_range_preserved (__main__.TestJit) ... ok (0.016s)
2023-01-11T21:25:29.7189960Z   test_peephole_optimize_shape_ops (__main__.TestJit) ... skip: Simple executor doesn't have shape information (0.002s)
2023-01-11T21:25:29.7190515Z   test_permute_inputs_binding (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7190960Z   test_pretty_printer (__main__.TestJit) ... ok (0.024s)
2023-01-11T21:25:29.7191364Z   test_print_classes_module (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7191786Z   test_print_op_module (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7192348Z   test_print_torch_ops_modules (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7193744Z   test_profiler (__main__.TestJit) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/65521 for allplatform(s) . If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:25:29.7194676Z   test_python_bindings (__main__.TestJit) ... ok (0.219s)
2023-01-11T21:25:29.7195105Z   test_python_ir (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7195525Z   test_python_ir_utils (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7195928Z   test_python_ir_utils_graph (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:29.7196412Z   test_python_ivalue (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:29.7196820Z   test_pytorch_jit_env_off (__main__.TestJit) ... ok (1.417s)
2023-01-11T21:25:29.7197232Z   test_recursive_cse (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7197684Z   test_repeat_interleave_script (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7198150Z   test_restore_device (__main__.TestJit) ... ok (0.005s)
2023-01-11T21:25:29.7198632Z   test_restore_device_cuda (__main__.TestJit) ... skip: restore device requires CUDA (0.001s)
2023-01-11T21:25:29.7199205Z   test_restore_shared_storage_on_cuda (__main__.TestJit) ... skip: restore device requires CUDA (0.001s)
2023-01-11T21:25:29.7199722Z   test_script_autograd_grad (__main__.TestJit) ... ok (0.357s)
2023-01-11T21:25:29.7200182Z   test_script_backward (__main__.TestJit) ... ok (0.012s)
2023-01-11T21:25:29.7201223Z   test_script_backward_twice (__main__.TestJit) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_script.py:1243: UserWarning: `optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead
2023-01-11T21:25:29.7201875Z   warnings.warn(
2023-01-11T21:25:29.7202157Z ok (0.014s)
2023-01-11T21:25:29.7202504Z   test_script_fn_pkl (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:29.7202931Z   test_script_tensor_type (__main__.TestJit) ... ok (0.004s)
2023-01-11T21:25:29.7203403Z   test_shape_analysis_broadcast (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7203857Z   test_shape_analysis_masked_select (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7204326Z   test_shape_analysis_unsqueeze_in_loop (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7204776Z   test_sparse_csr_tensors (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7205223Z   test_sparse_tensors (__main__.TestJit) ... ok (0.032s)
2023-01-11T21:25:29.7207229Z   test_torch_complex (__main__.TestJit) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:471: UserWarning: An output with one or more elements was resized since it had shape [3, 4], which does not match the required output shape [2]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:25:29.7208556Z   return callable(*args, **kwargs)
2023-01-11T21:25:29.7210353Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:471: UserWarning: An output with one or more elements was resized since it had shape [5, 2], which does not match the required output shape [2]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:25:29.7211047Z   return callable(*args, **kwargs)
2023-01-11T21:25:29.7211236Z ok (0.028s)
2023-01-11T21:25:29.7211764Z   test_torch_load_error (__main__.TestJit) ... skip: TODO: re-enable with https://github.com/pytorch/pytorch/pull/29339 (0.001s)
2023-01-11T21:25:29.7212105Z   test_torch_load_zipfile_check (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7212359Z   test_torch_ops_kwonly (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7212620Z   test_torch_ops_overloaded (__main__.TestJit) ... ok (0.002s)
2023-01-11T21:25:29.7212870Z   test_torch_sum (__main__.TestJit) ... ok (0.010s)
2023-01-11T21:25:29.7213105Z   test_trace_retains_train (__main__.TestJit) ... ok (0.006s)
2023-01-11T21:25:29.7213349Z   test_train_eval (__main__.TestJit) ... ok (0.044s)
2023-01-11T21:25:29.7213586Z   test_transpose (__main__.TestJit) ... ok (0.003s)
2023-01-11T21:25:29.7213819Z   test_unchecked_cast (__main__.TestJit) ... ok (0.011s)
2023-01-11T21:25:29.7214067Z   test_unique_state_dict (__main__.TestJit) ... ok (0.001s)
2023-01-11T21:25:29.7214373Z   test_verify (__main__.TestJit) ... skip: verify needs to be updated to work with GraphExecutors (0.001s)
2023-01-11T21:25:29.7214667Z   test_warnings (__main__.TestJit) ... ok (0.009s)
2023-01-11T21:25:29.7214935Z   test_nn_AdaptiveAvgPool1d (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:29.7215272Z   test_nn_AdaptiveAvgPool1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7215617Z   test_nn_AdaptiveAvgPool1d_one_output (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7215948Z   test_nn_AdaptiveAvgPool2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7216285Z   test_nn_AdaptiveAvgPool2d_single (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7216630Z   test_nn_AdaptiveAvgPool2d_single_1x1output (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7216970Z   test_nn_AdaptiveAvgPool2d_tuple (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7217295Z   test_nn_AdaptiveAvgPool2d_tuple_none (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7217633Z   test_nn_AdaptiveAvgPool3d_last_dim (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7217974Z   test_nn_AdaptiveAvgPool3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7218303Z   test_nn_AdaptiveAvgPool3d_single (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7218631Z   test_nn_AdaptiveAvgPool3d_tuple (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:29.7218967Z   test_nn_AdaptiveAvgPool3d_tuple_none (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7219295Z   test_nn_AdaptiveMaxPool1d (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:29.7219615Z   test_nn_AdaptiveMaxPool1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7219959Z   test_nn_AdaptiveMaxPool2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:29.7220293Z   test_nn_AdaptiveMaxPool2d_single (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7220625Z   test_nn_AdaptiveMaxPool2d_tuple (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7220948Z   test_nn_AdaptiveMaxPool2d_tuple_none (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7221391Z   test_nn_AdaptiveMaxPool3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.059s)
2023-01-11T21:25:29.7221727Z   test_nn_AdaptiveMaxPool3d_single (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:29.7222059Z   test_nn_AdaptiveMaxPool3d_single_nonatomic (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:29.7222400Z   test_nn_AdaptiveMaxPool3d_tuple (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:29.7222745Z   test_nn_AdaptiveMaxPool3d_tuple_nonatomic (__main__.TestJitGeneratedModule) ... ok (0.050s)
2023-01-11T21:25:29.7223091Z   test_nn_AdaptiveMaxPool3d_tuple_none (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7223462Z   test_nn_AvgPool1d (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7223808Z   test_nn_AvgPool1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7224124Z   test_nn_AvgPool1d_stride (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7224427Z   test_nn_AvgPool1d_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7224727Z   test_nn_AvgPool2d (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7225029Z   test_nn_AvgPool2d_divisor (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7225346Z   test_nn_AvgPool2d_divisor_stride (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7225662Z   test_nn_AvgPool2d_divisor_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7226091Z   test_nn_AvgPool2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7226405Z   test_nn_AvgPool2d_stride (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7226707Z   test_nn_AvgPool2d_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7227010Z   test_nn_AvgPool3d (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7227314Z   test_nn_AvgPool3d_divisor (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7227631Z   test_nn_AvgPool3d_divisor_stride (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7227961Z   test_nn_AvgPool3d_divisor_stride1_pad0_gpu_input (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7228307Z   test_nn_AvgPool3d_divisor_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7228659Z   test_nn_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7229025Z   test_nn_AvgPool3d_divisor_stride_pad_gpu_general_output (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7229399Z   test_nn_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7229742Z   test_nn_AvgPool3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7230055Z   test_nn_AvgPool3d_stride (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7230365Z   test_nn_AvgPool3d_stride1_pad0_gpu_input (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7230690Z   test_nn_AvgPool3d_stride_pad (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7231026Z   test_nn_AvgPool3d_stride_pad_gpu_fixedkw_output (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:29.7231372Z   test_nn_AvgPool3d_stride_pad_gpu_general_output (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:29.7231726Z   test_nn_AvgPool3d_stride_pad_gpu_input_nooverlap (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7232046Z   test_nn_BCELoss (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7232354Z   test_nn_BCELoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7232662Z   test_nn_BCELoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7233014Z   test_nn_BCELoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7233325Z   test_nn_BCELoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7233624Z   test_nn_BCELoss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7233943Z   test_nn_BCELoss_scalar_weights (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7234252Z   test_nn_BCELoss_weights (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7234568Z   test_nn_BCELoss_weights_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7234885Z   test_nn_BCELoss_weights_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7235238Z   test_nn_BCEWithLogitsLoss (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7235565Z   test_nn_BCEWithLogitsLoss_legacy_enum (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7235912Z   test_nn_BCEWithLogitsLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7236244Z   test_nn_BCEWithLogitsLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7236584Z   test_nn_BCEWithLogitsLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7236921Z   test_nn_BCEWithLogitsLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7237252Z   test_nn_BCEWithLogitsLoss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7237597Z   test_nn_BCEWithLogitsLoss_scalar_weights (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7237936Z   test_nn_BCEWithLogitsLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7238259Z   test_nn_BatchNorm1d_3d_input (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:29.7238573Z   test_nn_BatchNorm1d_3d_input_not_affine (__main__.TestJitGeneratedModule) ... ok (0.094s)
2023-01-11T21:25:29.7238893Z   test_nn_BatchNorm1d_affine (__main__.TestJitGeneratedModule) ... ok (0.096s)
2023-01-11T21:25:29.7239220Z   test_nn_BatchNorm1d_affine_simple_average (__main__.TestJitGeneratedModule) ... ok (0.099s)
2023-01-11T21:25:29.7239538Z   test_nn_BatchNorm1d_not_affine (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:29.7239863Z   test_nn_BatchNorm1d_not_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.092s)
2023-01-11T21:25:29.7240187Z   test_nn_BatchNorm1d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.085s)
2023-01-11T21:25:29.7240491Z   test_nn_BatchNorm2d (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:29.7240792Z   test_nn_BatchNorm2d_2d_simple_average (__main__.TestJitGeneratedModule) ... ok (0.099s)
2023-01-11T21:25:29.7241113Z   test_nn_BatchNorm2d_momentum (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:29.7241432Z   test_nn_BatchNorm2d_not_affine (__main__.TestJitGeneratedModule) ... ok (0.093s)
2023-01-11T21:25:29.7241748Z   test_nn_BatchNorm2d_not_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.092s)
2023-01-11T21:25:29.7242070Z   test_nn_BatchNorm2d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.085s)
2023-01-11T21:25:29.7242377Z   test_nn_BatchNorm3d (__main__.TestJitGeneratedModule) ... ok (0.098s)
2023-01-11T21:25:29.7242689Z   test_nn_BatchNorm3d_3d_simple_average (__main__.TestJitGeneratedModule) ... ok (0.100s)
2023-01-11T21:25:29.7242998Z   test_nn_BatchNorm3d_momentum (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:29.7243308Z   test_nn_BatchNorm3d_not_affine (__main__.TestJitGeneratedModule) ... ok (0.094s)
2023-01-11T21:25:29.7243633Z   test_nn_BatchNorm3d_not_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.090s)
2023-01-11T21:25:29.7243950Z   test_nn_BatchNorm3d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.084s)
2023-01-11T21:25:29.7244253Z   test_nn_Bilinear (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:29.7244566Z   test_nn_CELU (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7244858Z   test_nn_CELU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7245147Z   test_nn_CELU_scalar (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7245501Z   test_nn_CTCLoss_2d_int_target_lengths_intlists (__main__.TestJitGeneratedModule) ... skip: module test skipped on JIT (0.002s)
2023-01-11T21:25:29.7245877Z   test_nn_CTCLoss_2d_int_target_lengths_tensors (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7246201Z   test_nn_CTCLoss_2d_lengths_tensors (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7246558Z   test_nn_CTCLoss_lengths_intlists (__main__.TestJitGeneratedModule) ... skip: module test skipped on JIT (0.003s)
2023-01-11T21:25:29.7246929Z   test_nn_CTCLoss_lengths_tensors (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7247247Z   test_nn_ConstantPad1d (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7247548Z   test_nn_ConstantPad1d_batch (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:29.7247868Z   test_nn_ConstantPad1d_complex (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7248183Z   test_nn_ConstantPad2d (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7248503Z   test_nn_ConstantPad2d_complex (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7248817Z   test_nn_ConstantPad2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7249397Z   test_nn_ConstantPad3d (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7249718Z   test_nn_ConstantPad3d_complex (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7250037Z   test_nn_ConstantPad3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7250344Z   test_nn_Conv1d (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:29.7250659Z   test_nn_Conv1d_circular_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.061s)
2023-01-11T21:25:29.7250968Z   test_nn_Conv1d_dilated (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7251270Z   test_nn_Conv1d_groups (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7251565Z   test_nn_Conv1d_pad1 (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7251869Z   test_nn_Conv1d_pad1size1 (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:29.7252158Z   test_nn_Conv1d_pad2 (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7252463Z   test_nn_Conv1d_pad2size1 (__main__.TestJitGeneratedModule) ... ok (0.060s)
2023-01-11T21:25:29.7252771Z   test_nn_Conv1d_pad_same (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7253070Z   test_nn_Conv1d_pad_same2 (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:29.7253383Z   test_nn_Conv1d_pad_same_dilated (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7253699Z   test_nn_Conv1d_pad_valid (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7254019Z   test_nn_Conv1d_reflect_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.060s)
2023-01-11T21:25:29.7254338Z   test_nn_Conv1d_replicate_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.060s)
2023-01-11T21:25:29.7254652Z   test_nn_Conv1d_stride (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7254957Z   test_nn_Conv1d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.050s)
2023-01-11T21:25:29.7255257Z   test_nn_Conv1d_zeros_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7255559Z   test_nn_Conv2d (__main__.TestJitGeneratedModule) ... ok (0.060s)
2023-01-11T21:25:29.7255876Z   test_nn_Conv2d_circular_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.064s)
2023-01-11T21:25:29.7256196Z   test_nn_Conv2d_depthwise (__main__.TestJitGeneratedModule) ... ok (0.060s)
2023-01-11T21:25:29.7256558Z   test_nn_Conv2d_depthwise_dilated (__main__.TestJitGeneratedModule) ... ok (0.061s)
2023-01-11T21:25:29.7256882Z   test_nn_Conv2d_depthwise_padded (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7257211Z   test_nn_Conv2d_depthwise_strided (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:29.7257531Z   test_nn_Conv2d_depthwise_with_multiplier (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7257849Z   test_nn_Conv2d_dilated (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7258149Z   test_nn_Conv2d_groups (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:29.7258452Z   test_nn_Conv2d_groups_thnn (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7258777Z   test_nn_Conv2d_no_bias (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:29.7259081Z   test_nn_Conv2d_pad_same (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:29.7259398Z   test_nn_Conv2d_pad_same_dilated (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:29.7259698Z   test_nn_Conv2d_pad_valid (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:29.7259997Z   test_nn_Conv2d_padding (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:29.7260310Z   test_nn_Conv2d_reflect_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7260639Z   test_nn_Conv2d_replicate_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:29.7260938Z   test_nn_Conv2d_strided (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:29.7261238Z   test_nn_Conv2d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.052s)
2023-01-11T21:25:29.7261551Z   test_nn_Conv2d_zeros_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.071s)
2023-01-11T21:25:29.7261843Z   test_nn_Conv3d (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7262139Z   test_nn_Conv3d_1x1x1_no_bias (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:29.7262458Z   test_nn_Conv3d_circular_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.061s)
2023-01-11T21:25:29.7262771Z   test_nn_Conv3d_dilated (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7263066Z   test_nn_Conv3d_dilated_strided (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7263439Z   test_nn_Conv3d_groups (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7263742Z   test_nn_Conv3d_no_bias (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:29.7264031Z   test_nn_Conv3d_pad_same (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7264341Z   test_nn_Conv3d_pad_same_dilated (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7264655Z   test_nn_Conv3d_pad_valid (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7264974Z   test_nn_Conv3d_replicate_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.057s)
2023-01-11T21:25:29.7265281Z   test_nn_Conv3d_stride (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7265587Z   test_nn_Conv3d_stride_padding (__main__.TestJitGeneratedModule) ... ok (0.053s)
2023-01-11T21:25:29.7265897Z   test_nn_Conv3d_zero_batch (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:29.7266197Z   test_nn_Conv3d_zeros_stride2_pad2 (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7266512Z   test_nn_ConvTranspose1d (__main__.TestJitGeneratedModule) ... ok (0.102s)
2023-01-11T21:25:29.7266830Z   test_nn_ConvTranspose1d_dilated (__main__.TestJitGeneratedModule) ... ok (0.101s)
2023-01-11T21:25:29.7267153Z   test_nn_ConvTranspose1d_groups (__main__.TestJitGeneratedModule) ... ok (0.104s)
2023-01-11T21:25:29.7267464Z   test_nn_ConvTranspose1d_no_bias (__main__.TestJitGeneratedModule) ... ok (0.102s)
2023-01-11T21:25:29.7267774Z   test_nn_ConvTranspose2d (__main__.TestJitGeneratedModule) ... ok (0.105s)
2023-01-11T21:25:29.7268129Z   test_nn_ConvTranspose2d_dilated (__main__.TestJitGeneratedModule) ... ok (0.107s)
2023-01-11T21:25:29.7268442Z   test_nn_ConvTranspose2d_groups (__main__.TestJitGeneratedModule) ... ok (0.106s)
2023-01-11T21:25:29.7268762Z   test_nn_ConvTranspose2d_no_bias (__main__.TestJitGeneratedModule) ... ok (0.108s)
2023-01-11T21:25:29.7269072Z   test_nn_ConvTranspose3d (__main__.TestJitGeneratedModule) ... ok (0.108s)
2023-01-11T21:25:29.7269389Z   test_nn_ConvTranspose3d_dilated (__main__.TestJitGeneratedModule) ... ok (0.108s)
2023-01-11T21:25:29.7269698Z   test_nn_CosineEmbeddingLoss (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7270031Z   test_nn_CosineEmbeddingLoss_margin (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7270379Z   test_nn_CosineEmbeddingLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:29.7270757Z   test_nn_CosineEmbeddingLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7271116Z   test_nn_CosineEmbeddingLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7271448Z   test_nn_CrossEntropyLoss (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7271766Z   test_nn_CrossEntropyLoss_2d (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7272084Z   test_nn_CrossEntropyLoss_2d_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7272440Z   test_nn_CrossEntropyLoss_2d_indices_target_smoothing (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7272826Z   test_nn_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7273229Z   test_nn_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7273609Z   test_nn_CrossEntropyLoss_2d_indices_target_smoothing_weight (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7273975Z   test_nn_CrossEntropyLoss_2d_prob_target (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7274327Z   test_nn_CrossEntropyLoss_2d_prob_target_smoothing (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7274695Z   test_nn_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7275081Z   test_nn_CrossEntropyLoss_2d_prob_target_smoothing_weight (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7275448Z   test_nn_CrossEntropyLoss_2d_prob_target_weights (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7275793Z   test_nn_CrossEntropyLoss_2d_weights (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7276137Z   test_nn_CrossEntropyLoss_3d_indices_target_smoothing (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7276520Z   test_nn_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7276919Z   test_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7277332Z   test_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7277700Z   test_nn_CrossEntropyLoss_3d_prob_target (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7278057Z   test_nn_CrossEntropyLoss_3d_prob_target_smoothing (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7278433Z   test_nn_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7278809Z   test_nn_CrossEntropyLoss_3d_prob_target_weights (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7279147Z   test_nn_CrossEntropyLoss_4d_prob_target (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7279497Z   test_nn_CrossEntropyLoss_4d_prob_target_weights (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7279870Z   test_nn_CrossEntropyLoss_dim_is_3 (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7280189Z   test_nn_CrossEntropyLoss_higher_dim (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7280524Z   test_nn_CrossEntropyLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7280841Z   test_nn_CrossMapLRN2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7281133Z   test_nn_ELU (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7281410Z   test_nn_ELU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7281709Z   test_nn_ELU_scalar (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7282029Z   test_nn_Embedding (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7282336Z   test_nn_EmbeddingBag_discontiguous (__main__.TestJitGeneratedModule) ... ok (0.072s)
2023-01-11T21:25:29.7304996Z   test_nn_EmbeddingBag_max (__main__.TestJitGeneratedModule) ... ok (0.062s)
2023-01-11T21:25:29.7305394Z   test_nn_EmbeddingBag_max_padding_idx (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7305731Z   test_nn_EmbeddingBag_mean (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7306050Z   test_nn_EmbeddingBag_mean_padding_idx (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7306384Z   test_nn_EmbeddingBag_sparse (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7306703Z   test_nn_EmbeddingBag_sum (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7307032Z   test_nn_EmbeddingBag_sum_padding_idx (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7307366Z   test_nn_Embedding_discontiguous (__main__.TestJitGeneratedModule) ... ok (0.024s)
2023-01-11T21:25:29.7307688Z   test_nn_Embedding_sparse (__main__.TestJitGeneratedModule) ... ok (0.023s)
2023-01-11T21:25:29.7307994Z   test_nn_Flatten (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7308285Z   test_nn_Flatten_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7308584Z   test_nn_Fold (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:29.7308879Z   test_nn_Fold_int_input (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7309194Z   test_nn_Fold_no_batch_dim_input (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7309507Z   test_nn_Fold_no_batch_dim_int_input (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7309843Z   test_nn_FractionalMaxPool2d_ratio (__main__.TestJitGeneratedModule) ... ok (0.083s)
2023-01-11T21:25:29.7310200Z   test_nn_FractionalMaxPool2d_ratio_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.069s)
2023-01-11T21:25:29.7310600Z   test_nn_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples (__main__.TestJitGeneratedModule) ... ok (0.071s)
2023-01-11T21:25:29.7310991Z   test_nn_FractionalMaxPool2d_ratio_return_indices (__main__.TestJitGeneratedModule) ... ok (0.064s)
2023-01-11T21:25:29.7311343Z   test_nn_FractionalMaxPool2d_size (__main__.TestJitGeneratedModule) ... ok (0.067s)
2023-01-11T21:25:29.7311692Z   test_nn_FractionalMaxPool2d_size_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.067s)
2023-01-11T21:25:29.7312062Z   test_nn_FractionalMaxPool2d_size_no_batch_dim_no_random_samples (__main__.TestJitGeneratedModule) ... ok (0.070s)
2023-01-11T21:25:29.7312432Z   test_nn_FractionalMaxPool3d_asymsize (__main__.TestJitGeneratedModule) ... ok (0.085s)
2023-01-11T21:25:29.7312778Z   test_nn_FractionalMaxPool3d_ratio (__main__.TestJitGeneratedModule) ... ok (0.073s)
2023-01-11T21:25:29.7313121Z   test_nn_FractionalMaxPool3d_ratio_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.073s)
2023-01-11T21:25:29.7313504Z   test_nn_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples (__main__.TestJitGeneratedModule) ... ok (0.075s)
2023-01-11T21:25:29.7314007Z   test_nn_FractionalMaxPool3d_ratio_return_indices (__main__.TestJitGeneratedModule) ... ok (0.069s)
2023-01-11T21:25:29.7314362Z   test_nn_FractionalMaxPool3d_size (__main__.TestJitGeneratedModule) ... ok (0.071s)
2023-01-11T21:25:29.7314699Z   test_nn_FractionalMaxPool3d_size_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.070s)
2023-01-11T21:25:29.7315082Z   test_nn_FractionalMaxPool3d_size_no_batch_dim_no_random_samples (__main__.TestJitGeneratedModule) ... ok (0.074s)
2023-01-11T21:25:29.7315420Z   test_nn_GELU (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7315718Z   test_nn_GELU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7316006Z   test_nn_GELU_scalar (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7316335Z   test_nn_GLU (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7316623Z   test_nn_GLU_dim (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7316908Z   test_nn_GLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7317206Z   test_nn_GRUCell (__main__.TestJitGeneratedModule) ... ok (0.066s)
2023-01-11T21:25:29.7317509Z   test_nn_GroupNorm_1d_affine (__main__.TestJitGeneratedModule) ... ok (0.078s)
2023-01-11T21:25:29.7317828Z   test_nn_GroupNorm_1d_affine_GN (__main__.TestJitGeneratedModule) ... ok (0.064s)
2023-01-11T21:25:29.7318143Z   test_nn_GroupNorm_1d_affine_large_batch (__main__.TestJitGeneratedModule) ... ok (0.065s)
2023-01-11T21:25:29.7318473Z   test_nn_GroupNorm_1d_no_affine_IN (__main__.TestJitGeneratedModule) ... ok (0.062s)
2023-01-11T21:25:29.7318796Z   test_nn_GroupNorm_1d_no_affine_LN (__main__.TestJitGeneratedModule) ... ok (0.061s)
2023-01-11T21:25:29.7319098Z   test_nn_GroupNorm_2d_affine (__main__.TestJitGeneratedModule) ... ok (0.065s)
2023-01-11T21:25:29.7319430Z   test_nn_GroupNorm_2d_affine_large_feature (__main__.TestJitGeneratedModule) ... ok (0.070s)
2023-01-11T21:25:29.7319764Z   test_nn_GroupNorm_2d_no_affine_IN (__main__.TestJitGeneratedModule) ... ok (0.062s)
2023-01-11T21:25:29.7320083Z   test_nn_GroupNorm_2d_no_affine_LN (__main__.TestJitGeneratedModule) ... ok (0.062s)
2023-01-11T21:25:29.7320402Z   test_nn_GroupNorm_2d_no_affine_large_feature (__main__.TestJitGeneratedModule) ... ok (0.077s)
2023-01-11T21:25:29.7320725Z   test_nn_Hardshrink (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7321035Z   test_nn_Hardshrink_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7321340Z   test_nn_Hardshrink_scalar (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7321695Z   test_nn_Hardsigmoid_no_batch_dim (__main__.TestJitGeneratedModule) ... skip: module test skipped on JIT (0.002s)
2023-01-11T21:25:29.7322082Z   test_nn_Hardswish_no_batch_dim (__main__.TestJitGeneratedModule) ... skip: module test skipped on JIT (0.002s)
2023-01-11T21:25:29.7322416Z   test_nn_Hardtanh (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7322712Z   test_nn_Hardtanh_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7323026Z   test_nn_Hardtanh_scalar (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7323340Z   test_nn_HingeEmbeddingLoss (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7323656Z   test_nn_HingeEmbeddingLoss_margin (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7324005Z   test_nn_HingeEmbeddingLoss_margin_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7324363Z   test_nn_HingeEmbeddingLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7324723Z   test_nn_HingeEmbeddingLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:29.7325068Z   test_nn_HingeEmbeddingLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:29.7325417Z   test_nn_HingeEmbeddingLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7325811Z   test_nn_HingeEmbeddingLoss_scalar_margin (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7326135Z   test_nn_HuberLoss (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:29.7326423Z   test_nn_HuberLoss_delta (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7326741Z   test_nn_HuberLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.022s)
2023-01-11T21:25:29.7327068Z   test_nn_HuberLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.022s)
2023-01-11T21:25:29.7327381Z   test_nn_HuberLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.021s)
2023-01-11T21:25:29.7327697Z   test_nn_InstanceNorm1d (__main__.TestJitGeneratedModule) ... ok (0.090s)
2023-01-11T21:25:29.7328047Z   test_nn_InstanceNorm1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.087s)
2023-01-11T21:25:29.7328387Z   test_nn_InstanceNorm1d_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.094s)
2023-01-11T21:25:29.7328726Z   test_nn_InstanceNorm1d_tracking_stats_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.096s)
2023-01-11T21:25:29.7329178Z   test_nn_InstanceNorm2d (__main__.TestJitGeneratedModule) ... ok (0.090s)
2023-01-11T21:25:29.7329501Z   test_nn_InstanceNorm2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.088s)
2023-01-11T21:25:29.7329822Z   test_nn_InstanceNorm2d_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.094s)
2023-01-11T21:25:29.7330177Z   test_nn_InstanceNorm2d_tracking_stats_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.095s)
2023-01-11T21:25:29.7330510Z   test_nn_InstanceNorm3d (__main__.TestJitGeneratedModule) ... ok (0.089s)
2023-01-11T21:25:29.7330828Z   test_nn_InstanceNorm3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.088s)
2023-01-11T21:25:29.7331150Z   test_nn_InstanceNorm3d_tracking_stats (__main__.TestJitGeneratedModule) ... ok (0.094s)
2023-01-11T21:25:29.7331502Z   test_nn_InstanceNorm3d_tracking_stats_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.099s)
2023-01-11T21:25:29.7331829Z   test_nn_KLDivLoss (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7332123Z   test_nn_KLDivLoss_log_target (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7332451Z   test_nn_KLDivLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7332781Z   test_nn_KLDivLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7333108Z   test_nn_KLDivLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7333415Z   test_nn_KLDivLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7333740Z   test_nn_KLDivLoss_no_reduce_log_target (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7334075Z   test_nn_KLDivLoss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7334406Z   test_nn_KLDivLoss_no_reduce_scalar_log_target (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7334740Z   test_nn_KLDivLoss_scalar (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7335061Z   test_nn_KLDivLoss_scalar_log_target (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7335400Z   test_nn_KLDivLoss_with_log_target_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7335732Z   test_nn_KLDivLoss_with_target_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7336041Z   test_nn_L1Loss (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7336342Z   test_nn_L1Loss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:29.7336650Z   test_nn_L1Loss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7336968Z   test_nn_L1Loss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7337329Z   test_nn_L1Loss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7337641Z   test_nn_L1Loss_no_reduce_complex (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7337949Z   test_nn_L1Loss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7338255Z   test_nn_L1Loss_scalar (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7338553Z   test_nn_LPPool1d (__main__.TestJitGeneratedModule) ... ok (0.110s)
2023-01-11T21:25:29.7338847Z   test_nn_LPPool1d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.109s)
2023-01-11T21:25:29.7339156Z   test_nn_LPPool1d_norm (__main__.TestJitGeneratedModule) ... ok (0.117s)
2023-01-11T21:25:29.7339452Z   test_nn_LPPool2d (__main__.TestJitGeneratedModule) ... ok (0.133s)
2023-01-11T21:25:29.7339781Z   test_nn_LPPool2d_norm (__main__.TestJitGeneratedModule) ... ok (0.149s)
2023-01-11T21:25:29.7340066Z   test_nn_LSTMCell (__main__.TestJitGeneratedModule) ... ok (0.075s)
2023-01-11T21:25:29.7340387Z   test_nn_LayerNorm_1d_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:29.7340734Z   test_nn_LayerNorm_1d_empty_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7341069Z   test_nn_LayerNorm_1d_no_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:29.7341411Z   test_nn_LayerNorm_3d_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:29.7341759Z   test_nn_LayerNorm_3d_no_affine_large_feature (__main__.TestJitGeneratedModule) ... ok (0.162s)
2023-01-11T21:25:29.7342108Z   test_nn_LayerNorm_3d_no_elementwise_affine (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:29.7342415Z   test_nn_LeakyReLU (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7342723Z   test_nn_LeakyReLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7343045Z   test_nn_LeakyReLU_with_negval (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7343437Z   test_nn_LeakyReLU_with_negval_scalar (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7343777Z   test_nn_LeakyReLU_with_zero_negval (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7344081Z   test_nn_Linear (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:29.7344382Z   test_nn_Linear_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:29.7344678Z   test_nn_Linear_no_bias (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:29.7344994Z   test_nn_LocalResponseNorm_1d (__main__.TestJitGeneratedModule) ... ok (0.168s)
2023-01-11T21:25:29.7345329Z   test_nn_LocalResponseNorm_2d_uneven_pad (__main__.TestJitGeneratedModule) ... ok (0.176s)
2023-01-11T21:25:29.7345670Z   test_nn_LocalResponseNorm_3d_custom_params (__main__.TestJitGeneratedModule) ... ok (0.181s)
2023-01-11T21:25:29.7345997Z   test_nn_LogSigmoid (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7346313Z   test_nn_LogSigmoid_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7346629Z   test_nn_LogSigmoid_scalar (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7346920Z   test_nn_LogSoftmax (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:29.7347230Z   test_nn_LogSoftmax_multiparam (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:29.7347559Z   test_nn_LogSoftmax_multiparam_scalar (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7347874Z   test_nn_LogSoftmax_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:29.7348180Z   test_nn_MSELoss (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7348487Z   test_nn_MSELoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7348806Z   test_nn_MSELoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:29.7349143Z   test_nn_MSELoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7349455Z   test_nn_MSELoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7349768Z   test_nn_MSELoss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7350061Z   test_nn_MSELoss_prec (__main__.TestJitGeneratedModule) ... ok (0.045s)
2023-01-11T21:25:29.7350359Z   test_nn_MSELoss_scalar (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7350667Z   test_nn_MarginRankingLoss (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7350994Z   test_nn_MarginRankingLoss_margin (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7351326Z   test_nn_MarginRankingLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7351718Z   test_nn_MarginRankingLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7352074Z   test_nn_MarginRankingLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7352393Z   test_nn_MaxPool1d (__main__.TestJitGeneratedModule) ... ok (0.051s)
2023-01-11T21:25:29.7352691Z   test_nn_MaxPool1d_return_indices (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:29.7353007Z   test_nn_MaxPool1d_stride (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:29.7353315Z   test_nn_MaxPool2d_3d_input (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:29.7353611Z   test_nn_MaxPool2d_4d_input (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:29.7353932Z   test_nn_MaxPool2d_return_indices (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7354242Z   test_nn_MaxPool3d (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:29.7354541Z   test_nn_MaxPool3d_return_indices (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7354860Z   test_nn_MaxPool3d_stride (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:29.7355179Z   test_nn_MaxPool3d_stride_padding (__main__.TestJitGeneratedModule) ... ok (0.050s)
2023-01-11T21:25:29.7355482Z   test_nn_Mish (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7355763Z   test_nn_Mish_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7356063Z   test_nn_Mish_scalar (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7356377Z   test_nn_MultiLabelMarginLoss (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7356707Z   test_nn_MultiLabelMarginLoss_0d_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7357053Z   test_nn_MultiLabelMarginLoss_1d (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:29.7357400Z   test_nn_MultiLabelMarginLoss_1d_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7357750Z   test_nn_MultiLabelMarginLoss_index_neg (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7358096Z   test_nn_MultiLabelMarginLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:29.7358458Z   test_nn_MultiLabelMarginLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:29.7358824Z   test_nn_MultiLabelMarginLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:29.7359177Z   test_nn_MultiLabelMarginLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7359514Z   test_nn_MultiLabelSoftMarginLoss (__main__.TestJitGeneratedModule) ... ok (0.077s)
2023-01-11T21:25:29.7359884Z   test_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7360267Z   test_nn_MultiLabelSoftMarginLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7360635Z   test_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.058s)
2023-01-11T21:25:29.7361034Z   test_nn_MultiLabelSoftMarginLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.003s)
2023-01-11T21:25:29.7361400Z   test_nn_MultiLabelSoftMarginLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.073s)
2023-01-11T21:25:29.7361775Z   test_nn_MultiLabelSoftMarginLoss_weights_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.003s)
2023-01-11T21:25:29.7362108Z   test_nn_MultiMarginLoss (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7362421Z   test_nn_MultiMarginLoss_1d (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:29.7362747Z   test_nn_MultiMarginLoss_1d_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7363066Z   test_nn_MultiMarginLoss_margin (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:29.7363428Z   test_nn_MultiMarginLoss_margin_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7363765Z   test_nn_MultiMarginLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7364091Z   test_nn_MultiMarginLoss_p (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7364403Z   test_nn_MultiMarginLoss_p_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7364735Z   test_nn_MultiMarginLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7365076Z   test_nn_MultiMarginLoss_weights_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7365486Z   test_nn_MultiheadAttention (__main__.TestJitGeneratedModule) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:25:29.7365844Z   test_nn_NLLLoss (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7366147Z   test_nn_NLLLoss2d_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7366477Z   test_nn_NLLLoss2d_no_reduce_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7366804Z   test_nn_NLLLoss2d_no_reduce_weights (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7367121Z   test_nn_NLLLossNd_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7367444Z   test_nn_NLLLossNd_no_reduce_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7367766Z   test_nn_NLLLossNd_no_reduce_weights (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7368073Z   test_nn_NLLLoss_2d (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7368372Z   test_nn_NLLLoss_2d_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7368682Z   test_nn_NLLLoss_2d_weights (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7368973Z   test_nn_NLLLoss_dim_is_3 (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:29.7369384Z   test_nn_NLLLoss_higher_dim (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7369690Z   test_nn_NLLLoss_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7369998Z   test_nn_NLLLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7370312Z   test_nn_NLLLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:29.7370627Z   test_nn_NLLLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.025s)
2023-01-11T21:25:29.7370939Z   test_nn_NLLLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7371245Z   test_nn_NLLLoss_no_reduce_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7371574Z   test_nn_NLLLoss_no_reduce_weights (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7371909Z   test_nn_NLLLoss_no_reduce_weights_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7372254Z   test_nn_NLLLoss_no_reduce_weights_ignore_index_neg (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7372629Z   test_nn_NLLLoss_weights (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:29.7372942Z   test_nn_NLLLoss_weights_ignore_index (__main__.TestJitGeneratedModule) ... ok (0.026s)
2023-01-11T21:25:29.7373275Z   test_nn_NLLLoss_weights_ignore_index_neg (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:29.7373572Z   test_nn_PReLU_1d (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7373873Z   test_nn_PReLU_1d_multiparam (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7374170Z   test_nn_PReLU_2d (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7374456Z   test_nn_PReLU_2d_multiparam (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7374753Z   test_nn_PReLU_3d (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7375087Z   test_nn_PReLU_3d_multiparam (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7375395Z   test_nn_PReLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7375685Z   test_nn_PReLU_scalar (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7375994Z   test_nn_Padding122112_3dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7376319Z   test_nn_Padding1221_2dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7376622Z   test_nn_Padding12_1dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7376934Z   test_nn_Padding2322_2dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7377249Z   test_nn_Padding31_1dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7377565Z   test_nn_Padding322112_3dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7377870Z   test_nn_Padding332122_3dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7378186Z   test_nn_Padding3331_2dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7378502Z   test_nn_Padding33_1dcircular (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7378802Z   test_nn_PairwiseDistance (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7379133Z   test_nn_PairwiseDistance_broadcast_lhs (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7379476Z   test_nn_PairwiseDistance_broadcast_rhs (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7379812Z   test_nn_PairwiseDistance_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7380146Z   test_nn_PairwiseDistance_with_non_default_args (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7380471Z   test_nn_PixelShuffle (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7380772Z   test_nn_PixelUnshuffle (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7381076Z   test_nn_PoissonNLLLoss_full_loss (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7381416Z   test_nn_PoissonNLLLoss_full_loss_no_log_input (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7381758Z   test_nn_PoissonNLLLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7382095Z   test_nn_PoissonNLLLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7382418Z   test_nn_PoissonNLLLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7382751Z   test_nn_PoissonNLLLoss_no_full_loss (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7383092Z   test_nn_PoissonNLLLoss_no_full_loss_no_log_input (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7383510Z   test_nn_PoissonNLLLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7383823Z   test_nn_RNNCell (__main__.TestJitGeneratedModule) ... ok (0.070s)
2023-01-11T21:25:29.7384105Z   test_nn_RReLU (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7384435Z   test_nn_RReLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7384727Z   test_nn_RReLU_with_up_down (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7385041Z   test_nn_RReLU_with_up_down_scalar (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7385340Z   test_nn_ReLU (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7385606Z   test_nn_ReLU6 (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7385896Z   test_nn_ReLU6_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7386191Z   test_nn_ReLU6_scalar (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7386490Z   test_nn_ReLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7386810Z   test_nn_ReLU_scalar (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7387113Z   test_nn_ReflectionPad1d (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7387432Z   test_nn_ReflectionPad1d_batch (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7387746Z   test_nn_ReflectionPad1d_complex (__main__.TestJitGeneratedModule) ... ok (0.046s)
2023-01-11T21:25:29.7388063Z   test_nn_ReflectionPad2d (__main__.TestJitGeneratedModule) ... ok (0.043s)
2023-01-11T21:25:29.7388382Z   test_nn_ReflectionPad2d_complex (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7388717Z   test_nn_ReflectionPad2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7389024Z   test_nn_ReflectionPad3d (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7389339Z   test_nn_ReflectionPad3d_complex (__main__.TestJitGeneratedModule) ... ok (0.042s)
2023-01-11T21:25:29.7389671Z   test_nn_ReflectionPad3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7389980Z   test_nn_ReplicationPad1d (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7390304Z   test_nn_ReplicationPad1d_batch (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7390631Z   test_nn_ReplicationPad1d_complex (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7390952Z   test_nn_ReplicationPad2d (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7391261Z   test_nn_ReplicationPad2d_complex (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7391598Z   test_nn_ReplicationPad2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7391923Z   test_nn_ReplicationPad3d (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7392229Z   test_nn_ReplicationPad3d_complex (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7392561Z   test_nn_ReplicationPad3d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.039s)
2023-01-11T21:25:29.7392868Z   test_nn_SELU (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7393009Z   test_nn_SELU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7393145Z   test_nn_SELU_scalar (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7393258Z   test_nn_SiLU (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7393398Z   test_nn_SiLU_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7393532Z   test_nn_SiLU_scalar (__main__.TestJitGeneratedModule) ... ok (0.037s)
2023-01-11T21:25:29.7393663Z   test_nn_Sigmoid (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7393807Z   test_nn_Sigmoid_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7393944Z   test_nn_Sigmoid_scalar (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7394083Z   test_nn_SmoothL1Loss (__main__.TestJitGeneratedModule) ... ok (0.036s)
2023-01-11T21:25:29.7394225Z   test_nn_SmoothL1Loss_beta (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7394405Z   test_nn_SmoothL1Loss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7394562Z   test_nn_SmoothL1Loss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.028s)
2023-01-11T21:25:29.7394714Z   test_nn_SmoothL1Loss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7394861Z   test_nn_SmoothL1Loss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7395014Z   test_nn_SmoothL1Loss_no_reduce_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7395158Z   test_nn_SmoothL1Loss_scalar (__main__.TestJitGeneratedModule) ... ok (0.030s)
2023-01-11T21:25:29.7395303Z   test_nn_SmoothL1Loss_zero_beta (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7395472Z   test_nn_SoftMarginLoss (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7395635Z   test_nn_SoftMarginLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:29.7395784Z   test_nn_SoftMarginLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:29.7395941Z   test_nn_SoftMarginLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.027s)
2023-01-11T21:25:29.7396092Z   test_nn_SoftMarginLoss_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7396221Z   test_nn_Softmax (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7396352Z   test_nn_Softmax2d (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:29.7396499Z   test_nn_Softmax2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.055s)
2023-01-11T21:25:29.7396645Z   test_nn_Softmax_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.049s)
2023-01-11T21:25:29.7396785Z   test_nn_Softmax_scalar (__main__.TestJitGeneratedModule) ... ok (0.048s)
2023-01-11T21:25:29.7396903Z   test_nn_Softmin (__main__.TestJitGeneratedModule) ... ok (0.062s)
2023-01-11T21:25:29.7397045Z   test_nn_Softmin_multidim (__main__.TestJitGeneratedModule) ... ok (0.060s)
2023-01-11T21:25:29.7397188Z   test_nn_Softmin_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.056s)
2023-01-11T21:25:29.7397324Z   test_nn_Softmin_scalar (__main__.TestJitGeneratedModule) ... ok (0.054s)
2023-01-11T21:25:29.7397457Z   test_nn_Softplus (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7397594Z   test_nn_Softplus_beta (__main__.TestJitGeneratedModule) ... ok (0.035s)
2023-01-11T21:25:29.7397746Z   test_nn_Softplus_beta_threshold (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7397900Z   test_nn_Softplus_beta_threshold_scalar (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7398033Z   test_nn_Softplus_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7398170Z   test_nn_Softshrink (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7398311Z   test_nn_Softshrink_lambda (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7398463Z   test_nn_Softshrink_lambda_scalar (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7398610Z   test_nn_Softshrink_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7398743Z   test_nn_Softsign (__main__.TestJitGeneratedModule) ... ok (0.087s)
2023-01-11T21:25:29.7398888Z   test_nn_Softsign_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.072s)
2023-01-11T21:25:29.7399027Z   test_nn_Softsign_scalar (__main__.TestJitGeneratedModule) ... ok (0.070s)
2023-01-11T21:25:29.7399141Z   test_nn_Tanh (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7399282Z   test_nn_Tanh_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.032s)
2023-01-11T21:25:29.7399417Z   test_nn_Tanh_scalar (__main__.TestJitGeneratedModule) ... ok (0.031s)
2023-01-11T21:25:29.7399552Z   test_nn_Tanhshrink (__main__.TestJitGeneratedModule) ... ok (0.126s)
2023-01-11T21:25:29.7399702Z   test_nn_Tanhshrink_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.070s)
2023-01-11T21:25:29.7399888Z   test_nn_Tanhshrink_scalar (__main__.TestJitGeneratedModule) ... ok (0.066s)
2023-01-11T21:25:29.7400034Z   test_nn_Threshold_large_value (__main__.TestJitGeneratedModule) ... ok (0.044s)
2023-01-11T21:25:29.7400181Z   test_nn_Threshold_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7400318Z   test_nn_Threshold_threshold_value (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7400475Z   test_nn_Threshold_threshold_value_scalar (__main__.TestJitGeneratedModule) ... ok (0.040s)
2023-01-11T21:25:29.7400682Z   test_nn_Transformer (__main__.TestJitGeneratedModule) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:25:29.7400890Z   test_nn_TransformerDecoderLayer_gelu_activation (__main__.TestJitGeneratedModule) ... ok (1.956s)
2023-01-11T21:25:29.7401068Z   test_nn_TransformerDecoderLayer_relu_activation (__main__.TestJitGeneratedModule) ... ok (1.868s)
2023-01-11T21:25:29.7401240Z   test_nn_TransformerEncoderLayer_gelu_activation (__main__.TestJitGeneratedModule) ... ok (1.711s)
2023-01-11T21:25:29.7401407Z   test_nn_TransformerEncoderLayer_relu_activation (__main__.TestJitGeneratedModule) ... ok (1.720s)
2023-01-11T21:25:29.7401562Z   test_nn_Transformer_multilayer_coder (__main__.TestJitGeneratedModule) ... ok (5.513s)
2023-01-11T21:25:29.7401727Z   test_nn_TripletMarginLoss_no_batch_dim_mean (__main__.TestJitGeneratedModule) ... ok (0.038s)
2023-01-11T21:25:29.7401881Z   test_nn_TripletMarginLoss_no_batch_dim_none (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7402045Z   test_nn_TripletMarginLoss_no_batch_dim_sum (__main__.TestJitGeneratedModule) ... ok (0.029s)
2023-01-11T21:25:29.7402193Z   test_nn_Unflatten_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7402324Z   test_nn_Unfold (__main__.TestJitGeneratedModule) ... ok (0.047s)
2023-01-11T21:25:29.7402464Z   test_nn_Unfold_int_input (__main__.TestJitGeneratedModule) ... ok (0.041s)
2023-01-11T21:25:29.7402603Z   test_nn_ZeroPad2d (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7402745Z   test_nn_ZeroPad2d_complex (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7402897Z   test_nn_ZeroPad2d_negative_dims (__main__.TestJitGeneratedModule) ... ok (0.033s)
2023-01-11T21:25:29.7403033Z   test_nn_ZeroPad2d_no_batch_dim (__main__.TestJitGeneratedModule) ... ok (0.034s)
2023-01-11T21:25:29.7403179Z   test_nn_interpolate_bicubic_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7403336Z   test_nn_interpolate_bicubic_2d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7403490Z   test_nn_interpolate_bicubic_scale_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7403662Z   test_nn_interpolate_bicubic_scale_tuple_shared_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7403828Z   test_nn_interpolate_bicubic_scale_tuple_skewed_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7404015Z   test_nn_interpolate_bicubic_scale_tuple_skewed_2d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7404167Z   test_nn_interpolate_bicubic_tuple_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7404338Z   test_nn_interpolate_bicubic_tuple_2d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7404476Z   test_nn_interpolate_bilinear_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7404631Z   test_nn_interpolate_bilinear_2d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7404785Z   test_nn_interpolate_bilinear_scale_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7404959Z   test_nn_interpolate_bilinear_scale_tuple_shared_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7405128Z   test_nn_interpolate_bilinear_scale_tuple_skewed_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7405347Z   test_nn_interpolate_bilinear_scale_tuple_skewed_2d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7405503Z   test_nn_interpolate_bilinear_tuple_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7405677Z   test_nn_interpolate_bilinear_tuple_2d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7405813Z   test_nn_interpolate_linear_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7405974Z   test_nn_interpolate_linear_1d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7406129Z   test_nn_interpolate_linear_1d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7406308Z   test_nn_interpolate_linear_scale_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7406480Z   test_nn_interpolate_linear_scale_1d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7406633Z   test_nn_interpolate_linear_tuple_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7406782Z   test_nn_interpolate_nearest_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7406938Z   test_nn_interpolate_nearest_1d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7407084Z   test_nn_interpolate_nearest_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7407237Z   test_nn_interpolate_nearest_2d_launch_configs (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7407391Z   test_nn_interpolate_nearest_2d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7407536Z   test_nn_interpolate_nearest_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7407692Z   test_nn_interpolate_nearest_3d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7407844Z   test_nn_interpolate_nearest_scale_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7408001Z   test_nn_interpolate_nearest_scale_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7408151Z   test_nn_interpolate_nearest_scale_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7408306Z   test_nn_interpolate_nearest_tuple_1d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7408447Z   test_nn_interpolate_nearest_tuple_2d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7408598Z   test_nn_interpolate_nearest_tuple_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7408748Z   test_nn_interpolate_trilinear_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7408911Z   test_nn_interpolate_trilinear_3d_zero_dim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7409165Z   test_nn_interpolate_trilinear_scale_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7409342Z   test_nn_interpolate_trilinear_scale_3d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7409498Z   test_nn_interpolate_trilinear_tuple_3d (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7409671Z   test_nn_interpolate_trilinear_tuple_3d_align_corners (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7409810Z   test_nn_log_softmax_dim0 (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7409937Z   test_nn_log_softmax_dim3 (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7410080Z   test_nn_log_softmax_lastdim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7410220Z   test_nn_log_softmax_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7410365Z   test_nn_log_softmax_spatial (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7410518Z   test_nn_log_softmax_spatial_special (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7410736Z   test_nn_multimarginloss_1d_input_0d_target_no_reduce (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7410885Z   test_nn_softmax_functional_dim0 (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7411029Z   test_nn_softmax_functional_dim3 (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7411169Z   test_nn_softmax_functional_scalar (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7411307Z   test_nn_softmax_lastdim (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7411453Z   test_nn_softmax_lastdim_dtype (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7411591Z   test_nn_softmax_spatial (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7411775Z   test_nn_softmax_spatial_dtype (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7411926Z   test_nn_softmax_spatial_special (__main__.TestJitGeneratedModule) ... ok (0.002s)
2023-01-11T21:25:29.7412093Z   test_checkscriptassertraisesregex (jit.test_jit_utils.TestJitUtils) ... ok (0.003s)
2023-01-11T21:25:29.7412250Z   test_get_callable_argument_names_hybrid (jit.test_jit_utils.TestJitUtils) ... ok (0.006s)
2023-01-11T21:25:29.7412402Z   test_get_callable_argument_names_keyword_only (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:29.7412572Z   test_get_callable_argument_names_positional_only (jit.test_jit_utils.TestJitUtils) ... ok (0.005s)
2023-01-11T21:25:29.7412747Z   test_get_callable_argument_names_positional_or_keyword (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:29.7412909Z   test_get_callable_argument_names_var_keyword (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:29.7413079Z   test_get_callable_argument_names_var_positional (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:29.7413230Z   test_no_tracer_warn_context_manager (jit.test_jit_utils.TestJitUtils) ... ok (0.001s)
2023-01-11T21:25:29.7413370Z   test_comprehension_iterable (jit.test_list_dict.TestList) ... ok (0.017s)
2023-01-11T21:25:29.7413519Z   test_comprehension_out_type_not_in_type (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:29.7413654Z   test_comprehensions_basic (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:29.7413784Z   test_comprehensions_basic_float (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:29.7413923Z   test_comprehensions_two_comps (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7414054Z   test_copy_list_immutable (jit.test_list_dict.TestList) ... ok (0.002s)
2023-01-11T21:25:29.7414182Z   test_copy_list_mutable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7414296Z   test_del (jit.test_list_dict.TestList) ... ok (0.010s)
2023-01-11T21:25:29.7414444Z   test_dict_keyword_is_correctly_typed (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7414596Z   test_dict_keyword_with_dict_comprehension (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7414765Z   test_dict_keyword_with_dict_comprehension_and_kwargs (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7414913Z   test_dict_keyword_with_empty_dict_comprehension (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7415060Z   test_dict_keyword_with_empty_iterable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7415222Z   test_dict_keyword_with_internal_aggregate_function (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7415361Z   test_dict_keyword_with_iterable (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7415498Z   test_dict_keyword_with_kwargs (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7415660Z   test_dict_keyword_with_kwargs_using_container_values (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:29.7415802Z   test_dict_keyword_with_mapping (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7415954Z   test_dict_keyword_with_mapping_and_kwargs (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7416142Z   test_dict_keyword_with_mismatched_annotations (jit.test_list_dict.TestList) ... ok (0.002s)
2023-01-11T21:25:29.7416284Z   test_dict_keyword_with_nested_call (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7416449Z   test_dict_keyword_with_previously_declared_variable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7416625Z   test_dict_keyword_with_previously_declared_variable_and_kwargs (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7416756Z   test_extend_list_immutable (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7416886Z   test_extend_list_mutable (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7417005Z   test_in_check (jit.test_list_dict.TestList) ... ok (0.012s)
2023-01-11T21:25:29.7417165Z   test_list_bool_conversion (jit.test_list_dict.TestList) ... ok (0.018s)
2023-01-11T21:25:29.7417275Z   test_list_count (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7417413Z   test_list_count_not_existing (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7417534Z   test_list_gather (jit.test_list_dict.TestList) ... ok (0.010s)
2023-01-11T21:25:29.7417657Z   test_list_index (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7417788Z   test_list_index_not_existing (jit.test_list_dict.TestList) ... ok (0.017s)
2023-01-11T21:25:29.7417912Z   test_list_keyword (jit.test_list_dict.TestList) ... ok (0.014s)
2023-01-11T21:25:29.7418029Z   test_list_len (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7418148Z   test_list_literal (jit.test_list_dict.TestList) ... ok (0.012s)
2023-01-11T21:25:29.7418255Z   test_list_none (jit.test_list_dict.TestList) ... ok (0.001s)
2023-01-11T21:25:29.7418375Z   test_list_ops (jit.test_list_dict.TestList) ... ok (0.054s)
2023-01-11T21:25:29.7418493Z   test_list_slice (jit.test_list_dict.TestList) ... ok (0.020s)
2023-01-11T21:25:29.7418613Z   test_list_sort (jit.test_list_dict.TestList) ... ok (0.034s)
2023-01-11T21:25:29.7418745Z   test_list_unification_hint (jit.test_list_dict.TestList) ... ok (0.001s)
2023-01-11T21:25:29.7418854Z   test_list_variance (jit.test_list_dict.TestList)
2023-01-11T21:25:29.7418983Z `List[T1]` is not a subtype of `List[T2]`, even if `T1` is a ... ok (0.009s)
2023-01-11T21:25:29.7419107Z   test_min_bool_list (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7419216Z   test_min_max_list (jit.test_list_dict.TestList) ... ok (0.057s)
2023-01-11T21:25:29.7419346Z   test_min_max_single_list (jit.test_list_dict.TestList) ... ok (0.040s)
2023-01-11T21:25:29.7419478Z   test_mutable_list_append (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7419610Z   test_mutable_list_append_2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7419746Z   test_mutable_list_append_if (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7419887Z   test_mutable_list_append_if_else (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7420025Z   test_mutable_list_append_loop (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7420166Z   test_mutable_list_append_loop_if (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7420283Z   test_mutable_list_clear (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7420420Z   test_mutable_list_clear_empty (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7420560Z   test_mutable_list_function_inline (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7420689Z   test_mutable_list_insert (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7420837Z   test_mutable_list_insert_neg_out_of_bounds (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7420981Z   test_mutable_list_insert_negative (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7421125Z   test_mutable_list_insert_out_of_bounds (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7421295Z   test_mutable_list_nested_loop (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7421413Z   test_mutable_list_pop (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7421543Z   test_mutable_list_pop2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7421674Z   test_mutable_list_pop_at (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7421803Z   test_mutable_list_pop_at2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7421944Z   test_mutable_list_pop_at_negative (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7422086Z   test_mutable_list_pop_at_negative2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7422222Z   test_mutable_list_pop_empty (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7422385Z   test_mutable_list_pop_slice (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7422504Z   test_mutable_list_remove (jit.test_list_dict.TestList) ... ok (0.007s)
2023-01-11T21:25:29.7422635Z   test_mutable_list_remove2 (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7422779Z   test_mutable_list_remove_not_existing (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7422917Z   test_mutable_list_remove_tensor (jit.test_list_dict.TestList) ... ok (0.006s)
2023-01-11T21:25:29.7423047Z   test_mutable_list_reverse (jit.test_list_dict.TestList) ... ok (0.004s)
2023-01-11T21:25:29.7423187Z   test_mutable_list_reverse_empty (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7423326Z   test_mutable_tensor_list_reverse (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7423539Z   test_no_element_type_annotation (jit.test_list_dict.TestList) ... ok (0.003s)
2023-01-11T21:25:29.7423653Z   test_slice_index (jit.test_list_dict.TestList) ... ok (0.027s)
2023-01-11T21:25:29.7423781Z   test_tensor_list_count (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7423925Z   test_tensor_list_count_not_existing (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7424049Z   test_tensor_list_index (jit.test_list_dict.TestList) ... ok (0.005s)
2023-01-11T21:25:29.7424190Z   test_tensor_list_index_not_existing (jit.test_list_dict.TestList) ... ok (0.018s)
2023-01-11T21:25:29.7424294Z   test_to_list (jit.test_list_dict.TestList)
2023-01-11T21:25:29.7424409Z Unit tests for Tensor.tolist() function. ... ok (0.219s)
2023-01-11T21:25:29.7424518Z   test_to_list_gpu (jit.test_list_dict.TestList)
2023-01-11T21:25:29.7424651Z GPU tests for Tensor.tolist() function. ... skip: CUDA is not available (0.001s)
2023-01-11T21:25:29.7424789Z   test_bump_numeric_counter (jit.test_logging.TestLogging) ... ok (0.008s)
2023-01-11T21:25:29.7424927Z   test_counter_aggregation (jit.test_logging.TestLogging) ... ok (0.007s)
2023-01-11T21:25:29.7425065Z   test_logging_levels_set (jit.test_logging.TestLogging) ... ok (0.001s)
2023-01-11T21:25:29.7425207Z   test_time_measurement_counter (jit.test_logging.TestLogging) ... ok (0.002s)
2023-01-11T21:25:29.7425361Z   test_time_measurement_counter_script (jit.test_logging.TestLogging) ... ok (0.007s)
2023-01-11T21:25:29.7425498Z   test_trace_numeric_counter (jit.test_logging.TestLogging) ... ok (0.006s)
2023-01-11T21:25:29.7425660Z   test_always_alive_values (jit.test_freezing.TestMKLDNNReinplacing) ... ok (0.019s)
2023-01-11T21:25:29.7425801Z   test_merge_liveness (jit.test_freezing.TestMKLDNNReinplacing) ... ok (0.010s)
2023-01-11T21:25:29.7425951Z   test_successful (jit.test_freezing.TestMKLDNNReinplacing) ... ok (0.012s)
2023-01-11T21:25:29.7426115Z   test_switch_inputs_to_inplace (jit.test_freezing.TestMKLDNNReinplacing) ... ok (0.010s)
2023-01-11T21:25:29.7426231Z   test_broadcasting_list (jit.test_misc.TestMisc)
2023-01-11T21:25:29.7426361Z Test BroadcastingList and torch.nn._size_N_t alias ... ok (0.007s)
2023-01-11T21:25:29.7426497Z   test_export_opnames_interface (jit.test_misc.TestMisc) ... ok (0.023s)
2023-01-11T21:25:29.7426620Z   test_future_isinstance (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:29.7426772Z   test_hacked_twin (jit.test_misc.TestMisc) ... ok (0.002s)
2023-01-11T21:25:29.7426871Z   test_if_returning_any (jit.test_misc.TestMisc)
2023-01-11T21:25:29.7426994Z Check that an if statement can return different ... ok (0.005s)
2023-01-11T21:25:29.7427110Z   test_joined_str (jit.test_misc.TestMisc) ... ok (0.004s)
2023-01-11T21:25:29.7427231Z   test_kwarg_support (jit.test_misc.TestMisc) ... ok (0.008s)
2023-01-11T21:25:29.7427365Z   test_legacy_tensor_constructor (jit.test_misc.TestMisc) ... ok (0.017s)
2023-01-11T21:25:29.7427487Z   test_list_literal_infer (jit.test_misc.TestMisc) ... ok (0.008s)
2023-01-11T21:25:29.7427600Z   test_math_inf (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:29.7427711Z   test_parse_ir_annotate (jit.test_misc.TestMisc) ... ok (0.001s)
2023-01-11T21:25:29.7427885Z   test_parse_ir_single_element_tensor_negative (jit.test_misc.TestMisc) ... ok (0.001s)
2023-01-11T21:25:29.7428036Z   test_parse_ir_single_element_tensor_positive (jit.test_misc.TestMisc) ... ok (0.001s)
2023-01-11T21:25:29.7428171Z   test_script_many_decorators (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:29.7428291Z   test_str_refine_any (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:29.7428429Z   test_subexpression_Dict_int_Future (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:29.7428567Z   test_subexpression_Future_annotate (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:29.7428700Z   test_subexpression_List_Future (jit.test_misc.TestMisc) ... ok (0.002s)
2023-01-11T21:25:29.7428821Z   test_subexpression_Optional (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:29.7428965Z   test_subexpression_Tuple_int_int_Future (jit.test_misc.TestMisc) ... ok (0.003s)
2023-01-11T21:25:29.7429099Z   test_tuple_subscripted_assign (jit.test_misc.TestMisc) ... ok (0.002s)
2023-01-11T21:25:29.7429277Z   test_call_script_fn_from_traced_module (jit.test_tracer.TestMixTracingScripting) ... ok (0.011s)
2023-01-11T21:25:29.7429463Z   test_call_script_module_from_traced_module (jit.test_tracer.TestMixTracingScripting) ... ok (0.016s)
2023-01-11T21:25:29.7429635Z   test_call_traced_fn_from_script_fn (jit.test_tracer.TestMixTracingScripting) ... ok (0.006s)
2023-01-11T21:25:29.7429810Z   test_call_traced_mod_from_script_fn (jit.test_tracer.TestMixTracingScripting) ... ok (0.010s)
2023-01-11T21:25:29.7429986Z   test_call_tracing_fn_from_script_module (jit.test_tracer.TestMixTracingScripting) ... ok (0.008s)
2023-01-11T21:25:29.7430166Z   test_call_tracing_mod_from_script_module (jit.test_tracer.TestMixTracingScripting) ... ok (0.012s)
2023-01-11T21:25:29.7430330Z   test_script_inline_trace_multiple_args (jit.test_tracer.TestMixTracingScripting) ... ok (0.011s)
2023-01-11T21:25:29.7430493Z   test_trace_dict_mix_script (jit.test_tracer.TestMixTracingScripting) ... ok (0.045s)
2023-01-11T21:25:29.7430648Z   test_trace_hierarchy (jit.test_tracer.TestMixTracingScripting) ... ok (0.026s)
2023-01-11T21:25:29.7430802Z   test_trace_linear (jit.test_tracer.TestMixTracingScripting) ... ok (0.022s)
2023-01-11T21:25:29.7430983Z   test_trace_mixed_by_script_with_dict_output (jit.test_tracer.TestMixTracingScripting) ... ok (0.012s)
2023-01-11T21:25:29.7431139Z   test_trace_of_script (jit.test_tracer.TestMixTracingScripting) ... ok (0.016s)
2023-01-11T21:25:29.7431295Z   test_trace_parameter (jit.test_tracer.TestMixTracingScripting) ... ok (0.033s)
2023-01-11T21:25:29.7431466Z   test_trace_returning_dict_with_tensor_tuples (jit.test_tracer.TestMixTracingScripting)
2023-01-11T21:25:29.7431614Z Tracing over a module returning a dictionary whose values are tuples of tensors ... ok (0.012s)
2023-01-11T21:25:29.7431766Z   test_trace_script (jit.test_tracer.TestMixTracingScripting) ... ok (0.153s)
2023-01-11T21:25:29.7431937Z   test_trace_script_returning_complex_dict (jit.test_tracer.TestMixTracingScripting)
2023-01-11T21:25:29.7432083Z Tracing over a script function returning a dictionary should work. ... ok (0.025s)
2023-01-11T21:25:29.7432267Z   test_trace_with_size (jit.test_tracer.TestMixTracingScripting) ... ok (0.008s)
2023-01-11T21:25:29.7432462Z   test_traced_module_contains_scripted_interface_types (jit.test_tracer.TestMixTracingScripting) ... ok (0.066s)
2023-01-11T21:25:29.7432641Z   test_traced_module_implements_interface (jit.test_tracer.TestMixTracingScripting) ... ok (0.174s)
2023-01-11T21:25:29.7432802Z   test_tracing_indexing (jit.test_tracer.TestMixTracingScripting) ... ok (0.008s)
2023-01-11T21:25:29.7432944Z   test_tracing_slicing (jit.test_tracer.TestMixTracingScripting) ... ok (0.008s)
2023-01-11T21:25:29.7433070Z   test_alexnet (jit.test_models.TestModels) ... ok (0.614s)
2023-01-11T21:25:29.7433197Z   test_dcgan_models (jit.test_models.TestModels) ... ok (0.298s)
2023-01-11T21:25:29.7433369Z   test_dcgan_models_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7433490Z   test_mnist (jit.test_models.TestModels) ... ok (0.350s)
2023-01-11T21:25:29.7433625Z   test_mnist_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7433791Z   test_mnist_training_leaks_no_memory_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7433986Z   test_neural_style (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:29.7434128Z   test_neural_style_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.000s)
2023-01-11T21:25:29.7434255Z   test_reinforcement_learning (jit.test_models.TestModels) ... ok (0.041s)
2023-01-11T21:25:29.7434412Z   test_reinforcement_learning_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.000s)
2023-01-11T21:25:29.7434623Z   test_script_module_script_resnet (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.003s)
2023-01-11T21:25:29.7434836Z   test_script_module_trace_resnet18 (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:29.7435020Z   test_snli (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:25:29.7435153Z   test_snli_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.000s)
2023-01-11T21:25:29.7435283Z   test_snli_quantized (jit.test_models.TestModels) ... ok (0.553s)
2023-01-11T21:25:29.7435481Z   test_super_resolution (jit.test_models.TestModels) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:29.7435629Z   test_super_resolution_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.000s)
2023-01-11T21:25:29.7435760Z   test_time_sequence_prediction (jit.test_models.TestModels) ... ok (0.131s)
2023-01-11T21:25:29.7435875Z   test_vae (jit.test_models.TestModels) ... ok (0.158s)
2023-01-11T21:25:29.7436010Z   test_vae_cuda (jit.test_models.TestModels) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7436137Z   test_vae_quantized (jit.test_models.TestModels) ... ok (0.085s)
2023-01-11T21:25:29.7436287Z   test_customized_state_dict_methods (jit.test_module_apis.TestModuleAPIs)
2023-01-11T21:25:29.7436419Z Tests that customized state dict methods are in effect ... ok (0.033s)
2023-01-11T21:25:29.7436562Z   test_default_state_dict_methods (jit.test_module_apis.TestModuleAPIs)
2023-01-11T21:25:29.7436698Z Tests that default state dict methods are automatically available ... ok (0.025s)
2023-01-11T21:25:29.7436854Z   test_submodule_customized_state_dict_methods (jit.test_module_apis.TestModuleAPIs)
2023-01-11T21:25:29.7436999Z Tests that customized state dict methods on submodules are in effect ... ok (0.040s)
2023-01-11T21:25:29.7437180Z   test_custom_container_forward (jit.test_module_containers.TestModuleContainers) ... ok (0.075s)
2023-01-11T21:25:29.7437364Z   test_empty_dict_override_contains (jit.test_module_containers.TestModuleContainers) ... ok (0.021s)
2023-01-11T21:25:29.7437545Z   test_module_inplace_construct (jit.test_module_containers.TestModuleContainers) ... ok (0.018s)
2023-01-11T21:25:29.7437776Z   test_module_interface_special_methods (jit.test_module_containers.TestModuleContainers) ... ok (0.054s)
2023-01-11T21:25:29.7437943Z   test_module_properties (jit.test_module_containers.TestModuleContainers) ... ok (0.031s)
2023-01-11T21:25:29.7438104Z   test_moduledict (jit.test_module_containers.TestModuleContainers) ... ok (0.191s)
2023-01-11T21:25:29.7438260Z   test_moduledict_getitem (jit.test_module_containers.TestModuleContainers) ... ok (0.034s)
2023-01-11T21:25:29.7438432Z   test_moduledict_keyerror (jit.test_module_containers.TestModuleContainers) ... ok (0.011s)
2023-01-11T21:25:29.7438611Z   test_normal_list_attribute_with_modules_error (jit.test_module_containers.TestModuleContainers)
2023-01-11T21:25:29.7438760Z Test that an attempt to script a module with a regular list attribute ... ok (0.003s)
2023-01-11T21:25:29.7438973Z   test_parameterdict_script_getitem (jit.test_module_containers.TestModuleContainers) ... ok (0.014s)
2023-01-11T21:25:29.7439154Z   test_parameterlist_script_getitem (jit.test_module_containers.TestModuleContainers) ... ok (0.044s)
2023-01-11T21:25:29.7439337Z   test_parameterlist_script_iter (jit.test_module_containers.TestModuleContainers) ... ok (0.063s)
2023-01-11T21:25:29.7439519Z   test_script_module_list_sequential (jit.test_module_containers.TestModuleContainers) ... ok (0.025s)
2023-01-11T21:25:29.7439695Z   test_script_modulelist_index (jit.test_module_containers.TestModuleContainers) ... ok (0.108s)
2023-01-11T21:25:29.7439869Z   test_sequential_intermediary_types (jit.test_module_containers.TestModuleContainers) ... ok (0.026s)
2023-01-11T21:25:29.7440048Z   test_special_method_with_override (jit.test_module_containers.TestModuleContainers) ... ok (0.024s)
2023-01-11T21:25:29.7440209Z   test_typed_module_dict (jit.test_module_containers.TestModuleContainers)
2023-01-11T21:25:29.7440369Z Test that a type annotation can be provided for a ModuleDict that allows ... ok (0.061s)
2023-01-11T21:25:29.7440525Z   test_typed_module_list (jit.test_module_containers.TestModuleContainers)
2023-01-11T21:25:29.7440679Z Test that a type annotation can be provided for a ModuleList that allows ... ok (0.051s)
2023-01-11T21:25:29.7440881Z   test_freeze_module_with_inplace_mutation_in_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.023s)
2023-01-11T21:25:29.7441059Z   test_freeze_module_with_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.019s)
2023-01-11T21:25:29.7441235Z   test_freeze_module_with_interface_and_fork (jit.test_module_interface.TestModuleInterface) ... ok (0.027s)
2023-01-11T21:25:29.7441422Z   test_freeze_module_with_mutated_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.024s)
2023-01-11T21:25:29.7441609Z   test_freeze_module_with_setattr_in_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.020s)
2023-01-11T21:25:29.7441778Z   test_module_apis_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.014s)
2023-01-11T21:25:29.7441940Z   test_module_doc_string (jit.test_module_interface.TestModuleInterface) ... ok (0.020s)
2023-01-11T21:25:29.7442107Z   test_module_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.090s)
2023-01-11T21:25:29.7442287Z   test_module_interface_inheritance (jit.test_module_interface.TestModuleInterface) ... ok (0.001s)
2023-01-11T21:25:29.7442458Z   test_module_interface_subtype (jit.test_module_interface.TestModuleInterface) ... ok (0.050s)
2023-01-11T21:25:29.7442616Z   test_module_swap (jit.test_module_interface.TestModuleInterface) ... ok (0.021s)
2023-01-11T21:25:29.7442780Z   test_module_swap_no_lazy_compile (jit.test_module_interface.TestModuleInterface) ... ok (0.024s)
2023-01-11T21:25:29.7442959Z   test_module_swap_no_module_interface (jit.test_module_interface.TestModuleInterface) ... ok (0.016s)
2023-01-11T21:25:29.7443130Z   test_module_swap_wrong_module (jit.test_module_interface.TestModuleInterface) ... ok (0.019s)
2023-01-11T21:25:29.7443305Z   test_not_submodule_interface_call (jit.test_module_interface.TestModuleInterface) ... ok (0.010s)
2023-01-11T21:25:29.7443514Z   test_script_module_as_interface_swap (jit.test_module_interface.TestModuleInterface) ... ok (0.022s)
2023-01-11T21:25:29.7443653Z   test_script_module_with_constants_list (jit.test_modules.TestModules)
2023-01-11T21:25:29.7443785Z Test that a module that has __constants__ set to something ... ok (0.009s)
2023-01-11T21:25:29.7443921Z   test_namedtuple (jit.test_list_dict.TestNamedTuple) ... ok (0.004s)
2023-01-11T21:25:29.7444052Z   test_namedtuple_as_attr (jit.test_list_dict.TestNamedTuple) ... ok (0.005s)
2023-01-11T21:25:29.7444196Z   test_namedtuple_constant (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:29.7444347Z   test_namedtuple_kwarg_construct (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:29.7444516Z   test_namedtuple_lower (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:29.7444664Z   test_namedtuple_resolution (jit.test_list_dict.TestNamedTuple) ... ok (0.004s)
2023-01-11T21:25:29.7444863Z   test_namedtuple_serialization (jit.test_list_dict.TestNamedTuple) ... skip: broken while these tests were not in CI (0.001s)
2023-01-11T21:25:29.7445012Z   test_namedtuple_slice_unpack (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:29.7445165Z   test_namedtuple_type_annotation (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:29.7445313Z   test_namedtuple_wrong_types (jit.test_list_dict.TestNamedTuple) ... ok (0.002s)
2023-01-11T21:25:29.7445442Z   test_return_named_tuple (jit.test_list_dict.TestNamedTuple) ... ok (0.003s)
2023-01-11T21:25:29.7445849Z   test_adaptive_avg_pool2d (jit.test_backend_nnapi.TestNnapiBackend) ... /var/lib/jenkins/workspace/test/test_nnapi.py:14: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:25:29.7445927Z   t = torch.tensor(t)
2023-01-11T21:25:29.7445992Z ok (0.308s)
2023-01-11T21:25:29.7446140Z   test_avg_pool2d (jit.test_backend_nnapi.TestNnapiBackend) ... ok (1.032s)
2023-01-11T21:25:29.7446275Z   test_cat (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.048s)
2023-01-11T21:25:29.7446428Z   test_compile_spec_santiy (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.009s)
2023-01-11T21:25:29.7446565Z   test_conv2d (jit.test_backend_nnapi.TestNnapiBackend) ... ok (3.345s)
2023-01-11T21:25:29.7446702Z   test_conv2d_transpose (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.637s)
2023-01-11T21:25:29.7446843Z   test_dequantize (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.017s)
2023-01-11T21:25:29.7446979Z   test_detach (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.026s)
2023-01-11T21:25:29.7447117Z   test_flatten (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.095s)
2023-01-11T21:25:29.7447259Z   test_hardtanh (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.024s)
2023-01-11T21:25:29.7447397Z   test_linear (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.023s)
2023-01-11T21:25:29.7448008Z   test_log_softmax (jit.test_backend_nnapi.TestNnapiBackend) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1482: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
2023-01-11T21:25:29.7448102Z   return forward_call(*args, **kwargs)
2023-01-11T21:25:29.7448487Z /opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py:460: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
2023-01-11T21:25:29.7448584Z   outs = wrap_retval(mod(*_clone_inputs(inputs)))
2023-01-11T21:25:29.7448649Z ok (0.020s)
2023-01-11T21:25:29.7448791Z   test_max_pool2d (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.494s)
2023-01-11T21:25:29.7448932Z   test_mean (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.063s)
2023-01-11T21:25:29.7449267Z   test_multi_output (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.012s)
2023-01-11T21:25:29.7449475Z   test_pointwise_binary (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.094s)
2023-01-11T21:25:29.7449632Z   test_pointwise_binary_const (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.042s)
2023-01-11T21:25:29.7449781Z   test_pointwise_unary (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.042s)
2023-01-11T21:25:29.7449907Z   test_prelu (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.031s)
2023-01-11T21:25:29.7450042Z   test_qadd (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.142s)
2023-01-11T21:25:29.7450181Z   test_qlinear (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.114s)
2023-01-11T21:25:29.7450319Z   test_quantize (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.012s)
2023-01-11T21:25:29.7450457Z   test_reshape (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.027s)
2023-01-11T21:25:29.7450634Z   test_seblock_mul (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.011s)
2023-01-11T21:25:29.7450772Z   test_slice (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.042s)
2023-01-11T21:25:29.7451266Z   test_softmax (jit.test_backend_nnapi.TestNnapiBackend) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1482: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
2023-01-11T21:25:29.7451358Z   return forward_call(*args, **kwargs)
2023-01-11T21:25:29.7451723Z /opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py:460: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
2023-01-11T21:25:29.7451832Z   outs = wrap_retval(mod(*_clone_inputs(inputs)))
2023-01-11T21:25:29.7451897Z ok (0.033s)
2023-01-11T21:25:29.7452041Z   test_tensor_input (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.018s)
2023-01-11T21:25:29.7452176Z   test_to (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.031s)
2023-01-11T21:25:29.7452320Z   test_unsqueeze (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.054s)
2023-01-11T21:25:29.7452477Z   test_upsample_nearest2d (jit.test_backend_nnapi.TestNnapiBackend) ... ok (0.373s)
2023-01-11T21:25:29.7452656Z   test_op_decomposition (jit.test_op_decompositions.TestOpDecompositions) ... ok (0.072s)
2023-01-11T21:25:29.7452833Z   test_registered_decomposition (jit.test_op_decompositions.TestOpDecompositions) ... ok (0.006s)
2023-01-11T21:25:29.7453092Z   test_fuse_activation_with_pack_ops_linear_conv2d_1 (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.028s)
2023-01-11T21:25:29.7453348Z   test_fuse_activation_with_pack_ops_linear_conv2d_2 (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.026s)
2023-01-11T21:25:29.7453605Z   test_fuse_activation_with_pack_ops_linear_conv2d_3 (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.025s)
2023-01-11T21:25:29.7453858Z   test_fuse_activation_with_pack_ops_linear_conv2d_4 (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.025s)
2023-01-11T21:25:29.7454124Z   test_insert_pre_packed_linear_before_inline_and_conv_2d_op (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.009s)
2023-01-11T21:25:29.7454366Z   test_insert_pre_packed_linear_op (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.010s)
2023-01-11T21:25:29.7454604Z   test_replace_conv1d_with_conv2d (jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo) ... ok (0.006s)
2023-01-11T21:25:29.7454710Z   test_any (jit.test_pdt.TestPDT) ... ok (0.012s)
2023-01-11T21:25:29.7454843Z   test_class_as_profiled_types (jit.test_pdt.TestPDT) ... ok (0.017s)
2023-01-11T21:25:29.7454952Z   test_class_methods (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:29.7455093Z   test_class_with_args_as_profiled_types (jit.test_pdt.TestPDT) ... ok (0.014s)
2023-01-11T21:25:29.7455263Z   test_class_with_multiple_methods (jit.test_pdt.TestPDT) ... ok (0.009s)
2023-01-11T21:25:29.7455391Z   test_fx_tracing_with_typing (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:29.7455527Z   test_multiple_class_with_same_method (jit.test_pdt.TestPDT) ... ok (0.014s)
2023-01-11T21:25:29.7455660Z   test_nested_function_in_forward (jit.test_pdt.TestPDT) ... ok (0.008s)
2023-01-11T21:25:29.7455785Z   test_nested_list_and_tuple (jit.test_pdt.TestPDT) ... ok (0.015s)
2023-01-11T21:25:29.7455910Z   test_nested_nn_module_class (jit.test_pdt.TestPDT) ... ok (0.010s)
2023-01-11T21:25:29.7456035Z   test_nested_nn_module_class_with_args (jit.test_pdt.TestPDT) ... ok (0.011s)
2023-01-11T21:25:29.7456147Z   test_nn_module (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:29.7456286Z   test_nn_module_with_export_function (jit.test_pdt.TestPDT) ... ok (0.009s)
2023-01-11T21:25:29.7456436Z   test_nn_parameter_as_arg (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:29.7456570Z   test_nonetype_as_optional_of_type (jit.test_pdt.TestPDT) ... ok (0.007s)
2023-01-11T21:25:29.7456675Z   test_pdt (jit.test_pdt.TestPDT) ... ok (0.017s)
2023-01-11T21:25:29.7456783Z   test_pdt_dict (jit.test_pdt.TestPDT) ... ok (0.006s)
2023-01-11T21:25:29.7456893Z   test_pdt_list_and_tuple (jit.test_pdt.TestPDT) ... ok (0.011s)
2023-01-11T21:25:29.7457057Z   test_scriptable (jit.test_parametrization.TestParametrization) ... ok (0.045s)
2023-01-11T21:25:29.7457208Z   test_traceable (jit.test_parametrization.TestParametrization)
2023-01-11T21:25:29.7457348Z Test the jit scripting and tracing of a parametrized model. ... ok (0.033s)
2023-01-11T21:25:29.7457488Z   test_conv_dim_folding (jit.test_peephole.TestPeephole) ... ok (0.060s)
2023-01-11T21:25:29.7457632Z   test_integer_refinement (jit.test_peephole.TestPeephole) ... ok (0.016s)
2023-01-11T21:25:29.7457772Z   test_noop_peephole (jit.test_peephole.TestPeephole) ... ok (0.015s)
2023-01-11T21:25:29.7457909Z   test_normalized_is_op (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:29.7458041Z   test_normalized_isnot_op (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:29.7458180Z   test_normalized_rsub (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:29.7458339Z   test_optimize_out_comparison_same_value (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:29.7458474Z   test_peephole (jit.test_peephole.TestPeephole) ... ok (0.008s)
2023-01-11T21:25:29.7458613Z   test_peephole_add_zero (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7458749Z   test_peephole_arith (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:29.7458917Z   test_peephole_cuda (jit.test_peephole.TestPeephole) ... skip: cpp tests require CUDA (0.001s)
2023-01-11T21:25:29.7459104Z   test_peephole_dict_getitem_no_optimization_dict_modified (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:29.7459275Z   test_peephole_dict_getitem_no_optimization_get_input_arg (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:29.7459463Z   test_peephole_dict_getitem_no_optimization_keys_might_overlap (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7459642Z   test_peephole_dict_getitem_no_optimization_missing_key (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7459826Z   test_peephole_dict_getitem_no_optimization_overlapping_keys (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7460007Z   test_peephole_dict_getitem_no_optimization_unsupported_type (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:29.7460161Z   test_peephole_dict_getitem_simple (jit.test_peephole.TestPeephole) ... ok (0.008s)
2023-01-11T21:25:29.7460297Z   test_peephole_dict_len (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7460480Z   test_peephole_dict_len_no_optimization_keys_might_overlap (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7460662Z   test_peephole_dict_len_no_optimization_overlapping_keys (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7460864Z   test_peephole_dict_len_no_optimization_unsupported_type (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:29.7461004Z   test_peephole_dynamic (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7461138Z   test_peephole_int (jit.test_peephole.TestPeephole) ... ok (0.002s)
2023-01-11T21:25:29.7461272Z   test_peephole_len_list (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:29.7461408Z   test_peephole_list_len (jit.test_peephole.TestPeephole) ... ok (0.038s)
2023-01-11T21:25:29.7461546Z   test_peephole_list_ops (jit.test_peephole.TestPeephole) ... ok (0.012s)
2023-01-11T21:25:29.7461696Z   test_peephole_no_output_aliasing (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:29.7461873Z   test_peephole_optional_refine (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:29.7462016Z   test_peephole_slice_all_three_args (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:29.7462165Z   test_peephole_slice_one_empty_arg (jit.test_peephole.TestPeephole) ... ok (0.010s)
2023-01-11T21:25:29.7462350Z   test_peephole_slice_optimization_not_applied_list_modified (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:29.7462532Z   test_peephole_slice_optimization_not_applied_non_const_args (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:29.7462682Z   test_peephole_slice_two_empty_args (jit.test_peephole.TestPeephole) ... ok (0.011s)
2023-01-11T21:25:29.7462833Z   test_peephole_type_refinements (jit.test_peephole.TestPeephole) ... ok (0.010s)
2023-01-11T21:25:29.7462988Z   test_peephole_with_non_output_writes (jit.test_peephole.TestPeephole) ... ok (0.006s)
2023-01-11T21:25:29.7463128Z   test_peephole_with_writes (jit.test_peephole.TestPeephole) ... ok (0.004s)
2023-01-11T21:25:29.7463271Z   test_refine_integer_values (jit.test_peephole.TestPeephole) ... ok (0.003s)
2023-01-11T21:25:29.7463485Z   test_short_circuit_optimization (jit.test_peephole.TestPeephole) ... ok (0.005s)
2023-01-11T21:25:29.7463615Z   test_version (__main__.TestProducerVersion) ... ok (0.000s)
2023-01-11T21:25:29.7463752Z   test_aliasing_merge (jit.test_profiler.TestProfiler) ... ok (0.044s)
2023-01-11T21:25:29.7463898Z   test_autograd_fallback_graph (jit.test_profiler.TestProfiler) ... ok (0.038s)
2023-01-11T21:25:29.7464054Z   test_fallback_graph_not_specialized (jit.test_profiler.TestProfiler) ... ok (0.023s)
2023-01-11T21:25:29.7464188Z   test_iterative_fusion (jit.test_profiler.TestProfiler) ... ok (0.043s)
2023-01-11T21:25:29.7464327Z   test_local_fusion_strategy (jit.test_profiler.TestProfiler) ... ok (0.025s)
2023-01-11T21:25:29.7464469Z   test_not_fusing_scalar_ops (jit.test_profiler.TestProfiler) ... ok (0.001s)
2023-01-11T21:25:29.7464603Z   test_not_optimizing_property (jit.test_profiler.TestProfiler) ... ok (0.024s)
2023-01-11T21:25:29.7464751Z   test_specialize_backward (jit.test_profiler.TestProfiler) ... ok (0.092s)
2023-01-11T21:25:29.7464891Z   test_specialized_types (jit.test_profiler.TestProfiler) ... ok (0.022s)
2023-01-11T21:25:29.7465027Z   test_tensor_constant (jit.test_profiler.TestProfiler) ... ok (0.023s)
2023-01-11T21:25:29.7465181Z   test_tensor_type_not_determined_by_inputs (jit.test_profiler.TestProfiler) ... ok (0.051s)
2023-01-11T21:25:29.7465318Z   test_use_not_profiled (jit.test_profiler.TestProfiler) ... ok (0.025s)
2023-01-11T21:25:29.7465469Z   test_add_input (jit.test_python_bindings.TestPythonBindings) ... ok (0.002s)
2023-01-11T21:25:29.7465612Z   test_aliasdb (jit.test_python_bindings.TestPythonBindings) ... ok (0.003s)
2023-01-11T21:25:29.7465757Z   test_canonicalize (jit.test_python_bindings.TestPythonBindings) ... ok (0.001s)
2023-01-11T21:25:29.7465916Z   test_cu_create_function (jit.test_python_bindings.TestPythonBindings) ... ok (0.004s)
2023-01-11T21:25:29.7466073Z   test_cu_get_functions (jit.test_python_bindings.TestPythonBindings) ... ok (0.004s)
2023-01-11T21:25:29.7466227Z   test_graph_create (jit.test_python_bindings.TestPythonBindings) ... ok (0.002s)
2023-01-11T21:25:29.7466424Z   test_graph_iterator_keepalive (jit.test_python_bindings.TestPythonBindings) ... ok (0.003s)
2023-01-11T21:25:29.7466576Z   test_invalidation (jit.test_python_bindings.TestPythonBindings) ... ok (0.003s)
2023-01-11T21:25:29.7466717Z   test_add (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.009s)
2023-01-11T21:25:29.7466874Z   test_adv_indexing_list (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.021s)
2023-01-11T21:25:29.7467023Z   test_advancedindex (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.022s)
2023-01-11T21:25:29.7467174Z   test_gather (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.004s)
2023-01-11T21:25:29.7467316Z   test_index (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.026s)
2023-01-11T21:25:29.7467506Z   test_index_ellipses (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.090s)
2023-01-11T21:25:29.7467648Z   test_inf (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.005s)
2023-01-11T21:25:29.7467802Z   test_matmul_py3 (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.008s)
2023-01-11T21:25:29.7467942Z   test_mul (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.006s)
2023-01-11T21:25:29.7468081Z   test_pow (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.033s)
2023-01-11T21:25:29.7468218Z   test_random (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.003s)
2023-01-11T21:25:29.7468363Z   test_slice (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.014s)
2023-01-11T21:25:29.7468528Z   test_stepped_tuple_slicing (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.005s)
2023-01-11T21:25:29.7468682Z   test_str_to_float (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.006s)
2023-01-11T21:25:29.7468826Z   test_triple (jit.test_python_builtins.TestPythonBuiltinOP) ... ok (0.005s)
2023-01-11T21:25:29.7468961Z   test_param_strides (jit.test_python_ir.TestPythonIr) ... ok (0.004s)
2023-01-11T21:25:29.7469112Z   test_attributes (jit.test_recursive_script.TestRecursiveScript) ... ok (0.044s)
2023-01-11T21:25:29.7469268Z   test_class_compile (jit.test_recursive_script.TestRecursiveScript) ... ok (0.018s)
2023-01-11T21:25:29.7469419Z   test_constants_with_final (jit.test_recursive_script.TestRecursiveScript) ... ok (0.018s)
2023-01-11T21:25:29.7469558Z   test_dir (jit.test_recursive_script.TestRecursiveScript) ... ok (0.043s)
2023-01-11T21:25:29.7469708Z   test_error_stack (jit.test_recursive_script.TestRecursiveScript) ... ok (0.004s)
2023-01-11T21:25:29.7469872Z   test_error_stack_annotation (jit.test_recursive_script.TestRecursiveScript) ... ok (0.008s)
2023-01-11T21:25:29.7470031Z   test_error_stack_class (jit.test_recursive_script.TestRecursiveScript) ... ok (0.008s)
2023-01-11T21:25:29.7470191Z   test_error_stack_module (jit.test_recursive_script.TestRecursiveScript) ... ok (0.006s)
2023-01-11T21:25:29.7470363Z   test_failed_function_compilation (jit.test_recursive_script.TestRecursiveScript) ... ok (0.003s)
2023-01-11T21:25:29.7470539Z   test_function_attribute_in_submodule (jit.test_recursive_script.TestRecursiveScript) ... ok (0.018s)
2023-01-11T21:25:29.7470696Z   test_ignore_class (jit.test_recursive_script.TestRecursiveScript) ... ok (0.008s)
2023-01-11T21:25:29.7470846Z   test_inferred_nonetype (jit.test_recursive_script.TestRecursiveScript) ... ok (0.007s)
2023-01-11T21:25:29.7470996Z   test_init_error (jit.test_recursive_script.TestRecursiveScript) ... ok (0.001s)
2023-01-11T21:25:29.7471155Z   test_inner_traced_module (jit.test_recursive_script.TestRecursiveScript) ... ok (0.017s)
2023-01-11T21:25:29.7471315Z   test_iterable_modules (jit.test_recursive_script.TestRecursiveScript) ... ok (0.046s)
2023-01-11T21:25:29.7471468Z   test_method_call (jit.test_recursive_script.TestRecursiveScript) ... ok (0.009s)
2023-01-11T21:25:29.7471623Z   test_module_basic (jit.test_recursive_script.TestRecursiveScript) ... ok (0.013s)
2023-01-11T21:25:29.7471789Z   test_module_function_export (jit.test_recursive_script.TestRecursiveScript) ... ok (0.015s)
2023-01-11T21:25:29.7471977Z   test_module_name (jit.test_recursive_script.TestRecursiveScript) ... ok (0.004s)
2023-01-11T21:25:29.7472116Z   test_module_repr (jit.test_recursive_script.TestRecursiveScript) ... ok (0.019s)
2023-01-11T21:25:29.7472274Z   test_optional_module (jit.test_recursive_script.TestRecursiveScript) ... ok (0.018s)
2023-01-11T21:25:29.7472449Z   test_override_instance_method_ignore (jit.test_recursive_script.TestRecursiveScript) ... ok (0.003s)
2023-01-11T21:25:29.7472616Z   test_prepare_scriptable_basic (jit.test_recursive_script.TestRecursiveScript) ... ok (0.005s)
2023-01-11T21:25:29.7472783Z   test_prepare_scriptable_cycle (jit.test_recursive_script.TestRecursiveScript) ... ok (0.003s)
2023-01-11T21:25:29.7472965Z   test_prepare_scriptable_iterable_modules (jit.test_recursive_script.TestRecursiveScript) ... ok (0.048s)
2023-01-11T21:25:29.7473163Z   test_python_function_attribute (jit.test_recursive_script.TestRecursiveScript) ... ok (0.006s)
2023-01-11T21:25:29.7473327Z   test_repeated_error_stack (jit.test_recursive_script.TestRecursiveScript) ... ok (0.004s)
2023-01-11T21:25:29.7473489Z   test_script_after_eval (jit.test_recursive_script.TestRecursiveScript) ... ok (0.008s)
2023-01-11T21:25:29.7473633Z   test_script_basic (jit.test_recursive_script.TestRecursiveScript) ... ok (0.005s)
2023-01-11T21:25:29.7473798Z   test_script_function_attribute (jit.test_recursive_script.TestRecursiveScript) ... ok (0.015s)
2023-01-11T21:25:29.7473950Z   test_script_loaded_module (jit.test_recursive_script.TestRecursiveScript)
2023-01-11T21:25:29.7474084Z Test that we can hold a loaded ScriptModule as a submodule. ... ok (0.012s)
2023-01-11T21:25:29.7474232Z   test_aten_inplace (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.016s)
2023-01-11T21:25:29.7474396Z   test_common_pytorch_list_ops (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.027s)
2023-01-11T21:25:29.7474543Z   test_if_output (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.003s)
2023-01-11T21:25:29.7474695Z   test_if_output_fail (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.006s)
2023-01-11T21:25:29.7474848Z   test_list_indexing_removal (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.014s)
2023-01-11T21:25:29.7474998Z   test_lists_append (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.005s)
2023-01-11T21:25:29.7475144Z   test_lists_insert (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.003s)
2023-01-11T21:25:29.7475305Z   test_special_mapped_op (jit.test_remove_mutation.TestRemoveMutation) ... ok (0.009s)
2023-01-11T21:25:29.7475433Z   test_different_functions (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:29.7475567Z Exercise the situation where we have the same qualified name ... ok (0.024s)
2023-01-11T21:25:29.7475694Z   test_different_interfaces (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:29.7475831Z Exercise the situation where we have the same qualified name ... ok (0.046s)
2023-01-11T21:25:29.7475945Z   test_different_modules (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:29.7476083Z Exercise the situation where we have the same qualified name ... ok (0.033s)
2023-01-11T21:25:29.7476216Z   test_many_collisions (jit.test_save_load.TestSaveLoad) ... ok (0.181s)
2023-01-11T21:25:29.7476338Z   test_save_load_meta_tensors (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:29.7476492Z Check that parameters, buffers, and submodules are the same after loading ... ok (0.019s)
2023-01-11T21:25:29.7476633Z   test_save_load_params_buffers_submodules (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:29.7476787Z Check that parameters, buffers, and submodules are the same after loading. ... ok (0.007s)
2023-01-11T21:25:29.7476930Z   test_save_load_using_pathlib (jit.test_save_load.TestSaveLoad) ... ok (0.005s)
2023-01-11T21:25:29.7477062Z   test_save_load_with_extra_files (jit.test_save_load.TestSaveLoad) ... ok (0.012s)
2023-01-11T21:25:29.7477193Z   test_save_namedtuple_input_only (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:29.7477336Z Even if a NamedTuple is only used as an input argument, saving and ... ok (0.007s)
2023-01-11T21:25:29.7477515Z   test_save_namedtuple_output_only (jit.test_save_load.TestSaveLoad)
2023-01-11T21:25:29.7477660Z Even if a NamedTuple is only used as an output argument, saving and ... ok (0.006s)
2023-01-11T21:25:29.7477790Z   test_save_nonexit_file (jit.test_save_load.TestSaveLoad) ... ok (0.007s)
2023-01-11T21:25:29.7477938Z   test_different_functions (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:29.7478133Z Exercise the situation where we have the same qualified name ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:29.7478270Z   test_different_interfaces (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:29.7478466Z Exercise the situation where we have the same qualified name ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:29.7478638Z   test_different_modules (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:29.7478833Z Exercise the situation where we have the same qualified name ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:29.7479043Z   test_many_collisions (jit.test_save_load.TestSaveLoadFlatbuffer) ... skip: Need to enable flatbuffer to run the below tests (0.002s)
2023-01-11T21:25:29.7479259Z   test_module_info_flatbuffer (jit.test_save_load.TestSaveLoadFlatbuffer) ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:29.7479423Z   test_save_load_params_buffers_submodules (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:29.7479633Z Check that parameters, buffers, and submodules are the same after loading. ... skip: Need to enable flatbuffer to run the below tests (0.001s)
2023-01-11T21:25:29.7479854Z   test_save_load_using_pathlib (jit.test_save_load.TestSaveLoadFlatbuffer) ... skip: Need to enable flatbuffer to run the below tests (0.000s)
2023-01-11T21:25:29.7480005Z   test_save_load_with_extra_files (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:29.7480203Z Check that parameters, buffers, and submodules are the same after loading. ... skip: Need to enable flatbuffer to run the below tests (0.000s)
2023-01-11T21:25:29.7480357Z   test_save_namedtuple_input_only (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:29.7480554Z Even if a NamedTuple is only used as an input argument, saving and ... skip: Need to enable flatbuffer to run the below tests (0.000s)
2023-01-11T21:25:29.7480706Z   test_save_namedtuple_output_only (jit.test_save_load.TestSaveLoadFlatbuffer)
2023-01-11T21:25:29.7480899Z Even if a NamedTuple is only used as an output argument, saving and ... skip: Need to enable flatbuffer to run the below tests (0.000s)
2023-01-11T21:25:29.7481138Z   test_versioned_div_scalar (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... Falsifying explicit example: test_versioned_div_scalar(
2023-01-11T21:25:29.7481331Z     self=<jit.test_save_load_for_op_version.TestSaveLoadForOpVersion testMethod=test_versioned_div_scalar>,
2023-01-11T21:25:29.7481418Z     sample_input=(2, 3, 2.0, 3.0),
2023-01-11T21:25:29.7481477Z )
2023-01-11T21:25:29.7481560Z skip: Failed to load fixture! (0.123s)
2023-01-11T21:25:29.7481752Z   test_versioned_div_scalar_inplace (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.417s)
2023-01-11T21:25:29.7481946Z   test_versioned_div_scalar_reciprocal (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.101s)
2023-01-11T21:25:29.7482132Z   test_versioned_div_scalar_scalar (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.006s)
2023-01-11T21:25:29.7482312Z   test_versioned_div_tensor (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.084s)
2023-01-11T21:25:29.7482503Z   test_versioned_div_tensor_inplace (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.530s)
2023-01-11T21:25:29.7482692Z   test_versioned_div_tensor_out (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (1.154s)
2023-01-11T21:25:29.7482905Z   test_versioned_linspace (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.015s)
2023-01-11T21:25:29.7483083Z   test_versioned_linspace_out (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.012s)
2023-01-11T21:25:29.7483268Z   test_versioned_logspace (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.014s)
2023-01-11T21:25:29.7483453Z   test_versioned_logspace_out (jit.test_save_load_for_op_version.TestSaveLoadForOpVersion) ... ok (0.010s)
2023-01-11T21:25:29.7483561Z   test_add_out (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7483687Z   test_add_tuple_different_types (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7483809Z   test_add_tuple_non_optional (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7483932Z   test_add_tuple_optional (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7484079Z   test_add_tuple_same_types (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7484163Z   test_addmm_grad (__main__.TestScript)
2023-01-11T21:25:29.7484268Z This test checks several things: ... ok (0.005s)
2023-01-11T21:25:29.7484403Z   test_alias_covariant_type_containers (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7484500Z   test_all (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7484614Z   test_annot_ast_mypy_fn (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:29.7484730Z   test_annot_ast_mypy_method (__main__.TestScript) ... ok (0.034s)
2023-01-11T21:25:29.7484845Z   test_annot_ast_py3_fn (__main__.TestScript) ... ok (0.022s)
2023-01-11T21:25:29.7484960Z   test_annot_ast_py3_method (__main__.TestScript) ... ok (0.035s)
2023-01-11T21:25:29.7485067Z   test_annot_string_mypy_fn (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7485187Z   test_annot_string_mypy_method (__main__.TestScript) ... ok (0.024s)
2023-01-11T21:25:29.7485304Z   test_annot_string_py3_fn (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7485428Z   test_annot_string_py3_method (__main__.TestScript) ... ok (0.024s)
2023-01-11T21:25:29.7485550Z   test_annotated_script_fn (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7485686Z   test_annotated_script_fn_arg_mismatch (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7485826Z   test_annotated_script_fn_return_mismatch (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7485952Z   test_annotated_script_method (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7486058Z   test_annoying_doubles (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7486159Z   test_any (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:29.7486295Z   test_assert_is_scripting_metacompile (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7486424Z   test_assertion_optional_refinement (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7486545Z   test_attr_module_constants (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:29.7486662Z   test_attr_qscheme_script (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7486779Z   test_attribute_in_init (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7486896Z   test_attribute_serialization (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7487016Z   test_attribute_unpickling (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7487129Z   test_augmented_assign (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7487259Z   test_autodiff_complex (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7487382Z   test_backend_cudnn_enabled (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7487510Z   test_bad_multiline_annotations (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7487652Z   test_bailout_loop_carried_deps_name_clash (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7487787Z   test_bailout_loop_counter_transition (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7487963Z   test_batch_norm_inference_backward_cuda (__main__.TestScript) ... skip: running tests on cuda to verify cudnn fix (0.001s)
2023-01-11T21:25:29.7488116Z   test_batchnorm_fuser_cpu (__main__.TestScript) ... ok (0.320s)
2023-01-11T21:25:29.7488235Z   test_big_float_literals (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7488351Z   test_big_int_literals (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7488467Z   test_binary_op_shape (__main__.TestScript) ... ok (0.214s)
2023-01-11T21:25:29.7488579Z   test_bitwise_ops (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:29.7488705Z   test_block_input_grad_in_loop (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7488830Z   test_bool_augassign_bitwise_and (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7488945Z   test_bool_augassign_bitwise_or (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7489191Z   test_bool_augassign_bitwise_xor (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7489352Z   test_bool_dispatch (__main__.TestScript) ... ok (0.028s)
2023-01-11T21:25:29.7489492Z   test_boolean_literal_constant_metacompile (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:29.7489613Z   test_break_continue_error (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7489728Z   test_breaks_continues (__main__.TestScript) ... ok (0.112s)
2023-01-11T21:25:29.7489836Z   test_builtin_args (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7489992Z   test_builtin_args_fails (__main__.TestScript) ... You have not run this instance of FileCheck!
2023-01-11T21:25:29.7490054Z FileCheck checks:
2023-01-11T21:25:29.7490397Z [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[List[int], int]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:29.7490520Z   File "/var/lib/jenkins/workspace/test/test_jit.py", line 10812
2023-01-11T21:25:29.7490602Z             @torch.jit.script
2023-01-11T21:25:29.7490672Z             def f6(a):
2023-01-11T21:25:29.7490754Z                 a.expand(size=[3, [4]])
2023-01-11T21:25:29.7490912Z                               ~~~~~~ <--- HERE
2023-01-11T21:25:29.7491000Z  (function emitListLiteral)
2023-01-11T21:25:29.7491055Z ok (0.006s)
2023-01-11T21:25:29.7491182Z   test_builtin_function_attributes (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7491297Z   test_builtin_use_as_value (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7491402Z   test_call_ge (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7491529Z   test_call_python_fn_from_script_fn (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7491661Z   test_call_python_fn_from_script_module (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7491793Z   test_call_python_fn_from_traced_module (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7491915Z   test_call_python_fn_from_tracing_fn (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7492042Z   test_call_python_mod_from_script_fn (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7492180Z   test_call_python_mod_from_script_module (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7492311Z   test_call_python_mod_from_traced_module (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:29.7492444Z   test_call_python_mod_from_tracing_fn (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7492573Z   test_call_script_fn_from_script_fn (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7492708Z   test_call_script_fn_from_script_module (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7492836Z   test_call_script_fn_from_tracing_fn (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7492954Z   test_call_script_mod_from_script_fn (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7493085Z   test_call_script_mod_from_script_module (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7493249Z   test_call_script_mod_from_tracing_fn (__main__.TestScript) ... skip: error in first class mode (0.001s)
2023-01-11T21:25:29.7493376Z   test_call_traced_fn_from_tracing_fn (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7493575Z   test_call_traced_mod_from_tracing_fn (__main__.TestScript) ... skip: error in first class mode (0.001s)
2023-01-11T21:25:29.7493851Z   test_calls_in_type_annotations (__main__.TestScript) ... /opt/conda/lib/python3.10/site-packages/torch/__init__.py
2023-01-11T21:25:29.7493915Z ok (0.001s)
2023-01-11T21:25:29.7494047Z   test_canonicalize_control_outputs (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7494234Z   test_cast (__main__.TestScript) ... skip: RuntimeError: VariableType::ID() not implemented (0.000s)
2023-01-11T21:25:29.7494325Z   test_cat (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:29.7494431Z   test_cat_lifts (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7494526Z   test_chr (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7494663Z   test_circular_dependency (__main__.TestScript)
2023-01-11T21:25:29.7494805Z https://github.com/pytorch/pytorch/issues/25871 ... ok (0.028s)
2023-01-11T21:25:29.7494925Z   test_class_as_attribute (__main__.TestScript) ... ok (0.307s)
2023-01-11T21:25:29.7495037Z   test_class_attribute (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7495153Z   test_class_attribute_in_script (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7495295Z   test_class_with_comment_at_lower_indentation (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7495401Z   test_code_with_constants (__main__.TestScript)
2023-01-11T21:25:29.7495568Z Check that the `code_with_constants` property correctly returns graph CONSTANTS in the ... ok (0.006s)
2023-01-11T21:25:29.7495686Z   test_code_with_constants_restore (__main__.TestScript)
2023-01-11T21:25:29.7495861Z Check that the `code_with_constants` property correctly works on restoration after save() + load() ... ok (0.008s)
2023-01-11T21:25:29.7495986Z   test_comment_ignore_indent (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7496108Z   test_compare_two_bool_inputs (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7496297Z   test_compile_module_with_constant (__main__.TestScript) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:29.7496418Z   test_conditional_casting (__main__.TestScript) ... ok (0.042s)
2023-01-11T21:25:29.7496533Z   test_constant_as_attr (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7496671Z   test_constant_pooling_introduce_aliasing (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7496794Z   test_constant_pooling_none (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7496927Z   test_constant_pooling_same_identity (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7497044Z   test_context_manager (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:29.7497153Z   test_conv_error (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7497256Z   test_convert_base (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:29.7497382Z   test_cpp_function_tensor_str (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7497504Z   test_cpp_module_iterator (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7497618Z   test_desugar_module (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7497730Z   test_device_kwarg (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7497844Z   test_device_type (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7497983Z   test_device_type_cuda (__main__.TestScript) ... skip: Requires CUDA (0.000s)
2023-01-11T21:25:29.7498084Z   test_dir (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7498177Z   test_divmod (__main__.TestScript) ... ok (0.028s)
2023-01-11T21:25:29.7498338Z   test_dominated_bailout (__main__.TestScript) ... skip: bailouts are being deprecated (0.001s)
2023-01-11T21:25:29.7498451Z   test_dropout_eval (__main__.TestScript) ... ok (0.225s)
2023-01-11T21:25:29.7498563Z   test_dtype_attr (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7498679Z   test_dtype_op_shape (__main__.TestScript) ... ok (0.042s)
2023-01-11T21:25:29.7498829Z   test_dtype_op_shape2 (__main__.TestScript) ... ok (0.048s)
2023-01-11T21:25:29.7498949Z   test_early_return_closure (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7499059Z   test_early_return_fork_join (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7499179Z   test_early_return_rewrite (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:29.7499311Z   test_early_return_type_refinement (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7499431Z   test_early_returns_loops (__main__.TestScript) ... ok (0.034s)
2023-01-11T21:25:29.7499549Z   test_ellipsis_const_end (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7499668Z   test_ellipsis_const_mid (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7499797Z   test_ellipsis_const_mid_select (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7499944Z   test_ellipsis_const_start (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7500046Z   test_ellipsis_end (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7500158Z   test_ellipsis_mid (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7500279Z   test_ellipsis_mid_select (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7500394Z   test_ellipsis_start (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7500525Z   test_embedding_renorm_grad_error (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:29.7500652Z   test_empty_like_memory_format_bc (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7500768Z   test_empty_tuple_str (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7500894Z   test_enumerate_modlist_range (__main__.TestScript) ... ok (0.031s)
2023-01-11T21:25:29.7501000Z   test_erase_number_types (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7501102Z   test_error (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:29.7501222Z   test_error_stacktrace (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:29.7501351Z   test_error_stacktrace_interface (__main__.TestScript) ... ok (0.729s)
2023-01-11T21:25:29.7501464Z   test_eval_python (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7501588Z   test_exception_exits_closure (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7501721Z   test_exceptions_with_control_flow (__main__.TestScript) ... ok (0.033s)
2023-01-11T21:25:29.7501815Z   test_expand (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7501915Z   test_fibb (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7502033Z   test_fibb_totally_better (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7502162Z   test_file_format_serialization (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7502276Z   test_file_line_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7502402Z   test_file_line_error_class_defn (__main__.TestScript) ... ok (0.682s)
2023-01-11T21:25:29.7502520Z   test_file_line_graph (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7502636Z   test_file_line_save_load (__main__.TestScript) ... ok (0.265s)
2023-01-11T21:25:29.7502741Z   test_file_line_string (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7502853Z   test_file_line_trace (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7502962Z   test_filecheck (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7503078Z   test_filecheck_parse (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7503198Z   test_first_class_calls (__main__.TestScript) ... ok (0.394s)
2023-01-11T21:25:29.7503315Z   test_first_class_module (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7503518Z   test_floor_div (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7503616Z   test_floordiv (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:29.7503720Z   test_for_else (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7503827Z   test_for_in_dict (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7503946Z   test_for_in_enumerate (__main__.TestScript) ... ok (0.030s)
2023-01-11T21:25:29.7504056Z   test_for_in_range (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7504204Z   test_for_in_range_ast (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:29.7504322Z   test_for_in_range_dynamic (__main__.TestScript) ... ok (0.018s)
2023-01-11T21:25:29.7504443Z   test_for_in_range_if_ast (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7504552Z   test_for_in_range_start_end (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7504681Z   test_for_in_range_start_end_step (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:29.7504804Z   test_for_in_range_zero_step (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7504917Z   test_for_in_string (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:29.7505032Z   test_for_in_tensors (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7505191Z   test_for_in_tensors_fail_scalar (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7505316Z   test_for_in_tensors_nested (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7505434Z   test_for_in_tensors_rank0 (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7505533Z   test_for_in_zip (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:29.7505653Z   test_for_in_zip_enumerate (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:29.7505770Z   test_for_tuple_assign (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7505885Z   test_for_tuple_unpack (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:29.7505988Z   test_format (__main__.TestScript) ... ok (0.033s)
2023-01-11T21:25:29.7506094Z   test_func_call (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:29.7506227Z   test_function_compilation_caching (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7506342Z   test_function_overload_misuse (__main__.TestScript) ... ok (0.762s)
2023-01-11T21:25:29.7506481Z   test_function_overloading_isinstance (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7506603Z   test_function_overloads (__main__.TestScript) ... ok (0.048s)
2023-01-11T21:25:29.7506800Z   test_fuser_double_float_codegen (__main__.TestScript) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:25:29.7506938Z   test_fuser_double_literal_precision (__main__.TestScript) ... ok (0.237s)
2023-01-11T21:25:29.7507061Z   test_fuser_multiple_blocks (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7507181Z   test_gather_dynamic_index (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7507301Z   test_generic_list_errors (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7507402Z   test_get_set_state (__main__.TestScript) ... ok (0.028s)
2023-01-11T21:25:29.7507529Z   test_get_set_state_with_tensors (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7507647Z   test_grad_from_script (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7507749Z   test_hash (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7507862Z   test_hex_literals (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7507962Z   test_id (__main__.TestScript) ... ok (0.359s)
2023-01-11T21:25:29.7508064Z   test_if (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7508173Z   test_if_define (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7508280Z   test_if_different_type (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7508395Z   test_if_for_in_range (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7508516Z   test_if_is_none_dispatch (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:29.7508626Z   test_if_list_cat (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7508738Z   test_if_nest_while (__main__.TestScript) ... ok (0.054s)
2023-01-11T21:25:29.7508844Z   test_if_noelse (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7508962Z   test_if_not_defined_error (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7509066Z   test_if_supertype (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7509184Z   test_ignore_decorator (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7509301Z   test_ignored_as_value (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7509458Z   test_ignored_method_binding (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7509572Z   test_ignored_props (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7509709Z   test_import_constants_not_specialized (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7509828Z   test_in_for_and_comp_expr (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7509956Z   test_in_operator_with_two_strings (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7510048Z   test_index (__main__.TestScript) ... ok (0.079s)
2023-01-11T21:25:29.7510175Z   test_index_select_shape_prop (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7510291Z   test_index_with_tuple (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7510435Z   test_indexing_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7510545Z   test_infer_size (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7510650Z   test_inferred_error_msg (__main__.TestScript)
2023-01-11T21:25:29.7510798Z Test that when we get a type mismatch on a function where we inferred ... ok (0.002s)
2023-01-11T21:25:29.7510902Z   test_inherit_method (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7511105Z   test_inline_and_run_annotated_script_fn (__main__.TestScript) ... skip: https://github.com/pytorch/pytorch/issues/9595 (0.000s)
2023-01-11T21:25:29.7511205Z   test_inlined_graph (__main__.TestScript)
2023-01-11T21:25:29.7511357Z Check that the `inlined_graph` property correctly returns an inlined ... ok (0.010s)
2023-01-11T21:25:29.7511475Z   test_inlining_cleanup (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7511585Z   test_inplace_add (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7511707Z   test_inplace_copy_script (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7511837Z   test_input_keyword_in_schema (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7511931Z   test_int_cast (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7512060Z   test_integral_shape_inference (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7512176Z   test_interpret_graph (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7512294Z   test_interpreter_fuzz (__main__.TestScript) ... ok (0.284s)
2023-01-11T21:25:29.7512406Z   test_intlist_args (__main__.TestScript) ... ok (0.121s)
2023-01-11T21:25:29.7512532Z   test_invalid_call_arguments (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7512653Z   test_invalid_lhs_assignment (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7512782Z   test_invalid_prefix_annotation (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7512879Z   test_irparser (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7512988Z   test_is_after_use (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7513159Z   test_is_isnot (__main__.TestScript) ... <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7513287Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7513413Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7513534Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7513653Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7513772Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7513878Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7513996Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7514122Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7514243Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7514364Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7514482Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7514643Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7514750Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7514869Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7514995Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7515116Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7515238Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7515357Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7515475Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7515593Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7515728Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7515854Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7515979Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7516099Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7516222Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7516342Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7516461Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7516579Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7516688Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7516808Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7516930Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517049Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517167Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517290Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517411Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517531Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517638Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517757Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517875Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7517995Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7518114Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7518235Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7518357Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7518467Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7518586Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7518705Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7518828Z <string>:4: SyntaxWarning: "is" with a literal. Did you mean "=="?
2023-01-11T21:25:29.7518956Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7519079Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7519208Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7519337Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7519454Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7519580Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7519707Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7519863Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7519990Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7520117Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7520241Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7520366Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7520481Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7520605Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7520730Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7520879Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521005Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521136Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521261Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521385Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521499Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521624Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521749Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521873Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7521998Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7522122Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7522247Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7522372Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7522486Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7522609Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7522734Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7522857Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7522981Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7523106Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7523231Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7523358Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7523470Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7523596Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7523721Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7523844Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7523967Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7524089Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7524213Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7524325Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7524450Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7524574Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7524699Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7524824Z <string>:4: SyntaxWarning: "is not" with a literal. Did you mean "!="?
2023-01-11T21:25:29.7524918Z ok (0.044s)
2023-01-11T21:25:29.7525032Z   test_is_optional (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7525145Z   test_is_scripting (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7525263Z   test_is_scripting_metacompile (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7525372Z   test_isinstance (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7525495Z   test_isinstance_dynamic (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7525621Z   test_isinstance_metacompile (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7525743Z   test_isinstance_refinement (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7525851Z   test_jitter_bug (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7525985Z   test_keyword (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7526112Z   test_kwarg_expansion_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7526219Z   test_kwargs_error_msg (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7526330Z   test_lazy_script (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:29.7526467Z   test_lhs_advanced_indexing_assignment (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7526619Z   test_lhs_advanced_indexing_augmented_assignment (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7526731Z   test_lhs_indexing (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7526856Z   test_lhs_indexing_increment (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7526984Z   test_lhs_indexing_increment_list (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7527108Z   test_lhs_indexing_increment_list_prim (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7527227Z   test_lhs_indexing_list (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7527348Z   test_lhs_indexing_multi (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7527460Z   test_linear_grad (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7527598Z   test_list_comprehension_modulelist (__main__.TestScript) ... ok (0.044s)
2023-01-11T21:25:29.7527738Z   test_list_comprehension_variable_write (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7527853Z   test_list_iterables (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7527968Z   test_list_python_op (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7528065Z   test_list_unify (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7528170Z   test_literal (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:29.7528278Z   test_literals (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7528400Z   test_logical_short_circuit (__main__.TestScript) ... ok (0.021s)
2023-01-11T21:25:29.7528514Z   test_loop_liveness (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7528637Z   test_loop_unroll_negative (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7528766Z   test_loop_unroll_unused_counter (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7528883Z   test_loop_unrolling (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7528992Z   test_loop_unrolling_const (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7529231Z   test_loop_unrolling_nested (__main__.TestScript) ... ok (0.052s)
2023-01-11T21:25:29.7529353Z   test_lower_nested_tuples (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7529460Z   test_math_ops (__main__.TestScript) ... ok (0.204s)
2023-01-11T21:25:29.7529631Z   test_maxpool_guard_elimination (__main__.TestScript) ... skip: bailouts are being deprecated (0.001s)
2023-01-11T21:25:29.7529833Z   test_meshgrid (__main__.TestScript) ... skip: Profiling executor fails to recognize that tensors in a list require gradients (0.001s)
2023-01-11T21:25:29.7529954Z   test_method_casts_script (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7530070Z   test_method_no_self (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7530179Z   test_method_overloading (__main__.TestScript) ... ok (7.486s)
2023-01-11T21:25:29.7530350Z   test_missing_getstate (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7530462Z   test_mm_batching (__main__.TestScript) ... ok (0.512s)
2023-01-11T21:25:29.7530573Z   test_module_apis (__main__.TestScript) ... ok (0.048s)
2023-01-11T21:25:29.7530685Z   test_module_attrs (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7530817Z   test_module_copy_with_attributes (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7530933Z   test_module_copying (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:29.7531031Z   test_module_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7531161Z   test_module_method_reassignment (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7531282Z   test_module_none_attrs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7531448Z   test_module_parameters_and_buffers (__main__.TestScript) ... ok (0.024s)
2023-01-11T21:25:29.7531558Z   test_module_str (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7531696Z   test_module_with_params_called_fails (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7531813Z   test_multi_reduction (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7531936Z   test_multi_starred_expr_lhs (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7532051Z   test_multiline_annot_ast_py3_fn (__main__.TestScript) ... ok (0.022s)
2023-01-11T21:25:29.7532193Z   test_multiline_optional_future_refinement (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7532320Z   test_multiline_string_dedents (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7532435Z   test_multiple_assign (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7532557Z   test_multiple_assignment (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7532669Z   test_mutable_dce (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7532789Z   test_mutable_dce_block (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7532916Z   test_mutable_dce_graph_input (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7533046Z   test_mutable_dce_indirect_wildcard_write (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7533182Z   test_mutable_dce_indirect_wildcards (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7533300Z   test_mutable_dce_list (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7533416Z   test_mutable_dce_loop (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7533540Z   test_mutable_dce_wildcards (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7533655Z   test_mutate_constant (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7533772Z   test_mypy_type_ignore (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7533891Z   test_named_buffers_are_iterable (__main__.TestScript) ... ok (0.043s)
2023-01-11T21:25:29.7534009Z   test_namedtuple_attr (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7534155Z   test_namedtuple_default_values_Tensor_type (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7534306Z   test_namedtuple_default_values_container_type (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7534447Z   test_namedtuple_default_values_missing (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7534591Z   test_namedtuple_default_values_simple_type (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7534751Z   test_namedtuple_default_values_using_factory_constructor (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7534873Z   test_namedtuple_python (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7534991Z   test_namedtuple_type_inference (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7535103Z   test_narrow_copy (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7535222Z   test_nested_aug_assign (__main__.TestScript) ... ok (1.048s)
2023-01-11T21:25:29.7535343Z   test_nested_bailouts (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7535457Z   test_nested_breaks (__main__.TestScript) ... ok (0.026s)
2023-01-11T21:25:29.7535610Z   test_nested_list_construct (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7535731Z   test_nested_select_assign (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:29.7535835Z   test_nn_GRU (__main__.TestScript) ... ok (0.163s)
2023-01-11T21:25:29.7535929Z   test_nn_LSTM (__main__.TestScript) ... ok (0.104s)
2023-01-11T21:25:29.7536046Z   test_nn_LSTM_with_layers (__main__.TestScript) ... ok (0.107s)
2023-01-11T21:25:29.7536153Z   test_nn_init (__main__.TestScript) ... ok (0.031s)
2023-01-11T21:25:29.7536268Z   test_no_dtype_shape (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7536396Z   test_no_self_arg_ignore_function (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7536511Z   test_non_final_return (__main__.TestScript) ... ok (0.079s)
2023-01-11T21:25:29.7536649Z   test_none_type_str (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7536738Z   test_not (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7536858Z   test_not_initialized_err (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7536976Z   test_ntuple_builtins (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7537087Z   test_number_abs (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7537203Z   test_number_augassign (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7537336Z   test_number_augassign_bitwise_lshift (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7537467Z   test_number_augassign_bitwise_pow (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7537601Z   test_number_augassign_bitwise_rshift (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7537697Z   test_number_div (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7537806Z   test_number_math (__main__.TestScript) ... ok (1.878s)
2023-01-11T21:25:29.7537914Z   test_number_neg (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7538082Z   test_old_models_bc (__main__.TestScript) ... skip: PyTorch is build without Caffe2 support (0.001s)
2023-01-11T21:25:29.7538197Z   test_oneline_func (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7538308Z   test_op_dtype (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7538430Z   test_operator_precedence (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7538754Z   test_optional_list (__main__.TestScript) ... skip: the current version of Profiler doesn't profile/specialize Optionals (0.001s)
2023-01-11T21:25:29.7539041Z   test_optional_tensor (__main__.TestScript) ... skip: the current version of Profiler doesn't profile/specialize Optionals (0.001s)
2023-01-11T21:25:29.7539145Z   test_ord (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7539261Z   test_override_magic (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7539387Z   test_pack_tuple_into_non_var (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7539510Z   test_pack_unpack_nested (__main__.TestScript) ... ok (0.026s)
2023-01-11T21:25:29.7539631Z   test_pack_unpack_state (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:29.7539786Z   test_parameter_order (__main__.TestScript) ... tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
2023-01-11T21:25:29.7539884Z         14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
2023-01-11T21:25:29.7539970Z         28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
2023-01-11T21:25:29.7540058Z         42., 43., 44., 45., 46., 47., 48., 49., 50., 51.],
2023-01-11T21:25:29.7540146Z        grad_fn=<CatBackward0>)
2023-01-11T21:25:29.7540249Z tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
2023-01-11T21:25:29.7540342Z         14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
2023-01-11T21:25:29.7540435Z         28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
2023-01-11T21:25:29.7540525Z         42., 43., 44., 45., 46., 47., 48., 49., 50., 51.],
2023-01-11T21:25:29.7540607Z        grad_fn=<CatBackward0>)
2023-01-11T21:25:29.7540660Z ok (0.009s)
2023-01-11T21:25:29.7540842Z   test_parse_empty_tuple_annotation (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7540991Z   test_parse_empty_tuple_annotation_element_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7541111Z   test_parse_nested_names (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7541242Z   test_parse_none_type_annotation (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7541366Z   test_parse_tensor_constants (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7541483Z   test_parser_kwargonly (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7541599Z   test_parser_type_annotations (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7541736Z   test_parser_type_annotations_comment (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7541922Z   test_parser_type_annotations_incompatible_expression (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7542074Z   test_parser_type_annotations_subscript_non_ident (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7542224Z   test_parser_type_annotations_subscript_tensor (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7542366Z   test_parser_type_annotations_unknown_type (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7542484Z   test_partial_returns (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7542587Z   test_pass (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7542700Z   test_pickle_checkpoint (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7542840Z   test_pickle_checkpoint_cuda (__main__.TestScript) ... skip: no CUDA (0.000s)
2023-01-11T21:25:29.7542963Z   test_pickle_checkpoint_tup (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7543124Z   test_pow_scalar_backward_cuda (__main__.TestScript) ... skip: device tests require CUDA (0.001s)
2023-01-11T21:25:29.7543249Z   test_pretty_print_function (__main__.TestScript) ... ok (0.030s)
2023-01-11T21:25:29.7543492Z   test_prim_grad_undefined (__main__.TestScript) ... skip: shape analysis is only enabled in Legacy (0.000s)
2023-01-11T21:25:29.7543600Z   test_print (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7543713Z   test_print_kwargs (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7543872Z   test_profiling_graph_executor (__main__.TestScript) ... skip: bailouts are being deprecated (0.001s)
2023-01-11T21:25:29.7543990Z   test_profiling_merge (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7544118Z   test_pybind_type_comparisons (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7544231Z   test_python_call (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7544355Z   test_python_call_annotation (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7544491Z   test_python_call_annoytation_failure (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7544613Z   test_python_call_failure (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7544736Z   test_python_call_non_tensor (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7544857Z   test_python_call_non_tensor_wrong (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7544973Z   test_python_frontend (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7545094Z   test_python_frontend_py3 (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7545224Z   test_python_frontend_source_range (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7545347Z   test_python_op_builtins (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7545462Z   test_python_op_name (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7545591Z   test_python_val_doesnt_have_attr (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7545737Z   test_rand (__main__.TestScript) ... skip: the original version of test_rand (0.001s)
2023-01-11T21:25:29.7545842Z   test_rand_profiling (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7545954Z   test_range_args (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7546074Z   test_reassign_module_lhs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7546229Z   test_reassign_module_rhs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7546349Z   test_refine_tuple_types (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7546464Z   test_remove_dropout (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7546598Z   test_repeated_script_on_function (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7546702Z   test_request_bailout (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7546853Z   test_requires_grad_loop (__main__.TestScript) ... skip: Peeling is now disabled (0.001s)
2023-01-11T21:25:29.7546984Z   test_rescripting_loaded_modules (__main__.TestScript) ... ok (0.027s)
2023-01-11T21:25:29.7547101Z   test_resize_input_ops (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7547205Z   test_return (__main__.TestScript) ... ok (0.015s)
2023-01-11T21:25:29.7547357Z   test_return_stmt_not_at_end (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7547470Z   test_return_tuple (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7547594Z   test_robust_op_resolution (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7547685Z   test_round (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7547805Z   test_save_load_attr_error (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7547924Z   test_script_annotation (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7548046Z   test_script_bool_constant (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7548157Z   test_script_chunk (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7548275Z   test_script_clamp_none (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:29.7548388Z   test_script_copy (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7548496Z   test_script_cu (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7548607Z   test_script_define_order (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7548745Z   test_script_define_order_recursive_fail (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7548869Z   test_script_docstring (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7549009Z   test_script_forward_method_replacement (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7549155Z   test_script_get_device_cuda (__main__.TestScript) ... skip: requires CUDA (0.000s)
2023-01-11T21:25:29.7549286Z   test_script_get_tracing_state (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7549405Z   test_script_is_tracing (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7549526Z   test_script_kwargs_fn_call (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7549640Z   test_script_method_docstring (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7549782Z   test_script_method_torch_function_overload (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7549897Z   test_script_module (__main__.TestScript) ... ok (0.017s)
2023-01-11T21:25:29.7550028Z   test_script_module_call_noscript (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7550151Z   test_script_module_const (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7550292Z   test_script_module_const_submodule_fail (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:29.7550423Z   test_script_module_export_blocks (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7550898Z   test_script_module_export_shared_storage (__main__.TestScript) ... /var/lib/jenkins/workspace/test/test_jit.py:10624: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:29.7551079Z   self.assertTrue(m_import.param1.storage().data_ptr() == m_import.param2.storage().data_ptr())
2023-01-11T21:25:29.7551480Z /var/lib/jenkins/workspace/test/test_jit.py:10625: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:29.7551720Z   self.assertTrue(m_import.param1.storage().data_ptr() != m_import.param3.storage().data_ptr())
2023-01-11T21:25:29.7551813Z ok (0.008s)
2023-01-11T21:25:29.7552031Z   test_script_module_export_submodule (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:29.7552265Z   test_script_module_export_tensor_cuda (__main__.TestScript) ... skip: testing cuda tensors require CUDA (0.001s)
2023-01-11T21:25:29.7553096Z   test_script_module_export_tensor_type (__main__.TestScript) ... /var/lib/jenkins/workspace/test/test_jit.py:10560: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:29.7553283Z   self.assertTrue(m_orig.param.storage().size() == 25)
2023-01-11T21:25:29.7553379Z ok (0.010s)
2023-01-11T21:25:29.7553571Z   test_script_module_fail_exist (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7553749Z   test_script_module_for (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7553917Z   test_script_module_for2 (__main__.TestScript) ... ok (0.033s)
2023-01-11T21:25:29.7554118Z   test_script_module_invalid_consts (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7554324Z   test_script_module_nochange_submodule (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7554893Z   test_script_module_none_exist_fail (__main__.TestScript) ... skip: [module dedupe] currently NoneType refinement on optional attributes doesn't work. (0.001s)
2023-01-11T21:25:29.7555087Z   test_script_module_not_tuple (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7555297Z   test_script_module_param_buffer_mutation (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7555494Z   test_script_module_star_assign2 (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:29.7555698Z   test_script_module_star_assign2_inplace (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:29.7555899Z   test_script_module_star_assign_fail_builtin (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7556116Z   test_script_module_star_assign_fail_pythonop (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7556330Z   test_script_module_tensor_subclass_argument (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7556520Z   test_script_nested_mod_list (__main__.TestScript) ... ok (0.037s)
2023-01-11T21:25:29.7556721Z   test_script_non_tensor_args_outputs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7556903Z   test_script_optional_none (__main__.TestScript) ... ok (0.013s)
2023-01-11T21:25:29.7557079Z   test_script_outputs (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7557281Z   test_script_pack_padded_sequence (__main__.TestScript) ... ok (0.037s)
2023-01-11T21:25:29.7557481Z   test_script_pad_sequence_pack_sequence (__main__.TestScript) ... ok (0.041s)
2023-01-11T21:25:29.7557648Z   test_script_scope (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7557836Z   test_script_sequential_for (__main__.TestScript) ... ok (0.025s)
2023-01-11T21:25:29.7558040Z   test_script_sequential_in_mod_list (__main__.TestScript) ... ok (0.041s)
2023-01-11T21:25:29.7558251Z   test_script_sequential_multi_output_fail (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:29.7558450Z   test_script_sequential_orderdict (__main__.TestScript) ... ok (0.030s)
2023-01-11T21:25:29.7558657Z   test_script_sequential_sliced_iteration (__main__.TestScript) ... ok (0.031s)
2023-01-11T21:25:29.7558836Z   test_script_star_assign (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7559004Z   test_script_star_expr (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:29.7559272Z   test_script_star_expr_string (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:29.7559474Z   test_scriptable_fn_as_attr (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7559671Z   test_scriptmodule_multi_head_attn_cuda (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7559869Z   test_scriptmodule_releases_tensors_cuda (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7560065Z   test_scriptmodule_transformer_cuda (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7560222Z   test_select_after_chunk (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7560378Z   test_sequence_parsing (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7560544Z   test_sequential_intermediary_types (__main__.TestScript) ... ok (0.025s)
2023-01-11T21:25:29.7560709Z   test_serialization_big_ints (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7560908Z   test_serialization_sharing (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7561068Z   test_serialize_long_lines (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7561240Z   test_serialized_source_ranges (__main__.TestScript) ... ok (0.279s)
2023-01-11T21:25:29.7561410Z   test_serialized_source_ranges2 (__main__.TestScript) ... ok (0.385s)
2023-01-11T21:25:29.7561595Z   test_serialized_source_ranges_dont_jitter (__main__.TestScript) ... ok (0.068s)
2023-01-11T21:25:29.7561759Z   test_serialized_source_ranges_graph (__main__.TestScript) ... ok (0.275s)
2023-01-11T21:25:29.7561938Z   test_serialized_source_ranges_no_dups (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7562110Z   test_set_attribute_through_optional (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7562283Z   test_shape_analysis_grad_property (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7562441Z   test_shape_analysis_loop (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7562618Z   test_shape_prop_promote_scalar_arg (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7562778Z   test_shape_prop_promotion (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7562932Z   test_signed_float_zero (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7563093Z   test_single_starred_expr_for_loop (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7563249Z   test_single_starred_lhs (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7563410Z   test_singleton_tuple_unpack (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7563630Z   test_slice_guard_elimination (__main__.TestScript) ... skip: bailouts are being deprecated (0.000s)
2023-01-11T21:25:29.7563763Z   test_split (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7563897Z   test_stack (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7564046Z   test_static_if_prop (__main__.TestScript) ... ok (1.957s)
2023-01-11T21:25:29.7564193Z   test_static_method_on_module (__main__.TestScript)
2023-01-11T21:25:29.7564387Z Check that the `@staticmethod` annotation on a function on a module works. ... ok (0.008s)
2023-01-11T21:25:29.7564539Z   test_static_methods (__main__.TestScript) ... ok (0.019s)
2023-01-11T21:25:29.7564680Z   test_str_cast (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7564821Z   test_string_cu (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7565002Z   test_string_device_implicit_conversion (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7565158Z   test_string_frontend_elif (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7565303Z   test_string_index (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7565443Z   test_string_len (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7565574Z   test_string_list (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7565725Z   test_string_new_line (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7565865Z   test_string_ops (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7566013Z   test_string_print (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7566168Z   test_string_single_escape (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7566366Z   test_string_slicing (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7566506Z   test_string_sort (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7566644Z   test_string_sorted (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7566825Z   test_submodule_attribute_serialization (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:29.7566975Z   test_submodule_twice (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7567105Z   test_sum (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7567260Z   test_sum_list_diff_elms (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7567405Z   test_sum_list_empty (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7567555Z   test_sum_list_literal (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7567726Z   test_sum_list_one (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7567869Z   test_sum_list_wrong_type (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7568028Z   test_sys_stdout_override (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7568433Z   test_tensor_as_tensor_shape_prop (__main__.TestScript) ... skip: Simple Executor doesn't have any shapes to propagate (0.001s)
2023-01-11T21:25:29.7568576Z   test_tensor_data (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7568768Z   test_tensor_device (__main__.TestScript) ... skip: device tests require CUDA (0.000s)
2023-01-11T21:25:29.7568913Z   test_tensor_dtype (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7569289Z   test_tensor_grad (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7569450Z   test_tensor_import_export (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7569582Z   test_tensor_len (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7569734Z   test_tensor_number_math (__main__.TestScript) ... ok (0.458s)
2023-01-11T21:25:29.7569918Z   test_tensor_number_math_cuda (__main__.TestScript) ... skip: No CUDA (0.000s)
2023-01-11T21:25:29.7570118Z   test_tensor_requires_grad (__main__.TestScript) ... skip: testing legacy behavior (0.001s)
2023-01-11T21:25:29.7570270Z   test_tensor_shape (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7571043Z   test_tensor_subclasses (__main__.TestScript) ... /opt/conda/lib/python3.10/site-packages/torch/jit/annotations.py:309: UserWarning: TorchScript will treat type annotations of Tensor dtype-specific subtypes as if they are normal Tensors. dtype constraints are not enforced in compilation either.
2023-01-11T21:25:29.7571223Z   warnings.warn("TorchScript will treat type annotations of Tensor "
2023-01-11T21:25:29.7571308Z ok (0.008s)
2023-01-11T21:25:29.7571434Z   test_tensor_to (__main__.TestScript) ... ok (0.022s)
2023-01-11T21:25:29.7571584Z   test_tensor_to_cpu (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7571779Z   test_tensor_to_cuda (__main__.TestScript) ... skip: device tests require CUDA (0.000s)
2023-01-11T21:25:29.7571974Z   test_tensor_to_device (__main__.TestScript) ... skip: device tests require CUDA (0.000s)
2023-01-11T21:25:29.7572113Z   test_ternary (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7572281Z   test_ternary_module_type_hint (__main__.TestScript) ... ok (0.035s)
2023-01-11T21:25:29.7572451Z   test_ternary_right_associative (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7572604Z   test_ternary_static_if (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7572734Z   test_torch_any (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:29.7572943Z   test_torch_functional (__main__.TestScript) ... skip: Skipping while landing PR stack (0.002s)
2023-01-11T21:25:29.7573119Z   test_torch_functional_tensordot_int (__main__.TestScript) ... ok (0.042s)
2023-01-11T21:25:29.7573295Z   test_torch_functional_tensordot_list (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7573477Z   test_torch_functional_tensordot_tensor (__main__.TestScript) ... ok (0.022s)
2023-01-11T21:25:29.7573655Z   test_torch_functional_tensordot_tuple (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7573900Z   test_torch_ignore_conversion_to_none (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7574055Z   test_torch_manual_seed (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7574186Z   test_torch_pow (__main__.TestScript) ... ok (0.032s)
2023-01-11T21:25:29.7574963Z   test_torch_tensor_as_tensor (__main__.TestScript) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:471: UserWarning: Casting complex values to real discards the imaginary part (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Copy.cpp:276.)
2023-01-11T21:25:29.7575077Z   return callable(*args, **kwargs)
2023-01-11T21:25:29.7575160Z ok (0.559s)
2023-01-11T21:25:29.7575336Z   test_torch_tensor_as_tensor_empty_list (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7575533Z   test_torch_tensor_bad_input (__main__.TestScript) ... ok (0.027s)
2023-01-11T21:25:29.7575691Z   test_torch_tensor_dtype (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7575865Z   test_torchscript_memoryformat (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7576022Z   test_torchscript_multi_head_attn (__main__.TestScript) ... ok (0.076s)
2023-01-11T21:25:29.7576209Z   test_torchscript_multi_head_attn_fast_path (__main__.TestScript) ... ok (0.156s)
2023-01-11T21:25:29.7576361Z   test_training_param (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7576518Z   test_tuple_assignments (__main__.TestScript) ... ok (0.349s)
2023-01-11T21:25:29.7576668Z   test_tuple_error_msg (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7576823Z   test_tuple_index_to_list (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7576975Z   test_tuple_indexing (__main__.TestScript) ... ok (0.020s)
2023-01-11T21:25:29.7577118Z   test_tuple_len (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7577261Z   test_tuple_nested_sort (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7577402Z   test_tuple_sort (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7577558Z   test_tuple_sort_reverse (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7577704Z   test_tuple_sorted (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7577856Z   test_tuple_to_opt_list (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7578024Z   test_tuple_unsortable_diff_type (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7578197Z   test_tuple_unsortable_element_type (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7578366Z   test_tuple_unsortable_nested_diff_type (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7578514Z   test_type_annotate (__main__.TestScript) ... ok (0.014s)
2023-01-11T21:25:29.7578673Z   test_type_annotation_module (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7578829Z   test_type_annotation_py3 (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7578987Z   test_type_annotations (__main__.TestScript) ... ok (0.016s)
2023-01-11T21:25:29.7579161Z   test_type_annotations_repeated_list (__main__.TestScript) ... ok (0.012s)
2023-01-11T21:25:29.7579332Z   test_type_annotations_varargs (__main__.TestScript) ... ok (0.011s)
2023-01-11T21:25:29.7579488Z   test_type_call_in_script (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7579614Z   test_type_cast (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7579774Z   test_type_comments_in_body (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7579939Z   test_type_inferred_from_empty_annotation (__main__.TestScript)
2023-01-11T21:25:29.7580181Z Test that the type inferred from an empty or missing annotation is Torch.Tensor wtih `inferred=true` ... ok (0.002s)
2023-01-11T21:25:29.7580467Z   test_unbind (__main__.TestScript) ... skip: Profiling executor will be using different heuristics for constructing differentiable graphs (0.001s)
2023-01-11T21:25:29.7580623Z   test_unfold_zero_dim (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7580774Z   test_unicode_comments (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7580963Z   test_uninitialized (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7581101Z   test_union_to_number (__main__.TestScript) ... ok (0.002s)
2023-01-11T21:25:29.7581250Z   test_unknown_builtin (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7581418Z   test_unmatched_type_annotation (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7581588Z   test_unspecialized_any_binding (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7581812Z   test_unsqueeze_guard_elimination (__main__.TestScript) ... skip: bailouts are being deprecated (0.000s)
2023-01-11T21:25:29.7581978Z   test_unsupported_builtin_error (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7582131Z   test_unused_decorator (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7582295Z   test_unwrap_optional_builtin (__main__.TestScript) ... ok (0.010s)
2023-01-11T21:25:29.7582465Z   test_var_aug_assign (__main__.TestScript) ... ok (0.961s)
2023-01-11T21:25:29.7582614Z   test_vararg_zeros (__main__.TestScript) ... ok (0.004s)
2023-01-11T21:25:29.7582791Z   test_view_listconstruct_shape_prop (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7582945Z   test_view_shape_prop (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7583085Z   test_view_write (__main__.TestScript) ... ok (0.006s)
2023-01-11T21:25:29.7583240Z   test_weak_cuda (__main__.TestScript) ... skip: no CUDA (0.001s)
2023-01-11T21:25:29.7583430Z   test_where (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7583574Z   test_where_method (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7583694Z   test_while (__main__.TestScript) ... ok (0.008s)
2023-01-11T21:25:29.7583838Z   test_while_nest_if (__main__.TestScript) ... ok (0.067s)
2023-01-11T21:25:29.7584010Z   test_while_nonexistent_cond_value (__main__.TestScript) ... ok (0.027s)
2023-01-11T21:25:29.7584176Z   test_while_nonexistent_value (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7584341Z   test_while_write_outer_then_read (__main__.TestScript) ... ok (0.007s)
2023-01-11T21:25:29.7584499Z   test_wrong_attr_lookup (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7584658Z   test_wrong_implicit_expand (__main__.TestScript) ... ok (0.005s)
2023-01-11T21:25:29.7584810Z   test_wrong_method_call_inputs (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7584975Z   test_wrong_module_attr_lookup (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7585129Z   test_wrong_return_type (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7585286Z   test_wrong_use_as_callable (__main__.TestScript) ... ok (0.001s)
2023-01-11T21:25:29.7585441Z   test_wrong_use_as_tuple (__main__.TestScript) ... ok (0.003s)
2023-01-11T21:25:29.7585572Z   test_zeros (__main__.TestScript) ... ok (0.009s)
2023-01-11T21:25:29.7585739Z   test_zip_enumerate_modulelist (__main__.TestScript) ... ok (0.436s)
2023-01-11T21:25:29.7585885Z   test_bool (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7586033Z Test the __bool__ method. This should return True ... ok (0.002s)
2023-01-11T21:25:29.7586185Z   test_contains (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7586337Z Test membership checks (x in y, x not in y). ... ok (0.001s)
2023-01-11T21:25:29.7586484Z   test_delitem (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7586592Z Test deletion. ... ok (0.001s)
2023-01-11T21:25:29.7586740Z   test_getitem (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7586914Z Test accessing dictionary values using the [] operator. ... ok (0.001s)
2023-01-11T21:25:29.7587046Z   test_items (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7587145Z Test .items(). ... ok (0.001s)
2023-01-11T21:25:29.7587284Z   test_iter (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7587507Z Test iteration over a dictionary's keys. ... ok (0.001s)
2023-01-11T21:25:29.7587648Z   test_len (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7587778Z Test len() builtin function. ... ok (0.001s)
2023-01-11T21:25:29.7587925Z   test_nested (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7588274Z Test that reference semantics are honoured when the ScriptDict that is ... skip: Cannot pass until all dicts returned from TorchScript are ScriptDicts (0.001s)
2023-01-11T21:25:29.7588430Z   test_reference_semantics (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7588626Z Test that reference semantics are honoured; that modifications made ... ok (0.003s)
2023-01-11T21:25:29.7588768Z   test_repr (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7588887Z Test the __repr__ method. ... ok (0.001s)
2023-01-11T21:25:29.7589030Z   test_setitem (jit.test_list_dict.TestScriptDict)
2023-01-11T21:25:29.7589199Z Test setting dictionary values using the [] operator. ... ok (0.001s)
2023-01-11T21:25:29.7589344Z   test_append (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7589448Z Test append method. ... ok (0.001s)
2023-01-11T21:25:29.7589618Z   test_bool (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7589776Z Test the __bool__ method. This should return True ... ok (0.001s)
2023-01-11T21:25:29.7589923Z   test_clear (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7590025Z Test clear. ... ok (0.001s)
2023-01-11T21:25:29.7590173Z   test_contains (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7590325Z Test membership checks (x in y, x not in y). ... ok (0.001s)
2023-01-11T21:25:29.7590467Z   test_count (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7590564Z Test count method. ... ok (0.001s)
2023-01-11T21:25:29.7590707Z   test_delitem (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7590812Z Test deletion. ... ok (0.001s)
2023-01-11T21:25:29.7590954Z   test_extend (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7591056Z Test extend. ... ok (0.002s)
2023-01-11T21:25:29.7591203Z   test_getitem (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7591371Z Test accessing list elements using the [] operator. ... ok (0.003s)
2023-01-11T21:25:29.7591505Z   test_insert (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7591607Z Test insert. ... ok (0.001s)
2023-01-11T21:25:29.7591747Z   test_iter (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7591965Z Test iteration over a list's elements. ... ok (0.001s)
2023-01-11T21:25:29.7592104Z   test_len (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7592230Z Test len() builtin function. ... ok (0.001s)
2023-01-11T21:25:29.7592373Z   test_nested (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7592667Z Test that reference semantics are honoured when the ScriptList that is ... skip: Cannot pass until all list returned from TorchScript are ScriptLists (0.001s)
2023-01-11T21:25:29.7592809Z   test_pop (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7592906Z Test pop. ... ok (0.001s)
2023-01-11T21:25:29.7593074Z   test_reference_semantics (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7593272Z Test that reference semantics are honoured; that modifications made ... ok (0.003s)
2023-01-11T21:25:29.7593415Z   test_remove (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7593531Z Test remove method. ... ok (0.001s)
2023-01-11T21:25:29.7593660Z   test_repr (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7593778Z Test the __repr__ method. ... ok (0.001s)
2023-01-11T21:25:29.7593923Z   test_setitem (jit.test_list_dict.TestScriptList)
2023-01-11T21:25:29.7594082Z Test setting list elements using the [] operator. ... ok (0.002s)
2023-01-11T21:25:29.7594424Z   test_annotated_class_level_annotation_and_init_annotation (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.012s)
2023-01-11T21:25:29.7594748Z   test_annotated_class_level_annotation_only (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.007s)
2023-01-11T21:25:29.7595067Z   test_annotated_class_level_jit_annotation (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.008s)
2023-01-11T21:25:29.7595366Z   test_annotated_empty_dict (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.009s)
2023-01-11T21:25:29.7595692Z   test_annotated_empty_list (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.008s)
2023-01-11T21:25:29.7595982Z   test_annotated_empty_optional (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:29.7596281Z   test_annotated_empty_tensor (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:29.7596585Z   test_annotated_falsy_base_type (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:29.7596895Z   test_annotated_nonempty_container (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:29.7597232Z   test_annotated_with_jit_attribute (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:29.7597539Z   test_annotated_with_jit_empty_dict (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.006s)
2023-01-11T21:25:29.7597850Z   test_annotated_with_jit_empty_list (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.005s)
2023-01-11T21:25:29.7598163Z   test_annotated_with_jit_empty_optional (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.005s)
2023-01-11T21:25:29.7598473Z   test_annotated_with_torch_jit_import (jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation) ... ok (0.005s)
2023-01-11T21:25:29.7598656Z   test_basic (jit.test_script_profile.TestScriptProfile) ... ok (0.067s)
2023-01-11T21:25:29.7598829Z   test_empty (jit.test_script_profile.TestScriptProfile) ... ok (0.001s)
2023-01-11T21:25:29.7599010Z   test_multi (jit.test_script_profile.TestScriptProfile) ... ok (0.067s)
2023-01-11T21:25:29.7599201Z   test_script (jit.test_script_profile.TestScriptProfile) ... ok (0.190s)
2023-01-11T21:25:29.7599393Z   test_section (jit.test_script_profile.TestScriptProfile) ... ok (0.253s)
2023-01-11T21:25:29.7599564Z   test_module_list_slicing (jit.test_slice.TestSlice) ... ok (0.021s)
2023-01-11T21:25:29.7599728Z   test_slice_as_variable (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7599894Z   test_slice_dynamic_index (jit.test_slice.TestSlice) ... ok (0.005s)
2023-01-11T21:25:29.7600050Z   test_slice_kwarg (jit.test_slice.TestSlice) ... ok (0.001s)
2023-01-11T21:25:29.7600195Z   test_slice_one_none (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7600357Z   test_slice_start_stop (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7600529Z   test_slice_start_stop_step (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7600706Z   test_slice_start_stop_with_none (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7600879Z   test_slice_stop_clipped (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7601040Z   test_slice_stop_only (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7601215Z   test_slice_stop_only_with_nones (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7601376Z   test_slice_string (jit.test_slice.TestSlice) ... ok (0.003s)
2023-01-11T21:25:29.7601522Z   test_slice_tensor (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7601690Z   test_slice_tensor_multidim (jit.test_slice.TestSlice) ... ok (0.005s)
2023-01-11T21:25:29.7601876Z   test_slice_tensor_multidim_with_dots (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7602037Z   test_slice_three_nones (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7602193Z   test_slice_two_nones (jit.test_slice.TestSlice) ... ok (0.004s)
2023-01-11T21:25:29.7602352Z   test_tuple_slicing (jit.test_slice.TestSlice) ... ok (0.005s)
2023-01-11T21:25:29.7602526Z   test_freeze_sparse_coo (jit.test_sparse.TestSparse) ... ok (0.008s)
2023-01-11T21:25:29.7602691Z   test_freeze_sparse_csr (jit.test_sparse.TestSparse) ... ok (0.009s)
2023-01-11T21:25:29.7602869Z   test_serialize_sparse_coo (jit.test_sparse.TestSparse) ... ok (0.007s)
2023-01-11T21:25:29.7603111Z   test_serialize_sparse_csr (jit.test_sparse.TestSparse) ... ok (0.007s)
2023-01-11T21:25:29.7603328Z   test_modulo_operator (jit.test_string_formatting.TestStringFormatting) ... ok (0.004s)
2023-01-11T21:25:29.7603609Z   test_string_interpolation_with_alternate_digit_placeholder (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7603909Z   test_string_interpolation_with_capital_exponent_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7604192Z   test_string_interpolation_with_char_placeholder_and_char_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7604510Z   test_string_interpolation_with_char_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7604808Z   test_string_interpolation_with_char_placeholder_and_true_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:29.7605097Z   test_string_interpolation_with_digit_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7605372Z   test_string_interpolation_with_digit_placeholder_and_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:29.7605635Z   test_string_interpolation_with_double_percent_in_string (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7605926Z   test_string_interpolation_with_exponent_placeholder_and_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:29.7606210Z   test_string_interpolation_with_float_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7606494Z   test_string_interpolation_with_float_placeholder_and_float_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7606803Z   test_string_interpolation_with_lowercase_exponent_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7607068Z   test_string_interpolation_with_multiple_placeholders (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7607325Z   test_string_interpolation_with_percent_in_string (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:29.7607608Z   test_string_interpolation_with_string_placeholder_and_digit_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7607905Z   test_string_interpolation_with_string_placeholder_and_format_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7608181Z   test_string_interpolation_with_string_placeholder_and_string_variable (jit.test_string_formatting.TestStringFormatting) ... ok (0.003s)
2023-01-11T21:25:29.7608431Z   test_string_interpolation_with_subscript (jit.test_string_formatting.TestStringFormatting) ... ok (0.004s)
2023-01-11T21:25:29.7608684Z   test_string_interpolation_with_too_few_arguments (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:29.7608941Z   test_string_interpolation_with_too_many_arguments (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:29.7609330Z   test_string_interpolation_with_unknown_format_specifier (jit.test_string_formatting.TestStringFormatting) ... ok (0.006s)
2023-01-11T21:25:29.7609580Z   test_adaptive_avg_pool2d (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.028s)
2023-01-11T21:25:29.7609816Z   test_arange_shape (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.017s)
2023-01-11T21:25:29.7610067Z   test_binary_shape_fns_inplace (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.005s)
2023-01-11T21:25:29.7610315Z   test_binary_shape_functions (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.004s)
2023-01-11T21:25:29.7610606Z   test_convolution_backward (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.014s)
2023-01-11T21:25:29.7610840Z   test_if_propagation (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.004s)
2023-01-11T21:25:29.7611090Z   test_partial_eval_graph_conv (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.011s)
2023-01-11T21:25:29.7611335Z   test_partial_eval_stitching (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.035s)
2023-01-11T21:25:29.7611600Z   test_refinement_through_graph_stitching (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.020s)
2023-01-11T21:25:29.7611906Z   test_register_function_error_checking (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.011s)
2023-01-11T21:25:29.7612167Z   test_returning_input_symbolic_shapes (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.008s)
2023-01-11T21:25:29.7612407Z   test_shape_analysis (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.007s)
2023-01-11T21:25:29.7612638Z   test_shape_concat (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.083s)
2023-01-11T21:25:29.7612872Z   test_shape_embedding_bag (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.015s)
2023-01-11T21:25:29.7613178Z   test_shape_function_includes (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... skip: shape functions not loaded in python (0.001s)
2023-01-11T21:25:29.7613422Z   test_shared_shape_graph (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.001s)
2023-01-11T21:25:29.7613655Z   test_size_and_sizes (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.004s)
2023-01-11T21:25:29.7613897Z   test_stitching_concat (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.021s)
2023-01-11T21:25:29.7614142Z   test_stitching_multi_output (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.018s)
2023-01-11T21:25:29.7614381Z   test_sym_ir_parsing (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.001s)
2023-01-11T21:25:29.7614631Z   test_unary_shape_fns_inplace (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.001s)
2023-01-11T21:25:29.7614877Z   test_unary_shape_functions (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.002s)
2023-01-11T21:25:29.7615096Z   test_write (jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis) ... ok (0.002s)
2023-01-11T21:25:29.7615280Z   test_method_on_number (jit.test_builtins.TestTensorBuiltins) ... ok (0.002s)
2023-01-11T21:25:29.7615487Z   test_scalar_to_num_conversions (jit.test_builtins.TestTensorBuiltins) ... ok (0.015s)
2023-01-11T21:25:29.7615672Z   test_tensor_item (jit.test_builtins.TestTensorBuiltins) ... ok (0.005s)
2023-01-11T21:25:29.7615870Z   test_tensor_properties (jit.test_builtins.TestTensorBuiltins) ... ok (0.012s)
2023-01-11T21:25:29.7616081Z   test_tensor_subscript_assign (jit.test_builtins.TestTensorBuiltins) ... ok (0.026s)
2023-01-11T21:25:29.7616298Z   test_tensor_subscript_assign_device (jit.test_builtins.TestTensorBuiltins) ... skip: requires CUDA (0.000s)
2023-01-11T21:25:29.7616472Z   test_randperm_default_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:29.7616650Z   test_randperm_specifed_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.004s)
2023-01-11T21:25:29.7616815Z   test_tril_indices_default_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:29.7616994Z   test_tril_indices_specified_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:29.7617169Z   test_triu_indices_default_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.004s)
2023-01-11T21:25:29.7617348Z   test_triu_indices_specified_dtype (jit.test_tensor_creation_ops.TestTensorCreationOps) ... ok (0.005s)
2023-01-11T21:25:29.7617526Z   test_getitem (jit.test_tensor_methods.TestTensorMethods) ... ok (0.005s)
2023-01-11T21:25:29.7617678Z   test_getitem_invalid (jit.test_tensor_methods.TestTensorMethods) ... ok (0.001s)
2023-01-11T21:25:29.7617814Z   test_default_args (jit.test_torchbind.TestTorchbind) ... ok (0.010s)
2023-01-11T21:25:29.7617961Z   test_lambda_as_constructor (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:29.7618336Z   test_profiler_custom_op (jit.test_torchbind.TestTorchbind) ... STAGE:2023-01-11 21:25:25 1919:1919 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:29.7618575Z STAGE:2023-01-11 21:25:25 1919:1919 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:29.7618882Z STAGE:2023-01-11 21:25:25 1919:1919 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:29.7618951Z ok (0.003s)
2023-01-11T21:25:29.7619093Z   test_staticmethod (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:29.7619229Z   test_torchbind (jit.test_torchbind.TestTorchbind) ... ok (0.007s)
2023-01-11T21:25:29.7619382Z   test_torchbind_attr_exception (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:29.7619541Z   test_torchbind_class_attr_recursive (jit.test_torchbind.TestTorchbind) ... ok (0.003s)
2023-01-11T21:25:29.7619691Z   test_torchbind_class_attribute (jit.test_torchbind.TestTorchbind) ... ok (0.004s)
2023-01-11T21:25:29.7619826Z   test_torchbind_deepcopy (jit.test_torchbind.TestTorchbind) ... ok (0.003s)
2023-01-11T21:25:29.7619992Z   test_torchbind_def_property_getter_setter (jit.test_torchbind.TestTorchbind) ... ok (0.006s)
2023-01-11T21:25:29.7620154Z   test_torchbind_def_property_just_getter (jit.test_torchbind.TestTorchbind) ... ok (0.004s)
2023-01-11T21:25:29.7620319Z   test_torchbind_def_property_readwrite (jit.test_torchbind.TestTorchbind) ... ok (0.004s)
2023-01-11T21:25:29.7620461Z   test_torchbind_getattr (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:29.7620608Z   test_torchbind_getstate (jit.test_torchbind.TestTorchbind) ... ok (0.004s)
2023-01-11T21:25:29.7620772Z   test_torchbind_instantiate_missing_class (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:29.7620922Z   test_torchbind_lambda_method (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:29.7621052Z   test_torchbind_no_init (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:29.7621211Z   test_torchbind_optional_explicit_attr (jit.test_torchbind.TestTorchbind) ... ok (0.005s)
2023-01-11T21:25:29.7621363Z   test_torchbind_pass_wrong_type (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:29.7621521Z   test_torchbind_pickle_serialization (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:29.7621679Z   test_torchbind_python_deepcopy (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:29.7621828Z   test_torchbind_return_instance (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:29.7621997Z   test_torchbind_return_instance_from_method (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:29.7622146Z   test_torchbind_return_tuple (jit.test_torchbind.TestTorchbind) ... ok (0.001s)
2023-01-11T21:25:29.7622281Z   test_torchbind_save_load (jit.test_torchbind.TestTorchbind) ... ok (0.003s)
2023-01-11T21:25:29.7622426Z   test_torchbind_take_as_arg (jit.test_torchbind.TestTorchbind) ... ok (0.012s)
2023-01-11T21:25:29.7622593Z   test_torchbind_take_instance_as_method_arg (jit.test_torchbind.TestTorchbind) ... ok (0.002s)
2023-01-11T21:25:29.7622737Z   test_torchbind_tracing (jit.test_torchbind.TestTorchbind) ... ok (0.010s)
2023-01-11T21:25:29.7622888Z   test_torchbind_tracing_nested (jit.test_torchbind.TestTorchbind) ... ok (0.012s)
2023-01-11T21:25:29.7623039Z   test_call_traced_fn_from_traced_module (jit.test_tracer.TestTracer) ... ok (0.012s)
2023-01-11T21:25:29.7623193Z   test_call_traced_module_from_traced_module (jit.test_tracer.TestTracer) ... ok (0.018s)
2023-01-11T21:25:29.7623432Z   test_canonicalize_tensor_iterator (jit.test_tracer.TestTracer) ... ok (0.010s)
2023-01-11T21:25:29.7623559Z   test_constant (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7623661Z   test_conv (jit.test_tracer.TestTracer) ... ok (0.026s)
2023-01-11T21:25:29.7623793Z   test_export_no_reorder (jit.test_tracer.TestTracer) ... ok (0.016s)
2023-01-11T21:25:29.7623934Z   test_force_outplace_check_fill (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7624072Z   test_force_outplace_check_zero (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7624184Z   test_ge (jit.test_tracer.TestTracer) ... ok (0.013s)
2023-01-11T21:25:29.7624324Z   test_ge_cuda (jit.test_tracer.TestTracer) ... skip: requires CUDA (0.000s)
2023-01-11T21:25:29.7624486Z   test_ge_optimized (jit.test_tracer.TestTracer) ... ok (0.177s)
2023-01-11T21:25:29.7624606Z   test_ge_unoptimized (jit.test_tracer.TestTracer) ... ok (0.035s)
2023-01-11T21:25:29.7624730Z   test_index_put (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7624867Z   test_index_put_trace_with_view (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7625009Z   test_index_put_trace_without_view (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7625135Z   test_inplace_check (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:29.7625260Z   test_inplace_copy (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7625405Z   test_inplace_copy_force_outplace (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7625529Z   test_inplace_flags (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:29.7625652Z   test_inplace_transplant (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:29.7625777Z   test_inplace_warn (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7625920Z   test_input_dict_checkTrace_mut (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7626048Z   test_input_dict_empty (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:29.7626182Z   test_input_dict_empty_list (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:29.7626311Z   test_input_dict_insertion_order (jit.test_tracer.TestTracer)
2023-01-11T21:25:29.7626520Z Check that dictionary access doesn't care about insertion order ... ok (0.010s)
2023-01-11T21:25:29.7626651Z   test_input_dict_of_dicts (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7626771Z   test_input_dict_of_lists (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7626906Z   test_input_dict_recursive (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7627034Z   test_input_dict_remembers_keys (jit.test_tracer.TestTracer)
2023-01-11T21:25:29.7627173Z Check that the trace remembers which keys were in a dict input ... ok (0.011s)
2023-01-11T21:25:29.7627305Z   test_input_dict_unify (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7627417Z   test_input_flatten (jit.test_tracer.TestTracer)
2023-01-11T21:25:29.7627549Z Check that inputs to traced functions are flattened ... ok (0.011s)
2023-01-11T21:25:29.7627681Z   test_input_list_mixed_type (jit.test_tracer.TestTracer) ... ok (0.003s)
2023-01-11T21:25:29.7627804Z   test_input_list_of_tuples (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7627944Z   test_input_list_toplevel_flatten (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7628093Z   test_input_list_toplevel_flatten_direct (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7628225Z   test_input_tuple_of_dicts (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7628354Z   test_interpolate_trace (jit.test_tracer.TestTracer) ... ok (0.027s)
2023-01-11T21:25:29.7628507Z   test_large_nbr_kernel_args (jit.test_tracer.TestTracer) ... skip: requires CUDA (0.001s)
2023-01-11T21:25:29.7628637Z   test_lhs_index_fails (jit.test_tracer.TestTracer) ... ok (0.012s)
2023-01-11T21:25:29.7628764Z   test_lhs_index_trivial (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7628906Z   test_max_pool (jit.test_tracer.TestTracer) ... ok (0.013s)
2023-01-11T21:25:29.7629032Z   test_nested_inplace (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:29.7629162Z   test_non_tensor_tracing (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:29.7629279Z   test_output_unflatten (jit.test_tracer.TestTracer)
2023-01-11T21:25:29.7629455Z Check that outputs of traced functions retain the original structure and nesting ... expected failure (0.007s)
2023-01-11T21:25:29.7629585Z   test_python_function (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:29.7629716Z   test_python_function_tup (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7629841Z   test_repeated_input (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7629985Z   test_repeated_output (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7630111Z   test_shared_param (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:29.7630232Z   test_simple (jit.test_tracer.TestTracer) ... ok (0.015s)
2023-01-11T21:25:29.7630374Z   test_tensor_with_grad_as_constant (jit.test_tracer.TestTracer) ... ok (0.001s)
2023-01-11T21:25:29.7630512Z   test_trace_aliased_parameter (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7630640Z   test_trace_annotation (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7630763Z   test_trace_arange (jit.test_tracer.TestTracer) ... ok (0.017s)
2023-01-11T21:25:29.7630886Z   test_trace_arange_with_grad (jit.test_tracer.TestTracer) ... ok (0.018s)
2023-01-11T21:25:29.7631025Z   test_trace_autograd_function (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7631206Z   test_trace_c10_ops (jit.test_tracer.TestTracer) ... skip: Skip the test since c2 ops are not registered. (0.002s)
2023-01-11T21:25:29.7631329Z   test_trace_casts (jit.test_tracer.TestTracer) ... ok (0.036s)
2023-01-11T21:25:29.7631467Z   test_trace_checker_control_flow (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7631608Z   test_trace_checker_dot_data (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7631755Z   test_trace_checker_dropout_notrain (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7631897Z   test_trace_checker_dropout_train (jit.test_tracer.TestTracer) ... ok (0.017s)
2023-01-11T21:25:29.7632039Z   test_trace_checker_inplace_on_view (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7632169Z   test_trace_checker_memoization (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7632304Z   test_trace_checker_slice_lhs (jit.test_tracer.TestTracer) ... ok (0.011s)
2023-01-11T21:25:29.7632453Z   test_trace_checking_with_global_name (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7632925Z   test_trace_contiguous (jit.test_tracer.TestTracer) ... /var/lib/jenkins/workspace/test/jit/test_tracer.py:1673: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:29.7633066Z   self.assertNotEqual(x.storage().data_ptr(), y.storage().data_ptr())
2023-01-11T21:25:29.7633134Z ok (0.008s)
2023-01-11T21:25:29.7633282Z   test_trace_contiguous_short_circuit (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:29.7633405Z   test_trace_detach (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7633539Z   test_trace_detach_inplace (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7633679Z   test_trace_detach_inplace_redispatch (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:29.7633817Z   test_trace_detach_redispatch (jit.test_tracer.TestTracer) ... ok (0.002s)
2023-01-11T21:25:29.7633951Z   test_trace_dict_input (jit.test_tracer.TestTracer) ... ok (0.010s)
2023-01-11T21:25:29.7634084Z   test_trace_dict_output (jit.test_tracer.TestTracer) ... ok (0.016s)
2023-01-11T21:25:29.7634246Z   test_trace_export_fns (jit.test_tracer.TestTracer) ... ok (0.018s)
2023-01-11T21:25:29.7634387Z   test_trace_export_fns_recursive (jit.test_tracer.TestTracer) ... ok (0.040s)
2023-01-11T21:25:29.7634524Z   test_trace_fork_join_and_module (jit.test_tracer.TestTracer) ... ok (0.030s)
2023-01-11T21:25:29.7634651Z   test_trace_full_dynamic_shape (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7634802Z   test_trace_func_argument_names_captured (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:29.7634928Z   test_trace_index (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7635063Z   test_trace_index_constant (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7635202Z   test_trace_indexed_assignment (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7635362Z   test_trace_inline_shape (jit.test_tracer.TestTracer) ... ok (0.014s)
2023-01-11T21:25:29.7635487Z   test_trace_inverse (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:29.7635635Z   test_trace_invert_module_hierarchy (jit.test_tracer.TestTracer) ... ok (0.018s)
2023-01-11T21:25:29.7635751Z   test_trace_legacy_ctor (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:29.7635909Z   test_trace_module_argument_names_captured (jit.test_tracer.TestTracer) ... ok (0.035s)
2023-01-11T21:25:29.7636038Z   test_trace_modulelist (jit.test_tracer.TestTracer) ... ok (0.024s)
2023-01-11T21:25:29.7636214Z   test_trace_multi_output_function (jit.test_tracer.TestTracer) ... graph(%self : __torch__.jit.test_tracer.Bar,
2023-01-11T21:25:29.7636334Z       %x : Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu),
2023-01-11T21:25:29.7636454Z       %y : Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu)):
2023-01-11T21:25:29.7636683Z   %5 : Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu) = aten::relu(%x) # /var/lib/jenkins/workspace/test/jit/test_tracer.py:1429:0
2023-01-11T21:25:29.7636883Z   %6 : Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu) = aten::relu(%y) # /var/lib/jenkins/workspace/test/jit/test_tracer.py:1430:0
2023-01-11T21:25:29.7637391Z   %9 : (Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu), Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu)) = ^Foo[inplace=0, module="jit.test_tracer", Subgraph=<Graph>]()(%5, %6) # /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py:429:0
2023-01-11T21:25:29.7637595Z   %10 : Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu), %11 : Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu) = prim::TupleUnpack(%9)
2023-01-11T21:25:29.7637804Z   %12 : (Double(1, 2, strides=[2, 1], requires_grad=0, device=cpu), Double(3, 2, strides=[2, 1], requires_grad=0, device=cpu)) = prim::TupleConstruct(%10, %11)
2023-01-11T21:25:29.7637874Z   return (%12)
2023-01-11T21:25:29.7637884Z 
2023-01-11T21:25:29.7637939Z ok (0.007s)
2023-01-11T21:25:29.7638075Z   test_trace_namedtuple (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7638216Z   test_trace_nested_datatypes (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:29.7638344Z   test_trace_nested_fn (jit.test_tracer.TestTracer) ... ok (0.014s)
2023-01-11T21:25:29.7638466Z   test_trace_numel (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:29.7638602Z   test_trace_optioanl_dtype (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7638965Z   test_trace_optional (jit.test_tracer.TestTracer) ... ok (0.013s)
2023-01-11T21:25:29.7639125Z   test_trace_partial_func_argument_names_captured (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7639240Z   test_trace_random (jit.test_tracer.TestTracer) ... ok (0.003s)
2023-01-11T21:25:29.7639371Z   test_trace_records_names (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:29.7639494Z   test_trace_save (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7639627Z   test_trace_save_load_copy (jit.test_tracer.TestTracer) ... ok (0.032s)
2023-01-11T21:25:29.7639758Z   test_trace_single_tuple (jit.test_tracer.TestTracer) ... ok (0.004s)
2023-01-11T21:25:29.7639915Z   test_trace_size (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7640047Z   test_trace_size_with_grad (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7640185Z   test_trace_skip_none_submodule (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7640296Z   test_trace_slice (jit.test_tracer.TestTracer) ... ok (0.042s)
2023-01-11T21:25:29.7640443Z   test_trace_slice_expr_complete_type (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7640573Z   test_trace_slice_full_dim (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7640722Z   test_trace_slice_setitem_dynamic_shape (jit.test_tracer.TestTracer) ... ok (0.007s)
2023-01-11T21:25:29.7640884Z   test_trace_slice_with_grad (jit.test_tracer.TestTracer) ... ok (0.044s)
2023-01-11T21:25:29.7641017Z   test_trace_tensor_factory (jit.test_tracer.TestTracer) ... ok (0.034s)
2023-01-11T21:25:29.7641142Z   test_trace_topk (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:29.7641264Z   test_trace_tuple (jit.test_tracer.TestTracer) ... ok (0.009s)
2023-01-11T21:25:29.7641399Z   test_trace_variable_instantiation (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7641520Z   test_trace_warn (jit.test_tracer.TestTracer) ... ok (0.008s)
2023-01-11T21:25:29.7641669Z   test_trace_with_conditional_property (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7641818Z   test_trace_with_nested_tensor_list_output (jit.test_tracer.TestTracer) ... ok (0.003s)
2023-01-11T21:25:29.7641961Z   test_trace_with_number_list_output (jit.test_tracer.TestTracer) ... ok (0.003s)
2023-01-11T21:25:29.7642101Z   test_trace_with_tensor_list_output (jit.test_tracer.TestTracer) ... ok (0.015s)
2023-01-11T21:25:29.7642255Z   test_traced_module_cuda (jit.test_tracer.TestTracer) ... skip: calls .cuda() (0.001s)
2023-01-11T21:25:29.7642398Z   test_tracing_backward_hook_error (jit.test_tracer.TestTracer) ... ok (0.001s)
2023-01-11T21:25:29.7642514Z   test_tracing_hooks (jit.test_tracer.TestTracer) ... ok (0.040s)
2023-01-11T21:25:29.7642653Z   test_tracing_multiple_methods (jit.test_tracer.TestTracer) ... ok (0.126s)
2023-01-11T21:25:29.7642785Z   test_typeas_trace_check (jit.test_tracer.TestTracer) ... ok (0.005s)
2023-01-11T21:25:29.7642913Z   test_wrapped_number (jit.test_tracer.TestTracer) ... ok (0.006s)
2023-01-11T21:25:29.7643052Z   test_assign_python_attr (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7643279Z Assigning a new (python-only) attribute should not change type sharing ... ok (0.034s)
2023-01-11T21:25:29.7643415Z   test_basic (jit.test_type_sharing.TestTypeSharing) ... ok (0.006s)
2023-01-11T21:25:29.7643575Z   test_builtin_function_different (jit.test_type_sharing.TestTypeSharing) ... ok (0.007s)
2023-01-11T21:25:29.7643718Z   test_builtin_function_same (jit.test_type_sharing.TestTypeSharing) ... ok (0.005s)
2023-01-11T21:25:29.7643847Z   test_constants (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7644032Z Types should be shared for identical constant values, and different for different constant values ... ok (0.010s)
2023-01-11T21:25:29.7644167Z   test_diff_attr_values (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7644299Z Types should be shared even if attribute values differ ... ok (0.006s)
2023-01-11T21:25:29.7644453Z   test_failed_attribute_compilation (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7644610Z Attributes whose type cannot be inferred should fail cleanly with nice hints ... ok (0.002s)
2023-01-11T21:25:29.7644754Z   test_ignored_fns (jit.test_type_sharing.TestTypeSharing) ... ok (0.005s)
2023-01-11T21:25:29.7644863Z   test_linear (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7644975Z Simple example with a real nn Module ... ok (0.011s)
2023-01-11T21:25:29.7645127Z   test_loaded_modules_work (jit.test_type_sharing.TestTypeSharing) ... ok (0.016s)
2023-01-11T21:25:29.7645282Z   test_module_dict_same_type_different_name (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7645477Z We should be able to differentiate between two ModuleDict instances ... ok (0.031s)
2023-01-11T21:25:29.7645612Z   test_mutate_attr_value (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7645758Z Mutating the value of an attribute should not change type sharing ... ok (0.018s)
2023-01-11T21:25:29.7645893Z   test_param_vs_attribute (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7646097Z The same module with an `foo` as a parameter vs. attribute shouldn't ... ok (0.007s)
2023-01-11T21:25:29.7646254Z   test_python_function_attribute_different (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7646394Z Different functions passed in should lead to different types ... ok (0.010s)
2023-01-11T21:25:29.7646572Z   test_python_function_attribute_same (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7646700Z Same functions passed in should lead to same types ... ok (0.007s)
2023-01-11T21:25:29.7646846Z   test_same_but_different_classes (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7646996Z Even if everything about the module is the same, different originating ... ok (0.022s)
2023-01-11T21:25:29.7647153Z   test_script_function_attribute_different (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7647284Z Different functions passed in should lead to different types ... ok (0.010s)
2023-01-11T21:25:29.7647433Z   test_script_function_attribute_same (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7647559Z Same functions passed in should lead to same types ... ok (0.007s)
2023-01-11T21:25:29.7647735Z   test_script_module_containing_traced_module (jit.test_type_sharing.TestTypeSharing) ... ok (0.021s)
2023-01-11T21:25:29.7647862Z   test_submodules (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7647989Z If submodules differ, the types should differ. ... ok (0.043s)
2023-01-11T21:25:29.7648138Z   test_tracing_gives_different_types (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7648354Z Since we can't guarantee that methods are the same between different ... ok (0.012s)
2023-01-11T21:25:29.7648497Z   test_type_not_shared_ignored_attributes (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7648630Z Test that types are not shared if the exclusion of their ... ok (0.007s)
2023-01-11T21:25:29.7648780Z   test_type_shared_ignored_attributes (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7648908Z Test that types are shared if the exclusion of their ... ok (0.005s)
2023-01-11T21:25:29.7649173Z   test_type_sharing_define_in_init (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7649306Z Tests that types between instances of a ScriptModule ... ok (0.006s)
2023-01-11T21:25:29.7649444Z   test_type_sharing_disabled (jit.test_type_sharing.TestTypeSharing)
2023-01-11T21:25:29.7649559Z Test that type sharing can be disabled. ... ok (0.019s)
2023-01-11T21:25:29.7649708Z   test_annotate_outside_init (jit.test_types.TestTypesAndAnnotation) ... ok (0.016s)
2023-01-11T21:25:29.7649856Z   test_bad_types (jit.test_types.TestTypesAndAnnotation) ... ok (0.002s)
2023-01-11T21:25:29.7650014Z   test_ignore_with_types (jit.test_types.TestTypesAndAnnotation) ... ok (0.004s)
2023-01-11T21:25:29.7650167Z   test_ignoring_module_attributes (jit.test_types.TestTypesAndAnnotation)
2023-01-11T21:25:29.7650285Z Test that module attributes can be ignored. ... ok (0.007s)
2023-01-11T21:25:29.7650451Z   test_inferred_type_error_message (jit.test_types.TestTypesAndAnnotation) ... ok (0.003s)
2023-01-11T21:25:29.7650611Z   test_mismatched_annotation (jit.test_types.TestTypesAndAnnotation) ... ok (0.001s)
2023-01-11T21:25:29.7650773Z   test_optional_no_element_type_annotation (jit.test_types.TestTypesAndAnnotation)
2023-01-11T21:25:29.7650910Z Test that using an optional with no contained types produces an error. ... ok (0.002s)
2023-01-11T21:25:29.7651058Z   test_parser_bug (jit.test_types.TestTypesAndAnnotation) ... ok (0.000s)
2023-01-11T21:25:29.7651204Z   test_pep585_type (jit.test_types.TestTypesAndAnnotation) ... ok (0.008s)
2023-01-11T21:25:29.7651407Z   test_python_callable (jit.test_types.TestTypesAndAnnotation) ... ok (0.002s)
2023-01-11T21:25:29.7651552Z   test_reannotate (jit.test_types.TestTypesAndAnnotation) ... ok (0.001s)
2023-01-11T21:25:29.7651710Z   test_tuple_no_element_type_annotation (jit.test_types.TestTypesAndAnnotation)
2023-01-11T21:25:29.7651855Z Test that using a tuple with no contained types produces an error. ... ok (0.002s)
2023-01-11T21:25:29.7652008Z   test_type_annotate_py3 (jit.test_types.TestTypesAndAnnotation) ... ok (0.011s)
2023-01-11T21:25:29.7652147Z   test_types_as_values (jit.test_types.TestTypesAndAnnotation) ... ok (0.011s)
2023-01-11T21:25:29.7652311Z   test_unimported_type_resolution (jit.test_types.TestTypesAndAnnotation) ... ok (0.002s)
2023-01-11T21:25:29.7652472Z   test_bool_list_io (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:29.7652610Z   test_dict_comprehension (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:29.7652755Z   test_dict_comprehension_scope (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:29.7652913Z   test_dict_comprehension_with_type_annotation (jit.test_typing.TestTyping) ... ok (0.007s)
2023-01-11T21:25:29.7653041Z   test_dict_in_not_in (jit.test_typing.TestTyping) ... ok (0.022s)
2023-01-11T21:25:29.7653180Z   test_dict_invalid_annotations (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:29.7653590Z   test_dict_type_refinement_annotation_key_mismatch (jit.test_typing.TestTyping) ... [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[int, str]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:29.7653726Z   File "/var/lib/jenkins/workspace/test/jit/test_typing.py", line 90
2023-01-11T21:25:29.7653793Z         def fn():
2023-01-11T21:25:29.7653867Z             l1 = [1, 2, "foo", 3]
2023-01-11T21:25:29.7653997Z                  ~~~~~~~~~~~~~~~ <--- HERE
2023-01-11T21:25:29.7654087Z             l2 = ["foo", "bar", "baz", "qux"]
2023-01-11T21:25:29.7654200Z             d: Dict[int, str] = {k : v for k, v in zip(l1, l2)}
2023-01-11T21:25:29.7654290Z  (function emitListLiteral)
2023-01-11T21:25:29.7654344Z ok (0.002s)
2023-01-11T21:25:29.7654775Z   test_dict_type_refinement_annotation_value_mismatch (jit.test_typing.TestTyping) ... [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[int, str]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:29.7654915Z   File "/var/lib/jenkins/workspace/test/jit/test_typing.py", line 104
2023-01-11T21:25:29.7654985Z         def fn():
2023-01-11T21:25:29.7655076Z             l1 = ["foo", "bar", "baz", "qux"]
2023-01-11T21:25:29.7655150Z             l2 = [1, 2, "foo", 3]
2023-01-11T21:25:29.7655274Z                  ~~~~~~~~~~~~~~~ <--- HERE
2023-01-11T21:25:29.7655389Z             d: Dict[str, int] = {k : v for k, v in zip(l1, l2)}
2023-01-11T21:25:29.7655446Z             return d
2023-01-11T21:25:29.7655532Z  (function emitListLiteral)
2023-01-11T21:25:29.7655599Z ok (0.002s)
2023-01-11T21:25:29.7655724Z   test_for_in_dict (jit.test_typing.TestTyping) ... ok (0.010s)
2023-01-11T21:25:29.7655853Z   test_for_in_string (jit.test_typing.TestTyping) ... ok (0.017s)
2023-01-11T21:25:29.7655988Z   test_for_tuple_assign (jit.test_typing.TestTyping) ... ok (0.009s)
2023-01-11T21:25:29.7656115Z   test_for_tuple_unpack (jit.test_typing.TestTyping) ... ok (0.016s)
2023-01-11T21:25:29.7656224Z   test_list_io (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:29.7656352Z   test_list_iterables (jit.test_typing.TestTyping) ... ok (0.001s)
2023-01-11T21:25:29.7656473Z   test_list_sum (jit.test_typing.TestTyping) ... ok (0.011s)
2023-01-11T21:25:29.7656641Z   test_list_type_refinement_annotation_element_mismatch (jit.test_typing.TestTyping) ... ok (0.001s)
2023-01-11T21:25:29.7656805Z   test_list_unification (jit.test_typing.TestTyping) ... ok (0.007s)
2023-01-11T21:25:29.7656936Z   test_multiple_assign (jit.test_typing.TestTyping) ... ok (0.007s)
2023-01-11T21:25:29.7657073Z   test_namedtuple_good_error (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:29.7657201Z   test_namedtuple_py2 (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:29.7657325Z   test_namedtuple_redefine (jit.test_typing.TestTyping) ... ok (0.007s)
2023-01-11T21:25:29.7657449Z   test_nested_list (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:29.7657581Z   test_opt_opt_refinement (jit.test_typing.TestTyping) ... ok (0.003s)
2023-01-11T21:25:29.7657713Z   test_optional_conversion (jit.test_typing.TestTyping) ... ok (0.013s)
2023-01-11T21:25:29.7657874Z   test_optional_refinement (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:29.7658004Z   test_optional_tuple (jit.test_typing.TestTyping) ... ok (0.008s)
2023-01-11T21:25:29.7658145Z   test_singleton_tuple_unpack (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:29.7658275Z   test_sum_list_diff_elms (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:29.7658391Z   test_sum_list_empty (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:29.7658520Z   test_sum_list_literal (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:29.7658644Z   test_sum_list_one (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:29.7658777Z   test_sum_list_wrong_type (jit.test_typing.TestTyping) ... ok (0.001s)
2023-01-11T21:25:29.7658912Z   test_tuple_assignments (jit.test_typing.TestTyping) ... ok (0.021s)
2023-01-11T21:25:29.7659043Z   test_tuple_create_return (jit.test_typing.TestTyping) ... ok (0.005s)
2023-01-11T21:25:29.7659166Z   test_tuple_io (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:29.7659295Z   test_tuple_keyword (jit.test_typing.TestTyping) ... ok (0.004s)
2023-01-11T21:25:29.7659424Z   test_tuple_specialization (jit.test_typing.TestTyping) ... ok (0.006s)
2023-01-11T21:25:29.7659556Z   test_check_union_annotation (jit.test_union.TestUnion) ... ok (0.006s)
2023-01-11T21:25:29.7659710Z   test_union_T_None_is_equivalent_to_optional_T (jit.test_union.TestUnion) ... ok (0.011s)
2023-01-11T21:25:29.7659854Z   test_union_argument_order_is_ignored (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:29.7660009Z   test_union_argument_order_is_ignored_container (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:29.7660137Z   test_union_as_annotation (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:29.7660290Z   test_union_as_annotation_in_typed_container (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:29.7660422Z   test_union_as_annotation_py2 (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:29.7660538Z   test_union_as_dict_key (jit.test_union.TestUnion) ... ok (0.008s)
2023-01-11T21:25:29.7660667Z   test_union_as_dict_value (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:29.7660807Z   test_union_as_internal_tuple_type (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:29.7660974Z   test_union_branching_does_not_autoinfer_undeclared_union (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:29.7661143Z   test_union_branching_does_not_widen_existing_inferred_type (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:29.7661311Z   test_union_branching_with_union_return_and_homogenous_types (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:29.7661474Z   test_union_does_not_replace_existing_annotated_type (jit.test_union.TestUnion) ... ok (0.001s)
2023-01-11T21:25:29.7661653Z   test_union_does_not_replace_existing_annotated_type_empty_container (jit.test_union.TestUnion) ... ok (0.001s)
2023-01-11T21:25:29.7661812Z   test_union_does_not_replace_existing_annotated_type_union (jit.test_union.TestUnion) ... ok (0.001s)
2023-01-11T21:25:29.7661949Z   test_union_in_class_constructor (jit.test_union.TestUnion) ... ok (0.010s)
2023-01-11T21:25:29.7662587Z   test_union_memory_aliasing (jit.test_union.TestUnion) ... /opt/conda/lib/python3.10/site-packages/torch/_jit_internal.py:1282: UserWarning: The inner type of a container is lost when calling torch.jit.isinstance in eager mode. For example, List[int] would become list and therefore falsely return True for List[float] or List[str].
2023-01-11T21:25:29.7662663Z   warnings.warn(
2023-01-11T21:25:29.7662730Z ok (0.010s)
2023-01-11T21:25:29.7662882Z   test_union_module_with_union_class_variable (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:29.7663036Z   test_union_module_with_union_instance_variable (jit.test_union.TestUnion) ... ok (0.010s)
2023-01-11T21:25:29.7663184Z   test_union_optional_of_union_is_flattened (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:29.7663439Z   test_union_redundant_arguments_are_skipped (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:29.7663596Z   test_union_redundant_arguments_are_skipped_container (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:29.7663763Z   test_union_redundant_arguments_are_skipped_optional (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:29.7663927Z   test_union_redundant_arguments_are_skipped_subtyping (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:29.7664058Z   test_union_return_type (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:29.7664209Z   test_union_schema_matching_on_internal_type (jit.test_union.TestUnion) ... ok (0.009s)
2023-01-11T21:25:29.7664371Z   test_union_serialization_preserves_type_annotations (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:29.7664510Z   test_union_subclasses_larger_union (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:29.7664652Z   test_union_subtractive_refinement (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:29.7664814Z   test_union_subtractive_refinement_with_container (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:29.7664937Z   test_union_type_refinement (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:29.7665093Z   test_union_type_refinement_internal_declaration (jit.test_union.TestUnion) ... ok (0.005s)
2023-01-11T21:25:29.7665267Z   test_union_type_refinement_partial_static_refinement_tuple_rhs (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:29.7665439Z   test_union_type_refinement_partial_static_refinement_union_rhs (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:29.7665593Z   test_union_type_refinement_statically_false (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:29.7665741Z   test_union_type_refinement_statically_true (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:29.7665884Z   test_union_type_refinement_tuple_rhs (jit.test_union.TestUnion) ... ok (0.011s)
2023-01-11T21:25:29.7666050Z   test_union_type_refinement_tuple_rhs_noncontained_type (jit.test_union.TestUnion) ... ok (0.008s)
2023-01-11T21:25:29.7666187Z   test_union_type_refinement_tuple_rhs_union (jit.test_union.TestUnion) ... ok (0.003s)
2023-01-11T21:25:29.7666329Z   test_union_type_refinement_union_rhs (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:29.7666474Z   test_union_variable_can_be_reassigned (jit.test_union.TestUnion) ... ok (0.008s)
2023-01-11T21:25:29.7666605Z   test_union_with_collections (jit.test_union.TestUnion) ... ok (0.007s)
2023-01-11T21:25:29.7666742Z   test_union_with_dict_assignment (jit.test_union.TestUnion) ... ok (0.029s)
2023-01-11T21:25:29.7666863Z   test_union_with_enum (jit.test_union.TestUnion) ... ok (0.012s)
2023-01-11T21:25:29.7667269Z   test_union_with_list_assignment (jit.test_union.TestUnion) ... [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[Tensor, int]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:29.7667348Z   File "<string>", line 3
2023-01-11T21:25:29.7667393Z 
2023-01-11T21:25:29.7667459Z def fn():
2023-01-11T21:25:29.7667596Z     x: Union[List[str], List[torch.Tensor]] = [torch.add(1, x) for x in [torch.arange(5), 1]]
2023-01-11T21:25:29.7667787Z                                                                         ~~~~~~~~~~~~~~~~~~~ <--- HERE
2023-01-11T21:25:29.7667902Z     if torch.jit.isinstance(x, List[torch.Tensor]):
2023-01-11T21:25:29.7667993Z         x.append(torch.tensor(3))
2023-01-11T21:25:29.7668079Z  (function emitListLiteral)
2023-01-11T21:25:29.7668419Z [W ir_emitter.cpp:4385] Warning: List consists of heterogeneous types, which means that it has been typed as containing Union[Tensor, int]. To use any of the values in this List, it will be necessary to add an `assert isinstance` statement before first use to trigger type refinement.
2023-01-11T21:25:29.7668496Z   File "<string>", line 3
2023-01-11T21:25:29.7668533Z 
2023-01-11T21:25:29.7668598Z def fn():
2023-01-11T21:25:29.7668742Z     x: Union[List[torch.Tensor], int] = [torch.add(1, x) for x in [torch.arange(5), 1]]
2023-01-11T21:25:29.7668910Z                                                                   ~~~~~~~~~~~~~~~~~~~ <--- HERE
2023-01-11T21:25:29.7669025Z     if torch.jit.isinstance(x, List[torch.Tensor]):
2023-01-11T21:25:29.7669111Z         x.append(torch.tensor(3))
2023-01-11T21:25:29.7669195Z  (function emitListLiteral)
2023-01-11T21:25:29.7669260Z ok (0.023s)
2023-01-11T21:25:29.7669394Z   test_union_with_scalar_values (jit.test_union.TestUnion) ... ok (0.004s)
2023-01-11T21:25:29.7669540Z   test_unions_of_a_single_argument_vanish (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:29.7669670Z   test_unions_of_unions_are_flattened (jit.test_union.TestUnion) ... ok (0.002s)
2023-01-11T21:25:29.7669844Z   test_factory_ops_requires_grad_fail (jit.test_unsupported_ops.TestUnsupportedOps) ... ok (0.003s)
2023-01-11T21:25:29.7669993Z   test_init_ops (jit.test_unsupported_ops.TestUnsupportedOps) ... ok (0.017s)
2023-01-11T21:25:29.7670144Z   test_add_value_to_version_map (jit.test_upgraders.TestUpgraders) ... ok (0.002s)
2023-01-11T21:25:29.7670293Z   test_aten_div_scalar_at_3 (jit.test_upgraders.TestUpgraders) ... ok (0.004s)
2023-01-11T21:25:29.7670437Z   test_aten_div_tensor_at_3 (jit.test_upgraders.TestUpgraders) ... ok (0.004s)
2023-01-11T21:25:29.7670585Z   test_aten_div_tensor_out_at_3 (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:29.7670722Z   test_aten_full_at_4 (jit.test_upgraders.TestUpgraders) ... ok (0.004s)
2023-01-11T21:25:29.7670861Z   test_aten_full_other_variants (jit.test_upgraders.TestUpgraders) ... ok (0.005s)
2023-01-11T21:25:29.7671002Z   test_aten_full_out_at_4 (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:29.7671140Z   test_aten_linspace (jit.test_upgraders.TestUpgraders) ... ok (0.002s)
2023-01-11T21:25:29.7671284Z   test_aten_linspace_out (jit.test_upgraders.TestUpgraders) ... ok (0.002s)
2023-01-11T21:25:29.7671423Z   test_aten_logspace (jit.test_upgraders.TestUpgraders) ... ok (0.002s)
2023-01-11T21:25:29.7671569Z   test_aten_logspace_out (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:29.7671720Z   test_aten_test_serialization (jit.test_upgraders.TestUpgraders) ... ok (0.005s)
2023-01-11T21:25:29.7671882Z   test_populated_test_upgrader_graph (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:29.7672027Z   test_populated_upgrader_graph (jit.test_upgraders.TestUpgraders) ... ok (0.003s)
2023-01-11T21:25:29.7672141Z   test_warn (jit.test_warn.TestWarn) ... ok (0.004s)
2023-01-11T21:25:29.7672289Z   test_warn_multiple_calls_multiple_warnings (jit.test_warn.TestWarn) ... ok (0.003s)
2023-01-11T21:25:29.7672438Z   test_warn_multiple_calls_same_func_diff_stack (jit.test_warn.TestWarn) ... ok (0.007s)
2023-01-11T21:25:29.7672562Z   test_warn_once_per_func (jit.test_warn.TestWarn) ... ok (0.006s)
2023-01-11T21:25:29.7672694Z   test_warn_once_per_func_in_loop (jit.test_warn.TestWarn) ... ok (0.007s)
2023-01-11T21:25:29.7672813Z   test_warn_only_once (jit.test_warn.TestWarn) ... ok (0.003s)
2023-01-11T21:25:29.7672978Z   test_warn_only_once_in_loop_func (jit.test_warn.TestWarn) ... ok (0.005s)
2023-01-11T21:25:29.7673062Z   test_with_as (jit.test_with.TestWith)
2023-01-11T21:25:29.7673285Z Check that with statements that use the 'as' keyword to bind expressions ... ok (0.070s)
2023-01-11T21:25:29.7673387Z   test_with_errors (jit.test_with.TestWith)
2023-01-11T21:25:29.7673628Z Check that errors related to with-statements are detected and reported correctly. ... ok (0.027s)
2023-01-11T21:25:29.7673738Z   test_with_exceptions (jit.test_with.TestWith)
2023-01-11T21:25:29.7673948Z Check that exceptions thrown in the bodies of with-statements are ... ok (0.027s)
2023-01-11T21:25:29.7674046Z   test_with_no_as (jit.test_with.TestWith)
2023-01-11T21:25:29.7674274Z Check that with statements that do not use the 'as' keyword to bind expressions ... ok (0.070s)
2023-01-11T21:25:29.7674397Z   test_with_no_grad (jit.test_with.TestWith)
2023-01-11T21:25:29.7674541Z Check that torch.no_grad() works. Most of these are adapted from ... ok (0.017s)
2023-01-11T21:25:29.7674656Z   test_with_record_function (jit.test_with.TestWith)
2023-01-11T21:25:29.7675038Z Check that torch.autograd.profiler.record_function context manager is ... STAGE:2023-01-11 21:25:28 1919:1919 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:25:29.7675290Z STAGE:2023-01-11 21:25:28 1919:1919 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:25:29.7675544Z STAGE:2023-01-11 21:25:28 1919:1919 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:25:29.7675611Z ok (0.026s)
2023-01-11T21:25:29.7675616Z 
2023-01-11T21:25:29.7675811Z ----------------------------------------------------------------------
2023-01-11T21:25:29.7675877Z Ran 2544 tests in 126.514s
2023-01-11T21:25:29.7675894Z 
2023-01-11T21:25:29.7675982Z OK (skipped=114, expected failures=2)
2023-01-11T21:25:29.7675987Z 
2023-01-11T21:25:29.7676069Z Generating XML reports...
2023-01-11T21:25:29.7676394Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_alias_analysis.TestAliasAnalysis-20230111212322.xml
2023-01-11T21:25:29.7676675Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_async.TestAsync-20230111212322.xml
2023-01-11T21:25:29.7676960Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_aten_pow.TestAtenPow-20230111212322.xml
2023-01-11T21:25:29.7677264Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_autodiff.TestAutodiffJit-20230111212322.xml
2023-01-11T21:25:29.7677632Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing-20230111212322.xml
2023-01-11T21:25:29.7677925Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_backends.TestBackends-20230111212322.xml
2023-01-11T21:25:29.7678258Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_backends.TestBackendsWithCompiler-20230111212322.xml
2023-01-11T21:25:29.7678531Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_batch_mm.TestBatchMM-20230111212322.xml
2023-01-11T21:25:29.7678823Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_builtins.TestBuiltins-20230111212322.xml
2023-01-11T21:25:29.7679113Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_class_type.TestClassType-20230111212322.xml
2023-01-11T21:25:29.7679399Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_complex.TestComplex-20230111212322.xml
2023-01-11T21:25:29.7679729Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_custom_operators.TestCustomOperators-20230111212322.xml
2023-01-11T21:25:29.7679991Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_dce.TestDCE-20230111212322.xml
2023-01-11T21:25:29.7680301Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_dataclasses.TestDataclasses-20230111212322.xml
2023-01-11T21:25:29.7680655Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_device_analysis.TestDeviceAnalysis-20230111212322.xml
2023-01-11T21:25:29.7680927Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestDict-20230111212322.xml
2023-01-11T21:25:29.7681232Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_dtype_analysis.TestDtypeAnalysis-20230111212322.xml
2023-01-11T21:25:29.7681496Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_enum.TestEnum-20230111212322.xml
2023-01-11T21:25:29.7681784Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_freezing.TestFreezing-20230111212322.xml
2023-01-11T21:25:29.7682038Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestFrontend-20230111212322.xml
2023-01-11T21:25:29.7682392Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_freezing.TestFrozenOptimizations-20230111212322.xml
2023-01-11T21:25:29.7682727Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_functional_blocks.TestFunctionalBlocks-20230111212322.xml
2023-01-11T21:25:29.7683107Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_convert_activation.TestFunctionalToInplaceActivation-20230111212322.xml
2023-01-11T21:25:29.7683403Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_attr.TestGetDefaultAttr-20230111212322.xml
2023-01-11T21:25:29.7683741Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_graph_rewrite_passes.TestGraphRewritePasses-20230111212322.xml
2023-01-11T21:25:29.7684007Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_hash.TestHash-20230111212322.xml
2023-01-11T21:25:29.7684271Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_hooks.TestHooks-20230111212322.xml
2023-01-11T21:25:29.7684577Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_ignorable_args.TestIgnorableArgs-20230111212322.xml
2023-01-11T21:25:29.7684930Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_ignore_context_manager.TestIgnoreContextManager-20230111212322.xml
2023-01-11T21:25:29.7685312Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_convert_activation.TestInplaceToFunctionalActivation-20230111212322.xml
2023-01-11T21:25:29.7685614Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_isinstance.TestIsinstance-20230111212322.xml
2023-01-11T21:25:29.7685854Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestJit-20230111212322.xml
2023-01-11T21:25:29.7686141Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestJitGeneratedModule-20230111212322.xml
2023-01-11T21:25:29.7686429Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_jit_utils.TestJitUtils-20230111212322.xml
2023-01-11T21:25:29.7686700Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestList-20230111212322.xml
2023-01-11T21:25:29.7686974Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_logging.TestLogging-20230111212322.xml
2023-01-11T21:25:29.7687292Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_freezing.TestMKLDNNReinplacing-20230111212322.xml
2023-01-11T21:25:29.7687558Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_misc.TestMisc-20230111212322.xml
2023-01-11T21:25:29.7687879Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_tracer.TestMixTracingScripting-20230111212322.xml
2023-01-11T21:25:29.7688161Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_models.TestModels-20230111212322.xml
2023-01-11T21:25:29.7688455Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_module_apis.TestModuleAPIs-20230111212322.xml
2023-01-11T21:25:29.7688787Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_module_containers.TestModuleContainers-20230111212322.xml
2023-01-11T21:25:29.7689270Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_module_interface.TestModuleInterface-20230111212322.xml
2023-01-11T21:25:29.7689559Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_modules.TestModules-20230111212322.xml
2023-01-11T21:25:29.7689851Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestNamedTuple-20230111212322.xml
2023-01-11T21:25:29.7690149Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_backend_nnapi.TestNnapiBackend-20230111212322.xml
2023-01-11T21:25:29.7690494Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_op_decompositions.TestOpDecompositions-20230111212322.xml
2023-01-11T21:25:29.7690959Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_optimize_for_mobile_preserve_debug_info.TestOptimizeForMobilePreserveDebugInfo-20230111212322.xml
2023-01-11T21:25:29.7691229Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_pdt.TestPDT-20230111212322.xml
2023-01-11T21:25:29.7691566Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_parametrization.TestParametrization-20230111212322.xml
2023-01-11T21:25:29.7691858Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_peephole.TestPeephole-20230111212322.xml
2023-01-11T21:25:29.7692134Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestProducerVersion-20230111212322.xml
2023-01-11T21:25:29.7692423Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_profiler.TestProfiler-20230111212322.xml
2023-01-11T21:25:29.7692742Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_python_bindings.TestPythonBindings-20230111212322.xml
2023-01-11T21:25:29.7693071Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_python_builtins.TestPythonBuiltinOP-20230111212322.xml
2023-01-11T21:25:29.7693348Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_python_ir.TestPythonIr-20230111212322.xml
2023-01-11T21:25:29.7693669Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_recursive_script.TestRecursiveScript-20230111212322.xml
2023-01-11T21:25:29.7693986Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_remove_mutation.TestRemoveMutation-20230111212322.xml
2023-01-11T21:25:29.7694271Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_save_load.TestSaveLoad-20230111212322.xml
2023-01-11T21:25:29.7694625Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_save_load_for_op_version.TestSaveLoadForOpVersion-20230111212322.xml
2023-01-11T21:25:29.7694876Z Generated XML report: test-reports/python-unittest/test_jit/TEST-TestScript-20230111212322.xml
2023-01-11T21:25:29.7695166Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestScriptDict-20230111212322.xml
2023-01-11T21:25:29.7695453Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestScriptList-20230111212322.xml
2023-01-11T21:25:29.7695868Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_scriptmod_ann.TestScriptModuleInstanceAttributeTypeAnnotation-20230111212322.xml
2023-01-11T21:25:29.7696183Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_script_profile.TestScriptProfile-20230111212322.xml
2023-01-11T21:25:29.7696446Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_slice.TestSlice-20230111212322.xml
2023-01-11T21:25:29.7696727Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_sparse.TestSparse-20230111212322.xml
2023-01-11T21:25:29.7697066Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_string_formatting.TestStringFormatting-20230111212322.xml
2023-01-11T21:25:29.7697423Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_symbolic_shape_analysis.TestSymbolicShapeAnalysis-20230111212322.xml
2023-01-11T21:25:29.7697771Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_builtins.TestTensorBuiltins-20230111212322.xml
2023-01-11T21:25:29.7698103Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_tensor_creation_ops.TestTensorCreationOps-20230111212322.xml
2023-01-11T21:25:29.7698415Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_tensor_methods.TestTensorMethods-20230111212322.xml
2023-01-11T21:25:29.7698711Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_torchbind.TestTorchbind-20230111212322.xml
2023-01-11T21:25:29.7698990Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_tracer.TestTracer-20230111212322.xml
2023-01-11T21:25:29.7699322Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_type_sharing.TestTypeSharing-20230111212322.xml
2023-01-11T21:25:29.7699625Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_types.TestTypesAndAnnotation-20230111212322.xml
2023-01-11T21:25:29.7699906Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_typing.TestTyping-20230111212322.xml
2023-01-11T21:25:29.7700177Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_union.TestUnion-20230111212322.xml
2023-01-11T21:25:29.7700494Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_unsupported_ops.TestUnsupportedOps-20230111212322.xml
2023-01-11T21:25:29.7700791Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_upgraders.TestUpgraders-20230111212322.xml
2023-01-11T21:25:29.7701056Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_warn.TestWarn-20230111212322.xml
2023-01-11T21:25:29.7701327Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_with.TestWith-20230111212322.xml
2023-01-11T21:25:29.7701637Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_data_parallel.TestDataParallel-20230111212322.xml
2023-01-11T21:25:29.7701955Z Generated XML report: test-reports/python-unittest/test_jit/TEST-jit.test_save_load.TestSaveLoadFlatbuffer-20230111212322.xml
2023-01-11T21:25:29.7701961Z 
2023-01-11T21:25:29.7702328Z ##[endgroup]
2023-01-11T21:25:29.7702586Z FINISHED PRINTING LOG FILE of test_jit (/var/lib/jenkins/workspace/test/test-reports/test_jit_lpu6xhd1)
2023-01-11T21:25:29.7702591Z 
2023-01-11T21:25:31.5528707Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:25:31.6282600Z Ignoring disabled issues:  []
2023-01-11T21:25:31.7535402Z Running test_masked ... [2023-01-11 21:25:31.753177]
2023-01-11T21:25:31.7537333Z Executing ['/opt/conda/bin/python', '-bb', 'test_masked.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:25:31.753466]
2023-01-11T21:25:34.3561648Z 
2023-01-11T21:25:34.3562224Z Expand the folded group to see the log file of test_masked
2023-01-11T21:25:34.3563214Z ##[group]PRINTING LOG FILE of test_masked (/var/lib/jenkins/workspace/test/test-reports/test_masked_mzzp1glv)
2023-01-11T21:25:34.3563486Z 
2023-01-11T21:25:34.3563583Z Running tests...
2023-01-11T21:25:34.3564222Z ----------------------------------------------------------------------
2023-01-11T21:25:34.3564553Z 
2023-01-11T21:25:34.3564814Z ----------------------------------------------------------------------
2023-01-11T21:25:34.3565057Z Ran 0 tests in 0.000s
2023-01-11T21:25:34.3565160Z 
2023-01-11T21:25:34.3569749Z OK
2023-01-11T21:25:34.3570009Z 
2023-01-11T21:25:34.3570168Z Generating XML reports...
2023-01-11T21:25:34.3570889Z Test results will be stored in test-reports/python-unittest/test_masked
2023-01-11T21:25:34.3571154Z 
2023-01-11T21:25:34.3571488Z ##[endgroup]
2023-01-11T21:25:34.3571889Z FINISHED PRINTING LOG FILE of test_masked (/var/lib/jenkins/workspace/test/test-reports/test_masked_mzzp1glv)
2023-01-11T21:25:34.3572099Z 
2023-01-11T21:25:36.1968953Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:25:36.2615336Z Ignoring disabled issues:  []
2023-01-11T21:25:36.2754635Z Running test_meta ... [2023-01-11 21:25:36.275179]
2023-01-11T21:25:36.2756138Z Executing ['/opt/conda/bin/python', '-bb', 'test_meta.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:25:36.275414]
2023-01-11T21:25:39.0419106Z 
2023-01-11T21:25:39.0419613Z Expand the folded group to see the log file of test_meta
2023-01-11T21:25:39.0420623Z ##[group]PRINTING LOG FILE of test_meta (/var/lib/jenkins/workspace/test/test-reports/test_meta_dkjnyeqj)
2023-01-11T21:25:39.0421003Z 
2023-01-11T21:25:39.0421144Z Running tests...
2023-01-11T21:25:39.0421823Z ----------------------------------------------------------------------
2023-01-11T21:25:39.0422667Z Test results will be stored in test-reports/python-unittest/test_meta
2023-01-11T21:25:39.0423194Z   test_channels_last (__main__.TestMetaConverter) ... ok (0.114s)
2023-01-11T21:25:39.0423638Z   test_channels_last_leaf (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:39.0424078Z   test_channels_last_non_leaf (__main__.TestMetaConverter) ... ok (0.002s)
2023-01-11T21:25:39.0425601Z   test_complex_noncontiguous_bug (__main__.TestMetaConverter) ... /var/lib/jenkins/workspace/test/test_meta.py:233: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:25:39.0426689Z   x = torch.randn((2, 2, 4, 9), dtype=torch.complex32)[:, 0, :, :]
2023-01-11T21:25:39.0427109Z ok (0.002s)
2023-01-11T21:25:39.0427610Z   test_empty_strided_non_dense_leaf (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:39.0428197Z   test_imag (__main__.TestMetaConverter) ... ok (0.002s)
2023-01-11T21:25:39.0428726Z   test_leaf (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:39.0429270Z   test_non_leaf (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:39.0430626Z   test_non_leaf_torture (__main__.TestMetaConverter) ... /var/lib/jenkins/workspace/test/test_meta.py:212: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:25:39.0431799Z   x.set_(x.storage(), 10, (2,), (2,))
2023-01-11T21:25:39.0432157Z ok (0.001s)
2023-01-11T21:25:39.0432636Z   test_requires_grad_false (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:39.0433271Z   test_tensor_outlives_converter (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:39.0433892Z   test_view_as_complex (__main__.TestMetaConverter) ... ok (0.002s)
2023-01-11T21:25:39.0434462Z   test_view_as_real (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:39.0435033Z   test_view_dtype (__main__.TestMetaConverter) ... ok (0.001s)
2023-01-11T21:25:39.0435605Z   test_view_of_leaf (__main__.TestMetaConverter) ... ok (0.003s)
2023-01-11T21:25:39.0436161Z   test_view_of_non_leaf (__main__.TestMetaConverter) ... ok (0.003s)
2023-01-11T21:25:39.0436754Z   test_view_of_view_of_leaf (__main__.TestMetaConverter) ... ok (0.002s)
2023-01-11T21:25:39.0437325Z   test_weakref (__main__.TestMetaConverter) ... ok (0.002s)
2023-01-11T21:25:39.0437639Z 
2023-01-11T21:25:39.0438047Z ----------------------------------------------------------------------
2023-01-11T21:25:39.0438544Z Ran 18 tests in 0.143s
2023-01-11T21:25:39.0438780Z 
2023-01-11T21:25:39.0438899Z OK
2023-01-11T21:25:39.0439090Z 
2023-01-11T21:25:39.0439263Z Generating XML reports...
2023-01-11T21:25:39.0440105Z Generated XML report: test-reports/python-unittest/test_meta/TEST-TestMetaConverter-20230111212538.xml
2023-01-11T21:25:39.0440593Z 
2023-01-11T21:25:39.0441101Z ##[endgroup]
2023-01-11T21:25:39.0441864Z FINISHED PRINTING LOG FILE of test_meta (/var/lib/jenkins/workspace/test/test-reports/test_meta_dkjnyeqj)
2023-01-11T21:25:39.0442453Z 
2023-01-11T21:25:40.9191096Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:25:41.0003729Z Ignoring disabled issues:  []
2023-01-11T21:25:41.0140974Z Running test_proxy_tensor ... [2023-01-11 21:25:41.013822]
2023-01-11T21:25:41.0142731Z Executing ['/opt/conda/bin/python', '-bb', 'test_proxy_tensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:25:41.014058]
2023-01-11T21:26:19.7390806Z 
2023-01-11T21:26:19.7391335Z Expand the folded group to see the log file of test_proxy_tensor
2023-01-11T21:26:19.7392251Z ##[group]PRINTING LOG FILE of test_proxy_tensor (/var/lib/jenkins/workspace/test/test-reports/test_proxy_tensor_2tiaehkk)
2023-01-11T21:26:19.7392606Z 
2023-01-11T21:26:19.7392720Z Running tests...
2023-01-11T21:26:19.7393516Z ----------------------------------------------------------------------
2023-01-11T21:26:19.7394419Z Test results will be stored in test-reports/python-unittest/test_proxy_tensor
2023-01-11T21:26:19.7410025Z   test_alias (__main__.TestFakeProxyTensor) ... ok (0.106s)
2023-01-11T21:26:19.7410484Z   test_issue82547 (__main__.TestFakeProxyTensor) ... ok (0.006s)
2023-01-11T21:26:19.7410955Z   test_meta (__main__.TestFakeProxyTensor) ... ok (0.009s)
2023-01-11T21:26:19.7411400Z   test_use_fake_and_tensor (__main__.TestFakeProxyTensor) ... ok (0.011s)
2023-01-11T21:26:19.7411869Z   test_allclose (__main__.TestGenericProxyTensorFake) ... ok (0.006s)
2023-01-11T21:26:19.7412654Z   test_amp_cache (__main__.TestGenericProxyTensorFake) ... skip: CUDA-only test (0.001s)
2023-01-11T21:26:19.7413236Z   test_constant_blowup (__main__.TestGenericProxyTensorFake) ... ok (0.006s)
2023-01-11T21:26:19.7413806Z   test_constant_proxy_tensor_mut (__main__.TestGenericProxyTensorFake) ... ok (0.011s)
2023-01-11T21:26:19.7414351Z   test_constant_random (__main__.TestGenericProxyTensorFake) ... ok (0.004s)
2023-01-11T21:26:19.7433845Z   test_constant_unbind (__main__.TestGenericProxyTensorFake) ... ok (0.004s)
2023-01-11T21:26:19.7434494Z   test_decomp_of_capture (__main__.TestGenericProxyTensorFake) ... ok (0.031s)
2023-01-11T21:26:19.7434891Z   test_decomposition_interpreter (__main__.TestGenericProxyTensorFake) ... ok (0.024s)
2023-01-11T21:26:19.7435230Z   test_inplace_metadata (__main__.TestGenericProxyTensorFake) ... ok (0.005s)
2023-01-11T21:26:19.7435661Z   test_isolated_graphmodule (__main__.TestGenericProxyTensorFake) ... ok (0.246s)
2023-01-11T21:26:19.7436054Z   test_make_fx_model_double_param (__main__.TestGenericProxyTensorFake) ... ok (0.058s)
2023-01-11T21:26:19.7436494Z   test_make_fx_model_fwd_bwd (__main__.TestGenericProxyTensorFake) ... ok (0.042s)
2023-01-11T21:26:19.7436836Z   test_make_fx_model_fwd_bwd_wgtupdate (__main__.TestGenericProxyTensorFake) ... ok (0.042s)
2023-01-11T21:26:19.7437165Z   test_make_fx_overloads (__main__.TestGenericProxyTensorFake) ... ok (0.009s)
2023-01-11T21:26:19.7437497Z   test_make_fx_reentrant_dispatch (__main__.TestGenericProxyTensorFake) ... ok (0.005s)
2023-01-11T21:26:19.7437828Z   test_make_fx_simple (__main__.TestGenericProxyTensorFake) ... ok (0.003s)
2023-01-11T21:26:19.7440437Z   test_mode_tracing_factory_function (__main__.TestGenericProxyTensorFake) ... ok (0.009s)
2023-01-11T21:26:19.7441007Z   test_partial_decomp (__main__.TestGenericProxyTensorFake) ... ok (0.099s)
2023-01-11T21:26:19.7441442Z   test_pickle_issue89626 (__main__.TestGenericProxyTensorFake) ... ok (0.006s)
2023-01-11T21:26:19.7442068Z   test_pr_86917 (__main__.TestGenericProxyTensorFake) ... ok (0.026s)
2023-01-11T21:26:19.7442529Z   test_proxy_tensor (__main__.TestGenericProxyTensorFake) ... ok (0.031s)
2023-01-11T21:26:19.7442936Z   test_proxy_tensor_mode_with_decomp_table_preserves_proxy (__main__.TestGenericProxyTensorFake) ... ok (0.020s)
2023-01-11T21:26:19.7443302Z   test_resnet18_backward_trace (__main__.TestGenericProxyTensorFake) ... ok (2.107s)
2023-01-11T21:26:19.7443629Z   test_scalar_device (__main__.TestGenericProxyTensorFake) ... ok (0.005s)
2023-01-11T21:26:19.7443921Z   test_strides (__main__.TestGenericProxyTensorFake) ... ok (0.062s)
2023-01-11T21:26:19.7444381Z   test_tensor_constants (__main__.TestGenericProxyTensorFake) ... ok (0.009s)
2023-01-11T21:26:19.7444700Z   test_trace_subclasses (__main__.TestGenericProxyTensorFake) ... ok (0.008s)
2023-01-11T21:26:19.7445014Z   test_val_metadata_mutation (__main__.TestGenericProxyTensorFake) ... ok (0.004s)
2023-01-11T21:26:19.7445332Z   test_varargs (__main__.TestGenericProxyTensorFake) ... ok (0.005s)
2023-01-11T21:26:19.7445633Z   test_allclose (__main__.TestGenericProxyTensorReal) ... ok (0.008s)
2023-01-11T21:26:19.7446102Z   test_amp_cache (__main__.TestGenericProxyTensorReal) ... skip: CUDA-only test (0.001s)
2023-01-11T21:26:19.7446423Z   test_constant_blowup (__main__.TestGenericProxyTensorReal) ... ok (0.020s)
2023-01-11T21:26:19.7446816Z   test_constant_proxy_tensor_mut (__main__.TestGenericProxyTensorReal) ... ok (0.020s)
2023-01-11T21:26:19.7447149Z   test_constant_random (__main__.TestGenericProxyTensorReal) ... ok (0.014s)
2023-01-11T21:26:19.7447456Z   test_constant_unbind (__main__.TestGenericProxyTensorReal) ... ok (0.015s)
2023-01-11T21:26:19.7447782Z   test_decomp_of_capture (__main__.TestGenericProxyTensorReal) ... ok (0.031s)
2023-01-11T21:26:19.7448118Z   test_decomposition_interpreter (__main__.TestGenericProxyTensorReal) ... ok (0.031s)
2023-01-11T21:26:19.7448453Z   test_inplace_metadata (__main__.TestGenericProxyTensorReal) ... ok (0.019s)
2023-01-11T21:26:19.7448765Z   test_isolated_graphmodule (__main__.TestGenericProxyTensorReal) ... ok (0.230s)
2023-01-11T21:26:19.7449311Z   test_make_fx_model_double_param (__main__.TestGenericProxyTensorReal) ... ok (0.057s)
2023-01-11T21:26:19.7452740Z   test_make_fx_model_fwd_bwd (__main__.TestGenericProxyTensorReal) ... ok (0.255s)
2023-01-11T21:26:19.7453383Z   test_make_fx_model_fwd_bwd_wgtupdate (__main__.TestGenericProxyTensorReal) ... ok (0.288s)
2023-01-11T21:26:19.7454922Z   test_make_fx_overloads (__main__.TestGenericProxyTensorReal) ... ok (0.025s)
2023-01-11T21:26:19.7455545Z   test_make_fx_reentrant_dispatch (__main__.TestGenericProxyTensorReal) ... ok (0.029s)
2023-01-11T21:26:19.7456033Z   test_make_fx_simple (__main__.TestGenericProxyTensorReal) ... ok (0.013s)
2023-01-11T21:26:19.7456375Z   test_mode_tracing_factory_function (__main__.TestGenericProxyTensorReal) ... ok (0.018s)
2023-01-11T21:26:19.7456706Z   test_partial_decomp (__main__.TestGenericProxyTensorReal) ... ok (0.087s)
2023-01-11T21:26:19.7457024Z   test_pickle_issue89626 (__main__.TestGenericProxyTensorReal) ... ok (0.013s)
2023-01-11T21:26:19.7457317Z   test_pr_86917 (__main__.TestGenericProxyTensorReal) ... ok (0.023s)
2023-01-11T21:26:19.7457619Z   test_proxy_tensor (__main__.TestGenericProxyTensorReal) ... ok (0.232s)
2023-01-11T21:26:19.7457976Z   test_proxy_tensor_mode_with_decomp_table_preserves_proxy (__main__.TestGenericProxyTensorReal) ... ok (0.021s)
2023-01-11T21:26:19.7458330Z   test_resnet18_backward_trace (__main__.TestGenericProxyTensorReal) ... ok (6.888s)
2023-01-11T21:26:19.7458656Z   test_scalar_device (__main__.TestGenericProxyTensorReal) ... ok (0.017s)
2023-01-11T21:26:19.7458963Z   test_strides (__main__.TestGenericProxyTensorReal) ... ok (0.067s)
2023-01-11T21:26:19.7459273Z   test_tensor_constants (__main__.TestGenericProxyTensorReal) ... ok (0.023s)
2023-01-11T21:26:19.7459576Z   test_trace_subclasses (__main__.TestGenericProxyTensorReal) ... ok (0.069s)
2023-01-11T21:26:19.7459901Z   test_val_metadata_mutation (__main__.TestGenericProxyTensorReal) ... ok (0.029s)
2023-01-11T21:26:19.7460216Z   test_varargs (__main__.TestGenericProxyTensorReal) ... ok (0.031s)
2023-01-11T21:26:19.7460514Z   test_allclose (__main__.TestGenericProxyTensorSymbolic) ... ok (0.074s)
2023-01-11T21:26:19.7460989Z   test_amp_cache (__main__.TestGenericProxyTensorSymbolic) ... skip: CUDA-only test (0.001s)
2023-01-11T21:26:19.7461344Z   test_constant_blowup (__main__.TestGenericProxyTensorSymbolic) ... ok (0.014s)
2023-01-11T21:26:19.7461692Z   test_constant_proxy_tensor_mut (__main__.TestGenericProxyTensorSymbolic) ... ok (0.024s)
2023-01-11T21:26:19.7462150Z   test_constant_random (__main__.TestGenericProxyTensorSymbolic) ... ok (0.015s)
2023-01-11T21:26:19.7462483Z   test_constant_unbind (__main__.TestGenericProxyTensorSymbolic) ... ok (0.008s)
2023-01-11T21:26:19.7462822Z   test_decomp_of_capture (__main__.TestGenericProxyTensorSymbolic) ... ok (0.043s)
2023-01-11T21:26:19.7463162Z   test_decomposition_interpreter (__main__.TestGenericProxyTensorSymbolic) ... ok (0.092s)
2023-01-11T21:26:19.7463609Z   test_inplace_metadata (__main__.TestGenericProxyTensorSymbolic) ... ok (0.038s)
2023-01-11T21:26:19.7463953Z   test_isolated_graphmodule (__main__.TestGenericProxyTensorSymbolic) ... ok (0.291s)
2023-01-11T21:26:19.7464312Z   test_make_fx_model_double_param (__main__.TestGenericProxyTensorSymbolic) ... ok (0.086s)
2023-01-11T21:26:19.7464704Z   test_make_fx_model_fwd_bwd (__main__.TestGenericProxyTensorSymbolic) ... ok (0.244s)
2023-01-11T21:26:19.7465064Z   test_make_fx_model_fwd_bwd_wgtupdate (__main__.TestGenericProxyTensorSymbolic) ... ok (0.240s)
2023-01-11T21:26:19.7465435Z   test_make_fx_overloads (__main__.TestGenericProxyTensorSymbolic) ... expected failure (0.030s)
2023-01-11T21:26:19.7465799Z   test_make_fx_reentrant_dispatch (__main__.TestGenericProxyTensorSymbolic) ... ok (0.023s)
2023-01-11T21:26:19.7466131Z   test_make_fx_simple (__main__.TestGenericProxyTensorSymbolic) ... ok (0.015s)
2023-01-11T21:26:19.7468013Z   test_mode_tracing_factory_function (__main__.TestGenericProxyTensorSymbolic) ... ok (0.040s)
2023-01-11T21:26:19.7468567Z   test_partial_decomp (__main__.TestGenericProxyTensorSymbolic) ... ok (0.129s)
2023-01-11T21:26:19.7469119Z   test_pickle_issue89626 (__main__.TestGenericProxyTensorSymbolic) ... ok (0.016s)
2023-01-11T21:26:19.7470663Z   test_pr_86917 (__main__.TestGenericProxyTensorSymbolic) ... ok (0.066s)
2023-01-11T21:26:19.7471238Z   test_proxy_tensor (__main__.TestGenericProxyTensorSymbolic) ... ok (0.238s)
2023-01-11T21:26:19.7471921Z   test_proxy_tensor_mode_with_decomp_table_preserves_proxy (__main__.TestGenericProxyTensorSymbolic) ... ok (0.034s)
2023-01-11T21:26:19.7472627Z   test_resnet18_backward_trace (__main__.TestGenericProxyTensorSymbolic) ... ok (20.972s)
2023-01-11T21:26:19.7473248Z   test_scalar_device (__main__.TestGenericProxyTensorSymbolic) ... ok (0.024s)
2023-01-11T21:26:19.7473652Z   test_strides (__main__.TestGenericProxyTensorSymbolic) ... ok (0.065s)
2023-01-11T21:26:19.7475163Z   test_tensor_constants (__main__.TestGenericProxyTensorSymbolic) ... ok (0.011s)
2023-01-11T21:26:19.7475823Z   test_trace_subclasses (__main__.TestGenericProxyTensorSymbolic) ... expected failure (0.009s)
2023-01-11T21:26:19.7476476Z   test_val_metadata_mutation (__main__.TestGenericProxyTensorSymbolic) ... ok (0.022s)
2023-01-11T21:26:19.7477012Z   test_varargs (__main__.TestGenericProxyTensorSymbolic) ... ok (0.025s)
2023-01-11T21:26:19.7477321Z   test_binary_broadcast (__main__.TestSymbolicTracing) ... ok (0.034s)
2023-01-11T21:26:19.7477596Z   test_cat (__main__.TestSymbolicTracing) ... ok (0.068s)
2023-01-11T21:26:19.7477862Z   test_debug_interpreter (__main__.TestSymbolicTracing) ... ok (0.023s)
2023-01-11T21:26:19.7478171Z   test_elementwise_meta_with_sym_numbers (__main__.TestSymbolicTracing) ... ok (0.050s)
2023-01-11T21:26:19.7478465Z   test_expand (__main__.TestSymbolicTracing) ... ok (0.039s)
2023-01-11T21:26:19.7478724Z   test_guards_equal (__main__.TestSymbolicTracing) ... ok (0.509s)
2023-01-11T21:26:19.7478994Z   test_item (__main__.TestSymbolicTracing) ... ok (0.012s)
2023-01-11T21:26:19.7479259Z   test_mega_guard (__main__.TestSymbolicTracing) ... ok (0.028s)
2023-01-11T21:26:19.7479532Z   test_metadata (__main__.TestSymbolicTracing) ... ok (0.024s)
2023-01-11T21:26:19.7479797Z   test_metadata_fresh (__main__.TestSymbolicTracing) ... ok (0.019s)
2023-01-11T21:26:19.7480088Z   test_multiply_shape (__main__.TestSymbolicTracing) ... ok (0.017s)
2023-01-11T21:26:19.7480381Z   test_neg_shape (__main__.TestSymbolicTracing) ... ok (0.028s)
2023-01-11T21:26:19.7480716Z   test_new_empty (__main__.TestSymbolicTracing) ... ok (0.034s)
2023-01-11T21:26:19.7481013Z   test_nonidentity_transitive_guards (__main__.TestSymbolicTracing) ... ok (0.149s)
2023-01-11T21:26:19.7481317Z   test_resize_from_zero (__main__.TestSymbolicTracing) ... ok (0.012s)
2023-01-11T21:26:19.7481602Z   test_return_symint (__main__.TestSymbolicTracing) ... ok (0.028s)
2023-01-11T21:26:19.7481864Z   test_rmethod (__main__.TestSymbolicTracing) ... ok (0.015s)
2023-01-11T21:26:19.7482144Z   test_size_with_tensor (__main__.TestSymbolicTracing) ... ok (0.040s)
2023-01-11T21:26:19.7482423Z   test_sqrt_size (__main__.TestSymbolicTracing) ... ok (0.015s)
2023-01-11T21:26:19.7482691Z   test_sym_storage_offset (__main__.TestSymbolicTracing) ... ok (0.021s)
2023-01-11T21:26:19.7482977Z   test_symint_to_tensor (__main__.TestSymbolicTracing) ... ok (0.030s)
2023-01-11T21:26:19.7483307Z   test_unary (__main__.TestSymbolicTracing) ... ok (0.027s)
2023-01-11T21:26:19.7483459Z 
2023-01-11T21:26:19.7483698Z ----------------------------------------------------------------------
2023-01-11T21:26:19.7483929Z Ran 113 tests in 35.708s
2023-01-11T21:26:19.7484043Z 
2023-01-11T21:26:19.7484138Z OK (skipped=3, expected failures=2)
2023-01-11T21:26:19.7484268Z 
2023-01-11T21:26:19.7484354Z Generating XML reports...
2023-01-11T21:26:19.7484772Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestFakeProxyTensor-20230111212543.xml
2023-01-11T21:26:19.7485336Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestGenericProxyTensorFake-20230111212543.xml
2023-01-11T21:26:19.7485903Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestGenericProxyTensorReal-20230111212543.xml
2023-01-11T21:26:19.7486485Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestGenericProxyTensorSymbolic-20230111212543.xml
2023-01-11T21:26:19.7487026Z Generated XML report: test-reports/python-unittest/test_proxy_tensor/TEST-TestSymbolicTracing-20230111212543.xml
2023-01-11T21:26:19.7487267Z 
2023-01-11T21:26:19.7487628Z ##[endgroup]
2023-01-11T21:26:19.7488012Z FINISHED PRINTING LOG FILE of test_proxy_tensor (/var/lib/jenkins/workspace/test/test-reports/test_proxy_tensor_2tiaehkk)
2023-01-11T21:26:19.7488229Z 
2023-01-11T21:26:21.5744318Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:21.6385793Z Ignoring disabled issues:  []
2023-01-11T21:26:21.6523291Z Running test_public_bindings ... [2023-01-11 21:26:21.652070]
2023-01-11T21:26:21.6525490Z Executing ['/opt/conda/bin/python', '-bb', 'test_public_bindings.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:21.652327]
2023-01-11T21:26:25.3979987Z 
2023-01-11T21:26:25.3980522Z Expand the folded group to see the log file of test_public_bindings
2023-01-11T21:26:25.3981530Z ##[group]PRINTING LOG FILE of test_public_bindings (/var/lib/jenkins/workspace/test/test-reports/test_public_bindings_l6qk6ffc)
2023-01-11T21:26:25.3981977Z 
2023-01-11T21:26:25.3982125Z Running tests...
2023-01-11T21:26:25.3982801Z ----------------------------------------------------------------------
2023-01-11T21:26:25.3983558Z Test results will be stored in test-reports/python-unittest/test_public_bindings
2023-01-11T21:26:25.3984109Z   test_correct_module_names (__main__.TestPublicBindings)
2023-01-11T21:26:25.3984920Z An API is considered public, if  its  `__module__` starts with `torch.` ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:25.3985423Z ok (1.929s)
2023-01-11T21:26:25.3985791Z   test_no_new_bindings (__main__.TestPublicBindings)
2023-01-11T21:26:25.3986326Z This test aims to stop the introduction of new JIT bindings into torch._C ... ok (0.002s)
2023-01-11T21:26:25.3986665Z 
2023-01-11T21:26:25.3987029Z ----------------------------------------------------------------------
2023-01-11T21:26:25.3987354Z Ran 2 tests in 1.931s
2023-01-11T21:26:25.3987519Z 
2023-01-11T21:26:25.3987604Z OK
2023-01-11T21:26:25.3987736Z 
2023-01-11T21:26:25.3988067Z Generating XML reports...
2023-01-11T21:26:25.3988693Z Generated XML report: test-reports/python-unittest/test_public_bindings/TEST-TestPublicBindings-20230111212622.xml
2023-01-11T21:26:25.3988946Z 
2023-01-11T21:26:25.3989205Z ##[endgroup]
2023-01-11T21:26:25.3989726Z FINISHED PRINTING LOG FILE of test_public_bindings (/var/lib/jenkins/workspace/test/test-reports/test_public_bindings_l6qk6ffc)
2023-01-11T21:26:25.3989953Z 
2023-01-11T21:26:27.2659218Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:27.3482304Z Ignoring disabled issues:  []
2023-01-11T21:26:27.3631249Z Running test_python_dispatch ... [2023-01-11 21:26:27.362841]
2023-01-11T21:26:27.3633669Z Executing ['/opt/conda/bin/python', '-bb', 'test_python_dispatch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:27.363102]
2023-01-11T21:26:29.4154563Z 
2023-01-11T21:26:29.4155096Z Expand the folded group to see the log file of test_python_dispatch
2023-01-11T21:26:29.4156260Z ##[group]PRINTING LOG FILE of test_python_dispatch (/var/lib/jenkins/workspace/test/test-reports/test_python_dispatch_fcx9wh2a)
2023-01-11T21:26:29.4156701Z 
2023-01-11T21:26:29.4156830Z Running tests...
2023-01-11T21:26:29.4157484Z ----------------------------------------------------------------------
2023-01-11T21:26:29.4158233Z Test results will be stored in test-reports/python-unittest/test_python_dispatch
2023-01-11T21:26:29.4158794Z   test_all_same_mode (__main__.TestPythonDispatch) ... ok (0.223s)
2023-01-11T21:26:29.4159244Z   test_autograd_in_attr (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4168620Z   test_basic (__main__.TestPythonDispatch) ... ok (0.006s)
2023-01-11T21:26:29.4169345Z   test_capture_logs_with_torch_dispatch_mode (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4169958Z   test_construct_int_tensor (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4170498Z   test_custom_autograd (__main__.TestPythonDispatch) ... ok (0.004s)
2023-01-11T21:26:29.4171076Z   test_deepcopy_non_wrapper_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4171649Z   test_deepcopy_wrapper_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4172315Z   test_deepcopy_wrapper_subclass_with_clone_returning_different_type (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4172987Z   test_detach_appears_twice_when_called_once (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4173566Z   test_device_slowpath (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4174075Z   test_dim_slowpath (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4174605Z   test_dispatch_super_call (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4175164Z   test_dispatch_super_call_list_arg (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4175743Z   test_dispatch_super_dont_autograd (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4176337Z   test_error_using_class_method_on_mode (__main__.TestPythonDispatch) ... ok (0.005s)
2023-01-11T21:26:29.4176905Z   test_exception_handling (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4177418Z   test_fancy_strides (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4177918Z   test_format (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4178410Z   test_get_cur_mode (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4178922Z   test_get_mode_stack (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4179473Z   test_index_put_where_only_index_is_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4180280Z   test_invalid_ret (__main__.TestPythonDispatch) ... /var/lib/jenkins/workspace/test/test_python_dispatch.py:447: DeprecationWarning: Please use assertRaisesRegex instead.
2023-01-11T21:26:29.4180948Z   self.assertRaisesRegexp(
2023-01-11T21:26:29.4181359Z ok (0.001s)
2023-01-11T21:26:29.4181774Z   test_is_contiguous_slow_path (__main__.TestPythonDispatch) ... ok (0.003s)
2023-01-11T21:26:29.4182486Z   test_kwarg_only (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4183038Z   test_kwarg_only_and_positional_default (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4183672Z   test_layout_slow_path (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4184154Z   test_like (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4184638Z   test_list_ret (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4185157Z   test_make_subclass_with_modes (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4185718Z   test_make_wrapper_subclass_noalloc (__main__.TestPythonDispatch) ... ok (0.000s)
2023-01-11T21:26:29.4186340Z   test_make_wrapper_subclass_propagates_metadata (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4187044Z   test_maybe_tuple_bug (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4187591Z   test_mode_with_make_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4188137Z   test_multiple_ops_subclass (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4188717Z   test_nested_push_logging_tensor_mode (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4189278Z   test_nesting_same_mode (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4189776Z   test_new_ones (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4190284Z   test_none_wrapping (__main__.TestPythonDispatch) ... ok (0.008s)
2023-01-11T21:26:29.4190822Z   test_notimplemented_mode (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4191347Z   test_optional_tensor_list (__main__.TestPythonDispatch) ... woof
2023-01-11T21:26:29.4191751Z ok (0.001s)
2023-01-11T21:26:29.4192140Z   test_out (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4192641Z   test_produce_real_type (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4193148Z   test_set_data (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4193675Z   test_shallow_copy_and_detach (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4194214Z   test_sizes_slow_path (__main__.TestPythonDispatch) ... ok (0.003s)
2023-01-11T21:26:29.4194745Z   test_standard_is_not_subclass (__main__.TestPythonDispatch) ... ok (0.000s)
2023-01-11T21:26:29.4195954Z   test_storage (__main__.TestPythonDispatch) ... /var/lib/jenkins/workspace/test/test_python_dispatch.py:469: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:26:29.4197074Z   self.assertRaises(RuntimeError, lambda: x.storage())
2023-01-11T21:26:29.4197465Z ok (0.002s)
2023-01-11T21:26:29.4198597Z   test_storage_can_be_converted_to_python_object (__main__.TestPythonDispatch) ... /var/lib/jenkins/workspace/test/test_python_dispatch.py:1197: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:26:29.4199656Z   s = torch.Storage()
2023-01-11T21:26:29.4199961Z ok (0.001s)
2023-01-11T21:26:29.4200376Z   test_strides_slow_path (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4200949Z   test_subclass_autograd_device_check (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4201505Z   test_subclass_creation (__main__.TestPythonDispatch) ... ok (0.005s)
2023-01-11T21:26:29.4202042Z   test_subclass_priority (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4202634Z   test_tolist_numpy_with_torch_dispatch_mode (__main__.TestPythonDispatch) ... ok (0.003s)
2023-01-11T21:26:29.4203212Z   test_torch_dispatch_mode_basic (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4203901Z   test_torch_dispatch_mode_respects_no_dispatch (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4204536Z   test_torch_dispatch_mode_subclass_priority (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4205154Z   test_torch_dispatch_mode_unrelated_tensors (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4205687Z   test_version (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4206231Z   test_with_mode_created_separately (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4206786Z   test_with_nested_modes (__main__.TestPythonDispatch) ... ok (0.001s)
2023-01-11T21:26:29.4207324Z   test_wrapper_subclass_serializes (__main__.TestPythonDispatch) ... ok (0.002s)
2023-01-11T21:26:29.4207913Z   test_basic (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T21:26:29.4208415Z   test_lstsq (__main__.TestPythonDispatcher) ... ok (0.009s)
2023-01-11T21:26:29.4208926Z   test_alias_analysis (__main__.TestPythonRegistration) ... ok (0.006s)
2023-01-11T21:26:29.4209560Z   test_create_new_library (__main__.TestPythonRegistration) ... ok (0.002s)
2023-01-11T21:26:29.4210091Z   test_error_for_unsupported_ns_or_kind (__main__.TestPythonRegistration) ... ok (0.001s)
2023-01-11T21:26:29.4210477Z   test_error_if_fn_not_callable (__main__.TestPythonRegistration) ... ok (0.001s)
2023-01-11T21:26:29.4210926Z   test_extend_library_with_dispatch_key_arg (__main__.TestPythonRegistration) ... ok (0.002s)
2023-01-11T21:26:29.4211681Z   test_override_aten_ops_with_multiple_libraries (__main__.TestPythonRegistration) ... /opt/conda/lib/python3.10/site-packages/torch/library.py:126: UserWarning: Overriding a previously registered kernel for the same operator and the same dispatch key
2023-01-11T21:26:29.4212234Z   operator: aten::mul.Tensor(Tensor self, Tensor other) -> Tensor
2023-01-11T21:26:29.4212548Z     registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6
2023-01-11T21:26:29.4212803Z   dispatch key: ZeroTensor
2023-01-11T21:26:29.4213120Z   previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1070
2023-01-11T21:26:29.4213562Z        new kernel: registered at /dev/null:549 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:159.)
2023-01-11T21:26:29.4213966Z   self.m.impl(name, dispatch_key if dispatch_key != "" else "CompositeImplicitAutograd", fn)
2023-01-11T21:26:29.4214211Z ok (0.004s)
2023-01-11T21:26:29.4214448Z   test_override_cpu_sum (__main__.TestPythonRegistration) ... ok (0.001s)
2023-01-11T21:26:29.4214763Z   test_override_cuda_with_jiterator (__main__.TestPythonRegistration) ... ok (0.002s)
2023-01-11T21:26:29.4214947Z 
2023-01-11T21:26:29.4215140Z ----------------------------------------------------------------------
2023-01-11T21:26:29.4215381Z Ran 72 tests in 0.356s
2023-01-11T21:26:29.4215492Z 
2023-01-11T21:26:29.4215554Z OK
2023-01-11T21:26:29.4215644Z 
2023-01-11T21:26:29.4215713Z Generating XML reports...
2023-01-11T21:26:29.4216138Z Generated XML report: test-reports/python-unittest/test_python_dispatch/TEST-TestPythonDispatch-20230111212628.xml
2023-01-11T21:26:29.4216684Z Generated XML report: test-reports/python-unittest/test_python_dispatch/TEST-TestPythonDispatcher-20230111212628.xml
2023-01-11T21:26:29.4217236Z Generated XML report: test-reports/python-unittest/test_python_dispatch/TEST-TestPythonRegistration-20230111212628.xml
2023-01-11T21:26:29.4217480Z 
2023-01-11T21:26:29.4217742Z ##[endgroup]
2023-01-11T21:26:29.4218140Z FINISHED PRINTING LOG FILE of test_python_dispatch (/var/lib/jenkins/workspace/test/test-reports/test_python_dispatch_fcx9wh2a)
2023-01-11T21:26:29.4218367Z 
2023-01-11T21:26:31.2323912Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:31.2971505Z Ignoring disabled issues:  []
2023-01-11T21:26:31.3111786Z Running test_scatter_gather_ops ... [2023-01-11 21:26:31.310866]
2023-01-11T21:26:31.3113408Z Executing ['/opt/conda/bin/python', '-bb', 'test_scatter_gather_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:31.311135]
2023-01-11T21:26:33.1449835Z 
2023-01-11T21:26:33.1450480Z Expand the folded group to see the log file of test_scatter_gather_ops
2023-01-11T21:26:33.1451653Z ##[group]PRINTING LOG FILE of test_scatter_gather_ops (/var/lib/jenkins/workspace/test/test-reports/test_scatter_gather_ops_ka9qp4dv)
2023-01-11T21:26:33.1451947Z 
2023-01-11T21:26:33.1452009Z Running tests...
2023-01-11T21:26:33.1452408Z ----------------------------------------------------------------------
2023-01-11T21:26:33.1452586Z 
2023-01-11T21:26:33.1452781Z ----------------------------------------------------------------------
2023-01-11T21:26:33.1453015Z Ran 0 tests in 0.000s
2023-01-11T21:26:33.1453115Z 
2023-01-11T21:26:33.1453383Z OK
2023-01-11T21:26:33.1453479Z 
2023-01-11T21:26:33.1453564Z Generating XML reports...
2023-01-11T21:26:33.1453906Z Test results will be stored in test-reports/python-unittest/test_scatter_gather_ops
2023-01-11T21:26:33.1454106Z 
2023-01-11T21:26:33.1454319Z ##[endgroup]
2023-01-11T21:26:33.1454730Z FINISHED PRINTING LOG FILE of test_scatter_gather_ops (/var/lib/jenkins/workspace/test/test-reports/test_scatter_gather_ops_ka9qp4dv)
2023-01-11T21:26:33.1454960Z 
2023-01-11T21:26:34.9801524Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:35.0482395Z Ignoring disabled issues:  []
2023-01-11T21:26:35.0622072Z Running test_sort_and_select ... [2023-01-11 21:26:35.061957]
2023-01-11T21:26:35.0624759Z Executing ['/opt/conda/bin/python', '-bb', 'test_sort_and_select.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:35.062205]
2023-01-11T21:26:36.8939321Z 
2023-01-11T21:26:36.8939894Z Expand the folded group to see the log file of test_sort_and_select
2023-01-11T21:26:36.8940917Z ##[group]PRINTING LOG FILE of test_sort_and_select (/var/lib/jenkins/workspace/test/test-reports/test_sort_and_select_17vi88c_)
2023-01-11T21:26:36.8941318Z 
2023-01-11T21:26:36.8941449Z Running tests...
2023-01-11T21:26:36.8942074Z ----------------------------------------------------------------------
2023-01-11T21:26:36.8942341Z 
2023-01-11T21:26:36.8942661Z ----------------------------------------------------------------------
2023-01-11T21:26:36.8943016Z Ran 0 tests in 0.000s
2023-01-11T21:26:36.8943192Z 
2023-01-11T21:26:36.8943294Z OK
2023-01-11T21:26:36.8943525Z 
2023-01-11T21:26:36.8943660Z Generating XML reports...
2023-01-11T21:26:36.8944238Z Test results will be stored in test-reports/python-unittest/test_sort_and_select
2023-01-11T21:26:36.8944534Z 
2023-01-11T21:26:36.8944966Z ##[endgroup]
2023-01-11T21:26:36.8945640Z FINISHED PRINTING LOG FILE of test_sort_and_select (/var/lib/jenkins/workspace/test/test-reports/test_sort_and_select_17vi88c_)
2023-01-11T21:26:36.8946024Z 
2023-01-11T21:26:38.7235368Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:38.7876651Z Ignoring disabled issues:  []
2023-01-11T21:26:38.8019474Z Running test_sparse ... [2023-01-11 21:26:38.801711]
2023-01-11T21:26:38.8021328Z Executing ['/opt/conda/bin/python', '-bb', 'test_sparse.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:38.801938]
2023-01-11T21:26:41.3513080Z 
2023-01-11T21:26:41.3513632Z Expand the folded group to see the log file of test_sparse
2023-01-11T21:26:41.3514670Z ##[group]PRINTING LOG FILE of test_sparse (/var/lib/jenkins/workspace/test/test-reports/test_sparse_l8sq0pxx)
2023-01-11T21:26:41.3515026Z 
2023-01-11T21:26:41.3515103Z Running tests...
2023-01-11T21:26:41.3515509Z ----------------------------------------------------------------------
2023-01-11T21:26:41.3515880Z Test results will be stored in test-reports/python-unittest/test_sparse
2023-01-11T21:26:41.3516179Z   test_basic (__main__.TestSparseMeta) ... ok (0.003s)
2023-01-11T21:26:41.3516457Z   test_cuda_from_cpu (__main__.TestSparseOneOff) ... skip: CUDA not available (0.001s)
2023-01-11T21:26:41.3517058Z   test_cuda_sparse_cpu_dense_add (__main__.TestSparseOneOff) ... skip: CUDA not available (0.001s)
2023-01-11T21:26:41.3517250Z 
2023-01-11T21:26:41.3517450Z ----------------------------------------------------------------------
2023-01-11T21:26:41.3517678Z Ran 3 tests in 0.005s
2023-01-11T21:26:41.3517789Z 
2023-01-11T21:26:41.3517861Z OK (skipped=2)
2023-01-11T21:26:41.3517967Z 
2023-01-11T21:26:41.3518051Z Generating XML reports...
2023-01-11T21:26:41.3518455Z Generated XML report: test-reports/python-unittest/test_sparse/TEST-TestSparseMeta-20230111212640.xml
2023-01-11T21:26:41.3518939Z Generated XML report: test-reports/python-unittest/test_sparse/TEST-TestSparseOneOff-20230111212640.xml
2023-01-11T21:26:41.3519163Z 
2023-01-11T21:26:41.3519389Z ##[endgroup]
2023-01-11T21:26:41.3519825Z FINISHED PRINTING LOG FILE of test_sparse (/var/lib/jenkins/workspace/test/test-reports/test_sparse_l8sq0pxx)
2023-01-11T21:26:41.3520021Z 
2023-01-11T21:26:43.2254263Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:43.3130411Z Ignoring disabled issues:  []
2023-01-11T21:26:43.3274658Z Running test_stateless ... [2023-01-11 21:26:43.327242]
2023-01-11T21:26:43.3277833Z Executing ['/opt/conda/bin/python', '-bb', 'test_stateless.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:43.327498]
2023-01-11T21:26:50.7306838Z 
2023-01-11T21:26:50.7308614Z Expand the folded group to see the log file of test_stateless
2023-01-11T21:26:50.7309637Z ##[group]PRINTING LOG FILE of test_stateless (/var/lib/jenkins/workspace/test/test-reports/test_stateless_g9ija168)
2023-01-11T21:26:50.7310010Z 
2023-01-11T21:26:50.7310183Z Running tests...
2023-01-11T21:26:50.7310820Z ----------------------------------------------------------------------
2023-01-11T21:26:50.7311526Z Test results will be stored in test-reports/python-unittest/test_stateless
2023-01-11T21:26:50.7312070Z   test_runs_with_optimize_flag (__main__.TestPythonOptimizeMode) ... ok (4.003s)
2023-01-11T21:26:50.7312653Z   test_private_stateless_warns (__main__.TestStatelessDeprecation) ... ok (1.435s)
2023-01-11T21:26:50.7313228Z   test_circular_references (__main__.TestStatelessFunctionalAPI) ... ok (0.003s)
2023-01-11T21:26:50.7313797Z   test_functional_batch_norm (__main__.TestStatelessFunctionalAPI) ... ok (0.002s)
2023-01-11T21:26:50.7314374Z   test_functional_call (__main__.TestStatelessFunctionalAPI) ... ok (0.001s)
2023-01-11T21:26:50.7315262Z   test_functional_call_with_data_parallel (__main__.TestStatelessFunctionalAPI) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:26:50.7315921Z   test_functional_call_with_gradient (__main__.TestStatelessFunctionalAPI) ... ok (0.002s)
2023-01-11T21:26:50.7316499Z   test_functional_call_with_jit (__main__.TestStatelessFunctionalAPI) ... ok (0.055s)
2023-01-11T21:26:50.7317148Z   test_reparamertize_module_fail_reset_to_original (__main__.TestStatelessFunctionalAPI) ... ok (0.027s)
2023-01-11T21:26:50.7317869Z   test_reparametrized_module_change_parametrization_original (__main__.TestStatelessFunctionalAPI) ... ok (0.004s)
2023-01-11T21:26:50.7318482Z   test_setattr (__main__.TestStatelessFunctionalAPI) ... ok (0.001s)
2023-01-11T21:26:50.7319015Z   test_tied_weights_warns (__main__.TestStatelessFunctionalAPI) ... ok (0.002s)
2023-01-11T21:26:50.7319335Z 
2023-01-11T21:26:50.7319723Z ----------------------------------------------------------------------
2023-01-11T21:26:50.7320142Z Ran 12 tests in 5.537s
2023-01-11T21:26:50.7320329Z 
2023-01-11T21:26:50.7320434Z OK (skipped=1)
2023-01-11T21:26:50.7320611Z 
2023-01-11T21:26:50.7320754Z Generating XML reports...
2023-01-11T21:26:50.7321523Z Generated XML report: test-reports/python-unittest/test_stateless/TEST-TestPythonOptimizeMode-20230111212644.xml
2023-01-11T21:26:50.7322527Z Generated XML report: test-reports/python-unittest/test_stateless/TEST-TestStatelessDeprecation-20230111212644.xml
2023-01-11T21:26:50.7323542Z Generated XML report: test-reports/python-unittest/test_stateless/TEST-TestStatelessFunctionalAPI-20230111212644.xml
2023-01-11T21:26:50.7324319Z 
2023-01-11T21:26:50.7324725Z ##[endgroup]
2023-01-11T21:26:50.7325403Z FINISHED PRINTING LOG FILE of test_stateless (/var/lib/jenkins/workspace/test/test-reports/test_stateless_g9ija168)
2023-01-11T21:26:50.7325788Z 
2023-01-11T21:26:52.5409392Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:26:52.6052738Z Ignoring disabled issues:  []
2023-01-11T21:26:52.6192564Z Running test_testing ... [2023-01-11 21:26:52.618985]
2023-01-11T21:26:52.6194799Z Executing ['/opt/conda/bin/python', '-bb', 'test_testing.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:26:52.619243]
2023-01-11T21:27:15.4602495Z 
2023-01-11T21:27:15.4602973Z Expand the folded group to see the log file of test_testing
2023-01-11T21:27:15.4607016Z ##[group]PRINTING LOG FILE of test_testing (/var/lib/jenkins/workspace/test/test-reports/test_testing_pj0p5dx5)
2023-01-11T21:27:15.4607451Z 
2023-01-11T21:27:15.4608336Z Running tests...
2023-01-11T21:27:15.4609484Z ----------------------------------------------------------------------
2023-01-11T21:27:15.4610401Z Test results will be stored in test-reports/python-unittest/test_testing
2023-01-11T21:27:15.4611200Z   test_bool (__main__.TestAssertClose) ... ok (0.003s)
2023-01-11T21:27:15.4611915Z   test_default_tolerance_selection_mismatching_dtypes (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4612694Z   test_docstring_examples (__main__.TestAssertClose) ... ok (0.031s)
2023-01-11T21:27:15.4613353Z   test_matching (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4613943Z   test_matching_atol (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4614601Z   test_matching_conjugate_bit (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4615251Z   test_matching_nan (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4615936Z   test_matching_nan_with_equal_nan (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4616612Z   test_matching_rtol (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4617236Z   test_meta (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4617855Z   test_mismatching_dtype (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4618520Z   test_mismatching_dtype_no_check (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4620161Z   test_mismatching_layout (__main__.TestAssertClose) ... /var/lib/jenkins/workspace/test/test_testing.py:619: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:27:15.4621685Z   sparse_csr = strided.to_sparse_csr()
2023-01-11T21:27:15.4622115Z ok (0.002s)
2023-01-11T21:27:15.4622643Z   test_mismatching_layout_no_check (__main__.TestAssertClose) ... ok (0.004s)
2023-01-11T21:27:15.4623429Z   test_mismatching_shape (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4624127Z   test_mismatching_stride (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4624849Z   test_mismatching_stride_no_check (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4625548Z   test_mismatching_types (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4626259Z   test_mismatching_types_subclasses (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4627027Z   test_mismatching_types_type_equality (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4627741Z   test_mismatching_values (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4628440Z   test_mismatching_values_atol (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4629159Z   test_mismatching_values_rtol (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4629743Z   test_none (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4630314Z   test_none_mismatch (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4631170Z   test_numpy (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4631775Z   test_only_atol (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4632365Z   test_only_rtol (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4632974Z   test_scalar (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4633639Z   test_unexpected_error_compare (__main__.TestAssertClose) ... ok (0.002s)
2023-01-11T21:27:15.4634370Z   test_unexpected_error_originate (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4635221Z   test_unknown_layout (__main__.TestAssertClose) ... ok (0.001s)
2023-01-11T21:27:15.4635872Z   test_unknown_type (__main__.TestAssertClose) ... ok (0.011s)
2023-01-11T21:27:15.4636735Z   test_mapping_mismatching_keys (__main__.TestAssertCloseContainer) ... ok (0.001s)
2023-01-11T21:27:15.4637550Z   test_mapping_mismatching_values_msg (__main__.TestAssertCloseContainer) ... ok (0.001s)
2023-01-11T21:27:15.4638375Z   test_sequence_mismatching_len (__main__.TestAssertCloseContainer) ... ok (0.001s)
2023-01-11T21:27:15.4639209Z   test_sequence_mismatching_values_msg (__main__.TestAssertCloseContainer) ... ok (0.001s)
2023-01-11T21:27:15.4639994Z   test_abs_diff (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:15.4640718Z   test_abs_diff_scalar (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:15.4641438Z   test_atol (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:15.4642190Z   test_identifier_scalars (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:15.4643012Z   test_identifier_tensor_likes (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:15.4643820Z   test_mismatched_elements (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:15.4644608Z   test_msg_callable (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:15.4645360Z   test_msg_str (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:15.4646107Z   test_not_close (__main__.TestAssertCloseErrorMessage) ... ok (0.007s)
2023-01-11T21:27:15.4646837Z   test_not_equal (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:15.4647568Z   test_rel_diff (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:15.4648314Z   test_rel_diff_scalar (__main__.TestAssertCloseErrorMessage) ... ok (0.001s)
2023-01-11T21:27:15.4649169Z   test_rtol (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:15.4649855Z   test_zero_div_zero (__main__.TestAssertCloseErrorMessage) ... ok (0.003s)
2023-01-11T21:27:15.4650609Z   test_matching_per_channel (__main__.TestAssertCloseQuantized) ... ok (0.002s)
2023-01-11T21:27:15.4651390Z   test_matching_per_tensor (__main__.TestAssertCloseQuantized) ... ok (0.002s)
2023-01-11T21:27:15.4652187Z   test_mismatching_is_quantized (__main__.TestAssertCloseQuantized) ... ok (0.001s)
2023-01-11T21:27:15.4652964Z   test_mismatching_qscheme (__main__.TestAssertCloseQuantized) ... ok (0.001s)
2023-01-11T21:27:15.4653726Z   test_matching (__main__.TestAssertCloseSparseBSC) ... ok (0.002s)
2023-01-11T21:27:15.4654528Z   test_mismatching_ccol_indices_msg (__main__.TestAssertCloseSparseBSC) ... ok (0.003s)
2023-01-11T21:27:15.4655377Z   test_mismatching_row_indices_msg (__main__.TestAssertCloseSparseBSC) ... ok (0.003s)
2023-01-11T21:27:15.4656161Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseBSC) ... ok (0.003s)
2023-01-11T21:27:15.4656914Z   test_matching (__main__.TestAssertCloseSparseBSR) ... ok (0.002s)
2023-01-11T21:27:15.4657684Z   test_mismatching_col_indices_msg (__main__.TestAssertCloseSparseBSR) ... ok (0.003s)
2023-01-11T21:27:15.4658524Z   test_mismatching_crow_indices_msg (__main__.TestAssertCloseSparseBSR) ... ok (0.003s)
2023-01-11T21:27:15.4659340Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseBSR) ... ok (0.003s)
2023-01-11T21:27:15.4660117Z   test_matching_coalesced (__main__.TestAssertCloseSparseCOO) ... ok (0.002s)
2023-01-11T21:27:15.4661046Z   test_matching_uncoalesced (__main__.TestAssertCloseSparseCOO) ... ok (0.001s)
2023-01-11T21:27:15.4661827Z   test_mismatching_indices_msg (__main__.TestAssertCloseSparseCOO) ... ok (0.003s)
2023-01-11T21:27:15.4662621Z   test_mismatching_nnz (__main__.TestAssertCloseSparseCOO) ... ok (0.001s)
2023-01-11T21:27:15.4663488Z   test_mismatching_sparse_dims (__main__.TestAssertCloseSparseCOO) ... ok (0.001s)
2023-01-11T21:27:15.4664291Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseCOO) ... ok (0.003s)
2023-01-11T21:27:15.4665038Z   test_matching (__main__.TestAssertCloseSparseCSC) ... ok (0.002s)
2023-01-11T21:27:15.4665817Z   test_mismatching_ccol_indices_msg (__main__.TestAssertCloseSparseCSC) ... ok (0.003s)
2023-01-11T21:27:15.4666755Z   test_mismatching_row_indices_msg (__main__.TestAssertCloseSparseCSC) ... ok (0.003s)
2023-01-11T21:27:15.4667552Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseCSC) ... ok (0.003s)
2023-01-11T21:27:15.4668386Z   test_hybrid_support (__main__.TestAssertCloseSparseCSR) ... expected failure (0.007s)
2023-01-11T21:27:15.4669180Z   test_matching (__main__.TestAssertCloseSparseCSR) ... ok (0.002s)
2023-01-11T21:27:15.4669934Z   test_mismatching_col_indices_msg (__main__.TestAssertCloseSparseCSR) ... ok (0.003s)
2023-01-11T21:27:15.4670766Z   test_mismatching_crow_indices_msg (__main__.TestAssertCloseSparseCSR) ... ok (0.003s)
2023-01-11T21:27:15.4671577Z   test_mismatching_values_msg (__main__.TestAssertCloseSparseCSR) ... ok (0.003s)
2023-01-11T21:27:15.4672306Z   test_filtering_env_var (__main__.TestFrameworkUtils) ... ok (7.443s)
2023-01-11T21:27:15.4672920Z   test_circular_dependencies (__main__.TestImports)
2023-01-11T21:27:15.4673958Z Checks that all modules inside torch can be imported ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:15.4675160Z 2023-01-11 21:27:09,457 - torch.distributed.nn.jit.instantiator - INFO - Created a temporary directory at /tmp/tmpstg7rxxl
2023-01-11T21:27:15.4676439Z 2023-01-11 21:27:09,457 - torch.distributed.nn.jit.instantiator - INFO - Writing /tmp/tmpstg7rxxl/_remote_module_non_scriptable.py
2023-01-11T21:27:15.4677122Z ok (8.167s)
2023-01-11T21:27:15.4677705Z   test_no_mutate_global_logging_on_import_path_functorch (__main__.TestImports) ... ok (1.397s)
2023-01-11T21:27:15.4678493Z   test_no_mutate_global_logging_on_import_path_torch (__main__.TestImports) ... ok (1.368s)
2023-01-11T21:27:15.4679424Z   test_no_warning_on_import (__main__.TestImports) ... /var/lib/jenkins/workspace/test/test_testing.py:1836: DeprecationWarning: Please use assertEqual instead.
2023-01-11T21:27:15.4680174Z   self.assertEquals(out, "")
2023-01-11T21:27:15.4680577Z ok (1.449s)
2023-01-11T21:27:15.4681011Z   test_sample_input (__main__.TestOpInfos) ... ok (0.002s)
2023-01-11T21:27:15.4681601Z   test_sample_input_metadata (__main__.TestOpInfos) ... ok (0.001s)
2023-01-11T21:27:15.4682269Z   test_default_names (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4683064Z   test_modules_decorator_misuse_error (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4683904Z   test_multiple_handling_of_same_param_error (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4684674Z   test_name_fn (__main__.TestTestParametrization) ... ok (0.002s)
2023-01-11T21:27:15.4685418Z   test_ops_decorator_misuse_error (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4686202Z   test_subtest_expected_failure_x_1 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4687059Z   test_subtest_expected_failure_x_2 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:15.4687923Z   test_subtest_expected_failure_x_3 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4688697Z   test_subtest_names (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4689672Z   test_two_things_subtest_expected_failure_x_1_y_4 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:15.4690407Z   test_two_things_subtest_expected_failure_x_1_y_5 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:15.4690963Z   test_two_things_subtest_expected_failure_x_1_y_6 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:15.4691499Z   test_two_things_subtest_expected_failure_x_2_y_4 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4691998Z   test_two_things_subtest_expected_failure_x_2_y_5 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4692562Z   test_two_things_subtest_expected_failure_x_2_y_6 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:15.4693107Z   test_two_things_subtest_expected_failure_x_3_y_4 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4693742Z   test_two_things_subtest_expected_failure_x_3_y_5 (__main__.TestTestParametrization) ... ok (0.001s)
2023-01-11T21:27:15.4694290Z   test_two_things_subtest_expected_failure_x_3_y_6 (__main__.TestTestParametrization) ... expected failure (0.001s)
2023-01-11T21:27:15.4694603Z 
2023-01-11T21:27:15.4694931Z ----------------------------------------------------------------------
2023-01-11T21:27:15.4695277Z Ran 103 tests in 20.040s
2023-01-11T21:27:15.4695450Z 
2023-01-11T21:27:15.4695557Z OK (expected failures=7)
2023-01-11T21:27:15.4695727Z 
2023-01-11T21:27:15.4695853Z Generating XML reports...
2023-01-11T21:27:15.4696448Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertClose-20230111212654.xml
2023-01-11T21:27:15.4697260Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseContainer-20230111212654.xml
2023-01-11T21:27:15.4698068Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseErrorMessage-20230111212654.xml
2023-01-11T21:27:15.4698904Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseQuantized-20230111212654.xml
2023-01-11T21:27:15.4699704Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseBSC-20230111212654.xml
2023-01-11T21:27:15.4700454Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseBSR-20230111212654.xml
2023-01-11T21:27:15.4701217Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseCOO-20230111212654.xml
2023-01-11T21:27:15.4702031Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseCSC-20230111212654.xml
2023-01-11T21:27:15.4702815Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestAssertCloseSparseCSR-20230111212654.xml
2023-01-11T21:27:15.4703655Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestFrameworkUtils-20230111212654.xml
2023-01-11T21:27:15.4704381Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestImports-20230111212654.xml
2023-01-11T21:27:15.4705086Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestOpInfos-20230111212654.xml
2023-01-11T21:27:15.4705815Z Generated XML report: test-reports/python-unittest/test_testing/TEST-TestTestParametrization-20230111212654.xml
2023-01-11T21:27:15.4706165Z 
2023-01-11T21:27:15.4706561Z ##[endgroup]
2023-01-11T21:27:15.4707120Z FINISHED PRINTING LOG FILE of test_testing (/var/lib/jenkins/workspace/test/test-reports/test_testing_pj0p5dx5)
2023-01-11T21:27:15.4707411Z 
2023-01-11T21:27:17.2942554Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:17.3582354Z Ignoring disabled issues:  []
2023-01-11T21:27:17.3720593Z Running test_transformers ... [2023-01-11 21:27:17.371803]
2023-01-11T21:27:17.3722836Z Executing ['/opt/conda/bin/python', '-bb', 'test_transformers.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:17.372038]
2023-01-11T21:27:24.9411662Z 
2023-01-11T21:27:24.9412412Z Expand the folded group to see the log file of test_transformers
2023-01-11T21:27:24.9413947Z ##[group]PRINTING LOG FILE of test_transformers (/var/lib/jenkins/workspace/test/test-reports/test_transformers_wxam3gc1)
2023-01-11T21:27:24.9414346Z 
2023-01-11T21:27:24.9414491Z Running tests...
2023-01-11T21:27:24.9415172Z ----------------------------------------------------------------------
2023-01-11T21:27:24.9415876Z Test results will be stored in test-reports/python-unittest/test_transformers
2023-01-11T21:27:24.9416431Z   test_bias_is_none (__main__.TestTransformers) ... ok (0.003s)
2023-01-11T21:27:24.9416992Z   test_decoder_only_layer (__main__.TestTransformers) ... skip: Fairseq not found (0.005s)
2023-01-11T21:27:24.9417630Z   test_flash_autocast_fp32_bfloat16 (__main__.TestTransformers) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:27:24.9418439Z   test_flash_autocast_fp32_float16 (__main__.TestTransformers) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:27:24.9419047Z   test_flash_fail_fp32t (__main__.TestTransformers) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:27:24.9419631Z   test_fused_sdp_choice_type_dense (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9420187Z   test_fused_sdp_choice_type_nested (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9420724Z   test_mask_check_fastpath (__main__.TestTransformers)
2023-01-11T21:27:24.9421724Z Test that fastpath is executed independently of the masks that are passed. ... /var/lib/jenkins/workspace/test/test_transformers.py:916: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:27:24.9422847Z   nested_tensor_return_value = torch.nested.nested_tensor([torch.ones((2, 2), dtype=torch.float)])
2023-01-11T21:27:24.9423372Z ok (0.011s)
2023-01-11T21:27:24.9423911Z   test_scaled_dot_product_attention_3D_input_dim_2D_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9424669Z   test_scaled_dot_product_attention_3D_input_dim_2D_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9425418Z   test_scaled_dot_product_attention_3D_input_dim_2D_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9426149Z   test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:24.9426916Z   test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9427690Z   test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9428463Z   test_scaled_dot_product_attention_3D_input_dim_3D_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9429206Z   test_scaled_dot_product_attention_3D_input_dim_3D_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9429953Z   test_scaled_dot_product_attention_3D_input_dim_3D_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9430690Z   test_scaled_dot_product_attention_3D_input_dim_3D_causal_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9431451Z   test_scaled_dot_product_attention_3D_input_dim_3D_causal_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9432216Z   test_scaled_dot_product_attention_3D_input_dim_3D_causal_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9432973Z   test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.019s)
2023-01-11T21:27:24.9433706Z   test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.015s)
2023-01-11T21:27:24.9434565Z   test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.016s)
2023-01-11T21:27:24.9435304Z   test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9436035Z   test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9436769Z   test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9437516Z   test_scaled_dot_product_attention_4D_input_dim_2D_causal_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9438339Z   test_scaled_dot_product_attention_4D_input_dim_2D_causal_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:24.9439098Z   test_scaled_dot_product_attention_4D_input_dim_2D_causal_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:24.9439840Z   test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9440574Z   test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9441315Z   test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9442038Z   test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9442800Z   test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9443562Z   test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.009s)
2023-01-11T21:27:24.9444306Z   test_scaled_dot_product_attention_4D_input_dim_no_attn_mask_dropout_p_0_0_device_cpu (__main__.TestTransformers) ... ok (0.014s)
2023-01-11T21:27:24.9445066Z   test_scaled_dot_product_attention_4D_input_dim_no_attn_mask_dropout_p_0_2_device_cpu (__main__.TestTransformers) ... ok (0.015s)
2023-01-11T21:27:24.9445790Z   test_scaled_dot_product_attention_4D_input_dim_no_attn_mask_dropout_p_0_5_device_cpu (__main__.TestTransformers) ... ok (0.016s)
2023-01-11T21:27:24.9446647Z   test_scaled_dot_product_attention_fused_kernels_packed_accuracy_type_dense_fused_kernel_flash (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.002s)
2023-01-11T21:27:24.9447655Z   test_scaled_dot_product_attention_fused_kernels_packed_accuracy_type_dense_fused_kernel_mem_efficient (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.002s)
2023-01-11T21:27:24.9448632Z   test_scaled_dot_product_attention_fused_kernels_packed_accuracy_type_nested_fused_kernel_flash (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.002s)
2023-01-11T21:27:24.9449817Z   test_scaled_dot_product_attention_fused_kernels_packed_accuracy_type_nested_fused_kernel_mem_efficient (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.002s)
2023-01-11T21:27:24.9450752Z   test_scaled_dot_product_attention_fused_kernels_packed_type_dense_is_contiguous_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9451686Z   test_scaled_dot_product_attention_fused_kernels_packed_type_dense_is_contiguous_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9452661Z   test_scaled_dot_product_attention_fused_kernels_packed_type_nested_is_contiguous_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9453739Z   test_scaled_dot_product_attention_fused_kernels_packed_type_nested_is_contiguous_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9454667Z   test_scaled_dot_product_attention_fused_kernels_type_dense_is_contiguous_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9455548Z   test_scaled_dot_product_attention_fused_kernels_type_dense_is_contiguous_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9456517Z   test_scaled_dot_product_attention_fused_kernels_type_nested_is_contiguous_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9457390Z   test_scaled_dot_product_attention_fused_kernels_type_nested_is_contiguous_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9458229Z   test_sdp_fused_grad_against_math_contiguous_inputs_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9459006Z   test_sdp_fused_grad_against_math_contiguous_inputs_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9459670Z   test_sdp_math_gradcheck_contiguous_inputs_False (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9460289Z   test_sdp_math_gradcheck_contiguous_inputs_True (__main__.TestTransformers) ... skip: Flash Attention was not built for this system (0.001s)
2023-01-11T21:27:24.9460903Z   test_sdp_runtime_dispatch (__main__.TestTransformers) ... skip: CUDA unavailable (0.002s)
2023-01-11T21:27:24.9461845Z   test_self_attn_TxT_attn_mask (__main__.TestTransformers) ... skip: 4D mask not supported yet - activate when 4D mask supported (0.001s)
2023-01-11T21:27:24.9462627Z   test_train_with_is_causal_device_cpu (__main__.TestTransformers) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:27:24.9463403Z   test_train_with_pad_and_catch_error_device_cpu (__main__.TestTransformers) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:27:24.9464214Z   test_transformerencoder_batch_first_False_training_False_enable_nested_tensor_False_device_cpu (__main__.TestTransformers) ... ok (0.030s)
2023-01-11T21:27:24.9464979Z   test_transformerencoder_batch_first_False_training_False_enable_nested_tensor_True_device_cpu (__main__.TestTransformers) ... ok (0.029s)
2023-01-11T21:27:24.9465780Z   test_transformerencoder_batch_first_False_training_True_enable_nested_tensor_False_device_cpu (__main__.TestTransformers) ... ok (0.031s)
2023-01-11T21:27:24.9466588Z   test_transformerencoder_batch_first_False_training_True_enable_nested_tensor_True_device_cpu (__main__.TestTransformers) ... ok (0.031s)
2023-01-11T21:27:24.9467342Z   test_transformerencoder_batch_first_True_training_False_enable_nested_tensor_False_device_cpu (__main__.TestTransformers) ... ok (0.026s)
2023-01-11T21:27:24.9468167Z   test_transformerencoder_batch_first_True_training_False_enable_nested_tensor_True_device_cpu (__main__.TestTransformers) ... ok (0.026s)
2023-01-11T21:27:24.9468948Z   test_transformerencoder_batch_first_True_training_True_enable_nested_tensor_False_device_cpu (__main__.TestTransformers) ... ok (0.031s)
2023-01-11T21:27:24.9469747Z   test_transformerencoder_batch_first_True_training_True_enable_nested_tensor_True_device_cpu (__main__.TestTransformers) ... ok (0.031s)
2023-01-11T21:27:24.9470576Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_False_use_autocast_False_d_model_12 (__main__.TestTransformers)
2023-01-11T21:27:24.9471779Z Test TransformerEncoder fastpath output matches slowpath output ... /var/lib/jenkins/workspace/test/test_transformers.py:215: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:27:24.9472906Z   torch.tensor(pair[0], device=device, dtype=torch.get_default_dtype()),  # float input
2023-01-11T21:27:24.9473334Z ok (0.479s)
2023-01-11T21:27:24.9473924Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_False_use_autocast_False_d_model_256 (__main__.TestTransformers)
2023-01-11T21:27:24.9474633Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.742s)
2023-01-11T21:27:24.9475375Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_False_use_autocast_True_d_model_12 (__main__.TestTransformers)
2023-01-11T21:27:24.9476796Z Test TransformerEncoder fastpath output matches slowpath output ... /opt/conda/lib/python3.10/site-packages/torch/amp/autocast_mode.py:204: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
2023-01-11T21:27:24.9477811Z   warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
2023-01-11T21:27:24.9478277Z ok (0.442s)
2023-01-11T21:27:24.9478857Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_False_use_autocast_True_d_model_256 (__main__.TestTransformers)
2023-01-11T21:27:24.9479591Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.754s)
2023-01-11T21:27:24.9480279Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_True_use_autocast_False_d_model_12 (__main__.TestTransformers)
2023-01-11T21:27:24.9480997Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.335s)
2023-01-11T21:27:24.9481746Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_True_use_autocast_False_d_model_256 (__main__.TestTransformers)
2023-01-11T21:27:24.9482484Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.609s)
2023-01-11T21:27:24.9483196Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_True_use_autocast_True_d_model_12 (__main__.TestTransformers)
2023-01-11T21:27:24.9483902Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.386s)
2023-01-11T21:27:24.9484667Z   test_transformerencoder_fastpath_device_cpu_use_torchscript_False_enable_nested_tensor_True_use_autocast_True_d_model_256 (__main__.TestTransformers)
2023-01-11T21:27:24.9485378Z Test TransformerEncoder fastpath output matches slowpath output ... ok (0.686s)
2023-01-11T21:27:24.9486089Z   test_transformerencoder_square_input_with_no_grad_False_training_False_enable_nested_tensor_False_device_cpu (__main__.TestTransformers)
2023-01-11T21:27:24.9486831Z Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has ... ok (0.007s)
2023-01-11T21:27:24.9487565Z   test_transformerencoder_square_input_with_no_grad_False_training_True_enable_nested_tensor_False_device_cpu (__main__.TestTransformers)
2023-01-11T21:27:24.9488277Z Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has ... ok (0.006s)
2023-01-11T21:27:24.9489168Z   test_transformerencoder_square_input_with_no_grad_True_training_False_enable_nested_tensor_False_device_cpu (__main__.TestTransformers)
2023-01-11T21:27:24.9489991Z Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has ... ok (0.005s)
2023-01-11T21:27:24.9490734Z   test_transformerencoder_square_input_with_no_grad_True_training_True_enable_nested_tensor_False_device_cpu (__main__.TestTransformers)
2023-01-11T21:27:24.9491454Z Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has ... ok (0.005s)
2023-01-11T21:27:24.9492124Z   test_transformerencoderlayer_src_mask_device_cpu_nhead_1 (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9492970Z   test_transformerencoderlayer_src_mask_device_cpu_nhead_4 (__main__.TestTransformers) ... ok (0.010s)
2023-01-11T21:27:24.9493665Z   test_transformerencoderlayer_src_mask_device_cpu_nhead_8 (__main__.TestTransformers) ... ok (0.002s)
2023-01-11T21:27:24.9494284Z   test_unaligned_tensors (__main__.TestTransformers) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:27:24.9494608Z 
2023-01-11T21:27:24.9494932Z ----------------------------------------------------------------------
2023-01-11T21:27:24.9495340Z Ran 82 tests in 4.994s
2023-01-11T21:27:24.9495537Z 
2023-01-11T21:27:24.9495655Z OK (skipped=25)
2023-01-11T21:27:24.9495822Z 
2023-01-11T21:27:24.9495973Z Generating XML reports...
2023-01-11T21:27:24.9496755Z Generated XML report: test-reports/python-unittest/test_transformers/TEST-TestTransformers-20230111212719.xml
2023-01-11T21:27:24.9497307Z 
2023-01-11T21:27:24.9497783Z ##[endgroup]
2023-01-11T21:27:24.9498501Z FINISHED PRINTING LOG FILE of test_transformers (/var/lib/jenkins/workspace/test/test-reports/test_transformers_wxam3gc1)
2023-01-11T21:27:24.9498912Z 
2023-01-11T21:27:26.7904294Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:26.8558109Z Ignoring disabled issues:  []
2023-01-11T21:27:26.8714098Z Running test_utils ... [2023-01-11 21:27:26.871076]
2023-01-11T21:27:26.8715791Z Executing ['/opt/conda/bin/python', '-bb', 'test_utils.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:26.871344]
2023-01-11T21:27:31.3309104Z 
2023-01-11T21:27:31.3309583Z Expand the folded group to see the log file of test_utils
2023-01-11T21:27:31.3310373Z ##[group]PRINTING LOG FILE of test_utils (/var/lib/jenkins/workspace/test/test-reports/test_utils_ihl22tdk)
2023-01-11T21:27:31.3313051Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:31.3313327Z 
2023-01-11T21:27:31.3313460Z Running tests...
2023-01-11T21:27:31.3313978Z ----------------------------------------------------------------------
2023-01-11T21:27:31.3314567Z Test results will be stored in test-reports/python-unittest/test_utils
2023-01-11T21:27:31.3314852Z   test_assert_scriptable (__main__.TestAssert) ... ok (0.230s)
2023-01-11T21:27:31.3315112Z   test_assert_true (__main__.TestAssert) ... ok (0.001s)
2023-01-11T21:27:31.3315942Z   test_bottleneck_cpu_only (__main__.TestBottleneck) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/68433 for allplatform(s) . If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:27:31.3316516Z   test_bottleneck_cuda (__main__.TestBottleneck) ... skip: No CUDA (0.000s)
2023-01-11T21:27:31.3316788Z   test_checkpoint (__main__.TestCheckpoint) ... ok (0.011s)
2023-01-11T21:27:31.3317072Z   test_checkpoint_module_list (__main__.TestCheckpoint) ... ok (0.010s)
2023-01-11T21:27:31.3317651Z   test_checkpoint_no_tensors (__main__.TestCheckpoint) ... /opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
2023-01-11T21:27:31.3318094Z   warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
2023-01-11T21:27:31.3318326Z ok (0.003s)
2023-01-11T21:27:31.3370646Z   test_checkpoint_non_tensor (__main__.TestCheckpoint) ... ok (0.001s)
2023-01-11T21:27:31.3370959Z   test_checkpoint_non_tensor_inputs_outputs (__main__.TestCheckpoint) ... ok (0.004s)
2023-01-11T21:27:31.3371541Z   test_checkpoint_not_preserve_rng_state_and_without_reentrant (__main__.TestCheckpoint) ... skip: No CUDA (0.000s)
2023-01-11T21:27:31.3372092Z   test_checkpoint_partial_grad (__main__.TestCheckpoint) ... ok (0.001s)
2023-01-11T21:27:31.3372611Z   test_checkpoint_rng_cpu (__main__.TestCheckpoint) ... ok (0.009s)
2023-01-11T21:27:31.3373085Z   test_checkpoint_rng_cuda (__main__.TestCheckpoint) ... skip: No CUDA (0.001s)
2023-01-11T21:27:31.3373683Z   test_checkpoint_sequential_deprecated_multiple_args (__main__.TestCheckpoint) ... ok (0.001s)
2023-01-11T21:27:31.3374025Z   test_checkpoint_sequential_deprecated_no_args (__main__.TestCheckpoint) ... ok (0.001s)
2023-01-11T21:27:31.3374322Z   test_checkpoint_trigger (__main__.TestCheckpoint) ... ok (0.004s)
2023-01-11T21:27:31.3374612Z   test_checkpoint_valid (__main__.TestCheckpoint) ... ok (0.003s)
2023-01-11T21:27:31.3374946Z   test_checkpointing_without_reentrant_early_free (__main__.TestCheckpoint) ... skip: Test requires CUDA (0.001s)
2023-01-11T21:27:31.3375911Z   test_smoke (__main__.TestCollectEnv) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/77345 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:27:31.3376513Z   test_cc_compiler_is_ok (__main__.TestCppExtensionUtils) ... ok (0.010s)
2023-01-11T21:27:31.3376811Z   test_cpp_compiler_is_ok (__main__.TestCppExtensionUtils) ... ok (0.009s)
2023-01-11T21:27:31.3377636Z   test_multi_drop (__main__.TestDataLoaderUtils) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82865 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:27:31.3378392Z   test_multi_keep (__main__.TestDataLoaderUtils) ... skip: FIXME: Intermittent CUDA out-of-memory error on Windows and time-out under ASAN (0.000s)
2023-01-11T21:27:31.3378761Z   test_random_seed (__main__.TestDataLoaderUtils) ... ok (0.110s)
2023-01-11T21:27:31.3379029Z   test_single_drop (__main__.TestDataLoaderUtils) ... ok (0.002s)
2023-01-11T21:27:31.3379305Z   test_single_keep (__main__.TestDataLoaderUtils) ... ok (0.001s)
2023-01-11T21:27:31.3379598Z   test_external_module_register (__main__.TestExtensionUtils) ... ok (0.002s)
2023-01-11T21:27:31.3379884Z   test_import_hipify (__main__.TestHipify) ... ok (0.000s)
2023-01-11T21:27:31.3380139Z   test_check_onnx_broadcast (__main__.TestONNXUtils) ... ok (0.001s)
2023-01-11T21:27:31.3385250Z   test_prepare_onnx_paddings (__main__.TestONNXUtils) ... ok (0.001s)
2023-01-11T21:27:31.3385789Z   test_load_standalone (__main__.TestStandaloneCPPJIT) ... ok (2.013s)
2023-01-11T21:27:31.3386104Z 
2023-01-11T21:27:31.3386391Z ----------------------------------------------------------------------
2023-01-11T21:27:31.3386633Z Ran 31 tests in 2.433s
2023-01-11T21:27:31.3386748Z 
2023-01-11T21:27:31.3386807Z OK (skipped=8)
2023-01-11T21:27:31.3386914Z 
2023-01-11T21:27:31.3386998Z Generating XML reports...
2023-01-11T21:27:31.3387391Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestAssert-20230111212728.xml
2023-01-11T21:27:31.3387871Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestCheckpoint-20230111212728.xml
2023-01-11T21:27:31.3388369Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestCppExtensionUtils-20230111212728.xml
2023-01-11T21:27:31.3388888Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestDataLoaderUtils-20230111212728.xml
2023-01-11T21:27:31.3389392Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestExtensionUtils-20230111212728.xml
2023-01-11T21:27:31.3389863Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestHipify-20230111212728.xml
2023-01-11T21:27:31.3390316Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestONNXUtils-20230111212728.xml
2023-01-11T21:27:31.3390813Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestStandaloneCPPJIT-20230111212728.xml
2023-01-11T21:27:31.3391309Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestBottleneck-20230111212728.xml
2023-01-11T21:27:31.3391772Z Generated XML report: test-reports/python-unittest/test_utils/TEST-TestCollectEnv-20230111212728.xml
2023-01-11T21:27:31.3392069Z 
2023-01-11T21:27:31.3392415Z ##[endgroup]
2023-01-11T21:27:31.3392784Z FINISHED PRINTING LOG FILE of test_utils (/var/lib/jenkins/workspace/test/test-reports/test_utils_ihl22tdk)
2023-01-11T21:27:31.3392988Z 
2023-01-11T21:27:33.1641837Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:33.2288127Z Ignoring disabled issues:  []
2023-01-11T21:27:33.2427641Z Running backends/xeon/test_launch ... [2023-01-11 21:27:33.242513]
2023-01-11T21:27:33.2430186Z Executing ['/opt/conda/bin/python', '-bb', 'backends/xeon/test_launch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:33.242753]
2023-01-11T21:27:36.6026943Z 
2023-01-11T21:27:36.6027453Z Expand the folded group to see the log file of backends/xeon/test_launch
2023-01-11T21:27:36.6028943Z ##[group]PRINTING LOG FILE of backends/xeon/test_launch (/var/lib/jenkins/workspace/test/test-reports/backends-xeon-test_launch_22ixxi0n)
2023-01-11T21:27:36.6029444Z 
2023-01-11T21:27:36.6029577Z Running tests...
2023-01-11T21:27:36.6030254Z ----------------------------------------------------------------------
2023-01-11T21:27:36.6031001Z Test results will be stored in test-reports/python-unittest/backends.xeon.test_launch
2023-01-11T21:27:36.6032562Z   test_cpu_info (__main__.TestTorchrun) ... 2023-01-11 21:27:34,713 - torch.backends.xeon.run_cpu - WARNING - Numa Aware: cores:[2, 3, 4, 5] on different NUMA nodes:[0, 1]. To avoid this behavior, please use --ncores_per_instance knob to make sure number of cores is divisible by --ncores_per_instance. Alternatively, please use --skip_cross_node_cores knob.
2023-01-11T21:27:36.6033447Z ok (0.137s)
2023-01-11T21:27:36.6034016Z   test_multi_threads (__main__.TestTorchrun) ... ok (1.630s)
2023-01-11T21:27:36.6034303Z 
2023-01-11T21:27:36.6034680Z ----------------------------------------------------------------------
2023-01-11T21:27:36.6035116Z Ran 2 tests in 1.767s
2023-01-11T21:27:36.6035296Z 
2023-01-11T21:27:36.6035387Z OK
2023-01-11T21:27:36.6035523Z 
2023-01-11T21:27:36.6035651Z Generating XML reports...
2023-01-11T21:27:36.6036375Z Generated XML report: test-reports/python-unittest/backends.xeon.test_launch/TEST-TestTorchrun-20230111212734.xml
2023-01-11T21:27:36.6036826Z 
2023-01-11T21:27:36.6037229Z ##[endgroup]
2023-01-11T21:27:36.6038013Z FINISHED PRINTING LOG FILE of backends/xeon/test_launch (/var/lib/jenkins/workspace/test/test-reports/backends-xeon-test_launch_22ixxi0n)
2023-01-11T21:27:36.6038460Z 
2023-01-11T21:27:38.4221936Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:38.4872064Z Ignoring disabled issues:  []
2023-01-11T21:27:38.5014103Z Running benchmark_utils/test_benchmark_utils ... [2023-01-11 21:27:38.501091]
2023-01-11T21:27:38.5015734Z Executing ['/opt/conda/bin/python', '-bb', 'benchmark_utils/test_benchmark_utils.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:38.501346]
2023-01-11T21:27:40.9403581Z 
2023-01-11T21:27:40.9404193Z Expand the folded group to see the log file of benchmark_utils/test_benchmark_utils
2023-01-11T21:27:40.9405505Z ##[group]PRINTING LOG FILE of benchmark_utils/test_benchmark_utils (/var/lib/jenkins/workspace/test/test-reports/benchmark_utils-test_benchmark_utils_qchm89_j)
2023-01-11T21:27:40.9406421Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:40.9406624Z 
2023-01-11T21:27:40.9406701Z Running tests...
2023-01-11T21:27:40.9406995Z ----------------------------------------------------------------------
2023-01-11T21:27:40.9407415Z Test results will be stored in test-reports/python-unittest/benchmark_utils.test_benchmark_utils
2023-01-11T21:27:40.9407748Z   test_adaptive_timer (__main__.TestBenchmarkUtils) ... ok (0.310s)
2023-01-11T21:27:40.9408103Z   test_collect_callgrind (__main__.TestBenchmarkUtils) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:27:40.9408487Z   test_collect_cpp_callgrind (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.001s)
2023-01-11T21:27:40.9408982Z   test_compare (__main__.TestBenchmarkUtils) ... ok (0.128s)
2023-01-11T21:27:40.9409445Z   test_cpp_timer (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.000s)
2023-01-11T21:27:40.9409729Z   test_fuzzer (__main__.TestBenchmarkUtils) ... ok (0.002s)
2023-01-11T21:27:40.9410022Z   test_manipulate_callgrind_stats (__main__.TestBenchmarkUtils) ... ok (0.036s)
2023-01-11T21:27:40.9410309Z   test_timer (__main__.TestBenchmarkUtils) ... ok (0.031s)
2023-01-11T21:27:40.9410609Z   test_timer_tiny_fast_snippet (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.000s)
2023-01-11T21:27:40.9410803Z 
2023-01-11T21:27:40.9411007Z ----------------------------------------------------------------------
2023-01-11T21:27:40.9411248Z Ran 9 tests in 0.510s
2023-01-11T21:27:40.9411432Z 
2023-01-11T21:27:40.9411504Z OK (skipped=4)
2023-01-11T21:27:40.9411598Z 
2023-01-11T21:27:40.9411682Z Generating XML reports...
2023-01-11T21:27:40.9412154Z Generated XML report: test-reports/python-unittest/benchmark_utils.test_benchmark_utils/TEST-TestBenchmarkUtils-20230111212740.xml
2023-01-11T21:27:40.9412411Z 
2023-01-11T21:27:40.9412644Z ##[endgroup]
2023-01-11T21:27:40.9413083Z FINISHED PRINTING LOG FILE of benchmark_utils/test_benchmark_utils (/var/lib/jenkins/workspace/test/test-reports/benchmark_utils-test_benchmark_utils_qchm89_j)
2023-01-11T21:27:40.9413335Z 
2023-01-11T21:27:42.7938663Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:42.8584732Z Ignoring disabled issues:  []
2023-01-11T21:27:42.8726273Z Running dynamo/test_aot_autograd ... [2023-01-11 21:27:42.872345]
2023-01-11T21:27:42.8728034Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:42.872600]
2023-01-11T21:27:48.5556237Z 
2023-01-11T21:27:48.5556774Z Expand the folded group to see the log file of dynamo/test_aot_autograd
2023-01-11T21:27:48.5557988Z ##[group]PRINTING LOG FILE of dynamo/test_aot_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_autograd_tgu05r7x)
2023-01-11T21:27:48.5558503Z 
2023-01-11T21:27:48.5558619Z Running tests...
2023-01-11T21:27:48.5559273Z ----------------------------------------------------------------------
2023-01-11T21:27:48.5560111Z Test results will be stored in test-reports/python-unittest/dynamo.test_aot_autograd
2023-01-11T21:27:48.5561123Z   test_LSTM (__main__.AotAutogradFallbackTests) ... [2023-01-11 21:27:47,425] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of LSTM
2023-01-11T21:27:48.5561672Z ok (3.221s)
2023-01-11T21:27:48.5562292Z   test_arg_dupe_via_dynamo_recompiles (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:48.5563006Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:27:48.5563532Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:27:48.5564159Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:27:48.5564645Z aot_autograd [('total', 3), ('ok', 3)]
2023-01-11T21:27:48.5564979Z ok (0.092s)
2023-01-11T21:27:48.5565761Z   test_arg_dupe_via_dynamo_recompiles_many_args (__main__.AotAutogradFallbackTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:27:48.5566458Z stats [('calls_captured', 28), ('fusions_possible', 24), ('unique_graphs', 4)]
2023-01-11T21:27:48.5566977Z aot_autograd [('total', 4), ('ok', 4)]
2023-01-11T21:27:48.5567282Z ok (0.168s)
2023-01-11T21:27:48.5568153Z   test_call_fn_with_non_const_inputs_aot_safe (__main__.AotAutogradFallbackTests) ... inline_call [('inline in skipfiles: tree_flatten_spec /opt/conda/lib/python3.10/site-packages/torch/fx/_pytree.py', 1)]
2023-01-11T21:27:48.5569038Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:27:48.5569710Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:27:48.5570034Z unimplemented []
2023-01-11T21:27:48.5571368Z graph_break [('inline in skipfiles: tree_flatten_spec /opt/conda/lib/python3.10/site-packages/torch/fx/_pytree.py', 1), ('call_function args: ListVariable() UserDefinedObjectVariable(LeafSpec) ', 1)]
2023-01-11T21:27:48.5572048Z ok (0.045s)
2023-01-11T21:27:48.5573021Z   test_call_fn_with_non_const_inputs_aot_unsafe (__main__.AotAutogradFallbackTests) ... inline_call [('inline in skipfiles: tree_flatten_spec /opt/conda/lib/python3.10/site-packages/torch/fx/_pytree.py', 1)]
2023-01-11T21:27:48.5573869Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:27:48.5574343Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:27:48.5574659Z unimplemented []
2023-01-11T21:27:48.5575775Z graph_break [('inline in skipfiles: tree_flatten_spec /opt/conda/lib/python3.10/site-packages/torch/fx/_pytree.py', 1), ('call_function args: ListVariable() UserDefinedObjectVariable(LeafSpec) ', 1)]
2023-01-11T21:27:48.5576554Z ok (0.020s)
2023-01-11T21:27:48.5577285Z   test_call_fn_with_non_const_inputs_aot_unsafe_control_flow (__main__.AotAutogradFallbackTests) ... frames [('total', 8), ('ok', 8)]
2023-01-11T21:27:48.5578075Z inline_call [('generic_jump TensorVariable()', 2)]
2023-01-11T21:27:48.5578450Z unimplemented []
2023-01-11T21:27:48.5578900Z graph_break [('generic_jump TensorVariable()', 2)]
2023-01-11T21:27:48.5579488Z stats [('calls_captured', 14), ('unique_graphs', 8), ('fusions_possible', 6)]
2023-01-11T21:27:48.5579841Z ok (0.044s)
2023-01-11T21:27:48.5580403Z   test_mutation (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:48.5581061Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:27:48.5581455Z ok (0.006s)
2023-01-11T21:27:48.5582012Z   test_mutation1 (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:48.5582786Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:27:48.5583189Z ok (0.126s)
2023-01-11T21:27:48.5583858Z   test_negative_testing (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:48.5584656Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:27:48.5585020Z ok (0.006s)
2023-01-11T21:27:48.5585617Z   test_negative_testing_mutation (__main__.AotAutogradFallbackTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:48.5586292Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:27:48.5586668Z ok (0.131s)
2023-01-11T21:27:48.5587322Z   test_requires_grad_fake_via_dynamo_recompiles (__main__.AotAutogradFallbackTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:27:48.5588022Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:27:48.5588536Z aot_autograd [('total', 3), ('ok', 3)]
2023-01-11T21:27:48.5588880Z ok (0.042s)
2023-01-11T21:27:48.5589093Z 
2023-01-11T21:27:48.5589477Z ----------------------------------------------------------------------
2023-01-11T21:27:48.5589943Z Ran 11 tests in 3.902s
2023-01-11T21:27:48.5590128Z 
2023-01-11T21:27:48.5590225Z OK
2023-01-11T21:27:48.5590397Z 
2023-01-11T21:27:48.5590584Z Generating XML reports...
2023-01-11T21:27:48.5591482Z Generated XML report: test-reports/python-unittest/dynamo.test_aot_autograd/TEST-AotAutogradFallbackTests-20230111212744.xml
2023-01-11T21:27:48.5591938Z 
2023-01-11T21:27:48.5592379Z ##[endgroup]
2023-01-11T21:27:48.5593089Z FINISHED PRINTING LOG FILE of dynamo/test_aot_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_autograd_tgu05r7x)
2023-01-11T21:27:48.5596883Z 
2023-01-11T21:27:50.3632170Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:50.4280121Z Ignoring disabled issues:  []
2023-01-11T21:27:50.4420294Z Running dynamo/test_aot_cudagraphs ... [2023-01-11 21:27:50.441806]
2023-01-11T21:27:50.4422428Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_aot_cudagraphs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:50.442058]
2023-01-11T21:27:52.1295562Z 
2023-01-11T21:27:52.1296201Z Expand the folded group to see the log file of dynamo/test_aot_cudagraphs
2023-01-11T21:27:52.1297826Z ##[group]PRINTING LOG FILE of dynamo/test_aot_cudagraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_cudagraphs_pa7ugkv0)
2023-01-11T21:27:52.1298462Z 
2023-01-11T21:27:52.1298624Z Running tests...
2023-01-11T21:27:52.1299449Z ----------------------------------------------------------------------
2023-01-11T21:27:52.1300556Z   test_basic (__main__.TestAotCudagraphs) ... Test results will be stored in test-reports/python-unittest/dynamo.test_aot_cudagraphs
2023-01-11T21:27:52.1301341Z skip: these tests require cuda (0.000s)
2023-01-11T21:27:52.1302014Z   test_dead_fill (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T21:27:52.1303045Z   test_dtoh (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:27:52.1303897Z   test_factory (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:27:52.1304679Z   test_htod (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:27:52.1305476Z   test_mutate_constant (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:27:52.1306291Z   test_mutate_input (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.000s)
2023-01-11T21:27:52.1307100Z   test_mutated_metadata (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T21:27:52.1307563Z 
2023-01-11T21:27:52.1308063Z ----------------------------------------------------------------------
2023-01-11T21:27:52.1308620Z Ran 8 tests in 0.004s
2023-01-11T21:27:52.1308877Z 
2023-01-11T21:27:52.1309027Z OK (skipped=8)
2023-01-11T21:27:52.1309278Z 
2023-01-11T21:27:52.1309488Z Generating XML reports...
2023-01-11T21:27:52.1310560Z Generated XML report: test-reports/python-unittest/dynamo.test_aot_cudagraphs/TEST-TestAotCudagraphs-20230111212751.xml
2023-01-11T21:27:52.1311182Z 
2023-01-11T21:27:52.1311732Z ##[endgroup]
2023-01-11T21:27:52.1312747Z FINISHED PRINTING LOG FILE of dynamo/test_aot_cudagraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_cudagraphs_pa7ugkv0)
2023-01-11T21:27:52.1313328Z 
2023-01-11T21:27:53.9512746Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:54.0163720Z Ignoring disabled issues:  []
2023-01-11T21:27:54.0305000Z Running dynamo/test_comptime ... [2023-01-11 21:27:54.030227]
2023-01-11T21:27:54.0306949Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_comptime.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:54.030472]
2023-01-11T21:27:56.1575705Z 
2023-01-11T21:27:56.1576158Z Expand the folded group to see the log file of dynamo/test_comptime
2023-01-11T21:27:56.1577320Z ##[group]PRINTING LOG FILE of dynamo/test_comptime (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_comptime_xcqmr_ts)
2023-01-11T21:27:56.1577833Z 
2023-01-11T21:27:56.1577922Z Running tests...
2023-01-11T21:27:56.1578334Z ----------------------------------------------------------------------
2023-01-11T21:27:56.1578723Z Test results will be stored in test-reports/python-unittest/dynamo.test_comptime
2023-01-11T21:27:56.1579008Z   test_get_local (__main__.ComptimeTests) ... ok (0.241s)
2023-01-11T21:27:56.1579358Z   test_graph_break (__main__.ComptimeTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:56.1579715Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:27:56.1579981Z frames [('total', 6), ('ok', 6)]
2023-01-11T21:27:56.1580290Z stats [('calls_captured', 5), ('unique_graphs', 4), ('fusions_possible', 1)]
2023-01-11T21:27:56.1580521Z unimplemented []
2023-01-11T21:27:56.1580774Z graph_break [('ComptimeContext.graph_break', 2)]
2023-01-11T21:27:56.1581078Z inline_call [('ComptimeContext.graph_break', 1)]
2023-01-11T21:27:56.1581286Z ok (0.018s)
2023-01-11T21:27:56.1581559Z   test_print_bt (__main__.ComptimeTests) ...   File "/var/lib/jenkins/workspace/test/dynamo/test_comptime.py", line 152, in f
2023-01-11T21:27:56.1582028Z     y = g(y)
2023-01-11T21:27:56.1582267Z   File "/var/lib/jenkins/workspace/test/dynamo/test_comptime.py", line 145, in g
2023-01-11T21:27:56.1582511Z     comptime.print_bt()
2023-01-11T21:27:56.1582618Z 
2023-01-11T21:27:56.1582738Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:56.1582920Z inline_call []
2023-01-11T21:27:56.1583212Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:27:56.1583500Z ok (0.088s)
2023-01-11T21:27:56.1583735Z   test_print_disas (__main__.ComptimeTests) ...  54           0 LOAD_FAST                0 (x)
2023-01-11T21:27:56.1583987Z               2 LOAD_CONST               1 (2)
2023-01-11T21:27:56.1584170Z               4 BINARY_MULTIPLY
2023-01-11T21:27:56.1584435Z               6 STORE_FAST               1 (y)
2023-01-11T21:27:56.1584559Z 
2023-01-11T21:27:56.1584657Z  56           8 LOAD_GLOBAL              0 (comptime)
2023-01-11T21:27:56.1584788Z 
2023-01-11T21:27:56.1584979Z  57          10 LOAD_CONST               2 (<code object _ at 0x7f6726206810, file "/var/lib/jenkins/workspace/test/dynamo/test_comptime.py", line 56>)
2023-01-11T21:27:56.1585400Z              12 LOAD_CONST               3 ('ComptimeTests.test_print_disas.<locals>.f.<locals>._')
2023-01-11T21:27:56.1585660Z              14 MAKE_FUNCTION            0
2023-01-11T21:27:56.1585861Z              16 CALL_FUNCTION            1
2023-01-11T21:27:56.1586050Z              18 STORE_FAST               2 (_)
2023-01-11T21:27:56.1586170Z 
2023-01-11T21:27:56.1586267Z  60          20 LOAD_GLOBAL              0 (comptime)
2023-01-11T21:27:56.1586494Z              22 LOAD_METHOD              1 (print_disas)
2023-01-11T21:27:56.1586705Z              24 CALL_METHOD              0
2023-01-11T21:27:56.1586910Z     -->      26 POP_TOP
2023-01-11T21:27:56.1587019Z 
2023-01-11T21:27:56.1587105Z  62          28 LOAD_FAST                1 (y)
2023-01-11T21:27:56.1587308Z              30 LOAD_CONST               4 (3)
2023-01-11T21:27:56.1587480Z              32 BINARY_ADD
2023-01-11T21:27:56.1587658Z              34 RETURN_VALUE
2023-01-11T21:27:56.1587768Z 
2023-01-11T21:27:56.1587884Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:56.1588055Z inline_call []
2023-01-11T21:27:56.1588348Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:27:56.1588572Z ok (0.007s)
2023-01-11T21:27:56.1588765Z   test_print_graph (__main__.ComptimeTests) ... 
2023-01-11T21:27:56.1588905Z 
2023-01-11T21:27:56.1588910Z 
2023-01-11T21:27:56.1589004Z def forward(self, x : torch.Tensor):
2023-01-11T21:27:56.1589279Z     # File: /var/lib/jenkins/workspace/test/dynamo/test_comptime.py:26, code: y = x * 2
2023-01-11T21:27:56.1589522Z     mul = x * 2;  x = None
2023-01-11T21:27:56.1589675Z     
2023-01-11T21:27:56.1589883Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:56.1590063Z inline_call []
2023-01-11T21:27:56.1590344Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:27:56.1590571Z ok (0.006s)
2023-01-11T21:27:56.1590778Z   test_print_guards (__main__.ComptimeTests) ... -
2023-01-11T21:27:56.1591022Z             local 'x' TENSOR_MATCH
2023-01-11T21:27:56.1591199Z             {
2023-01-11T21:27:56.1591410Z                 'guard_types': None,
2023-01-11T21:27:56.1591615Z                 'code': None,
2023-01-11T21:27:56.1591836Z                 'obj_weakref': None
2023-01-11T21:27:56.1592067Z                 'guarded_class': None
2023-01-11T21:27:56.1592232Z             }
2023-01-11T21:27:56.1592386Z             
2023-01-11T21:27:56.1592592Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:56.1592757Z inline_call []
2023-01-11T21:27:56.1593046Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:27:56.1593269Z ok (0.006s)
2023-01-11T21:27:56.1593497Z   test_print_locals (__main__.ComptimeTests) ... x = TensorVariable()
2023-01-11T21:27:56.1593717Z y = TensorVariable()
2023-01-11T21:27:56.1606591Z _ = ConstantVariable(NoneType)
2023-01-11T21:27:56.1606913Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:56.1607087Z inline_call []
2023-01-11T21:27:56.1607392Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:27:56.1607622Z ok (0.006s)
2023-01-11T21:27:56.1607921Z   test_print_value_stack (__main__.ComptimeTests) ... - TensorVariable()
2023-01-11T21:27:56.1608189Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:27:56.1608370Z inline_call []
2023-01-11T21:27:56.1608661Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:27:56.1608874Z ok (0.007s)
2023-01-11T21:27:56.1608975Z 
2023-01-11T21:27:56.1609363Z ----------------------------------------------------------------------
2023-01-11T21:27:56.1609608Z Ran 8 tests in 0.379s
2023-01-11T21:27:56.1609864Z 
2023-01-11T21:27:56.1609913Z OK
2023-01-11T21:27:56.1610003Z 
2023-01-11T21:27:56.1610089Z Generating XML reports...
2023-01-11T21:27:56.1610515Z Generated XML report: test-reports/python-unittest/dynamo.test_comptime/TEST-ComptimeTests-20230111212755.xml
2023-01-11T21:27:56.1610748Z 
2023-01-11T21:27:56.1611017Z ##[endgroup]
2023-01-11T21:27:56.1611414Z FINISHED PRINTING LOG FILE of dynamo/test_comptime (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_comptime_xcqmr_ts)
2023-01-11T21:27:56.1611643Z 
2023-01-11T21:27:58.0078649Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:27:58.0726565Z Ignoring disabled issues:  []
2023-01-11T21:27:58.0868499Z Running dynamo/test_dynamic_shapes ... [2023-01-11 21:27:58.086579]
2023-01-11T21:27:58.0870563Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_dynamic_shapes.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:27:58.086834]
2023-01-11T21:28:38.0959170Z 
2023-01-11T21:28:38.0961380Z Expand the folded group to see the log file of dynamo/test_dynamic_shapes
2023-01-11T21:28:38.0962386Z ##[group]PRINTING LOG FILE of dynamo/test_dynamic_shapes (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_dynamic_shapes_xm_m_su8)
2023-01-11T21:28:38.0962819Z 
2023-01-11T21:28:38.0962954Z Running tests...
2023-01-11T21:28:38.0963566Z ----------------------------------------------------------------------
2023-01-11T21:28:38.0966236Z Test results will be stored in test-reports/python-unittest/dynamo.test_dynamic_shapes
2023-01-11T21:28:38.0966723Z   test_dict_return_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.383s)
2023-01-11T21:28:38.0967648Z   test_dict_return_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.0968401Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.0968761Z ok (0.073s)
2023-01-11T21:28:38.0969763Z   test_dupes_2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.0970135Z ok (0.027s)
2023-01-11T21:28:38.1010462Z   test_dupes_2_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1011111Z ok (0.039s)
2023-01-11T21:28:38.1012052Z   test_dupes_and_bypass_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1012646Z ok (0.031s)
2023-01-11T21:28:38.1013620Z   test_dupes_and_bypass_reorder_with_non_tensor_arg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1014277Z ok (0.030s)
2023-01-11T21:28:38.1015563Z   test_dupes_and_bypass_reorder_with_non_tensor_arg_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1016149Z ok (0.041s)
2023-01-11T21:28:38.1016660Z   test_dupes_and_bypass_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1016995Z ok (0.040s)
2023-01-11T21:28:38.1017824Z   test_dupes_and_bypass_with_non_tensor_arg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1018470Z ok (0.029s)
2023-01-11T21:28:38.1019268Z   test_dupes_and_bypass_with_non_tensor_arg_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1019794Z ok (0.040s)
2023-01-11T21:28:38.1020985Z   test_dupes_and_bypass_with_non_tensor_output_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1021765Z ok (0.033s)
2023-01-11T21:28:38.1022985Z   test_dupes_and_bypass_with_non_tensor_output_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1023858Z ok (0.033s)
2023-01-11T21:28:38.1024868Z   test_dupes_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1025568Z ok (0.026s)
2023-01-11T21:28:38.1026660Z   test_dupes_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1027379Z ok (0.038s)
2023-01-11T21:28:38.1028527Z   test_export_compare_optimize_with_make_fx_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:38.1029305Z ok (0.322s)
2023-01-11T21:28:38.1029983Z   test_export_decomp_asserts_bad_args_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:38.1030951Z   test_export_decomp_asserts_bad_args_mode_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:38.1032327Z   test_export_decomp_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1033055Z ok (0.091s)
2023-01-11T21:28:38.1033693Z   test_export_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1034575Z stats [('calls_captured', 80), ('fusions_possible', 78), ('unique_graphs', 2)]
2023-01-11T21:28:38.1035053Z ok (0.113s)
2023-01-11T21:28:38.1036133Z   test_export_graph_bypass_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1036852Z ok (0.034s)
2023-01-11T21:28:38.1037989Z   test_export_graph_bypass_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1038756Z ok (0.045s)
2023-01-11T21:28:38.1039887Z   test_export_graph_with_complex_reorder_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1089617Z ok (0.050s)
2023-01-11T21:28:38.1090603Z   test_export_graph_with_complex_reorder_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1091284Z ok (0.075s)
2023-01-11T21:28:38.1092085Z   test_export_graph_with_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1092638Z ok (0.036s)
2023-01-11T21:28:38.1093629Z   test_export_graph_with_list_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1094242Z ok (0.047s)
2023-01-11T21:28:38.1095067Z   test_export_meta_val_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1095695Z ok (0.069s)
2023-01-11T21:28:38.1096519Z   test_export_mismatched_out_2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1097143Z ok (0.032s)
2023-01-11T21:28:38.1098118Z   test_export_mismatched_out_2_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1098765Z ok (0.045s)
2023-01-11T21:28:38.1099645Z   test_export_mismatched_out_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1100221Z ok (0.041s)
2023-01-11T21:28:38.1101154Z   test_export_mismatched_out_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1101733Z ok (0.044s)
2023-01-11T21:28:38.1102472Z   test_export_shape_control_flow_1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1103205Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:38.1103643Z ok (0.048s)
2023-01-11T21:28:38.1104200Z   test_export_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1104944Z stats [('calls_captured', 80), ('fusions_possible', 78), ('unique_graphs', 2)]
2023-01-11T21:28:38.1105293Z ok (0.359s)
2023-01-11T21:28:38.1106213Z   test_export_with_constant_dict_values_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1106848Z ok (0.031s)
2023-01-11T21:28:38.1107728Z   test_export_with_constant_free_function_and_class_method_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1108387Z ok (0.047s)
2023-01-11T21:28:38.1109404Z   test_export_with_constant_free_function_and_class_method_multiarg_diff_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1110065Z ok (0.023s)
2023-01-11T21:28:38.1111052Z   test_export_with_constant_free_function_and_class_method_multiarg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1111834Z ok (0.060s)
2023-01-11T21:28:38.1164287Z   test_export_with_constant_free_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1164931Z ok (0.059s)
2023-01-11T21:28:38.1165840Z   test_export_with_constant_list_nonzero_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('call_function BuiltinVariable(iter) [TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1166516Z expected failure (0.013s)
2023-01-11T21:28:38.1167302Z   test_export_with_constant_list_nonzero_free_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('call_function BuiltinVariable(iter) [TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1167707Z expected failure (0.011s)
2023-01-11T21:28:38.1168240Z   test_export_with_constant_method_on_module_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1168590Z ok (0.046s)
2023-01-11T21:28:38.1169245Z   test_export_with_constant_method_on_module_invoke_twice_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1169789Z ok (0.058s)
2023-01-11T21:28:38.1170593Z   test_export_with_constant_none_control_flow_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1171110Z ok (0.007s)
2023-01-11T21:28:38.1171839Z   test_export_with_constant_none_control_flow_free_func_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1172215Z ok (0.007s)
2023-01-11T21:28:38.1172739Z   test_export_with_constant_not_none_control_flow_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1173095Z ok (0.021s)
2023-01-11T21:28:38.1173612Z   test_export_with_constant_not_none_control_flow_free_func_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1173975Z ok (0.020s)
2023-01-11T21:28:38.1174499Z   test_export_with_constant_not_none_control_flow_pos_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1174858Z ok (0.020s)
2023-01-11T21:28:38.1175354Z   test_export_with_constant_not_return_const_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1175701Z ok (0.008s)
2023-01-11T21:28:38.1176223Z   test_export_with_constant_tuple_nonzero_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('call_function BuiltinVariable(iter) [TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1176601Z expected failure (0.011s)
2023-01-11T21:28:38.1176913Z   test_export_with_module_layer_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1177335Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1177562Z ok (0.076s)
2023-01-11T21:28:38.1178039Z   test_export_with_stack_trace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:38.1178459Z ok (0.170s)
2023-01-11T21:28:38.1178751Z   test_func_return_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1179159Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1179369Z ok (0.060s)
2023-01-11T21:28:38.1179676Z   test_func_return_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1180096Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1180305Z ok (0.097s)
2023-01-11T21:28:38.1180839Z   test_input_container_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1181181Z ok (0.169s)
2023-01-11T21:28:38.1181654Z   test_list_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1181969Z ok (0.035s)
2023-01-11T21:28:38.1182461Z   test_list_unpack_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1182800Z ok (0.048s)
2023-01-11T21:28:38.1183390Z   test_zeroes_in_and_out_different_shape_on_test_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1183732Z ok (0.047s)
2023-01-11T21:28:38.1184274Z   test_zeroes_in_and_out_different_shape_on_test_with_aten_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1184643Z ok (0.076s)
2023-01-11T21:28:38.1185134Z   test_zeroes_in_new_shape_scalar_out_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 16), ('fusions_possible', 14), ('unique_graphs', 2)]
2023-01-11T21:28:38.1185476Z ok (0.053s)
2023-01-11T21:28:38.1186019Z   test_zeroes_in_new_shape_scalar_out_permute_dupe_and_bypass_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 22), ('fusions_possible', 20), ('unique_graphs', 2)]
2023-01-11T21:28:38.1186382Z ok (0.069s)
2023-01-11T21:28:38.1186882Z   test_zeroes_in_new_shape_scalar_out_permute_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 22), ('fusions_possible', 20), ('unique_graphs', 2)]
2023-01-11T21:28:38.1187231Z ok (0.070s)
2023-01-11T21:28:38.1187684Z   test_T_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1187998Z ok (0.033s)
2023-01-11T21:28:38.1188516Z   test_add__dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_functions.py:73: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:38.1189033Z   a_copy = torch.tensor(a)
2023-01-11T21:28:38.1189655Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:38.1190128Z   return node.target(*args, **kwargs)
2023-01-11T21:28:38.1190539Z <eval_with_key>.331:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:38.1190909Z   tensor = torch.tensor(a);  a = None
2023-01-11T21:28:38.1191229Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1191454Z ok (0.038s)
2023-01-11T21:28:38.1191902Z   test_add_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1192223Z ok (0.019s)
2023-01-11T21:28:38.1192788Z   test_addcdiv__dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_functions.py:84: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:38.1193311Z   a_copy = torch.tensor(a)
2023-01-11T21:28:38.1193915Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:38.1194354Z   return node.target(*args, **kwargs)
2023-01-11T21:28:38.1194761Z <eval_with_key>.336:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:38.1195146Z   tensor = torch.tensor(a);  a = None
2023-01-11T21:28:38.1195455Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1195680Z ok (0.045s)
2023-01-11T21:28:38.1196146Z   test_addcdiv_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1196469Z ok (0.037s)
2023-01-11T21:28:38.1196755Z   test_build_list_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1197163Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1197383Z ok (0.063s)
2023-01-11T21:28:38.1197680Z   test_chunks1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1198295Z   test_const_tuple_add1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1198627Z ok (0.040s)
2023-01-11T21:28:38.1199098Z   test_const_tuple_add2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1199417Z ok (0.040s)
2023-01-11T21:28:38.1199884Z   test_constant1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1200207Z ok (0.038s)
2023-01-11T21:28:38.1200669Z   test_constant2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1200974Z ok (0.037s)
2023-01-11T21:28:38.1201438Z   test_constant3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1201799Z ok (0.018s)
2023-01-11T21:28:38.1202252Z   test_constant4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1202569Z ok (0.020s)
2023-01-11T21:28:38.1203042Z   test_default_dict_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1203368Z ok (0.031s)
2023-01-11T21:28:38.1203811Z   test_del_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1204127Z ok (0.034s)
2023-01-11T21:28:38.1205200Z   test_device_constant_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1205532Z ok (0.026s)
2023-01-11T21:28:38.1205989Z   test_device_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1206310Z ok (0.018s)
2023-01-11T21:28:38.1206778Z   test_dict_copy_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1207101Z ok (0.018s)
2023-01-11T21:28:38.1207551Z   test_dict_ops_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:38.1207877Z ok (0.071s)
2023-01-11T21:28:38.1208348Z   test_dict_param_keys_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1208661Z ok (0.019s)
2023-01-11T21:28:38.1209289Z   test_distributed_is_available_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1209634Z ok (0.019s)
2023-01-11T21:28:38.1210126Z   test_distributed_is_initialized_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1210456Z ok (0.017s)
2023-01-11T21:28:38.1210931Z   test_dtype_compare_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1211258Z ok (0.027s)
2023-01-11T21:28:38.1211718Z   test_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1212027Z ok (0.018s)
2023-01-11T21:28:38.1212484Z   test_finfo_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1212802Z ok (0.023s)
2023-01-11T21:28:38.1213248Z   test_float_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1213566Z ok (0.018s)
2023-01-11T21:28:38.1214036Z   test_fn_with_self_set_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1214364Z ok (0.101s)
2023-01-11T21:28:38.1214819Z   test_fstrings1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1215204Z ok (0.021s)
2023-01-11T21:28:38.1215518Z   test_fstrings2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1216121Z   test_fstrings3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1216430Z ok (0.019s)
2023-01-11T21:28:38.1216728Z   test_funcdef_closure_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1217145Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:28:38.1217357Z ok (0.085s)
2023-01-11T21:28:38.1217873Z   test_get_default_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1218207Z ok (0.019s)
2023-01-11T21:28:38.1218670Z   test_globalfn_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1218984Z ok (0.020s)
2023-01-11T21:28:38.1219455Z   test_globalmodule_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1219780Z ok (0.033s)
2023-01-11T21:28:38.1220240Z   test_globalvar_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1220551Z ok (0.029s)
2023-01-11T21:28:38.1221012Z   test_import1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1221338Z ok (0.028s)
2023-01-11T21:28:38.1221793Z   test_indirect1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1222114Z ok (0.020s)
2023-01-11T21:28:38.1222572Z   test_indirect2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1222891Z ok (0.020s)
2023-01-11T21:28:38.1223396Z   test_indirect3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1223723Z ok (0.020s)
2023-01-11T21:28:38.1224026Z   test_inline_jit_annotations_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1224452Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1224662Z ok (0.033s)
2023-01-11T21:28:38.1225140Z   test_inline_softmax_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1225467Z ok (0.072s)
2023-01-11T21:28:38.1225755Z   test_inline_with_default_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1226170Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1226391Z ok (0.030s)
2023-01-11T21:28:38.1226686Z   test_inner_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1227085Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1227344Z ok (0.020s)
2023-01-11T21:28:38.1227675Z   test_is_contiguous_memory_format_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1228295Z   test_is_fx_tracing_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1228617Z ok (0.020s)
2023-01-11T21:28:38.1229091Z   test_is_in_onnx_export_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1229419Z ok (0.019s)
2023-01-11T21:28:38.1229903Z   test_is_not_null_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1230231Z ok (0.020s)
2023-01-11T21:28:38.1230705Z   test_is_quantized_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1231030Z ok (0.019s)
2023-01-11T21:28:38.1231482Z   test_is_sparse_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1231807Z ok (0.018s)
2023-01-11T21:28:38.1232280Z   test_islice_chain_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1232592Z ok (0.057s)
2023-01-11T21:28:38.1233064Z   test_jit_annotate_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1233385Z ok (0.026s)
2023-01-11T21:28:38.1233860Z   test_len_constant_dict_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1234177Z ok (0.018s)
2023-01-11T21:28:38.1234650Z   test_len_constant_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1234980Z ok (0.018s)
2023-01-11T21:28:38.1235471Z   test_len_constant_misc_iterables_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1235796Z ok (0.018s)
2023-01-11T21:28:38.1236101Z   test_len_tensor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... expected failure (0.007s)
2023-01-11T21:28:38.1236696Z   test_list_add_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1237018Z ok (0.021s)
2023-01-11T21:28:38.1237477Z   test_list_clear_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1237803Z ok (0.034s)
2023-01-11T21:28:38.1238275Z   test_list_convert_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1238590Z ok (0.042s)
2023-01-11T21:28:38.1239061Z   test_list_reversed_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1239383Z ok (0.049s)
2023-01-11T21:28:38.1239868Z   test_list_slice_assignment_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1240221Z ok (0.019s)
2023-01-11T21:28:38.1240688Z   test_list_truth_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1241010Z ok (0.020s)
2023-01-11T21:28:38.1241470Z   test_listarg1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1241782Z ok (0.029s)
2023-01-11T21:28:38.1242272Z   test_listarg2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1242592Z ok (0.032s)
2023-01-11T21:28:38.1243040Z   test_listarg3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1243356Z ok (0.029s)
2023-01-11T21:28:38.1243813Z   test_listarg4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1244136Z ok (0.029s)
2023-01-11T21:28:38.1244578Z   test_listarg5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1244895Z ok (0.032s)
2023-01-11T21:28:38.1245366Z   test_load_global_bool_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1245693Z ok (0.019s)
2023-01-11T21:28:38.1245967Z   test_map_sum_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1246373Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:38.1246596Z ok (0.076s)
2023-01-11T21:28:38.1246876Z   test_methodcall1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1247286Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1247508Z ok (0.037s)
2023-01-11T21:28:38.1247799Z   test_methodcall2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1248193Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1248413Z ok (0.034s)
2023-01-11T21:28:38.1248705Z   test_methodcall3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1249214Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1249439Z ok (0.034s)
2023-01-11T21:28:38.1249909Z   test_min_max_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 11), ('fusions_possible', 10), ('unique_graphs', 1)]
2023-01-11T21:28:38.1250234Z ok (0.049s)
2023-01-11T21:28:38.1250697Z   test_module_constant_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1251028Z ok (0.044s)
2023-01-11T21:28:38.1251490Z   test_ndim_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1251796Z ok (0.018s)
2023-01-11T21:28:38.1252253Z   test_pop_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1252657Z ok (0.042s)
2023-01-11T21:28:38.1253117Z   test_range1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1253427Z ok (0.014s)
2023-01-11T21:28:38.1253889Z   test_range2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 15), ('fusions_possible', 14), ('unique_graphs', 1)]
2023-01-11T21:28:38.1254208Z ok (0.112s)
2023-01-11T21:28:38.1254696Z   test_reduce_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1255019Z ok (0.039s)
2023-01-11T21:28:38.1255488Z   test_return_dict2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1255816Z ok (0.023s)
2023-01-11T21:28:38.1256276Z   test_return_dict_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1256605Z ok (0.021s)
2023-01-11T21:28:38.1257077Z   test_return_tuple1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1257403Z ok (0.028s)
2023-01-11T21:28:38.1257864Z   test_return_tuple2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1258185Z ok (0.018s)
2023-01-11T21:28:38.1258494Z   test_shape1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1258939Z   test_shape2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1259528Z   test_slice1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1259846Z ok (0.014s)
2023-01-11T21:28:38.1260303Z   test_slice2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1260615Z ok (0.013s)
2023-01-11T21:28:38.1261076Z   test_slice3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1261395Z ok (0.041s)
2023-01-11T21:28:38.1261853Z   test_slice4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1262154Z ok (0.016s)
2023-01-11T21:28:38.1262605Z   test_slice5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1262927Z ok (0.032s)
2023-01-11T21:28:38.1263453Z   test_slice6_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1263759Z ok (0.040s)
2023-01-11T21:28:38.1264229Z   test_startswith_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1264589Z ok (0.028s)
2023-01-11T21:28:38.1264878Z   test_tensor_len_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... expected failure (0.018s)
2023-01-11T21:28:38.1265332Z   test_tensor_new_with_shape_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1265806Z   test_tensor_new_with_size_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.000s)
2023-01-11T21:28:38.1266264Z   test_tensor_type2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.000s)
2023-01-11T21:28:38.1266901Z   test_tensor_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1267219Z ok (0.050s)
2023-01-11T21:28:38.1267703Z   test_transpose_for_scores_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1268041Z ok (0.035s)
2023-01-11T21:28:38.1268487Z   test_tuple1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1268810Z ok (0.021s)
2023-01-11T21:28:38.1269269Z   test_tuple2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1269589Z ok (0.020s)
2023-01-11T21:28:38.1270049Z   test_tuple_contains_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1270378Z ok (0.021s)
2023-01-11T21:28:38.1270841Z   test_tuple_iadd_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1271161Z ok (0.029s)
2023-01-11T21:28:38.1271608Z   test_unpack1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1271926Z ok (0.034s)
2023-01-11T21:28:38.1272390Z   test_unpack2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1272696Z ok (0.033s)
2023-01-11T21:28:38.1273158Z   test_unpack3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1273474Z ok (0.032s)
2023-01-11T21:28:38.1273932Z   test_unpack_ex1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1274243Z ok (0.048s)
2023-01-11T21:28:38.1274704Z   test_unpack_ex2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1275020Z ok (0.048s)
2023-01-11T21:28:38.1275486Z   test_unpack_ex3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1275790Z ok (0.048s)
2023-01-11T21:28:38.1276260Z   test_viamethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1276624Z ok (0.020s)
2023-01-11T21:28:38.1277076Z   test_viatorch_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1277397Z ok (0.020s)
2023-01-11T21:28:38.1277812Z   test_allow_in_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1278234Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1278444Z ok (0.041s)
2023-01-11T21:28:38.1278917Z   test_autocast_cpu_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1279245Z ok (0.032s)
2023-01-11T21:28:38.1279575Z   test_autocast_device_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:38.1280020Z   test_autocast_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:38.1280457Z   test_autocast_float64_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:38.1280903Z   test_autograd_function_equivalence_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1281329Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:28:38.1281541Z ok (0.078s)
2023-01-11T21:28:38.1282081Z   test_autograd_profiler_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... STAGE:2023-01-11 21:28:06 3567:3567 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:28:38.1282628Z STAGE:2023-01-11 21:28:06 3567:3567 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:28:38.1283064Z STAGE:2023-01-11 21:28:06 3567:3567 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:28:38.1283479Z [2023-01-11 21:28:06,721] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:28:38.1283767Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1283954Z unimplemented []
2023-01-11T21:28:38.1284172Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:28:38.1284486Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1284707Z ok (0.063s)
2023-01-11T21:28:38.1285239Z   test_autograd_profiler_enabled_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... STAGE:2023-01-11 21:28:06 3567:3567 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:28:38.1285791Z STAGE:2023-01-11 21:28:06 3567:3567 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:28:38.1286232Z STAGE:2023-01-11 21:28:06 3567:3567 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:28:38.1286544Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1286720Z unimplemented []
2023-01-11T21:28:38.1287021Z graph_break [('torch.autograd._profiler_enabled not supported yet', 1)]
2023-01-11T21:28:38.1287381Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1287592Z ok (0.042s)
2023-01-11T21:28:38.1288059Z   test_boolarg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:38.1288382Z ok (0.054s)
2023-01-11T21:28:38.1288671Z   test_build_tuple_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1289209Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1289436Z ok (0.060s)
2023-01-11T21:28:38.1289945Z   test_builder_for_class_with_metaclass_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1290371Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1290594Z ok (0.019s)
2023-01-11T21:28:38.1291076Z   test_builtin_isinstance_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1291411Z ok (0.017s)
2023-01-11T21:28:38.1291719Z   test_builtin_subclasses_as_method_on_class_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.003s)
2023-01-11T21:28:38.1292215Z   test_builtin_subclasses_as_method_on_var_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.004s)
2023-01-11T21:28:38.1292671Z   test_call_parent_non_class_methods_from_child_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1293095Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1293318Z ok (0.029s)
2023-01-11T21:28:38.1293790Z   test_callpacked_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1294114Z ok (0.056s)
2023-01-11T21:28:38.1294511Z   test_cell_output1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1294925Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1295143Z ok (0.017s)
2023-01-11T21:28:38.1295539Z   test_cell_output2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1295838Z unimplemented []
2023-01-11T21:28:38.1296225Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1296634Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1296846Z ok (0.027s)
2023-01-11T21:28:38.1297669Z   test_change_backends_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T21:28:38.1298336Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T21:28:38.1298682Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:38.1298945Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1299126Z ok (0.064s)
2023-01-11T21:28:38.1299521Z   test_cond_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1299800Z inline_call []
2023-01-11T21:28:38.1300092Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1300315Z ok (0.025s)
2023-01-11T21:28:38.1300608Z   test_cond_export_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1301003Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1301228Z ok (0.044s)
2023-01-11T21:28:38.1302094Z   test_cond_export_single_arg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91143 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:28:38.1302931Z   test_cond_nested_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1303213Z inline_call []
2023-01-11T21:28:38.1303572Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1303794Z ok (0.043s)
2023-01-11T21:28:38.1304097Z   test_cond_side_effects_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... expected failure (0.001s)
2023-01-11T21:28:38.1304694Z   test_config_getattr_default_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1305129Z stats [('calls_captured', 21), ('fusions_possible', 18), ('unique_graphs', 3)]
2023-01-11T21:28:38.1305355Z ok (0.155s)
2023-01-11T21:28:38.1305757Z   test_config_log_level_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1306180Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1306400Z ok (0.016s)
2023-01-11T21:28:38.1306790Z   test_config_obj_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1307205Z stats [('calls_captured', 8), ('fusions_possible', 4), ('unique_graphs', 4)]
2023-01-11T21:28:38.1307424Z ok (0.070s)
2023-01-11T21:28:38.1307732Z   test_const_dict_variable_python_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:38.1308503Z   test_cross_entropy_loss_fancy_ctor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:28:38.1308971Z   warnings.warn(warning.format(ret))
2023-01-11T21:28:38.1309163Z ok (0.002s)
2023-01-11T21:28:38.1309469Z   test_cross_entropy_loss_simple_ctor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.002s)
2023-01-11T21:28:38.1310007Z   test_dataclass_fields_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1310306Z inline_call []
2023-01-11T21:28:38.1310601Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1310811Z ok (0.045s)
2023-01-11T21:28:38.1311245Z   test_dict_mutation_side_effect_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1311677Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1311899Z ok (0.016s)
2023-01-11T21:28:38.1312338Z   test_dict_reconstruct_keeps_original_order_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 13), ('ok', 12)]
2023-01-11T21:28:38.1312869Z unimplemented [("Guard setup for uninitialized class <class 'torch.nn.modules.container.ModuleDict'>", 1)]
2023-01-11T21:28:38.1313809Z graph_break [('UnspecializedNNModuleVariable missing add_module', 3), ('construct nn.Module: ReLU', 1), ('call_function in skip_files /opt/conda/lib/python3.10/collections/__init__.py', 1), ('construct nn.Module: ModuleDict', 1), ('Patched init cannot be inlined.', 1), ('construct nn.Module: Linear', 1), ('construct nn.Module: Sigmoid', 1), ('call_method ConstDictVariable() update [TupleVariable()] {}', 1)]
2023-01-11T21:28:38.1314471Z inline_call [('inline __setitem__', 2), ('Patched init cannot be inlined.', 1)]
2023-01-11T21:28:38.1314723Z ok (0.037s)
2023-01-11T21:28:38.1315122Z   test_dictcomp_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1315419Z inline_call []
2023-01-11T21:28:38.1315712Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1315922Z ok (0.020s)
2023-01-11T21:28:38.1316211Z   test_disable_flag_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.002s)
2023-01-11T21:28:38.1316624Z   test_disable_optimize_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.002s)
2023-01-11T21:28:38.1317147Z   test_disallow_in_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1317485Z unimplemented []
2023-01-11T21:28:38.1317862Z graph_break [('call_function UserDefinedObjectVariable(sub) [TensorVariable(), ConstantVariable(int)] {}', 1)]
2023-01-11T21:28:38.1318268Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1318476Z ok (0.041s)
2023-01-11T21:28:38.1318774Z   test_dunder_methods_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1319185Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1319394Z ok (0.057s)
2023-01-11T21:28:38.1319698Z   test_duplicate_graph_break_warning_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... break
2023-01-11T21:28:38.1319983Z break
2023-01-11T21:28:38.1320174Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:28:38.1320506Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:28:38.1320762Z unimplemented []
2023-01-11T21:28:38.1321082Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 4)]
2023-01-11T21:28:38.1321444Z stats [('calls_captured', 6), ('unique_graphs', 4), ('fusions_possible', 2)]
2023-01-11T21:28:38.1321668Z ok (0.110s)
2023-01-11T21:28:38.1322168Z   test_dynamo_min_operator_with_shape_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1322499Z ok (0.011s)
2023-01-11T21:28:38.1322902Z   test_empty_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1323315Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1323537Z ok (0.037s)
2023-01-11T21:28:38.1324009Z   test_enum_no_graphbreaks_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1324349Z ok (0.029s)
2023-01-11T21:28:38.1324773Z   test_error_on_nested_fx_trace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1325191Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1325412Z ok (0.019s)
2023-01-11T21:28:38.1325869Z   test_fold_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1326189Z ok (0.017s)
2023-01-11T21:28:38.1326676Z   test_frozenset_torch_func_contains_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1327018Z ok (0.027s)
2023-01-11T21:28:38.1327500Z   test_function_annotation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1327869Z ok (0.027s)
2023-01-11T21:28:38.1328323Z   test_generate_tensor_from_list_of_numpy_primitive_type_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1328656Z unimplemented []
2023-01-11T21:28:38.1328871Z graph_break [('numpy', 1)]
2023-01-11T21:28:38.1329306Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1329530Z ok (0.008s)
2023-01-11T21:28:38.1329838Z   test_get_device_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.000s)
2023-01-11T21:28:38.1330423Z   test_grad_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1330719Z unimplemented []
2023-01-11T21:28:38.1330956Z graph_break [('Tensor.backward', 1)]
2023-01-11T21:28:38.1331282Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1331494Z ok (0.064s)
2023-01-11T21:28:38.1331909Z   test_grad_mode_guard_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1332212Z unimplemented []
2023-01-11T21:28:38.1332431Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:28:38.1332745Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1332968Z ok (0.044s)
2023-01-11T21:28:38.1333363Z   test_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1333659Z unimplemented []
2023-01-11T21:28:38.1334034Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 2)]
2023-01-11T21:28:38.1334431Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:28:38.1334643Z ok (0.079s)
2023-01-11T21:28:38.1335124Z   test_guard_failure_fn2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1335457Z ok (0.028s)
2023-01-11T21:28:38.1335924Z   test_guard_failure_fn_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:38.1336255Z ok (0.068s)
2023-01-11T21:28:38.1336731Z   test_id_of_nn_module_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1337057Z ok (0.029s)
2023-01-11T21:28:38.1337520Z   test_if_cond_nn_mod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1337847Z ok (0.041s)
2023-01-11T21:28:38.1338266Z   test_inference_mode_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1338686Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1338896Z ok (0.022s)
2023-01-11T21:28:38.1339314Z   test_inline_dict_mutation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1339612Z inline_call []
2023-01-11T21:28:38.1339893Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1340113Z ok (0.016s)
2023-01-11T21:28:38.1340556Z   test_inline_func_jump_on_tensor_condition_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1341026Z inline_call [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:38.1341229Z unimplemented []
2023-01-11T21:28:38.1341489Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:38.1341822Z stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:38.1342030Z ok (0.038s)
2023-01-11T21:28:38.1342446Z   test_inline_list_mutation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1342746Z inline_call []
2023-01-11T21:28:38.1343025Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1343245Z ok (0.015s)
2023-01-11T21:28:38.1343839Z   test_inplace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1344169Z ok (0.018s)
2023-01-11T21:28:38.1344641Z   test_inplace_param_update_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1344983Z ok (0.010s)
2023-01-11T21:28:38.1345391Z   test_is_compiling_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1345794Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1346016Z ok (0.009s)
2023-01-11T21:28:38.1346492Z   test_is_floating_point2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1346824Z ok (0.034s)
2023-01-11T21:28:38.1347291Z   test_is_floating_point_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1347617Z ok (0.034s)
2023-01-11T21:28:38.1348017Z   test_is_tensor2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1348428Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1348638Z ok (0.035s)
2023-01-11T21:28:38.1349100Z   test_is_tensor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1349419Z ok (0.034s)
2023-01-11T21:28:38.1349815Z   test_is_tensor_like2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1350114Z unimplemented []
2023-01-11T21:28:38.1350440Z graph_break [('call_function args: UserDefinedObjectVariable(MyTensor) ', 1)]
2023-01-11T21:28:38.1350798Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1351018Z ok (0.016s)
2023-01-11T21:28:38.1351492Z   test_is_tensor_like_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1351818Z ok (0.022s)
2023-01-11T21:28:38.1352280Z   test_item_changes_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1352611Z ok (0.041s)
2023-01-11T21:28:38.1353098Z   test_item_changes_new_shape_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1353435Z ok (0.043s)
2023-01-11T21:28:38.1353886Z   test_item_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1354253Z ok (0.021s)
2023-01-11T21:28:38.1354550Z   test_large_reduction_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.012s)
2023-01-11T21:28:38.1354957Z   test_linetable_writer_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:38.1355503Z   test_list_append_return_none_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1355933Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1356157Z ok (0.012s)
2023-01-11T21:28:38.1356584Z   test_list_mul_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1356870Z ok (0.002s)
2023-01-11T21:28:38.1357269Z   test_listcomp_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1357563Z inline_call []
2023-01-11T21:28:38.1357842Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1358064Z ok (0.042s)
2023-01-11T21:28:38.1358384Z   test_lnotab_writer_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: use lnotab when python < 3.10 (0.000s)
2023-01-11T21:28:38.1358984Z   test_manual_seed_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1359306Z ok (0.029s)
2023-01-11T21:28:38.1359771Z   test_matmul1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1360092Z ok (0.013s)
2023-01-11T21:28:38.1360378Z   test_module_complex_iter_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.011s)
2023-01-11T21:28:38.1360913Z   test_module_deepcopy_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 6), ('ok', 6)]
2023-01-11T21:28:38.1361215Z unimplemented []
2023-01-11T21:28:38.1361520Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 2)]
2023-01-11T21:28:38.1361761Z inline_call []
2023-01-11T21:28:38.1362053Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1362281Z ok (0.063s)
2023-01-11T21:28:38.1362564Z   test_named_parameters_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.024s)
2023-01-11T21:28:38.1363095Z   test_namedtuple1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1363519Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1363729Z ok (0.022s)
2023-01-11T21:28:38.1364133Z   test_namedtuple2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1364551Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1364773Z ok (0.027s)
2023-01-11T21:28:38.1365161Z   test_namedtuple3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1365578Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1365800Z ok (0.020s)
2023-01-11T21:28:38.1366179Z   test_nan_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1366582Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1366845Z ok (0.019s)
2023-01-11T21:28:38.1367130Z   test_nested_closure_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1367540Z stats [('calls_captured', 9), ('fusions_possible', 7), ('unique_graphs', 2)]
2023-01-11T21:28:38.1367762Z ok (0.067s)
2023-01-11T21:28:38.1368065Z   test_nested_closure_mutation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1368477Z stats [('calls_captured', 11), ('fusions_possible', 9), ('unique_graphs', 2)]
2023-01-11T21:28:38.1368697Z ok (0.036s)
2023-01-11T21:28:38.1369468Z   test_nested_disable_decorator_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... [2023-01-11 21:28:09,255] torch._dynamo.convert_frame: [ERROR] WON'T CONVERT fn3 /var/lib/jenkins/workspace/test/dynamo/test_misc.py line 1197 
2023-01-11T21:28:38.1369878Z due to: 
2023-01-11T21:28:38.1370053Z Traceback (most recent call last):
2023-01-11T21:28:38.1370429Z   File "/opt/conda/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 67, in unimplemented
2023-01-11T21:28:38.1370699Z     raise Unsupported(msg)
2023-01-11T21:28:38.1371040Z torch._dynamo.exc.Unsupported: call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f705437fb50>
2023-01-11T21:28:38.1371297Z 
2023-01-11T21:28:38.1371367Z from user code:
2023-01-11T21:28:38.1371608Z    File "/var/lib/jenkins/workspace/test/dynamo/test_misc.py", line 1199, in fn3
2023-01-11T21:28:38.1371841Z     return fn2(x)
2023-01-11T21:28:38.1372067Z   File "/var/lib/jenkins/workspace/test/dynamo/test_misc.py", line 1192, in fn2
2023-01-11T21:28:38.1372304Z     x = fn1(x)  # graph break
2023-01-11T21:28:38.1372420Z 
2023-01-11T21:28:38.1372550Z Set torch._dynamo.config.verbose=True for more information
2023-01-11T21:28:38.1372696Z 
2023-01-11T21:28:38.1372712Z 
2023-01-11T21:28:38.1372820Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1373009Z unimplemented []
2023-01-11T21:28:38.1373437Z graph_break [('call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f705437fb50>', 1)]
2023-01-11T21:28:38.1373863Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1374323Z inline_call [('call torch._dynamo.disable() wrapped function <function MiscTests.test_nested_disable_decorator.<locals>.fn1 at 0x7f705437fb50>', 1)]
2023-01-11T21:28:38.1374614Z ok (0.076s)
2023-01-11T21:28:38.1374924Z   test_nested_optimize_decorator_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1375335Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1375560Z ok (0.049s)
2023-01-11T21:28:38.1376039Z   test_nested_optimize_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:38.1376374Z ok (0.080s)
2023-01-11T21:28:38.1376846Z   test_nested_optimize_run_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:38.1377181Z ok (0.173s)
2023-01-11T21:28:38.1377669Z   test_nn_functional_reduction_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1377993Z ok (0.024s)
2023-01-11T21:28:38.1378298Z   test_nn_sequential_invocation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1378719Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1378940Z ok (0.071s)
2023-01-11T21:28:38.1379301Z   test_nn_sequential_invocation_reposition_indices_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1379740Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1379959Z ok (0.051s)
2023-01-11T21:28:38.1380376Z   test_no_error_on_nested_fx_trace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1380803Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1381025Z ok (0.020s)
2023-01-11T21:28:38.1381492Z   test_no_grad_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 40), ('fusions_possible', 32), ('unique_graphs', 8)]
2023-01-11T21:28:38.1381840Z ok (0.270s)
2023-01-11T21:28:38.1382258Z   test_not_dynamic_scope_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1382561Z inline_call []
2023-01-11T21:28:38.1382842Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1383065Z ok (0.012s)
2023-01-11T21:28:38.1383615Z   test_numel_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1383935Z ok (0.033s)
2023-01-11T21:28:38.1384343Z   test_numpy_int_constant_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1384770Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1384990Z ok (0.021s)
2023-01-11T21:28:38.1385409Z   test_numpy_variable_isinstance_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1385841Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1386063Z ok (0.011s)
2023-01-11T21:28:38.1386372Z   test_object_classmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1386769Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1386989Z ok (0.011s)
2023-01-11T21:28:38.1387292Z   test_object_staticmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1387694Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1387916Z ok (0.010s)
2023-01-11T21:28:38.1388406Z   test_onnx_shape_as_tensor_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 15), ('fusions_possible', 10), ('unique_graphs', 5)]
2023-01-11T21:28:38.1388743Z ok (0.044s)
2023-01-11T21:28:38.1389207Z   test_optimize_on_module_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1389537Z ok (0.021s)
2023-01-11T21:28:38.1389991Z   test_pair_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:38.1390308Z ok (0.040s)
2023-01-11T21:28:38.1390703Z   test_python_slice_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1391116Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1391336Z ok (0.041s)
2023-01-11T21:28:38.1391735Z   test_raise_on_backend_error_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1)]
2023-01-11T21:28:38.1392171Z stats [('calls_captured', 3), ('fusions_possible', 2)]
2023-01-11T21:28:38.1392373Z ok (0.027s)
2023-01-11T21:28:38.1392760Z   test_raises_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1393056Z unimplemented []
2023-01-11T21:28:38.1393363Z graph_break [('call_function BuiltinVariable(str) [TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1393722Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1393929Z ok (0.038s)
2023-01-11T21:28:38.1394226Z   test_rand_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:38.1394682Z   test_range_input_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1395082Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1395304Z ok (0.034s)
2023-01-11T21:28:38.1395743Z   test_recursive_inline_list_mutation_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1396056Z inline_call []
2023-01-11T21:28:38.1396336Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1396556Z ok (0.015s)
2023-01-11T21:28:38.1396975Z   test_release_input_memory_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1397390Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1397608Z ok (0.013s)
2023-01-11T21:28:38.1398145Z   test_release_module_memory_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1398571Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1398781Z ok (0.031s)
2023-01-11T21:28:38.1399292Z   test_repro_graph_breaks_in__get_item_by_idx_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1399640Z ok (0.032s)
2023-01-11T21:28:38.1400047Z   test_restore_graphstate_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1400436Z inline_call [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:38.1400652Z unimplemented []
2023-01-11T21:28:38.1400918Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:38.1401241Z stats [('calls_captured', 6), ('unique_graphs', 4), ('fusions_possible', 2)]
2023-01-11T21:28:38.1401463Z ok (0.071s)
2023-01-11T21:28:38.1401966Z   test_restore_graphstate_internals_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1402303Z ok (0.031s)
2023-01-11T21:28:38.1402722Z   test_return_nested_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1403143Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 2)]
2023-01-11T21:28:38.1403364Z ok (0.058s)
2023-01-11T21:28:38.1403641Z   test_sample_input_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.574s)
2023-01-11T21:28:38.1404171Z   test_setattr_mutation1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1404683Z unimplemented [('call_method UserDefinedObjectVariable(member_descriptor) __mul__ [ConstantVariable(int)] {}', 1)]
2023-01-11T21:28:38.1405192Z graph_break [("isinstance called on UserDefinedClass UserDefinedObjectVariable(member_descriptor) <class 'torch.Tensor'>", 1)]
2023-01-11T21:28:38.1405586Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1405897Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:28:38.1406122Z ok (0.093s)
2023-01-11T21:28:38.1406527Z   test_setattr_mutation2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1406824Z inline_call []
2023-01-11T21:28:38.1407113Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:38.1407325Z ok (0.070s)
2023-01-11T21:28:38.1407739Z   test_setattr_mutation3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1408070Z inline_call []
2023-01-11T21:28:38.1408362Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:38.1408575Z ok (0.070s)
2023-01-11T21:28:38.1408981Z   test_shape_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1409521Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1409733Z ok (0.020s)
2023-01-11T21:28:38.1410179Z   test_side_effects_codegen_update_mutated_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 6), ('ok', 6)]
2023-01-11T21:28:38.1410502Z unimplemented []
2023-01-11T21:28:38.1410716Z graph_break [('Tensor.item', 4)]
2023-01-11T21:28:38.1411029Z stats [('calls_captured', 8), ('fusions_possible', 4), ('unique_graphs', 4)]
2023-01-11T21:28:38.1411248Z ok (0.128s)
2023-01-11T21:28:38.1411649Z   test_size_input_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1412051Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1412272Z ok (0.038s)
2023-01-11T21:28:38.1412744Z   test_slice_input_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('unique_graphs', 3), ('fusions_possible', 0)]
2023-01-11T21:28:38.1413057Z ok (0.031s)
2023-01-11T21:28:38.1413384Z   test_tensor_build_list_unpack_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1414006Z   test_tensor_data_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1414332Z ok (0.019s)
2023-01-11T21:28:38.1414724Z   test_tensor_dict1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1415144Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1415365Z ok (0.018s)
2023-01-11T21:28:38.1415768Z   test_tensor_dict2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1416171Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:38.1416393Z ok (0.036s)
2023-01-11T21:28:38.1416831Z   test_tensor_dot_grad_no_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1417134Z unimplemented []
2023-01-11T21:28:38.1417370Z graph_break [('Tensor.backward', 1)]
2023-01-11T21:28:38.1417686Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1417897Z ok (0.049s)
2023-01-11T21:28:38.1418315Z   test_tensor_is_contiguous_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1418803Z stats [('calls_captured', 10), ('fusions_possible', 8), ('unique_graphs', 2)]
2023-01-11T21:28:38.1419025Z ok (0.071s)
2023-01-11T21:28:38.1419432Z   test_tensor_item_capture_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1419857Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1420076Z ok (0.032s)
2023-01-11T21:28:38.1420480Z   test_tensor_item_no_capture_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1420787Z unimplemented []
2023-01-11T21:28:38.1421017Z graph_break [('Tensor.item', 1)]
2023-01-11T21:28:38.1421367Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1421578Z ok (0.026s)
2023-01-11T21:28:38.1422050Z   test_tensor_layout_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1422379Z ok (0.019s)
2023-01-11T21:28:38.1422778Z   test_tensor_types_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 10), ('ok', 10)]
2023-01-11T21:28:38.1423202Z stats [('calls_captured', 10), ('unique_graphs', 10), ('fusions_possible', 0)]
2023-01-11T21:28:38.1423493Z ok (0.078s)
2023-01-11T21:28:38.1423985Z   test_top_package_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1424305Z ok (0.020s)
2023-01-11T21:28:38.1424792Z   test_torch_cuda_is_available_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1425131Z ok (0.014s)
2023-01-11T21:28:38.1425449Z   test_torch_cudnn_is_acceptable_bad_inputs_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:38.1425927Z   test_torch_cudnn_is_acceptable_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.000s)
2023-01-11T21:28:38.1426511Z   test_torch_nn_parameter_isinstance_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1426945Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1427158Z ok (0.019s)
2023-01-11T21:28:38.1427687Z   test_torch_profiler_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... STAGE:2023-01-11 21:28:12 3567:3567 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:28:38.1428227Z STAGE:2023-01-11 21:28:12 3567:3567 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:28:38.1428670Z STAGE:2023-01-11 21:28:12 3567:3567 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:28:38.1429073Z [2023-01-11 21:28:12,038] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:28:38.1429464Z [2023-01-11 21:28:12,046] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:28:38.1429747Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1429921Z unimplemented []
2023-01-11T21:28:38.1430151Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:28:38.1430468Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1430690Z ok (0.063s)
2023-01-11T21:28:38.1431148Z   test_torch_seed_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1431531Z ok (0.010s)
2023-01-11T21:28:38.1431993Z   test_torch_size_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1432301Z ok (0.020s)
2023-01-11T21:28:38.1432701Z   test_type_copy_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1433121Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1433341Z ok (0.057s)
2023-01-11T21:28:38.1433755Z   test_typing_variable_isinstance_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1434218Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1434438Z ok (0.022s)
2023-01-11T21:28:38.1434890Z   test_unpack4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:38.1435216Z ok (0.049s)
2023-01-11T21:28:38.1435676Z   test_unpack5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:38.1435990Z ok (0.043s)
2023-01-11T21:28:38.1436430Z   test_update_locals_and_stack_uses_shared_cache_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1436754Z inline_call []
2023-01-11T21:28:38.1436928Z unimplemented []
2023-01-11T21:28:38.1437237Z graph_break [('call_method ListVariable() extend [ListIteratorVariable()] {}', 1)]
2023-01-11T21:28:38.1437479Z ok (0.020s)
2023-01-11T21:28:38.1437967Z   test_user_defined_class_name_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1438300Z ok (0.031s)
2023-01-11T21:28:38.1438616Z   test_user_function_variable_supports_enum_argument_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1438923Z ok (0.018s)
2023-01-11T21:28:38.1439254Z   test_user_function_variable_supports_function_argument_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1439685Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1439908Z ok (0.020s)
2023-01-11T21:28:38.1440242Z   test_user_function_variable_supports_type_abcmeta_argument_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1440685Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1440896Z ok (0.020s)
2023-01-11T21:28:38.1441307Z   test_user_getattr1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1441603Z inline_call []
2023-01-11T21:28:38.1441882Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1442104Z ok (0.021s)
2023-01-11T21:28:38.1442515Z   test_user_getattr2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1442807Z inline_call []
2023-01-11T21:28:38.1443086Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1443307Z ok (0.028s)
2023-01-11T21:28:38.1443714Z   test_user_property_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1443996Z inline_call []
2023-01-11T21:28:38.1444333Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1444553Z ok (0.021s)
2023-01-11T21:28:38.1444840Z   test_usr_cls_classmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1445251Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1445470Z ok (0.028s)
2023-01-11T21:28:38.1445770Z   test_usr_cls_staticmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1446172Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1446394Z ok (0.028s)
2023-01-11T21:28:38.1446724Z   test_version_ci_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:38.1447136Z   test_write_to_closures_in_inlining_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1447557Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1447776Z ok (0.039s)
2023-01-11T21:28:38.1448069Z   test_access_by_keys_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1448463Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1448684Z ok (0.110s)
2023-01-11T21:28:38.1449317Z   test_basicmodule1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1449638Z ok (0.058s)
2023-01-11T21:28:38.1450116Z   test_basicmodule2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1450443Z ok (0.055s)
2023-01-11T21:28:38.1450754Z   test_call_fn_with_non_const_inputs_safe_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1451165Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1451385Z ok (0.209s)
2023-01-11T21:28:38.1451847Z   test_cfgmod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1452170Z ok (0.095s)
2023-01-11T21:28:38.1452622Z   test_children_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1452950Z ok (0.077s)
2023-01-11T21:28:38.1453419Z   test_constloop_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1453731Z ok (0.144s)
2023-01-11T21:28:38.1454023Z   test_densenet_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1454429Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1454651Z ok (0.112s)
2023-01-11T21:28:38.1454931Z   test_enumvalues_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1455340Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1455559Z ok (0.108s)
2023-01-11T21:28:38.1456015Z   test_fnmember_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1456333Z ok (0.046s)
2023-01-11T21:28:38.1456804Z   test_fnmembercmp1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1457192Z ok (0.047s)
2023-01-11T21:28:38.1457653Z   test_fnmembercmp2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1457975Z ok (0.058s)
2023-01-11T21:28:38.1458272Z   test_forward_directly_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1458677Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1458898Z ok (0.076s)
2023-01-11T21:28:38.1459229Z   test_generation_tag_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.002s)
2023-01-11T21:28:38.1459824Z   test_hasattr_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1460139Z ok (0.036s)
2023-01-11T21:28:38.1460603Z   test_intarg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1460925Z ok (0.054s)
2023-01-11T21:28:38.1461386Z   test_iseval1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1461695Z ok (0.046s)
2023-01-11T21:28:38.1462153Z   test_iseval2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1462470Z ok (0.045s)
2023-01-11T21:28:38.1462932Z   test_isnonelayer_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1463261Z ok (0.032s)
2023-01-11T21:28:38.1463803Z   test_istraining1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1464129Z ok (0.045s)
2023-01-11T21:28:38.1464582Z   test_istraining2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1464906Z ok (0.045s)
2023-01-11T21:28:38.1465376Z   test_layerlist_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1465702Z ok (0.063s)
2023-01-11T21:28:38.1466365Z   test_lazy_module_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:28:38.1466962Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:28:38.1467626Z [2023-01-11 21:28:14,227] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7f7060372950> [SizeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:28:38.1468367Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:28:38.1468843Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:28:38.1469410Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:28:38.1469934Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:28:38.1470582Z [2023-01-11 21:28:14,353] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7f7060372950> [SizeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:28:38.1471316Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:28:38.1471813Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:28:38.1472098Z frames [('total', 16), ('ok', 14)]
2023-01-11T21:28:38.1472847Z inline_call [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2), ('call_function UserDefinedObjectVariable(_infer_parameters) [NNModuleVariable(), TupleVariable()] {}', 1), ('call_function UserDefinedObjectVariable(_infer_parameters) [UnspecializedNNModuleVariable(LazyModule), TupleVariable()] {}', 1)]
2023-01-11T21:28:38.1473515Z unimplemented [("Guard setup for uninitialized class <class 'test_modules.LazyModule'>", 2)]
2023-01-11T21:28:38.1473900Z graph_break [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2)]
2023-01-11T21:28:38.1474264Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:28:38.1474487Z ok (0.302s)
2023-01-11T21:28:38.1474788Z   test_module_attribute_precedence_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1475213Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1475434Z ok (0.044s)
2023-01-11T21:28:38.1475732Z   test_module_class_method_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1476136Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:38.1476355Z ok (0.123s)
2023-01-11T21:28:38.1476788Z   test_module_forward_has_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 2)]
2023-01-11T21:28:38.1477085Z inline_call []
2023-01-11T21:28:38.1477363Z unimplemented [('reconstruct: ConstantVariable(dict)', 2)]
2023-01-11T21:28:38.1477823Z graph_break [('call_function BuiltinVariable(dict) [ListIteratorVariable()] {}', 1), ('call_method NNModuleVariable() buffers [] {}', 1)]
2023-01-11T21:28:38.1478235Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1478448Z ok (0.180s)
2023-01-11T21:28:38.1478930Z   test_module_name_string_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1479263Z ok (0.053s)
2023-01-11T21:28:38.1479551Z   test_module_property_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1479959Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1480182Z ok (0.026s)
2023-01-11T21:28:38.1480482Z   test_module_static_method_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1480885Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:38.1481105Z ok (0.123s)
2023-01-11T21:28:38.1481578Z   test_moduledict_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1481971Z ok (0.032s)
2023-01-11T21:28:38.1482445Z   test_modulelist_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 40), ('fusions_possible', 39), ('unique_graphs', 1)]
2023-01-11T21:28:38.1482768Z ok (0.512s)
2023-01-11T21:28:38.1483064Z   test_modulemethod1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1483462Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:38.1483684Z ok (0.123s)
2023-01-11T21:28:38.1483977Z   test_modulemethod2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1484376Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:28:38.1484595Z ok (0.124s)
2023-01-11T21:28:38.1485116Z   test_nn_moduledict_contains_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:28:38.1485511Z frames [('total', 2), ('ok', 1)]
2023-01-11T21:28:38.1485770Z inline_call [('Patched init cannot be inlined.', 1)]
2023-01-11T21:28:38.1486223Z unimplemented [("Guard setup for uninitialized class <class 'test_modules.NNModuleTests.test_nn_moduledict_contains.<locals>.M'>", 1)]
2023-01-11T21:28:38.1486614Z graph_break [('Patched init cannot be inlined.', 1)]
2023-01-11T21:28:38.1486807Z ok (0.043s)
2023-01-11T21:28:38.1487103Z   test_parameters1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1487512Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1487734Z ok (0.022s)
2023-01-11T21:28:38.1488018Z   test_parameters2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1488421Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1488643Z ok (0.022s)
2023-01-11T21:28:38.1489213Z   test_parameters3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1489541Z ok (0.070s)
2023-01-11T21:28:38.1490019Z   test_self_mutating1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:38.1490351Z ok (0.067s)
2023-01-11T21:28:38.1490795Z   test_seq_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1491113Z ok (0.077s)
2023-01-11T21:28:38.1491416Z   test_simple_torch_function_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1491819Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1492042Z ok (0.106s)
2023-01-11T21:28:38.1492514Z   test_stringmember_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1492841Z ok (0.046s)
2023-01-11T21:28:38.1493122Z   test_submodules1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1493528Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1493748Z ok (0.108s)
2023-01-11T21:28:38.1494031Z   test_submodules2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1494438Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1494730Z ok (0.108s)
2023-01-11T21:28:38.1495018Z   test_super1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1495408Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1495633Z ok (0.063s)
2023-01-11T21:28:38.1495919Z   test_super2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1496309Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1496531Z ok (0.056s)
2023-01-11T21:28:38.1496829Z   test_super_class_method_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1497242Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1497533Z ok (0.020s)
2023-01-11T21:28:38.1498008Z   test_tensorlist_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1498335Z ok (0.058s)
2023-01-11T21:28:38.1498818Z   test_torch_function_with_closure_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1499161Z ok (0.102s)
2023-01-11T21:28:38.1499583Z   test_unsupportedmethod_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1500096Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1500375Z unimplemented []
2023-01-11T21:28:38.1500753Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1501157Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:28:38.1501370Z ok (0.093s)
2023-01-11T21:28:38.1501794Z   test_unsupportedmodule_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1502301Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1502588Z unimplemented []
2023-01-11T21:28:38.1502948Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1503426Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:28:38.1503652Z ok (0.095s)
2023-01-11T21:28:38.1503943Z   test_viamodulecall_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1504358Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1504584Z ok (0.071s)
2023-01-11T21:28:38.1504983Z   test_Size_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1505439Z inline_call [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:28:38.1505706Z unimplemented []
2023-01-11T21:28:38.1506059Z graph_break [('inline in skipfiles: assertIsInstance /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:28:38.1506432Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1506653Z ok (0.019s)
2023-01-11T21:28:38.1507059Z   test_abc_setattr_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1507358Z unimplemented []
2023-01-11T21:28:38.1507771Z graph_break [('setattr(UserDefinedObjectVariable) <function ReproTests.test_abc_setattr.<locals>.Derived.__setattr__ at 0x7f702611d6c0>', 1)]
2023-01-11T21:28:38.1508119Z inline_call []
2023-01-11T21:28:38.1508414Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1508626Z ok (0.023s)
2023-01-11T21:28:38.1509056Z   test_avoid_dupe_specialization_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1509489Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1509757Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1509940Z ok (0.154s)
2023-01-11T21:28:38.1510237Z   test_batch_norm_act_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1510644Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1510884Z ok (0.159s)
2023-01-11T21:28:38.1511342Z   test_batchnorm_e2e_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:38.1511717Z frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1511886Z inline_call []
2023-01-11T21:28:38.1512175Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1512454Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1512626Z ok (1.925s)
2023-01-11T21:28:38.1513058Z   test_bigbird_unsqueeze_inplace_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1513490Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1513770Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1513939Z ok (0.420s)
2023-01-11T21:28:38.1514233Z   test_boxes_len_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1514637Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1514846Z ok (0.026s)
2023-01-11T21:28:38.1515166Z   test_chunk_reformer_ff_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1515783Z   test_class_member_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1516108Z ok (0.043s)
2023-01-11T21:28:38.1516533Z   test_convert_boxes_to_pooler_format_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1516603Z inline_call []
2023-01-11T21:28:38.1516677Z unimplemented []
2023-01-11T21:28:38.1516884Z graph_break [('dynamic shape operator: aten.repeat_interleave.Tensor', 2)]
2023-01-11T21:28:38.1517083Z stats [('calls_captured', 18), ('fusions_possible', 14), ('unique_graphs', 4)]
2023-01-11T21:28:38.1517166Z expected failure (0.170s)
2023-01-11T21:28:38.1517408Z   test_create_rand_mask_from_inputs_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires static shapes (0.001s)
2023-01-11T21:28:38.1517592Z   test_dict_iter_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.005s)
2023-01-11T21:28:38.1517916Z   test_dict_list_values_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:38.1517989Z unimplemented []
2023-01-11T21:28:38.1518326Z graph_break [('call_function in skip_files Builtin count', 2), ('call_function BuiltinVariable(zip) [UserDefinedObjectVariable(count), ListVariable()] {}', 2)]
2023-01-11T21:28:38.1518392Z ok (0.056s)
2023-01-11T21:28:38.1518693Z   test_do_paste_mask_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1)]
2023-01-11T21:28:38.1518826Z expected failure (0.031s)
2023-01-11T21:28:38.1519229Z   test_dynamic_shapes_right_side_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1519295Z ok (0.047s)
2023-01-11T21:28:38.1519655Z   test_ellipsis_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1519719Z ok (0.138s)
2023-01-11T21:28:38.1520028Z   test_exec_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:28:38.1520245Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:38.1520352Z unimplemented []
2023-01-11T21:28:38.1520570Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:38.1520637Z ok (0.004s)
2023-01-11T21:28:38.1520966Z   test_exec_wildcard_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:28:38.1521167Z inline_call [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:38.1521241Z unimplemented []
2023-01-11T21:28:38.1521454Z graph_break [('call_function BuiltinVariable(exec) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:38.1521650Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1521714Z ok (0.016s)
2023-01-11T21:28:38.1522049Z   test_for_loop_graph_break_before_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1522126Z unimplemented []
2023-01-11T21:28:38.1522395Z graph_break [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:28:38.1522454Z inline_call []
2023-01-11T21:28:38.1522654Z stats [('calls_captured', 100), ('fusions_possible', 99), ('unique_graphs', 1)]
2023-01-11T21:28:38.1522718Z ok (0.700s)
2023-01-11T21:28:38.1523044Z   test_for_loop_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1523113Z inline_call []
2023-01-11T21:28:38.1523385Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:28:38.1523576Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1523644Z ok (0.029s)
2023-01-11T21:28:38.1523845Z   test_get_parameter_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1524038Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1524105Z ok (0.027s)
2023-01-11T21:28:38.1524340Z   test_grad_mode_carrying_correct_state_after_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... Break
2023-01-11T21:28:38.1524457Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1524531Z unimplemented []
2023-01-11T21:28:38.1524748Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:38.1524928Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1524992Z ok (0.030s)
2023-01-11T21:28:38.1525321Z   test_guard_fail_nested_tuple_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1525513Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1525580Z ok (0.034s)
2023-01-11T21:28:38.1525914Z   test_guard_fail_tensor_bool_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 12), ('ok', 12)]
2023-01-11T21:28:38.1526152Z unimplemented [('FOR_ITER UserDefinedObjectVariable(product)', 1)]
2023-01-11T21:28:38.1526595Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_guard_fail_tensor_bool.<locals>.fn.<locals>.get_expected at 0x7f70a054d1b0>', 5), ('data dependent operator: aten.allclose.default', 5)]
2023-01-11T21:28:38.1526786Z stats [('calls_captured', 5), ('unique_graphs', 5), ('fusions_possible', 0)]
2023-01-11T21:28:38.1526840Z ok (0.187s)
2023-01-11T21:28:38.1527051Z   test_guard_ordering_shape_fail_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.001s)
2023-01-11T21:28:38.1527286Z   test_hf_model_output_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1527483Z stats [('calls_captured', 4), ('unique_graphs', 4), ('fusions_possible', 0)]
2023-01-11T21:28:38.1527551Z ok (0.094s)
2023-01-11T21:28:38.1527751Z   test_hf_t5_forward_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1527948Z stats [('calls_captured', 14), ('fusions_possible', 13), ('unique_graphs', 1)]
2023-01-11T21:28:38.1528029Z expected failure (0.557s)
2023-01-11T21:28:38.1528339Z   test_indexing_with_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1528465Z inline_call [('Tensor.numpy', 1)]
2023-01-11T21:28:38.1528668Z unimplemented [('COMPARE_OP ConstantVariable(tuple) == SizeVariable()', 1)]
2023-01-11T21:28:38.1528793Z graph_break [('Tensor.numpy', 1)]
2023-01-11T21:28:38.1528857Z ok (0.032s)
2023-01-11T21:28:38.1529158Z   test_is_symbolic_tracing_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1529355Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1529422Z ok (0.014s)
2023-01-11T21:28:38.1529613Z   test_isinstance_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... ok (0.006s)
2023-01-11T21:28:38.1530150Z   test_isinstance_storage_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:28:38.1530272Z   bools = torch.BoolStorage.from_buffer(f, "big")
2023-01-11T21:28:38.1530392Z frames [('total', 9), ('ok', 9)]
2023-01-11T21:28:38.1530470Z unimplemented []
2023-01-11T21:28:38.1530839Z graph_break [('call_function BuiltinVariable(bytearray) [ListVariable()] {}', 1), ('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:28:38.1531105Z inline_call [('inline in skipfiles: from_buffer /opt/conda/lib/python3.10/site-packages/torch/storage.py', 1)]
2023-01-11T21:28:38.1531300Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1531384Z expected failure (0.021s)
2023-01-11T21:28:38.1531580Z   test_issue1466_size_aot_autograd_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... arf
2023-01-11T21:28:38.1531640Z arf
2023-01-11T21:28:38.1531759Z frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1531832Z unimplemented []
2023-01-11T21:28:38.1532051Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:38.1532245Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1532370Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1532489Z ok (0.158s)
2023-01-11T21:28:38.1532673Z   test_issue175_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1532872Z stats [('calls_captured', 12), ('fusions_possible', 11), ('unique_graphs', 1)]
2023-01-11T21:28:38.1532936Z ok (0.436s)
2023-01-11T21:28:38.1533324Z   test_longformer_chunk_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 38), ('fusions_possible', 36), ('unique_graphs', 2)]
2023-01-11T21:28:38.1533389Z ok (1.122s)
2023-01-11T21:28:38.1533610Z   test_maml_item_capture_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... expected failure (0.002s)
2023-01-11T21:28:38.1533978Z   test_maml_no_item_capture_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:28:38.1534178Z inline_call [('inlining disallowed: <function grad at 0x7f70602a1480>', 1)]
2023-01-11T21:28:38.1534245Z unimplemented []
2023-01-11T21:28:38.1534581Z graph_break [('Tensor.item', 2), ('call_function in skip_files /opt/conda/lib/python3.10/copy.py', 1), ('inlining disallowed: <function grad at 0x7f70602a1480>', 1)]
2023-01-11T21:28:38.1534778Z stats [('calls_captured', 36), ('fusions_possible', 31), ('unique_graphs', 5)]
2023-01-11T21:28:38.1534842Z ok (0.821s)
2023-01-11T21:28:38.1535216Z   test_modules_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1535280Z ok (0.062s)
2023-01-11T21:28:38.1535599Z   test_multi_dot_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1535887Z inline_call [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:28:38.1535964Z unimplemented []
2023-01-11T21:28:38.1536233Z graph_break [('inline in skipfiles: symbolic_trace /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py', 1)]
2023-01-11T21:28:38.1536427Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1536493Z ok (0.023s)
2023-01-11T21:28:38.1536714Z   test_multi_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires detectron2 (0.000s)
2023-01-11T21:28:38.1537094Z   test_named_buffers_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1537159Z ok (0.045s)
2023-01-11T21:28:38.1537475Z   test_nn_parameter_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1537709Z inline_call [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:28:38.1537773Z unimplemented []
2023-01-11T21:28:38.1538005Z graph_break [('inline in skipfiles: assertTrue /opt/conda/lib/python3.10/unittest/case.py', 1)]
2023-01-11T21:28:38.1538196Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1538262Z ok (0.014s)
2023-01-11T21:28:38.1538476Z   test_norm_dtype_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:38.1538842Z   test_not_rewrite_assert_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:38.1538907Z ok (0.019s)
2023-01-11T21:28:38.1539257Z   test_not_rewrite_assert_for_other_errors_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1539438Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1539543Z ok (0.022s)
2023-01-11T21:28:38.1539856Z   test_numpy_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:28:38.1539932Z unimplemented []
2023-01-11T21:28:38.1540240Z graph_break [('call torch._dynamo.disable() wrapped function <function ReproTests.test_numpy_list.<locals>.rand_gen at 0x7f709044aa70>', 1)]
2023-01-11T21:28:38.1540323Z expected failure (0.029s)
2023-01-11T21:28:38.1540650Z   test_optimized_deepcopy_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1540843Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1540895Z ok (0.045s)
2023-01-11T21:28:38.1541239Z   test_primtorch_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1541517Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:28:38.1541592Z unimplemented []
2023-01-11T21:28:38.1541865Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:28:38.1541930Z ok (0.013s)
2023-01-11T21:28:38.1542399Z   test_primtorch_no_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py', 1)]
2023-01-11T21:28:38.1542481Z expected failure (0.006s)
2023-01-11T21:28:38.1542801Z   test_recursive_map_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1542859Z inline_call []
2023-01-11T21:28:38.1543053Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1543120Z ok (0.041s)
2023-01-11T21:28:38.1543380Z   test_reformer_eval_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1543583Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:28:38.1543652Z ok (0.415s)
2023-01-11T21:28:38.1543860Z   test_reformer_min_chunk_len_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1544053Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1544106Z ok (0.016s)
2023-01-11T21:28:38.1544316Z   test_reformer_remove_unused_args_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... foo
2023-01-11T21:28:38.1544379Z foo
2023-01-11T21:28:38.1544496Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1544713Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:38.1544790Z unimplemented []
2023-01-11T21:28:38.1545006Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2)]
2023-01-11T21:28:38.1545185Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1545312Z aot_autograd [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1545378Z ok (0.153s)
2023-01-11T21:28:38.1545584Z   test_reformer_sorting_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... inline_call []
2023-01-11T21:28:38.1545783Z stats [('calls_captured', 28), ('fusions_possible', 27), ('unique_graphs', 1)]
2023-01-11T21:28:38.1545848Z ok (0.088s)
2023-01-11T21:28:38.1546167Z   test_reformer_train_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1546455Z inline_call [('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:28:38.1546561Z unimplemented []
2023-01-11T21:28:38.1546921Z graph_break [('autograd.Function with requires_grad', 1), ('inline in skipfiles: save_for_backward /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py', 1)]
2023-01-11T21:28:38.1547117Z stats [('calls_captured', 10), ('fusions_possible', 6), ('unique_graphs', 4)]
2023-01-11T21:28:38.1547182Z ok (0.404s)
2023-01-11T21:28:38.1547500Z   test_reinplacing_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1547694Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1547819Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1547883Z ok (0.434s)
2023-01-11T21:28:38.1548288Z   test_relative_import_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1548358Z ok (0.031s)
2023-01-11T21:28:38.1548766Z   test_relative_import_no_modulename_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1548831Z ok (0.028s)
2023-01-11T21:28:38.1549218Z   test_rewrite_assert_noop_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:38.1549283Z ok (0.082s)
2023-01-11T21:28:38.1549666Z   test_rewrite_assert_with_fstring_msg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... unimplemented [('generic_jump TensorVariable()', 1)]
2023-01-11T21:28:38.1549730Z ok (0.019s)
2023-01-11T21:28:38.1550126Z   test_rewrite_assert_with_msg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 18), ('fusions_possible', 15), ('unique_graphs', 3)]
2023-01-11T21:28:38.1550181Z ok (0.100s)
2023-01-11T21:28:38.1550583Z   test_rewrite_assert_without_msg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 12), ('fusions_possible', 10), ('unique_graphs', 2)]
2023-01-11T21:28:38.1550647Z ok (0.068s)
2023-01-11T21:28:38.1550956Z   test_rng_state_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1551190Z unimplemented [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 1)]
2023-01-11T21:28:38.1551418Z graph_break [('TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd', 2)]
2023-01-11T21:28:38.1551614Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1551678Z ok (0.035s)
2023-01-11T21:28:38.1552060Z   test_seq_append_list_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1552115Z ok (0.105s)
2023-01-11T21:28:38.1552782Z   test_sigmoid_out_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:38.1552873Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:28:38.1553649Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:28:38.1553768Z   warnings.warn(msg)
2023-01-11T21:28:38.1554352Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1543: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3, 5]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:38.1554441Z   torch.sigmoid(inp, out=out1)
2023-01-11T21:28:38.1554564Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:38.1554757Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:28:38.1554822Z ok (0.032s)
2023-01-11T21:28:38.1555223Z   test_slice_into_list_mutable_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 30), ('fusions_possible', 29), ('unique_graphs', 1)]
2023-01-11T21:28:38.1555288Z ok (0.086s)
2023-01-11T21:28:38.1555618Z   test_slicing_dynamic_shape_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1555771Z unimplemented [('Dynamic slicing not supported', 2)]
2023-01-11T21:28:38.1555965Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1556033Z ok (0.024s)
2023-01-11T21:28:38.1556373Z   test_slicing_dynamic_shape_setitem_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:28:38.1556539Z unimplemented [('Dynamic slicing not supported', 1)]
2023-01-11T21:28:38.1556695Z graph_break [('Dynamic slicing not supported', 1)]
2023-01-11T21:28:38.1556889Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1557002Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1557066Z ok (0.034s)
2023-01-11T21:28:38.1557728Z   test_sort_out_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:38.1557838Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:28:38.1558700Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:38.1558794Z   return node.target(*args, **kwargs)
2023-01-11T21:28:38.1559346Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:38.1559488Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:28:38.1560035Z /var/lib/jenkins/workspace/test/dynamo/test_repros.py:1527: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:28:38.1560173Z   torch.sort(tensor, out=(values1, indices1))
2023-01-11T21:28:38.1560293Z frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:38.1560489Z inline_call [('call_function UserDefinedClassVariable() [] {}', 1)]
2023-01-11T21:28:38.1560555Z ok (0.041s)
2023-01-11T21:28:38.1560888Z   test_specialized_stride_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1561081Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1561134Z ok (0.010s)
2023-01-11T21:28:38.1561461Z   test_swin_base_tensor_attr_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1561653Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1561719Z ok (0.081s)
2023-01-11T21:28:38.1562050Z   test_tensor_isinstance_tuple_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1562244Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1562310Z ok (0.009s)
2023-01-11T21:28:38.1562627Z   test_tokenization_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1562852Z inline_call [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:28:38.1562926Z unimplemented []
2023-01-11T21:28:38.1563161Z graph_break [('inline in skipfiles: __init__ /opt/conda/lib/python3.10/collections/__init__.py', 2)]
2023-01-11T21:28:38.1563226Z ok (0.015s)
2023-01-11T21:28:38.1563609Z   test_torch_ops_aten_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1563673Z ok (0.019s)
2023-01-11T21:28:38.1564065Z   test_vdd_duplicate_error_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1564131Z ok (0.062s)
2023-01-11T21:28:38.1564445Z   test_while_loop_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1564515Z inline_call []
2023-01-11T21:28:38.1564783Z unimplemented [('call_function in skip_files /opt/conda/lib/python3.10/site-packages/torch/_dynamo/__init__.py', 1)]
2023-01-11T21:28:38.1564976Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1565040Z ok (0.026s)
2023-01-11T21:28:38.1565249Z   test_with_on_graph_break_inst_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... Hello world
2023-01-11T21:28:38.1565317Z Hello world
2023-01-11T21:28:38.1565434Z frames [('total', 6), ('ok', 6)]
2023-01-11T21:28:38.1565639Z inline_call [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 1)]
2023-01-11T21:28:38.1565765Z unimplemented []
2023-01-11T21:28:38.1566023Z graph_break [('call_function BuiltinVariable(print) [ConstantVariable(str)] {}', 2), ('Tensor.backward', 1)]
2023-01-11T21:28:38.1566216Z stats [('calls_captured', 11), ('fusions_possible', 7), ('unique_graphs', 4)]
2023-01-11T21:28:38.1566281Z ok (0.107s)
2023-01-11T21:28:38.1566592Z   test_capi_call1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1566666Z unimplemented []
2023-01-11T21:28:38.1566927Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1567120Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1567185Z ok (0.022s)
2023-01-11T21:28:38.1567527Z   test_capi_call2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1567605Z unimplemented []
2023-01-11T21:28:38.1567880Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1568074Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1568137Z ok (0.043s)
2023-01-11T21:28:38.1568430Z   test_capi_call3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1568504Z unimplemented []
2023-01-11T21:28:38.1568776Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1568967Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1569031Z ok (0.022s)
2023-01-11T21:28:38.1569435Z   test_control_flow1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1569632Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:38.1569697Z ok (0.043s)
2023-01-11T21:28:38.1569997Z   test_control_flow2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1570188Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1570252Z ok (0.022s)
2023-01-11T21:28:38.1570559Z   test_control_flow3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1570751Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:38.1570816Z ok (0.071s)
2023-01-11T21:28:38.1571128Z   test_control_flow4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:28:38.1571321Z stats [('calls_captured', 5), ('unique_graphs', 3), ('fusions_possible', 2)]
2023-01-11T21:28:38.1571376Z ok (0.039s)
2023-01-11T21:28:38.1571686Z   test_control_flow5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:38.1571881Z stats [('calls_captured', 13), ('fusions_possible', 7), ('unique_graphs', 6)]
2023-01-11T21:28:38.1571947Z ok (0.098s)
2023-01-11T21:28:38.1572267Z   test_dynamic_duck_size_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1572459Z stats [('calls_captured', 10), ('fusions_possible', 8), ('unique_graphs', 2)]
2023-01-11T21:28:38.1572524Z ok (0.040s)
2023-01-11T21:28:38.1572839Z   test_dynamic_kwarg_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1573032Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1573137Z ok (0.022s)
2023-01-11T21:28:38.1573476Z   test_dynamic_order_dependence_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1573669Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:28:38.1573735Z ok (0.063s)
2023-01-11T21:28:38.1574062Z   test_dynamic_shapes_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 11), ('ok', 11)]
2023-01-11T21:28:38.1574259Z stats [('calls_captured', 22), ('fusions_possible', 11), ('unique_graphs', 11)]
2023-01-11T21:28:38.1574324Z ok (0.059s)
2023-01-11T21:28:38.1574695Z   test_dynamic_zero_inference_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1574879Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1574946Z ok (0.024s)
2023-01-11T21:28:38.1575285Z   test_enumerate_not_break_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1575476Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1575558Z expected failure (0.024s)
2023-01-11T21:28:38.1575870Z   test_extended_args_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1576075Z stats [('calls_captured', 1026), ('fusions_possible', 1023), ('unique_graphs', 3)]
2023-01-11T21:28:38.1576141Z ok (6.796s)
2023-01-11T21:28:38.1576455Z   test_graph_break_on_item_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1576529Z unimplemented []
2023-01-11T21:28:38.1576653Z graph_break [('Tensor.item', 1)]
2023-01-11T21:28:38.1576846Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:28:38.1576910Z ok (0.050s)
2023-01-11T21:28:38.1577237Z   test_indirect_unsupported1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1577514Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1577588Z unimplemented []
2023-01-11T21:28:38.1577850Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1578043Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1578109Z ok (0.042s)
2023-01-11T21:28:38.1578441Z   test_indirect_unsupported2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1578718Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1578795Z unimplemented []
2023-01-11T21:28:38.1579063Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1579257Z stats [('calls_captured', 5), ('unique_graphs', 3), ('fusions_possible', 2)]
2023-01-11T21:28:38.1579310Z ok (0.067s)
2023-01-11T21:28:38.1579639Z   test_indirect_unsupported3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1579913Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1579988Z unimplemented []
2023-01-11T21:28:38.1580260Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1580455Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:28:38.1580555Z ok (0.042s)
2023-01-11T21:28:38.1580870Z   test_multigraph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1581050Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:28:38.1581114Z ok (0.041s)
2023-01-11T21:28:38.1581441Z   test_no_graph_break_on_item_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1581633Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:38.1581698Z ok (0.046s)
2023-01-11T21:28:38.1582045Z   test_pop_after_resume_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1582122Z unimplemented []
2023-01-11T21:28:38.1582396Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1582576Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1582640Z ok (0.042s)
2023-01-11T21:28:38.1582952Z   test_restore_range_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1583028Z unimplemented []
2023-01-11T21:28:38.1583381Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1583577Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1583642Z ok (0.041s)
2023-01-11T21:28:38.1583967Z   test_restore_range_iter_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1584029Z unimplemented []
2023-01-11T21:28:38.1584303Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1584498Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:38.1584575Z ok (0.024s)
2023-01-11T21:28:38.1585057Z   test_restore_state_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 1)]
2023-01-11T21:28:38.1585193Z unimplemented []
2023-01-11T21:28:38.1585698Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1585910Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1585979Z expected failure (0.036s)
2023-01-11T21:28:38.1586290Z   test_resume1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1586364Z unimplemented []
2023-01-11T21:28:38.1586635Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1586828Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1586894Z ok (0.054s)
2023-01-11T21:28:38.1587196Z   test_resume2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1587466Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1587529Z unimplemented []
2023-01-11T21:28:38.1587798Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1587990Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:38.1588055Z ok (0.075s)
2023-01-11T21:28:38.1588359Z   test_resume3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1588679Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1588754Z unimplemented []
2023-01-11T21:28:38.1589025Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1589207Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:38.1589274Z ok (0.074s)
2023-01-11T21:28:38.1589578Z   test_resume4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1589851Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1589927Z unimplemented []
2023-01-11T21:28:38.1590232Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1590429Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:38.1590493Z ok (0.074s)
2023-01-11T21:28:38.1590701Z   test_resume5_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:28:38.1590766Z         1.5000])
2023-01-11T21:28:38.1590870Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:28:38.1590933Z         1.5000])
2023-01-11T21:28:38.1591033Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:28:38.1591095Z         1.5000])
2023-01-11T21:28:38.1591192Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:28:38.1591242Z         1.5000])
2023-01-11T21:28:38.1591361Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1591435Z unimplemented []
2023-01-11T21:28:38.1591638Z graph_break [('call_function BuiltinVariable(print) [TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1591833Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1591897Z ok (0.055s)
2023-01-11T21:28:38.1608168Z   test_resume_freevars_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1608324Z unimplemented []
2023-01-11T21:28:38.1608658Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1608865Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:28:38.1608933Z ok (0.053s)
2023-01-11T21:28:38.1609383Z   test_resume_paths_join_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:28:38.1609586Z stats [('calls_captured', 10), ('unique_graphs', 7), ('fusions_possible', 3)]
2023-01-11T21:28:38.1609662Z ok (0.115s)
2023-01-11T21:28:38.1609998Z   test_resume_tuple_iterator_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1610075Z unimplemented []
2023-01-11T21:28:38.1610339Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1610538Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:28:38.1610604Z ok (0.071s)
2023-01-11T21:28:38.1610932Z   test_resume_with_no_grad1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1611007Z unimplemented []
2023-01-11T21:28:38.1611136Z graph_break [('Tensor.tolist', 2)]
2023-01-11T21:28:38.1611340Z stats [('calls_captured', 18), ('fusions_possible', 14), ('unique_graphs', 4)]
2023-01-11T21:28:38.1611405Z ok (0.096s)
2023-01-11T21:28:38.1611720Z   test_resume_with_no_grad2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1611929Z unimplemented []
2023-01-11T21:28:38.1612059Z graph_break [('Tensor.tolist', 2)]
2023-01-11T21:28:38.1612259Z stats [('calls_captured', 13), ('fusions_possible', 10), ('unique_graphs', 3)]
2023-01-11T21:28:38.1612324Z ok (0.070s)
2023-01-11T21:28:38.1612653Z   test_resume_with_no_grad3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1612729Z unimplemented []
2023-01-11T21:28:38.1612842Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:28:38.1613041Z stats [('calls_captured', 19), ('fusions_possible', 17), ('unique_graphs', 2)]
2023-01-11T21:28:38.1613107Z ok (0.048s)
2023-01-11T21:28:38.1613496Z   test_stack_state1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1613576Z unimplemented []
2023-01-11T21:28:38.1613860Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1614056Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:28:38.1614119Z ok (0.056s)
2023-01-11T21:28:38.1614419Z   test_stack_state2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1614696Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1614773Z unimplemented []
2023-01-11T21:28:38.1615047Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1615245Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:28:38.1615312Z ok (0.077s)
2023-01-11T21:28:38.1615525Z   test_start1_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
2023-01-11T21:28:38.1615690Z tensor([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2.])
2023-01-11T21:28:38.1615769Z tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
2023-01-11T21:28:38.1615922Z tensor([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2.])
2023-01-11T21:28:38.1616041Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1616117Z unimplemented []
2023-01-11T21:28:38.1616330Z graph_break [('call_function BuiltinVariable(print) [TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1616527Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1616592Z ok (0.035s)
2023-01-11T21:28:38.1616901Z   test_start2_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1617165Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1617244Z unimplemented []
2023-01-11T21:28:38.1617519Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1617716Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:28:38.1617783Z ok (0.053s)
2023-01-11T21:28:38.1618087Z   test_start3_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:38.1618162Z unimplemented []
2023-01-11T21:28:38.1618439Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:28:38.1618620Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1618688Z ok (0.033s)
2023-01-11T21:28:38.1618989Z   test_start4_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1619219Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:28:38.1619284Z ok (0.048s)
2023-01-11T21:28:38.1619508Z   test_tuple_iterator_mutate_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: not working yet (0.001s)
2023-01-11T21:28:38.1619839Z   test_tuple_iterator_return_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1619915Z unimplemented []
2023-01-11T21:28:38.1620179Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:28:38.1620372Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:28:38.1620468Z ok (0.068s)
2023-01-11T21:28:38.1620696Z   test_builtin_functions_on_cuda_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... skip: requires cuda (0.001s)
2023-01-11T21:28:38.1621009Z   test_builtin_getitem_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1)]
2023-01-11T21:28:38.1621090Z expected failure (0.014s)
2023-01-11T21:28:38.1621405Z   test_builtin_max_min_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1621597Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:38.1621650Z ok (0.022s)
2023-01-11T21:28:38.1622000Z   test_feed_random_values_into_graph_only_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1622194Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1622259Z ok (0.018s)
2023-01-11T21:28:38.1622629Z   test_multiple_consecutive_random_calls_before_graph_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1622823Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1622887Z ok (0.027s)
2023-01-11T21:28:38.1623207Z   test_no_recompilations_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1623471Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:38.1623525Z ok (0.019s)
2023-01-11T21:28:38.1623852Z   test_numpy_correctness_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:28:38.1624038Z unimplemented [('reconstruct: ConstantVariable(float64)', 1)]
2023-01-11T21:28:38.1624189Z graph_break [('Tensor.numpy', 2), ('numpy', 2)]
2023-01-11T21:28:38.1624387Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:38.1624453Z ok (0.073s)
2023-01-11T21:28:38.1624786Z   test_random_call_with_while_loop_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1624977Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:38.1625028Z ok (0.017s)
2023-01-11T21:28:38.1625370Z   test_random_values_with_graph_break_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:28:38.1625445Z unimplemented []
2023-01-11T21:28:38.1625567Z graph_break [('Tensor.item', 2)]
2023-01-11T21:28:38.1625760Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:28:38.1625823Z ok (0.062s)
2023-01-11T21:28:38.1626155Z   test_unspec_float_precision_dynamic_shapes (torch._dynamo.testing.make_test_cls_with_patches.<locals>.DummyTestClass) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:38.1626387Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:28:38.1626452Z ok (0.661s)
2023-01-11T21:28:38.1626460Z 
2023-01-11T21:28:38.1626662Z ----------------------------------------------------------------------
2023-01-11T21:28:38.1626742Z Ran 510 tests in 37.636s
2023-01-11T21:28:38.1626747Z 
2023-01-11T21:28:38.1626842Z OK (skipped=24, expected failures=16)
2023-01-11T21:28:38.1626848Z 
2023-01-11T21:28:38.1626931Z Generating XML reports...
2023-01-11T21:28:38.1627326Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesExportTests-20230111212759.xml
2023-01-11T21:28:38.1627751Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesFunctionTests-20230111212759.xml
2023-01-11T21:28:38.1628125Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesMiscTests-20230111212759.xml
2023-01-11T21:28:38.1628510Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesNNModuleTests-20230111212759.xml
2023-01-11T21:28:38.1628882Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesReproTests-20230111212759.xml
2023-01-11T21:28:38.1629271Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesSubGraphTests-20230111212759.xml
2023-01-11T21:28:38.1629648Z Generated XML report: test-reports/python-unittest/dynamo.test_dynamic_shapes/TEST-torch._dynamo.testing.DynamicShapesUnspecTests-20230111212759.xml
2023-01-11T21:28:38.1629653Z 
2023-01-11T21:28:38.1630011Z ##[endgroup]
2023-01-11T21:28:38.1630329Z FINISHED PRINTING LOG FILE of dynamo/test_dynamic_shapes (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_dynamic_shapes_xm_m_su8)
2023-01-11T21:28:38.1630337Z 
2023-01-11T21:28:39.9272704Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:39.9912185Z Ignoring disabled issues:  []
2023-01-11T21:28:40.0052239Z Running dynamo/test_export_mutations ... [2023-01-11 21:28:40.004966]
2023-01-11T21:28:40.0054156Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_export_mutations.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:40.005209]
2023-01-11T21:28:42.0231643Z 
2023-01-11T21:28:42.0232211Z Expand the folded group to see the log file of dynamo/test_export_mutations
2023-01-11T21:28:42.0234610Z ##[group]PRINTING LOG FILE of dynamo/test_export_mutations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_mutations_qzu9z2jb)
2023-01-11T21:28:42.0235223Z 
2023-01-11T21:28:42.0235348Z Running tests...
2023-01-11T21:28:42.0235978Z ----------------------------------------------------------------------
2023-01-11T21:28:42.0236688Z Test results will be stored in test-reports/python-unittest/dynamo.test_export_mutations
2023-01-11T21:28:42.0239533Z   test_module_attribute_mutation_violation_negative_1 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88468 for platform(s) linux, macos, mac. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.225s)
2023-01-11T21:28:42.0241704Z   test_module_attribute_mutation_violation_negative_2 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88475 for platform(s) linux, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:28:42.0243898Z   test_module_attribute_mutation_violation_negative_3 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88466 for platform(s) linux, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:28:42.0246331Z   test_module_attribute_mutation_violation_negative_4 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88467 for platform(s) linux, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:28:42.0247615Z   test_module_attribute_mutation_violation_positive_1 (__main__.MutationExportTests) ... ok (0.025s)
2023-01-11T21:28:42.0248241Z   test_module_attribute_mutation_violation_positive_2 (__main__.MutationExportTests) ... ok (0.002s)
2023-01-11T21:28:42.0248892Z   test_module_attribute_mutation_violation_positive_3 (__main__.MutationExportTests) ... ok (0.002s)
2023-01-11T21:28:42.0249725Z   test_module_attribute_mutation_violation_positive_4 (__main__.MutationExportTests) ... inline_call []
2023-01-11T21:28:42.0250175Z ok (0.004s)
2023-01-11T21:28:42.0250338Z 
2023-01-11T21:28:42.0250722Z ----------------------------------------------------------------------
2023-01-11T21:28:42.0251126Z Ran 8 tests in 0.261s
2023-01-11T21:28:42.0251320Z 
2023-01-11T21:28:42.0251437Z OK (skipped=4)
2023-01-11T21:28:42.0251636Z 
2023-01-11T21:28:42.0251777Z Generating XML reports...
2023-01-11T21:28:42.0252548Z Generated XML report: test-reports/python-unittest/dynamo.test_export_mutations/TEST-MutationExportTests-20230111212841.xml
2023-01-11T21:28:42.0253002Z 
2023-01-11T21:28:42.0253403Z ##[endgroup]
2023-01-11T21:28:42.0254184Z FINISHED PRINTING LOG FILE of dynamo/test_export_mutations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_mutations_qzu9z2jb)
2023-01-11T21:28:42.0254608Z 
2023-01-11T21:28:43.9028944Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:43.9674839Z Ignoring disabled issues:  []
2023-01-11T21:28:43.9816244Z Running dynamo/test_functions ... [2023-01-11 21:28:43.981386]
2023-01-11T21:28:43.9818294Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_functions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:43.981637]
2023-01-11T21:28:46.9091243Z 
2023-01-11T21:28:46.9091798Z Expand the folded group to see the log file of dynamo/test_functions
2023-01-11T21:28:46.9092899Z ##[group]PRINTING LOG FILE of dynamo/test_functions (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_functions_ay3ysm_x)
2023-01-11T21:28:46.9093350Z 
2023-01-11T21:28:46.9093460Z Running tests...
2023-01-11T21:28:46.9094121Z ----------------------------------------------------------------------
2023-01-11T21:28:46.9094814Z Test results will be stored in test-reports/python-unittest/dynamo.test_functions
2023-01-11T21:28:46.9095315Z   test_T (__main__.FunctionTests) ... ok (0.338s)
2023-01-11T21:28:46.9096195Z   test_add (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9097500Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9097901Z ok (0.006s)
2023-01-11T21:28:46.9098732Z   test_add_ (__main__.FunctionTests) ... /var/lib/jenkins/workspace/test/dynamo/test_functions.py:73: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:46.9099571Z   a_copy = torch.tensor(a)
2023-01-11T21:28:46.9100730Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:46.9101793Z   return node.target(*args, **kwargs)
2023-01-11T21:28:46.9102510Z <eval_with_key>.7:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:46.9103218Z   tensor = torch.tensor(a);  a = None
2023-01-11T21:28:46.9103892Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9104441Z ok (0.016s)
2023-01-11T21:28:46.9105086Z   test_addcdiv (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9105536Z ok (0.008s)
2023-01-11T21:28:46.9106524Z   test_addcdiv_ (__main__.FunctionTests) ... /var/lib/jenkins/workspace/test/dynamo/test_functions.py:84: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:46.9107378Z   a_copy = torch.tensor(a)
2023-01-11T21:28:46.9108530Z /opt/conda/lib/python3.10/site-packages/torch/_dynamo/utils.py:1052: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:46.9109331Z   return node.target(*args, **kwargs)
2023-01-11T21:28:46.9110046Z <eval_with_key>.12:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:28:46.9110743Z   tensor = torch.tensor(a);  a = None
2023-01-11T21:28:46.9111320Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:46.9111705Z ok (0.015s)
2023-01-11T21:28:46.9112103Z   test_build_list_unpack (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9112731Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:46.9113118Z ok (0.012s)
2023-01-11T21:28:46.9113741Z   test_chunks1 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9114194Z ok (0.008s)
2023-01-11T21:28:46.9114855Z   test_const_tuple_add1 (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:46.9115312Z ok (0.008s)
2023-01-11T21:28:46.9115971Z   test_const_tuple_add2 (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:46.9116440Z ok (0.008s)
2023-01-11T21:28:46.9117074Z   test_constant1 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9117531Z ok (0.007s)
2023-01-11T21:28:46.9118175Z   test_constant2 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9118645Z ok (0.007s)
2023-01-11T21:28:46.9119273Z   test_constant3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9119736Z ok (0.006s)
2023-01-11T21:28:46.9120375Z   test_constant4 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9120813Z ok (0.005s)
2023-01-11T21:28:46.9121460Z   test_default_dict (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9121925Z ok (0.010s)
2023-01-11T21:28:46.9122555Z   test_del (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9122990Z ok (0.007s)
2023-01-11T21:28:46.9123621Z   test_device (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9124195Z ok (0.005s)
2023-01-11T21:28:46.9124853Z   test_device_constant (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9125333Z ok (0.011s)
2023-01-11T21:28:46.9125971Z   test_dict_copy (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9126417Z ok (0.005s)
2023-01-11T21:28:46.9127055Z   test_dict_ops (__main__.FunctionTests) ... stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:46.9127519Z ok (0.014s)
2023-01-11T21:28:46.9128172Z   test_dict_param_keys (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9128626Z ok (0.006s)
2023-01-11T21:28:46.9129643Z   test_distributed_is_available (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9130148Z ok (0.005s)
2023-01-11T21:28:46.9130846Z   test_distributed_is_initialized (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9131335Z ok (0.005s)
2023-01-11T21:28:46.9131971Z   test_dtype (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9132424Z ok (0.005s)
2023-01-11T21:28:46.9133064Z   test_dtype_compare (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9133534Z ok (0.007s)
2023-01-11T21:28:46.9134157Z   test_finfo (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9134604Z ok (0.008s)
2023-01-11T21:28:46.9135244Z   test_float (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9135693Z ok (0.005s)
2023-01-11T21:28:46.9136344Z   test_fn_with_self_set (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:46.9136813Z ok (0.010s)
2023-01-11T21:28:46.9137457Z   test_fstrings1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9137906Z ok (0.006s)
2023-01-11T21:28:46.9138526Z   test_fstrings2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9138992Z ok (0.006s)
2023-01-11T21:28:46.9139621Z   test_fstrings3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9140058Z ok (0.005s)
2023-01-11T21:28:46.9140447Z   test_funcdef_closure (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9141066Z stats [('calls_captured', 10), ('fusions_possible', 9), ('unique_graphs', 1)]
2023-01-11T21:28:46.9141442Z ok (0.014s)
2023-01-11T21:28:46.9142123Z   test_get_default_dtype (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9142591Z ok (0.005s)
2023-01-11T21:28:46.9143233Z   test_globalfn (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9143764Z ok (0.005s)
2023-01-11T21:28:46.9144419Z   test_globalmodule (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9144892Z ok (0.010s)
2023-01-11T21:28:46.9145526Z   test_globalvar (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9145981Z ok (0.006s)
2023-01-11T21:28:46.9146614Z   test_import1 (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9147068Z ok (0.007s)
2023-01-11T21:28:46.9147693Z   test_indirect1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9148289Z ok (0.005s)
2023-01-11T21:28:46.9148937Z   test_indirect2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9149373Z ok (0.005s)
2023-01-11T21:28:46.9150023Z   test_indirect3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9150473Z ok (0.006s)
2023-01-11T21:28:46.9150861Z   test_inline_jit_annotations (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9151494Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9151881Z ok (0.008s)
2023-01-11T21:28:46.9152615Z   test_inline_softmax (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9153071Z ok (0.010s)
2023-01-11T21:28:46.9153462Z   test_inline_with_default (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9154091Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9154468Z ok (0.007s)
2023-01-11T21:28:46.9154850Z   test_inner_function (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9155457Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9155838Z ok (0.006s)
2023-01-11T21:28:46.9156523Z   test_is_contiguous_memory_format (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9157014Z ok (0.006s)
2023-01-11T21:28:46.9157660Z   test_is_fx_tracing (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9158103Z ok (0.006s)
2023-01-11T21:28:46.9158763Z   test_is_in_onnx_export (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9159229Z ok (0.006s)
2023-01-11T21:28:46.9159859Z   test_is_not_null (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9160313Z ok (0.005s)
2023-01-11T21:28:46.9160968Z   test_is_quantized (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9161414Z ok (0.005s)
2023-01-11T21:28:46.9162050Z   test_is_sparse (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9162518Z ok (0.005s)
2023-01-11T21:28:46.9163168Z   test_islice_chain (__main__.FunctionTests) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:28:46.9163615Z ok (0.011s)
2023-01-11T21:28:46.9164261Z   test_jit_annotate (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9164721Z ok (0.006s)
2023-01-11T21:28:46.9165364Z   test_len_constant_dict (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9165849Z ok (0.005s)
2023-01-11T21:28:46.9166506Z   test_len_constant_list (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9166980Z ok (0.006s)
2023-01-11T21:28:46.9167665Z   test_len_constant_misc_iterables (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9168161Z ok (0.005s)
2023-01-11T21:28:46.9168813Z   test_len_tensor (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9169404Z ok (0.005s)
2023-01-11T21:28:46.9170053Z   test_list_add (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9170514Z ok (0.006s)
2023-01-11T21:28:46.9171145Z   test_list_clear (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9171722Z ok (0.008s)
2023-01-11T21:28:46.9172377Z   test_list_convert (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:46.9172835Z ok (0.008s)
2023-01-11T21:28:46.9173474Z   test_list_reversed (__main__.FunctionTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:46.9173951Z ok (0.009s)
2023-01-11T21:28:46.9174641Z   test_list_slice_assignment (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9175115Z ok (0.006s)
2023-01-11T21:28:46.9175756Z   test_list_truth (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9176315Z ok (0.005s)
2023-01-11T21:28:46.9176980Z   test_listarg1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9177429Z ok (0.006s)
2023-01-11T21:28:46.9178062Z   test_listarg2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9178509Z ok (0.006s)
2023-01-11T21:28:46.9179125Z   test_listarg3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9179571Z ok (0.006s)
2023-01-11T21:28:46.9180198Z   test_listarg4 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9180635Z ok (0.006s)
2023-01-11T21:28:46.9181266Z   test_listarg5 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9181717Z ok (0.006s)
2023-01-11T21:28:46.9182375Z   test_load_global_bool (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9182827Z ok (0.005s)
2023-01-11T21:28:46.9183197Z   test_map_sum (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9183877Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:28:46.9184251Z ok (0.014s)
2023-01-11T21:28:46.9184624Z   test_methodcall1 (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9185231Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9185604Z ok (0.007s)
2023-01-11T21:28:46.9185982Z   test_methodcall2 (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9186587Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9186957Z ok (0.007s)
2023-01-11T21:28:46.9187333Z   test_methodcall3 (__main__.FunctionTests) ... inline_call []
2023-01-11T21:28:46.9187948Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9188335Z ok (0.007s)
2023-01-11T21:28:46.9188964Z   test_min_max (__main__.FunctionTests) ... stats [('calls_captured', 11), ('fusions_possible', 10), ('unique_graphs', 1)]
2023-01-11T21:28:46.9189428Z ok (0.018s)
2023-01-11T21:28:46.9190086Z   test_module_constant (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:46.9190546Z ok (0.009s)
2023-01-11T21:28:46.9191172Z   test_ndim (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9191614Z ok (0.005s)
2023-01-11T21:28:46.9192230Z   test_pop (__main__.FunctionTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:28:46.9192675Z ok (0.010s)
2023-01-11T21:28:46.9193313Z   test_range1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9193774Z ok (0.005s)
2023-01-11T21:28:46.9194387Z   test_range2 (__main__.FunctionTests) ... stats [('calls_captured', 13), ('fusions_possible', 12), ('unique_graphs', 1)]
2023-01-11T21:28:46.9194947Z ok (0.016s)
2023-01-11T21:28:46.9195581Z   test_reduce (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9196010Z ok (0.008s)
2023-01-11T21:28:46.9196665Z   test_return_dict (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9197123Z ok (0.007s)
2023-01-11T21:28:46.9197773Z   test_return_dict2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9198219Z ok (0.007s)
2023-01-11T21:28:46.9198867Z   test_return_tuple1 (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9199337Z ok (0.006s)
2023-01-11T21:28:46.9200060Z   test_return_tuple2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9200539Z ok (0.005s)
2023-01-11T21:28:46.9201175Z   test_shape1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9201615Z ok (0.005s)
2023-01-11T21:28:46.9202252Z   test_shape2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9202710Z ok (0.005s)
2023-01-11T21:28:46.9203342Z   test_slice1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9203782Z ok (0.005s)
2023-01-11T21:28:46.9204418Z   test_slice2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9204880Z ok (0.005s)
2023-01-11T21:28:46.9205502Z   test_slice3 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9205951Z ok (0.005s)
2023-01-11T21:28:46.9206577Z   test_slice4 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9207024Z ok (0.005s)
2023-01-11T21:28:46.9207657Z   test_slice5 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9208104Z ok (0.005s)
2023-01-11T21:28:46.9208725Z   test_slice6 (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9209298Z ok (0.006s)
2023-01-11T21:28:46.9209947Z   test_startswith (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9210400Z ok (0.006s)
2023-01-11T21:28:46.9211017Z   test_tensor_len (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9211480Z ok (0.007s)
2023-01-11T21:28:46.9212164Z   test_tensor_new_with_shape (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9212642Z ok (0.017s)
2023-01-11T21:28:46.9213288Z   test_tensor_new_with_size (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9213761Z ok (0.017s)
2023-01-11T21:28:46.9214403Z   test_tensor_type (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9214853Z ok (0.018s)
2023-01-11T21:28:46.9215260Z   test_tensor_type2 (__main__.FunctionTests) ... skip: requires cuda (0.000s)
2023-01-11T21:28:46.9216067Z   test_transpose_for_scores (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9216525Z ok (0.007s)
2023-01-11T21:28:46.9217156Z   test_tuple1 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9217610Z ok (0.005s)
2023-01-11T21:28:46.9218374Z   test_tuple2 (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9218805Z ok (0.005s)
2023-01-11T21:28:46.9219465Z   test_tuple_contains (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9219934Z ok (0.006s)
2023-01-11T21:28:46.9220563Z   test_tuple_iadd (__main__.FunctionTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:46.9221024Z ok (0.007s)
2023-01-11T21:28:46.9221658Z   test_unpack1 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9222106Z ok (0.007s)
2023-01-11T21:28:46.9222834Z   test_unpack2 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9223295Z ok (0.007s)
2023-01-11T21:28:46.9224037Z   test_unpack3 (__main__.FunctionTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:28:46.9224478Z ok (0.007s)
2023-01-11T21:28:46.9225128Z   test_unpack_ex1 (__main__.FunctionTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:46.9225577Z ok (0.008s)
2023-01-11T21:28:46.9226201Z   test_unpack_ex2 (__main__.FunctionTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:46.9226655Z ok (0.008s)
2023-01-11T21:28:46.9227296Z   test_unpack_ex3 (__main__.FunctionTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:28:46.9227753Z ok (0.008s)
2023-01-11T21:28:46.9228389Z   test_viamethod (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9228852Z ok (0.005s)
2023-01-11T21:28:46.9229497Z   test_viatorch (__main__.FunctionTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:46.9229944Z ok (0.005s)
2023-01-11T21:28:46.9230113Z 
2023-01-11T21:28:46.9230466Z ----------------------------------------------------------------------
2023-01-11T21:28:46.9230882Z Ran 109 tests in 1.132s
2023-01-11T21:28:46.9231076Z 
2023-01-11T21:28:46.9231182Z OK (skipped=1)
2023-01-11T21:28:46.9231369Z 
2023-01-11T21:28:46.9231512Z Generating XML reports...
2023-01-11T21:28:46.9232249Z Generated XML report: test-reports/python-unittest/dynamo.test_functions/TEST-FunctionTests-20230111212845.xml
2023-01-11T21:28:46.9232691Z 
2023-01-11T21:28:46.9233183Z ##[endgroup]
2023-01-11T21:28:46.9233882Z FINISHED PRINTING LOG FILE of dynamo/test_functions (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_functions_ay3ysm_x)
2023-01-11T21:28:46.9234300Z 
2023-01-11T21:28:48.7402121Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:48.8048132Z Ignoring disabled issues:  []
2023-01-11T21:28:48.8191158Z Running dynamo/test_global ... [2023-01-11 21:28:48.818852]
2023-01-11T21:28:48.8193666Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_global.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:48.819097]
2023-01-11T21:28:50.9699432Z 
2023-01-11T21:28:50.9700002Z Expand the folded group to see the log file of dynamo/test_global
2023-01-11T21:28:50.9701248Z ##[group]PRINTING LOG FILE of dynamo/test_global (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_rblqkeky)
2023-01-11T21:28:50.9701544Z 
2023-01-11T21:28:50.9701619Z Running tests...
2023-01-11T21:28:50.9702024Z ----------------------------------------------------------------------
2023-01-11T21:28:50.9702397Z Test results will be stored in test-reports/python-unittest/dynamo.test_global
2023-01-11T21:28:50.9702695Z   test_store_global_1 (__main__.TestGlobals) ... ok (0.321s)
2023-01-11T21:28:50.9703059Z   test_store_global_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9703473Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:50.9703923Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:50.9704230Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:28:50.9704451Z ok (0.009s)
2023-01-11T21:28:50.9704747Z   test_store_global_cross_file (__main__.TestGlobals) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:28:50.9704991Z unimplemented []
2023-01-11T21:28:50.9705402Z graph_break [('call_function BuiltinVariable(setattr) [PythonModuleVariable(), ConstantVariable(str), TensorVariable()] {}', 1)]
2023-01-11T21:28:50.9705811Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:50.9706032Z ok (0.008s)
2023-01-11T21:28:50.9706328Z   test_store_global_dict (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9706721Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:50.9706946Z ok (0.005s)
2023-01-11T21:28:50.9707245Z   test_store_global_dict_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9707477Z inline_call []
2023-01-11T21:28:50.9707759Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:50.9707983Z ok (0.007s)
2023-01-11T21:28:50.9708284Z   test_store_global_inline_1 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9708501Z inline_call []
2023-01-11T21:28:50.9708789Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:50.9709009Z ok (0.009s)
2023-01-11T21:28:50.9709296Z   test_store_global_inline_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9709524Z inline_call []
2023-01-11T21:28:50.9709811Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:50.9710018Z ok (0.010s)
2023-01-11T21:28:50.9710313Z   test_store_global_list (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9710662Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:50.9710883Z ok (0.005s)
2023-01-11T21:28:50.9711168Z   test_store_global_list_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9711394Z inline_call []
2023-01-11T21:28:50.9711681Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:50.9711886Z ok (0.006s)
2023-01-11T21:28:50.9712181Z   test_store_global_new (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9712528Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:28:50.9712734Z ok (0.005s)
2023-01-11T21:28:50.9713032Z   test_store_global_object (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:28:50.9713385Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:28:50.9713595Z ok (0.005s)
2023-01-11T21:28:50.9713693Z 
2023-01-11T21:28:50.9713889Z ----------------------------------------------------------------------
2023-01-11T21:28:50.9714129Z Ran 11 tests in 0.391s
2023-01-11T21:28:50.9714239Z 
2023-01-11T21:28:50.9714299Z OK
2023-01-11T21:28:50.9714375Z 
2023-01-11T21:28:50.9714458Z Generating XML reports...
2023-01-11T21:28:50.9714856Z Generated XML report: test-reports/python-unittest/dynamo.test_global/TEST-TestGlobals-20230111212850.xml
2023-01-11T21:28:50.9715080Z 
2023-01-11T21:28:50.9715316Z ##[endgroup]
2023-01-11T21:28:50.9715691Z FINISHED PRINTING LOG FILE of dynamo/test_global (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_rblqkeky)
2023-01-11T21:28:50.9715913Z 
2023-01-11T21:28:52.8066337Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:52.8708005Z Ignoring disabled issues:  []
2023-01-11T21:28:52.8852948Z Running dynamo/test_global_declaration ... [2023-01-11 21:28:52.885022]
2023-01-11T21:28:52.8854664Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_global_declaration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:52.885258]
2023-01-11T21:28:54.2344210Z 
2023-01-11T21:28:54.2344857Z Expand the folded group to see the log file of dynamo/test_global_declaration
2023-01-11T21:28:54.2346166Z ##[group]PRINTING LOG FILE of dynamo/test_global_declaration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_declaration_ap7x6ltq)
2023-01-11T21:28:54.2346447Z 
2023-01-11T21:28:54.2346665Z ##[endgroup]
2023-01-11T21:28:54.2347202Z FINISHED PRINTING LOG FILE of dynamo/test_global_declaration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_declaration_ap7x6ltq)
2023-01-11T21:28:54.2347452Z 
2023-01-11T21:28:56.0438577Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:28:56.1080815Z Ignoring disabled issues:  []
2023-01-11T21:28:56.1223086Z Running dynamo/test_minifier ... [2023-01-11 21:28:56.122056]
2023-01-11T21:28:56.1225492Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_minifier.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:28:56.122310]
2023-01-11T21:29:36.8235136Z 
2023-01-11T21:29:36.8235788Z Expand the folded group to see the log file of dynamo/test_minifier
2023-01-11T21:29:36.8237087Z ##[group]PRINTING LOG FILE of dynamo/test_minifier (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_minifier_3gjkt5xj)
2023-01-11T21:29:36.8249608Z 
2023-01-11T21:29:36.8249917Z Running tests...
2023-01-11T21:29:36.8250638Z ----------------------------------------------------------------------
2023-01-11T21:29:36.8251349Z Test results will be stored in test-reports/python-unittest/dynamo.test_minifier
2023-01-11T21:29:36.8251887Z   test_after_dynamo_cpu_accuracy_backend_passes (__main__.MinifierTests) ... ok (1.793s)
2023-01-11T21:29:36.8252398Z   test_after_dynamo_cpu_accuracy_error (__main__.MinifierTests) ... ok (5.018s)
2023-01-11T21:29:36.8252949Z   test_after_dynamo_cpu_compile_backend_passes (__main__.MinifierTests) ... ok (1.619s)
2023-01-11T21:29:36.8253479Z   test_after_dynamo_cpu_compile_error (__main__.MinifierTests) ... ok (4.860s)
2023-01-11T21:29:36.8254001Z   test_after_dynamo_cpu_runtime_backend_passes (__main__.MinifierTests) ... ok (1.579s)
2023-01-11T21:29:36.8254496Z   test_after_dynamo_cpu_runtime_error (__main__.MinifierTests) ... ok (4.694s)
2023-01-11T21:29:36.8255036Z   test_after_dynamo_cuda_accuracy_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:29:36.8255607Z   test_after_dynamo_cuda_accuracy_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:29:36.8256151Z   test_after_dynamo_cuda_compile_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:29:36.8256746Z   test_after_dynamo_cuda_compile_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:29:36.8257342Z   test_after_dynamo_cuda_runtime_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:29:36.8257935Z   test_after_dynamo_cuda_runtime_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:29:36.8258447Z   test_after_dynamo_custom_backend (__main__.MinifierTests) ... ok (3.116s)
2023-01-11T21:29:36.8258977Z   test_after_dynamo_with_modified_config_cpu_accuracy_error (__main__.MinifierTests) ... ok (4.950s)
2023-01-11T21:29:36.8259798Z   test_after_dynamo_with_modified_config_cpu_compile_error (__main__.MinifierTests) ... ok (4.860s)
2023-01-11T21:29:36.8260374Z   test_cpu_cuda_module_after_dynamo (__main__.MinifierTests) ... skip: requires cuda (0.001s)
2023-01-11T21:29:36.8260891Z   test_dynamo_config_serialization (__main__.MinifierTests) ... ok (1.508s)
2023-01-11T21:29:36.8261398Z   test_if_graph_minified (__main__.MinifierTests) ... ok (4.893s)
2023-01-11T21:29:36.8261670Z 
2023-01-11T21:29:36.8262077Z ----------------------------------------------------------------------
2023-01-11T21:29:36.8262453Z Ran 18 tests in 38.897s
2023-01-11T21:29:36.8262639Z 
2023-01-11T21:29:36.8262754Z OK (skipped=7)
2023-01-11T21:29:36.8262922Z 
2023-01-11T21:29:36.8263425Z Generating XML reports...
2023-01-11T21:29:36.8264131Z Generated XML report: test-reports/python-unittest/dynamo.test_minifier/TEST-MinifierTests-20230111212857.xml
2023-01-11T21:29:36.8264485Z 
2023-01-11T21:29:36.8264932Z ##[endgroup]
2023-01-11T21:29:36.8265611Z FINISHED PRINTING LOG FILE of dynamo/test_minifier (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_minifier_3gjkt5xj)
2023-01-11T21:29:36.8265969Z 
2023-01-11T21:29:38.6475778Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:38.7120848Z Ignoring disabled issues:  []
2023-01-11T21:29:38.7263860Z Running dynamo/test_model_output ... [2023-01-11 21:29:38.726098]
2023-01-11T21:29:38.7265427Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_model_output.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:38.726348]
2023-01-11T21:29:40.3830679Z 
2023-01-11T21:29:40.3831322Z Expand the folded group to see the log file of dynamo/test_model_output
2023-01-11T21:29:40.3832111Z ##[group]PRINTING LOG FILE of dynamo/test_model_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_model_output_ctxt04f3)
2023-01-11T21:29:40.3832351Z 
2023-01-11T21:29:40.3832413Z Running tests...
2023-01-11T21:29:40.3832814Z ----------------------------------------------------------------------
2023-01-11T21:29:40.3833215Z Test results will be stored in test-reports/python-unittest/dynamo.test_model_output
2023-01-11T21:29:40.3833559Z   test_pretrained (__main__.TestHFPretrained) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:29:40.3833873Z   test_mo_assign (__main__.TestModelOutput) ... skip: requires HuggingFace (0.001s)
2023-01-11T21:29:40.3834195Z   test_mo_create (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:29:40.3834514Z   test_mo_getattr (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:29:40.3834817Z   test_mo_getitem (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:29:40.3835135Z   test_mo_index (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:29:40.3835441Z   test_mo_init (__main__.TestModelOutput) ... skip: requires HuggingFace (0.001s)
2023-01-11T21:29:40.3835749Z   test_mo_tuple (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T21:29:40.3835909Z 
2023-01-11T21:29:40.3836108Z ----------------------------------------------------------------------
2023-01-11T21:29:40.3836346Z Ran 8 tests in 0.003s
2023-01-11T21:29:40.3836459Z 
2023-01-11T21:29:40.3836529Z OK (skipped=8)
2023-01-11T21:29:40.3836634Z 
2023-01-11T21:29:40.3836706Z Generating XML reports...
2023-01-11T21:29:40.3837141Z Generated XML report: test-reports/python-unittest/dynamo.test_model_output/TEST-TestHFPretrained-20230111212940.xml
2023-01-11T21:29:40.3837681Z Generated XML report: test-reports/python-unittest/dynamo.test_model_output/TEST-TestModelOutput-20230111212940.xml
2023-01-11T21:29:40.3837922Z 
2023-01-11T21:29:40.3838122Z ##[endgroup]
2023-01-11T21:29:40.3838538Z FINISHED PRINTING LOG FILE of dynamo/test_model_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_model_output_ctxt04f3)
2023-01-11T21:29:40.3838767Z 
2023-01-11T21:29:42.2378163Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:42.3117556Z Ignoring disabled issues:  []
2023-01-11T21:29:42.3261090Z Running dynamo/test_modules ... [2023-01-11 21:29:42.325805]
2023-01-11T21:29:42.3262816Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_modules.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:42.326049]
2023-01-11T21:29:45.7871952Z 
2023-01-11T21:29:45.7872949Z Expand the folded group to see the log file of dynamo/test_modules
2023-01-11T21:29:45.7873906Z ##[group]PRINTING LOG FILE of dynamo/test_modules (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_modules_luscdvt3)
2023-01-11T21:29:45.7874146Z 
2023-01-11T21:29:45.7874207Z Running tests...
2023-01-11T21:29:45.7874712Z ----------------------------------------------------------------------
2023-01-11T21:29:45.7875569Z Test results will be stored in test-reports/python-unittest/dynamo.test_modules
2023-01-11T21:29:45.7892238Z   test_access_by_keys (__main__.NNModuleTests) ... ok (0.352s)
2023-01-11T21:29:45.7892708Z   test_basicmodule1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7893221Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:29:45.7893584Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.7893810Z ok (0.017s)
2023-01-11T21:29:45.7894249Z   test_basicmodule2 (__main__.NNModuleTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.7894539Z ok (0.013s)
2023-01-11T21:29:45.7894924Z   test_call_fn_with_non_const_inputs_safe (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7895351Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:45.7930533Z ok (0.023s)
2023-01-11T21:29:45.7931208Z   test_cfgmod (__main__.NNModuleTests) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:29:45.7931710Z ok (0.029s)
2023-01-11T21:29:45.7932392Z   test_children (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.7932855Z ok (0.019s)
2023-01-11T21:29:45.7933498Z   test_constloop (__main__.NNModuleTests) ... stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:29:45.7933765Z ok (0.027s)
2023-01-11T21:29:45.7933965Z   test_densenet (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7934468Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:29:45.7934695Z ok (0.017s)
2023-01-11T21:29:45.7937153Z   test_enumvalues (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7938380Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:29:45.7939006Z ok (0.016s)
2023-01-11T21:29:45.7939944Z   test_fnmember (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7940580Z ok (0.012s)
2023-01-11T21:29:45.7941509Z   test_fnmembercmp1 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7942181Z ok (0.012s)
2023-01-11T21:29:45.7943095Z   test_fnmembercmp2 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7943818Z ok (0.012s)
2023-01-11T21:29:45.7944363Z   test_forward_directly (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7945235Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.7945799Z ok (0.020s)
2023-01-11T21:29:45.7946340Z   test_generation_tag (__main__.NNModuleTests) ... ok (0.002s)
2023-01-11T21:29:45.7947313Z   test_hasattr (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7947963Z ok (0.009s)
2023-01-11T21:29:45.7948839Z   test_intarg (__main__.NNModuleTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.7949499Z ok (0.013s)
2023-01-11T21:29:45.7950388Z   test_iseval1 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7951015Z ok (0.012s)
2023-01-11T21:29:45.7951872Z   test_iseval2 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7952460Z ok (0.012s)
2023-01-11T21:29:45.7953307Z   test_isnonelayer (__main__.NNModuleTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:45.7953942Z ok (0.010s)
2023-01-11T21:29:45.7954814Z   test_istraining1 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7955697Z ok (0.012s)
2023-01-11T21:29:45.7956637Z   test_istraining2 (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7970373Z ok (0.012s)
2023-01-11T21:29:45.7971037Z   test_layerlist (__main__.NNModuleTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.7971387Z ok (0.017s)
2023-01-11T21:29:45.7972051Z   test_lazy_module (__main__.NNModuleTests) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:29:45.7972628Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:29:45.7973430Z [2023-01-11 21:29:44,497] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7fc3810eee60> [ShapeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:29:45.7974177Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:29:45.7974661Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:29:45.7975234Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:29:45.7975741Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:29:45.7976434Z [2023-01-11 21:29:44,569] torch._dynamo.symbolic_convert: [WARNING] /opt/conda/lib/python3.10/site-packages/torch/nn/parameter.py <function UninitializedTensorMixin.materialize at 0x7fc3810eee60> [ShapeVariable()] {} missing a required argument: 'shape'
2023-01-11T21:29:45.7977712Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:29:45.7978401Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:29:45.7978865Z frames [('total', 16), ('ok', 14)]
2023-01-11T21:29:45.7980203Z inline_call [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2), ('call_function UserDefinedObjectVariable(_infer_parameters) [NNModuleVariable(), TupleVariable()] {}', 1), ('call_function UserDefinedObjectVariable(_infer_parameters) [UnspecializedNNModuleVariable(LazyModule), TupleVariable()] {}', 1)]
2023-01-11T21:29:45.7981349Z unimplemented [("Guard setup for uninitialized class <class '__main__.LazyModule'>", 2)]
2023-01-11T21:29:45.7982041Z graph_break [('Patched init cannot be inlined.', 3), ('arg mismatch inlining', 2)]
2023-01-11T21:29:45.7982846Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.7983404Z ok (0.205s)
2023-01-11T21:29:45.7983898Z   test_module_attribute_precedence (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7984679Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.7985148Z ok (0.011s)
2023-01-11T21:29:45.7985617Z   test_module_class_method (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7986371Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:29:45.7986849Z ok (0.030s)
2023-01-11T21:29:45.7987550Z   test_module_forward_has_graph_break (__main__.NNModuleTests) ... frames [('total', 3), ('ok', 2)]
2023-01-11T21:29:45.7988081Z inline_call []
2023-01-11T21:29:45.7988691Z unimplemented [('reconstruct: ConstantVariable(dict)', 2)]
2023-01-11T21:29:45.7989666Z graph_break [('call_function BuiltinVariable(dict) [ListIteratorVariable()] {}', 1), ('call_method NNModuleVariable() buffers [] {}', 1)]
2023-01-11T21:29:45.7990778Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:29:45.7991264Z ok (0.077s)
2023-01-11T21:29:45.7992008Z   test_module_name_string (__main__.NNModuleTests) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.7992443Z ok (0.014s)
2023-01-11T21:29:45.7992667Z   test_module_property (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7993014Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:45.7993222Z ok (0.006s)
2023-01-11T21:29:45.7993468Z   test_module_static_method (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7993815Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:29:45.7994087Z ok (0.029s)
2023-01-11T21:29:45.7994445Z   test_moduledict (__main__.NNModuleTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:45.7994714Z ok (0.010s)
2023-01-11T21:29:45.7995082Z   test_modulelist (__main__.NNModuleTests) ... stats [('calls_captured', 40), ('fusions_possible', 39), ('unique_graphs', 1)]
2023-01-11T21:29:45.7995334Z ok (0.112s)
2023-01-11T21:29:45.7995555Z   test_modulemethod1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7995903Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:29:45.7996116Z ok (0.029s)
2023-01-11T21:29:45.7996339Z   test_modulemethod2 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.7996678Z stats [('calls_captured', 9), ('fusions_possible', 8), ('unique_graphs', 1)]
2023-01-11T21:29:45.7996896Z ok (0.029s)
2023-01-11T21:29:45.7997274Z   test_nn_moduledict_contains (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:29:45.7997601Z frames [('total', 2), ('ok', 1)]
2023-01-11T21:29:45.7997875Z inline_call [('Patched init cannot be inlined.', 1)]
2023-01-11T21:29:45.7998303Z unimplemented [("Guard setup for uninitialized class <class '__main__.NNModuleTests.test_nn_moduledict_contains.<locals>.M'>", 1)]
2023-01-11T21:29:45.7998687Z graph_break [('Patched init cannot be inlined.', 1)]
2023-01-11T21:29:45.8052522Z ok (0.018s)
2023-01-11T21:29:45.8054271Z   test_parameters1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8058365Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:45.8058627Z ok (0.009s)
2023-01-11T21:29:45.8058856Z   test_parameters2 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8059192Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:45.8059419Z ok (0.009s)
2023-01-11T21:29:45.8059799Z   test_parameters3 (__main__.NNModuleTests) ... stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:29:45.8060052Z ok (0.024s)
2023-01-11T21:29:45.8060423Z   test_self_mutating1 (__main__.NNModuleTests) ... stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:29:45.8060695Z ok (0.039s)
2023-01-11T21:29:45.8061042Z   test_seq (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.8061295Z ok (0.019s)
2023-01-11T21:29:45.8061525Z   test_simple_torch_function (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8061877Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.8062089Z ok (0.013s)
2023-01-11T21:29:45.8062456Z   test_stringmember (__main__.NNModuleTests) ... stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:29:45.8062722Z ok (0.012s)
2023-01-11T21:29:45.8062928Z   test_submodules1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8063267Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:29:45.8063573Z ok (0.025s)
2023-01-11T21:29:45.8063902Z   test_submodules2 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8064247Z stats [('calls_captured', 7), ('fusions_possible', 6), ('unique_graphs', 1)]
2023-01-11T21:29:45.8064472Z ok (0.025s)
2023-01-11T21:29:45.8064684Z   test_super1 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8065007Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.8065228Z ok (0.016s)
2023-01-11T21:29:45.8065438Z   test_super2 (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8065761Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.8065983Z ok (0.014s)
2023-01-11T21:29:45.8066212Z   test_super_class_method (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8066625Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:29:45.8066856Z ok (0.007s)
2023-01-11T21:29:45.8067223Z   test_tensorlist (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.8067476Z ok (0.011s)
2023-01-11T21:29:45.8067865Z   test_torch_function_with_closure (__main__.NNModuleTests) ... stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.8068142Z ok (0.008s)
2023-01-11T21:29:45.8068458Z   test_unsupportedmethod (__main__.NNModuleTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:45.8068899Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:29:45.8069194Z unimplemented []
2023-01-11T21:29:45.8069578Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:29:45.8069978Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:29:45.8070206Z ok (0.029s)
2023-01-11T21:29:45.8070518Z   test_unsupportedmodule (__main__.NNModuleTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:45.8070969Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:29:45.8071249Z unimplemented []
2023-01-11T21:29:45.8071627Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:29:45.8072035Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:29:45.8072250Z ok (0.030s)
2023-01-11T21:29:45.8072473Z   test_viamodulecall (__main__.NNModuleTests) ... inline_call []
2023-01-11T21:29:45.8072821Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:29:45.8073032Z ok (0.015s)
2023-01-11T21:29:45.8073250Z   test_attr (__main__.OptimizedModuleTest) ... ok (0.002s)
2023-01-11T21:29:45.8073618Z   test_composition (__main__.OptimizedModuleTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:45.8073863Z inline_call []
2023-01-11T21:29:45.8074147Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.8074377Z ok (0.008s)
2023-01-11T21:29:45.8074712Z   test_composition_with_opt_mod (__main__.OptimizedModuleTest) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:29:45.8075075Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:29:45.8075514Z inline_call [('inline in skipfiles: forward /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 1)]
2023-01-11T21:29:45.8075799Z unimplemented []
2023-01-11T21:29:45.8076168Z graph_break [('inline in skipfiles: forward /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 1)]
2023-01-11T21:29:45.8076437Z ok (0.010s)
2023-01-11T21:29:45.8076743Z   test_nn_module (__main__.OptimizedModuleTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:45.8077103Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.8077316Z ok (0.012s)
2023-01-11T21:29:45.8077621Z   test_recursion (__main__.OptimizedModuleTest) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:29:45.8078017Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:45.8078225Z ok (0.013s)
2023-01-11T21:29:45.8078519Z   test_to (__main__.OptimizedModuleTest) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:45.8078868Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:29:45.8079076Z ok (0.034s)
2023-01-11T21:29:45.8079177Z 
2023-01-11T21:29:45.8079378Z ----------------------------------------------------------------------
2023-01-11T21:29:45.8079619Z Ran 57 tests in 1.627s
2023-01-11T21:29:45.8079731Z 
2023-01-11T21:29:45.8079791Z OK
2023-01-11T21:29:45.8079871Z 
2023-01-11T21:29:45.8079955Z Generating XML reports...
2023-01-11T21:29:45.8080392Z Generated XML report: test-reports/python-unittest/dynamo.test_modules/TEST-NNModuleTests-20230111212943.xml
2023-01-11T21:29:45.8080925Z Generated XML report: test-reports/python-unittest/dynamo.test_modules/TEST-OptimizedModuleTest-20230111212943.xml
2023-01-11T21:29:45.8081167Z 
2023-01-11T21:29:45.8081519Z ##[endgroup]
2023-01-11T21:29:45.8081919Z FINISHED PRINTING LOG FILE of dynamo/test_modules (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_modules_luscdvt3)
2023-01-11T21:29:45.8082142Z 
2023-01-11T21:29:47.6254449Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:47.6900327Z Ignoring disabled issues:  []
2023-01-11T21:29:47.7042931Z Running dynamo/test_nops ... [2023-01-11 21:29:47.704026]
2023-01-11T21:29:47.7045025Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_nops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:47.704274]
2023-01-11T21:29:49.7293591Z 
2023-01-11T21:29:49.7294129Z Expand the folded group to see the log file of dynamo/test_nops
2023-01-11T21:29:49.7295273Z ##[group]PRINTING LOG FILE of dynamo/test_nops (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_nops_ffc6wtso)
2023-01-11T21:29:49.7295710Z 
2023-01-11T21:29:49.7295850Z Running tests...
2023-01-11T21:29:49.7296558Z ----------------------------------------------------------------------
2023-01-11T21:29:49.7297046Z Test results will be stored in test-reports/python-unittest/dynamo.test_nops
2023-01-11T21:29:49.7297309Z   test1 (__main__.NopTests) ... ok (0.223s)
2023-01-11T21:29:49.7297528Z   test2 (__main__.NopTests) ... ok (0.002s)
2023-01-11T21:29:49.7297742Z   test3 (__main__.NopTests) ... ok (0.002s)
2023-01-11T21:29:49.7297965Z   test_extended_args (__main__.NopTests) ... ok (0.031s)
2023-01-11T21:29:49.7298108Z 
2023-01-11T21:29:49.7298306Z ----------------------------------------------------------------------
2023-01-11T21:29:49.7298547Z Ran 4 tests in 0.259s
2023-01-11T21:29:49.7298661Z 
2023-01-11T21:29:49.7298721Z OK
2023-01-11T21:29:49.7298799Z 
2023-01-11T21:29:49.7298895Z Generating XML reports...
2023-01-11T21:29:49.7299297Z Generated XML report: test-reports/python-unittest/dynamo.test_nops/TEST-NopTests-20230111212949.xml
2023-01-11T21:29:49.7299517Z 
2023-01-11T21:29:49.7299740Z ##[endgroup]
2023-01-11T21:29:49.7300116Z FINISHED PRINTING LOG FILE of dynamo/test_nops (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_nops_ffc6wtso)
2023-01-11T21:29:49.7300332Z 
2023-01-11T21:29:51.5598052Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:51.6243018Z Ignoring disabled issues:  []
2023-01-11T21:29:51.6391447Z Running dynamo/test_optimizers ... [2023-01-11 21:29:51.638858]
2023-01-11T21:29:51.6393416Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_optimizers.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:51.639098]
2023-01-11T21:29:54.9970267Z 
2023-01-11T21:29:54.9970789Z Expand the folded group to see the log file of dynamo/test_optimizers
2023-01-11T21:29:54.9973949Z ##[group]PRINTING LOG FILE of dynamo/test_optimizers (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizers_fy1juneh)
2023-01-11T21:29:54.9975253Z 
2023-01-11T21:29:54.9975571Z Running tests...
2023-01-11T21:29:54.9976272Z ----------------------------------------------------------------------
2023-01-11T21:29:54.9976951Z Test results will be stored in test-reports/python-unittest/dynamo.test_optimizers
2023-01-11T21:29:54.9977562Z   test_optimizing_over_tensor_with_requires_grad (__main__.End2EndTests) ... ok (0.416s)
2023-01-11T21:29:54.9978106Z frames [('total', 8), ('ok', 8)]
2023-01-11T21:29:54.9978898Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 4), ('inline with __closure__', 1)]
2023-01-11T21:29:54.9979429Z unimplemented []
2023-01-11T21:29:54.9980272Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2), ('Tensor.backward', 1), ('inline with __closure__', 1)]
2023-01-11T21:29:54.9981175Z stats [('calls_captured', 24), ('fusions_possible', 22), ('unique_graphs', 2)]
2023-01-11T21:29:54.9981812Z   test_adadelta (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:54.9982604Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:29:54.9983419Z stats [('calls_captured', 48), ('fusions_possible', 47), ('unique_graphs', 1)]
2023-01-11T21:29:54.9983836Z ok (0.083s)
2023-01-11T21:29:54.9984339Z   test_adagrad (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:54.9985121Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:29:54.9985849Z stats [('calls_captured', 48), ('fusions_possible', 47), ('unique_graphs', 1)]
2023-01-11T21:29:54.9986247Z ok (0.085s)
2023-01-11T21:29:54.9986773Z   test_adam (__main__.OptimizerTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:29:54.9987570Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 3)]
2023-01-11T21:29:54.9988075Z unimplemented []
2023-01-11T21:29:54.9988751Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 1)]
2023-01-11T21:29:54.9989492Z stats [('calls_captured', 80), ('fusions_possible', 79), ('unique_graphs', 1)]
2023-01-11T21:29:54.9989890Z ok (0.133s)
2023-01-11T21:29:54.9990191Z   test_adamax (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:54.9990611Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:29:54.9991009Z stats [('calls_captured', 72), ('fusions_possible', 71), ('unique_graphs', 1)]
2023-01-11T21:29:54.9991220Z ok (0.112s)
2023-01-11T21:29:54.9991509Z   test_adamw (__main__.OptimizerTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:29:54.9991927Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 3)]
2023-01-11T21:29:54.9992201Z unimplemented []
2023-01-11T21:29:54.9992551Z graph_break [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 1)]
2023-01-11T21:29:54.9992954Z stats [('calls_captured', 80), ('fusions_possible', 79), ('unique_graphs', 1)]
2023-01-11T21:29:54.9993180Z ok (0.123s)
2023-01-11T21:29:54.9993452Z   test_asgd (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:54.9993868Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:29:54.9994266Z stats [('calls_captured', 76), ('fusions_possible', 75), ('unique_graphs', 1)]
2023-01-11T21:29:54.9994488Z ok (0.123s)
2023-01-11T21:29:54.9994762Z   test_nadam (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:54.9995177Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:29:54.9995578Z stats [('calls_captured', 152), ('fusions_possible', 151), ('unique_graphs', 1)]
2023-01-11T21:29:54.9995862Z ok (0.188s)
2023-01-11T21:29:54.9996256Z   test_radam (__main__.OptimizerTests) ... [2023-01-11 21:29:54,356] torch._dynamo.variables.torch: [WARNING] Profiler will be ignored
2023-01-11T21:29:54.9996591Z frames [('total', 8), ('ok', 8)]
2023-01-11T21:29:54.9997015Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 3), ('generic_jump TensorVariable()', 1)]
2023-01-11T21:29:54.9997327Z unimplemented []
2023-01-11T21:29:54.9997593Z graph_break [('generic_jump TensorVariable()', 1)]
2023-01-11T21:29:54.9997802Z ok (0.101s)
2023-01-11T21:29:54.9998079Z   test_rmsprop (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:54.9998497Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:29:54.9998929Z stats [('calls_captured', 28), ('fusions_possible', 27), ('unique_graphs', 1)]
2023-01-11T21:29:54.9999146Z ok (0.060s)
2023-01-11T21:29:54.9999436Z   test_rprop (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:54.9999852Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:29:55.0000250Z stats [('calls_captured', 64), ('fusions_possible', 63), ('unique_graphs', 1)]
2023-01-11T21:29:55.0000462Z ok (0.090s)
2023-01-11T21:29:55.0000747Z   test_sgd (__main__.OptimizerTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:29:55.0001164Z inline_call [('inline in skipfiles: _fn /opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py', 2)]
2023-01-11T21:29:55.0001548Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:29:55.0001777Z ok (0.037s)
2023-01-11T21:29:55.0001878Z 
2023-01-11T21:29:55.0002092Z ----------------------------------------------------------------------
2023-01-11T21:29:55.0002337Z Ran 12 tests in 1.552s
2023-01-11T21:29:55.0002437Z 
2023-01-11T21:29:55.0002498Z OK
2023-01-11T21:29:55.0002592Z 
2023-01-11T21:29:55.0002676Z Generating XML reports...
2023-01-11T21:29:55.0003090Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizers/TEST-End2EndTests-20230111212953.xml
2023-01-11T21:29:55.0003601Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizers/TEST-OptimizerTests-20230111212953.xml
2023-01-11T21:29:55.0003835Z 
2023-01-11T21:29:55.0004127Z ##[endgroup]
2023-01-11T21:29:55.0004543Z FINISHED PRINTING LOG FILE of dynamo/test_optimizers (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizers_fy1juneh)
2023-01-11T21:29:55.0004777Z 
2023-01-11T21:29:56.8628982Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:29:56.9275476Z Ignoring disabled issues:  []
2023-01-11T21:29:56.9418809Z Running dynamo/test_python_autograd ... [2023-01-11 21:29:56.941598]
2023-01-11T21:29:56.9420397Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:29:56.941843]
2023-01-11T21:29:59.3993822Z 
2023-01-11T21:29:59.3995632Z Expand the folded group to see the log file of dynamo/test_python_autograd
2023-01-11T21:29:59.3996371Z ##[group]PRINTING LOG FILE of dynamo/test_python_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_python_autograd_6k8pgcga)
2023-01-11T21:29:59.3996618Z 
2023-01-11T21:29:59.3996692Z Running tests...
2023-01-11T21:29:59.3997081Z ----------------------------------------------------------------------
2023-01-11T21:29:59.3997485Z Test results will be stored in test-reports/python-unittest/dynamo.test_python_autograd
2023-01-11T21:29:59.3997801Z   test_backwards1 (__main__.TestPythonAutograd) ... ok (0.421s)
2023-01-11T21:29:59.3998089Z   test_backwards2 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T21:29:59.3998448Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:29:59.3998679Z inline_call []
2023-01-11T21:29:59.3998974Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T21:29:59.3999400Z ok (0.099s)
2023-01-11T21:29:59.3999627Z   test_forwards1 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T21:29:59.3999977Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:59.4000188Z ok (0.030s)
2023-01-11T21:29:59.4000411Z   test_forwards2 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T21:29:59.4000757Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:29:59.4000965Z ok (0.029s)
2023-01-11T21:29:59.4001185Z   test_split (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T21:29:59.4001529Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:29:59.4001750Z ok (0.096s)
2023-01-11T21:29:59.4001839Z 
2023-01-11T21:29:59.4002093Z ----------------------------------------------------------------------
2023-01-11T21:29:59.4002336Z Ran 5 tests in 0.676s
2023-01-11T21:29:59.4002464Z 
2023-01-11T21:29:59.4002522Z OK
2023-01-11T21:29:59.4002599Z 
2023-01-11T21:29:59.4002683Z Generating XML reports...
2023-01-11T21:29:59.4003125Z Generated XML report: test-reports/python-unittest/dynamo.test_python_autograd/TEST-TestPythonAutograd-20230111212958.xml
2023-01-11T21:29:59.4003374Z 
2023-01-11T21:29:59.4003593Z ##[endgroup]
2023-01-11T21:29:59.4003999Z FINISHED PRINTING LOG FILE of dynamo/test_python_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_python_autograd_6k8pgcga)
2023-01-11T21:29:59.4004242Z 
2023-01-11T21:30:01.2728132Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:01.3375682Z Ignoring disabled issues:  []
2023-01-11T21:30:01.3520381Z Running dynamo/test_recompile_ux ... [2023-01-11 21:30:01.351745]
2023-01-11T21:30:01.3522671Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:01.352005]
2023-01-11T21:30:03.0814473Z 
2023-01-11T21:30:03.0815046Z Expand the folded group to see the log file of dynamo/test_recompile_ux
2023-01-11T21:30:03.0816000Z ##[group]PRINTING LOG FILE of dynamo/test_recompile_ux (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_recompile_ux_w3soobbe)
2023-01-11T21:30:03.0816241Z 
2023-01-11T21:30:03.0816463Z ##[endgroup]
2023-01-11T21:30:03.0816963Z FINISHED PRINTING LOG FILE of dynamo/test_recompile_ux (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_recompile_ux_w3soobbe)
2023-01-11T21:30:03.0817193Z 
2023-01-11T21:30:04.9588397Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:05.0241363Z Ignoring disabled issues:  []
2023-01-11T21:30:05.0416597Z Running dynamo/test_replay_record ... [2023-01-11 21:30:05.041403]
2023-01-11T21:30:05.0418872Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_replay_record.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:05.041671]
2023-01-11T21:30:06.7907489Z 
2023-01-11T21:30:06.7908003Z Expand the folded group to see the log file of dynamo/test_replay_record
2023-01-11T21:30:06.7909156Z ##[group]PRINTING LOG FILE of dynamo/test_replay_record (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_replay_record_hauiuzwo)
2023-01-11T21:30:06.7909585Z 
2023-01-11T21:30:06.7909661Z Running tests...
2023-01-11T21:30:06.7910048Z ----------------------------------------------------------------------
2023-01-11T21:30:06.7910446Z Test results will be stored in test-reports/python-unittest/dynamo.test_replay_record
2023-01-11T21:30:06.7910784Z   test_fn_call_args (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:06.7911098Z   test_local_module (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:06.7911404Z   test_nonlocal_fn_call (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:06.7911740Z   test_nonlocal_module_class (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:06.7912070Z   test_nonlocal_module_fn_call (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:06.7912568Z   test_successful_inline (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:06.7912895Z   test_unsuccessful_inline (__main__.ReplayRecordTests) ... skip: requires dill (0.000s)
2023-01-11T21:30:06.7913075Z 
2023-01-11T21:30:06.7913278Z ----------------------------------------------------------------------
2023-01-11T21:30:06.7913519Z Ran 7 tests in 0.003s
2023-01-11T21:30:06.7913620Z 
2023-01-11T21:30:06.7913692Z OK (skipped=7)
2023-01-11T21:30:06.7913798Z 
2023-01-11T21:30:06.7913883Z Generating XML reports...
2023-01-11T21:30:06.7914330Z Generated XML report: test-reports/python-unittest/dynamo.test_replay_record/TEST-ReplayRecordTests-20230111213006.xml
2023-01-11T21:30:06.7914574Z 
2023-01-11T21:30:06.7914795Z ##[endgroup]
2023-01-11T21:30:06.7915269Z FINISHED PRINTING LOG FILE of dynamo/test_replay_record (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_replay_record_hauiuzwo)
2023-01-11T21:30:06.7915513Z 
2023-01-11T21:30:08.6611497Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:08.7260862Z Ignoring disabled issues:  []
2023-01-11T21:30:08.7405157Z Running dynamo/test_skip_non_tensor ... [2023-01-11 21:30:08.740265]
2023-01-11T21:30:08.7407263Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:08.740525]
2023-01-11T21:30:10.8743891Z 
2023-01-11T21:30:10.8744389Z Expand the folded group to see the log file of dynamo/test_skip_non_tensor
2023-01-11T21:30:10.8745422Z ##[group]PRINTING LOG FILE of dynamo/test_skip_non_tensor (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_skip_non_tensor_1ggs2qfy)
2023-01-11T21:30:10.8745666Z 
2023-01-11T21:30:10.8745741Z Running tests...
2023-01-11T21:30:10.8746154Z ----------------------------------------------------------------------
2023-01-11T21:30:10.8746547Z Test results will be stored in test-reports/python-unittest/dynamo.test_skip_non_tensor
2023-01-11T21:30:10.8746914Z   test_add_skip (__main__.SkipNonTensorTests) ... ok (0.243s)
2023-01-11T21:30:10.8747669Z   test_add_tensor1 (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:10.8748099Z ok (0.086s)
2023-01-11T21:30:10.8748541Z   test_add_tensor2 (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:10.8748813Z ok (0.005s)
2023-01-11T21:30:10.8749251Z   test_add_tensor_dict (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:10.8749643Z ok (0.005s)
2023-01-11T21:30:10.8750322Z   test_add_tensor_list (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T21:30:10.8750810Z ok (0.004s)
2023-01-11T21:30:10.8751199Z   test_custom_list (__main__.SkipNonTensorTests) ... ok (0.001s)
2023-01-11T21:30:10.8751711Z   test_recursive_list (__main__.SkipNonTensorTests) ... ok (0.001s)
2023-01-11T21:30:10.8752005Z 
2023-01-11T21:30:10.8752341Z ----------------------------------------------------------------------
2023-01-11T21:30:10.8752586Z Ran 7 tests in 0.345s
2023-01-11T21:30:10.8752685Z 
2023-01-11T21:30:10.8752745Z OK
2023-01-11T21:30:10.8752835Z 
2023-01-11T21:30:10.8752922Z Generating XML reports...
2023-01-11T21:30:10.8753357Z Generated XML report: test-reports/python-unittest/dynamo.test_skip_non_tensor/TEST-SkipNonTensorTests-20230111213010.xml
2023-01-11T21:30:10.8753605Z 
2023-01-11T21:30:10.8753824Z ##[endgroup]
2023-01-11T21:30:10.8754242Z FINISHED PRINTING LOG FILE of dynamo/test_skip_non_tensor (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_skip_non_tensor_1ggs2qfy)
2023-01-11T21:30:10.8754477Z 
2023-01-11T21:30:12.7270730Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:12.7919343Z Ignoring disabled issues:  []
2023-01-11T21:30:12.8064481Z Running dynamo/test_subgraphs ... [2023-01-11 21:30:12.806184]
2023-01-11T21:30:12.8066804Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_subgraphs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:12.806449]
2023-01-11T21:30:16.4809968Z 
2023-01-11T21:30:16.4810651Z Expand the folded group to see the log file of dynamo/test_subgraphs
2023-01-11T21:30:16.4811690Z ##[group]PRINTING LOG FILE of dynamo/test_subgraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_subgraphs_55b9_xok)
2023-01-11T21:30:16.4812308Z 
2023-01-11T21:30:16.4812450Z Running tests...
2023-01-11T21:30:16.4813108Z ----------------------------------------------------------------------
2023-01-11T21:30:16.4813654Z Test results will be stored in test-reports/python-unittest/dynamo.test_subgraphs
2023-01-11T21:30:16.4814154Z   test_capi_call1 (__main__.SubGraphTests) ... ok (0.323s)
2023-01-11T21:30:16.4814499Z   test_capi_call2 (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:16.4814746Z unimplemented []
2023-01-11T21:30:16.4815140Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4815561Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:16.4815828Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4816023Z unimplemented []
2023-01-11T21:30:16.4816413Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4816816Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:30:16.4817047Z ok (0.012s)
2023-01-11T21:30:16.4817348Z   test_capi_call3 (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:16.4817569Z unimplemented []
2023-01-11T21:30:16.4817960Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4818378Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:16.4818608Z ok (0.006s)
2023-01-11T21:30:16.4818895Z   test_control_flow1 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4819256Z stats [('calls_captured', 5), ('fusions_possible', 4), ('unique_graphs', 1)]
2023-01-11T21:30:16.4819481Z ok (0.011s)
2023-01-11T21:30:16.4819768Z   test_control_flow2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4820122Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:16.4820352Z ok (0.006s)
2023-01-11T21:30:16.4820637Z   test_control_flow3 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4820996Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:16.4821222Z ok (0.015s)
2023-01-11T21:30:16.4821509Z   test_control_flow4 (__main__.SubGraphTests) ... frames [('total', 5), ('ok', 5)]
2023-01-11T21:30:16.4821865Z stats [('calls_captured', 5), ('unique_graphs', 3), ('fusions_possible', 2)]
2023-01-11T21:30:16.4822088Z ok (0.014s)
2023-01-11T21:30:16.4822382Z   test_control_flow5 (__main__.SubGraphTests) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:30:16.4822727Z stats [('calls_captured', 13), ('fusions_possible', 7), ('unique_graphs', 6)]
2023-01-11T21:30:16.4822948Z ok (0.037s)
2023-01-11T21:30:16.4823251Z   test_dynamic_duck_size (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4823699Z stats [('calls_captured', 10), ('fusions_possible', 8), ('unique_graphs', 2)]
2023-01-11T21:30:16.4823921Z ok (0.069s)
2023-01-11T21:30:16.4824220Z   test_dynamic_kwarg (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:16.4824566Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:16.4824789Z ok (0.021s)
2023-01-11T21:30:16.4825114Z   test_dynamic_order_dependence (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4825583Z stats [('calls_captured', 9), ('fusions_possible', 6), ('unique_graphs', 3)]
2023-01-11T21:30:16.4825795Z ok (0.062s)
2023-01-11T21:30:16.4826110Z   test_dynamic_shapes (__main__.SubGraphTests) ... frames [('total', 11), ('ok', 11)]
2023-01-11T21:30:16.4826478Z stats [('calls_captured', 22), ('fusions_possible', 11), ('unique_graphs', 11)]
2023-01-11T21:30:16.4826691Z ok (0.058s)
2023-01-11T21:30:16.4827006Z   test_dynamic_zero_inference (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4827371Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:16.4827581Z ok (0.023s)
2023-01-11T21:30:16.4827900Z   test_enumerate_not_break_graph (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:16.4828308Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T21:30:16.4828534Z ok (0.007s)
2023-01-11T21:30:16.4828826Z   test_extended_args (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4829196Z stats [('calls_captured', 1026), ('fusions_possible', 1023), ('unique_graphs', 3)]
2023-01-11T21:30:16.4829423Z ok (0.818s)
2023-01-11T21:30:16.4829722Z   test_graph_break_on_item (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4829961Z unimplemented []
2023-01-11T21:30:16.4830190Z graph_break [('Tensor.item', 1)]
2023-01-11T21:30:16.4830492Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:30:16.4830716Z ok (0.012s)
2023-01-11T21:30:16.4831029Z   test_indirect_unsupported1 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4831466Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4831759Z unimplemented []
2023-01-11T21:30:16.4832141Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4832555Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:30:16.4832766Z ok (0.010s)
2023-01-11T21:30:16.4833080Z   test_indirect_unsupported2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4833530Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4833810Z unimplemented []
2023-01-11T21:30:16.4834186Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4834599Z stats [('calls_captured', 5), ('unique_graphs', 3), ('fusions_possible', 2)]
2023-01-11T21:30:16.4834827Z ok (0.015s)
2023-01-11T21:30:16.4835131Z   test_indirect_unsupported3 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4835575Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4835866Z unimplemented []
2023-01-11T21:30:16.4836237Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4836644Z stats [('calls_captured', 3), ('unique_graphs', 2), ('fusions_possible', 1)]
2023-01-11T21:30:16.4836870Z ok (0.010s)
2023-01-11T21:30:16.4837153Z   test_multigraph (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4837509Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:30:16.4837729Z ok (0.010s)
2023-01-11T21:30:16.4838040Z   test_no_graph_break_on_item (__main__.SubGraphTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:16.4838390Z stats [('calls_captured', 6), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:30:16.4838616Z ok (0.008s)
2023-01-11T21:30:16.4838921Z   test_pop_after_resume (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4839148Z unimplemented []
2023-01-11T21:30:16.4839583Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4839993Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:16.4840207Z ok (0.014s)
2023-01-11T21:30:16.4840506Z   test_restore_range (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4840741Z unimplemented []
2023-01-11T21:30:16.4841122Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4841521Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:30:16.4841745Z ok (0.011s)
2023-01-11T21:30:16.4842053Z   test_restore_range_iter (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4842314Z unimplemented []
2023-01-11T21:30:16.4842699Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4843111Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T21:30:16.4843325Z ok (0.010s)
2023-01-11T21:30:16.4843625Z   test_restore_state (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4843862Z unimplemented []
2023-01-11T21:30:16.4844244Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4844642Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:30:16.4844865Z ok (0.011s)
2023-01-11T21:30:16.4845152Z   test_resume1 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4845363Z unimplemented []
2023-01-11T21:30:16.4845749Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4846160Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:16.4846374Z ok (0.011s)
2023-01-11T21:30:16.4846658Z   test_resume2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4847089Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4847377Z unimplemented []
2023-01-11T21:30:16.4847745Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4848155Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:16.4848377Z ok (0.015s)
2023-01-11T21:30:16.4848654Z   test_resume3 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4849290Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4849599Z unimplemented []
2023-01-11T21:30:16.4849973Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4850386Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:16.4850614Z ok (0.015s)
2023-01-11T21:30:16.4850906Z   test_resume4 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4851321Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4851608Z unimplemented []
2023-01-11T21:30:16.4851987Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4852380Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:16.4852607Z ok (0.015s)
2023-01-11T21:30:16.4852853Z   test_resume5 (__main__.SubGraphTests) ... tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:30:16.4853085Z         1.5000])
2023-01-11T21:30:16.4853271Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:30:16.4853540Z         1.5000])
2023-01-11T21:30:16.4853736Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:30:16.4853913Z         1.5000])
2023-01-11T21:30:16.4854107Z tensor([1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000, 1.5000,
2023-01-11T21:30:16.4854301Z         1.5000])
2023-01-11T21:30:16.4854502Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:30:16.4854693Z unimplemented []
2023-01-11T21:30:16.4855014Z graph_break [('call_function BuiltinVariable(print) [TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4855366Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:16.4855590Z ok (0.012s)
2023-01-11T21:30:16.4855933Z   test_resume_freevars (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4856162Z unimplemented []
2023-01-11T21:30:16.4856543Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4856955Z stats [('calls_captured', 5), ('fusions_possible', 3), ('unique_graphs', 2)]
2023-01-11T21:30:16.4857181Z ok (0.011s)
2023-01-11T21:30:16.4857473Z   test_resume_paths_join (__main__.SubGraphTests) ... frames [('total', 7), ('ok', 7)]
2023-01-11T21:30:16.4857834Z stats [('calls_captured', 10), ('unique_graphs', 7), ('fusions_possible', 3)]
2023-01-11T21:30:16.4858060Z ok (0.032s)
2023-01-11T21:30:16.4858363Z   test_resume_tuple_iterator (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4858611Z unimplemented []
2023-01-11T21:30:16.4858996Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4859400Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T21:30:16.4859630Z ok (0.017s)
2023-01-11T21:30:16.4859946Z   test_resume_with_no_grad1 (__main__.SubGraphTests) ... frames [('total', 4), ('ok', 4)]
2023-01-11T21:30:16.4860196Z unimplemented []
2023-01-11T21:30:16.4860418Z graph_break [('Tensor.tolist', 2)]
2023-01-11T21:30:16.4860744Z stats [('calls_captured', 18), ('fusions_possible', 14), ('unique_graphs', 4)]
2023-01-11T21:30:16.4860972Z ok (0.023s)
2023-01-11T21:30:16.4861269Z   test_resume_with_no_grad2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4861513Z unimplemented []
2023-01-11T21:30:16.4861752Z graph_break [('Tensor.tolist', 2)]
2023-01-11T21:30:16.4862062Z stats [('calls_captured', 13), ('fusions_possible', 10), ('unique_graphs', 3)]
2023-01-11T21:30:16.4862291Z ok (0.019s)
2023-01-11T21:30:16.4862602Z   test_resume_with_no_grad3 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4862829Z unimplemented []
2023-01-11T21:30:16.4863063Z graph_break [('Tensor.tolist', 1)]
2023-01-11T21:30:16.4863470Z stats [('calls_captured', 19), ('fusions_possible', 17), ('unique_graphs', 2)]
2023-01-11T21:30:16.4863704Z ok (0.017s)
2023-01-11T21:30:16.4863997Z   test_stack_state1 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4864233Z unimplemented []
2023-01-11T21:30:16.4864619Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4865022Z stats [('calls_captured', 6), ('fusions_possible', 4), ('unique_graphs', 2)]
2023-01-11T21:30:16.4865247Z ok (0.012s)
2023-01-11T21:30:16.4865547Z   test_stack_state2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4865966Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4866258Z unimplemented []
2023-01-11T21:30:16.4866639Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4867048Z stats [('calls_captured', 7), ('fusions_possible', 4), ('unique_graphs', 3)]
2023-01-11T21:30:16.4867303Z ok (0.016s)
2023-01-11T21:30:16.4867534Z   test_start1 (__main__.SubGraphTests) ... tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
2023-01-11T21:30:16.4867852Z tensor([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2.])
2023-01-11T21:30:16.4868058Z tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
2023-01-11T21:30:16.4868326Z tensor([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2.])
2023-01-11T21:30:16.4868570Z frames [('total', 4), ('ok', 4)]
2023-01-11T21:30:16.4868751Z unimplemented []
2023-01-11T21:30:16.4869066Z graph_break [('call_function BuiltinVariable(print) [TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4869431Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:16.4869646Z ok (0.009s)
2023-01-11T21:30:16.4869930Z   test_start2 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4870394Z inline_call [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4870688Z unimplemented []
2023-01-11T21:30:16.4871058Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4871472Z stats [('calls_captured', 4), ('fusions_possible', 2), ('unique_graphs', 2)]
2023-01-11T21:30:16.4871699Z ok (0.012s)
2023-01-11T21:30:16.4871975Z   test_start3 (__main__.SubGraphTests) ... frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:16.4872206Z unimplemented []
2023-01-11T21:30:16.4872590Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 1)]
2023-01-11T21:30:16.4872988Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:16.4887817Z ok (0.008s)
2023-01-11T21:30:16.4888485Z   test_start4 (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4889175Z stats [('calls_captured', 4), ('unique_graphs', 3), ('fusions_possible', 1)]
2023-01-11T21:30:16.4889535Z ok (0.012s)
2023-01-11T21:30:16.4889919Z   test_tuple_iterator_mutate (__main__.SubGraphTests) ... skip: not working yet (0.001s)
2023-01-11T21:30:16.4890469Z   test_tuple_iterator_return (__main__.SubGraphTests) ... frames [('total', 3), ('ok', 3)]
2023-01-11T21:30:16.4890826Z unimplemented []
2023-01-11T21:30:16.4891431Z graph_break [('call_function UserDefinedObjectVariable(unsupported) [TensorVariable(), TensorVariable()] {}', 2)]
2023-01-11T21:30:16.4892023Z stats [('calls_captured', 6), ('fusions_possible', 3), ('unique_graphs', 3)]
2023-01-11T21:30:16.4892353Z ok (0.023s)
2023-01-11T21:30:16.4892507Z 
2023-01-11T21:30:16.4892807Z ----------------------------------------------------------------------
2023-01-11T21:30:16.4893137Z Ran 44 tests in 1.875s
2023-01-11T21:30:16.4893251Z 
2023-01-11T21:30:16.4893321Z OK (skipped=1)
2023-01-11T21:30:16.4893425Z 
2023-01-11T21:30:16.4893513Z Generating XML reports...
2023-01-11T21:30:16.4893937Z Generated XML report: test-reports/python-unittest/dynamo.test_subgraphs/TEST-SubGraphTests-20230111213014.xml
2023-01-11T21:30:16.4894165Z 
2023-01-11T21:30:16.4894491Z ##[endgroup]
2023-01-11T21:30:16.4894898Z FINISHED PRINTING LOG FILE of dynamo/test_subgraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_subgraphs_55b9_xok)
2023-01-11T21:30:16.4895125Z 
2023-01-11T21:30:18.3325270Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:18.3972985Z Ignoring disabled issues:  []
2023-01-11T21:30:18.4117453Z Running dynamo/test_torchxla_num_output ... [2023-01-11 21:30:18.411477]
2023-01-11T21:30:18.4120283Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_torchxla_num_output.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:18.411743]
2023-01-11T21:30:19.9472022Z 
2023-01-11T21:30:19.9472563Z Expand the folded group to see the log file of dynamo/test_torchxla_num_output
2023-01-11T21:30:19.9473733Z ##[group]PRINTING LOG FILE of dynamo/test_torchxla_num_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_num_output_bpyz7zwh)
2023-01-11T21:30:19.9474448Z 
2023-01-11T21:30:19.9474828Z ##[endgroup]
2023-01-11T21:30:19.9475731Z FINISHED PRINTING LOG FILE of dynamo/test_torchxla_num_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_num_output_bpyz7zwh)
2023-01-11T21:30:19.9476183Z 
2023-01-11T21:30:21.7629963Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:21.8273942Z Ignoring disabled issues:  []
2023-01-11T21:30:21.8417618Z Running dynamo/test_torchxla_util ... [2023-01-11 21:30:21.841551]
2023-01-11T21:30:21.8419812Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_torchxla_util.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:21.841785]
2023-01-11T21:30:21.8761840Z 
2023-01-11T21:30:21.8762270Z Expand the folded group to see the log file of dynamo/test_torchxla_util
2023-01-11T21:30:21.8763508Z ##[group]PRINTING LOG FILE of dynamo/test_torchxla_util (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_util_zeh12lhp)
2023-01-11T21:30:21.8763771Z 
2023-01-11T21:30:21.8763986Z ##[endgroup]
2023-01-11T21:30:21.8764471Z FINISHED PRINTING LOG FILE of dynamo/test_torchxla_util (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_util_zeh12lhp)
2023-01-11T21:30:21.8764703Z 
2023-01-11T21:30:23.6619706Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:23.7259351Z Ignoring disabled issues:  []
2023-01-11T21:30:23.7403197Z Running dynamo/test_verify_correctness ... [2023-01-11 21:30:23.740003]
2023-01-11T21:30:23.7405766Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:23.740263]
2023-01-11T21:30:25.9828859Z 
2023-01-11T21:30:25.9829394Z Expand the folded group to see the log file of dynamo/test_verify_correctness
2023-01-11T21:30:25.9830596Z ##[group]PRINTING LOG FILE of dynamo/test_verify_correctness (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_verify_correctness_bua5ds4z)
2023-01-11T21:30:25.9831084Z 
2023-01-11T21:30:25.9831238Z Running tests...
2023-01-11T21:30:25.9831914Z ----------------------------------------------------------------------
2023-01-11T21:30:25.9832650Z Test results will be stored in test-reports/python-unittest/dynamo.test_verify_correctness
2023-01-11T21:30:25.9833245Z   test_example_inputs (__main__.TestVerifyCorrectness) ... ok (0.315s)
2023-01-11T21:30:25.9833678Z   test_incorrect_verify_false (__main__.TestVerifyCorrectness)
2023-01-11T21:30:25.9834434Z The bad optimization return a graph that ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T21:30:25.9834869Z frames [('total', 2), ('ok', 2)]
2023-01-11T21:30:25.9835236Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 2)]
2023-01-11T21:30:25.9835451Z ok (0.016s)
2023-01-11T21:30:25.9835710Z   test_incorrect_verify_true (__main__.TestVerifyCorrectness)
2023-01-11T21:30:25.9836236Z If a bad optimization return a graph that ... [2023-01-11 21:30:25,518] torch._dynamo.output_graph: [ERROR] error in verify_correctness
2023-01-11T21:30:25.9836601Z Traceback (most recent call last):
2023-01-11T21:30:25.9837015Z   File "/opt/conda/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 173, in __call__
2023-01-11T21:30:25.9837387Z     raise RuntimeError(f"incorrect results of backend {self}")
2023-01-11T21:30:25.9837737Z RuntimeError: incorrect results of backend <torch._dynamo.output_graph.WrapperBackend object at 0x7fe2ca95a470>
2023-01-11T21:30:25.9838117Z frames [('total', 2), ('ok', 1)]
2023-01-11T21:30:25.9838418Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T21:30:25.9838645Z ok (0.015s)
2023-01-11T21:30:25.9838946Z   test_ipex_fp32 (__main__.TestVerifyCorrectness) ... skip: requires ipex (0.001s)
2023-01-11T21:30:25.9839397Z   test_nnc (__main__.TestVerifyCorrectness) ... frames [('total', 1), ('ok', 1)]
2023-01-11T21:30:25.9839810Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T21:30:25.9840220Z ok (0.090s)
2023-01-11T21:30:25.9840375Z 
2023-01-11T21:30:25.9840563Z ----------------------------------------------------------------------
2023-01-11T21:30:25.9840808Z Ran 5 tests in 0.437s
2023-01-11T21:30:25.9840924Z 
2023-01-11T21:30:25.9840999Z OK (skipped=1)
2023-01-11T21:30:25.9841153Z 
2023-01-11T21:30:25.9841239Z Generating XML reports...
2023-01-11T21:30:25.9841676Z Generated XML report: test-reports/python-unittest/dynamo.test_verify_correctness/TEST-TestVerifyCorrectness-20230111213025.xml
2023-01-11T21:30:25.9841990Z 
2023-01-11T21:30:25.9842268Z ##[endgroup]
2023-01-11T21:30:25.9842758Z FINISHED PRINTING LOG FILE of dynamo/test_verify_correctness (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_verify_correctness_bua5ds4z)
2023-01-11T21:30:25.9842994Z 
2023-01-11T21:30:27.8088517Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:30:27.8735011Z Ignoring disabled issues:  []
2023-01-11T21:30:27.8880603Z Running inductor/test_minifier ... [2023-01-11 21:30:27.887739]
2023-01-11T21:30:27.8881978Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_minifier.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:30:27.887977]
2023-01-11T21:32:01.9406187Z 
2023-01-11T21:32:01.9406916Z Expand the folded group to see the log file of inductor/test_minifier
2023-01-11T21:32:01.9409268Z ##[group]PRINTING LOG FILE of inductor/test_minifier (/var/lib/jenkins/workspace/test/test-reports/inductor-test_minifier_04v09_xu)
2023-01-11T21:32:01.9409865Z 
2023-01-11T21:32:01.9410039Z Running tests...
2023-01-11T21:32:01.9428744Z ----------------------------------------------------------------------
2023-01-11T21:32:01.9429261Z Test results will be stored in test-reports/python-unittest/inductor.test_minifier
2023-01-11T21:32:01.9429653Z   test_after_aot_cpu_accuracy_backend_passes (__main__.MinifierTests) ... ok (9.669s)
2023-01-11T21:32:01.9430460Z   test_after_aot_cpu_accuracy_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:01.9431019Z   test_after_aot_cpu_compile_backend_passes (__main__.MinifierTests) ... ok (8.142s)
2023-01-11T21:32:01.9431687Z   test_after_aot_cpu_compile_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:01.9432243Z   test_after_aot_cpu_runtime_backend_passes (__main__.MinifierTests) ... ok (7.935s)
2023-01-11T21:32:01.9433020Z   test_after_aot_cpu_runtime_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:01.9433606Z   test_after_aot_cuda_accuracy_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:32:01.9434217Z   test_after_aot_cuda_accuracy_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:32:01.9434728Z   test_after_aot_cuda_compile_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:32:01.9435189Z   test_after_aot_cuda_compile_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T21:32:01.9435951Z   test_after_aot_cuda_runtime_backend_passes (__main__.MinifierTests) ... skip:  (0.000s)
2023-01-11T21:32:01.9436522Z   test_after_aot_cuda_runtime_error (__main__.MinifierTests) ... skip:  (0.000s)
2023-01-11T21:32:01.9437231Z   test_after_aot_with_modified_config_accuracy_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:01.9437714Z   test_after_aot_with_modified_config_compile_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:01.9438083Z   test_inductor_config_serialization (__main__.MinifierTests) ... ok (1.522s)
2023-01-11T21:32:01.9438459Z   test_torch_compile_after_aot_accuracy_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:01.9439155Z   test_torch_compile_after_aot_compile_error (__main__.MinifierTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:32:01.9439535Z   test_torch_compile_after_dynamo_accuracy_error (__main__.MinifierTests) ... ok (29.012s)
2023-01-11T21:32:01.9439850Z   test_torch_compile_after_dynamo_compile_error (__main__.MinifierTests) ... ok (35.949s)
2023-01-11T21:32:01.9440030Z 
2023-01-11T21:32:01.9440263Z ----------------------------------------------------------------------
2023-01-11T21:32:01.9440508Z Ran 19 tests in 92.233s
2023-01-11T21:32:01.9440627Z 
2023-01-11T21:32:01.9440702Z OK (skipped=13)
2023-01-11T21:32:01.9440797Z 
2023-01-11T21:32:01.9440882Z Generating XML reports...
2023-01-11T21:32:01.9441388Z Generated XML report: test-reports/python-unittest/inductor.test_minifier/TEST-MinifierTests-20230111213029.xml
2023-01-11T21:32:01.9441625Z 
2023-01-11T21:32:01.9442008Z ##[endgroup]
2023-01-11T21:32:01.9442413Z FINISHED PRINTING LOG FILE of inductor/test_minifier (/var/lib/jenkins/workspace/test/test-reports/inductor-test_minifier_04v09_xu)
2023-01-11T21:32:01.9442645Z 
2023-01-11T21:32:03.7785145Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:03.8431104Z Ignoring disabled issues:  []
2023-01-11T21:32:03.8574877Z Running inductor/test_perf ... [2023-01-11 21:32:03.857189]
2023-01-11T21:32:03.8577504Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_perf.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:03.857430]
2023-01-11T21:32:11.8585511Z 
2023-01-11T21:32:11.8586033Z Expand the folded group to see the log file of inductor/test_perf
2023-01-11T21:32:11.8587081Z ##[group]PRINTING LOG FILE of inductor/test_perf (/var/lib/jenkins/workspace/test/test-reports/inductor-test_perf_t2sizwmp)
2023-01-11T21:32:11.8587876Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:11.8588139Z 
2023-01-11T21:32:11.8588486Z ##[endgroup]
2023-01-11T21:32:11.8589191Z FINISHED PRINTING LOG FILE of inductor/test_perf (/var/lib/jenkins/workspace/test/test-reports/inductor-test_perf_t2sizwmp)
2023-01-11T21:32:11.8589578Z 
2023-01-11T21:32:13.6960566Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:13.7606916Z Ignoring disabled issues:  []
2023-01-11T21:32:13.7753069Z Running inductor/test_smoke ... [2023-01-11 21:32:13.774932]
2023-01-11T21:32:13.7754088Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_smoke.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:13.775167]
2023-01-11T21:32:15.4920144Z 
2023-01-11T21:32:15.4920654Z Expand the folded group to see the log file of inductor/test_smoke
2023-01-11T21:32:15.4921906Z ##[group]PRINTING LOG FILE of inductor/test_smoke (/var/lib/jenkins/workspace/test/test-reports/inductor-test_smoke_ezx46jf2)
2023-01-11T21:32:15.4922337Z 
2023-01-11T21:32:15.4922687Z ##[endgroup]
2023-01-11T21:32:15.4923182Z FINISHED PRINTING LOG FILE of inductor/test_smoke (/var/lib/jenkins/workspace/test/test-reports/inductor-test_smoke_ezx46jf2)
2023-01-11T21:32:15.4923415Z 
2023-01-11T21:32:17.3218329Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:17.3869138Z Ignoring disabled issues:  []
2023-01-11T21:32:17.4013222Z Running lazy/test_bindings ... [2023-01-11 21:32:17.401092]
2023-01-11T21:32:17.4015652Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_bindings.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:17.401335]
2023-01-11T21:32:18.7449499Z 
2023-01-11T21:32:18.7450207Z Expand the folded group to see the log file of lazy/test_bindings
2023-01-11T21:32:18.7451437Z ##[group]PRINTING LOG FILE of lazy/test_bindings (/var/lib/jenkins/workspace/test/test-reports/lazy-test_bindings_kf5y4qi9)
2023-01-11T21:32:18.7451850Z 
2023-01-11T21:32:18.7452139Z ##[endgroup]
2023-01-11T21:32:18.7452746Z FINISHED PRINTING LOG FILE of lazy/test_bindings (/var/lib/jenkins/workspace/test/test-reports/lazy-test_bindings_kf5y4qi9)
2023-01-11T21:32:18.7453180Z 
2023-01-11T21:32:20.5653834Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:20.6297720Z Ignoring disabled issues:  []
2023-01-11T21:32:20.6442426Z Running lazy/test_debug_util ... [2023-01-11 21:32:20.643954]
2023-01-11T21:32:20.6444238Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_debug_util.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:20.644200]
2023-01-11T21:32:22.5680520Z 
2023-01-11T21:32:22.5680968Z Expand the folded group to see the log file of lazy/test_debug_util
2023-01-11T21:32:22.5682061Z ##[group]PRINTING LOG FILE of lazy/test_debug_util (/var/lib/jenkins/workspace/test/test-reports/lazy-test_debug_util_rfpyxxs1)
2023-01-11T21:32:22.5682509Z 
2023-01-11T21:32:22.5682635Z Running tests...
2023-01-11T21:32:22.5683538Z ----------------------------------------------------------------------
2023-01-11T21:32:22.5684245Z Test results will be stored in test-reports/python-unittest/lazy.test_debug_util
2023-01-11T21:32:22.5684812Z   test_get_python_frames (__main__.DebugUtilTest) ... ok (0.263s)
2023-01-11T21:32:22.5685102Z 
2023-01-11T21:32:22.5685466Z ----------------------------------------------------------------------
2023-01-11T21:32:22.5685881Z Ran 1 test in 0.264s
2023-01-11T21:32:22.5686079Z 
2023-01-11T21:32:22.5686185Z OK
2023-01-11T21:32:22.5686349Z 
2023-01-11T21:32:22.5686498Z Generating XML reports...
2023-01-11T21:32:22.5687270Z Generated XML report: test-reports/python-unittest/lazy.test_debug_util/TEST-DebugUtilTest-20230111213221.xml
2023-01-11T21:32:22.5687685Z 
2023-01-11T21:32:22.5688073Z ##[endgroup]
2023-01-11T21:32:22.5688811Z FINISHED PRINTING LOG FILE of lazy/test_debug_util (/var/lib/jenkins/workspace/test/test-reports/lazy-test_debug_util_rfpyxxs1)
2023-01-11T21:32:22.5689438Z 
2023-01-11T21:32:24.4285681Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:24.4933103Z Ignoring disabled issues:  []
2023-01-11T21:32:24.5079725Z Running lazy/test_extract_compiled_graph ... [2023-01-11 21:32:24.507727]
2023-01-11T21:32:24.5082436Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_extract_compiled_graph.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:24.507979]
2023-01-11T21:32:25.8811717Z 
2023-01-11T21:32:25.8812238Z Expand the folded group to see the log file of lazy/test_extract_compiled_graph
2023-01-11T21:32:25.8813562Z ##[group]PRINTING LOG FILE of lazy/test_extract_compiled_graph (/var/lib/jenkins/workspace/test/test-reports/lazy-test_extract_compiled_graph_vp_yooe7)
2023-01-11T21:32:25.8814043Z 
2023-01-11T21:32:25.8814372Z ##[endgroup]
2023-01-11T21:32:25.8814935Z FINISHED PRINTING LOG FILE of lazy/test_extract_compiled_graph (/var/lib/jenkins/workspace/test/test-reports/lazy-test_extract_compiled_graph_vp_yooe7)
2023-01-11T21:32:25.8815192Z 
2023-01-11T21:32:27.6942484Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:27.7629525Z Ignoring disabled issues:  []
2023-01-11T21:32:27.7779977Z Running lazy/test_meta_kernel ... [2023-01-11 21:32:27.777685]
2023-01-11T21:32:27.7781278Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_meta_kernel.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:27.777921]
2023-01-11T21:32:29.3821345Z 
2023-01-11T21:32:29.3821899Z Expand the folded group to see the log file of lazy/test_meta_kernel
2023-01-11T21:32:29.3822829Z ##[group]PRINTING LOG FILE of lazy/test_meta_kernel (/var/lib/jenkins/workspace/test/test-reports/lazy-test_meta_kernel_aj6zjv0u)
2023-01-11T21:32:29.3823123Z 
2023-01-11T21:32:29.3823325Z ##[endgroup]
2023-01-11T21:32:29.3823947Z FINISHED PRINTING LOG FILE of lazy/test_meta_kernel (/var/lib/jenkins/workspace/test/test-reports/lazy-test_meta_kernel_aj6zjv0u)
2023-01-11T21:32:29.3824170Z 
2023-01-11T21:32:31.2653861Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:31.3309858Z Ignoring disabled issues:  []
2023-01-11T21:32:31.3457775Z Running lazy/test_reuse_ir ... [2023-01-11 21:32:31.345462]
2023-01-11T21:32:31.3459518Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_reuse_ir.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:31.345720]
2023-01-11T21:32:33.6062094Z 
2023-01-11T21:32:33.6062627Z Expand the folded group to see the log file of lazy/test_reuse_ir
2023-01-11T21:32:33.6063742Z ##[group]PRINTING LOG FILE of lazy/test_reuse_ir (/var/lib/jenkins/workspace/test/test-reports/lazy-test_reuse_ir_dyxkmm7w)
2023-01-11T21:32:33.6064187Z 
2023-01-11T21:32:33.6064320Z Running tests...
2023-01-11T21:32:33.6064993Z ----------------------------------------------------------------------
2023-01-11T21:32:33.6065643Z Test results will be stored in test-reports/python-unittest/lazy.test_reuse_ir
2023-01-11T21:32:33.6066132Z   testAdd (__main__.TestLazyReuseIr) ... ok (0.272s)
2023-01-11T21:32:33.6066816Z   testAddSub (__main__.TestLazyReuseIr) ... ok (0.064s)
2023-01-11T21:32:33.6067291Z   testAddSubFallback (__main__.TestLazyReuseIr) ... ok (0.003s)
2023-01-11T21:32:33.6067755Z   testBatchNorm (__main__.TestLazyReuseIr) ... ok (0.273s)
2023-01-11T21:32:33.6068015Z 
2023-01-11T21:32:33.6068413Z ----------------------------------------------------------------------
2023-01-11T21:32:33.6068843Z Ran 4 tests in 0.611s
2023-01-11T21:32:33.6069056Z 
2023-01-11T21:32:33.6069143Z OK
2023-01-11T21:32:33.6069293Z 
2023-01-11T21:32:33.6069456Z Generating XML reports...
2023-01-11T21:32:33.6070229Z Generated XML report: test-reports/python-unittest/lazy.test_reuse_ir/TEST-TestLazyReuseIr-20230111213232.xml
2023-01-11T21:32:33.6070612Z 
2023-01-11T21:32:33.6071027Z ##[endgroup]
2023-01-11T21:32:33.6071754Z FINISHED PRINTING LOG FILE of lazy/test_reuse_ir (/var/lib/jenkins/workspace/test/test-reports/lazy-test_reuse_ir_dyxkmm7w)
2023-01-11T21:32:33.6072118Z 
2023-01-11T21:32:35.4231660Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:35.4875153Z Ignoring disabled issues:  []
2023-01-11T21:32:35.5020781Z Running lazy/test_step_closures ... [2023-01-11 21:32:35.501700]
2023-01-11T21:32:35.5021726Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_step_closures.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:35.501937]
2023-01-11T21:32:39.4248915Z 
2023-01-11T21:32:39.4249875Z Expand the folded group to see the log file of lazy/test_step_closures
2023-01-11T21:32:39.4250805Z ##[group]PRINTING LOG FILE of lazy/test_step_closures (/var/lib/jenkins/workspace/test/test-reports/lazy-test_step_closures_ie9zqgpk)
2023-01-11T21:32:39.4251050Z 
2023-01-11T21:32:39.4251136Z Running tests...
2023-01-11T21:32:39.4251540Z ----------------------------------------------------------------------
2023-01-11T21:32:39.4251938Z Test results will be stored in test-reports/python-unittest/lazy.test_step_closures
2023-01-11T21:32:39.4252265Z   test_asynchronous (__main__.ClosuresTest) ... ok (0.225s)
2023-01-11T21:32:39.4252531Z   test_asynchronous_exception (__main__.ClosuresTest) ... ok (1.001s)
2023-01-11T21:32:39.4252819Z   test_synchronous (__main__.ClosuresTest) ... ok (1.002s)
2023-01-11T21:32:39.4253091Z   test_synchronous_exception (__main__.ClosuresTest) ... ok (0.001s)
2023-01-11T21:32:39.4253253Z 
2023-01-11T21:32:39.4253440Z ----------------------------------------------------------------------
2023-01-11T21:32:39.4253681Z Ran 4 tests in 2.230s
2023-01-11T21:32:39.4253795Z 
2023-01-11T21:32:39.4253857Z OK
2023-01-11T21:32:39.4253948Z 
2023-01-11T21:32:39.4254035Z Generating XML reports...
2023-01-11T21:32:39.4254450Z Generated XML report: test-reports/python-unittest/lazy.test_step_closures/TEST-ClosuresTest-20230111213236.xml
2023-01-11T21:32:39.4254679Z 
2023-01-11T21:32:39.4254906Z ##[endgroup]
2023-01-11T21:32:39.4255320Z FINISHED PRINTING LOG FILE of lazy/test_step_closures (/var/lib/jenkins/workspace/test/test-reports/lazy-test_step_closures_ie9zqgpk)
2023-01-11T21:32:39.4255543Z 
2023-01-11T21:32:41.2518938Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:41.3162197Z Ignoring disabled issues:  []
2023-01-11T21:32:41.3308076Z Running lazy/test_ts_opinfo ... [2023-01-11 21:32:41.330476]
2023-01-11T21:32:41.3309429Z Executing ['/opt/conda/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:41.330707]
2023-01-11T21:32:44.0325260Z 
2023-01-11T21:32:44.0325787Z Expand the folded group to see the log file of lazy/test_ts_opinfo
2023-01-11T21:32:44.0326885Z ##[group]PRINTING LOG FILE of lazy/test_ts_opinfo (/var/lib/jenkins/workspace/test/test-reports/lazy-test_ts_opinfo_7_exk4ml)
2023-01-11T21:32:44.0327328Z 
2023-01-11T21:32:44.0327456Z Running tests...
2023-01-11T21:32:44.0328090Z ----------------------------------------------------------------------
2023-01-11T21:32:44.0328512Z Test results will be stored in test-reports/python-unittest/lazy.test_ts_opinfo
2023-01-11T21:32:44.0329178Z   test_nonzero_dynamic (__main__.TestLazyDynamicOps) ... ok (0.163s)
2023-01-11T21:32:44.0329547Z   testConvolutionBackward (__main__.TestLazyTensor) ... skip: Disable until autograd supports symints (0.001s)
2023-01-11T21:32:44.0329870Z   test_tensor_ctr (__main__.TestLazyTensor) ... ok (0.002s)
2023-01-11T21:32:44.0330148Z   test_view_mark_step_preserved (__main__.TestLazyTensor) ... ok (0.002s)
2023-01-11T21:32:44.0330314Z 
2023-01-11T21:32:44.0330517Z ----------------------------------------------------------------------
2023-01-11T21:32:44.0330748Z Ran 4 tests in 0.169s
2023-01-11T21:32:44.0330863Z 
2023-01-11T21:32:44.0330936Z OK (skipped=1)
2023-01-11T21:32:44.0331043Z 
2023-01-11T21:32:44.0331128Z Generating XML reports...
2023-01-11T21:32:44.0331547Z Generated XML report: test-reports/python-unittest/lazy.test_ts_opinfo/TEST-TestLazyDynamicOps-20230111213243.xml
2023-01-11T21:32:44.0332077Z Generated XML report: test-reports/python-unittest/lazy.test_ts_opinfo/TEST-TestLazyTensor-20230111213243.xml
2023-01-11T21:32:44.0332305Z 
2023-01-11T21:32:44.0332545Z ##[endgroup]
2023-01-11T21:32:44.0332941Z FINISHED PRINTING LOG FILE of lazy/test_ts_opinfo (/var/lib/jenkins/workspace/test/test-reports/lazy-test_ts_opinfo_7_exk4ml)
2023-01-11T21:32:44.0333150Z 
2023-01-11T21:32:45.9143694Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:45.9805219Z Ignoring disabled issues:  []
2023-01-11T21:32:45.9952405Z Running nn/test_dropout ... [2023-01-11 21:32:45.994918]
2023-01-11T21:32:45.9953744Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_dropout.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:45.995176]
2023-01-11T21:32:48.0137847Z 
2023-01-11T21:32:48.0138505Z Expand the folded group to see the log file of nn/test_dropout
2023-01-11T21:32:48.0139475Z ##[group]PRINTING LOG FILE of nn/test_dropout (/var/lib/jenkins/workspace/test/test-reports/nn-test_dropout_q8e6pe57)
2023-01-11T21:32:48.0139753Z 
2023-01-11T21:32:48.0139853Z Running tests...
2023-01-11T21:32:48.0140257Z ----------------------------------------------------------------------
2023-01-11T21:32:48.0140694Z Test results will be stored in test-reports/python-unittest/nn.test_dropout
2023-01-11T21:32:48.0141000Z   test_AlphaDropout (__main__.TestDropoutNN) ... ok (0.003s)
2023-01-11T21:32:48.0141335Z   test_FeatureAlphaDropout (__main__.TestDropoutNN) ... ok (0.079s)
2023-01-11T21:32:48.0141609Z   test_invalid_dropout_p (__main__.TestDropoutNN) ... ok (0.002s)
2023-01-11T21:32:48.0141921Z   test_native_dropout_corner_case (__main__.TestDropoutNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:32:48.0142158Z 
2023-01-11T21:32:48.0142358Z ----------------------------------------------------------------------
2023-01-11T21:32:48.0142597Z Ran 4 tests in 0.086s
2023-01-11T21:32:48.0142700Z 
2023-01-11T21:32:48.0142811Z OK (skipped=1)
2023-01-11T21:32:48.0142921Z 
2023-01-11T21:32:48.0143004Z Generating XML reports...
2023-01-11T21:32:48.0143417Z Generated XML report: test-reports/python-unittest/nn.test_dropout/TEST-TestDropoutNN-20230111213247.xml
2023-01-11T21:32:48.0143756Z 
2023-01-11T21:32:48.0144180Z ##[endgroup]
2023-01-11T21:32:48.0144623Z FINISHED PRINTING LOG FILE of nn/test_dropout (/var/lib/jenkins/workspace/test/test-reports/nn-test_dropout_q8e6pe57)
2023-01-11T21:32:48.0144840Z 
2023-01-11T21:32:49.8714901Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:49.9361322Z Ignoring disabled issues:  []
2023-01-11T21:32:49.9507907Z Running nn/test_embedding ... [2023-01-11 21:32:49.950425]
2023-01-11T21:32:49.9508522Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_embedding.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:49.950653]
2023-01-11T21:32:51.8669892Z 
2023-01-11T21:32:51.8670384Z Expand the folded group to see the log file of nn/test_embedding
2023-01-11T21:32:51.8671680Z ##[group]PRINTING LOG FILE of nn/test_embedding (/var/lib/jenkins/workspace/test/test-reports/nn-test_embedding_xwe0h0hs)
2023-01-11T21:32:51.8671987Z 
2023-01-11T21:32:51.8672064Z Running tests...
2023-01-11T21:32:51.8672470Z ----------------------------------------------------------------------
2023-01-11T21:32:51.8672969Z Test results will be stored in test-reports/python-unittest/nn.test_embedding
2023-01-11T21:32:51.8673291Z   test_embedding_bag_from_pretrained (__main__.TestEmbeddingNN) ... ok (0.003s)
2023-01-11T21:32:51.8673619Z   test_embedding_bag_from_pretrained_padding_idx (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8673922Z   test_embedding_bag_functional (__main__.TestEmbeddingNN) ... ok (0.002s)
2023-01-11T21:32:51.8674226Z   test_embedding_bag_padding_idx_error (__main__.TestEmbeddingNN) ... ok (0.011s)
2023-01-11T21:32:51.8674540Z   test_embedding_from_pretrained_float32 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8674855Z   test_embedding_from_pretrained_float64 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8675155Z   test_embedding_from_pretrained_int16 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8675460Z   test_embedding_from_pretrained_int32 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8675762Z   test_embedding_from_pretrained_int64 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8676056Z   test_embedding_from_pretrained_int8 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8676366Z   test_embedding_from_pretrained_options (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8676686Z   test_embedding_from_pretrained_padding_idx (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8677003Z   test_embedding_from_pretrained_uint8 (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8677286Z   test_embedding_functional (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8677568Z   test_embedding_max_norm (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8677904Z   test_embedding_max_norm_unsorted_repeating_indices (__main__.TestEmbeddingNN) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:32:51.8678228Z   test_embedding_sparse_basic (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8678525Z   test_embedding_sparse_empty_tensor (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8678832Z   test_embeddingbag_from_pretrained (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8679146Z   test_embeddingbag_from_pretrained_options (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8679451Z   test_embeddingbag_include_last_offset (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8679755Z   test_move_sparse_half_embedding (__main__.TestEmbeddingNN) ... ok (0.001s)
2023-01-11T21:32:51.8679920Z 
2023-01-11T21:32:51.8680123Z ----------------------------------------------------------------------
2023-01-11T21:32:51.8680351Z Ran 22 tests in 0.038s
2023-01-11T21:32:51.8680462Z 
2023-01-11T21:32:51.8680533Z OK (skipped=1)
2023-01-11T21:32:51.8680639Z 
2023-01-11T21:32:51.8680726Z Generating XML reports...
2023-01-11T21:32:51.8681146Z Generated XML report: test-reports/python-unittest/nn.test_embedding/TEST-TestEmbeddingNN-20230111213251.xml
2023-01-11T21:32:51.8681467Z 
2023-01-11T21:32:51.8681713Z ##[endgroup]
2023-01-11T21:32:51.8682104Z FINISHED PRINTING LOG FILE of nn/test_embedding (/var/lib/jenkins/workspace/test/test-reports/nn-test_embedding_xwe0h0hs)
2023-01-11T21:32:51.8682321Z 
2023-01-11T21:32:53.6853216Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:53.7522268Z Ignoring disabled issues:  []
2023-01-11T21:32:53.7667696Z Running nn/test_init ... [2023-01-11 21:32:53.766537]
2023-01-11T21:32:53.7669823Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_init.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:53.766763]
2023-01-11T21:32:55.5805536Z 
2023-01-11T21:32:55.5806080Z Expand the folded group to see the log file of nn/test_init
2023-01-11T21:32:55.5807182Z ##[group]PRINTING LOG FILE of nn/test_init (/var/lib/jenkins/workspace/test/test-reports/nn-test_init_vyuzj9zc)
2023-01-11T21:32:55.5807486Z 
2023-01-11T21:32:55.5807704Z ##[endgroup]
2023-01-11T21:32:55.5808265Z FINISHED PRINTING LOG FILE of nn/test_init (/var/lib/jenkins/workspace/test/test-reports/nn-test_init_vyuzj9zc)
2023-01-11T21:32:55.5808476Z 
2023-01-11T21:32:57.4315057Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:32:57.4964760Z Ignoring disabled issues:  []
2023-01-11T21:32:57.5112201Z Running nn/test_lazy_modules ... [2023-01-11 21:32:57.510931]
2023-01-11T21:32:57.5114560Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_lazy_modules.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:32:57.511201]
2023-01-11T21:32:59.8696484Z 
2023-01-11T21:32:59.8697024Z Expand the folded group to see the log file of nn/test_lazy_modules
2023-01-11T21:32:59.8698039Z ##[group]PRINTING LOG FILE of nn/test_lazy_modules (/var/lib/jenkins/workspace/test/test-reports/nn-test_lazy_modules_33fct_xm)
2023-01-11T21:32:59.8698492Z 
2023-01-11T21:32:59.8698623Z Running tests...
2023-01-11T21:32:59.8699203Z ----------------------------------------------------------------------
2023-01-11T21:32:59.8699910Z Test results will be stored in test-reports/python-unittest/nn.test_lazy_modules
2023-01-11T21:32:59.8700462Z   test_chained_initialization (__main__.TestLazyModules) ... ok (0.011s)
2023-01-11T21:32:59.8700953Z   test_invalid_functions (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8702083Z   test_lazy_batchnorm1d (__main__.TestLazyModules) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:32:59.8703034Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:32:59.8703444Z ok (0.010s)
2023-01-11T21:32:59.8703935Z   test_lazy_batchnorm1d_pickle (__main__.TestLazyModules) ... ok (0.008s)
2023-01-11T21:32:59.8704407Z   test_lazy_batchnorm1d_state (__main__.TestLazyModules) ... ok (0.003s)
2023-01-11T21:32:59.8704888Z   test_lazy_batchnorm2d (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8705356Z   test_lazy_batchnorm2d_pickle (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:32:59.8705821Z   test_lazy_batchnorm2d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8706301Z   test_lazy_batchnorm3d (__main__.TestLazyModules) ... ok (0.007s)
2023-01-11T21:32:59.8733094Z   test_lazy_batchnorm3d_pickle (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8733666Z   test_lazy_batchnorm3d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8734195Z   test_lazy_conv1d (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8734672Z   test_lazy_conv1d_pickle (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8735003Z   test_lazy_conv1d_state (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8735495Z   test_lazy_conv2d (__main__.TestLazyModules) ... ok (0.003s)
2023-01-11T21:32:59.8736000Z   test_lazy_conv2d_pickle (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8736681Z   test_lazy_conv2d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8737102Z   test_lazy_conv3d (__main__.TestLazyModules) ... ok (0.015s)
2023-01-11T21:32:59.8738828Z   test_lazy_conv3d_pickle (__main__.TestLazyModules) ... ok (0.007s)
2023-01-11T21:32:59.8739351Z   test_lazy_conv3d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8739920Z   test_lazy_conv_transpose1d_pickle (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8740447Z   test_lazy_conv_transpose1d_state (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8740979Z   test_lazy_conv_transpose2d (__main__.TestLazyModules) ... ok (0.015s)
2023-01-11T21:32:59.8741398Z   test_lazy_conv_transpose2d_pickle (__main__.TestLazyModules) ... ok (0.006s)
2023-01-11T21:32:59.8741968Z   test_lazy_conv_transpose2d_state (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8742412Z   test_lazy_conv_transpose3d (__main__.TestLazyModules) ... ok (0.173s)
2023-01-11T21:32:59.8742984Z   test_lazy_conv_transpose3d_pickle (__main__.TestLazyModules) ... ok (0.060s)
2023-01-11T21:32:59.8743582Z   test_lazy_conv_transpose3d_state (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8744091Z   test_lazy_conv_transposed1d (__main__.TestLazyModules) ... ok (0.009s)
2023-01-11T21:32:59.8744368Z   test_lazy_forward_hook (__main__.TestLazyModules)
2023-01-11T21:32:59.8745131Z This test is to test whether lazymodule can register other forward hook ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:32:59.8745747Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:32:59.8745969Z ok (0.001s)
2023-01-11T21:32:59.8746203Z   test_lazy_instancenorm1d (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8746504Z   test_lazy_instancenorm1d_pickle (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8746797Z   test_lazy_instancenorm1d_state (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:32:59.8747092Z   test_lazy_instancenorm2d (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8747388Z   test_lazy_instancenorm2d_pickle (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8747689Z   test_lazy_instancenorm2d_state (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:32:59.8747966Z   test_lazy_instancenorm3d (__main__.TestLazyModules) ... ok (0.007s)
2023-01-11T21:32:59.8748258Z   test_lazy_instancenorm3d_pickle (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8748554Z   test_lazy_instancenorm3d_state (__main__.TestLazyModules) ... ok (0.004s)
2023-01-11T21:32:59.8748827Z   test_lazy_linear_pickle (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8749107Z   test_lazy_module_buffer (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8749389Z   test_lazy_module_jit_buffer (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8749678Z   test_lazy_module_jit_param (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8749951Z   test_lazy_module_parameter (__main__.TestLazyModules) ... ok (0.002s)
2023-01-11T21:32:59.8750223Z   test_lazy_pre_forward_hook (__main__.TestLazyModules)
2023-01-11T21:32:59.8750898Z This test is to test whether lazymodule can register other pre-forward hook ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:32:59.8751448Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:32:59.8751683Z ok (0.001s)
2023-01-11T21:32:59.8751913Z   test_lazy_share_memory_buffer (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8752205Z   test_lazy_share_memory_param (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8752460Z   test_linear (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8752794Z   test_linear_state (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8753099Z   test_materialize_device (__main__.TestLazyModules) ... skip: CUDA not available (0.000s)
2023-01-11T21:32:59.8753403Z   test_materialize_dtype (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8753681Z   test_optimizer_pass (__main__.TestLazyModules) ... ok (0.005s)
2023-01-11T21:32:59.8753956Z   test_spectral_norm (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8754244Z   test_weight_norm (__main__.TestLazyModules) ... ok (0.001s)
2023-01-11T21:32:59.8754381Z 
2023-01-11T21:32:59.8754586Z ----------------------------------------------------------------------
2023-01-11T21:32:59.8754829Z Ran 54 tests in 0.438s
2023-01-11T21:32:59.8754943Z 
2023-01-11T21:32:59.8755016Z OK (skipped=1)
2023-01-11T21:32:59.8755158Z 
2023-01-11T21:32:59.8755232Z Generating XML reports...
2023-01-11T21:32:59.8755658Z Generated XML report: test-reports/python-unittest/nn.test_lazy_modules/TEST-TestLazyModules-20230111213259.xml
2023-01-11T21:32:59.8755896Z 
2023-01-11T21:32:59.8756204Z ##[endgroup]
2023-01-11T21:32:59.8756589Z FINISHED PRINTING LOG FILE of nn/test_lazy_modules (/var/lib/jenkins/workspace/test/test-reports/nn-test_lazy_modules_33fct_xm)
2023-01-11T21:32:59.8756813Z 
2023-01-11T21:33:01.7386409Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:01.8035928Z Ignoring disabled issues:  []
2023-01-11T21:33:01.8182666Z Running nn/test_module_hooks ... [2023-01-11 21:33:01.817997]
2023-01-11T21:33:01.8185252Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_module_hooks.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:01.818242]
2023-01-11T21:33:03.8094982Z 
2023-01-11T21:33:03.8095811Z Expand the folded group to see the log file of nn/test_module_hooks
2023-01-11T21:33:03.8096965Z ##[group]PRINTING LOG FILE of nn/test_module_hooks (/var/lib/jenkins/workspace/test/test-reports/nn-test_module_hooks_aq1dmqhu)
2023-01-11T21:33:03.8097385Z 
2023-01-11T21:33:03.8097476Z Running tests...
2023-01-11T21:33:03.8098102Z ----------------------------------------------------------------------
2023-01-11T21:33:03.8098746Z Test results will be stored in test-reports/python-unittest/nn.test_module_hooks
2023-01-11T21:33:03.8099982Z   test_global_and_local_hooks_order (__main__.TestModuleGlobalHooks) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1331: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.
2023-01-11T21:33:03.8100767Z   warnings.warn("Using a non-full backward hook when the forward contains multiple autograd Nodes "
2023-01-11T21:33:03.8101036Z ok (0.011s)
2023-01-11T21:33:03.8101337Z   test_module_backward_global_hook_writeable (__main__.TestModuleGlobalHooks) ... ok (0.002s)
2023-01-11T21:33:03.8101677Z   test_module_forward_forward_hook_removable (__main__.TestModuleGlobalHooks)
2023-01-11T21:33:03.8102139Z This test is to test when multiple forward hook functions can be registered ... ok (0.002s)
2023-01-11T21:33:03.8102605Z   test_module_forward_preforward_hook_removable (__main__.TestModuleGlobalHooks)
2023-01-11T21:33:03.8103190Z This test is to test when multiple pre-forward hook functions can be ... ok (0.001s)
2023-01-11T21:33:03.8103908Z   test_module_global_forward_preforward_hook_writeable (__main__.TestModuleGlobalHooks) ... ok (0.001s)
2023-01-11T21:33:03.8104375Z   test_module_global_hook_invalid_outputs (__main__.TestModuleGlobalHooks) ... ok (0.001s)
2023-01-11T21:33:03.8104699Z   test_module_global_hooks (__main__.TestModuleGlobalHooks) ... ok (0.009s)
2023-01-11T21:33:03.8105014Z   test_backward_hooks_interaction (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8105364Z   test_hook_backward_size (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T21:33:03.8105881Z   test_hook_backward_writeable (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8106209Z   test_hook_buffer_registration (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8106494Z   test_hook_cpp (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T21:33:03.8106816Z   test_hook_extra_input (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8107105Z   test_hook_forward_preforward_writable (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8107507Z   test_hook_inplace (__main__.TestModuleHookNN) ... ok (0.024s)
2023-01-11T21:33:03.8108033Z   test_hook_invalid_outputs (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T21:33:03.8108512Z   test_hook_last_arg_requires_grad (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8108860Z   test_hook_no_requires_grad (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T21:33:03.8109151Z   test_hook_non_full_warning (__main__.TestModuleHookNN) ... ok (0.010s)
2023-01-11T21:33:03.8109451Z   test_hook_parameter_registration (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8109731Z   test_hook_requires_grad (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8110025Z   test_hook_submodule_registration (__main__.TestModuleHookNN) ... ok (0.001s)
2023-01-11T21:33:03.8110883Z   test_hooks (__main__.TestModuleHookNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1331: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.
2023-01-11T21:33:03.8111576Z   warnings.warn("Using a non-full backward hook when the forward contains multiple autograd Nodes "
2023-01-11T21:33:03.8111835Z ok (0.014s)
2023-01-11T21:33:03.8112044Z   test_forward_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:03.8112319Z   test_forward_pre_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:03.8112607Z   test_full_backward_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:03.8112881Z   test_full_backward_pre_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:03.8113158Z   test_kwarg_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:03.8113419Z   test_mixed_hooks (__main__.TestModuleHooks) ... ok (0.004s)
2023-01-11T21:33:03.8113681Z   test_remove_kwarg_hooks (__main__.TestModuleHooks) ... ok (0.003s)
2023-01-11T21:33:03.8113980Z   test_load_state_dict_module_pre_hook (__main__.TestStateDictHooks) ... ok (0.002s)
2023-01-11T21:33:03.8114291Z   test_load_state_dict_post_hook (__main__.TestStateDictHooks) ... ok (0.002s)
2023-01-11T21:33:03.8114627Z   test_load_state_dict_post_hook_backward_compatibility (__main__.TestStateDictHooks) ... ok (0.002s)
2023-01-11T21:33:03.8114947Z   test_load_state_dict_pre_hook (__main__.TestStateDictHooks) ... ok (0.002s)
2023-01-11T21:33:03.8115241Z   test_no_extra_ref_to_module (__main__.TestStateDictHooks) ... ok (0.001s)
2023-01-11T21:33:03.8115530Z   test_pickled_hook (__main__.TestStateDictHooks) ... ok (0.001s)
2023-01-11T21:33:03.8115688Z 
2023-01-11T21:33:03.8115877Z ----------------------------------------------------------------------
2023-01-11T21:33:03.8116117Z Ran 36 tests in 0.132s
2023-01-11T21:33:03.8116229Z 
2023-01-11T21:33:03.8116290Z OK
2023-01-11T21:33:03.8116380Z 
2023-01-11T21:33:03.8116463Z Generating XML reports...
2023-01-11T21:33:03.8116881Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleGlobalHooks-20230111213303.xml
2023-01-11T21:33:03.8117416Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleHookNN-20230111213303.xml
2023-01-11T21:33:03.8117931Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleHooks-20230111213303.xml
2023-01-11T21:33:03.8118437Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestStateDictHooks-20230111213303.xml
2023-01-11T21:33:03.8118721Z 
2023-01-11T21:33:03.8119007Z ##[endgroup]
2023-01-11T21:33:03.8119405Z FINISHED PRINTING LOG FILE of nn/test_module_hooks (/var/lib/jenkins/workspace/test/test-reports/nn-test_module_hooks_aq1dmqhu)
2023-01-11T21:33:03.8119630Z 
2023-01-11T21:33:05.6123361Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:05.6768002Z Ignoring disabled issues:  []
2023-01-11T21:33:05.6913510Z Running nn/test_multihead_attention ... [2023-01-11 21:33:05.691120]
2023-01-11T21:33:05.6915860Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_multihead_attention.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:05.691343]
2023-01-11T21:33:12.2502635Z 
2023-01-11T21:33:12.2503175Z Expand the folded group to see the log file of nn/test_multihead_attention
2023-01-11T21:33:12.2504647Z ##[group]PRINTING LOG FILE of nn/test_multihead_attention (/var/lib/jenkins/workspace/test/test-reports/nn-test_multihead_attention_l_tw5w9m)
2023-01-11T21:33:12.2505077Z 
2023-01-11T21:33:12.2505143Z Running tests...
2023-01-11T21:33:12.2505546Z ----------------------------------------------------------------------
2023-01-11T21:33:12.2505949Z Test results will be stored in test-reports/python-unittest/nn.test_multihead_attention
2023-01-11T21:33:12.2506452Z   test_multihead_attention_average_attn_weights_False (__main__.TestMultiheadAttentionNN) ... ok (2.357s)
2023-01-11T21:33:12.2507116Z   test_multihead_attention_average_attn_weights_True (__main__.TestMultiheadAttentionNN) ... ok (2.288s)
2023-01-11T21:33:12.2507575Z   test_multihead_attn_3d_attn_mask (__main__.TestMultiheadAttentionNN) ... ok (0.007s)
2023-01-11T21:33:12.2507925Z   test_multihead_attn_fast_path_invalid_shape (__main__.TestMultiheadAttentionNN) ... ok (0.004s)
2023-01-11T21:33:12.2508274Z   test_multihead_attn_invalid_shape (__main__.TestMultiheadAttentionNN) ... ok (0.001s)
2023-01-11T21:33:12.2508898Z   test_multihead_attn_nested_tensor_outside_fast_path (__main__.TestMultiheadAttentionNN) ... /var/lib/jenkins/workspace/test/nn/test_multihead_attention.py:494: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:33:12.2509471Z   nt = torch.nested.nested_tensor([torch.randn(4, 4)])
2023-01-11T21:33:12.2509683Z ok (0.003s)
2023-01-11T21:33:12.2509931Z   test_multihead_attn_no_bias (__main__.TestMultiheadAttentionNN) ... ok (0.001s)
2023-01-11T21:33:12.2510109Z 
2023-01-11T21:33:12.2510303Z ----------------------------------------------------------------------
2023-01-11T21:33:12.2510542Z Ran 7 tests in 4.661s
2023-01-11T21:33:12.2510655Z 
2023-01-11T21:33:12.2510716Z OK
2023-01-11T21:33:12.2510805Z 
2023-01-11T21:33:12.2510890Z Generating XML reports...
2023-01-11T21:33:12.2511349Z Generated XML report: test-reports/python-unittest/nn.test_multihead_attention/TEST-TestMultiheadAttentionNN-20230111213307.xml
2023-01-11T21:33:12.2511617Z 
2023-01-11T21:33:12.2511867Z ##[endgroup]
2023-01-11T21:33:12.2512286Z FINISHED PRINTING LOG FILE of nn/test_multihead_attention (/var/lib/jenkins/workspace/test/test-reports/nn-test_multihead_attention_l_tw5w9m)
2023-01-11T21:33:12.2512512Z 
2023-01-11T21:33:14.0837142Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:14.1479380Z Ignoring disabled issues:  []
2023-01-11T21:33:14.1625377Z Running nn/test_packed_sequence ... [2023-01-11 21:33:14.162242]
2023-01-11T21:33:14.1627241Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_packed_sequence.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:14.162495]
2023-01-11T21:33:16.2113660Z 
2023-01-11T21:33:16.2117544Z Expand the folded group to see the log file of nn/test_packed_sequence
2023-01-11T21:33:16.2118321Z ##[group]PRINTING LOG FILE of nn/test_packed_sequence (/var/lib/jenkins/workspace/test/test-reports/nn-test_packed_sequence_pjs3ln7k)
2023-01-11T21:33:16.2118799Z 
2023-01-11T21:33:16.2118896Z Running tests...
2023-01-11T21:33:16.2119322Z ----------------------------------------------------------------------
2023-01-11T21:33:16.2119769Z Test results will be stored in test-reports/python-unittest/nn.test_packed_sequence
2023-01-11T21:33:16.2120099Z   test_pack_padded_sequence (__main__.PackedSequenceTest) ... ok (0.249s)
2023-01-11T21:33:16.2120396Z   test_pack_sequence (__main__.PackedSequenceTest) ... ok (0.077s)
2023-01-11T21:33:16.2120717Z   test_pad_sequence (__main__.PackedSequenceTest) ... ok (0.013s)
2023-01-11T21:33:16.2121031Z   test_pad_sequence_with_non_iterable_sequences (__main__.PackedSequenceTest) ... ok (0.001s)
2023-01-11T21:33:16.2121403Z   test_pad_sequence_with_tensor_sequences (__main__.PackedSequenceTest) ... ok (0.001s)
2023-01-11T21:33:16.2121766Z   test_to (__main__.PackedSequenceTest) ... ok (0.002s)
2023-01-11T21:33:16.2122092Z   test_to_memory_format (__main__.PackedSequenceTest) ... ok (0.001s)
2023-01-11T21:33:16.2122374Z   test_total_length (__main__.PackedSequenceTest) ... ok (0.003s)
2023-01-11T21:33:16.2122630Z   test_type_casts (__main__.PackedSequenceTest)
2023-01-11T21:33:16.2122977Z Test type casting of `PackedSequence` against type casting of tensor ... ok (0.024s)
2023-01-11T21:33:16.2123289Z   test_unpack_sequence (__main__.PackedSequenceTest) ... ok (0.010s)
2023-01-11T21:33:16.2123609Z   test_unpad_sequence (__main__.PackedSequenceTest) ... ok (0.009s)
2023-01-11T21:33:16.2123893Z   test_wrong_order (__main__.PackedSequenceTest) ... ok (0.004s)
2023-01-11T21:33:16.2124054Z 
2023-01-11T21:33:16.2124300Z ----------------------------------------------------------------------
2023-01-11T21:33:16.2124543Z Ran 12 tests in 0.396s
2023-01-11T21:33:16.2124644Z 
2023-01-11T21:33:16.2124705Z OK
2023-01-11T21:33:16.2124795Z 
2023-01-11T21:33:16.2124881Z Generating XML reports...
2023-01-11T21:33:16.2125374Z Generated XML report: test-reports/python-unittest/nn.test_packed_sequence/TEST-PackedSequenceTest-20230111213315.xml
2023-01-11T21:33:16.2125622Z 
2023-01-11T21:33:16.2125895Z ##[endgroup]
2023-01-11T21:33:16.2126309Z FINISHED PRINTING LOG FILE of nn/test_packed_sequence (/var/lib/jenkins/workspace/test/test-reports/nn-test_packed_sequence_pjs3ln7k)
2023-01-11T21:33:16.2126590Z 
2023-01-11T21:33:18.0262211Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:18.0906174Z Ignoring disabled issues:  []
2023-01-11T21:33:18.1054028Z Running nn/test_parametrization ... [2023-01-11 21:33:18.105128]
2023-01-11T21:33:18.1056284Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_parametrization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:18.105376]
2023-01-11T21:33:20.3705680Z 
2023-01-11T21:33:20.3706195Z Expand the folded group to see the log file of nn/test_parametrization
2023-01-11T21:33:20.3707391Z ##[group]PRINTING LOG FILE of nn/test_parametrization (/var/lib/jenkins/workspace/test/test-reports/nn-test_parametrization_fmmttay5)
2023-01-11T21:33:20.3707839Z 
2023-01-11T21:33:20.3707971Z Running tests...
2023-01-11T21:33:20.3708630Z ----------------------------------------------------------------------
2023-01-11T21:33:20.3709191Z Test results will be stored in test-reports/python-unittest/nn.test_parametrization
2023-01-11T21:33:20.3709662Z   test_caching_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:20.3710059Z Test the caching system of a parametrization ... ok (0.011s)
2023-01-11T21:33:20.3710404Z   test_caching_parametrization_with_transfer_parametrizations_and_params (__main__.TestNNParametrization)
2023-01-11T21:33:20.3710859Z Test that transferring parametrizations doesn't cause issues with caching ... ok (0.002s)
2023-01-11T21:33:20.3711174Z   test_deepcopy_after_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:20.3711591Z Test that we are able to create a deepcopy of the module when it's parametrized. ... ok (0.007s)
2023-01-11T21:33:20.3711952Z   test_errors_parametrized_tensor_parametrization (__main__.TestNNParametrization) ... ok (0.003s)
2023-01-11T21:33:20.3712532Z   test_errors_unparametrized_tensor_parametrization (__main__.TestNNParametrization) ... ok (0.005s)
2023-01-11T21:33:20.3712861Z   test_initialization_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:20.3713176Z Test that it is possible to initialize a parametrization when it ... ok (0.004s)
2023-01-11T21:33:20.3713502Z   test_multiple_inputs_parametrization (__main__.TestNNParametrization) ... ok (0.007s)
2023-01-11T21:33:20.3713810Z   test_new_spectral_norm (__main__.TestNNParametrization) ... ok (0.040s)
2023-01-11T21:33:20.3714113Z   test_new_spectral_norm_dim (__main__.TestNNParametrization) ... ok (0.003s)
2023-01-11T21:33:20.3714423Z   test_new_spectral_norm_forward (__main__.TestNNParametrization) ... ok (0.003s)
2023-01-11T21:33:20.3714807Z   test_new_spectral_norm_load_state_dict (__main__.TestNNParametrization) ... ok (0.015s)
2023-01-11T21:33:20.3715113Z   test_orthogonal_errors (__main__.TestNNParametrization) ... ok (0.005s)
2023-01-11T21:33:20.3715432Z   test_orthogonal_parametrization (__main__.TestNNParametrization) ... ok (0.181s)
2023-01-11T21:33:20.3715752Z   test_parametrization_same_training_mode (__main__.TestNNParametrization)
2023-01-11T21:33:20.3716053Z Test training mode updated on parametrization registration ... ok (0.001s)
2023-01-11T21:33:20.3716375Z   test_register_and_remove_buffer_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:20.3716703Z Test that it is possible to add and remove parametrizations on buffers ... ok (0.002s)
2023-01-11T21:33:20.3717031Z   test_register_and_remove_nested_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:20.3717328Z Test that it is possible to nest the parametrizations ... ok (0.002s)
2023-01-11T21:33:20.3717631Z   test_register_and_remove_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:20.3717941Z Test that it is possible to add a few parametrizations ... ok (0.018s)
2023-01-11T21:33:20.3718229Z   test_serialization_parametrization (__main__.TestNNParametrization)
2023-01-11T21:33:20.3718550Z Test that it is possible to serialize a parametrized model via state_dict ... ok (0.005s)
2023-01-11T21:33:20.3718875Z   test_transfer_parametrizations_and_params (__main__.TestNNParametrization)
2023-01-11T21:33:20.3719214Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.004s)
2023-01-11T21:33:20.3719566Z   test_transfer_parametrizations_and_params_many_to_one (__main__.TestNNParametrization) ... ok (0.005s)
2023-01-11T21:33:20.3719929Z   test_transfer_parametrizations_and_params_right_inverse (__main__.TestNNParametrization)
2023-01-11T21:33:20.3720283Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.002s)
2023-01-11T21:33:20.3720635Z   test_transfer_parametrizations_and_params_single_param (__main__.TestNNParametrization)
2023-01-11T21:33:20.3720980Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.003s)
2023-01-11T21:33:20.3721307Z   test_type_before_parametrizations (__main__.TestNNParametrization)
2023-01-11T21:33:20.3721622Z Test that type_before_parametrizations always retrieves original type ... ok (0.001s)
2023-01-11T21:33:20.3721803Z 
2023-01-11T21:33:20.3722000Z ----------------------------------------------------------------------
2023-01-11T21:33:20.3722239Z Ran 23 tests in 0.332s
2023-01-11T21:33:20.3722352Z 
2023-01-11T21:33:20.3722412Z OK
2023-01-11T21:33:20.3722501Z 
2023-01-11T21:33:20.3722584Z Generating XML reports...
2023-01-11T21:33:20.3723020Z Generated XML report: test-reports/python-unittest/nn.test_parametrization/TEST-TestNNParametrization-20230111213319.xml
2023-01-11T21:33:20.3723278Z 
2023-01-11T21:33:20.3723547Z ##[endgroup]
2023-01-11T21:33:20.3723947Z FINISHED PRINTING LOG FILE of nn/test_parametrization (/var/lib/jenkins/workspace/test/test-reports/nn-test_parametrization_fmmttay5)
2023-01-11T21:33:20.3724187Z 
2023-01-11T21:33:22.1861792Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:22.2506359Z Ignoring disabled issues:  []
2023-01-11T21:33:22.2653351Z Running nn/test_pruning ... [2023-01-11 21:33:22.265067]
2023-01-11T21:33:22.2654994Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_pruning.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:22.265314]
2023-01-11T21:33:24.1838174Z 
2023-01-11T21:33:24.1838743Z Expand the folded group to see the log file of nn/test_pruning
2023-01-11T21:33:24.1839979Z ##[group]PRINTING LOG FILE of nn/test_pruning (/var/lib/jenkins/workspace/test/test-reports/nn-test_pruning_nm3kiv_6)
2023-01-11T21:33:24.1840403Z 
2023-01-11T21:33:24.1840534Z Running tests...
2023-01-11T21:33:24.1841183Z ----------------------------------------------------------------------
2023-01-11T21:33:24.1841844Z Test results will be stored in test-reports/python-unittest/nn.test_pruning
2023-01-11T21:33:24.1842617Z   test_compute_nparams_to_prune (__main__.TestPruningNN)
2023-01-11T21:33:24.1843128Z Test that requested pruning `amount` gets translated into the ... ok (0.010s)
2023-01-11T21:33:24.1843660Z   test_custom_from_mask_pruning (__main__.TestPruningNN)
2023-01-11T21:33:24.1844121Z Test that the CustomFromMask is capable of receiving ... ok (0.001s)
2023-01-11T21:33:24.1844664Z   test_global_pruning (__main__.TestPruningNN)
2023-01-11T21:33:24.1845151Z Test that global l1 unstructured pruning over 2 parameters removes ... ok (0.002s)
2023-01-11T21:33:24.1845650Z   test_global_pruning_importance_scores (__main__.TestPruningNN)
2023-01-11T21:33:24.1846228Z Test that global l1 unstructured pruning over 2 parameters removes ... ok (0.002s)
2023-01-11T21:33:24.1846723Z   test_identity_pruning (__main__.TestPruningNN)
2023-01-11T21:33:24.1847193Z Test that a mask of 1s does not change forward or backward. ... ok (0.003s)
2023-01-11T21:33:24.1847662Z   test_l1_unstructured_pruning (__main__.TestPruningNN)
2023-01-11T21:33:24.1848147Z Test that l1 unstructured pruning actually removes the lowest ... ok (0.002s)
2023-01-11T21:33:24.1848740Z   test_l1_unstructured_pruning_with_importance_scores (__main__.TestPruningNN)
2023-01-11T21:33:24.1849425Z Test that l1 unstructured pruning actually removes the lowest ... ok (0.002s)
2023-01-11T21:33:24.1849890Z   test_ln_structured_pruning (__main__.TestPruningNN)
2023-01-11T21:33:24.1850424Z Check Ln structured pruning by hand. ... ok (0.002s)
2023-01-11T21:33:24.1850914Z   test_ln_structured_pruning_importance_scores (__main__.TestPruningNN)
2023-01-11T21:33:24.1851373Z Check Ln structured pruning by hand. ... ok (0.002s)
2023-01-11T21:33:24.1851842Z   test_multiple_pruning_calls (__main__.TestPruningNN) ... ok (0.002s)
2023-01-11T21:33:24.1852313Z   test_prune (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:33:24.1852743Z   test_prune_importance_scores (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:33:24.1853299Z   test_prune_importance_scores_mimic_default (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:33:24.1853847Z   test_pruning_container (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:33:24.1854341Z   test_pruning_container_compute_mask (__main__.TestPruningNN)
2023-01-11T21:33:24.1854868Z Test `compute_mask` of pruning container with a known `t` and ... ok (0.002s)
2023-01-11T21:33:24.1855354Z   test_pruning_id_consistency (__main__.TestPruningNN)
2023-01-11T21:33:24.1856050Z Test that pruning doesn't change the id of the parameters, which ... ok (0.001s)
2023-01-11T21:33:24.1856514Z   test_pruning_rollback (__main__.TestPruningNN)
2023-01-11T21:33:24.1856982Z Test that if something fails when the we try to compute the mask, ... ok (0.002s)
2023-01-11T21:33:24.1857529Z   test_pruning_serialization_model (__main__.TestPruningNN) ... ok (0.003s)
2023-01-11T21:33:24.1858045Z   test_pruning_serialization_state_dict (__main__.TestPruningNN) ... ok (0.003s)
2023-01-11T21:33:24.1858527Z   test_random_pruning (__main__.TestPruningNN) ... ok (0.002s)
2023-01-11T21:33:24.1858962Z   test_random_pruning_0perc (__main__.TestPruningNN)
2023-01-11T21:33:24.1859511Z Test that a mask of 1s does not change forward or backward. ... ok (0.003s)
2023-01-11T21:33:24.1860178Z   test_random_pruning_forward (__main__.TestPruningNN)
2023-01-11T21:33:24.1860591Z check forward with mask (by hand). ... ok (0.002s)
2023-01-11T21:33:24.1861025Z   test_random_pruning_new_weight (__main__.TestPruningNN)
2023-01-11T21:33:24.1861487Z Test that module.name now contains a pruned version of ... ok (0.002s)
2023-01-11T21:33:24.1861946Z   test_random_pruning_orig (__main__.TestPruningNN)
2023-01-11T21:33:24.1862579Z Test that original tensor is correctly stored in 'orig' ... ok (0.002s)
2023-01-11T21:33:24.1863059Z   test_random_pruning_pickle (__main__.TestPruningNN) ... ok (0.004s)
2023-01-11T21:33:24.1863604Z   test_random_pruning_sizes (__main__.TestPruningNN)
2023-01-11T21:33:24.1864149Z Test that the new parameters and buffers created by the pruning ... ok (0.002s)
2023-01-11T21:33:24.1864848Z   test_random_structured_pruning_amount (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T21:33:24.1865382Z   test_remove_pruning (__main__.TestPruningNN)
2023-01-11T21:33:24.1865852Z `prune.remove` removes the hook and the reparametrization ... ok (0.002s)
2023-01-11T21:33:24.1866342Z   test_remove_pruning_exception (__main__.TestPruningNN)
2023-01-11T21:33:24.1866857Z Removing from an unpruned tensor throws an assertion error ... ok (0.001s)
2023-01-11T21:33:24.1867338Z   test_remove_pruning_forward (__main__.TestPruningNN)
2023-01-11T21:33:24.1867817Z Remove pruning and check forward is unchanged from previous ... ok (0.001s)
2023-01-11T21:33:24.1868302Z   test_rnn_pruning (__main__.TestPruningNN) ... ok (0.002s)
2023-01-11T21:33:24.1868749Z   test_unstructured_pruning_same_magnitude (__main__.TestPruningNN)
2023-01-11T21:33:24.1869308Z Since it may happen that the tensor to prune has entries with the ... ok (0.001s)
2023-01-11T21:33:24.1869812Z   test_validate_pruning_amount (__main__.TestPruningNN)
2023-01-11T21:33:24.1870266Z Tests the second util function that validates the pruning ... ok (0.001s)
2023-01-11T21:33:24.1870804Z   test_validate_pruning_amount_init (__main__.TestPruningNN)
2023-01-11T21:33:24.1871313Z Test the first util function that validates the pruning ... ok (0.001s)
2023-01-11T21:33:24.1871604Z 
2023-01-11T21:33:24.1871994Z ----------------------------------------------------------------------
2023-01-11T21:33:24.1872383Z Ran 34 tests in 0.073s
2023-01-11T21:33:24.1872580Z 
2023-01-11T21:33:24.1872682Z OK
2023-01-11T21:33:24.1872840Z 
2023-01-11T21:33:24.1872979Z Generating XML reports...
2023-01-11T21:33:24.1873696Z Generated XML report: test-reports/python-unittest/nn.test_pruning/TEST-TestPruningNN-20230111213323.xml
2023-01-11T21:33:24.1874075Z 
2023-01-11T21:33:24.1874526Z ##[endgroup]
2023-01-11T21:33:24.1875269Z FINISHED PRINTING LOG FILE of nn/test_pruning (/var/lib/jenkins/workspace/test/test-reports/nn-test_pruning_nm3kiv_6)
2023-01-11T21:33:24.1875660Z 
2023-01-11T21:33:26.0283341Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:26.0926773Z Ignoring disabled issues:  []
2023-01-11T21:33:26.1073975Z Running profiler/test_memory_profiler ... [2023-01-11 21:33:26.107145]
2023-01-11T21:33:26.1076235Z Executing ['/opt/conda/bin/python', '-bb', 'profiler/test_memory_profiler.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:26.107394]
2023-01-11T21:33:30.8581809Z 
2023-01-11T21:33:30.8582322Z Expand the folded group to see the log file of profiler/test_memory_profiler
2023-01-11T21:33:30.8583414Z ##[group]PRINTING LOG FILE of profiler/test_memory_profiler (/var/lib/jenkins/workspace/test/test-reports/profiler-test_memory_profiler_b6udbbia)
2023-01-11T21:33:30.8584039Z 
2023-01-11T21:33:30.8584160Z Running tests...
2023-01-11T21:33:30.8584853Z ----------------------------------------------------------------------
2023-01-11T21:33:30.8585293Z Test results will be stored in test-reports/python-unittest/profiler.test_memory_profiler
2023-01-11T21:33:30.8585848Z   test_data_flow_graph_complicated (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8586539Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8586989Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8587621Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:336: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8588251Z   lines.append(f"{name + ':':<8} T{storage_to_id[t.storage().data_ptr()]}")
2023-01-11T21:33:30.8588696Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8589135Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8589580Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8589842Z ok (0.287s)
2023-01-11T21:33:30.8590278Z   test_data_flow_graph_non_op_allocations (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8590762Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8591203Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8591449Z ok (0.052s)
2023-01-11T21:33:30.8591874Z   test_data_flow_graph_simple (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8592350Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8592790Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8593980Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:337: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:33:30.8594724Z   if t.grad is not None:
2023-01-11T21:33:30.8595069Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8595500Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8595942Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8596199Z ok (0.096s)
2023-01-11T21:33:30.8596631Z   test_data_flow_graph_simple_backward (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8597121Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8597557Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8598181Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:338: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8598757Z   grad_id = storage_to_id[t.grad.storage().data_ptr()]
2023-01-11T21:33:30.8598962Z ok (0.051s)
2023-01-11T21:33:30.8599405Z   test_data_flow_graph_simple_inplace (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8599894Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8600319Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8600752Z STAGE:2023-01-11 21:33:27 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8601211Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8601639Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8601897Z ok (0.091s)
2023-01-11T21:33:30.8602325Z   test_data_flow_graph_stacked (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8602798Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8603223Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8603654Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8604078Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8604507Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8604938Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8605364Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8605799Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8606042Z ok (0.177s)
2023-01-11T21:33:30.8606481Z   test_data_flow_graph_with_annotations (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8606965Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8607403Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8607647Z ok (0.055s)
2023-01-11T21:33:30.8608061Z   test_match_schemas (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8608528Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8608954Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8609408Z ok (0.007s)
2023-01-11T21:33:30.8609848Z   test_match_schemas_backward (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8610326Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8610753Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8611011Z ok (0.004s)
2023-01-11T21:33:30.8611442Z   test_match_schemas_tensorlist (__main__.TestDataFlow) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8611923Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8612422Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8612679Z ok (0.002s)
2023-01-11T21:33:30.8613140Z   test_extract_gradients_from_module (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8613626Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8614067Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8614730Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:117: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8615290Z   return tensor.storage().data_ptr() == key.storage.ptr
2023-01-11T21:33:30.8615856Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:147: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8616376Z   allowed_set = {t.storage().data_ptr() for t in tensors}
2023-01-11T21:33:30.8616758Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8617190Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8617634Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8618051Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8618482Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8618922Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8619340Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8619765Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8620204Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8620462Z ok (0.034s)
2023-01-11T21:33:30.8620939Z   test_extract_gradients_from_module_and_optimizer (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8621453Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8621894Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8622155Z ok (0.007s)
2023-01-11T21:33:30.8622608Z   test_extract_gradients_from_optimizer (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8623114Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8623637Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8624063Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8624496Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8624934Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8625406Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8625818Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8626256Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8626683Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8627106Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8627530Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8627822Z ok (0.038s)
2023-01-11T21:33:30.8628311Z   test_extract_gradients_from_optimizer_set_to_none (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8628810Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8629247Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8629673Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8630098Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8630519Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8630946Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8631373Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8631812Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8632225Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8632645Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8633079Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8633322Z ok (0.044s)
2023-01-11T21:33:30.8633779Z   test_extract_gradients_low_level (__main__.TestIdentifyGradients) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8634272Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8634709Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8635128Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8635552Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8635990Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8636246Z ok (0.010s)
2023-01-11T21:33:30.8636660Z   test_config_check (__main__.TestMemoryProfiler) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8637135Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8637570Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8637985Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8638411Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8638883Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8639309Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8639722Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8640161Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8640419Z ok (0.004s)
2023-01-11T21:33:30.8640867Z   test_categories_e2e_sequential_fwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8641589Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:81: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8642141Z   if isinstance(t, torch.Tensor) and t.storage()
2023-01-11T21:33:30.8642699Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:78: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8643196Z   return tuple(
2023-01-11T21:33:30.8643700Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:79: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8644208Z   (t._cdata, t.storage().data_ptr())
2023-01-11T21:33:30.8644585Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8645030Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8645275Z ok (0.071s)
2023-01-11T21:33:30.8645744Z   test_categories_e2e_sequential_fwd_bwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8646248Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8646692Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8646941Z ok (0.317s)
2023-01-11T21:33:30.8647395Z   test_categories_e2e_simple_fwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8647892Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8648333Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8648578Z ok (0.036s)
2023-01-11T21:33:30.8649177Z   test_categories_e2e_simple_fwd_bwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8649703Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8650134Z STAGE:2023-01-11 21:33:28 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8650393Z ok (0.185s)
2023-01-11T21:33:30.8650863Z   test_categories_e2e_simple_fwd_bwd_step (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8651441Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8651870Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8652129Z ok (0.228s)
2023-01-11T21:33:30.8652595Z   test_categories_e2e_simple_module_fwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8653096Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8653526Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8653829Z ok (0.028s)
2023-01-11T21:33:30.8654307Z   test_categories_e2e_simple_module_fwd_bwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8654800Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8655243Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8655503Z ok (0.119s)
2023-01-11T21:33:30.8655980Z   test_categories_e2e_simple_module_fwd_bwd_step (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8656475Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8656919Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8657176Z ok (0.198s)
2023-01-11T21:33:30.8657591Z   test_inputs_fwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8658071Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8658504Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8659125Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:828: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8659639Z   storage = t.storage()
2023-01-11T21:33:30.8660157Z /var/lib/jenkins/workspace/test/profiler/test_memory_profiler.py:836: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:30.8660728Z   if key.storage.ptr == storage.data_ptr() and key.device == storage.device
2023-01-11T21:33:30.8660963Z ok (0.025s)
2023-01-11T21:33:30.8661401Z   test_inputs_fwd_bwd (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8661877Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8662316Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8662577Z ok (0.146s)
2023-01-11T21:33:30.8663002Z   test_inputs_fwd_lazy (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8663488Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8664086Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8664343Z ok (0.029s)
2023-01-11T21:33:30.8664774Z   test_lazily_initialized (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8665261Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8665701Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8665962Z ok (0.075s)
2023-01-11T21:33:30.8666445Z   test_manual_optimizer_step (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8666945Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8667392Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8667637Z ok (0.039s)
2023-01-11T21:33:30.8668073Z   test_memory_timeline (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8668555Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8668990Z STAGE:2023-01-11 21:33:29 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8669233Z ok (0.178s)
2023-01-11T21:33:30.8669693Z   test_parameters_and_gradients (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8670191Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8670630Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8671048Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8671473Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8671911Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8672328Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8672754Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8673197Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8673624Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8674041Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8674479Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8674736Z ok (0.152s)
2023-01-11T21:33:30.8675211Z   test_parameters_and_gradients_set_to_none (__main__.TestMemoryProfilerE2E) ... STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8675694Z [W CPUAllocator.cpp:231] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
2023-01-11T21:33:30.8676183Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8676623Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8677050Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:30.8677499Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:30.8677934Z STAGE:2023-01-11 21:33:30 5728:5728 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:30.8678190Z ok (0.230s)
2023-01-11T21:33:30.8678295Z 
2023-01-11T21:33:30.8678481Z ----------------------------------------------------------------------
2023-01-11T21:33:30.8678726Z Ran 32 tests in 3.018s
2023-01-11T21:33:30.8678843Z 
2023-01-11T21:33:30.8678903Z OK
2023-01-11T21:33:30.8678993Z 
2023-01-11T21:33:30.8679065Z Generating XML reports...
2023-01-11T21:33:30.8679489Z Generated XML report: test-reports/python-unittest/profiler.test_memory_profiler/TEST-TestDataFlow-20230111213327.xml
2023-01-11T21:33:30.8680084Z Generated XML report: test-reports/python-unittest/profiler.test_memory_profiler/TEST-TestIdentifyGradients-20230111213327.xml
2023-01-11T21:33:30.8680653Z Generated XML report: test-reports/python-unittest/profiler.test_memory_profiler/TEST-TestMemoryProfiler-20230111213327.xml
2023-01-11T21:33:30.8681202Z Generated XML report: test-reports/python-unittest/profiler.test_memory_profiler/TEST-TestMemoryProfilerE2E-20230111213327.xml
2023-01-11T21:33:30.8681459Z 
2023-01-11T21:33:30.8681786Z ##[endgroup]
2023-01-11T21:33:30.8682222Z FINISHED PRINTING LOG FILE of profiler/test_memory_profiler (/var/lib/jenkins/workspace/test/test-reports/profiler-test_memory_profiler_b6udbbia)
2023-01-11T21:33:30.8682466Z 
2023-01-11T21:33:32.7492085Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:32.8143957Z Ignoring disabled issues:  []
2023-01-11T21:33:32.8294016Z Running profiler/test_profiler ... [2023-01-11 21:33:32.829110]
2023-01-11T21:33:32.8295567Z Executing ['/opt/conda/bin/python', '-bb', 'profiler/test_profiler.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:32.829363]
2023-01-11T21:33:40.9202749Z 
2023-01-11T21:33:40.9204031Z Expand the folded group to see the log file of profiler/test_profiler
2023-01-11T21:33:40.9205029Z ##[group]PRINTING LOG FILE of profiler/test_profiler (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_58kwhjzf)
2023-01-11T21:33:40.9205740Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:40.9205955Z 
2023-01-11T21:33:40.9206057Z Running tests...
2023-01-11T21:33:40.9206546Z ----------------------------------------------------------------------
2023-01-11T21:33:40.9207482Z Test results will be stored in test-reports/python-unittest/profiler.test_profiler
2023-01-11T21:33:40.9208258Z   test_execution_graph_alone (__main__.TestExecutionGraph) ... ok (0.009s)
2023-01-11T21:33:40.9208976Z   test_execution_graph_no_capture (__main__.TestExecutionGraph) ... ok (0.001s)
2023-01-11T21:33:40.9209979Z   test_execution_graph_repeat_in_loop (__main__.TestExecutionGraph) ... ok (0.008s)
2023-01-11T21:33:40.9210743Z   test_execution_graph_start_stop (__main__.TestExecutionGraph) ... ok (0.006s)
2023-01-11T21:33:40.9211712Z   test_execution_graph_with_kineto (__main__.TestExecutionGraph) ... [W kineto_shim.cpp:330] Profiler is not initialized: skipping step() invocation
2023-01-11T21:33:40.9212657Z [W kineto_shim.cpp:330] Profiler is not initialized: skipping step() invocation
2023-01-11T21:33:40.9213420Z [W kineto_shim.cpp:330] Profiler is not initialized: skipping step() invocation
2023-01-11T21:33:40.9214470Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9215517Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9216555Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9217621Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9237150Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9238683Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9239322Z ok (0.019s)
2023-01-11T21:33:40.9239854Z   test_bfs (__main__.TestExperimentalUtils) ... ok (0.001s)
2023-01-11T21:33:40.9240517Z   test_dfs (__main__.TestExperimentalUtils) ... ok (0.001s)
2023-01-11T21:33:40.9241880Z   test_profiler_conv2d_bias_followed_by_batchnorm2d_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9243145Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9244356Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9245542Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9246614Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9247669Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9248686Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9249902Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9250977Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9251591Z ok (0.008s)
2023-01-11T21:33:40.9252235Z   test_profiler_extra_cuda_copy_pattern (__main__.TestExperimentalUtils) ... skip: CUDA is required (0.001s)
2023-01-11T21:33:40.9253198Z   test_profiler_extra_cuda_copy_pattern_benchmark (__main__.TestExperimentalUtils) ... skip: CUDA is required (0.000s)
2023-01-11T21:33:40.9254644Z   test_profiler_for_loop_indexing_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9255904Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9256985Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9258029Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9259086Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9260156Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9261225Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9262255Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9263359Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9264458Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9265498Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9266580Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9267620Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9268649Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9269729Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9270379Z ok (0.087s)
2023-01-11T21:33:40.9271168Z   test_profiler_fp32_matmul_pattern (__main__.TestExperimentalUtils) ... skip: CUDA is required (0.000s)
2023-01-11T21:33:40.9272593Z   test_profiler_grad_not_set_to_none_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9273843Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9274931Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9275963Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9276987Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9278176Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9279197Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9280219Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9281274Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9282293Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9283302Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9284291Z STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9284865Z ok (0.044s)
2023-01-11T21:33:40.9285483Z   test_profiler_matmul_dim_fp16_pattern (__main__.TestExperimentalUtils) ... skip: CUDA is required (0.001s)
2023-01-11T21:33:40.9286795Z   test_profiler_name_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:34 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9288003Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9289282Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9289907Z ok (2.129s)
2023-01-11T21:33:40.9291125Z   test_profiler_optimizer_single_tensor_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9292382Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9293453Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9294340Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9294930Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9295598Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9296233Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9296863Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9297513Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9298151Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9298765Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9299405Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9300211Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9300831Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9301461Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9302084Z STAGE:2023-01-11 21:33:36 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9302711Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9303359Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9303803Z ok (0.086s)
2023-01-11T21:33:40.9304595Z   test_profiler_pattern_match_helper (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9305307Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9305946Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9306319Z ok (0.004s)
2023-01-11T21:33:40.9307017Z   test_profiler_pattern_matcher_json_report (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9307761Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9308389Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9308764Z ok (0.097s)
2023-01-11T21:33:40.9309488Z   test_profiler_synchronized_dataloader_pattern (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9310240Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9310851Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9311211Z ok (0.095s)
2023-01-11T21:33:40.9311566Z   test_utils_compute_idle_time (__main__.TestExperimentalUtils) ... ok (0.002s)
2023-01-11T21:33:40.9312015Z   test_utils_compute_queue_depth (__main__.TestExperimentalUtils) ... ok (0.001s)
2023-01-11T21:33:40.9312840Z   test_utils_compute_queue_depth_when_no_cuda_events (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9313603Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9314262Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9314620Z ok (0.044s)
2023-01-11T21:33:40.9315284Z   test_utils_compute_self_time (__main__.TestExperimentalUtils) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9315991Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9316602Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9316930Z ok (0.007s)
2023-01-11T21:33:40.9317267Z   test_utils_get_optimizable_events (__main__.TestExperimentalUtils) ... ok (0.007s)
2023-01-11T21:33:40.9317740Z   test_utils_intervals_overlap (__main__.TestExperimentalUtils) ... 5
2023-01-11T21:33:40.9318079Z ok (0.001s)
2023-01-11T21:33:40.9318718Z   test_export_stacks (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9319437Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9320191Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9320539Z ok (0.005s)
2023-01-11T21:33:40.9321118Z   test_flops (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9321806Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9322423Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9322802Z ok (0.052s)
2023-01-11T21:33:40.9323097Z   test_high_level_trace (__main__.TestProfiler)
2023-01-11T21:33:40.9323884Z Checks that python side high level events are recorded. ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9324611Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9325230Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9325794Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9326398Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9327074Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9327730Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9328390Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9329192Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9329583Z ok (0.114s)
2023-01-11T21:33:40.9330715Z   test_kineto (__main__.TestProfiler) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88377 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:33:40.9331570Z   test_kineto_multigpu (__main__.TestProfiler) ... skip: Multiple GPUs needed (0.001s)
2023-01-11T21:33:40.9332308Z   test_kineto_profiler_api (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9332989Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9333637Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9334330Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9334984Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9335630Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9336219Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9336827Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9337456Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9338042Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9338662Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9339291Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9339776Z ok (0.009s)
2023-01-11T21:33:40.9340449Z   test_kineto_profiler_multiple_steppers (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9341152Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9341780Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9342385Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9342997Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9343827Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9344484Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9345093Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9345740Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9346114Z ok (0.029s)
2023-01-11T21:33:40.9347286Z   test_memory_profiler (__main__.TestProfiler) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/72280 for platform(s) linux, mac. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.002s)
2023-01-11T21:33:40.9348582Z   test_module_hierarchy (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9349227Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9349889Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9350584Z ERROR:2023-01-11 21:33:37 5775:5775 CudaDeviceProperties.cpp:27] cudaGetDeviceCount failed with code 35
2023-01-11T21:33:40.9350963Z ok (0.019s)
2023-01-11T21:33:40.9351717Z   test_nested_tensor_with_shapes (__main__.TestProfiler) ... /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1318: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:33:40.9352484Z   inp = torch.nested.nested_tensor([a, b])
2023-01-11T21:33:40.9353066Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9353879Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9354539Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9354912Z ok (0.003s)
2023-01-11T21:33:40.9355228Z   test_oom_tracing (__main__.TestProfiler) ... ok (0.001s)
2023-01-11T21:33:40.9355601Z   test_profiler_correlation_id (__main__.TestProfiler)
2023-01-11T21:33:40.9356398Z We expect the correlation_id to be unique across multiple invokation of the profiler, ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9357143Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9357828Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9358457Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9359089Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9359821Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9360465Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9361099Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9361725Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9362375Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9362974Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9363725Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9364341Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9364984Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9365604Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9365957Z ok (0.131s)
2023-01-11T21:33:40.9366513Z   test_profiler_fwd_bwd_link (__main__.TestProfiler) ... skip: Disable forward->backward link to workaround profiler crash (0.002s)
2023-01-11T21:33:40.9367329Z   test_profiler_metadata (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9368058Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9368705Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9369226Z ok (0.003s)
2023-01-11T21:33:40.9369843Z   test_profiler_tracing (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9370501Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9371138Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9371758Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9372405Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9373036Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9373670Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9374305Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9374955Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9375593Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9376203Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9376851Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9377221Z ok (0.007s)
2023-01-11T21:33:40.9377799Z   test_profiler_type (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9378506Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9379180Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9379677Z ok (0.001s)
2023-01-11T21:33:40.9379960Z   test_source (__main__.TestProfiler)
2023-01-11T21:33:40.9380717Z Checks that source code attribution works for eager, TS and autograd mode ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9381499Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9382144Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9382530Z ok (0.056s)
2023-01-11T21:33:40.9383178Z   test_tensorboard_trace_handler (__main__.TestProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9384107Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9384781Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9385442Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9386079Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9386734Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9387369Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9387984Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9388637Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9389265Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9389893Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9390548Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9391148Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9391790Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9392440Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9393081Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9393720Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9394357Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9394970Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9395581Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9396201Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9396572Z ok (0.017s)
2023-01-11T21:33:40.9396963Z   test_custom_module_input_op_ids (__main__.TestProfilerCUDA) ... skip: CUDA is required (0.001s)
2023-01-11T21:33:40.9397368Z   test_mem_leak (__main__.TestProfilerCUDA)
2023-01-11T21:33:40.9397921Z Checks that there's no memory leak when using profiler with CUDA ... skip: CUDA is required (0.001s)
2023-01-11T21:33:40.9398382Z   test_custom_module_input_op_ids (__main__.TestProfilerITT) ... ok (0.001s)
2023-01-11T21:33:40.9398825Z   test_datapipe_delegation_with_profiler (__main__.TestRecordFunction) ... ok (0.002s)
2023-01-11T21:33:40.9399607Z   test_datapipe_with_record_function (__main__.TestRecordFunction) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9400461Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9401099Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9401456Z ok (0.004s)
2023-01-11T21:33:40.9402141Z   test_datapipe_with_record_function_fork (__main__.TestRecordFunction) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9402880Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9403508Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9403955Z ok (0.004s)
2023-01-11T21:33:40.9404593Z   test_record_function (__main__.TestRecordFunction) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9405300Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9405951Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9406282Z ok (0.002s)
2023-01-11T21:33:40.9406903Z   test_allocation_id_uniqueness (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9407611Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9408207Z STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9408588Z ok (0.249s)
2023-01-11T21:33:40.9409385Z   test_allocation_ids (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:37 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9410103Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9410714Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9411048Z ok (0.155s)
2023-01-11T21:33:40.9411716Z   test_allocation_ids_with_other_ops (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9412437Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9413075Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9413438Z ok (0.126s)
2023-01-11T21:33:40.9414071Z   test_allocations (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9414758Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9415400Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9416029Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9416650Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9417273Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9417610Z ok (0.129s)
2023-01-11T21:33:40.9418241Z   test_extra_fields (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9418909Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9419668Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9420038Z ok (0.002s)
2023-01-11T21:33:40.9420638Z   test_impl_reuse (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9421322Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9421938Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9422294Z ok (0.205s)
2023-01-11T21:33:40.9422939Z   test_mkldnn_tensors (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9423823Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9424485Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9424869Z ok (0.003s)
2023-01-11T21:33:40.9425504Z   test_module_and_optimizer_ids (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9426245Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9426870Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9427497Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9428097Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9428738Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9429093Z ok (0.014s)
2023-01-11T21:33:40.9429711Z   test_nnmodule_params (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9430425Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9431105Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9431970Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1985: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9432891Z   expected = [(name, val.storage().data_ptr(), val.grad.storage().data_ptr()) for name, val in net.fc1._parameters.items()]
2023-01-11T21:33:40.9433813Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1986: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9434762Z   expected += [(name, val.storage().data_ptr(), val.grad.storage().data_ptr()) for name, val in net.fc2._parameters.items()]
2023-01-11T21:33:40.9435176Z ok (0.004s)
2023-01-11T21:33:40.9435836Z   test_optimizer (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9436520Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9437207Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9437602Z ok (0.011s)
2023-01-11T21:33:40.9438293Z   test_optimizer_parameters_adam (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9439141Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9439807Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9440738Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:2009: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9441580Z   [(v.storage().data_ptr()) for v in group.get("params", [])],
2023-01-11T21:33:40.9442506Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:2022: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9443410Z   {name: value.storage().data_ptr() for name, value in parameter_state.items()},
2023-01-11T21:33:40.9444314Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:2023: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9445129Z   observed_state.get(parameter.storage().data_ptr(), [])
2023-01-11T21:33:40.9445444Z ok (0.036s)
2023-01-11T21:33:40.9446153Z   test_optimizer_parameters_sgd (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9446933Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9447557Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9447937Z ok (0.019s)
2023-01-11T21:33:40.9448811Z   test_pointers_and_ids (__main__.TestTorchTidyProfiler) ... /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1366: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9449867Z   a_initial_storage_data = a.storage().data_ptr()
2023-01-11T21:33:40.9450633Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1371: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9451399Z   c_initial_storage_data = c.storage().data_ptr()
2023-01-11T21:33:40.9451971Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9452566Z [W CPUAllocator.cpp:231] Memory block of unknown size was allocated before the profiling started, profiler results will not include the deallocation event
2023-01-11T21:33:40.9453541Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1384: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9454261Z   c.set_(d.storage())
2023-01-11T21:33:40.9454957Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9455585Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9455953Z ok (0.004s)
2023-01-11T21:33:40.9456580Z   test_scalar_ins (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9457278Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9457915Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9458280Z ok (0.002s)
2023-01-11T21:33:40.9459030Z   test_sparse_tensors (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9459731Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9460377Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9460742Z ok (0.004s)
2023-01-11T21:33:40.9461350Z   test_tensor_lists (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9462050Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9462690Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9463657Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1958: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9464522Z   self.assertEqual(x.storage().data_ptr(), inputs[0][0].storage_data_ptr)
2023-01-11T21:33:40.9465364Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9466197Z   self.assertEqual(y.storage().data_ptr(), inputs[0][1].storage_data_ptr)
2023-01-11T21:33:40.9466508Z ok (0.002s)
2023-01-11T21:33:40.9467194Z   test_tensor_properties (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9467893Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9468504Z STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9468889Z ok (0.003s)
2023-01-11T21:33:40.9469544Z   test_tensorimpl_invalidation_full (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:38 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9470560Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1546: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9471313Z   x_storages = [x.storage()]
2023-01-11T21:33:40.9472112Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1549: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9472985Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:33:40.9473795Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1550: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9474573Z   x_storages.append(x.storage())
2023-01-11T21:33:40.9475460Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1559: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9476198Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:33:40.9476984Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1563: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9477725Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:33:40.9478292Z STAGE:2023-01-11 21:33:39 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9478905Z STAGE:2023-01-11 21:33:39 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9479243Z ok (0.424s)
2023-01-11T21:33:40.9479948Z   test_tensorimpl_invalidation_keep_alive (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:39 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9480926Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1478: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9481699Z   x_storages = [x.storage()]
2023-01-11T21:33:40.9482484Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1481: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9483244Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:33:40.9484012Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1485: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9484768Z   x_storages.append(x.storage())
2023-01-11T21:33:40.9485550Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1499: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9486272Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:33:40.9486831Z STAGE:2023-01-11 21:33:39 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9487490Z STAGE:2023-01-11 21:33:39 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9488117Z STAGE:2023-01-11 21:33:39 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9488828Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9489613Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9489977Z ok (0.948s)
2023-01-11T21:33:40.9490668Z   test_tensorimpl_invalidation_scalar_args (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9491349Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9492005Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9492492Z ok (0.136s)
2023-01-11T21:33:40.9493180Z   test_tensorimpl_invalidation_set (__main__.TestTorchTidyProfiler) ... STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9494136Z /var/lib/jenkins/workspace/test/profiler/test_profiler.py:1454: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:40.9494874Z   x.set_(torch.ones((1,)).storage())
2023-01-11T21:33:40.9495404Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9496042Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9496647Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:33:40.9497250Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:33:40.9497866Z STAGE:2023-01-11 21:33:40 5775:5775 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:33:40.9498221Z ok (0.261s)
2023-01-11T21:33:40.9498350Z 
2023-01-11T21:33:40.9498636Z ----------------------------------------------------------------------
2023-01-11T21:33:40.9498987Z Ran 71 tests in 5.868s
2023-01-11T21:33:40.9499144Z 
2023-01-11T21:33:40.9499241Z OK (skipped=10)
2023-01-11T21:33:40.9499386Z 
2023-01-11T21:33:40.9499487Z Generating XML reports...
2023-01-11T21:33:40.9500104Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestExecutionGraph-20230111213334.xml
2023-01-11T21:33:40.9500924Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestExperimentalUtils-20230111213334.xml
2023-01-11T21:33:40.9501711Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestProfiler-20230111213334.xml
2023-01-11T21:33:40.9502436Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestProfilerITT-20230111213334.xml
2023-01-11T21:33:40.9503226Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestRecordFunction-20230111213334.xml
2023-01-11T21:33:40.9504106Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestTorchTidyProfiler-20230111213334.xml
2023-01-11T21:33:40.9504868Z Generated XML report: test-reports/python-unittest/profiler.test_profiler/TEST-TestProfilerCUDA-20230111213334.xml
2023-01-11T21:33:40.9505174Z 
2023-01-11T21:33:40.9505577Z ##[endgroup]
2023-01-11T21:33:40.9506233Z FINISHED PRINTING LOG FILE of profiler/test_profiler (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_58kwhjzf)
2023-01-11T21:33:40.9506583Z 
2023-01-11T21:33:42.7622222Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:42.8266392Z Ignoring disabled issues:  []
2023-01-11T21:33:42.8413548Z Running test_autocast ... [2023-01-11 21:33:42.841068]
2023-01-11T21:33:42.8415139Z Executing ['/opt/conda/bin/python', '-bb', 'test_autocast.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:42.841304]
2023-01-11T21:33:44.8624274Z 
2023-01-11T21:33:44.8624812Z Expand the folded group to see the log file of test_autocast
2023-01-11T21:33:44.8625933Z ##[group]PRINTING LOG FILE of test_autocast (/var/lib/jenkins/workspace/test/test-reports/test_autocast_82qx57ye)
2023-01-11T21:33:44.8626356Z 
2023-01-11T21:33:44.8626487Z Running tests...
2023-01-11T21:33:44.8627133Z ----------------------------------------------------------------------
2023-01-11T21:33:44.8627791Z Test results will be stored in test-reports/python-unittest/test_autocast
2023-01-11T21:33:44.8628347Z   test_autocast_methods_expect_builtin_promote (__main__.TestAutocastCPU) ... ok (0.229s)
2023-01-11T21:33:44.8629148Z   test_autocast_nn_bf16 (__main__.TestAutocastCPU) ... ok (0.006s)
2023-01-11T21:33:44.8629660Z   test_autocast_nn_fp32 (__main__.TestAutocastCPU) ... ok (0.008s)
2023-01-11T21:33:44.8630172Z   test_autocast_torch_bf16 (__main__.TestAutocastCPU) ... ok (0.016s)
2023-01-11T21:33:44.8630753Z   test_autocast_torch_expect_builtin_promote (__main__.TestAutocastCPU) ... ok (0.006s)
2023-01-11T21:33:44.8631288Z   test_autocast_torch_fp32 (__main__.TestAutocastCPU) ... ok (0.079s)
2023-01-11T21:33:44.8631832Z   test_autocast_torch_need_autocast_promote (__main__.TestAutocastCPU) ... ok (0.006s)
2023-01-11T21:33:44.8632334Z   test_cast_cache_is_global (__main__.TestAutocastGPU)
2023-01-11T21:33:44.8632851Z Verifies that the autocast cache is global. This is done by ... skip: requires cuda (0.001s)
2023-01-11T21:33:44.8633401Z   test_autocast_fast_dtype (__main__.TestTorchAutocast) ... ok (0.001s)
2023-01-11T21:33:44.8633691Z 
2023-01-11T21:33:44.8634074Z ----------------------------------------------------------------------
2023-01-11T21:33:44.8634500Z Ran 9 tests in 0.352s
2023-01-11T21:33:44.8634704Z 
2023-01-11T21:33:44.8634824Z OK (skipped=1)
2023-01-11T21:33:44.8635013Z 
2023-01-11T21:33:44.8635161Z Generating XML reports...
2023-01-11T21:33:44.8635878Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestAutocastCPU-20230111213344.xml
2023-01-11T21:33:44.8636818Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestTorchAutocast-20230111213344.xml
2023-01-11T21:33:44.8637731Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestAutocastGPU-20230111213344.xml
2023-01-11T21:33:44.8638141Z 
2023-01-11T21:33:44.8638566Z ##[endgroup]
2023-01-11T21:33:44.8639243Z FINISHED PRINTING LOG FILE of test_autocast (/var/lib/jenkins/workspace/test/test-reports/test_autocast_82qx57ye)
2023-01-11T21:33:44.8639618Z 
2023-01-11T21:33:46.7052664Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:46.7698846Z Ignoring disabled issues:  []
2023-01-11T21:33:46.7848280Z Running test_binary_ufuncs ... [2023-01-11 21:33:46.784476]
2023-01-11T21:33:46.7849676Z Executing ['/opt/conda/bin/python', '-bb', 'test_binary_ufuncs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:46.784726]
2023-01-11T21:33:49.3220334Z 
2023-01-11T21:33:49.3220882Z Expand the folded group to see the log file of test_binary_ufuncs
2023-01-11T21:33:49.3221946Z ##[group]PRINTING LOG FILE of test_binary_ufuncs (/var/lib/jenkins/workspace/test/test-reports/test_binary_ufuncs_t8btn9in)
2023-01-11T21:33:49.3224991Z 
2023-01-11T21:33:49.3225127Z Running tests...
2023-01-11T21:33:49.3225780Z ----------------------------------------------------------------------
2023-01-11T21:33:49.3226072Z 
2023-01-11T21:33:49.3226450Z ----------------------------------------------------------------------
2023-01-11T21:33:49.3226792Z Ran 0 tests in 0.000s
2023-01-11T21:33:49.3226906Z 
2023-01-11T21:33:49.3226964Z OK
2023-01-11T21:33:49.3227053Z 
2023-01-11T21:33:49.3227137Z Generating XML reports...
2023-01-11T21:33:49.3227471Z Test results will be stored in test-reports/python-unittest/test_binary_ufuncs
2023-01-11T21:33:49.3227862Z 
2023-01-11T21:33:49.3228108Z ##[endgroup]
2023-01-11T21:33:49.3228508Z FINISHED PRINTING LOG FILE of test_binary_ufuncs (/var/lib/jenkins/workspace/test/test-reports/test_binary_ufuncs_t8btn9in)
2023-01-11T21:33:49.3228727Z 
2023-01-11T21:33:51.1896926Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:51.2540358Z Ignoring disabled issues:  []
2023-01-11T21:33:51.2689474Z Running test_bundled_inputs ... [2023-01-11 21:33:51.268601]
2023-01-11T21:33:51.2690404Z Executing ['/opt/conda/bin/python', '-bb', 'test_bundled_inputs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:51.268837]
2023-01-11T21:33:53.9796454Z 
2023-01-11T21:33:53.9796962Z Expand the folded group to see the log file of test_bundled_inputs
2023-01-11T21:33:53.9798187Z ##[group]PRINTING LOG FILE of test_bundled_inputs (/var/lib/jenkins/workspace/test/test-reports/test_bundled_inputs_3m17p5u9)
2023-01-11T21:33:53.9798405Z 
2023-01-11T21:33:53.9798515Z Running tests...
2023-01-11T21:33:53.9798926Z ----------------------------------------------------------------------
2023-01-11T21:33:53.9799317Z Test results will be stored in test-reports/python-unittest/test_bundled_inputs
2023-01-11T21:33:53.9799623Z   test_bad_inputs (__main__.TestBundledInputs) ... ok (0.230s)
2023-01-11T21:33:53.9800227Z   test_dict_args (__main__.TestBundledInputs) ... /var/lib/jenkins/workspace/test/test_bundled_inputs.py:361: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:53.9800775Z   assert ret.storage().size() == 1
2023-01-11T21:33:53.9801563Z /opt/conda/lib/python3.10/site-packages/torch/utils/bundled_inputs.py:394: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:53.9802137Z   if arg._typed_storage().size() <= MAX_RAW_TENSOR_SIZE or skip_size_check:
2023-01-11T21:33:53.9802363Z ok (0.613s)
2023-01-11T21:33:53.9802581Z   test_double_augment_fail (__main__.TestBundledInputs) ... ok (0.007s)
2023-01-11T21:33:53.9802880Z   test_double_augment_non_mutator (__main__.TestBundledInputs) ... ok (0.007s)
2023-01-11T21:33:53.9803174Z   test_double_augment_success (__main__.TestBundledInputs) ... ok (0.009s)
2023-01-11T21:33:53.9803464Z   test_large_tensor_with_inflation (__main__.TestBundledInputs) ... ok (0.011s)
2023-01-11T21:33:53.9803772Z   test_multiple_methods_with_inputs (__main__.TestBundledInputs) ... ok (0.047s)
2023-01-11T21:33:53.9804109Z   test_multiple_methods_with_inputs_both_defined_failure (__main__.TestBundledInputs) ... ok (0.005s)
2023-01-11T21:33:53.9804470Z   test_multiple_methods_with_inputs_neither_defined_failure (__main__.TestBundledInputs) ... ok (0.004s)
2023-01-11T21:33:53.9804771Z   test_non_tensors (__main__.TestBundledInputs) ... ok (0.008s)
2023-01-11T21:33:53.9805665Z   test_rejected_tensors (__main__.TestBundledInputs) ... /opt/conda/lib/python3.10/site-packages/torch/utils/bundled_inputs.py:410: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:33:53.9806277Z   f"a tensor with storage size {arg._typed_storage().size()}. "
2023-01-11T21:33:53.9806488Z ok (0.008s)
2023-01-11T21:33:53.9806698Z   test_single_tensors (__main__.TestBundledInputs) ... ok (0.032s)
2023-01-11T21:33:53.9806854Z 
2023-01-11T21:33:53.9807053Z ----------------------------------------------------------------------
2023-01-11T21:33:53.9807292Z Ran 12 tests in 0.985s
2023-01-11T21:33:53.9807482Z 
2023-01-11T21:33:53.9807530Z OK
2023-01-11T21:33:53.9807620Z 
2023-01-11T21:33:53.9807707Z Generating XML reports...
2023-01-11T21:33:53.9808127Z Generated XML report: test-reports/python-unittest/test_bundled_inputs/TEST-TestBundledInputs-20230111213352.xml
2023-01-11T21:33:53.9808368Z 
2023-01-11T21:33:53.9808595Z ##[endgroup]
2023-01-11T21:33:53.9808988Z FINISHED PRINTING LOG FILE of test_bundled_inputs (/var/lib/jenkins/workspace/test/test-reports/test_bundled_inputs_3m17p5u9)
2023-01-11T21:33:53.9809375Z 
2023-01-11T21:33:55.8067427Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:55.8716336Z Ignoring disabled issues:  []
2023-01-11T21:33:55.8865060Z Running test_comparison_utils ... [2023-01-11 21:33:55.886200]
2023-01-11T21:33:55.8867185Z Executing ['/opt/conda/bin/python', '-bb', 'test_comparison_utils.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:55.886469]
2023-01-11T21:33:57.4723775Z 
2023-01-11T21:33:57.4724373Z Expand the folded group to see the log file of test_comparison_utils
2023-01-11T21:33:57.4725332Z ##[group]PRINTING LOG FILE of test_comparison_utils (/var/lib/jenkins/workspace/test/test-reports/test_comparison_utils_gcf13j3m)
2023-01-11T21:33:57.4725568Z 
2023-01-11T21:33:57.4725784Z ##[endgroup]
2023-01-11T21:33:57.4726288Z FINISHED PRINTING LOG FILE of test_comparison_utils (/var/lib/jenkins/workspace/test/test-reports/test_comparison_utils_gcf13j3m)
2023-01-11T21:33:57.4726509Z 
2023-01-11T21:33:59.2966830Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:33:59.3621779Z Ignoring disabled issues:  []
2023-01-11T21:33:59.3772478Z Running test_complex ... [2023-01-11 21:33:59.376946]
2023-01-11T21:33:59.3774558Z Executing ['/opt/conda/bin/python', '-bb', 'test_complex.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:33:59.377192]
2023-01-11T21:34:01.2568495Z 
2023-01-11T21:34:01.2569187Z Expand the folded group to see the log file of test_complex
2023-01-11T21:34:01.2570253Z ##[group]PRINTING LOG FILE of test_complex (/var/lib/jenkins/workspace/test/test-reports/test_complex_7p3eol9c)
2023-01-11T21:34:01.2570633Z 
2023-01-11T21:34:01.2570755Z Running tests...
2023-01-11T21:34:01.2571368Z ----------------------------------------------------------------------
2023-01-11T21:34:01.2571663Z 
2023-01-11T21:34:01.2572021Z ----------------------------------------------------------------------
2023-01-11T21:34:01.2572417Z Ran 0 tests in 0.000s
2023-01-11T21:34:01.2572612Z 
2023-01-11T21:34:01.2572713Z OK
2023-01-11T21:34:01.2572854Z 
2023-01-11T21:34:01.2572998Z Generating XML reports...
2023-01-11T21:34:01.2573565Z Test results will be stored in test-reports/python-unittest/test_complex
2023-01-11T21:34:01.2573859Z 
2023-01-11T21:34:01.2574259Z ##[endgroup]
2023-01-11T21:34:01.2574917Z FINISHED PRINTING LOG FILE of test_complex (/var/lib/jenkins/workspace/test/test-reports/test_complex_7p3eol9c)
2023-01-11T21:34:01.2575295Z 
2023-01-11T21:34:03.1300239Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:03.1945255Z Ignoring disabled issues:  []
2023-01-11T21:34:03.2093953Z Running test_cuda_sanitizer ... [2023-01-11 21:34:03.209156]
2023-01-11T21:34:03.2096692Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_sanitizer.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:03.209393]
2023-01-11T21:34:04.7756524Z 
2023-01-11T21:34:04.7758378Z Expand the folded group to see the log file of test_cuda_sanitizer
2023-01-11T21:34:04.7759507Z ##[group]PRINTING LOG FILE of test_cuda_sanitizer (/var/lib/jenkins/workspace/test/test-reports/test_cuda_sanitizer_y8az38v2)
2023-01-11T21:34:04.7760105Z CUDA not available, skipping tests
2023-01-11T21:34:04.7760320Z 
2023-01-11T21:34:04.7760444Z Running tests...
2023-01-11T21:34:04.7760949Z ----------------------------------------------------------------------
2023-01-11T21:34:04.7761123Z 
2023-01-11T21:34:04.7761321Z ----------------------------------------------------------------------
2023-01-11T21:34:04.7761736Z Ran 0 tests in 0.000s
2023-01-11T21:34:04.7761848Z 
2023-01-11T21:34:04.7761908Z OK
2023-01-11T21:34:04.7761997Z 
2023-01-11T21:34:04.7762082Z Generating XML reports...
2023-01-11T21:34:04.7762421Z Test results will be stored in test-reports/python-unittest/test_cuda_sanitizer
2023-01-11T21:34:04.7762594Z 
2023-01-11T21:34:04.7762823Z ##[endgroup]
2023-01-11T21:34:04.7763224Z FINISHED PRINTING LOG FILE of test_cuda_sanitizer (/var/lib/jenkins/workspace/test/test-reports/test_cuda_sanitizer_y8az38v2)
2023-01-11T21:34:04.7763448Z 
2023-01-11T21:34:06.6450556Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:34:06.7094158Z Ignoring disabled issues:  []
2023-01-11T21:34:06.7242671Z Running test_dataloader ... [2023-01-11 21:34:06.724021]
2023-01-11T21:34:06.7244832Z Executing ['/opt/conda/bin/python', '-bb', 'test_dataloader.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:34:06.724268]
2023-01-11T21:35:33.3649655Z 
2023-01-11T21:35:33.3650289Z Expand the folded group to see the log file of test_dataloader
2023-01-11T21:35:33.3651435Z ##[group]PRINTING LOG FILE of test_dataloader (/var/lib/jenkins/workspace/test/test-reports/test_dataloader_l3fzk_j0)
2023-01-11T21:35:33.3718938Z 
2023-01-11T21:35:33.3719295Z Running tests...
2023-01-11T21:35:33.3720040Z ----------------------------------------------------------------------
2023-01-11T21:35:33.3721628Z Test results will be stored in test-reports/python-unittest/test_dataloader
2023-01-11T21:35:33.3722292Z   test_shuffler_iterdatapipe (__main__.IntegrationTestDataLoaderDataPipe)
2023-01-11T21:35:33.3722699Z Verify ``IterDataPipe.shuffle`` is controlled by ``DataLoader`` ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:35:33.3723061Z   test_add_dataset (__main__.TestConcatDataset) ... ok (0.223s)
2023-01-11T21:35:33.3723363Z   test_concat_raises_index_error (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:35:33.3723749Z   test_concat_two_non_singletons (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:35:33.3727219Z   test_concat_two_non_singletons_with_empty (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:35:33.3727827Z   test_concat_two_singletons (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:35:33.3728401Z   test_iterable_dataset_err (__main__.TestConcatDataset) ... ok (0.001s)
2023-01-11T21:35:33.3729692Z   test_conv_after_fork (__main__.TestConvAfterFork) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/75492 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T21:35:33.3730354Z   test_custom_batch_pin (__main__.TestCustomPinFn) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:35:33.3730696Z   test_custom_batch_pin_worker (__main__.TestCustomPinFn) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:35:33.3731242Z   test_batch_sampler (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3731621Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3732052Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3732398Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3732823Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3733155Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3733587Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3733919Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3734532Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3734859Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3735288Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3735616Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3736046Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3736375Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3738914Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3739537Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3739941Z ok (2.517s)
2023-01-11T21:35:33.3740195Z   test_builtin_collection_conversion (__main__.TestDataLoader) ... ok (0.240s)
2023-01-11T21:35:33.3740650Z   test_bulk_loading_nobatch (__main__.TestDataLoader) ... ok (0.070s)
2023-01-11T21:35:33.3741272Z   test_chain_iterable_style_dataset (__main__.TestDataLoader) ... ok (0.118s)
2023-01-11T21:35:33.3741933Z   test_default_collate_bad_numpy_types (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.3742605Z   test_default_collate_bad_sequence_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3743270Z   test_default_collate_dtype (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.3743968Z   test_default_collate_mapping_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3745761Z   test_default_collate_numpy_memmap (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py:172: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:210.)
2023-01-11T21:35:33.3746864Z   return collate([torch.as_tensor(b) for b in batch], collate_fn_map=collate_fn_map)
2023-01-11T21:35:33.3747303Z ok (0.004s)
2023-01-11T21:35:33.3747636Z   test_default_collate_sequence_dont_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3747964Z   test_default_collate_sequence_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3748265Z   test_default_collate_shared_tensor (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.3748582Z   test_default_convert_mapping_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3748944Z   test_default_convert_sequence_dont_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3749420Z   test_default_convert_sequence_keep_type (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3749919Z   test_distributed_sampler_invalid_rank (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3750463Z   test_duplicating_data_with_drop_last (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.3750960Z   test_error (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3751411Z   test_error_in_init (__main__.TestDataLoader) ... ok (0.076s)
2023-01-11T21:35:33.3751881Z   test_error_workers (__main__.TestDataLoader) ... ok (0.066s)
2023-01-11T21:35:33.3752330Z   test_excessive_thread_creation_warning (__main__.TestDataLoader) ... ok (0.008s)
2023-01-11T21:35:33.3752616Z   test_fd_limit_exceeded (__main__.TestDataLoader) ... ok (1.500s)
2023-01-11T21:35:33.3753140Z   test_get_worker_info (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3753642Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3754081Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3754424Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3754655Z ok (3.297s)
2023-01-11T21:35:33.3754883Z   test_growing_dataset (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.3755160Z   test_invalid_assign_after_init (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3755465Z   test_invalid_ctor_args_combinations (__main__.TestDataLoader) ... ok (0.004s)
2023-01-11T21:35:33.3755763Z   test_iterable_style_dataset (__main__.TestDataLoader) ... ok (0.856s)
2023-01-11T21:35:33.3756098Z   test_iterabledataset_len (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.3756618Z   test_large_sampler_indices (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3757009Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3757443Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3757780Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3758009Z ok (3.104s)
2023-01-11T21:35:33.3758220Z   test_len (__main__.TestDataLoader) ... ok (0.011s)
2023-01-11T21:35:33.3758490Z   test_multi_epochs_reproducibility (__main__.TestDataLoader) ... ok (0.117s)
2023-01-11T21:35:33.3759030Z   test_multiple_dataloaders (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3759420Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3759855Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3760196Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3760631Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3760965Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3761395Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3761733Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3762167Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3762499Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3762919Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3763268Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3763720Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3764056Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3764471Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3764815Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3765045Z ok (3.335s)
2023-01-11T21:35:33.3765516Z   test_multiprocessing_contexts (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3765897Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3766330Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3766732Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3767170Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3767486Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3767915Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3768259Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3768681Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3769223Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3769734Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3770259Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3770959Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3771551Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3772339Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3772954Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3773556Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3773889Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3774320Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3774656Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3775093Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3775423Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3775852Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3776183Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3776638Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3776967Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3777394Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3777723Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3778158Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3778488Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3778902Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3779244Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3779671Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3779999Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3780417Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3780759Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3781284Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3781615Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3782028Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3782370Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3782799Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3783112Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3783689Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3784042Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3784479Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3784794Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3785222Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3785562Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3785776Z ok (6.670s)
2023-01-11T21:35:33.3786482Z   test_multiprocessing_iterdatapipe (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/graph_settings.py:88: UserWarning: `shuffle=True` was set, but the datapipe does not contain a `Shuffler`. Adding one at the end. Be aware that the default buffer size might not be sufficient for your task.
2023-01-11T21:35:33.3786974Z   warnings.warn(
2023-01-11T21:35:33.3787360Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3787699Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3788118Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3788471Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3788906Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3789224Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3789655Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3789998Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3790436Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3790754Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3791184Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3791530Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3791960Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3792272Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3792703Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3793046Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3793466Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3811700Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3815227Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3820342Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3821049Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3821534Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3822201Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3822704Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3823606Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3824073Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3824692Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3825199Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3825858Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3826339Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3826966Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3827484Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3827765Z ok (6.393s)
2023-01-11T21:35:33.3828465Z   test_no_segfault (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3828999Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3829655Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3830151Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3830471Z ok (1.663s)
2023-01-11T21:35:33.3830768Z   test_numpy (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.3831119Z   test_numpy_gen_state (__main__.TestDataLoader) ... ok (0.003s)
2023-01-11T21:35:33.3831510Z   test_numpy_scalars (__main__.TestDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.3831873Z   test_partial_workers (__main__.TestDataLoader)
2023-01-11T21:35:33.3832277Z Check that workers exit even if the iterator is not exhausted. ... ok (0.080s)
2023-01-11T21:35:33.3832691Z   test_proper_exit (__main__.TestDataLoader)
2023-01-11T21:35:33.3833326Z There might be ConnectionResetError or leaked semaphore warning (due to dirty process exit), but they are all safe to ignore ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.003s)
2023-01-11T21:35:33.3833919Z   test_random_sampler (__main__.TestDataLoader) ... ok (0.003s)
2023-01-11T21:35:33.3834326Z   test_random_sampler_len_with_replacement (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3834775Z   test_random_sampler_len_without_replacement (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3835564Z   test_sampler (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3836095Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3836733Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3837255Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3837892Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3838452Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3839096Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3839598Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3840241Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3840696Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3841329Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3841821Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3842537Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3843013Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3843692Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3844212Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3844533Z ok (2.623s)
2023-01-11T21:35:33.3844882Z   test_sampler_reproducibility (__main__.TestDataLoader) ... ok (0.016s)
2023-01-11T21:35:33.3845664Z   test_segfault (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3846212Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3846871Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3847378Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3847715Z ok (4.906s)
2023-01-11T21:35:33.3848031Z   test_seqential_batch_workers (__main__.TestDataLoader) ... ok (0.120s)
2023-01-11T21:35:33.3848477Z   test_seqential_batch_workers_prefetch (__main__.TestDataLoader) ... ok (0.103s)
2023-01-11T21:35:33.3848919Z   test_sequential_batch (__main__.TestDataLoader) ... ok (0.040s)
2023-01-11T21:35:33.3849509Z   test_sequential_nonbatch (__main__.TestDataLoader) ... ok (0.021s)
2023-01-11T21:35:33.3849975Z   test_sequential_pin_memory (__main__.TestDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:35:33.3850424Z   test_sequential_workers (__main__.TestDataLoader) ... ok (0.177s)
2023-01-11T21:35:33.3850817Z   test_shuffle (__main__.TestDataLoader) ... ok (0.062s)
2023-01-11T21:35:33.3851184Z   test_shuffle_batch (__main__.TestDataLoader) ... ok (0.054s)
2023-01-11T21:35:33.3851595Z   test_shuffle_batch_none (__main__.TestDataLoader) ... ok (0.056s)
2023-01-11T21:35:33.3852011Z   test_shuffle_batch_workers (__main__.TestDataLoader) ... ok (0.167s)
2023-01-11T21:35:33.3852445Z   test_shuffle_batch_workers_prefetch (__main__.TestDataLoader) ... ok (0.172s)
2023-01-11T21:35:33.3852896Z   test_shuffle_pin_memory (__main__.TestDataLoader) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:35:33.3853363Z   test_shuffle_reproducibility (__main__.TestDataLoader) ... ok (0.319s)
2023-01-11T21:35:33.3853776Z   test_shuffle_workers (__main__.TestDataLoader) ... ok (0.188s)
2023-01-11T21:35:33.3854528Z   test_timeout (__main__.TestDataLoader) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3855066Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3855707Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3856206Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3856530Z ok (2.637s)
2023-01-11T21:35:33.3856990Z   test_typing (__main__.TestDataLoader) ... ok (0.001s)
2023-01-11T21:35:33.3857385Z   test_worker_init_fn (__main__.TestDataLoader) ... ok (0.038s)
2023-01-11T21:35:33.3857765Z   test_worker_seed (__main__.TestDataLoader) ... ok (0.086s)
2023-01-11T21:35:33.3858201Z   test_worker_seed_reproducibility (__main__.TestDataLoader) ... ok (0.150s)
2023-01-11T21:35:33.3859034Z   test_batch_sampler (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3859599Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3860223Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3860822Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3861495Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3861960Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3862588Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3863084Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3863773Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3864241Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3864898Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3865394Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3866027Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3866514Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3867154Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3867657Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3867968Z ok (2.481s)
2023-01-11T21:35:33.3868363Z   test_builtin_collection_conversion (__main__.TestDataLoaderPersistentWorkers) ... ok (0.245s)
2023-01-11T21:35:33.3868887Z   test_bulk_loading_nobatch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.080s)
2023-01-11T21:35:33.3869394Z   test_chain_iterable_style_dataset (__main__.TestDataLoaderPersistentWorkers) ... ok (0.089s)
2023-01-11T21:35:33.3869912Z   test_dataset_not_reset (__main__.TestDataLoaderPersistentWorkers) ... ok (0.078s)
2023-01-11T21:35:33.3870449Z   test_default_collate_bad_numpy_types (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3870962Z   test_default_collate_bad_sequence_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3871471Z   test_default_collate_dtype (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3871994Z   test_default_collate_mapping_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3872511Z   test_default_collate_numpy_memmap (__main__.TestDataLoaderPersistentWorkers) ... ok (0.003s)
2023-01-11T21:35:33.3873039Z   test_default_collate_sequence_dont_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3873588Z   test_default_collate_sequence_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3874125Z   test_default_collate_shared_tensor (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3874684Z   test_default_convert_mapping_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3875175Z   test_default_convert_sequence_dont_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3875856Z   test_default_convert_sequence_keep_type (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3876407Z   test_distributed_sampler_invalid_rank (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3876962Z   test_duplicating_data_with_drop_last (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3877444Z   test_early_exit (__main__.TestDataLoaderPersistentWorkers) ... ok (1.470s)
2023-01-11T21:35:33.3877906Z   test_error (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3878380Z   test_error_in_init (__main__.TestDataLoaderPersistentWorkers) ... ok (0.078s)
2023-01-11T21:35:33.3878925Z   test_error_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.061s)
2023-01-11T21:35:33.3879439Z   test_excessive_thread_creation_warning (__main__.TestDataLoaderPersistentWorkers) ... ok (0.012s)
2023-01-11T21:35:33.3879975Z   test_fd_limit_exceeded (__main__.TestDataLoaderPersistentWorkers) ... ok (1.511s)
2023-01-11T21:35:33.3880859Z   test_get_worker_info (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3881420Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3882048Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3882532Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3882833Z ok (3.394s)
2023-01-11T21:35:33.3883189Z   test_growing_dataset (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3883709Z   test_invalid_assign_after_init (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3884234Z   test_invalid_ctor_args_combinations (__main__.TestDataLoaderPersistentWorkers) ... ok (0.004s)
2023-01-11T21:35:33.3884883Z   test_iterable_style_dataset (__main__.TestDataLoaderPersistentWorkers) ... Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f518bad4b80>
2023-01-11T21:35:33.3885416Z Traceback (most recent call last):
2023-01-11T21:35:33.3885970Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1480, in __del__
2023-01-11T21:35:33.3886344Z     self._shutdown_workers()
2023-01-11T21:35:33.3886915Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1463, in _shutdown_workers
2023-01-11T21:35:33.3887304Z     if w.is_alive():
2023-01-11T21:35:33.3887659Z   File "/opt/conda/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
2023-01-11T21:35:33.3888201Z     assert self._parent_pid == os.getpid(), 'can only test a child process'
2023-01-11T21:35:33.3888611Z AssertionError: can only test a child process
2023-01-11T21:35:33.3889191Z Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f518bad4b80>
2023-01-11T21:35:33.3889614Z Traceback (most recent call last):
2023-01-11T21:35:33.3890174Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1480, in __del__
2023-01-11T21:35:33.3890692Z Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f518bad4b80>
2023-01-11T21:35:33.3891090Z Traceback (most recent call last):
2023-01-11T21:35:33.3891619Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1480, in __del__
2023-01-11T21:35:33.3892003Z     self._shutdown_workers()
2023-01-11T21:35:33.3892554Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1463, in _shutdown_workers
2023-01-11T21:35:33.3892926Z     if w.is_alive():
2023-01-11T21:35:33.3893290Z   File "/opt/conda/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
2023-01-11T21:35:33.3893837Z     assert self._parent_pid == os.getpid(), 'can only test a child process'
2023-01-11T21:35:33.3894335Z AssertionError: can only test a child process
2023-01-11T21:35:33.3894673Z     self._shutdown_workers()
2023-01-11T21:35:33.3895250Z   File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1463, in _shutdown_workers
2023-01-11T21:35:33.3895612Z     if w.is_alive():
2023-01-11T21:35:33.3895966Z   File "/opt/conda/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
2023-01-11T21:35:33.3896518Z     assert self._parent_pid == os.getpid(), 'can only test a child process'
2023-01-11T21:35:33.3896904Z AssertionError: can only test a child process
2023-01-11T21:35:33.3897186Z ok (0.792s)
2023-01-11T21:35:33.3897569Z   test_iterabledataset_len (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3898620Z   test_large_sampler_indices (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3899243Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3899858Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3900361Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3900693Z ok (3.061s)
2023-01-11T21:35:33.3901041Z   test_len (__main__.TestDataLoaderPersistentWorkers) ... ok (0.007s)
2023-01-11T21:35:33.3901569Z   test_multi_epochs_reproducibility (__main__.TestDataLoaderPersistentWorkers) ... ok (0.053s)
2023-01-11T21:35:33.3902509Z   test_multiple_dataloaders (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3903140Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3903870Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3904393Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3905057Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3905532Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3906201Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3906746Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3907395Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3907871Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3908534Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3909047Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3909701Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3910182Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3910835Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3911319Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3911636Z ok (3.304s)
2023-01-11T21:35:33.3912429Z   test_multiprocessing_contexts (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3913096Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3913693Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3914309Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3914937Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3915405Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3916012Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3916513Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3917155Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3917708Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3918337Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3918829Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3919445Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3919906Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3920519Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3921013Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3921637Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3922078Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3922702Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3923190Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3923800Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3924242Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3924871Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3925359Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3925978Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3926442Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3927078Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3927575Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3928204Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3928700Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3929479Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3929970Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3930723Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3931197Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3931834Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3932275Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3933044Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3933518Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3934171Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3934654Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3935302Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3935788Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3936419Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3937039Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3937718Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3938201Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3938823Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3939323Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3939644Z ok (7.221s)
2023-01-11T21:35:33.3940899Z   test_multiprocessing_iterdatapipe (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/graph_settings.py:88: UserWarning: `shuffle=True` was set, but the datapipe does not contain a `Shuffler`. Adding one at the end. Be aware that the default buffer size might not be sufficient for your task.
2023-01-11T21:35:33.3941727Z   warnings.warn(
2023-01-11T21:35:33.3942248Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3942705Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3943361Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3943921Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3944585Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3945083Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3945727Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3946221Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3946858Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3947345Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3947958Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3948466Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3949088Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3949573Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3950181Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3950689Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3951337Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3951800Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3952524Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3953006Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3953648Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3954077Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3954727Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3955237Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3955950Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3956398Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3957023Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3957509Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3958125Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3958599Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3959207Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3959693Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3959989Z ok (6.685s)
2023-01-11T21:35:33.3960717Z   test_no_segfault (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3961312Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3961938Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3962424Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3962739Z ok (1.651s)
2023-01-11T21:35:33.3963097Z   test_numpy (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3963557Z   test_numpy_gen_state (__main__.TestDataLoaderPersistentWorkers) ... ok (0.003s)
2023-01-11T21:35:33.3964034Z   test_numpy_scalars (__main__.TestDataLoaderPersistentWorkers) ... ok (0.002s)
2023-01-11T21:35:33.3964505Z   test_partial_workers (__main__.TestDataLoaderPersistentWorkers)
2023-01-11T21:35:33.3964944Z Check that workers exit even if the iterator is not exhausted. ... ok (0.078s)
2023-01-11T21:35:33.3965393Z   test_proper_exit (__main__.TestDataLoaderPersistentWorkers)
2023-01-11T21:35:33.3966009Z There might be ConnectionResetError or leaked semaphore warning (due to dirty process exit), but they are all safe to ignore ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.003s)
2023-01-11T21:35:33.3966644Z   test_random_sampler (__main__.TestDataLoaderPersistentWorkers) ... ok (0.003s)
2023-01-11T21:35:33.3967155Z   test_random_sampler_len_with_replacement (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3967790Z   test_random_sampler_len_without_replacement (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3968716Z   test_sampler (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3969474Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3970117Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3970614Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3971386Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3971856Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3972472Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3972966Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3973596Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3974059Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3974795Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3975298Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3975945Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3976375Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3976986Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3977498Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3977816Z ok (2.319s)
2023-01-11T21:35:33.3978226Z   test_sampler_reproducibility (__main__.TestDataLoaderPersistentWorkers) ... ok (0.026s)
2023-01-11T21:35:33.3979116Z   test_segfault (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3979749Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3980389Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3980903Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3981225Z ok (3.229s)
2023-01-11T21:35:33.3981602Z   test_seqential_batch_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.112s)
2023-01-11T21:35:33.3982147Z   test_seqential_batch_workers_prefetch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.099s)
2023-01-11T21:35:33.3982655Z   test_sequential_batch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.040s)
2023-01-11T21:35:33.3983161Z   test_sequential_nonbatch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.021s)
2023-01-11T21:35:33.3983785Z   test_sequential_pin_memory (__main__.TestDataLoaderPersistentWorkers) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:35:33.3984336Z   test_sequential_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.198s)
2023-01-11T21:35:33.3984816Z   test_shuffle (__main__.TestDataLoaderPersistentWorkers) ... ok (0.062s)
2023-01-11T21:35:33.3985286Z   test_shuffle_batch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.054s)
2023-01-11T21:35:33.3985793Z   test_shuffle_batch_none (__main__.TestDataLoaderPersistentWorkers) ... ok (0.055s)
2023-01-11T21:35:33.3986361Z   test_shuffle_batch_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.147s)
2023-01-11T21:35:33.3986910Z   test_shuffle_batch_workers_prefetch (__main__.TestDataLoaderPersistentWorkers) ... ok (0.150s)
2023-01-11T21:35:33.3987477Z   test_shuffle_pin_memory (__main__.TestDataLoaderPersistentWorkers) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:35:33.3988025Z   test_shuffle_reproducibility (__main__.TestDataLoaderPersistentWorkers) ... ok (0.280s)
2023-01-11T21:35:33.3988522Z   test_shuffle_workers (__main__.TestDataLoaderPersistentWorkers) ... ok (0.225s)
2023-01-11T21:35:33.3989399Z   test_timeout (__main__.TestDataLoaderPersistentWorkers) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T21:35:33.3990067Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T21:35:33.3990707Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T21:35:33.3991186Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T21:35:33.3991483Z ok (2.662s)
2023-01-11T21:35:33.3991820Z   test_typing (__main__.TestDataLoaderPersistentWorkers) ... ok (0.001s)
2023-01-11T21:35:33.3992284Z   test_worker_init_fn (__main__.TestDataLoaderPersistentWorkers) ... ok (0.045s)
2023-01-11T21:35:33.3992751Z   test_worker_seed (__main__.TestDataLoaderPersistentWorkers) ... ok (0.101s)
2023-01-11T21:35:33.3993246Z   test_worker_seed_reproducibility (__main__.TestDataLoaderPersistentWorkers) ... ok (0.193s)
2023-01-11T21:35:33.3993845Z   test_incomplete_fractional_splits (__main__.TestDatasetRandomSplit) ... ok (0.001s)
2023-01-11T21:35:33.3994331Z   test_lengths_must_equal_dataset_size (__main__.TestDatasetRandomSplit) ... ok (0.001s)
2023-01-11T21:35:33.3994789Z   test_slicing_of_subset_of_dataset (__main__.TestDatasetRandomSplit) ... ok (0.002s)
2023-01-11T21:35:33.3995253Z   test_slicing_of_subset_of_subset (__main__.TestDatasetRandomSplit) ... ok (0.002s)
2023-01-11T21:35:33.3995702Z   test_splits_are_mutually_exclusive (__main__.TestDatasetRandomSplit) ... ok (0.001s)
2023-01-11T21:35:33.3996151Z   test_splits_generator (__main__.TestDatasetRandomSplit) ... ok (0.002s)
2023-01-11T21:35:33.3996576Z   test_splits_have_correct_size (__main__.TestDatasetRandomSplit) ... ok (0.001s)
2023-01-11T21:35:33.3996994Z   test_splits_indexing_type (__main__.TestDatasetRandomSplit)
2023-01-11T21:35:33.3997379Z Indices generated by random_split ... ok (0.002s)
2023-01-11T21:35:33.3997792Z   test_splits_reproducibility (__main__.TestDatasetRandomSplit) ... ok (0.007s)
2023-01-11T21:35:33.3998262Z   test_pin_memory (__main__.TestDictDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:35:33.3998737Z   test_pin_memory_device (__main__.TestDictDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:35:33.3999244Z   test_pin_memory_with_only_device (__main__.TestDictDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:35:33.3999691Z   test_sequential_batch (__main__.TestDictDataLoader) ... ok (0.031s)
2023-01-11T21:35:33.4001050Z   test_ind_worker_queue (__main__.TestIndividualWorkerQueue) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/68643 for platform(s) macos, linux, win. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:35:33.4002095Z   test_dataloader_with_namedtuple (__main__.TestNamedTupleDataLoader) ... ok (0.002s)
2023-01-11T21:35:33.4002642Z   test_set_affinity_in_worker_init (__main__.TestSetAffinity) ... ok (0.030s)
2023-01-11T21:35:33.4003107Z   test_shuffle_pin_memory (__main__.TestStringDataLoader) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:35:33.4003511Z   test_getitem (__main__.TestTensorDataset) ... ok (0.006s)
2023-01-11T21:35:33.4003888Z   test_getitem_1d (__main__.TestTensorDataset) ... ok (0.005s)
2023-01-11T21:35:33.4004253Z   test_len (__main__.TestTensorDataset) ... ok (0.001s)
2023-01-11T21:35:33.4004641Z   test_many_tensors (__main__.TestTensorDataset) ... ok (0.005s)
2023-01-11T21:35:33.4005078Z   test_single_tensor (__main__.TestTensorDataset) ... ok (0.002s)
2023-01-11T21:35:33.4005332Z 
2023-01-11T21:35:33.4005687Z ----------------------------------------------------------------------
2023-01-11T21:35:33.4006069Z Ran 164 tests in 84.863s
2023-01-11T21:35:33.4006263Z 
2023-01-11T21:35:33.4006381Z OK (skipped=15)
2023-01-11T21:35:33.4006539Z 
2023-01-11T21:35:33.4006666Z Generating XML reports...
2023-01-11T21:35:33.4007274Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestConcatDataset-20230111213408.xml
2023-01-11T21:35:33.4008138Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestDataLoader-20230111213408.xml
2023-01-11T21:35:33.4008963Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestDataLoaderPersistentWorkers-20230111213408.xml
2023-01-11T21:35:33.4010180Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestDatasetRandomSplit-20230111213408.xml
2023-01-11T21:35:33.4010943Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestDictDataLoader-20230111213408.xml
2023-01-11T21:35:33.4011732Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestNamedTupleDataLoader-20230111213408.xml
2023-01-11T21:35:33.4012502Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestSetAffinity-20230111213408.xml
2023-01-11T21:35:33.4013379Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestTensorDataset-20230111213408.xml
2023-01-11T21:35:33.4014214Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-IntegrationTestDataLoaderDataPipe-20230111213408.xml
2023-01-11T21:35:33.4015047Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestConvAfterFork-20230111213408.xml
2023-01-11T21:35:33.4015755Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestCustomPinFn-20230111213408.xml
2023-01-11T21:35:33.4016529Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestIndividualWorkerQueue-20230111213408.xml
2023-01-11T21:35:33.4017347Z Generated XML report: test-reports/python-unittest/test_dataloader/TEST-TestStringDataLoader-20230111213408.xml
2023-01-11T21:35:33.4017699Z 
2023-01-11T21:35:33.4018160Z ##[endgroup]
2023-01-11T21:35:33.4018736Z FINISHED PRINTING LOG FILE of test_dataloader (/var/lib/jenkins/workspace/test/test-reports/test_dataloader_l3fzk_j0)
2023-01-11T21:35:33.4019030Z 
2023-01-11T21:35:35.2322935Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:35:35.2974693Z Ignoring disabled issues:  []
2023-01-11T21:35:35.3125171Z Running test_datapipe ... [2023-01-11 21:35:35.312268]
2023-01-11T21:35:35.3127829Z Executing ['/opt/conda/bin/python', '-bb', 'test_datapipe.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:35:35.312517]
2023-01-11T21:35:37.6322290Z 
2023-01-11T21:35:37.6322818Z Expand the folded group to see the log file of test_datapipe
2023-01-11T21:35:37.6323692Z ##[group]PRINTING LOG FILE of test_datapipe (/var/lib/jenkins/workspace/test/test-reports/test_datapipe_ltfsfxze)
2023-01-11T21:35:37.6324046Z 
2023-01-11T21:35:37.6324149Z Running tests...
2023-01-11T21:35:37.6324836Z ----------------------------------------------------------------------
2023-01-11T21:35:37.6325484Z   test_basic_capture (__main__.TestCaptureDataFrame) ... Test results will be stored in test-reports/python-unittest/test_datapipe
2023-01-11T21:35:37.6325865Z skip: no dataframes (pandas) (0.000s)
2023-01-11T21:35:37.6326168Z   test_circular_serialization_with_dill (__main__.TestCircularSerialization) ... skip: no dill (0.001s)
2023-01-11T21:35:37.6327070Z   test_circular_serialization_with_pickle (__main__.TestCircularSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py:297: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T21:35:37.6327722Z   warnings.warn("Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
2023-01-11T21:35:37.6327987Z ok (0.227s)
2023-01-11T21:35:37.6328235Z   test_as_string (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:35:37.6328490Z   test_getitem (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:35:37.6328746Z   test_iter (__main__.TestDataChunk) ... ok (0.000s)
2023-01-11T21:35:37.6329019Z   test_len (__main__.TestDataChunk) ... ok (0.000s)
2023-01-11T21:35:37.6329455Z   test_random_shuffle (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:35:37.6329900Z   test_reverse (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:35:37.6330195Z   test_sort (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T21:35:37.6330475Z   test_batch (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:35:37.6330861Z   test_capture (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:35:37.6331198Z   test_collate (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.001s)
2023-01-11T21:35:37.6331566Z   test_filter (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:35:37.6331897Z   test_shuffle (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:35:37.6332324Z   test_unbatch (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T21:35:37.6332664Z   test_batch_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.003s)
2023-01-11T21:35:37.6333453Z   test_collate_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6333962Z   warnings.warn(
2023-01-11T21:35:37.6334137Z ok (0.004s)
2023-01-11T21:35:37.6334378Z   test_concat_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.003s)
2023-01-11T21:35:37.6335218Z   test_demux_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6335690Z   warnings.warn(
2023-01-11T21:35:37.6336263Z /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6336639Z   warnings.warn(
2023-01-11T21:35:37.6337194Z /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6337607Z   warnings.warn(
2023-01-11T21:35:37.6338125Z /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6338545Z   warnings.warn(
2023-01-11T21:35:37.6338702Z ok (0.008s)
2023-01-11T21:35:37.6338943Z   test_filter_datapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.005s)
2023-01-11T21:35:37.6352603Z   test_fork_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.010s)
2023-01-11T21:35:37.6353116Z   test_iterable_wrapper_datapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.002s)
2023-01-11T21:35:37.6353665Z   test_map_dict_with_col_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.016s)
2023-01-11T21:35:37.6354769Z   test_map_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6355251Z   warnings.warn(
2023-01-11T21:35:37.6355425Z ok (0.005s)
2023-01-11T21:35:37.6355685Z   test_map_tuple_list_with_col_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.031s)
2023-01-11T21:35:37.6356034Z   test_mux_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.002s)
2023-01-11T21:35:37.6356369Z   test_sampler_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.001s)
2023-01-11T21:35:37.6357154Z   test_serializable (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py:297: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T21:35:37.6357841Z   warnings.warn("Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
2023-01-11T21:35:37.6358498Z /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py:297: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T21:35:37.6359002Z   warnings.warn("Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
2023-01-11T21:35:37.6359255Z ok (0.038s)
2023-01-11T21:35:37.6359485Z   test_serializable_with_dill (__main__.TestFunctionalIterDataPipe)
2023-01-11T21:35:37.6359870Z Only for DataPipes that take in a function as argument ... ok (0.020s)
2023-01-11T21:35:37.6360191Z   test_shuffler_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.005s)
2023-01-11T21:35:37.6360539Z   test_stream_reader_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.001s)
2023-01-11T21:35:37.6360873Z   test_unbatch_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.004s)
2023-01-11T21:35:37.6361203Z   test_zip_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.002s)
2023-01-11T21:35:37.6361536Z   test_batch_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.002s)
2023-01-11T21:35:37.6361851Z   test_concat_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.002s)
2023-01-11T21:35:37.6362570Z   test_map_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6363012Z   warnings.warn(
2023-01-11T21:35:37.6363183Z ok (0.003s)
2023-01-11T21:35:37.6363431Z   test_sequence_wrapper_datapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.002s)
2023-01-11T21:35:37.6363758Z   test_serializable (__main__.TestFunctionalMapDataPipe) ... ok (0.008s)
2023-01-11T21:35:37.6364068Z   test_serializable_with_dill (__main__.TestFunctionalMapDataPipe)
2023-01-11T21:35:37.6364353Z Only for DataPipes that take in a function as argument ... ok (0.005s)
2023-01-11T21:35:37.6364665Z   test_shuffler_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.004s)
2023-01-11T21:35:37.6364991Z   test_zip_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.002s)
2023-01-11T21:35:37.6365661Z   test_simple_traverse (__main__.TestGraph) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6366058Z   warnings.warn(
2023-01-11T21:35:37.6366228Z ok (0.002s)
2023-01-11T21:35:37.6366456Z   test_traverse_circular_datapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T21:35:37.6366715Z   test_traverse_forked (__main__.TestGraph) ... ok (0.002s)
2023-01-11T21:35:37.6366982Z   test_traverse_mapdatapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T21:35:37.6367258Z   test_traverse_mixdatapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T21:35:37.6367540Z   test_traverse_unhashable_datapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T21:35:37.6367897Z   test_iterdatapipe_sample_yielded_generator_function (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T21:35:37.6368345Z   test_iterdatapipe_sample_yielded_generator_function_exception (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.002s)
2023-01-11T21:35:37.6368766Z   test_iterdatapipe_sample_yielded_next (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T21:35:37.6369333Z   test_iterdatapipe_sample_yielded_next_exception (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T21:35:37.6369823Z   test_iterdatapipe_sample_yielded_return_self (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T21:35:37.6370229Z   test_simple_snapshot_custom_non_generator (__main__.TestIterDataPipeGraphFastForward) ... ok (0.001s)
2023-01-11T21:35:37.6370617Z   test_simple_snapshot_custom_self_next (__main__.TestIterDataPipeGraphFastForward) ... ok (0.001s)
2023-01-11T21:35:37.6370977Z   test_simple_snapshot_graph (__main__.TestIterDataPipeGraphFastForward) ... ok (0.012s)
2023-01-11T21:35:37.6371828Z   test_simple_snapshot_graph_repeated (__main__.TestIterDataPipeGraphFastForward) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py:297: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T21:35:37.6372473Z   warnings.warn("Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
2023-01-11T21:35:37.6372728Z ok (0.005s)
2023-01-11T21:35:37.6373007Z   test_simple_snapshot_graph_with_serialization (__main__.TestIterDataPipeGraphFastForward) ... ok (0.010s)
2023-01-11T21:35:37.6373398Z   test_iterdatapipe_singleton_buggy (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:35:37.6373847Z Buggy test case case where IterDataPipe's `__iter__` returns a new object, but also has ... ok (0.002s)
2023-01-11T21:35:37.6374235Z   test_iterdatapipe_singleton_constraint_multiple_outputs (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:35:37.6374609Z Testing for the case where IterDataPipe has multiple child DataPipes as outputs. ... ok (0.006s)
2023-01-11T21:35:37.6374971Z   test_iterdatapipe_singleton_generator (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:35:37.6375401Z Testing for the case where IterDataPipe's `__iter__` is a generator function. ... ok (0.003s)
2023-01-11T21:35:37.6375762Z   test_iterdatapipe_singleton_new_object (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:35:37.6376198Z Testing for the case where IterDataPipe's `__iter__` isn't a generator nor returns `self`, ... ok (0.002s)
2023-01-11T21:35:37.6376559Z   test_iterdatapipe_singleton_self_next (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T21:35:37.6377013Z Testing for the case where IterDataPipe's `__iter__` returns `self` and there is a `__next__` method ... ok (0.003s)
2023-01-11T21:35:37.6377749Z   test_demux_mux_datapipe (__main__.TestIterableDataPipeBasic) ... /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py:137: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T21:35:37.6378182Z   warnings.warn(
2023-01-11T21:35:37.6378356Z ok (0.005s)
2023-01-11T21:35:37.6378614Z   test_groupby_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.008s)
2023-01-11T21:35:37.6378951Z   test_listdirfiles_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.003s)
2023-01-11T21:35:37.6379334Z   test_listdirfilesdeterministic_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.004s)
2023-01-11T21:35:37.6379711Z   test_map_with_col_file_handle_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.003s)
2023-01-11T21:35:37.6380074Z   test_openfilesfromdisk_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.004s)
2023-01-11T21:35:37.6380426Z   test_routeddecoder_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.004s)
2023-01-11T21:35:37.6380759Z   test_spawn_lambdas_iter (__main__.TestSerialization) ... skip: no dill (0.000s)
2023-01-11T21:35:37.6381070Z   test_spawn_lambdas_map (__main__.TestSerialization) ... skip: no dill (0.000s)
2023-01-11T21:35:37.6381343Z   test_multi_sharding (__main__.TestSharding) ... ok (0.003s)
2023-01-11T21:35:37.6381605Z   test_old_dataloader (__main__.TestSharding) ... ok (0.051s)
2023-01-11T21:35:37.6381872Z   test_sharding_groups (__main__.TestSharding) ... ok (0.002s)
2023-01-11T21:35:37.6382136Z   test_sharding_length (__main__.TestSharding) ... ok (0.002s)
2023-01-11T21:35:37.6382423Z   test_simple_sharding (__main__.TestSharding) ... ok (0.003s)
2023-01-11T21:35:37.6382682Z   test_api (__main__.TestStreamWrapper) ... ok (0.001s)
2023-01-11T21:35:37.6382938Z   test_dir (__main__.TestStreamWrapper) ... ok (0.001s)
2023-01-11T21:35:37.6383184Z   test_pickle (__main__.TestStreamWrapper) ... ok (0.002s)
2023-01-11T21:35:37.6383510Z   test_repr (__main__.TestStreamWrapper) ... ok (0.002s)
2023-01-11T21:35:37.6383802Z   test_compile_time (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.002s)
2023-01-11T21:35:37.6384099Z   test_construct_time (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:35:37.6384377Z   test_isinstance (__main__.TestTyping) ... ok (0.001s)
2023-01-11T21:35:37.6384695Z   test_issubinstance (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:35:37.6384977Z   test_protocol (__main__.TestTyping) ... ok (0.001s)
2023-01-11T21:35:37.6385235Z   test_reinforce (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:35:37.6385531Z   test_runtime (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:35:37.6385823Z   test_subtype (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T21:35:37.6385986Z 
2023-01-11T21:35:37.6386180Z ----------------------------------------------------------------------
2023-01-11T21:35:37.6386424Z Ran 89 tests in 0.590s
2023-01-11T21:35:37.6386537Z 
2023-01-11T21:35:37.6386611Z OK (skipped=16)
2023-01-11T21:35:37.6386719Z 
2023-01-11T21:35:37.6386803Z Generating XML reports...
2023-01-11T21:35:37.6387230Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestCircularSerialization-20230111213536.xml
2023-01-11T21:35:37.6387754Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestDataChunk-20230111213536.xml
2023-01-11T21:35:37.6388287Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestFunctionalIterDataPipe-20230111213536.xml
2023-01-11T21:35:37.6388838Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestFunctionalMapDataPipe-20230111213536.xml
2023-01-11T21:35:37.6389347Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestGraph-20230111213536.xml
2023-01-11T21:35:37.6389895Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeCountSampleYielded-20230111213536.xml
2023-01-11T21:35:37.6390506Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeGraphFastForward-20230111213536.xml
2023-01-11T21:35:37.6391106Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeSingletonConstraint-20230111213536.xml
2023-01-11T21:35:37.6391687Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterableDataPipeBasic-20230111213536.xml
2023-01-11T21:35:37.6392200Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestSharding-20230111213536.xml
2023-01-11T21:35:37.6392694Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestStreamWrapper-20230111213536.xml
2023-01-11T21:35:37.6393164Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestTyping-20230111213536.xml
2023-01-11T21:35:37.6393665Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestCaptureDataFrame-20230111213536.xml
2023-01-11T21:35:37.6394186Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestDataFramesPipes-20230111213536.xml
2023-01-11T21:35:37.6394693Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestSerialization-20230111213536.xml
2023-01-11T21:35:37.6394905Z 
2023-01-11T21:35:37.6395200Z ##[endgroup]
2023-01-11T21:35:37.6395584Z FINISHED PRINTING LOG FILE of test_datapipe (/var/lib/jenkins/workspace/test/test-reports/test_datapipe_ltfsfxze)
2023-01-11T21:35:37.6395801Z 
2023-01-11T21:35:39.4729280Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:35:39.5372365Z Ignoring disabled issues:  []
2023-01-11T21:35:39.5522683Z Running test_decomp ... [2023-01-11 21:35:39.552014]
2023-01-11T21:35:39.5525023Z Executing ['/opt/conda/bin/python', '-bb', 'test_decomp.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:35:39.552276]
2023-01-11T21:35:42.0262122Z 
2023-01-11T21:35:42.0262649Z Expand the folded group to see the log file of test_decomp
2023-01-11T21:35:42.0263755Z ##[group]PRINTING LOG FILE of test_decomp (/var/lib/jenkins/workspace/test/test-reports/test_decomp_2v8n538b)
2023-01-11T21:35:42.0264129Z 
2023-01-11T21:35:42.0264245Z Running tests...
2023-01-11T21:35:42.0264860Z ----------------------------------------------------------------------
2023-01-11T21:35:42.0265161Z 
2023-01-11T21:35:42.0265512Z ----------------------------------------------------------------------
2023-01-11T21:35:42.0266174Z Ran 0 tests in 0.000s
2023-01-11T21:35:42.0266373Z 
2023-01-11T21:35:42.0266468Z OK
2023-01-11T21:35:42.0266618Z 
2023-01-11T21:35:42.0266769Z Generating XML reports...
2023-01-11T21:35:42.0267318Z Test results will be stored in test-reports/python-unittest/test_decomp
2023-01-11T21:35:42.0267622Z 
2023-01-11T21:35:42.0268037Z ##[endgroup]
2023-01-11T21:35:42.0268726Z FINISHED PRINTING LOG FILE of test_decomp (/var/lib/jenkins/workspace/test/test-reports/test_decomp_2v8n538b)
2023-01-11T21:35:42.0269148Z 
2023-01-11T21:35:43.8555216Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:35:43.9196730Z Ignoring disabled issues:  []
2023-01-11T21:35:43.9345876Z Running test_deploy ... [2023-01-11 21:35:43.934318]
2023-01-11T21:35:43.9348305Z Executing ['/opt/conda/bin/python', '-bb', '-m', 'pytest', 'test_deploy.py', '-v'] ... [2023-01-11 21:35:43.934566]
2023-01-11T21:35:46.5866121Z 
2023-01-11T21:35:46.5866665Z Expand the folded group to see the log file of test_deploy
2023-01-11T21:35:46.5867847Z ##[group]PRINTING LOG FILE of test_deploy (/var/lib/jenkins/workspace/test/test-reports/test_deploy_99a6qy20)
2023-01-11T21:35:46.5868465Z ============================= test session starts ==============================
2023-01-11T21:35:46.5869066Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:35:46.5869321Z cachedir: .pytest_cache
2023-01-11T21:35:46.5869750Z hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/var/lib/jenkins/workspace/test/.hypothesis/examples')
2023-01-11T21:35:46.5870109Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:35:46.5870529Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:35:46.5870824Z collecting ... collected 1 item
2023-01-11T21:35:46.5871088Z Running 1 items in this shard: test/test_deploy.py::TestFreezer::test_compile_string
2023-01-11T21:35:46.5871270Z 
2023-01-11T21:35:46.5871413Z test_deploy.py::TestFreezer::test_compile_string PASSED                  [100%]
2023-01-11T21:35:46.5871578Z 
2023-01-11T21:35:46.5871691Z ============================== 1 passed in 1.44s ===============================
2023-01-11T21:35:46.5871825Z 
2023-01-11T21:35:46.5872039Z ##[endgroup]
2023-01-11T21:35:46.5872391Z FINISHED PRINTING LOG FILE of test_deploy (/var/lib/jenkins/workspace/test/test-reports/test_deploy_99a6qy20)
2023-01-11T21:35:46.5872596Z 
2023-01-11T21:35:48.4327874Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:35:48.4982716Z Ignoring disabled issues:  []
2023-01-11T21:35:48.5136968Z Running test_dlpack ... [2023-01-11 21:35:48.513376]
2023-01-11T21:35:48.5138503Z Executing ['/opt/conda/bin/python', '-bb', 'test_dlpack.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:35:48.513642]
2023-01-11T21:35:50.3626928Z 
2023-01-11T21:35:50.3627448Z Expand the folded group to see the log file of test_dlpack
2023-01-11T21:35:50.3628508Z ##[group]PRINTING LOG FILE of test_dlpack (/var/lib/jenkins/workspace/test/test-reports/test_dlpack_hw3_2afr)
2023-01-11T21:35:50.3628913Z 
2023-01-11T21:35:50.3629292Z Running tests...
2023-01-11T21:35:50.3629943Z ----------------------------------------------------------------------
2023-01-11T21:35:50.3630233Z 
2023-01-11T21:35:50.3630559Z ----------------------------------------------------------------------
2023-01-11T21:35:50.3630885Z Ran 0 tests in 0.000s
2023-01-11T21:35:50.3631113Z 
2023-01-11T21:35:50.3631207Z OK
2023-01-11T21:35:50.3631375Z 
2023-01-11T21:35:50.3631528Z Generating XML reports...
2023-01-11T21:35:50.3631921Z Test results will be stored in test-reports/python-unittest/test_dlpack
2023-01-11T21:35:50.3632097Z 
2023-01-11T21:35:50.3632313Z ##[endgroup]
2023-01-11T21:35:50.3632687Z FINISHED PRINTING LOG FILE of test_dlpack (/var/lib/jenkins/workspace/test/test-reports/test_dlpack_hw3_2afr)
2023-01-11T21:35:50.3632892Z 
2023-01-11T21:35:52.2473629Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:35:52.3133810Z Ignoring disabled issues:  []
2023-01-11T21:35:52.3283263Z Running test_dynamic_shapes ... [2023-01-11 21:35:52.328071]
2023-01-11T21:35:52.3285245Z Executing ['/opt/conda/bin/python', '-bb', 'test_dynamic_shapes.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:35:52.328287]
2023-01-11T21:35:55.0780325Z 
2023-01-11T21:35:55.0780827Z Expand the folded group to see the log file of test_dynamic_shapes
2023-01-11T21:35:55.0783653Z ##[group]PRINTING LOG FILE of test_dynamic_shapes (/var/lib/jenkins/workspace/test/test-reports/test_dynamic_shapes_7__iorh2)
2023-01-11T21:35:55.0784163Z 
2023-01-11T21:35:55.0784291Z Running tests...
2023-01-11T21:35:55.0784968Z ----------------------------------------------------------------------
2023-01-11T21:35:55.0785673Z Test results will be stored in test-reports/python-unittest/test_dynamic_shapes
2023-01-11T21:35:55.0786103Z   test_arith_ops (__main__.TestPySymInt) ... ok (0.518s)
2023-01-11T21:35:55.0786463Z   test_aten_ops (__main__.TestPySymInt) ... ok (0.022s)
2023-01-11T21:35:55.0786811Z   test_binary (__main__.TestPySymInt) ... ok (0.025s)
2023-01-11T21:35:55.0787195Z   test_fx_trace_intlist (__main__.TestPySymInt) ... ok (0.010s)
2023-01-11T21:35:55.0787501Z   test_guard_int (__main__.TestPySymInt) ... ok (0.003s)
2023-01-11T21:35:55.0787761Z   test_int_conversion (__main__.TestPySymInt) ... ok (0.001s)
2023-01-11T21:35:55.0788018Z   test_int_to_float (__main__.TestPySymInt) ... ok (0.002s)
2023-01-11T21:35:55.0788257Z   test_meta_symint (__main__.TestPySymInt) ... ok (0.003s)
2023-01-11T21:35:55.0788501Z   test_numel (__main__.TestPySymInt) ... ok (0.003s)
2023-01-11T21:35:55.0788769Z   test_print_readable_with_symints (__main__.TestPySymInt) ... ok (0.087s)
2023-01-11T21:35:55.0789038Z   test_reverse_arith_ops (__main__.TestPySymInt) ... ok (0.063s)
2023-01-11T21:35:55.0789296Z   test_roundtrip (__main__.TestPySymInt) ... ok (0.027s)
2023-01-11T21:35:55.0789558Z   test_size_expressions (__main__.TestPySymInt) ... ok (0.006s)
2023-01-11T21:35:55.0789802Z   test_stride (__main__.TestPySymInt) ... ok (0.003s)
2023-01-11T21:35:55.0790053Z   test_sym_floor (__main__.TestPySymInt) ... ok (0.068s)
2023-01-11T21:35:55.0790295Z   test_sym_int (__main__.TestPySymInt) ... ok (0.020s)
2023-01-11T21:35:55.0790538Z   test_sym_sqrt (__main__.TestPySymInt) ... ok (0.040s)
2023-01-11T21:35:55.0790774Z   test_symint_args (__main__.TestPySymInt) ... ok (0.032s)
2023-01-11T21:35:55.0791029Z   test_symint_as_scalar (__main__.TestPySymInt) ... ok (0.001s)
2023-01-11T21:35:55.0791284Z   test_symint_vargs (__main__.TestPySymInt) ... ok (0.023s)
2023-01-11T21:35:55.0791627Z   test_method_fn_add_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: add is not a float magic method (0.001s)
2023-01-11T21:35:55.0792068Z   test_method_fn_add_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: add is not a float magic method (0.001s)
2023-01-11T21:35:55.0792475Z   test_method_fn_add_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.002s)
2023-01-11T21:35:55.0792845Z   test_method_fn_add_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0793423Z   test_method_fn_ceil_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: ceil is not a float magic method (0.001s)
2023-01-11T21:35:55.0793864Z   test_method_fn_ceil_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: ceil is not a float magic method (0.001s)
2023-01-11T21:35:55.0794295Z   test_method_fn_ceil_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: ceil is unary and already tested (0.001s)
2023-01-11T21:35:55.0794695Z   test_method_fn_ceil_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0795138Z   test_method_fn_eq_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: eq is not a float magic method (0.001s)
2023-01-11T21:35:55.0795573Z   test_method_fn_eq_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: eq is not a float magic method (0.001s)
2023-01-11T21:35:55.0795966Z   test_method_fn_eq_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.002s)
2023-01-11T21:35:55.0796328Z   test_method_fn_eq_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0796722Z   test_method_fn_floor_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: floor is not a float magic method (0.001s)
2023-01-11T21:35:55.0797161Z   test_method_fn_floor_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: floor is not a float magic method (0.001s)
2023-01-11T21:35:55.0797600Z   test_method_fn_floor_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: floor is unary and already tested (0.001s)
2023-01-11T21:35:55.0797999Z   test_method_fn_floor_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0798406Z   test_method_fn_floordiv_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: floordiv is not a float magic method (0.001s)
2023-01-11T21:35:55.0798859Z   test_method_fn_floordiv_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: floordiv is not a float magic method (0.001s)
2023-01-11T21:35:55.0799274Z   test_method_fn_floordiv_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.003s)
2023-01-11T21:35:55.0799651Z   test_method_fn_floordiv_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0800043Z   test_method_fn_ge_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: ge is not a float magic method (0.001s)
2023-01-11T21:35:55.0800473Z   test_method_fn_ge_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: ge is not a float magic method (0.001s)
2023-01-11T21:35:55.0800864Z   test_method_fn_ge_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0801228Z   test_method_fn_ge_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0801615Z   test_method_fn_gt_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: gt is not a float magic method (0.001s)
2023-01-11T21:35:55.0802044Z   test_method_fn_gt_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: gt is not a float magic method (0.001s)
2023-01-11T21:35:55.0802434Z   test_method_fn_gt_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0802796Z   test_method_fn_gt_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0803178Z   test_method_fn_le_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: le is not a float magic method (0.001s)
2023-01-11T21:35:55.0803603Z   test_method_fn_le_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: le is not a float magic method (0.001s)
2023-01-11T21:35:55.0804027Z   test_method_fn_le_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0804386Z   test_method_fn_le_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0804767Z   test_method_fn_lt_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: lt is not a float magic method (0.001s)
2023-01-11T21:35:55.0805191Z   test_method_fn_lt_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: lt is not a float magic method (0.001s)
2023-01-11T21:35:55.0805583Z   test_method_fn_lt_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0805975Z   test_method_fn_lt_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0806368Z   test_method_fn_max_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: max is not a float magic method (0.001s)
2023-01-11T21:35:55.0806799Z   test_method_fn_max_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: max is not a float magic method (0.001s)
2023-01-11T21:35:55.0807194Z   test_method_fn_max_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0807558Z   test_method_fn_max_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0807945Z   test_method_fn_min_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: min is not a float magic method (0.001s)
2023-01-11T21:35:55.0808369Z   test_method_fn_min_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: min is not a float magic method (0.001s)
2023-01-11T21:35:55.0808763Z   test_method_fn_min_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0809352Z   test_method_fn_min_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0809748Z   test_method_fn_mod_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: mod is not a float magic method (0.001s)
2023-01-11T21:35:55.0810177Z   test_method_fn_mod_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: mod is not a float magic method (0.001s)
2023-01-11T21:35:55.0810573Z   test_method_fn_mod_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0810923Z   test_method_fn_mod_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0811321Z   test_method_fn_mul_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: mul is not a float magic method (0.001s)
2023-01-11T21:35:55.0811747Z   test_method_fn_mul_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: mul is not a float magic method (0.001s)
2023-01-11T21:35:55.0812139Z   test_method_fn_mul_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0812489Z   test_method_fn_mul_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0812885Z   test_method_fn_neg_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: neg is not a float magic method (0.001s)
2023-01-11T21:35:55.0813315Z   test_method_fn_neg_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: neg is not a float magic method (0.001s)
2023-01-11T21:35:55.0813744Z   test_method_fn_neg_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: neg is unary and already tested (0.001s)
2023-01-11T21:35:55.0814127Z   test_method_fn_neg_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0814595Z   test_method_fn_pow_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: pow is not a float magic method (0.001s)
2023-01-11T21:35:55.0815022Z   test_method_fn_pow_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: pow is not a float magic method (0.001s)
2023-01-11T21:35:55.0815417Z   test_method_fn_pow_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0815767Z   test_method_fn_pow_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0816165Z   test_method_fn_sub_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sub is not a float magic method (0.001s)
2023-01-11T21:35:55.0816632Z   test_method_fn_sub_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: sub is not a float magic method (0.001s)
2023-01-11T21:35:55.0817025Z   test_method_fn_sub_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0817374Z   test_method_fn_sub_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0817790Z   test_method_fn_sym_float_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sym_float is not a float magic method (0.001s)
2023-01-11T21:35:55.0818242Z   test_method_fn_sym_float_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: sym_float is not a float magic method (0.001s)
2023-01-11T21:35:55.0818690Z   test_method_fn_sym_float_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sym_float is unary and already tested (0.001s)
2023-01-11T21:35:55.0819087Z   test_method_fn_sym_float_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0819501Z   test_method_fn_sym_sqrt_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sym_sqrt is not a float magic method (0.001s)
2023-01-11T21:35:55.0819954Z   test_method_fn_sym_sqrt_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: sym_sqrt is not a float magic method (0.001s)
2023-01-11T21:35:55.0820398Z   test_method_fn_sym_sqrt_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: sym_sqrt is unary and already tested (0.001s)
2023-01-11T21:35:55.0820805Z   test_method_fn_sym_sqrt_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0821206Z   test_method_fn_truediv_first_type_float_second_type_float (__main__.TestSymNumberMagicMethods) ... skip: truediv is not a float magic method (0.001s)
2023-01-11T21:35:55.0821659Z   test_method_fn_truediv_first_type_float_second_type_int (__main__.TestSymNumberMagicMethods) ... skip: truediv is not a float magic method (0.001s)
2023-01-11T21:35:55.0822070Z   test_method_fn_truediv_first_type_int_second_type_float (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0822446Z   test_method_fn_truediv_first_type_int_second_type_int (__main__.TestSymNumberMagicMethods) ... ok (0.001s)
2023-01-11T21:35:55.0822636Z 
2023-01-11T21:35:55.0822886Z ----------------------------------------------------------------------
2023-01-11T21:35:55.0823129Z Ran 96 tests in 1.035s
2023-01-11T21:35:55.0823241Z 
2023-01-11T21:35:55.0823312Z OK (skipped=43)
2023-01-11T21:35:55.0823489Z 
2023-01-11T21:35:55.0823561Z Generating XML reports...
2023-01-11T21:35:55.0823980Z Generated XML report: test-reports/python-unittest/test_dynamic_shapes/TEST-TestPySymInt-20230111213553.xml
2023-01-11T21:35:55.0824525Z Generated XML report: test-reports/python-unittest/test_dynamic_shapes/TEST-TestSymNumberMagicMethods-20230111213553.xml
2023-01-11T21:35:55.0824781Z 
2023-01-11T21:35:55.0825054Z ##[endgroup]
2023-01-11T21:35:55.0825446Z FINISHED PRINTING LOG FILE of test_dynamic_shapes (/var/lib/jenkins/workspace/test/test-reports/test_dynamic_shapes_7__iorh2)
2023-01-11T21:35:55.0825704Z 
2023-01-11T21:35:56.9430946Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:35:57.0088046Z Ignoring disabled issues:  []
2023-01-11T21:35:57.0255267Z Running test_expanded_weights ... [2023-01-11 21:35:57.025095]
2023-01-11T21:35:57.0256134Z Executing ['/opt/conda/bin/python', '-bb', 'test_expanded_weights.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:35:57.025333]
2023-01-11T21:35:59.9574976Z 
2023-01-11T21:35:59.9575503Z Expand the folded group to see the log file of test_expanded_weights
2023-01-11T21:35:59.9576373Z ##[group]PRINTING LOG FILE of test_expanded_weights (/var/lib/jenkins/workspace/test/test-reports/test_expanded_weights_9i0ulrtk)
2023-01-11T21:35:59.9576813Z 
2023-01-11T21:35:59.9576921Z Running tests...
2023-01-11T21:35:59.9577641Z ----------------------------------------------------------------------
2023-01-11T21:35:59.9578230Z Test results will be stored in test-reports/python-unittest/test_expanded_weights
2023-01-11T21:35:59.9578570Z   test_Conv1d (__main__.TestExpandedWeightModule) ... ok (0.005s)
2023-01-11T21:35:59.9578881Z   test_Conv1d_circular_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9579249Z   test_Conv1d_circular_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.042s)
2023-01-11T21:35:59.9579595Z   test_Conv1d_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:35:59.9579908Z   test_Conv1d_pad1 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9580214Z   test_Conv1d_pad1_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9580533Z   test_Conv1d_pad1size1 (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9580862Z   test_Conv1d_pad1size1_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9581168Z   test_Conv1d_pad2 (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9581484Z   test_Conv1d_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9581802Z   test_Conv1d_pad2size1 (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9582127Z   test_Conv1d_pad2size1_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9582448Z   test_Conv1d_reflect_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9582801Z   test_Conv1d_reflect_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9583149Z   test_Conv1d_replicate_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9583571Z   test_Conv1d_replicate_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9583924Z   test_Conv1d_stride (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9584247Z   test_Conv1d_stride_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9584773Z   test_Conv1d_zero_batch (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:35:59.9585344Z   test_Conv1d_zero_batch_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:35:59.9585749Z   test_Conv1d_zeros_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9586097Z   test_Conv1d_zeros_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9586414Z   test_Conv2d (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9586717Z   test_Conv2d_circular_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9587075Z   test_Conv2d_circular_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.005s)
2023-01-11T21:35:59.9587412Z   test_Conv2d_dilated (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9587835Z   test_Conv2d_dilated_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9588169Z   test_Conv2d_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9588481Z   test_Conv2d_no_bias (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9588805Z   test_Conv2d_no_bias_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9589117Z   test_Conv2d_padding (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9589445Z   test_Conv2d_padding_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9589787Z   test_Conv2d_reflect_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9590167Z   test_Conv2d_reflect_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9590524Z   test_Conv2d_replicate_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9590883Z   test_Conv2d_replicate_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9591226Z   test_Conv2d_strided (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9591537Z   test_Conv2d_strided_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:35:59.9592049Z   test_Conv2d_zero_batch (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:35:59.9592630Z   test_Conv2d_zero_batch_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:35:59.9593030Z   test_Conv2d_zeros_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9593364Z   test_Conv2d_zeros_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9593688Z   test_Conv3d (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9593991Z   test_Conv3d_1x1x1_no_bias (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9594308Z   test_Conv3d_1x1x1_no_bias_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9594647Z   test_Conv3d_circular_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:35:59.9594998Z   test_Conv3d_circular_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.005s)
2023-01-11T21:35:59.9595340Z   test_Conv3d_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9595639Z   test_Conv3d_no_bias (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9595963Z   test_Conv3d_no_bias_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9596298Z   test_Conv3d_replicate_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9596644Z   test_Conv3d_replicate_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9596981Z   test_Conv3d_stride (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9597299Z   test_Conv3d_stride_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9597624Z   test_Conv3d_stride_padding (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9597953Z   test_Conv3d_stride_padding_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9598467Z   test_Conv3d_zero_batch (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:35:59.9599048Z   test_Conv3d_zero_batch_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:35:59.9599450Z   test_Conv3d_zeros_stride2_pad2 (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9599831Z   test_Conv3d_zeros_stride2_pad2_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:35:59.9600158Z   test_Embedding (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9600479Z   test_Embedding_discontiguous (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9600818Z   test_Embedding_discontiguous_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9601170Z   test_Embedding_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.001s)
2023-01-11T21:35:59.9601500Z   test_GroupNorm_1d_affine (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:35:59.9601822Z   test_GroupNorm_1d_affine_GN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9602186Z   test_GroupNorm_1d_affine_GN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9602544Z   test_GroupNorm_1d_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9602888Z   test_GroupNorm_1d_no_affine_IN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9603236Z   test_GroupNorm_1d_no_affine_IN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9603565Z   test_GroupNorm_1d_no_affine_LN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9603914Z   test_GroupNorm_1d_no_affine_LN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9604252Z   test_GroupNorm_2d_affine (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9604573Z   test_GroupNorm_2d_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9604913Z   test_GroupNorm_2d_no_affine_IN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9605258Z   test_GroupNorm_2d_no_affine_IN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9605602Z   test_GroupNorm_2d_no_affine_LN (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9605931Z   test_GroupNorm_2d_no_affine_LN_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9606284Z   test_LayerNorm_1d_elementwise_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9606650Z   test_LayerNorm_1d_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9607198Z   test_LayerNorm_1d_empty_elementwise_affine (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:35:59.9607836Z   test_LayerNorm_1d_empty_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients when no batch dim or batch dim is 0 (0.001s)
2023-01-11T21:35:59.9608273Z   test_LayerNorm_1d_no_elementwise_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9608642Z   test_LayerNorm_1d_no_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9608992Z   test_LayerNorm_3d_elementwise_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9609690Z   test_LayerNorm_3d_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9610063Z   test_LayerNorm_3d_no_affine_large_feature (__main__.TestExpandedWeightModule) ... ok (0.030s)
2023-01-11T21:35:59.9610438Z   test_LayerNorm_3d_no_affine_large_feature_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.013s)
2023-01-11T21:35:59.9610791Z   test_LayerNorm_3d_no_elementwise_affine (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9611162Z   test_LayerNorm_3d_no_elementwise_affine_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.004s)
2023-01-11T21:35:59.9611498Z   test_Linear (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9611891Z   test_Linear_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9612371Z   test_Linear_no_batch_dim (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients for input of rank 1 (0.001s)
2023-01-11T21:35:59.9612925Z   test_Linear_no_batch_dim_multiple_inputs (__main__.TestExpandedWeightModule) ... skip: Can't get per sample gradients for input of rank 1 (0.001s)
2023-01-11T21:35:59.9613304Z   test_Linear_no_bias (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9613631Z   test_Linear_no_bias_multiple_inputs (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9613965Z   test_per_sample_api_compute_batch_size (__main__.TestExpandedWeightModule) ... ok (0.002s)
2023-01-11T21:35:59.9614413Z   test_per_sample_api_compute_batch_size_not_pytreeable (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9614769Z   test_per_sample_api_failing (__main__.TestExpandedWeightModule) ... ok (0.003s)
2023-01-11T21:35:59.9614951Z 
2023-01-11T21:35:59.9615144Z ----------------------------------------------------------------------
2023-01-11T21:35:59.9615390Z Ran 99 tests in 0.333s
2023-01-11T21:35:59.9615505Z 
2023-01-11T21:35:59.9615581Z OK (skipped=10)
2023-01-11T21:35:59.9615689Z 
2023-01-11T21:35:59.9615775Z Generating XML reports...
2023-01-11T21:35:59.9616217Z Generated XML report: test-reports/python-unittest/test_expanded_weights/TEST-TestExpandedWeightModule-20230111213559.xml
2023-01-11T21:35:59.9616476Z 
2023-01-11T21:35:59.9616772Z ##[endgroup]
2023-01-11T21:35:59.9617181Z FINISHED PRINTING LOG FILE of test_expanded_weights (/var/lib/jenkins/workspace/test/test-reports/test_expanded_weights_9i0ulrtk)
2023-01-11T21:35:59.9617399Z 
2023-01-11T21:36:01.8739960Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:01.9395403Z Ignoring disabled issues:  []
2023-01-11T21:36:01.9548737Z Running test_functional_autograd_benchmark ... [2023-01-11 21:36:01.954519]
2023-01-11T21:36:01.9550778Z Executing ['/opt/conda/bin/python', '-bb', 'test_functional_autograd_benchmark.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:01.954813]
2023-01-11T21:36:27.1253714Z 
2023-01-11T21:36:27.1255689Z Expand the folded group to see the log file of test_functional_autograd_benchmark
2023-01-11T21:36:27.1256694Z ##[group]PRINTING LOG FILE of test_functional_autograd_benchmark (/var/lib/jenkins/workspace/test/test-reports/test_functional_autograd_benchmark_kk9h06ut)
2023-01-11T21:36:27.1257165Z 
2023-01-11T21:36:27.1257299Z Running tests...
2023-01-11T21:36:27.1258667Z ----------------------------------------------------------------------
2023-01-11T21:36:27.1259435Z Test results will be stored in test-reports/python-unittest/test_functional_autograd_benchmark
2023-01-11T21:36:27.1259820Z   test_fast_tasks (__main__.TestFunctionalAutogradBenchmark) ... Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:36:27.1260156Z Results for model resnet18 on task vjp: nans (var: nan)
2023-01-11T21:36:27.1260431Z Results for model resnet18 on task vjp using Functorch: nans (var: nan)
2023-01-11T21:36:27.1260681Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:36:27.1260915Z Results for model ppl_simple_reg on task vjp: nans (var: nan)
2023-01-11T21:36:27.1261205Z Results for model ppl_simple_reg on task vjp using Functorch: nans (var: nan)
2023-01-11T21:36:27.1261459Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:36:27.1261689Z Results for model ppl_robust_reg on task vjp: nans (var: nan)
2023-01-11T21:36:27.1261974Z Results for model ppl_robust_reg on task vjp using Functorch: nans (var: nan)
2023-01-11T21:36:27.1262223Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:36:27.1262447Z Results for model wav2letter on task vjp: nans (var: nan)
2023-01-11T21:36:27.1262727Z Results for model wav2letter on task vjp using Functorch: nans (var: nan)
2023-01-11T21:36:27.1262974Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:36:27.1263379Z Results for model transformer on task vjp: nans (var: nan)
2023-01-11T21:36:27.1263751Z Results for model transformer on task vjp using Functorch: nans (var: nan)
2023-01-11T21:36:27.1264006Z Found functorch: 2.0.0a0+git8419ddd
2023-01-11T21:36:27.1264250Z Results for model multiheadattn on task vjp: nans (var: nan)
2023-01-11T21:36:27.1264528Z Results for model multiheadattn on task vjp using Functorch: nans (var: nan)
2023-01-11T21:36:27.1264764Z ok (23.485s)
2023-01-11T21:36:27.1265088Z   test_slow_tasks (__main__.TestFunctionalAutogradBenchmark) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:36:27.1265334Z 
2023-01-11T21:36:27.1265542Z ----------------------------------------------------------------------
2023-01-11T21:36:27.1265773Z Ran 2 tests in 23.486s
2023-01-11T21:36:27.1265884Z 
2023-01-11T21:36:27.1266046Z OK (skipped=1)
2023-01-11T21:36:27.1266153Z 
2023-01-11T21:36:27.1266236Z Generating XML reports...
2023-01-11T21:36:27.1266731Z Generated XML report: test-reports/python-unittest/test_functional_autograd_benchmark/TEST-TestFunctionalAutogradBenchmark-20230111213603.xml
2023-01-11T21:36:27.1267025Z 
2023-01-11T21:36:27.1267280Z ##[endgroup]
2023-01-11T21:36:27.1267731Z FINISHED PRINTING LOG FILE of test_functional_autograd_benchmark (/var/lib/jenkins/workspace/test/test-reports/test_functional_autograd_benchmark_kk9h06ut)
2023-01-11T21:36:27.1267990Z 
2023-01-11T21:36:28.9681780Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:29.0325708Z Ignoring disabled issues:  []
2023-01-11T21:36:29.0478706Z Running test_functional_optim ... [2023-01-11 21:36:29.047580]
2023-01-11T21:36:29.0480744Z Executing ['/opt/conda/bin/python', '-bb', 'test_functional_optim.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:29.047861]
2023-01-11T21:36:31.2398657Z 
2023-01-11T21:36:31.2399194Z Expand the folded group to see the log file of test_functional_optim
2023-01-11T21:36:31.2400094Z ##[group]PRINTING LOG FILE of test_functional_optim (/var/lib/jenkins/workspace/test/test-reports/test_functional_optim_r34v025h)
2023-01-11T21:36:31.2400537Z 
2023-01-11T21:36:31.2402618Z Running tests...
2023-01-11T21:36:31.2403355Z ----------------------------------------------------------------------
2023-01-11T21:36:31.2403790Z Test results will be stored in test-reports/python-unittest/test_functional_optim
2023-01-11T21:36:31.2404143Z   test_functional_optim_parity_adam (__main__.TestFunctionalOptimParity) ... ok (0.253s)
2023-01-11T21:36:31.2404488Z   test_functional_optim_parity_adam_w (__main__.TestFunctionalOptimParity) ... ok (0.030s)
2023-01-11T21:36:31.2404839Z   test_functional_optim_parity_sgd (__main__.TestFunctionalOptimParity) ... ok (0.026s)
2023-01-11T21:36:31.2410949Z   test_functional_optim_registration (__main__.TestFunctionalOptimParity) ... ok (0.001s)
2023-01-11T21:36:31.2411230Z 
2023-01-11T21:36:31.2411581Z ----------------------------------------------------------------------
2023-01-11T21:36:31.2414064Z Ran 4 tests in 0.310s
2023-01-11T21:36:31.2414190Z 
2023-01-11T21:36:31.2414263Z OK
2023-01-11T21:36:31.2414357Z 
2023-01-11T21:36:31.2414448Z Generating XML reports...
2023-01-11T21:36:31.2415006Z Generated XML report: test-reports/python-unittest/test_functional_optim/TEST-TestFunctionalOptimParity-20230111213630.xml
2023-01-11T21:36:31.2415273Z 
2023-01-11T21:36:31.2415635Z ##[endgroup]
2023-01-11T21:36:31.2416049Z FINISHED PRINTING LOG FILE of test_functional_optim (/var/lib/jenkins/workspace/test/test-reports/test_functional_optim_r34v025h)
2023-01-11T21:36:31.2416297Z 
2023-01-11T21:36:33.2908483Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:33.3555356Z Ignoring disabled issues:  []
2023-01-11T21:36:33.3707736Z Running test_functionalization ... [2023-01-11 21:36:33.370470]
2023-01-11T21:36:33.3709389Z Executing ['/opt/conda/bin/python', '-bb', 'test_functionalization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:33.370702]
2023-01-11T21:36:50.1316033Z 
2023-01-11T21:36:50.1316796Z Expand the folded group to see the log file of test_functionalization
2023-01-11T21:36:50.1317912Z ##[group]PRINTING LOG FILE of test_functionalization (/var/lib/jenkins/workspace/test/test-reports/test_functionalization_q4jtp1dh)
2023-01-11T21:36:50.1318397Z 
2023-01-11T21:36:50.1318524Z Running tests...
2023-01-11T21:36:50.1319178Z ----------------------------------------------------------------------
2023-01-11T21:36:50.1319877Z Test results will be stored in test-reports/python-unittest/test_functionalization
2023-01-11T21:36:50.1320479Z   test_advanced_indexing (__main__.TestCrossRefFunctionalization) ... ok (0.452s)
2023-01-11T21:36:50.1321143Z   test_advanced_indexing_correct_strides (__main__.TestCrossRefFunctionalization) ... ok (0.138s)
2023-01-11T21:36:50.1322748Z   test_aliases_maintained_after_pass_when_reapplying_views (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:19: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:36:50.1324022Z   x_storage = StorageWeakRef(x.storage())
2023-01-11T21:36:50.1325071Z /var/lib/jenkins/workspace/test/test_functionalization.py:20: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:36:50.1326086Z   y_storage = StorageWeakRef(y.storage())
2023-01-11T21:36:50.1326445Z ok (0.001s)
2023-01-11T21:36:50.1326966Z   test_as_strided (__main__.TestCrossRefFunctionalization) ... expected failure (0.006s)
2023-01-11T21:36:50.1327578Z   test_batch_norm (__main__.TestCrossRefFunctionalization) ... ok (0.971s)
2023-01-11T21:36:50.1328180Z   test_cat (__main__.TestCrossRefFunctionalization) ... ok (0.116s)
2023-01-11T21:36:50.1328784Z   test_copy_ (__main__.TestCrossRefFunctionalization) ... expected failure (0.033s)
2023-01-11T21:36:50.1329578Z   test_copy_stride_mismatch (__main__.TestCrossRefFunctionalization) ... ok (0.010s)
2023-01-11T21:36:50.1330221Z   test_diagonal (__main__.TestCrossRefFunctionalization) ... expected failure (0.010s)
2023-01-11T21:36:50.1330913Z   test_diagonal_mutated_input (__main__.TestCrossRefFunctionalization) ... expected failure (0.009s)
2023-01-11T21:36:50.1331604Z   test_everything (__main__.TestCrossRefFunctionalization) ... expected failure (0.014s)
2023-01-11T21:36:50.1332161Z   test_expand_symint (__main__.TestCrossRefFunctionalization) ... ok (0.041s)
2023-01-11T21:36:50.1332739Z   test_fill_ (__main__.TestCrossRefFunctionalization) ... expected failure (0.005s)
2023-01-11T21:36:50.1333284Z   test_freeze (__main__.TestCrossRefFunctionalization) ... ok (0.021s)
2023-01-11T21:36:50.1333851Z   test_index_mutation_on_non_input (__main__.TestCrossRefFunctionalization) ... ok (0.201s)
2023-01-11T21:36:50.1334450Z   test_inplace_on_non_view (__main__.TestCrossRefFunctionalization) ... ok (0.209s)
2023-01-11T21:36:50.1335029Z   test_instance_norm (__main__.TestCrossRefFunctionalization) ... ok (1.325s)
2023-01-11T21:36:50.1335637Z   test_metadata_change (__main__.TestCrossRefFunctionalization) ... ok (0.134s)
2023-01-11T21:36:50.1336236Z   test_metadata_change_out_op (__main__.TestCrossRefFunctionalization) ... ok (0.001s)
2023-01-11T21:36:50.1336895Z   test_mixed_wrappers_invalid (__main__.TestCrossRefFunctionalization) ... ok (0.004s)
2023-01-11T21:36:50.1337512Z   test_mixed_wrappers_valid (__main__.TestCrossRefFunctionalization) ... ok (0.001s)
2023-01-11T21:36:50.1338986Z   test_multi_out (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:36:50.1340417Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:36:50.1341781Z /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:36:50.1370354Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:36:50.1371603Z /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:36:50.1372833Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:36:50.1374081Z /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:36:50.1375221Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:36:50.1376443Z /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:36:50.1377646Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:36:50.1378013Z ok (0.196s)
2023-01-11T21:36:50.1378493Z   test_multiple_views_of_same_base (__main__.TestCrossRefFunctionalization) ... ok (0.098s)
2023-01-11T21:36:50.1379094Z   test_mutable_op_not_inplace_or_other (__main__.TestCrossRefFunctionalization) ... ok (0.077s)
2023-01-11T21:36:50.1379704Z   test_nested_functions_propagate_updates (__main__.TestCrossRefFunctionalization) ... ok (0.075s)
2023-01-11T21:36:50.1380289Z   test_only_one_view (__main__.TestCrossRefFunctionalization) ... ok (0.019s)
2023-01-11T21:36:50.1380842Z   test_optional_tensor_list (__main__.TestCrossRefFunctionalization) ... ok (0.229s)
2023-01-11T21:36:50.1381470Z   test_reapply_views_simple (__main__.TestCrossRefFunctionalization) ... ok (0.182s)
2023-01-11T21:36:50.1382068Z   test_resize_larger_invalid (__main__.TestCrossRefFunctionalization) ... ok (0.013s)
2023-01-11T21:36:50.1382700Z   test_resize_larger_valid (__main__.TestCrossRefFunctionalization) ... ok (0.247s)
2023-01-11T21:36:50.1383254Z   test_resize_smaller (__main__.TestCrossRefFunctionalization) ... ok (0.554s)
2023-01-11T21:36:50.1383950Z   test_save_for_backwards_segfault (__main__.TestCrossRefFunctionalization) ... ok (0.001s)
2023-01-11T21:36:50.1384519Z   test_scalars (__main__.TestCrossRefFunctionalization) ... ok (0.202s)
2023-01-11T21:36:50.1385182Z   test_set_ (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:149: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:36:50.1385744Z   y.set_(x.storage())
2023-01-11T21:36:50.1385902Z ok (0.001s)
2023-01-11T21:36:50.1386143Z   test_simple (__main__.TestCrossRefFunctionalization) ... ok (0.272s)
2023-01-11T21:36:50.1387000Z   test_simple_out (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:266: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [4, 2]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:36:50.1387704Z   torch.add(y, tmp, out=z)
2023-01-11T21:36:50.1388674Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:36:50.1389262Z   warnings.warn(msg)
2023-01-11T21:36:50.1390134Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:36:50.1390722Z   warnings.warn(msg)
2023-01-11T21:36:50.1391600Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:36:50.1392168Z   warnings.warn(msg)
2023-01-11T21:36:50.1393047Z /opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py:145: UserWarning: An output with one or more elements was resized since it had shape torch.Size([]) which does not match the required output shape {str(shape)}. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0).
2023-01-11T21:36:50.1393630Z   warnings.warn(msg)
2023-01-11T21:36:50.1393790Z ok (0.240s)
2023-01-11T21:36:50.1394048Z   test_split (__main__.TestCrossRefFunctionalization) ... expected failure (0.012s)
2023-01-11T21:36:50.1394377Z   test_tensor_ctr (__main__.TestCrossRefFunctionalization) ... ok (0.180s)
2023-01-11T21:36:50.1394696Z   test_tensor_list_composite (__main__.TestCrossRefFunctionalization) ... ok (0.041s)
2023-01-11T21:36:50.1395062Z   test_tensor_list_mixed_functional_nonfunctional (__main__.TestCrossRefFunctionalization) ... ok (0.017s)
2023-01-11T21:36:50.1395652Z   test_view_clone_view_inplace (__main__.TestCrossRefFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:185: UserWarning: Anomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.
2023-01-11T21:36:50.1396122Z   with torch.autograd.detect_anomaly(check_nan=False):
2023-01-11T21:36:50.1396610Z /opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:197: UserWarning: Error detected in SumBackward0. Traceback of forward call that caused the error:
2023-01-11T21:36:50.1397017Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 1487, in <module>
2023-01-11T21:36:50.1397262Z     run_tests()
2023-01-11T21:36:50.1397614Z   File "/opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 787, in run_tests
2023-01-11T21:36:50.1397983Z     unittest.main(argv=argv, testRunner=xmlrunner.XMLTestRunner(
2023-01-11T21:36:50.1398279Z   File "/opt/conda/lib/python3.10/unittest/main.py", line 101, in __init__
2023-01-11T21:36:50.1398512Z     self.runTests()
2023-01-11T21:36:50.1398733Z   File "/opt/conda/lib/python3.10/unittest/main.py", line 271, in runTests
2023-01-11T21:36:50.1398991Z     self.result = testRunner.run(self.test)
2023-01-11T21:36:50.1399340Z   File "/opt/conda/lib/python3.10/site-packages/xmlrunner/runner.py", line 67, in run
2023-01-11T21:36:50.1399569Z     test(result)
2023-01-11T21:36:50.1399799Z   File "/opt/conda/lib/python3.10/unittest/suite.py", line 84, in __call__
2023-01-11T21:36:50.1400039Z     return self.run(*args, **kwds)
2023-01-11T21:36:50.1400273Z   File "/opt/conda/lib/python3.10/unittest/suite.py", line 122, in run
2023-01-11T21:36:50.1400490Z     test(result)
2023-01-11T21:36:50.1400720Z   File "/opt/conda/lib/python3.10/unittest/suite.py", line 84, in __call__
2023-01-11T21:36:50.1400960Z     return self.run(*args, **kwds)
2023-01-11T21:36:50.1401191Z   File "/opt/conda/lib/python3.10/unittest/suite.py", line 122, in run
2023-01-11T21:36:50.1401411Z     test(result)
2023-01-11T21:36:50.1401640Z   File "/opt/conda/lib/python3.10/unittest/case.py", line 650, in __call__
2023-01-11T21:36:50.1401868Z     return self.run(*args, **kwds)
2023-01-11T21:36:50.1402240Z   File "/opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 2225, in run
2023-01-11T21:36:50.1402511Z     self._run_with_retry(
2023-01-11T21:36:50.1402876Z   File "/opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 2154, in _run_with_retry
2023-01-11T21:36:50.1403158Z     super().run(result=result)
2023-01-11T21:36:50.1403398Z   File "/opt/conda/lib/python3.10/unittest/case.py", line 591, in run
2023-01-11T21:36:50.1403644Z     self._callTestMethod(testMethod)
2023-01-11T21:36:50.1403902Z   File "/opt/conda/lib/python3.10/unittest/case.py", line 549, in _callTestMethod
2023-01-11T21:36:50.1404129Z     method()
2023-01-11T21:36:50.1404403Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 186, in test_view_clone_view_inplace
2023-01-11T21:36:50.1404719Z     logs = self.get_logs(g, torch.ones(16, 64, 128, 128, requires_grad=True))
2023-01-11T21:36:50.1405020Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 66, in get_logs
2023-01-11T21:36:50.1405408Z     traced_f = make_fx(_functionalize(func, reapply_views=reapply_views, crossref=self.crossref))(*inpts)
2023-01-11T21:36:50.1405855Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 692, in wrapped
2023-01-11T21:36:50.1406218Z     t = dispatch_trace(wrap_key(func, args, fx_tracer), tracer=fx_tracer, concrete_args=tuple(phs))
2023-01-11T21:36:50.1406657Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 431, in dispatch_trace
2023-01-11T21:36:50.1406968Z     graph = tracer.trace(root, concrete_args)
2023-01-11T21:36:50.1407332Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 739, in trace
2023-01-11T21:36:50.1407630Z     (self.create_arg(fn(*args)),),
2023-01-11T21:36:50.1408004Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 447, in wrapped
2023-01-11T21:36:50.1408269Z     out = f(*tensors)
2023-01-11T21:36:50.1408449Z   File "<string>", line 1, in <lambda>
2023-01-11T21:36:50.1408725Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 40, in wrapped
2023-01-11T21:36:50.1408984Z     out = f(*inputs_functional)
2023-01-11T21:36:50.1409532Z   File "/var/lib/jenkins/workspace/test/test_functionalization.py", line 177, in g
2023-01-11T21:36:50.1409783Z     loss = f(x).sum()
2023-01-11T21:36:50.1410223Z  (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/autograd/python_anomaly_mode.cpp:119.)
2023-01-11T21:36:50.1410972Z   Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
2023-01-11T21:36:50.1411231Z expected failure (0.290s)
2023-01-11T21:36:50.1411515Z   test_view_inplace (__main__.TestCrossRefFunctionalization) ... expected failure (0.012s)
2023-01-11T21:36:50.1411855Z   test_advanced_indexing (__main__.TestFunctionalization) ... ok (0.077s)
2023-01-11T21:36:50.1412177Z   test_advanced_indexing_correct_strides (__main__.TestFunctionalization) ... ok (0.109s)
2023-01-11T21:36:50.1412872Z   test_aliases_maintained_after_pass_when_reapplying_views (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:19: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:36:50.1413499Z   x_storage = StorageWeakRef(x.storage())
2023-01-11T21:36:50.1414051Z /var/lib/jenkins/workspace/test/test_functionalization.py:20: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:36:50.1414576Z   y_storage = StorageWeakRef(y.storage())
2023-01-11T21:36:50.1414761Z ok (0.001s)
2023-01-11T21:36:50.1414990Z   test_as_strided (__main__.TestFunctionalization) ... ok (0.103s)
2023-01-11T21:36:50.1415278Z   test_batch_norm (__main__.TestFunctionalization) ... ok (0.621s)
2023-01-11T21:36:50.1415558Z   test_cat (__main__.TestFunctionalization) ... ok (0.067s)
2023-01-11T21:36:50.1415818Z   test_copy_ (__main__.TestFunctionalization) ... ok (0.902s)
2023-01-11T21:36:50.1416112Z   test_copy_stride_mismatch (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:36:50.1416409Z   test_diagonal (__main__.TestFunctionalization) ... ok (0.208s)
2023-01-11T21:36:50.1416698Z   test_diagonal_mutated_input (__main__.TestFunctionalization) ... ok (0.118s)
2023-01-11T21:36:50.1416999Z   test_everything (__main__.TestFunctionalization) ... ok (1.179s)
2023-01-11T21:36:50.1417295Z   test_expand_symint (__main__.TestFunctionalization) ... ok (0.032s)
2023-01-11T21:36:50.1417562Z   test_fill_ (__main__.TestFunctionalization) ... ok (0.154s)
2023-01-11T21:36:50.1417835Z   test_freeze (__main__.TestFunctionalization) ... ok (0.009s)
2023-01-11T21:36:50.1418136Z   test_index_mutation_on_non_input (__main__.TestFunctionalization) ... ok (0.152s)
2023-01-11T21:36:50.1418451Z   test_inplace_on_non_view (__main__.TestFunctionalization) ... ok (0.147s)
2023-01-11T21:36:50.1418736Z   test_instance_norm (__main__.TestFunctionalization) ... ok (1.236s)
2023-01-11T21:36:50.1419030Z   test_metadata_change (__main__.TestFunctionalization) ... ok (0.106s)
2023-01-11T21:36:50.1419336Z   test_metadata_change_out_op (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:36:50.1419635Z   test_mixed_wrappers_invalid (__main__.TestFunctionalization) ... ok (0.004s)
2023-01-11T21:36:50.1419948Z   test_mixed_wrappers_valid (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:36:50.1420786Z   test_multi_out (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:304: UserWarning: An output with one or more elements was resized since it had shape [4], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:36:50.1421486Z   torch.aminmax(x, dim=0, out=(out_max, out_min))
2023-01-11T21:36:50.1421687Z ok (0.121s)
2023-01-11T21:36:50.1421921Z   test_multiple_views_of_same_base (__main__.TestFunctionalization) ... ok (0.079s)
2023-01-11T21:36:50.1422281Z   test_mutable_op_not_inplace_or_other (__main__.TestFunctionalization) ... ok (0.062s)
2023-01-11T21:36:50.1422618Z   test_nested_functions_propagate_updates (__main__.TestFunctionalization) ... ok (0.062s)
2023-01-11T21:36:50.1422921Z   test_only_one_view (__main__.TestFunctionalization) ... ok (0.016s)
2023-01-11T21:36:50.1423218Z   test_optional_tensor_list (__main__.TestFunctionalization) ... ok (0.138s)
2023-01-11T21:36:50.1423595Z   test_reapply_views_simple (__main__.TestFunctionalization) ... ok (0.135s)
2023-01-11T21:36:50.1423909Z   test_resize_larger_invalid (__main__.TestFunctionalization) ... ok (0.004s)
2023-01-11T21:36:50.1424200Z   test_resize_larger_valid (__main__.TestFunctionalization) ... ok (0.202s)
2023-01-11T21:36:50.1424495Z   test_resize_smaller (__main__.TestFunctionalization) ... ok (0.515s)
2023-01-11T21:36:50.1424803Z   test_save_for_backwards_segfault (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:36:50.1425096Z   test_scalars (__main__.TestFunctionalization) ... ok (0.151s)
2023-01-11T21:36:50.1425718Z   test_set_ (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:149: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:36:50.1426265Z   y.set_(x.storage())
2023-01-11T21:36:50.1426441Z ok (0.001s)
2023-01-11T21:36:50.1426650Z   test_simple (__main__.TestFunctionalization) ... ok (0.203s)
2023-01-11T21:36:50.1427423Z   test_simple_out (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:266: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [4, 2]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:36:50.1428107Z   torch.add(y, tmp, out=z)
2023-01-11T21:36:50.1428286Z ok (0.149s)
2023-01-11T21:36:50.1428504Z   test_split (__main__.TestFunctionalization) ... ok (0.271s)
2023-01-11T21:36:50.1428767Z   test_tensor_ctr (__main__.TestFunctionalization) ... ok (0.157s)
2023-01-11T21:36:50.1429063Z   test_tensor_list_composite (__main__.TestFunctionalization) ... ok (0.027s)
2023-01-11T21:36:50.1429394Z   test_tensor_list_mixed_functional_nonfunctional (__main__.TestFunctionalization) ... ok (0.001s)
2023-01-11T21:36:50.1429899Z   test_view_clone_view_inplace (__main__.TestFunctionalization) ... /var/lib/jenkins/workspace/test/test_functionalization.py:185: UserWarning: Anomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.
2023-01-11T21:36:50.1430356Z   with torch.autograd.detect_anomaly(check_nan=False):
2023-01-11T21:36:50.1430568Z ok (0.661s)
2023-01-11T21:36:50.1430837Z   test_view_inplace (__main__.TestFunctionalization) ... ok (0.203s)
2023-01-11T21:36:50.1430988Z 
2023-01-11T21:36:50.1431218Z ----------------------------------------------------------------------
2023-01-11T21:36:50.1431459Z Ran 84 tests in 15.053s
2023-01-11T21:36:50.1431575Z 
2023-01-11T21:36:50.1431657Z OK (expected failures=9)
2023-01-11T21:36:50.1431777Z 
2023-01-11T21:36:50.1431847Z Generating XML reports...
2023-01-11T21:36:50.1432327Z Generated XML report: test-reports/python-unittest/test_functionalization/TEST-TestCrossRefFunctionalization-20230111213634.xml
2023-01-11T21:36:50.1432915Z Generated XML report: test-reports/python-unittest/test_functionalization/TEST-TestFunctionalization-20230111213634.xml
2023-01-11T21:36:50.1433167Z 
2023-01-11T21:36:50.1433485Z ##[endgroup]
2023-01-11T21:36:50.1433926Z FINISHED PRINTING LOG FILE of test_functionalization (/var/lib/jenkins/workspace/test/test-reports/test_functionalization_q4jtp1dh)
2023-01-11T21:36:50.1434166Z 
2023-01-11T21:36:51.9908326Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:52.0621581Z Ignoring disabled issues:  []
2023-01-11T21:36:52.0776353Z Running test_futures ... [2023-01-11 21:36:52.077317]
2023-01-11T21:36:52.0777492Z Executing ['/opt/conda/bin/python', '-bb', 'test_futures.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:52.077555]
2023-01-11T21:36:54.5826230Z 
2023-01-11T21:36:54.5828341Z Expand the folded group to see the log file of test_futures
2023-01-11T21:36:54.5829446Z ##[group]PRINTING LOG FILE of test_futures (/var/lib/jenkins/workspace/test/test-reports/test_futures_v_kdf7li)
2023-01-11T21:36:54.5829890Z 
2023-01-11T21:36:54.5830021Z Running tests...
2023-01-11T21:36:54.5830680Z ----------------------------------------------------------------------
2023-01-11T21:36:54.5831350Z Test results will be stored in test-reports/python-unittest/test_futures
2023-01-11T21:36:54.5832072Z   test_add_done_callback_error_is_ignored (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: ValueError: Expected error
2023-01-11T21:36:54.5832521Z 
2023-01-11T21:36:54.5832625Z At:
2023-01-11T21:36:54.5833014Z   /var/lib/jenkins/workspace/test/test_futures.py(236): raise_value_error
2023-01-11T21:36:54.5833702Z   /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py(244): set_result
2023-01-11T21:36:54.5834278Z   /var/lib/jenkins/workspace/test/test_futures.py(229): _test_add_done_callback_error_ignored
2023-01-11T21:36:54.5834873Z   /var/lib/jenkins/workspace/test/test_futures.py(238): test_add_done_callback_error_is_ignored
2023-01-11T21:36:54.5835416Z   /opt/conda/lib/python3.10/unittest/case.py(549): _callTestMethod
2023-01-11T21:36:54.5835873Z   /opt/conda/lib/python3.10/unittest/case.py(591): run
2023-01-11T21:36:54.5836610Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(2154): _run_with_retry
2023-01-11T21:36:54.5837379Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(2225): run
2023-01-11T21:36:54.5837902Z   /opt/conda/lib/python3.10/unittest/case.py(650): __call__
2023-01-11T21:36:54.5838346Z   /opt/conda/lib/python3.10/unittest/suite.py(122): run
2023-01-11T21:36:54.5838796Z   /opt/conda/lib/python3.10/unittest/suite.py(84): __call__
2023-01-11T21:36:54.5839237Z   /opt/conda/lib/python3.10/unittest/suite.py(122): run
2023-01-11T21:36:54.5839700Z   /opt/conda/lib/python3.10/unittest/suite.py(84): __call__
2023-01-11T21:36:54.5840323Z   /opt/conda/lib/python3.10/site-packages/xmlrunner/runner.py(67): run
2023-01-11T21:36:54.5840820Z   /opt/conda/lib/python3.10/unittest/main.py(271): runTests
2023-01-11T21:36:54.5841256Z   /opt/conda/lib/python3.10/unittest/main.py(101): __init__
2023-01-11T21:36:54.5841927Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(787): run_tests
2023-01-11T21:36:54.5842491Z   /var/lib/jenkins/workspace/test/test_futures.py(340): <module>
2023-01-11T21:36:54.5842764Z 
2023-01-11T21:36:54.5843117Z ok (0.228s)
2023-01-11T21:36:54.5843536Z   test_add_done_callback_maintains_callback_order (__main__.TestFuture) ... ok (0.002s)
2023-01-11T21:36:54.5844439Z   test_add_done_callback_no_arg_error_is_ignored (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: TypeError: TestFuture.test_add_done_callback_no_arg_error_is_ignored.<locals>.no_arg() takes 0 positional arguments but 1 was given
2023-01-11T21:36:54.5845179Z ok (0.001s)
2023-01-11T21:36:54.5845549Z   test_add_done_callback_simple (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5846016Z   test_chained_then (__main__.TestFuture) ... ok (0.003s)
2023-01-11T21:36:54.5846434Z   test_collect_all (__main__.TestFuture) ... ok (0.101s)
2023-01-11T21:36:54.5846841Z   test_done (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5847407Z   test_done_exception (__main__.TestFuture) ... ok (0.002s)
2023-01-11T21:36:54.5847975Z   test_interleaving_then_and_add_done_callback_maintains_callback_order (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5848719Z   test_interleaving_then_and_add_done_callback_propagates_error (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: ValueError: Expected error
2023-01-11T21:36:54.5849356Z 
2023-01-11T21:36:54.5849452Z At:
2023-01-11T21:36:54.5849802Z   /var/lib/jenkins/workspace/test/test_futures.py(280): raise_value_error
2023-01-11T21:36:54.5850526Z   /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py(244): set_result
2023-01-11T21:36:54.5851124Z   /var/lib/jenkins/workspace/test/test_futures.py(285): test_interleaving_then_and_add_done_callback_propagates_error
2023-01-11T21:36:54.5851677Z   /opt/conda/lib/python3.10/unittest/case.py(549): _callTestMethod
2023-01-11T21:36:54.5852140Z   /opt/conda/lib/python3.10/unittest/case.py(591): run
2023-01-11T21:36:54.5852857Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(2154): _run_with_retry
2023-01-11T21:36:54.5853591Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(2225): run
2023-01-11T21:36:54.5854106Z   /opt/conda/lib/python3.10/unittest/case.py(650): __call__
2023-01-11T21:36:54.5854562Z   /opt/conda/lib/python3.10/unittest/suite.py(122): run
2023-01-11T21:36:54.5855012Z   /opt/conda/lib/python3.10/unittest/suite.py(84): __call__
2023-01-11T21:36:54.5855450Z   /opt/conda/lib/python3.10/unittest/suite.py(122): run
2023-01-11T21:36:54.5855897Z   /opt/conda/lib/python3.10/unittest/suite.py(84): __call__
2023-01-11T21:36:54.5856525Z   /opt/conda/lib/python3.10/site-packages/xmlrunner/runner.py(67): run
2023-01-11T21:36:54.5857002Z   /opt/conda/lib/python3.10/unittest/main.py(271): runTests
2023-01-11T21:36:54.5857433Z   /opt/conda/lib/python3.10/unittest/main.py(101): __init__
2023-01-11T21:36:54.5858112Z   /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py(787): run_tests
2023-01-11T21:36:54.5858655Z   /var/lib/jenkins/workspace/test/test_futures.py(340): <module>
2023-01-11T21:36:54.5858920Z 
2023-01-11T21:36:54.5859033Z ok (0.001s)
2023-01-11T21:36:54.5859390Z   test_mark_future_twice (__main__.TestFuture) ... ok (0.002s)
2023-01-11T21:36:54.5859819Z   test_pickle_future (__main__.TestFuture) ... ok (0.003s)
2023-01-11T21:36:54.5860207Z   test_set_exception (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5860659Z   test_set_exception_multithreading (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5861091Z   test_then (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5861465Z   test_then_no_arg (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5861867Z   test_then_raise (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5862274Z   test_then_wrong_arg (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5862680Z   test_wait (__main__.TestFuture) ... ok (0.001s)
2023-01-11T21:36:54.5863055Z   test_wait_all (__main__.TestFuture) ... [1, 2]
2023-01-11T21:36:54.5863384Z ok (0.001s)
2023-01-11T21:36:54.5864020Z   test_wait_multi_thread (__main__.TestFuture) ... ok (0.502s)
2023-01-11T21:36:54.5864445Z   test_wait_none (__main__.TestFuture) ... ok (0.004s)
2023-01-11T21:36:54.5864663Z 
2023-01-11T21:36:54.5865049Z ----------------------------------------------------------------------
2023-01-11T21:36:54.5865431Z Ran 22 tests in 0.860s
2023-01-11T21:36:54.5865610Z 
2023-01-11T21:36:54.5865688Z OK
2023-01-11T21:36:54.5865844Z 
2023-01-11T21:36:54.5865991Z Generating XML reports...
2023-01-11T21:36:54.5866698Z Generated XML report: test-reports/python-unittest/test_futures/TEST-TestFuture-20230111213653.xml
2023-01-11T21:36:54.5867071Z 
2023-01-11T21:36:54.5867472Z ##[endgroup]
2023-01-11T21:36:54.5868107Z FINISHED PRINTING LOG FILE of test_futures (/var/lib/jenkins/workspace/test/test-reports/test_futures_v_kdf7li)
2023-01-11T21:36:54.5868453Z 
2023-01-11T21:36:56.4030311Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:36:56.4697765Z Ignoring disabled issues:  []
2023-01-11T21:36:56.4850910Z Running test_fx_experimental ... [2023-01-11 21:36:56.484786]
2023-01-11T21:36:56.4852299Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx_experimental.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:36:56.485036]
2023-01-11T21:37:13.3747667Z 
2023-01-11T21:37:13.3748181Z Expand the folded group to see the log file of test_fx_experimental
2023-01-11T21:37:13.3749430Z ##[group]PRINTING LOG FILE of test_fx_experimental (/var/lib/jenkins/workspace/test/test-reports/test_fx_experimental__l0jerl7)
2023-01-11T21:37:13.3749911Z 
2023-01-11T21:37:13.3750049Z Running tests...
2023-01-11T21:37:13.3750692Z ----------------------------------------------------------------------
2023-01-11T21:37:13.3751284Z Test results will be stored in test-reports/python-unittest/test_fx_experimental
2023-01-11T21:37:13.3751754Z   test_annotate_getitem_node (__main__.TestFXExperimental) ... ok (0.004s)
2023-01-11T21:37:13.3752687Z   test_annotate_returns_with_schema (__main__.TestFXExperimental) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T21:37:13.3753331Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T21:37:13.3753558Z ok (1.767s)
2023-01-11T21:37:13.3753800Z   test_aot_based_partition (__main__.TestFXExperimental) ... ok (0.006s)
2023-01-11T21:37:13.3754090Z   test_call_to_assert_no_msg (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3754402Z   test_call_to_assert_with_empty_msg (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3754712Z   test_call_to_assert_with_msg (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3755023Z   test_call_to_assert_with_multiline_message (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3755335Z   test_conv_bn_fusion (__main__.TestFXExperimental) ... ok (1.014s)
2023-01-11T21:37:13.3755641Z   test_conv_bn_fusion_not_running_state (__main__.TestFXExperimental) ... ok (0.009s)
2023-01-11T21:37:13.3755939Z   test_cost_aware_partition (__main__.TestFXExperimental) ... ok (0.010s)
2023-01-11T21:37:13.3756221Z   test_fetch (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3756506Z   test_find_single_partition (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3756803Z   test_lack_of_devices (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3757082Z   test_large_node_error (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3757365Z   test_merge_matmuls (__main__.TestFXExperimental)
2023-01-11T21:37:13.3757668Z A collection of test cases for torch.fx.experimental.merge_matmul, ... ok (0.026s)
2023-01-11T21:37:13.3757967Z   test_meta_tracer (__main__.TestFXExperimental) ... ok (0.019s)
2023-01-11T21:37:13.3758249Z   test_normalize_args (__main__.TestFXExperimental) ... ok (0.543s)
2023-01-11T21:37:13.3759079Z   test_normalize_args_perserve_type (__main__.TestFXExperimental) ... /opt/conda/lib/python3.10/site-packages/torch/fx/operator_schemas.py:207: UserWarning: Does not support nested parametric types, got typing.List[~t]. Please file a bug.
2023-01-11T21:37:13.3759481Z   warnings.warn(
2023-01-11T21:37:13.3759645Z ok (0.008s)
2023-01-11T21:37:13.3759892Z   test_normalize_args_preserve_meta (__main__.TestFXExperimental) ... ok (0.007s)
2023-01-11T21:37:13.3760209Z   test_normalize_binary_operators (__main__.TestFXExperimental) ... ok (0.056s)
2023-01-11T21:37:13.3760503Z   test_normalize_modules_exhaustive (__main__.TestFXExperimental)
2023-01-11T21:37:13.3761384Z Exhaustively test `Node.normalized_arguments` on all standard ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py:309: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T21:37:13.3761945Z   return F.conv1d(input, weight, bias, self.stride,
2023-01-11T21:37:13.3762155Z ok (1.632s)
2023-01-11T21:37:13.3762391Z   test_optimize_for_inference_cpu (__main__.TestFXExperimental) ... ok (0.265s)
2023-01-11T21:37:13.3762721Z   test_optimize_for_inference_cpu_torchvision (__main__.TestFXExperimental) ... ok (8.284s)
2023-01-11T21:37:13.3763048Z   test_partition_device_mapping (__main__.TestFXExperimental) ... ok (0.008s)
2023-01-11T21:37:13.3763355Z   test_partition_latency (__main__.TestFXExperimental) ... ok (0.006s)
2023-01-11T21:37:13.3763655Z   test_partition_node_manipulation (__main__.TestFXExperimental) ... ok (0.004s)
2023-01-11T21:37:13.3763971Z   test_replace_target_nodes_with (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3764265Z   test_saturate_host (__main__.TestFXExperimental) ... [0, 4]
2023-01-11T21:37:13.3764470Z [1, 2]
2023-01-11T21:37:13.3764627Z ok (0.006s)
2023-01-11T21:37:13.3764866Z   test_size_based_partition (__main__.TestFXExperimental) ... ok (0.005s)
2023-01-11T21:37:13.3765156Z   test_sparse_nn_partition (__main__.TestFXExperimental) ... ok (0.120s)
2023-01-11T21:37:13.3765457Z   test_split_module_default_arg (__main__.TestFXExperimental) ... ok (0.007s)
2023-01-11T21:37:13.3765774Z   test_split_module_kwargs_expansion (__main__.TestFXExperimental) ... ok (0.004s)
2023-01-11T21:37:13.3766086Z   test_split_qualname_mapping (__main__.TestFXExperimental) ... ok (0.005s)
2023-01-11T21:37:13.3766376Z   test_subgraph_creation (__main__.TestFXExperimental) ... ok (0.007s)
2023-01-11T21:37:13.3766677Z   test_subgraph_trivial_resnet (__main__.TestFXExperimental) ... ok (0.159s)
2023-01-11T21:37:13.3766978Z   test_subgraph_uniquename (__main__.TestFXExperimental) ... ok (0.006s)
2023-01-11T21:37:13.3767594Z   test_to_folder (__main__.TestFXExperimental) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph_module.py:476: UserWarning: Was not able to save the following children modules as reprs -saved as pickled files instead: ['seq']
2023-01-11T21:37:13.3768098Z   warnings.warn("Was not able to save the following children modules as reprs -"
2023-01-11T21:37:13.3768336Z ok (0.010s)
2023-01-11T21:37:13.3768598Z   test_traceable_function_with_nonstandard_name (__main__.TestFXExperimental) ... ok (0.003s)
2023-01-11T21:37:13.3768897Z   test_type_matches (__main__.TestFXExperimental) ... ok (0.002s)
2023-01-11T21:37:13.3769237Z 
2023-01-11T21:37:13.3769442Z ----------------------------------------------------------------------
2023-01-11T21:37:13.3769691Z Ran 39 tests in 14.033s
2023-01-11T21:37:13.3769808Z 
2023-01-11T21:37:13.3769860Z OK
2023-01-11T21:37:13.3769954Z 
2023-01-11T21:37:13.3770040Z Generating XML reports...
2023-01-11T21:37:13.3770473Z Generated XML report: test-reports/python-unittest/test_fx_experimental/TEST-TestFXExperimental-20230111213658.xml
2023-01-11T21:37:13.3770716Z 
2023-01-11T21:37:13.3770984Z ##[endgroup]
2023-01-11T21:37:13.3771386Z FINISHED PRINTING LOG FILE of test_fx_experimental (/var/lib/jenkins/workspace/test/test-reports/test_fx_experimental__l0jerl7)
2023-01-11T21:37:13.3771678Z 
2023-01-11T21:37:15.2358303Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:15.3001318Z Ignoring disabled issues:  []
2023-01-11T21:37:15.3156806Z Running test_fx_passes ... [2023-01-11 21:37:15.315395]
2023-01-11T21:37:15.3158472Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx_passes.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:15.315634]
2023-01-11T21:37:17.5660835Z 
2023-01-11T21:37:17.5661634Z Expand the folded group to see the log file of test_fx_passes
2023-01-11T21:37:17.5662654Z ##[group]PRINTING LOG FILE of test_fx_passes (/var/lib/jenkins/workspace/test/test-reports/test_fx_passes__3_2bzfx)
2023-01-11T21:37:17.5663085Z 
2023-01-11T21:37:17.5663153Z Running tests...
2023-01-11T21:37:17.5663812Z ----------------------------------------------------------------------
2023-01-11T21:37:17.5664208Z Test results will be stored in test-reports/python-unittest/test_fx_passes
2023-01-11T21:37:17.5664543Z   test_fuser_pass_deep_model (__main__.TestFXGraphPasses) ... ok (0.270s)
2023-01-11T21:37:17.5664947Z   test_fuser_util_partition_[['add', 'add_1', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.006s)
2023-01-11T21:37:17.5665382Z   test_fuser_util_partition_[['add', 'add_1'], ['add_5', 'add_6']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5665918Z   test_fuser_util_partition_[['add', 'linear', 'add_1', 'param', 'add_2', 'add_3', 'add_4', 'linear2', 'add_5', 'add_6', 'relu']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5666370Z   test_fuser_util_partition_[['add_2', 'add_3']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5666759Z   test_fuser_util_partition_[['add_3', 'add_4']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5667180Z   test_fuser_util_partition_[['add_4', 'add_1', 'add_3', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5667639Z   test_fuser_util_partition_[['add_5', 'add_6'], ['add_1', 'add_2', 'add_3', 'add_4']] (__main__.TestFXGraphPasses) ... ok (0.006s)
2023-01-11T21:37:17.5668049Z   test_fuser_util_partition_[['add_5', 'linear2']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5668448Z   test_fuser_util_partition_[['add_6', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5668840Z   test_fuser_util_partition_[['add_6', 'relu']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5669253Z   test_fuser_util_partition_[['param', 'add_1', 'linear']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5669648Z   test_fuser_util_partition_[['param', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5670069Z   test_fuser_util_xfail_partition_[['add', 'add_1', 'add_3']] (__main__.TestFXGraphPasses) ... ok (0.003s)
2023-01-11T21:37:17.5670523Z   test_fuser_util_xfail_partition_[['add', 'add_1'], ['add_1', 'add_5', 'add_6']] (__main__.TestFXGraphPasses) ... ok (0.003s)
2023-01-11T21:37:17.5670965Z   test_fuser_util_xfail_partition_[['add_4', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.002s)
2023-01-11T21:37:17.5671361Z   test_fuser_util_xfail_partition_[['relu', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.003s)
2023-01-11T21:37:17.5672015Z   test_partitioner_fn_<function TestPartitionFunctions_forward1 at 0x7f8bb90bca60>_expected_partition_[['add_7', 'add_6'], ['add_5', 'add_4', 'add_3'], ['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.006s)
2023-01-11T21:37:17.5672754Z   test_partitioner_fn_<function TestPartitionFunctions_forward10 at 0x7f8bb90bcf70>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.012s)
2023-01-11T21:37:17.5673446Z   test_partitioner_fn_<function TestPartitionFunctions_forward11 at 0x7f8bb90bd000>_expected_partition_[['add_1'], ['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5674287Z   test_partitioner_fn_<function TestPartitionFunctions_forward12 at 0x7f8bb90bd090>_expected_partition_[['add_2'], ['add_3', 'add_4', 'add_1'], ['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.006s)
2023-01-11T21:37:17.5674980Z   test_partitioner_fn_<function TestPartitionFunctions_forward13 at 0x7f8bb90bd120>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5675702Z   test_partitioner_fn_<function TestPartitionFunctions_forward14 at 0x7f8bb90bd1b0>_expected_partition_[['add', 'std_mean', 'getitem', 'getitem_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:37:17.5676517Z   test_partitioner_fn_<function TestPartitionFunctions_forward15 at 0x7f8bb90bd240>_expected_partition_[['add_1', 'add', 'permute_1', 'view', 'permute_2', 'permute_3', 'permute']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5677258Z   test_partitioner_fn_<function TestPartitionFunctions_forward15 at 0x7f8bb90bd240>_expected_partition_[['permute_1', 'add_1', 'add']]_bookend_non_compute_pass_True (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5678011Z   test_partitioner_fn_<function TestPartitionFunctions_forward16 at 0x7f8bb90bd2d0>_expected_partition_[['add_1', 'add', 'permute_1', 'view', 'permute_2', 'permute_3', 'permute']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5678733Z   test_partitioner_fn_<function TestPartitionFunctions_forward16 at 0x7f8bb90bd2d0>_expected_partition_[['permute_1', 'add_1', 'add']]_bookend_non_compute_pass_True (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5679430Z   test_partitioner_fn_<function TestPartitionFunctions_forward2 at 0x7f8bb90bcaf0>_expected_partition_[['add_3', 'add_2'], ['add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5680123Z   test_partitioner_fn_<function TestPartitionFunctions_forward3 at 0x7f8bb90bcb80>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:37:17.5680811Z   test_partitioner_fn_<function TestPartitionFunctions_forward4 at 0x7f8bb90bcc10>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:37:17.5681490Z   test_partitioner_fn_<function TestPartitionFunctions_forward5 at 0x7f8bb90bcca0>_expected_partition_[['add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:37:17.5682136Z   test_partitioner_fn_<function TestPartitionFunctions_forward6 at 0x7f8bb90bcd30>_expected_partition_[['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:37:17.5682823Z   test_partitioner_fn_<function TestPartitionFunctions_forward7 at 0x7f8bb90bcdc0>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:37:17.5683529Z   test_partitioner_fn_<function TestPartitionFunctions_forward8 at 0x7f8bb90bce50>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T21:37:17.5684225Z   test_partitioner_fn_<function TestPartitionFunctions_forward9 at 0x7f8bb90bcee0>_expected_partition_[['add_3', 'add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T21:37:17.5684797Z   test_subgraph_matcher_test_model_<class '__main___ChainRepeatedPattern'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:37:17.5685299Z   test_subgraph_matcher_test_model_<class '__main___DiamondShapePatternTestCase'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5685838Z   test_subgraph_matcher_test_model_<class '__main___MultiOutputWithWithInvalidMatches'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:37:17.5686422Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsHorizontalPattern'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5686961Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsIdenticalAnchor'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5687527Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsMultipleNonOverlappingMatches'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:37:17.5688124Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsMultipleOverlappingMatches'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:37:17.5688709Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsWithDependency'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5689402Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsWithoutDependency'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5689894Z   test_subgraph_matcher_test_model_<class '__main___NoAnchorFound'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5690381Z   test_subgraph_matcher_test_model_<class '__main___NonFullyContainedMatches'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:37:17.5690879Z   test_subgraph_matcher_test_model_<class '__main___QuantizationFp8Pattern'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T21:37:17.5691365Z   test_subgraph_matcher_test_model_<class '__main___QuantizationModel'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5691847Z   test_subgraph_matcher_test_model_<class '__main___SimpleFullGraphMatching'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5692327Z   test_subgraph_matcher_test_model_<class '__main___SimplePattern'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T21:37:17.5692797Z   test_subgraph_matcher_test_model_<class '__main___SingleNodePattern'> (__main__.TestFXMatcherUtils) ... ok (0.003s)
2023-01-11T21:37:17.5693011Z 
2023-01-11T21:37:17.5693211Z ----------------------------------------------------------------------
2023-01-11T21:37:17.5693446Z Ran 51 tests in 0.492s
2023-01-11T21:37:17.5693562Z 
2023-01-11T21:37:17.5693624Z OK
2023-01-11T21:37:17.5693717Z 
2023-01-11T21:37:17.5693805Z Generating XML reports...
2023-01-11T21:37:17.5694224Z Generated XML report: test-reports/python-unittest/test_fx_passes/TEST-TestFXGraphPasses-20230111213716.xml
2023-01-11T21:37:17.5694736Z Generated XML report: test-reports/python-unittest/test_fx_passes/TEST-TestFXMatcherUtils-20230111213716.xml
2023-01-11T21:37:17.5694971Z 
2023-01-11T21:37:17.5695234Z ##[endgroup]
2023-01-11T21:37:17.5695615Z FINISHED PRINTING LOG FILE of test_fx_passes (/var/lib/jenkins/workspace/test/test-reports/test_fx_passes__3_2bzfx)
2023-01-11T21:37:17.5695812Z 
2023-01-11T21:37:19.5077573Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:19.5881517Z Ignoring disabled issues:  []
2023-01-11T21:37:19.6033464Z Running test_fx_reinplace_pass ... [2023-01-11 21:37:19.603083]
2023-01-11T21:37:19.6034877Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx_reinplace_pass.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:19.603309]
2023-01-11T21:37:22.8129714Z 
2023-01-11T21:37:22.8130237Z Expand the folded group to see the log file of test_fx_reinplace_pass
2023-01-11T21:37:22.8131251Z ##[group]PRINTING LOG FILE of test_fx_reinplace_pass (/var/lib/jenkins/workspace/test/test-reports/test_fx_reinplace_pass_saswhh0j)
2023-01-11T21:37:22.8131513Z 
2023-01-11T21:37:22.8131592Z Running tests...
2023-01-11T21:37:22.8131994Z ----------------------------------------------------------------------
2023-01-11T21:37:22.8132402Z Test results will be stored in test-reports/python-unittest/test_fx_reinplace_pass
2023-01-11T21:37:22.8132727Z   test_out_node_updated (__main__.TestReinplacePass) ... ok (0.285s)
2023-01-11T21:37:22.8133212Z   test_reinplace_basic (__main__.TestReinplacePass) ... ok (0.120s)
2023-01-11T21:37:22.8133517Z   test_reinplace_different_metadata (__main__.TestReinplacePass) ... ok (0.029s)
2023-01-11T21:37:22.8133818Z   test_reinplace_index_mutation (__main__.TestReinplacePass) ... ok (0.145s)
2023-01-11T21:37:22.8134129Z   test_reinplace_overlapping_memory (__main__.TestReinplacePass) ... ok (0.030s)
2023-01-11T21:37:22.8134435Z   test_reinplace_scatter_op (__main__.TestReinplacePass) ... ok (0.254s)
2023-01-11T21:37:22.8134724Z   test_reinplace_scatter_twice (__main__.TestReinplacePass) ... ok (0.238s)
2023-01-11T21:37:22.8135065Z   test_reinplace_scatter_twice_with_different_view_op_invalid (__main__.TestReinplacePass) ... ok (0.065s)
2023-01-11T21:37:22.8135490Z   test_reinplace_scatter_twice_with_different_view_op_invalid2 (__main__.TestReinplacePass) ... ok (0.064s)
2023-01-11T21:37:22.8135857Z   test_reinplace_scatter_twice_with_different_view_op_valid (__main__.TestReinplacePass) ... ok (0.065s)
2023-01-11T21:37:22.8136175Z   test_reinplace_with_view (__main__.TestReinplacePass) ... ok (0.036s)
2023-01-11T21:37:22.8136339Z 
2023-01-11T21:37:22.8136542Z ----------------------------------------------------------------------
2023-01-11T21:37:22.8136782Z Ran 11 tests in 1.332s
2023-01-11T21:37:22.8136894Z 
2023-01-11T21:37:22.8136953Z OK
2023-01-11T21:37:22.8137032Z 
2023-01-11T21:37:22.8137116Z Generating XML reports...
2023-01-11T21:37:22.8137545Z Generated XML report: test-reports/python-unittest/test_fx_reinplace_pass/TEST-TestReinplacePass-20230111213721.xml
2023-01-11T21:37:22.8137787Z 
2023-01-11T21:37:22.8138017Z ##[endgroup]
2023-01-11T21:37:22.8138406Z FINISHED PRINTING LOG FILE of test_fx_reinplace_pass (/var/lib/jenkins/workspace/test/test-reports/test_fx_reinplace_pass_saswhh0j)
2023-01-11T21:37:22.8138634Z 
2023-01-11T21:37:24.6893392Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:24.7783135Z Ignoring disabled issues:  []
2023-01-11T21:37:24.9570609Z Running test_hub ... [2023-01-11 21:37:24.956679]
2023-01-11T21:37:24.9571885Z Executing ['/opt/conda/bin/python', '-bb', 'test_hub.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:24.956961]
2023-01-11T21:37:26.5642198Z 
2023-01-11T21:37:26.5642679Z Expand the folded group to see the log file of test_hub
2023-01-11T21:37:26.5643807Z ##[group]PRINTING LOG FILE of test_hub (/var/lib/jenkins/workspace/test/test-reports/test_hub_ren2c9sh)
2023-01-11T21:37:26.5644188Z 
2023-01-11T21:37:26.5644574Z ##[endgroup]
2023-01-11T21:37:26.5645326Z FINISHED PRINTING LOG FILE of test_hub (/var/lib/jenkins/workspace/test/test-reports/test_hub_ren2c9sh)
2023-01-11T21:37:26.5645671Z 
2023-01-11T21:37:28.3853736Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:28.4544219Z Ignoring disabled issues:  []
2023-01-11T21:37:28.4698057Z Running test_import_stats ... [2023-01-11 21:37:28.469517]
2023-01-11T21:37:28.4699926Z Executing ['/opt/conda/bin/python', '-bb', 'test_import_stats.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:28.469785]
2023-01-11T21:37:33.1284285Z 
2023-01-11T21:37:33.1284768Z Expand the folded group to see the log file of test_import_stats
2023-01-11T21:37:33.1285737Z ##[group]PRINTING LOG FILE of test_import_stats (/var/lib/jenkins/workspace/test/test-reports/test_import_stats_wey_g2s2)
2023-01-11T21:37:33.1286106Z 
2023-01-11T21:37:33.1286197Z Running tests...
2023-01-11T21:37:33.1286617Z ----------------------------------------------------------------------
2023-01-11T21:37:33.1289532Z Test results will be stored in test-reports/python-unittest/test_import_stats
2023-01-11T21:37:33.1289989Z   test_time_cuda_device_count (__main__.TestImportTime) ... ok (1.626s)
2023-01-11T21:37:33.1290283Z   test_time_import_torch (__main__.TestImportTime) ... ok (1.389s)
2023-01-11T21:37:33.1290464Z 
2023-01-11T21:37:33.1290670Z ----------------------------------------------------------------------
2023-01-11T21:37:33.1291072Z Ran 2 tests in 3.016s
2023-01-11T21:37:33.1291187Z 
2023-01-11T21:37:33.1291247Z OK
2023-01-11T21:37:33.1291336Z 
2023-01-11T21:37:33.1291422Z Generating XML reports...
2023-01-11T21:37:33.1291845Z Generated XML report: test-reports/python-unittest/test_import_stats/TEST-TestImportTime-20230111213729.xml
2023-01-11T21:37:33.1292064Z 
2023-01-11T21:37:33.1292383Z ##[endgroup]
2023-01-11T21:37:33.1292771Z FINISHED PRINTING LOG FILE of test_import_stats (/var/lib/jenkins/workspace/test/test-reports/test_import_stats_wey_g2s2)
2023-01-11T21:37:33.1292989Z 
2023-01-11T21:37:34.9995762Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:35.0650905Z Ignoring disabled issues:  []
2023-01-11T21:37:35.0804958Z Running test_itt ... [2023-01-11 21:37:35.080235]
2023-01-11T21:37:35.0807073Z Executing ['/opt/conda/bin/python', '-bb', 'test_itt.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:35.080486]
2023-01-11T21:37:36.9540698Z 
2023-01-11T21:37:36.9541281Z Expand the folded group to see the log file of test_itt
2023-01-11T21:37:36.9542282Z ##[group]PRINTING LOG FILE of test_itt (/var/lib/jenkins/workspace/test/test-reports/test_itt_ty1txm0m)
2023-01-11T21:37:36.9542636Z 
2023-01-11T21:37:36.9542746Z Running tests...
2023-01-11T21:37:36.9543307Z ----------------------------------------------------------------------
2023-01-11T21:37:36.9543801Z Test results will be stored in test-reports/python-unittest/test_itt
2023-01-11T21:37:36.9544109Z   test_itt (__main__.TestItt) ... ok (0.221s)
2023-01-11T21:37:36.9544241Z 
2023-01-11T21:37:36.9544471Z ----------------------------------------------------------------------
2023-01-11T21:37:36.9544740Z Ran 1 test in 0.221s
2023-01-11T21:37:36.9544853Z 
2023-01-11T21:37:36.9544901Z OK
2023-01-11T21:37:36.9544990Z 
2023-01-11T21:37:36.9545080Z Generating XML reports...
2023-01-11T21:37:36.9545519Z Generated XML report: test-reports/python-unittest/test_itt/TEST-TestItt-20230111213736.xml
2023-01-11T21:37:36.9545882Z 
2023-01-11T21:37:36.9546224Z ##[endgroup]
2023-01-11T21:37:36.9546889Z FINISHED PRINTING LOG FILE of test_itt (/var/lib/jenkins/workspace/test/test-reports/test_itt_ty1txm0m)
2023-01-11T21:37:36.9547161Z 
2023-01-11T21:37:38.7963784Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:38.8615056Z Ignoring disabled issues:  []
2023-01-11T21:37:38.8767297Z Running test_jit_autocast ... [2023-01-11 21:37:38.876461]
2023-01-11T21:37:38.8769595Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_autocast.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:38.876704]
2023-01-11T21:37:55.2925582Z 
2023-01-11T21:37:55.2926108Z Expand the folded group to see the log file of test_jit_autocast
2023-01-11T21:37:55.2927047Z ##[group]PRINTING LOG FILE of test_jit_autocast (/var/lib/jenkins/workspace/test/test-reports/test_jit_autocast_guvw60ot)
2023-01-11T21:37:55.2927816Z CUDA not available, skipping tests
2023-01-11T21:37:55.2928126Z 
2023-01-11T21:37:55.2928299Z Running tests...
2023-01-11T21:37:55.2929273Z ----------------------------------------------------------------------
2023-01-11T21:37:55.2930187Z Test results will be stored in test-reports/python-unittest/test_jit_autocast
2023-01-11T21:37:55.2930910Z   test_autocast_api (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2931739Z   test_autocast_api_not_supported (__main__.TestAutocast) ... skip: we need to provide dtype argument at this moment (0.001s)
2023-01-11T21:37:55.2932593Z   test_autocast_autodiff (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2933402Z   test_autocast_decorator (__main__.TestAutocast) ... skip: autocast decorators not supported (0.000s)
2023-01-11T21:37:55.2934234Z   test_autocast_decorator_outside_jit (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2934997Z   test_autocast_mixed_dtypes (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2935682Z   test_callees (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2936660Z   test_callees_with_autocast_off (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2937371Z   test_callees_with_autocast_on (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2938105Z   test_conditional_autocast (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2938890Z   test_control_flow (__main__.TestAutocast) ... skip: broken due to lack of type propagation (0.001s)
2023-01-11T21:37:55.2939656Z   test_divergent_autocast (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2940306Z   test_divergent_types (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2941001Z   test_duplicate_inputs (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2941671Z   test_eager_and_script (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2942528Z   test_explicit_casts (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2943215Z   test_fp32_policy (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2943964Z   test_fp32_policy_with_fp64 (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2944660Z   test_fp32_set_opt_dtype_policy (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2945391Z   test_fp32_set_opt_dtype_policy_fp64 (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2946061Z   test_ignore_amp (__main__.TestAutocast) ... ok (0.013s)
2023-01-11T21:37:55.2946734Z   test_implicitly_nested_autocast (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2947414Z   test_inplace (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2948083Z   test_jit_autocast_softmax_cpu (__main__.TestAutocast) ... ok (0.056s)
2023-01-11T21:37:55.2948816Z   test_jit_autocast_softmax_gpu (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2949559Z   test_jit_call_method_under_autocast (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2950329Z   test_jit_executor_under_autocast (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2951071Z   test_jit_freeze_autocast_basic (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2951824Z   test_jit_freeze_autocast_constants (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2952503Z   test_linear_bf16 (__main__.TestAutocast) ... skip: No cuda bfloat16 support (0.000s)
2023-01-11T21:37:55.2953219Z   test_minimal (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2953883Z   test_minimal_cpu (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2954552Z   test_minimal_off (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2955202Z   test_nested_autocast (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2955918Z   test_promote_policy (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2956620Z   test_promote_policy_fp64 (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2957316Z   test_reused_autocast (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2958084Z   test_reused_autocast_expr (__main__.TestAutocast) ... skip: unsuported autocast syntax (0.001s)
2023-01-11T21:37:55.2958859Z   test_runtime_autocast_state (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2959617Z   test_runtime_autocast_state_expr (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2960315Z   test_script_and_tracing (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2961192Z   test_script_and_tracing_with_autocast (__main__.TestAutocast) ... skip: autocast(False) is ignored inside traced functions (0.000s)
2023-01-11T21:37:55.2962008Z   test_script_module (__main__.TestAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2962712Z   test_tracing_and_script (__main__.TestAutocast) ... skip: No cuda (0.000s)
2023-01-11T21:37:55.2963579Z   test_tracing_with_autocast_and_script (__main__.TestAutocast) ... skip: scripted called from traced TorchScript is not yet working (0.000s)
2023-01-11T21:37:55.2964555Z   test_cat_promote (__main__.TestJitTraceAutocast) ... ok (0.167s)
2023-01-11T21:37:55.2965300Z   test_generate_autocast_jit_trace_model (__main__.TestJitTraceAutocast) ... ok (3.541s)
2023-01-11T21:37:55.2966079Z   test_nchw_autocast_jit_trace_model (__main__.TestJitTraceAutocast) ... ok (4.483s)
2023-01-11T21:37:55.2966848Z   test_nhwc_autocast_jit_trace_model (__main__.TestJitTraceAutocast) ... ok (4.823s)
2023-01-11T21:37:55.2967605Z   test_script_autocast_cpu (__main__.TestJitTraceAutocast) ... ok (0.059s)
2023-01-11T21:37:55.2968362Z   test_script_autocast_cuda (__main__.TestJitTraceAutocast) ... skip: No cuda (0.001s)
2023-01-11T21:37:55.2969454Z   test_script_autocast_enable_and_check (__main__.TestJitTraceAutocast) ... ok (0.070s)
2023-01-11T21:37:55.2970243Z   test_scripted_aliasing (__main__.TestJitTraceAutocast) ... ok (0.063s)
2023-01-11T21:37:55.2970649Z 
2023-01-11T21:37:55.2971167Z ----------------------------------------------------------------------
2023-01-11T21:37:55.2971717Z Ran 53 tests in 13.313s
2023-01-11T21:37:55.2971973Z 
2023-01-11T21:37:55.2972134Z OK (skipped=44)
2023-01-11T21:37:55.2972385Z 
2023-01-11T21:37:55.2972582Z Generating XML reports...
2023-01-11T21:37:55.2973557Z Generated XML report: test-reports/python-unittest/test_jit_autocast/TEST-TestAutocast-20230111213741.xml
2023-01-11T21:37:55.2974844Z Generated XML report: test-reports/python-unittest/test_jit_autocast/TEST-TestJitTraceAutocast-20230111213741.xml
2023-01-11T21:37:55.2975466Z 
2023-01-11T21:37:55.2976052Z ##[endgroup]
2023-01-11T21:37:55.2977006Z FINISHED PRINTING LOG FILE of test_jit_autocast (/var/lib/jenkins/workspace/test/test-reports/test_jit_autocast_guvw60ot)
2023-01-11T21:37:55.2977547Z 
2023-01-11T21:37:57.1021594Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:37:57.1667500Z Ignoring disabled issues:  []
2023-01-11T21:37:57.1820100Z Running test_jit_fuser_te ... [2023-01-11 21:37:57.181725]
2023-01-11T21:37:57.1821915Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_fuser_te.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:37:57.181980]
2023-01-11T21:39:33.8209967Z 
2023-01-11T21:39:33.8210388Z Expand the folded group to see the log file of test_jit_fuser_te
2023-01-11T21:39:33.8211101Z ##[group]PRINTING LOG FILE of test_jit_fuser_te (/var/lib/jenkins/workspace/test/test-reports/test_jit_fuser_te_25znr0r4)
2023-01-11T21:39:33.8211846Z CUDA not available, skipping tests
2023-01-11T21:39:33.8250124Z 
2023-01-11T21:39:33.8250323Z Running tests...
2023-01-11T21:39:33.8250964Z ----------------------------------------------------------------------
2023-01-11T21:39:33.8251586Z Test results will be stored in test-reports/python-unittest/test_jit_fuser_te
2023-01-11T21:39:33.8252134Z   test_autodiff_fallback (jit.test_fuser_common.TestFuserCommon) ... ok (0.121s)
2023-01-11T21:39:33.8252821Z   test_abs (__main__.TestTEFuserDynamic) ... ok (0.294s)
2023-01-11T21:39:33.8253295Z   test_adaptive_avg_pool2d (__main__.TestTEFuserDynamic) ... ok (0.066s)
2023-01-11T21:39:33.8253580Z   test_add_bool (__main__.TestTEFuserDynamic) ... ok (0.280s)
2023-01-11T21:39:33.8253848Z   test_addcmul (__main__.TestTEFuserDynamic) ... ok (0.093s)
2023-01-11T21:39:33.8254156Z   test_arg_configurations_smoke (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:39:33.8254637Z   test_autocast_down (__main__.TestTEFuserDynamic) ... skip: half-precision NNC fusion requires CUDA (0.000s)
2023-01-11T21:39:33.8255099Z   test_autocast_up (__main__.TestTEFuserDynamic) ... skip: half-precision NNC fusion requires CUDA (0.000s)
2023-01-11T21:39:33.8255406Z   test_batch_norm (__main__.TestTEFuserDynamic) ... ok (0.687s)
2023-01-11T21:39:33.8255688Z   test_binary_div_ops (__main__.TestTEFuserDynamic) ... ok (2.440s)
2023-01-11T21:39:33.8255966Z   test_binary_ops (__main__.TestTEFuserDynamic) ... ok (8.681s)
2023-01-11T21:39:33.8256414Z   test_binary_pow (__main__.TestTEFuserDynamic) ... ok (0.792s)
2023-01-11T21:39:33.8256684Z   test_binary_scalar_ops (__main__.TestTEFuserDynamic) ... ok (1.031s)
2023-01-11T21:39:33.8267458Z   test_binary_tensor_scalar_ops (__main__.TestTEFuserDynamic) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:39:33.8267883Z   test_bitwise_ops (__main__.TestTEFuserDynamic) ... ok (1.898s)
2023-01-11T21:39:33.8268153Z   test_broadcast (__main__.TestTEFuserDynamic) ... ok (0.230s)
2023-01-11T21:39:33.8268433Z   test_cat_2k_args (__main__.TestTEFuserDynamic) ... ok (0.835s)
2023-01-11T21:39:33.8268715Z   test_cat_graph_opt (__main__.TestTEFuserDynamic) ... ok (0.784s)
2023-01-11T21:39:33.8269042Z   test_channels_last_dims_dynamic (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8269497Z   test_checks_cat_inputs (__main__.TestTEFuserDynamic) ... ok (0.467s)
2023-01-11T21:39:33.8269811Z   test_chunk (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:39:33.8270167Z   test_chunk_correctness (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:39:33.8270506Z   test_chunk_distributes (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:39:33.8270880Z   test_chunk_motion_deduplicates_inputs (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:39:33.8271248Z   test_chunk_mul_one (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:39:33.8271592Z   test_chunk_multiple (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:39:33.8271881Z   test_clamp (__main__.TestTEFuserDynamic) ... ok (3.627s)
2023-01-11T21:39:33.8272156Z   test_clamp_double (__main__.TestTEFuserDynamic) ... ok (0.143s)
2023-01-11T21:39:33.8272431Z   test_clamp_int (__main__.TestTEFuserDynamic) ... ok (0.136s)
2023-01-11T21:39:33.8272703Z   test_comparison_eq_ne (__main__.TestTEFuserDynamic) ... ok (0.286s)
2023-01-11T21:39:33.8272989Z   test_comparison_ge_le (__main__.TestTEFuserDynamic) ... ok (0.284s)
2023-01-11T21:39:33.8273267Z   test_comparison_gt_lt (__main__.TestTEFuserDynamic) ... ok (0.286s)
2023-01-11T21:39:33.8273535Z   test_concat (__main__.TestTEFuserDynamic) ... ok (0.573s)
2023-01-11T21:39:33.8273797Z   test_concat_invariant (__main__.TestTEFuserDynamic) ... ok (0.756s)
2023-01-11T21:39:33.8274125Z   test_constant_chunk_shapes (__main__.TestTEFuserDynamic) ... skip: TODO: chunk dynamic shapes (0.001s)
2023-01-11T21:39:33.8274612Z   test_conv2d (__main__.TestTEFuserDynamic) ... skip: don't run conv with dynamic shapes (0.001s)
2023-01-11T21:39:33.8275037Z   test_conv2d_depthwise (__main__.TestTEFuserDynamic) ... skip: don't run conv with dynamic shapes (0.001s)
2023-01-11T21:39:33.8275383Z   test_cuda_half (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8275672Z   test_dims (__main__.TestTEFuserDynamic) ... ok (0.126s)
2023-01-11T21:39:33.8275936Z   test_disabled (__main__.TestTEFuserDynamic) ... ok (0.005s)
2023-01-11T21:39:33.8276190Z   test_div_bool (__main__.TestTEFuserDynamic) ... ok (0.138s)
2023-01-11T21:39:33.8276458Z   test_dynamic_cat (__main__.TestTEFuserDynamic) ... ok (0.009s)
2023-01-11T21:39:33.8276739Z   test_dynamic_shapes (__main__.TestTEFuserDynamic) ... ok (2.085s)
2023-01-11T21:39:33.8277014Z   test_eq_unsqueeze_type_as (__main__.TestTEFuserDynamic) ... ok (0.311s)
2023-01-11T21:39:33.8277289Z   test_erf (__main__.TestTEFuserDynamic) ... ok (0.001s)
2023-01-11T21:39:33.8277571Z   test_exhaust_specializations (__main__.TestTEFuserDynamic) ... ok (0.029s)
2023-01-11T21:39:33.8277839Z   test_exp (__main__.TestTEFuserDynamic) ... ok (0.089s)
2023-01-11T21:39:33.8278146Z   test_fusion_reuse_multi_gpu (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8278448Z   test_gelu (__main__.TestTEFuserDynamic) ... ok (0.026s)
2023-01-11T21:39:33.8278784Z   test_hardsigmoid_fwd_bwd (__main__.TestTEFuserDynamic) ... ok (0.265s)
2023-01-11T21:39:33.8279062Z   test_hardswish_fwd_bwd (__main__.TestTEFuserDynamic) ... ok (0.291s)
2023-01-11T21:39:33.8279357Z   test_inlined_optimized_graph (__main__.TestTEFuserDynamic) ... ok (0.410s)
2023-01-11T21:39:33.8279638Z   test_isnan (__main__.TestTEFuserDynamic) ... ok (0.643s)
2023-01-11T21:39:33.8279931Z   test_kernel_cache_multi_gpu (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8280233Z   test_lerp (__main__.TestTEFuserDynamic) ... ok (0.332s)
2023-01-11T21:39:33.8280700Z   test_list_ops (__main__.TestTEFuserDynamic) ... skip: FIXME: fuser doesn't include ListConstruct nodes to the group causing a failure (0.001s)
2023-01-11T21:39:33.8281040Z   test_lstm (__main__.TestTEFuserDynamic) ... ok (0.750s)
2023-01-11T21:39:33.8281323Z   test_lstm_concat (__main__.TestTEFuserDynamic) ... ok (0.247s)
2023-01-11T21:39:33.8281616Z   test_lstm_gates_permutations (__main__.TestTEFuserDynamic) ... ok (10.760s)
2023-01-11T21:39:33.8281907Z   test_lstm_traced (__main__.TestTEFuserDynamic) ... ok (0.430s)
2023-01-11T21:39:33.8282196Z   test_masked_fill (__main__.TestTEFuserDynamic) ... skip: Temporarily disabled (0.001s)
2023-01-11T21:39:33.8282612Z   test_matmul (__main__.TestTEFuserDynamic) ... skip: don't run conv with dynamic shapes (0.002s)
2023-01-11T21:39:33.8283028Z   test_milstm (__main__.TestTEFuserDynamic) ... skip: don't run conv with dynamic shapes (0.001s)
2023-01-11T21:39:33.8283328Z   test_minmax (__main__.TestTEFuserDynamic) ... ok (1.138s)
2023-01-11T21:39:33.8283584Z   test_minmax_int_ops (__main__.TestTEFuserDynamic) ... ok (0.793s)
2023-01-11T21:39:33.8283856Z   test_mul_bool (__main__.TestTEFuserDynamic) ... ok (0.168s)
2023-01-11T21:39:33.8284120Z   test_neg_pow (__main__.TestTEFuserDynamic) ... ok (0.137s)
2023-01-11T21:39:33.8284497Z   test_nonzero_device_cuda (__main__.TestTEFuserDynamic) ... skip: needs non-zero device (0.001s)
2023-01-11T21:39:33.8284800Z   test_nop (__main__.TestTEFuserDynamic) ... ok (0.001s)
2023-01-11T21:39:33.8285273Z   test_profiler (__main__.TestTEFuserDynamic) ... STAGE:2023-01-11 21:38:43 12313:12313 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:39:33.8285755Z STAGE:2023-01-11 21:38:43 12313:12313 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:39:33.8286194Z STAGE:2023-01-11 21:38:43 12313:12313 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:39:33.8286454Z ok (0.076s)
2023-01-11T21:39:33.8286713Z   test_rand_broadcast_cuda (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8287033Z   test_rand_cuda (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8287358Z   test_rand_diamond (__main__.TestTEFuserDynamic) ... skip: fuser requires CUDA (0.000s)
2023-01-11T21:39:33.8287645Z   test_relu (__main__.TestTEFuserDynamic) ... ok (0.138s)
2023-01-11T21:39:33.8287908Z   test_relu_fwd_bwd (__main__.TestTEFuserDynamic) ... ok (0.150s)
2023-01-11T21:39:33.8288192Z   test_remove_output_used_only_in_size (__main__.TestTEFuserDynamic) ... ok (0.029s)
2023-01-11T21:39:33.8288480Z   test_scalar (__main__.TestTEFuserDynamic) ... ok (0.048s)
2023-01-11T21:39:33.8288747Z   test_scalar_arg (__main__.TestTEFuserDynamic) ... ok (0.506s)
2023-01-11T21:39:33.8289014Z   test_scalar_only_inputs (__main__.TestTEFuserDynamic) ... ok (0.046s)
2023-01-11T21:39:33.8289541Z   test_skip_grad_in_check (__main__.TestTEFuserDynamic) ... ok (0.165s)
2023-01-11T21:39:33.8289832Z   test_small_constant (__main__.TestTEFuserDynamic) ... ok (0.293s)
2023-01-11T21:39:33.8290097Z   test_sub_gt_and (__main__.TestTEFuserDynamic) ... ok (0.179s)
2023-01-11T21:39:33.8290365Z   test_sum_dim (__main__.TestTEFuserDynamic) ... ok (0.268s)
2023-01-11T21:39:33.8290645Z   test_sum_keepdim_cast (__main__.TestTEFuserDynamic) ... ok (0.138s)
2023-01-11T21:39:33.8290925Z   test_sum_simple (__main__.TestTEFuserDynamic) ... ok (0.093s)
2023-01-11T21:39:33.8291262Z   test_superslomo (__main__.TestTEFuserDynamic) ... ok (1.310s)
2023-01-11T21:39:33.8291542Z   test_tensor_scalar_ops (__main__.TestTEFuserDynamic) ... ok (0.654s)
2023-01-11T21:39:33.8291832Z   test_ternary_norm_ops (__main__.TestTEFuserDynamic) ... ok (0.415s)
2023-01-11T21:39:33.8292100Z   test_ternary_ops (__main__.TestTEFuserDynamic) ... ok (0.648s)
2023-01-11T21:39:33.8292376Z   test_threshold (__main__.TestTEFuserDynamic) ... ok (0.210s)
2023-01-11T21:39:33.8292647Z   test_to_device (__main__.TestTEFuserDynamic) ... ok (0.147s)
2023-01-11T21:39:33.8292903Z   test_to_dtype (__main__.TestTEFuserDynamic) ... ok (0.382s)
2023-01-11T21:39:33.8293170Z   test_torch_to (__main__.TestTEFuserDynamic) ... ok (3.900s)
2023-01-11T21:39:33.8293439Z   test_type_as_cat (__main__.TestTEFuserDynamic) ... ok (2.069s)
2023-01-11T21:39:33.8293751Z   test_typecheck (__main__.TestTEFuserDynamic) ... ok (0.049s)
2023-01-11T21:39:33.8294085Z   test_unary_ops (__main__.TestTEFuserDynamic) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:39:33.8294427Z   test_unrolled_cat (__main__.TestTEFuserDynamic) ... ok (0.012s)
2023-01-11T21:39:33.8294719Z   test_unsqueeze_size_calculation (__main__.TestTEFuserDynamic) ... ok (0.347s)
2023-01-11T21:39:33.8295005Z   test_unsqueeze_var_dim (__main__.TestTEFuserDynamic) ... ok (0.353s)
2023-01-11T21:39:33.8295733Z   test_unsupported_dtypes (__main__.TestTEFuserDynamic) ... /var/lib/jenkins/workspace/test/test_jit_fuser_te.py:1186: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:39:33.8296243Z   return v.to(dtype)
2023-01-11T21:39:33.8296421Z ok (0.042s)
2023-01-11T21:39:33.8296655Z   test_where_and_typing (__main__.TestTEFuserDynamic) ... ok (0.314s)
2023-01-11T21:39:33.8296920Z   test_where_ops (__main__.TestTEFuserDynamic) ... ok (2.671s)
2023-01-11T21:39:33.8297413Z   test_with_strict_fusion (__main__.TestTEFuserDynamic) ... /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py:216: UserWarning: Only works in script mode
2023-01-11T21:39:33.8297766Z   warnings.warn("Only works in script mode")
2023-01-11T21:39:33.8297953Z ok (0.172s)
2023-01-11T21:39:33.8298185Z   test_zero_element_tensors (__main__.TestTEFuserDynamic) ... ok (0.093s)
2023-01-11T21:39:33.8298457Z   test_abs (__main__.TestTEFuserStatic) ... ok (0.050s)
2023-01-11T21:39:33.8298732Z   test_adaptive_avg_pool2d (__main__.TestTEFuserStatic) ... ok (0.060s)
2023-01-11T21:39:33.8299001Z   test_add_bool (__main__.TestTEFuserStatic) ... ok (0.097s)
2023-01-11T21:39:33.8299264Z   test_addcmul (__main__.TestTEFuserStatic) ... ok (0.041s)
2023-01-11T21:39:33.8299552Z   test_arg_configurations_smoke (__main__.TestTEFuserStatic) ... ok (0.040s)
2023-01-11T21:39:33.8299966Z   test_autocast_down (__main__.TestTEFuserStatic) ... skip: half-precision NNC fusion requires CUDA (0.000s)
2023-01-11T21:39:33.8300422Z   test_autocast_up (__main__.TestTEFuserStatic) ... skip: half-precision NNC fusion requires CUDA (0.000s)
2023-01-11T21:39:33.8300744Z   test_batch_norm (__main__.TestTEFuserStatic) ... ok (0.278s)
2023-01-11T21:39:33.8301003Z   test_binary_div_ops (__main__.TestTEFuserStatic) ... ok (1.079s)
2023-01-11T21:39:33.8301275Z   test_binary_ops (__main__.TestTEFuserStatic) ... ok (3.149s)
2023-01-11T21:39:33.8301542Z   test_binary_pow (__main__.TestTEFuserStatic) ... ok (0.262s)
2023-01-11T21:39:33.8301817Z   test_binary_scalar_ops (__main__.TestTEFuserStatic) ... ok (1.015s)
2023-01-11T21:39:33.8302095Z   test_binary_tensor_scalar_ops (__main__.TestTEFuserStatic) ... ok (8.337s)
2023-01-11T21:39:33.8302377Z   test_bitwise_ops (__main__.TestTEFuserStatic) ... ok (0.701s)
2023-01-11T21:39:33.8302646Z   test_broadcast (__main__.TestTEFuserStatic) ... ok (0.055s)
2023-01-11T21:39:33.8302901Z   test_cat_2k_args (__main__.TestTEFuserStatic) ... ok (0.821s)
2023-01-11T21:39:33.8303170Z   test_cat_graph_opt (__main__.TestTEFuserStatic) ... ok (0.079s)
2023-01-11T21:39:33.8303525Z   test_channels_last_dims_dynamic (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8303922Z   test_checks_cat_inputs (__main__.TestTEFuserStatic) ... ok (0.069s)
2023-01-11T21:39:33.8304177Z   test_chunk (__main__.TestTEFuserStatic) ... ok (0.099s)
2023-01-11T21:39:33.8304451Z   test_chunk_correctness (__main__.TestTEFuserStatic) ... ok (1.188s)
2023-01-11T21:39:33.8304736Z   test_chunk_distributes (__main__.TestTEFuserStatic) ... ok (0.054s)
2023-01-11T21:39:33.8305025Z   test_chunk_motion_deduplicates_inputs (__main__.TestTEFuserStatic) ... ok (0.093s)
2023-01-11T21:39:33.8305324Z   test_chunk_mul_one (__main__.TestTEFuserStatic) ... ok (0.111s)
2023-01-11T21:39:33.8305600Z   test_chunk_multiple (__main__.TestTEFuserStatic) ... ok (0.176s)
2023-01-11T21:39:33.8305889Z   test_clamp (__main__.TestTEFuserStatic) ... ok (0.875s)
2023-01-11T21:39:33.8306154Z   test_clamp_double (__main__.TestTEFuserStatic) ... ok (0.057s)
2023-01-11T21:39:33.8306426Z   test_clamp_int (__main__.TestTEFuserStatic) ... ok (0.053s)
2023-01-11T21:39:33.8306701Z   test_comparison_eq_ne (__main__.TestTEFuserStatic) ... ok (0.081s)
2023-01-11T21:39:33.8306968Z   test_comparison_ge_le (__main__.TestTEFuserStatic) ... ok (0.097s)
2023-01-11T21:39:33.8307247Z   test_comparison_gt_lt (__main__.TestTEFuserStatic) ... ok (0.082s)
2023-01-11T21:39:33.8307517Z   test_concat (__main__.TestTEFuserStatic) ... ok (0.050s)
2023-01-11T21:39:33.8307772Z   test_concat_invariant (__main__.TestTEFuserStatic) ... ok (0.077s)
2023-01-11T21:39:33.8308406Z   test_constant_chunk_shapes (__main__.TestTEFuserStatic) ... /var/lib/jenkins/workspace/test/test_jit_fuser_te.py:2443: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
2023-01-11T21:39:33.8308959Z   r = torch.tensor(4)
2023-01-11T21:39:33.8309132Z ok (0.111s)
2023-01-11T21:39:33.8309334Z   test_conv2d (__main__.TestTEFuserStatic) ... ok (0.025s)
2023-01-11T21:39:33.8309608Z   test_conv2d_depthwise (__main__.TestTEFuserStatic) ... ok (0.907s)
2023-01-11T21:39:33.8309906Z   test_cuda_half (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8310184Z   test_dims (__main__.TestTEFuserStatic) ... ok (0.069s)
2023-01-11T21:39:33.8310445Z   test_disabled (__main__.TestTEFuserStatic) ... ok (0.006s)
2023-01-11T21:39:33.8310695Z   test_div_bool (__main__.TestTEFuserStatic) ... ok (0.036s)
2023-01-11T21:39:33.8310960Z   test_dynamic_cat (__main__.TestTEFuserStatic) ... ok (0.009s)
2023-01-11T21:39:33.8311232Z   test_dynamic_shapes (__main__.TestTEFuserStatic) ... ok (2.071s)
2023-01-11T21:39:33.8311505Z   test_eq_unsqueeze_type_as (__main__.TestTEFuserStatic) ... ok (0.108s)
2023-01-11T21:39:33.8311771Z   test_erf (__main__.TestTEFuserStatic) ... ok (0.001s)
2023-01-11T21:39:33.8312051Z   test_exhaust_specializations (__main__.TestTEFuserStatic) ... ok (0.029s)
2023-01-11T21:39:33.8312316Z   test_exp (__main__.TestTEFuserStatic) ... ok (0.045s)
2023-01-11T21:39:33.8312612Z   test_fusion_reuse_multi_gpu (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8312908Z   test_gelu (__main__.TestTEFuserStatic) ... ok (0.026s)
2023-01-11T21:39:33.8313179Z   test_hardsigmoid_fwd_bwd (__main__.TestTEFuserStatic) ... ok (0.100s)
2023-01-11T21:39:33.8313452Z   test_hardswish_fwd_bwd (__main__.TestTEFuserStatic) ... ok (0.117s)
2023-01-11T21:39:33.8313742Z   test_inlined_optimized_graph (__main__.TestTEFuserStatic) ... ok (0.085s)
2023-01-11T21:39:33.8314018Z   test_isnan (__main__.TestTEFuserStatic) ... ok (0.358s)
2023-01-11T21:39:33.8314310Z   test_kernel_cache_multi_gpu (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8314604Z   test_lerp (__main__.TestTEFuserStatic) ... ok (0.066s)
2023-01-11T21:39:33.8315101Z   test_list_ops (__main__.TestTEFuserStatic) ... skip: FIXME: fuser doesn't include ListConstruct nodes to the group causing a failure (0.001s)
2023-01-11T21:39:33.8315439Z   test_lstm (__main__.TestTEFuserStatic) ... ok (0.158s)
2023-01-11T21:39:33.8315686Z   test_lstm_concat (__main__.TestTEFuserStatic) ... ok (0.161s)
2023-01-11T21:39:33.8315971Z   test_lstm_gates_permutations (__main__.TestTEFuserStatic) ... ok (1.702s)
2023-01-11T21:39:33.8316257Z   test_lstm_traced (__main__.TestTEFuserStatic) ... ok (0.138s)
2023-01-11T21:39:33.8316541Z   test_masked_fill (__main__.TestTEFuserStatic) ... skip: Temporarily disabled (0.001s)
2023-01-11T21:39:33.8316833Z   test_matmul (__main__.TestTEFuserStatic) ... ok (0.814s)
2023-01-11T21:39:33.8317088Z   test_milstm (__main__.TestTEFuserStatic) ... ok (0.922s)
2023-01-11T21:39:33.8317362Z   test_minmax (__main__.TestTEFuserStatic) ... ok (0.277s)
2023-01-11T21:39:33.8317624Z   test_minmax_int_ops (__main__.TestTEFuserStatic) ... ok (0.290s)
2023-01-11T21:39:33.8317893Z   test_mul_bool (__main__.TestTEFuserStatic) ... ok (0.036s)
2023-01-11T21:39:33.8318152Z   test_neg_pow (__main__.TestTEFuserStatic) ... ok (0.133s)
2023-01-11T21:39:33.8318519Z   test_nonzero_device_cuda (__main__.TestTEFuserStatic) ... skip: needs non-zero device (0.001s)
2023-01-11T21:39:33.8318816Z   test_nop (__main__.TestTEFuserStatic) ... ok (0.001s)
2023-01-11T21:39:33.8319278Z   test_profiler (__main__.TestTEFuserStatic) ... STAGE:2023-01-11 21:39:27 12313:12313 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:39:33.8319743Z STAGE:2023-01-11 21:39:27 12313:12313 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:39:33.8320190Z STAGE:2023-01-11 21:39:27 12313:12313 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:39:33.8320452Z ok (0.026s)
2023-01-11T21:39:33.8320708Z   test_rand_broadcast_cuda (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8321022Z   test_rand_cuda (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.001s)
2023-01-11T21:39:33.8321343Z   test_rand_diamond (__main__.TestTEFuserStatic) ... skip: fuser requires CUDA (0.000s)
2023-01-11T21:39:33.8321626Z   test_relu (__main__.TestTEFuserStatic) ... ok (0.046s)
2023-01-11T21:39:33.8321874Z   test_relu_fwd_bwd (__main__.TestTEFuserStatic) ... ok (0.057s)
2023-01-11T21:39:33.8322162Z   test_remove_output_used_only_in_size (__main__.TestTEFuserStatic) ... ok (0.026s)
2023-01-11T21:39:33.8322443Z   test_scalar (__main__.TestTEFuserStatic) ... ok (0.047s)
2023-01-11T21:39:33.8322705Z   test_scalar_arg (__main__.TestTEFuserStatic) ... ok (0.087s)
2023-01-11T21:39:33.8322968Z   test_scalar_only_inputs (__main__.TestTEFuserStatic) ... ok (0.046s)
2023-01-11T21:39:33.8323249Z   test_skip_grad_in_check (__main__.TestTEFuserStatic) ... ok (0.028s)
2023-01-11T21:39:33.8323527Z   test_small_constant (__main__.TestTEFuserStatic) ... ok (0.070s)
2023-01-11T21:39:33.8323783Z   test_sub_gt_and (__main__.TestTEFuserStatic) ... ok (0.090s)
2023-01-11T21:39:33.8324047Z   test_sum_dim (__main__.TestTEFuserStatic) ... ok (0.117s)
2023-01-11T21:39:33.8324316Z   test_sum_keepdim_cast (__main__.TestTEFuserStatic) ... ok (0.062s)
2023-01-11T21:39:33.8324579Z   test_sum_simple (__main__.TestTEFuserStatic) ... ok (0.056s)
2023-01-11T21:39:33.8324844Z   test_superslomo (__main__.TestTEFuserStatic) ... ok (0.648s)
2023-01-11T21:39:33.8325116Z   test_tensor_scalar_ops (__main__.TestTEFuserStatic) ... ok (0.111s)
2023-01-11T21:39:33.8325396Z   test_ternary_norm_ops (__main__.TestTEFuserStatic) ... ok (0.223s)
2023-01-11T21:39:33.8325659Z   test_ternary_ops (__main__.TestTEFuserStatic) ... ok (0.253s)
2023-01-11T21:39:33.8325922Z   test_threshold (__main__.TestTEFuserStatic) ... ok (0.071s)
2023-01-11T21:39:33.8326184Z   test_to_device (__main__.TestTEFuserStatic) ... ok (0.046s)
2023-01-11T21:39:33.8326436Z   test_to_dtype (__main__.TestTEFuserStatic) ... ok (0.099s)
2023-01-11T21:39:33.8326696Z   test_torch_to (__main__.TestTEFuserStatic) ... ok (0.827s)
2023-01-11T21:39:33.8327007Z   test_type_as_cat (__main__.TestTEFuserStatic) ... ok (1.371s)
2023-01-11T21:39:33.8327258Z   test_typecheck (__main__.TestTEFuserStatic) ... ok (0.046s)
2023-01-11T21:39:33.8327590Z   test_unary_ops (__main__.TestTEFuserStatic) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:39:33.8327922Z   test_unrolled_cat (__main__.TestTEFuserStatic) ... ok (0.027s)
2023-01-11T21:39:33.8328210Z   test_unsqueeze_size_calculation (__main__.TestTEFuserStatic) ... ok (0.082s)
2023-01-11T21:39:33.8328491Z   test_unsqueeze_var_dim (__main__.TestTEFuserStatic) ... ok (0.223s)
2023-01-11T21:39:33.8328775Z   test_unsupported_dtypes (__main__.TestTEFuserStatic) ... ok (0.035s)
2023-01-11T21:39:33.8329212Z   test_where_and_typing (__main__.TestTEFuserStatic) ... ok (0.080s)
2023-01-11T21:39:33.8329577Z   test_where_ops (__main__.TestTEFuserStatic) ... ok (0.608s)
2023-01-11T21:39:33.8330082Z   test_with_strict_fusion (__main__.TestTEFuserStatic) ... /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py:216: UserWarning: Only works in script mode
2023-01-11T21:39:33.8330434Z   warnings.warn("Only works in script mode")
2023-01-11T21:39:33.8330632Z ok (0.067s)
2023-01-11T21:39:33.8330848Z   test_zero_element_tensors (__main__.TestTEFuserStatic) ... ok (0.043s)
2023-01-11T21:39:33.8331016Z 
2023-01-11T21:39:33.8331217Z ----------------------------------------------------------------------
2023-01-11T21:39:33.8331458Z Ran 207 tests in 93.592s
2023-01-11T21:39:33.8331571Z 
2023-01-11T21:39:33.8331630Z OK (skipped=39)
2023-01-11T21:39:33.8331738Z 
2023-01-11T21:39:33.8331821Z Generating XML reports...
2023-01-11T21:39:33.8332264Z Generated XML report: test-reports/python-unittest/test_jit_fuser_te/TEST-jit.test_fuser_common.TestFuserCommon-20230111213759.xml
2023-01-11T21:39:33.8332808Z Generated XML report: test-reports/python-unittest/test_jit_fuser_te/TEST-TestTEFuserDynamic-20230111213759.xml
2023-01-11T21:39:33.8333313Z Generated XML report: test-reports/python-unittest/test_jit_fuser_te/TEST-TestTEFuserStatic-20230111213759.xml
2023-01-11T21:39:33.8333538Z 
2023-01-11T21:39:33.8333859Z ##[endgroup]
2023-01-11T21:39:33.8334238Z FINISHED PRINTING LOG FILE of test_jit_fuser_te (/var/lib/jenkins/workspace/test/test-reports/test_jit_fuser_te_25znr0r4)
2023-01-11T21:39:33.8334448Z 
2023-01-11T21:39:35.6597403Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:39:35.7247099Z Ignoring disabled issues:  []
2023-01-11T21:39:35.7401125Z Running test_jit_llga_fuser ... [2023-01-11 21:39:35.739863]
2023-01-11T21:39:35.7403290Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_llga_fuser.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:39:35.740111]
2023-01-11T21:39:39.4723912Z 
2023-01-11T21:39:39.4724476Z Expand the folded group to see the log file of test_jit_llga_fuser
2023-01-11T21:39:39.4725503Z ##[group]PRINTING LOG FILE of test_jit_llga_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_llga_fuser_hah4ekg4)
2023-01-11T21:39:39.4725820Z 
2023-01-11T21:39:39.4725923Z Running tests...
2023-01-11T21:39:39.4726574Z ----------------------------------------------------------------------
2023-01-11T21:39:39.4727164Z   test_dynamo_aot_ts_onednn (__main__.TestDynamoAOT) ... Test results will be stored in test-reports/python-unittest/test_jit_llga_fuser
2023-01-11T21:39:39.4728012Z skip: Enable when integration with dynamo aot_autograd is more stable (0.001s)
2023-01-11T21:39:39.4728347Z   test_context_manager (__main__.TestEnableDisableLlgaFuser) ... ok (0.081s)
2023-01-11T21:39:39.4728675Z   test_vision_alexnet_bfloat16 (__main__.TestModel) ... ok (0.037s)
2023-01-11T21:39:39.4728972Z   test_vision_alexnet_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4729580Z   test_vision_densenet121_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4733067Z   test_vision_densenet121_float32 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4733775Z   test_vision_densenet161_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4734290Z   test_vision_densenet161_float32 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4734723Z   test_vision_densenet169_bfloat16 (__main__.TestModel) ... ok (0.037s)
2023-01-11T21:39:39.4735086Z   test_vision_densenet169_float32 (__main__.TestModel) ... ok (0.029s)
2023-01-11T21:39:39.4735538Z   test_vision_densenet201_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4736044Z   test_vision_densenet201_float32 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4736385Z   test_vision_efficientnet_b0_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4736659Z   test_vision_efficientnet_b0_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4737042Z   test_vision_efficientnet_b1_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4737338Z   test_vision_efficientnet_b1_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4737612Z   test_vision_efficientnet_b2_bfloat16 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4737893Z   test_vision_efficientnet_b2_float32 (__main__.TestModel) ... ok (0.029s)
2023-01-11T21:39:39.4738173Z   test_vision_efficientnet_b3_bfloat16 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4738450Z   test_vision_efficientnet_b3_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4738713Z   test_vision_efficientnet_b4_bfloat16 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4738994Z   test_vision_efficientnet_b4_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4739274Z   test_vision_efficientnet_b5_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4739540Z   test_vision_efficientnet_b5_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4739821Z   test_vision_efficientnet_b6_bfloat16 (__main__.TestModel) ... ok (0.029s)
2023-01-11T21:39:39.4740102Z   test_vision_efficientnet_b6_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4740380Z   test_vision_efficientnet_b7_bfloat16 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4772543Z   test_vision_efficientnet_b7_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4773240Z   test_vision_googlenet_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4773971Z   test_vision_googlenet_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4774639Z   test_vision_mnasnet1_0_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4775282Z   test_vision_mnasnet1_0_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4776061Z   test_vision_mobilenet_v2_bfloat16 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4776726Z   test_vision_mobilenet_v2_float32 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4777444Z   test_vision_mobilenet_v3_large_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4778120Z   test_vision_mobilenet_v3_large_float32 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4778819Z   test_vision_regnet_y_400mf_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4779504Z   test_vision_regnet_y_400mf_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4780133Z   test_vision_resnet50_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4780794Z   test_vision_resnet50_float32 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4781462Z   test_vision_resnext101_32x8d_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4782142Z   test_vision_resnext101_32x8d_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4782793Z   test_vision_resnext50_32x4d_bfloat16 (__main__.TestModel) ... ok (0.030s)
2023-01-11T21:39:39.4783465Z   test_vision_resnext50_32x4d_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4784240Z   test_vision_shufflenet_v2_x1_0_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4784909Z   test_vision_shufflenet_v2_x1_0_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4785786Z   test_vision_squeezenet1_0_bfloat16 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4786463Z   test_vision_squeezenet1_0_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4787130Z   test_vision_vgg16_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4787755Z   test_vision_vgg16_float32 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4788411Z   test_vision_wide_resnet50_2_bfloat16 (__main__.TestModel) ... ok (0.031s)
2023-01-11T21:39:39.4789073Z   test_vision_wide_resnet50_2_float32 (__main__.TestModel) ... ok (0.032s)
2023-01-11T21:39:39.4789461Z 
2023-01-11T21:39:39.4790011Z ----------------------------------------------------------------------
2023-01-11T21:39:39.4790597Z Ran 52 tests in 1.645s
2023-01-11T21:39:39.4790864Z 
2023-01-11T21:39:39.4791032Z OK (skipped=1)
2023-01-11T21:39:39.4791426Z 
2023-01-11T21:39:39.4791643Z Generating XML reports...
2023-01-11T21:39:39.4792782Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestEnableDisableLlgaFuser-20230111213937.xml
2023-01-11T21:39:39.4794126Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestModel-20230111213937.xml
2023-01-11T21:39:39.4795330Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestDynamoAOT-20230111213937.xml
2023-01-11T21:39:39.4795876Z 
2023-01-11T21:39:39.4796544Z ##[endgroup]
2023-01-11T21:39:39.4797522Z FINISHED PRINTING LOG FILE of test_jit_llga_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_llga_fuser_hah4ekg4)
2023-01-11T21:39:39.4798062Z 
2023-01-11T21:39:41.3030073Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:39:41.3676243Z Ignoring disabled issues:  []
2023-01-11T21:39:41.3829751Z Running test_jiterator ... [2023-01-11 21:39:41.382749]
2023-01-11T21:39:41.3832150Z Executing ['/opt/conda/bin/python', '-bb', 'test_jiterator.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:39:41.382991]
2023-01-11T21:39:43.2679794Z 
2023-01-11T21:39:43.2680356Z Expand the folded group to see the log file of test_jiterator
2023-01-11T21:39:43.2681359Z ##[group]PRINTING LOG FILE of test_jiterator (/var/lib/jenkins/workspace/test/test-reports/test_jiterator_seg31d00)
2023-01-11T21:39:43.2681944Z CUDA not available, skipping tests
2023-01-11T21:39:43.2682182Z 
2023-01-11T21:39:43.2682310Z Running tests...
2023-01-11T21:39:43.2682941Z ----------------------------------------------------------------------
2023-01-11T21:39:43.2683157Z 
2023-01-11T21:39:43.2683356Z ----------------------------------------------------------------------
2023-01-11T21:39:43.2683602Z Ran 0 tests in 0.000s
2023-01-11T21:39:43.2683716Z 
2023-01-11T21:39:43.2683778Z OK
2023-01-11T21:39:43.2683855Z 
2023-01-11T21:39:43.2683941Z Generating XML reports...
2023-01-11T21:39:43.2684284Z Test results will be stored in test-reports/python-unittest/test_jiterator
2023-01-11T21:39:43.2684462Z 
2023-01-11T21:39:43.2684684Z ##[endgroup]
2023-01-11T21:39:43.2685064Z FINISHED PRINTING LOG FILE of test_jiterator (/var/lib/jenkins/workspace/test/test-reports/test_jiterator_seg31d00)
2023-01-11T21:39:43.2685276Z 
2023-01-11T21:39:45.0943289Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:39:45.1589840Z Ignoring disabled issues:  []
2023-01-11T21:39:45.1744418Z Running test_legacy_vmap ... [2023-01-11 21:39:45.174167]
2023-01-11T21:39:45.1746332Z Executing ['/opt/conda/bin/python', '-bb', 'test_legacy_vmap.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:39:45.174410]
2023-01-11T21:39:48.7984400Z 
2023-01-11T21:39:48.7985481Z Expand the folded group to see the log file of test_legacy_vmap
2023-01-11T21:39:48.7986576Z ##[group]PRINTING LOG FILE of test_legacy_vmap (/var/lib/jenkins/workspace/test/test-reports/test_legacy_vmap_fmc2e1t3)
2023-01-11T21:39:48.7986952Z 
2023-01-11T21:39:48.7987106Z Running tests...
2023-01-11T21:39:48.7987816Z ----------------------------------------------------------------------
2023-01-11T21:39:48.7988827Z Test results will be stored in test-reports/python-unittest/test_legacy_vmap
2023-01-11T21:39:48.7989542Z   test_accepts_nested_inputs (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:377: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.7990133Z   out = vmap(lambda z: z[0] + z[1])((x, y))
2023-01-11T21:39:48.7990638Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:379: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.7991106Z   out = vmap(lambda z: z[0] + z[1], in_dims=(0,))((x, y))
2023-01-11T21:39:48.7991600Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:381: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.7992214Z   out = vmap(lambda z: z[0] + z[1], in_dims=((0, 0),))((x, y))
2023-01-11T21:39:48.7992707Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:384: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.7993176Z   out = vmap(lambda z: z[0] + z[1])([x, y])
2023-01-11T21:39:48.7993658Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:386: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.7994133Z   out = vmap(lambda z: z[0] + z[1], in_dims=(0,))([x, y])
2023-01-11T21:39:48.7994789Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:388: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.7995607Z   out = vmap(lambda z: z[0] + z[1], in_dims=([0, 0],))([x, y])
2023-01-11T21:39:48.7996425Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:391: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.7997418Z   out = vmap(lambda z: z['x'] + z['y'])({'x': x, 'y': y})
2023-01-11T21:39:48.7998225Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:393: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.7999235Z   out = vmap(lambda z: z['x'] + z['y'], in_dims=(0,))({'x': x, 'y': y})
2023-01-11T21:39:48.8000093Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:395: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8001136Z   out = vmap(lambda z: z['x'] + z['y'], in_dims=({'x': 0, 'y': 0},))({'x': x, 'y': y})
2023-01-11T21:39:48.8002006Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:399: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8003004Z   out_fn = vmap(lambda z: z['x'][0] + z['x'][1][0] + z['y'][0] + z['y'][1])
2023-01-11T21:39:48.8003488Z ok (0.005s)
2023-01-11T21:39:48.8004373Z   test_backward_unsupported_interaction (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:749: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8005360Z   vmap(backward_on_vmapped_tensor)(x)
2023-01-11T21:39:48.8006200Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:755: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8007038Z   vmap(backward_with_vmapped_grad)(x, grad)
2023-01-11T21:39:48.8007837Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:761: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8008636Z   vmap(completely_unrelated_backward)(y)
2023-01-11T21:39:48.8009419Z ok (0.004s)
2023-01-11T21:39:48.8010324Z   test_batched_gradient_basic (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:793: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8011269Z   jacobian = vmap(vjp_mul)(batched_v)
2023-01-11T21:39:48.8014085Z ok (0.001s)
2023-01-11T21:39:48.8014936Z   test_constant_function (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:64: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8015920Z   output = vmap(lambda x: torch.tensor(3.14))(torch.ones(3))
2023-01-11T21:39:48.8016611Z ok (0.001s)
2023-01-11T21:39:48.8017502Z   test_different_map_dim_size_raises (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:42: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8018410Z   vmap(torch.mul)(x, y)
2023-01-11T21:39:48.8019189Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:44: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8019974Z   vmap(lambda z: z[0] + z[1], in_dims=((0, 0),))((x, y))
2023-01-11T21:39:48.8020713Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:46: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8021965Z   vmap(lambda z: z['x'] + z['y'], in_dims=({'x': 0, 'y': 0},))({'x': x, 'y': y})
2023-01-11T21:39:48.8022469Z ok (0.001s)
2023-01-11T21:39:48.8023324Z   test_fallback_atan2 (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:555: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8024293Z   result = vmap(op, (2, 0))(x, y)
2023-01-11T21:39:48.8025095Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:561: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8025884Z   result = vmap(vmap(op), (2, 0))(x, y)
2023-01-11T21:39:48.8026713Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:567: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8027467Z   result = vmap(vmap(vmap(op)))(x, y)
2023-01-11T21:39:48.8027912Z ok (0.091s)
2023-01-11T21:39:48.8028500Z   test_fallback_does_not_warn_by_default (__main__.TestVmapAPI) ... ok (0.001s)
2023-01-11T21:39:48.8029547Z   test_fallback_masked_fill (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:583: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8030536Z   result = vmap(torch.index_add, (0, None, None, 0))(x, dim, index, values)
2023-01-11T21:39:48.8031416Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:583: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8032239Z   result = vmap(torch.index_add, (0, None, None, 0))(x, dim, index, values)
2023-01-11T21:39:48.8032758Z ok (0.062s)
2023-01-11T21:39:48.8033639Z   test_fallback_multiple_returns (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:601: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8034577Z   result = vmap(torch.var_mean)(tensor)
2023-01-11T21:39:48.8035366Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:607: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8036170Z   result = vmap(vmap(torch.var_mean))(tensor)
2023-01-11T21:39:48.8037025Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:613: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8037872Z   result = vmap(vmap(vmap(torch.var_mean)))(tensor)
2023-01-11T21:39:48.8038353Z ok (0.069s)
2023-01-11T21:39:48.8038963Z   test_fallback_warns_when_warnings_are_enabled (__main__.TestVmapAPI) ... ok (0.001s)
2023-01-11T21:39:48.8039716Z   test_fallback_with_undefined_grad (__main__.TestVmapAPI) ... ok (0.003s)
2023-01-11T21:39:48.8040717Z   test_fallback_zero_dim (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:526: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8041578Z   vmap(op, (0, None))(x, y)
2023-01-11T21:39:48.8042345Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:528: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8043107Z   vmap(op, (None, 0))(y, x)
2023-01-11T21:39:48.8043868Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:530: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8044738Z   vmap(op)(x, x)
2023-01-11T21:39:48.8045518Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:535: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8046223Z   vmap(op, (0, None))(x, y)
2023-01-11T21:39:48.8047020Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:537: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8047783Z   vmap(op, (None, 0))(y, x)
2023-01-11T21:39:48.8048547Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:539: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8049419Z   vmap(op)(x, x)
2023-01-11T21:39:48.8049821Z ok (0.015s)
2023-01-11T21:39:48.8050856Z   test_func_with_no_inputs (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:58: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8051697Z   vmap(foo)()
2023-01-11T21:39:48.8052468Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:61: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8053188Z   vmap(bar)()
2023-01-11T21:39:48.8053576Z ok (0.001s)
2023-01-11T21:39:48.8054443Z   test_functools_partial (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:799: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8055421Z   result = vmap(functools.partial(torch.mul, x))(y)
2023-01-11T21:39:48.8055938Z ok (0.001s)
2023-01-11T21:39:48.8056861Z   test_grad_unsupported_interaction (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:774: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8057855Z   vmap(output_to_grad_is_vmapped)(input_tensor)
2023-01-11T21:39:48.8058697Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:782: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8059517Z   vmap(input_to_grad_is_vmapped)(input_tensor)
2023-01-11T21:39:48.8060006Z ok (0.003s)
2023-01-11T21:39:48.8060906Z   test_in_dim_not_in_tensor_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:464: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8061791Z   vmap(foo)(torch.randn([]))
2023-01-11T21:39:48.8062556Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:466: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8063345Z   vmap(foo, in_dims=(0,))(torch.randn([]))
2023-01-11T21:39:48.8064313Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:468: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8065200Z   vmap(foo, in_dims=(-1,))(x)
2023-01-11T21:39:48.8065977Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:470: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8066713Z   vmap(foo, in_dims=(2,))(y)
2023-01-11T21:39:48.8067483Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:472: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8068234Z   vmap(lambda z: z[0] + z[1], in_dims=([3, 0],))([x, y])
2023-01-11T21:39:48.8069025Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:474: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8069807Z   vmap(foo, in_dims=(0,))(torch.randn(2, 3))
2023-01-11T21:39:48.8070613Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:475: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8071348Z   vmap(foo, in_dims=(1,))(torch.randn(2, 3))
2023-01-11T21:39:48.8071826Z ok (0.002s)
2023-01-11T21:39:48.8072655Z   test_in_dims_wrong_type_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:408: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8073702Z   vmap(torch.mul, [0, 0])(x, y)
2023-01-11T21:39:48.8074465Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:410: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8075258Z   vmap(torch.mul, set({0, 0}))(x, y)
2023-01-11T21:39:48.8076052Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:412: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8076932Z   vmap(torch.mul, 'lol')(x, y)
2023-01-11T21:39:48.8077740Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:414: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8078541Z   vmap(lambda z: z[0] + z[1], in_dims=[0, 0])([x, y])
2023-01-11T21:39:48.8079438Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:416: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8080201Z   vmap(torch.mul, (0, 0))(x, y)
2023-01-11T21:39:48.8080632Z ok (0.001s)
2023-01-11T21:39:48.8081594Z   test_inplace_fallback_nary_different_levels (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:708: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8082247Z   vmap(op, in_dims=(0, None))(x, y)
2023-01-11T21:39:48.8082691Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:714: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8083172Z   vmap(vmap(op, in_dims=(0, None)))(x, y)
2023-01-11T21:39:48.8083657Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:722: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8084111Z   vmap(op, in_dims=(None, 0))(x, y)
2023-01-11T21:39:48.8084622Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:727: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8085129Z   vmap(vmap(op, in_dims=(0, None)), in_dims=(None, 0))(x, y)
2023-01-11T21:39:48.8085664Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:732: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8086148Z   vmap(vmap(op, in_dims=(0, None)), in_dims=(None, 1))(x, y)
2023-01-11T21:39:48.8086659Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:737: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8087132Z   vmap(vmap(op, in_dims=(None, 0)))(x, y)
2023-01-11T21:39:48.8087411Z ok (0.013s)
2023-01-11T21:39:48.8087965Z   test_inplace_fallback_nary_same_levels (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:673: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8088502Z   vmap(op, (2, 0))(x, y)
2023-01-11T21:39:48.8088984Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:681: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8089589Z   vmap(vmap(op), (2, 0))(x, y)
2023-01-11T21:39:48.8090068Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:689: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8090542Z   result = vmap(vmap(vmap(op)))(x, y)
2023-01-11T21:39:48.8090810Z ok (0.077s)
2023-01-11T21:39:48.8091362Z   test_inplace_fallback_unary (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:632: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8091881Z   result = vmap(op)(x)
2023-01-11T21:39:48.8092357Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:639: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8092792Z   result = vmap(op, out_dims=(1,))(x)
2023-01-11T21:39:48.8093262Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:646: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8093716Z   result = vmap(vmap(op))(x)
2023-01-11T21:39:48.8094192Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:653: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8094829Z   result = vmap(vmap(vmap(op)))(x)
2023-01-11T21:39:48.8095111Z ok (0.316s)
2023-01-11T21:39:48.8095664Z   test_integer_in_dim_but_not_tensor_input_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:447: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8096182Z   vmap(torch.sum)(x, 0)
2023-01-11T21:39:48.8096639Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:449: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8097097Z   vmap(torch.sum, (0, 0))(x, 0)
2023-01-11T21:39:48.8097766Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:451: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8098239Z   vmap(lambda z: z[0] + z[1], in_dims=([0, 0],))([x, 1])
2023-01-11T21:39:48.8098719Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:453: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8099185Z   vmap(torch.sum, (0, None))(x, 0)
2023-01-11T21:39:48.8099463Z ok (0.002s)
2023-01-11T21:39:48.8099960Z   test_multiple_inputs (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:79: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8100473Z   output = vmap(torch.mul)(x, y)
2023-01-11T21:39:48.8100756Z ok (0.001s)
2023-01-11T21:39:48.8101250Z   test_multiple_out_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:221: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8101785Z   result = vmap(foo, out_dims=(0, 1))(x)
2023-01-11T21:39:48.8102273Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:224: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8102896Z   result = vmap(bar, out_dims=(-1, 0, 1, 2))(x, y)
2023-01-11T21:39:48.8103178Z ok (0.002s)
2023-01-11T21:39:48.8103796Z   test_multiple_outputs (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:87: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8104253Z   outputs = vmap(foo)(x)
2023-01-11T21:39:48.8104501Z ok (0.001s)
2023-01-11T21:39:48.8104997Z   test_multiple_outputs_error_cases (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:107: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8105540Z   vmap(returns_tuple_of_tensors)(x)
2023-01-11T21:39:48.8106050Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:112: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8106529Z   vmap(returns_list_of_two_tensors)(x)
2023-01-11T21:39:48.8107045Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:114: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8107528Z   vmap(returns_list_of_one_tensor)(x)
2023-01-11T21:39:48.8107798Z ok (0.001s)
2023-01-11T21:39:48.8108313Z   test_nested_non_default_in_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:338: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8108863Z   result = vmap(vmap(vmap(torch.mul), (1, 0)), (1, 2))(x, y)
2023-01-11T21:39:48.8109168Z ok (0.001s)
2023-01-11T21:39:48.8109675Z   test_nested_out_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:237: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8110235Z   result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y))(y)
2023-01-11T21:39:48.8110764Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:242: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8111303Z   result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y), out_dims=1)(y)
2023-01-11T21:39:48.8111955Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:247: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8112628Z   result = vmap(lambda y: vmap(lambda x: x, out_dims=-1)(y), out_dims=-1)(y)
2023-01-11T21:39:48.8113138Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:254: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8113719Z   result = vmap(lambda y: vmap(lambda x: x * y, out_dims=1)(x), out_dims=-1)(y)
2023-01-11T21:39:48.8114058Z ok (0.003s)
2023-01-11T21:39:48.8114623Z   test_nested_with_different_map_dim (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:128: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8115314Z   output = vmap(lambda x: vmap(lambda y: x * y)(y))(x)
2023-01-11T21:39:48.8115844Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:133: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8116567Z   output = vmap(lambda x: vmap(lambda y: vmap(lambda z: x * y * z)(z))(y))(x)
2023-01-11T21:39:48.8116905Z ok (0.002s)
2023-01-11T21:39:48.8117418Z   test_nested_with_same_map_dim (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:119: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8117984Z   output = vmap(vmap(torch.mul))(x, y)
2023-01-11T21:39:48.8118488Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:122: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8118987Z   output = vmap(vmap(vmap(torch.mul)))(x, y)
2023-01-11T21:39:48.8119275Z ok (0.001s)
2023-01-11T21:39:48.8119780Z   test_nn_module (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:805: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8120278Z   result = vmap(model)(tensor)
2023-01-11T21:39:48.8120535Z ok (0.001s)
2023-01-11T21:39:48.8121074Z   test_non_default_in_dims_out_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:345: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8121632Z   result = vmap(lambda x: x, in_dims=1, out_dims=1)(x)
2023-01-11T21:39:48.8122162Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:350: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8122680Z   result = vmap(lambda x: x, in_dims=2, out_dims=1)(x)
2023-01-11T21:39:48.8123179Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:359: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8123657Z   result = vmap(foo, in_dims=1, out_dims=1)(x)
2023-01-11T21:39:48.8124150Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:363: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8124626Z   result = vmap(foo, in_dims=2, out_dims=1)(x)
2023-01-11T21:39:48.8125121Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:368: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8125727Z   result = vmap(vmap(foo, 1, 1), 1, 1)(x)
2023-01-11T21:39:48.8125983Z ok (0.002s)
2023-01-11T21:39:48.8126492Z   test_non_tensor_output_raises (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:29: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8127040Z   output = vmap(lambda x: 3.14)(torch.ones(3))
2023-01-11T21:39:48.8127514Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:35: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8128005Z   vmap(multiple_outputs)(torch.ones(3))
2023-01-11T21:39:48.8128298Z ok (0.001s)
2023-01-11T21:39:48.8128804Z   test_non_zero_in_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:311: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8129599Z   output = vmap(lambda x: x, (1,))(tensor)
2023-01-11T21:39:48.8130086Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:317: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8130544Z   output = vmap(torch.mul, (0, 1))(x, y)
2023-01-11T21:39:48.8131128Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:319: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8131588Z   output = vmap(torch.mul, (1, 0))(x, y)
2023-01-11T21:39:48.8131868Z ok (0.002s)
2023-01-11T21:39:48.8132374Z   test_none_in_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:327: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8133036Z   output = vmap(torch.mul, (0, None))(x, y)
2023-01-11T21:39:48.8133555Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:332: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8134017Z   output = vmap(torch.mul, (0, None))(x, 2)
2023-01-11T21:39:48.8134303Z ok (0.001s)
2023-01-11T21:39:48.8134843Z   test_nonzero_out_dims (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:172: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8135477Z   result = vmap(lambda x: x, out_dims=1)(tensor)
2023-01-11T21:39:48.8136064Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:178: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8136557Z   result = vmap(lambda x: x, out_dims=2)(tensor)
2023-01-11T21:39:48.8137012Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:184: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8137608Z   result = vmap(lambda x: x, out_dims=-1)(tensor)
2023-01-11T21:39:48.8138146Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:191: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8138700Z   result = vmap(lambda x, y: (x, y), out_dims=2)(tensor, other)
2023-01-11T21:39:48.8139263Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:199: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8139745Z   result = vmap(lambda x: x, out_dims=2)(tensor)
2023-01-11T21:39:48.8140263Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:207: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8140743Z   result = vmap(foo, out_dims=1)(x, y)
2023-01-11T21:39:48.8141013Z ok (0.005s)
2023-01-11T21:39:48.8141545Z   test_noop_in_inner_vmap (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:140: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8142103Z   output = vmap(lambda x: vmap(lambda y: x)(y))(x)
2023-01-11T21:39:48.8142415Z ok (0.001s)
2023-01-11T21:39:48.8142942Z   test_not_enough_in_dims_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:424: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8143464Z   vmap(torch.mul, (0,))(x, y)
2023-01-11T21:39:48.8144036Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:426: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8144489Z   vmap(torch.mul, (0, 0, 0))(x, y)
2023-01-11T21:39:48.8144968Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:428: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8145474Z   vmap(lambda z: z[0] + z[1], in_dims=([0],))([x, y])
2023-01-11T21:39:48.8145978Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:430: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8146460Z   vmap(lambda z: z[0] + z[1], in_dims=((0, 0),))([x, y])
2023-01-11T21:39:48.8147080Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:432: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8147503Z   vmap(torch.mul, (0, 0))(x, y)
2023-01-11T21:39:48.8147757Z ok (0.001s)
2023-01-11T21:39:48.8148295Z   test_out_dim_out_of_bounds_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:303: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8148839Z   vmap(lambda x: x, out_dims=3)(x)
2023-01-11T21:39:48.8149337Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:305: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8149879Z   vmap(lambda x: x, out_dims=-4)(x)
2023-01-11T21:39:48.8150170Z ok (0.005s)
2023-01-11T21:39:48.8150797Z   test_out_dims_and_num_outputs_mismatch_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:286: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8151396Z   vmap(lambda x: x, out_dims=(0, 0))(x)
2023-01-11T21:39:48.8151873Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:288: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8152344Z   vmap(lambda x: (x, x, x), out_dims=(0, 0, 0, 0))(x)
2023-01-11T21:39:48.8152864Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:292: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8153314Z   vmap(lambda x: (x, x), out_dims=(0,))(x)
2023-01-11T21:39:48.8153793Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:294: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8154263Z   vmap(lambda x: (x, x, x), out_dims=(0, 0))(x)
2023-01-11T21:39:48.8154563Z ok (0.001s)
2023-01-11T21:39:48.8155076Z   test_out_dims_edge_case (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:264: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8155649Z   expected = vmap(foo, out_dims=1)(tensor)
2023-01-11T21:39:48.8156177Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:265: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8156648Z   result = vmap(foo, out_dims=(1,))(tensor)
2023-01-11T21:39:48.8156936Z ok (0.001s)
2023-01-11T21:39:48.8157472Z   test_out_dims_must_be_int_or_tuple_of_int_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:272: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8158118Z   vmap(lambda x: x, out_dims='lol')(tensor)
2023-01-11T21:39:48.8158602Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:274: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8159172Z   vmap(lambda x: x, out_dims=('lol',))(tensor)
2023-01-11T21:39:48.8159676Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:276: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8160156Z   vmap(lambda x: x, out_dims=None)(tensor)
2023-01-11T21:39:48.8160655Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:278: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8161125Z   vmap(lambda x: x, out_dims=(None,))(tensor)
2023-01-11T21:39:48.8161420Z ok (0.001s)
2023-01-11T21:39:48.8161916Z   test_single_input (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:73: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8162437Z   output = vmap(square)(x)
2023-01-11T21:39:48.8162710Z ok (0.001s)
2023-01-11T21:39:48.8163250Z   test_unsupported_op_err_msg (__main__.TestVmapAPI) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:151: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8163773Z   vmap(torch.ravel)(tensor)
2023-01-11T21:39:48.8164256Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:157: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8164848Z   vmap(out_op)(tensor, tensor)
2023-01-11T21:39:48.8165315Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:162: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8165799Z   vmap(lambda t: torch.atleast_1d([t]))(tensor)
2023-01-11T21:39:48.8166309Z /var/lib/jenkins/workspace/test/test_legacy_vmap.py:167: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8166783Z   vmap(torch.Tensor.item)(tensor)
2023-01-11T21:39:48.8167083Z ok (0.010s)
2023-01-11T21:39:48.8167421Z   test_T_numpy (__main__.TestVmapOperators) ... ok (0.005s)
2023-01-11T21:39:48.8167836Z   test_as_strided (__main__.TestVmapOperators) ... ok (0.024s)
2023-01-11T21:39:48.8168367Z   test_binary_pointwise_ops (__main__.TestVmapOperators) ... ok (0.121s)
2023-01-11T21:39:48.8168812Z   test_bmm (__main__.TestVmapOperators) ... ok (0.032s)
2023-01-11T21:39:48.8169354Z   test_cat (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:39:48.8169731Z   test_chunk (__main__.TestVmapOperators) ... ok (0.018s)
2023-01-11T21:39:48.8170127Z   test_clamp (__main__.TestVmapOperators) ... ok (0.015s)
2023-01-11T21:39:48.8170530Z   test_clone (__main__.TestVmapOperators) ... ok (0.014s)
2023-01-11T21:39:48.8170924Z   test_comparison_ops (__main__.TestVmapOperators) ... ok (0.056s)
2023-01-11T21:39:48.8171327Z   test_conj (__main__.TestVmapOperators) ... ok (0.007s)
2023-01-11T21:39:48.8171711Z   test_contiguous (__main__.TestVmapOperators) ... ok (0.007s)
2023-01-11T21:39:48.8172143Z   test_diagonal (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:39:48.8172531Z   test_dot (__main__.TestVmapOperators) ... ok (0.012s)
2023-01-11T21:39:48.8172945Z   test_expand_as (__main__.TestVmapOperators) ... ok (0.006s)
2023-01-11T21:39:48.8173389Z   test_fill_and_zero_inplace (__main__.TestVmapOperators) ... ok (0.014s)
2023-01-11T21:39:48.8173803Z   test_imag (__main__.TestVmapOperators) ... ok (0.008s)
2023-01-11T21:39:48.8174206Z   test_is_complex (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:39:48.8174623Z   test_is_contiguous (__main__.TestVmapOperators) ... ok (0.007s)
2023-01-11T21:39:48.8175051Z   test_is_floating_point (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:39:48.8175453Z   test_mm (__main__.TestVmapOperators) ... ok (0.013s)
2023-01-11T21:39:48.8175853Z   test_movedim (__main__.TestVmapOperators) ... ok (0.007s)
2023-01-11T21:39:48.8176232Z   test_mv (__main__.TestVmapOperators) ... ok (0.012s)
2023-01-11T21:39:48.8176605Z   test_narrow (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:39:48.8177007Z   test_new_empty (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:39:48.8177433Z   test_new_empty_strided (__main__.TestVmapOperators) ... ok (0.009s)
2023-01-11T21:39:48.8177838Z   test_new_zeros (__main__.TestVmapOperators) ... ok (0.002s)
2023-01-11T21:39:48.8178262Z   test_no_random_op_support (__main__.TestVmapOperators) ... ok (0.099s)
2023-01-11T21:39:48.8178673Z   test_real (__main__.TestVmapOperators) ... ok (0.009s)
2023-01-11T21:39:48.8179047Z   test_reshape (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:39:48.8179441Z   test_reshape_as (__main__.TestVmapOperators) ... ok (0.005s)
2023-01-11T21:39:48.8179853Z   test_result_type (__main__.TestVmapOperators) ... ok (0.005s)
2023-01-11T21:39:48.8180246Z   test_select (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:39:48.8180623Z   test_slice (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:39:48.8181031Z   test_split (__main__.TestVmapOperators) ... ok (0.036s)
2023-01-11T21:39:48.8181444Z   test_squeeze (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:39:48.8181824Z   test_stack (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:39:48.8182207Z   test_stride (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:39:48.8182581Z   test_sum_dim (__main__.TestVmapOperators) ... ok (0.006s)
2023-01-11T21:39:48.8183099Z   test_t (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:39:48.8183478Z   test_tensor_split (__main__.TestVmapOperators) ... ok (0.088s)
2023-01-11T21:39:48.8183947Z   test_to (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:39:48.8184319Z   test_trace (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:39:48.8184706Z   test_transpose (__main__.TestVmapOperators) ... ok (0.004s)
2023-01-11T21:39:48.8185140Z   test_unary_pointwise_ops (__main__.TestVmapOperators) ... ok (0.094s)
2023-01-11T21:39:48.8185557Z   test_unbind (__main__.TestVmapOperators) ... ok (0.106s)
2023-01-11T21:39:48.8185930Z   test_unfold (__main__.TestVmapOperators) ... ok (0.003s)
2023-01-11T21:39:48.8186322Z   test_view (__main__.TestVmapOperators) ... ok (0.010s)
2023-01-11T21:39:48.8186824Z   test_view_as (__main__.TestVmapOperators) ... ok (0.012s)
2023-01-11T21:39:48.8187217Z   test_view_as_complex (__main__.TestVmapOperators) ... ok (0.061s)
2023-01-11T21:39:48.8187630Z   test_view_as_real (__main__.TestVmapOperators) ... ok (0.008s)
2023-01-11T21:39:48.8188032Z   test_vmap_fallback_check (__main__.TestVmapOperators) ... ok (0.001s)
2023-01-11T21:39:48.8188708Z   test_vmap_fallback_check_ok (__main__.TestVmapOperators) ... /var/lib/jenkins/workspace/test/test_legacy_vmap.py:965: UserWarning: Please use torch.vmap instead of torch._vmap_internals.vmap. 
2023-01-11T21:39:48.8189247Z   vmap(op_using_fallback)(torch.rand(3))
2023-01-11T21:39:48.8189529Z ok (0.001s)
2023-01-11T21:39:48.8189684Z 
2023-01-11T21:39:48.8190022Z ----------------------------------------------------------------------
2023-01-11T21:39:48.8190365Z Ran 95 tests in 1.702s
2023-01-11T21:39:48.8190528Z 
2023-01-11T21:39:48.8190618Z OK
2023-01-11T21:39:48.8190766Z 
2023-01-11T21:39:48.8190898Z Generating XML reports...
2023-01-11T21:39:48.8191510Z Generated XML report: test-reports/python-unittest/test_legacy_vmap/TEST-TestVmapAPI-20230111213946.xml
2023-01-11T21:39:48.8192272Z Generated XML report: test-reports/python-unittest/test_legacy_vmap/TEST-TestVmapOperators-20230111213946.xml
2023-01-11T21:39:48.8192627Z 
2023-01-11T21:39:48.8193201Z ##[endgroup]
2023-01-11T21:39:48.8193800Z FINISHED PRINTING LOG FILE of test_legacy_vmap (/var/lib/jenkins/workspace/test/test-reports/test_legacy_vmap_fmc2e1t3)
2023-01-11T21:39:48.8194134Z 
2023-01-11T21:39:50.6391606Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:39:50.7038755Z Ignoring disabled issues:  []
2023-01-11T21:39:50.7194325Z Running test_license ... [2023-01-11 21:39:50.719139]
2023-01-11T21:39:50.7196552Z Executing ['/opt/conda/bin/python', '-bb', 'test_license.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:39:50.719411]
2023-01-11T21:39:52.5995770Z 
2023-01-11T21:39:52.5996723Z Expand the folded group to see the log file of test_license
2023-01-11T21:39:52.5997623Z ##[group]PRINTING LOG FILE of test_license (/var/lib/jenkins/workspace/test/test-reports/test_license_3aoq8056)
2023-01-11T21:39:52.5997853Z 
2023-01-11T21:39:52.5997928Z Running tests...
2023-01-11T21:39:52.5998323Z ----------------------------------------------------------------------
2023-01-11T21:39:52.5998706Z Test results will be stored in test-reports/python-unittest/test_license
2023-01-11T21:39:52.5998985Z   test_distinfo_license (__main__.TestLicense)
2023-01-11T21:39:52.5999256Z If run when pytorch is installed via a wheel, the license will be in ... ok (0.223s)
2023-01-11T21:39:52.5999585Z   test_license_for_wheel (__main__.TestLicense) ... skip: can only be run in a source tree (0.000s)
2023-01-11T21:39:52.5999769Z 
2023-01-11T21:39:52.5999969Z ----------------------------------------------------------------------
2023-01-11T21:39:52.6000213Z Ran 2 tests in 0.223s
2023-01-11T21:39:52.6000313Z 
2023-01-11T21:39:52.6000384Z OK (skipped=1)
2023-01-11T21:39:52.6000490Z 
2023-01-11T21:39:52.6000577Z Generating XML reports...
2023-01-11T21:39:52.6000972Z Generated XML report: test-reports/python-unittest/test_license/TEST-TestLicense-20230111213952.xml
2023-01-11T21:39:52.6001388Z 
2023-01-11T21:39:52.6001610Z ##[endgroup]
2023-01-11T21:39:52.6001983Z FINISHED PRINTING LOG FILE of test_license (/var/lib/jenkins/workspace/test/test-reports/test_license_3aoq8056)
2023-01-11T21:39:52.6002190Z 
2023-01-11T21:39:54.4295663Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:39:54.4948776Z Ignoring disabled issues:  []
2023-01-11T21:39:54.5109812Z Running test_logging ... [2023-01-11 21:39:54.510656]
2023-01-11T21:39:54.5112101Z Executing ['/opt/conda/bin/python', '-bb', 'test_logging.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:39:54.510986]
2023-01-11T21:39:58.2847267Z 
2023-01-11T21:39:58.2847806Z Expand the folded group to see the log file of test_logging
2023-01-11T21:39:58.2849510Z ##[group]PRINTING LOG FILE of test_logging (/var/lib/jenkins/workspace/test/test-reports/test_logging_ktxdq3z0)
2023-01-11T21:39:58.2850078Z 
2023-01-11T21:39:58.2850230Z Running tests...
2023-01-11T21:39:58.2851039Z ----------------------------------------------------------------------
2023-01-11T21:39:58.2851858Z Test results will be stored in test-reports/python-unittest/test_logging
2023-01-11T21:39:58.2852500Z   testApiUsage (__main__.LoggingTest)
2023-01-11T21:39:58.2853143Z This test verifies that api usage logging is not triggered via static ... ok (2.090s)
2023-01-11T21:39:58.2853582Z 
2023-01-11T21:39:58.2854066Z ----------------------------------------------------------------------
2023-01-11T21:39:58.2854615Z Ran 1 test in 2.090s
2023-01-11T21:39:58.2854872Z 
2023-01-11T21:39:58.2854994Z OK
2023-01-11T21:39:58.2855211Z 
2023-01-11T21:39:58.2855407Z Generating XML reports...
2023-01-11T21:39:58.2856350Z Generated XML report: test-reports/python-unittest/test_logging/TEST-LoggingTest-20230111213955.xml
2023-01-11T21:39:58.2856876Z 
2023-01-11T21:39:58.2857406Z ##[endgroup]
2023-01-11T21:39:58.2858280Z FINISHED PRINTING LOG FILE of test_logging (/var/lib/jenkins/workspace/test/test-reports/test_logging_ktxdq3z0)
2023-01-11T21:39:58.2858806Z 
2023-01-11T21:40:00.1461892Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:00.2119419Z Ignoring disabled issues:  []
2023-01-11T21:40:00.2274180Z Running test_maskedtensor ... [2023-01-11 21:40:00.227158]
2023-01-11T21:40:00.2276226Z Executing ['/opt/conda/bin/python', '-bb', 'test_maskedtensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:00.227406]
2023-01-11T21:40:03.2950442Z 
2023-01-11T21:40:03.2951412Z Expand the folded group to see the log file of test_maskedtensor
2023-01-11T21:40:03.2952467Z ##[group]PRINTING LOG FILE of test_maskedtensor (/var/lib/jenkins/workspace/test/test-reports/test_maskedtensor_0qyzhzi2)
2023-01-11T21:40:03.2952820Z 
2023-01-11T21:40:03.2952951Z Running tests...
2023-01-11T21:40:03.2953655Z ----------------------------------------------------------------------
2023-01-11T21:40:03.2954341Z Test results will be stored in test-reports/python-unittest/test_maskedtensor
2023-01-11T21:40:03.2955985Z   test_binary_fn_aten_add (__main__.TestBinary) ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:156: UserWarning: The PyTorch API of MaskedTensors is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.masked module for further information about the project.
2023-01-11T21:40:03.2957012Z   warnings.warn(("The PyTorch API of MaskedTensors is in prototype stage "
2023-01-11T21:40:03.2957420Z ok (0.004s)
2023-01-11T21:40:03.2957788Z   test_binary_fn_aten_arctan2 (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2958256Z   test_binary_fn_aten_atan2 (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2958688Z   test_binary_fn_aten_bitwise_and (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2959218Z   test_binary_fn_aten_bitwise_left_shift (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2959722Z   test_binary_fn_aten_bitwise_or (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2960507Z   test_binary_fn_aten_bitwise_right_shift (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2961030Z   test_binary_fn_aten_bitwise_xor (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2961535Z   test_binary_fn_aten_div (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2961931Z   test_binary_fn_aten_divide (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2962348Z   test_binary_fn_aten_eq (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2962687Z   test_binary_fn_aten_floor_divide (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2963103Z   test_binary_fn_aten_fmax (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2963572Z   test_binary_fn_aten_fmin (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2964146Z   test_binary_fn_aten_fmod (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2964630Z   test_binary_fn_aten_ge (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2964961Z   test_binary_fn_aten_greater (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2965239Z   test_binary_fn_aten_greater_equal (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2965507Z   test_binary_fn_aten_gt (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2965749Z   test_binary_fn_aten_le (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2966001Z   test_binary_fn_aten_less (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2966270Z   test_binary_fn_aten_less_equal (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2966545Z   test_binary_fn_aten_logaddexp (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2966812Z   test_binary_fn_aten_logaddexp2 (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2967080Z   test_binary_fn_aten_lt (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2967342Z   test_binary_fn_aten_maximum (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2967595Z   test_binary_fn_aten_minimum (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2967863Z   test_binary_fn_aten_mul (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2968132Z   test_binary_fn_aten_multiply (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2968387Z   test_binary_fn_aten_ne (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2968655Z   test_binary_fn_aten_nextafter (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2968929Z   test_binary_fn_aten_not_equal (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2969388Z   test_binary_fn_aten_remainder (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2969638Z   test_binary_fn_aten_sub (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2969907Z   test_binary_fn_aten_subtract (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2970187Z   test_binary_fn_aten_true_divide (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2970455Z   test_inplace_binary_fn_aten_add_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2970746Z   test_inplace_binary_fn_aten_arctan2_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2971037Z   test_inplace_binary_fn_aten_atan2_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2971333Z   test_inplace_binary_fn_aten_bitwise_and_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2971628Z   test_inplace_binary_fn_aten_bitwise_left_shift_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2971933Z   test_inplace_binary_fn_aten_bitwise_or_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2972240Z   test_inplace_binary_fn_aten_bitwise_right_shift_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2972534Z   test_inplace_binary_fn_aten_bitwise_xor_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2972823Z   test_inplace_binary_fn_aten_div_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2973111Z   test_inplace_binary_fn_aten_divide_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2973396Z   test_inplace_binary_fn_aten_eq_ (__main__.TestBinary) ... ok (0.012s)
2023-01-11T21:40:03.2973745Z   test_inplace_binary_fn_aten_floor_divide_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2974038Z   test_inplace_binary_fn_aten_fmod_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2974320Z   test_inplace_binary_fn_aten_ge_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2974593Z   test_inplace_binary_fn_aten_greater_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2974891Z   test_inplace_binary_fn_aten_greater_equal_ (__main__.TestBinary) ... ok (0.012s)
2023-01-11T21:40:03.2975180Z   test_inplace_binary_fn_aten_gt_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2975442Z   test_inplace_binary_fn_aten_le_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2975728Z   test_inplace_binary_fn_aten_less_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2976059Z   test_inplace_binary_fn_aten_less_equal_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2976343Z   test_inplace_binary_fn_aten_lt_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2976610Z   test_inplace_binary_fn_aten_mul_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2976894Z   test_inplace_binary_fn_aten_multiply_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2977174Z   test_inplace_binary_fn_aten_ne_ (__main__.TestBinary) ... ok (0.011s)
2023-01-11T21:40:03.2977444Z   test_inplace_binary_fn_aten_nextafter_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2977732Z   test_inplace_binary_fn_aten_not_equal_ (__main__.TestBinary) ... ok (0.012s)
2023-01-11T21:40:03.2978016Z   test_inplace_binary_fn_aten_remainder_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2978303Z   test_inplace_binary_fn_aten_sub_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2978575Z   test_inplace_binary_fn_aten_subtract_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2978870Z   test_inplace_binary_fn_aten_true_divide_ (__main__.TestBinary) ... ok (0.002s)
2023-01-11T21:40:03.2979157Z   test_masks_match_fn_name_add (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:03.2979416Z   test_masks_match_fn_name_add_ (__main__.TestBinary) ... ok (0.001s)
2023-01-11T21:40:03.2979670Z   test_all (__main__.TestReductions) ... ok (0.003s)
2023-01-11T21:40:03.2979910Z   test_amax (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:03.2980591Z   test_amax_grad (__main__.TestReductions) ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:161: UserWarning: It is not recommended to create a MaskedTensor with a tensor that requires_grad. To avoid this, you can use data.clone().detach()
2023-01-11T21:40:03.2981102Z   warnings.warn("It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
2023-01-11T21:40:03.2981362Z ok (0.003s)
2023-01-11T21:40:03.2981569Z   test_amin (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:03.2981806Z   test_amin_grad (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:03.2982064Z   test_grad_dtype (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:03.2982636Z   test_max_not_implemented (__main__.TestReductions) ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:299: UserWarning: max is not implemented in __torch_dispatch__ for MaskedTensor.
2023-01-11T21:40:03.2983203Z If you would like this operator to be supported, please file an issue for a feature request at https://github.com/pytorch/maskedtensor/issues with a minimal reproducible code snippet.
2023-01-11T21:40:03.2983752Z In the case that the semantics for the operator are not trivial, it would be appreciated to also include a proposal for the semantics.
2023-01-11T21:40:03.2984051Z   warnings.warn(msg)
2023-01-11T21:40:03.2984228Z ok (0.001s)
2023-01-11T21:40:03.2984436Z   test_mean (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:03.2984683Z   test_mean_dim_grad (__main__.TestReductions) ... ok (0.003s)
2023-01-11T21:40:03.2984937Z   test_mean_grad_case_1a (__main__.TestReductions)
2023-01-11T21:40:03.2985785Z values.requires_grad = True ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:156: UserWarning: The PyTorch API of MaskedTensors is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.masked module for further information about the project.
2023-01-11T21:40:03.2986371Z   warnings.warn(("The PyTorch API of MaskedTensors is in prototype stage "
2023-01-11T21:40:03.2986962Z /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:161: UserWarning: It is not recommended to create a MaskedTensor with a tensor that requires_grad. To avoid this, you can use data.clone().detach()
2023-01-11T21:40:03.2987431Z   warnings.warn("It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
2023-01-11T21:40:03.2987721Z ok (0.007s)
2023-01-11T21:40:03.2987915Z   test_mean_grad_case_1b (__main__.TestReductions)
2023-01-11T21:40:03.2988162Z values.requires_grad = False ... ok (0.003s)
2023-01-11T21:40:03.2988402Z   test_mean_grad_case_1c (__main__.TestReductions)
2023-01-11T21:40:03.2989186Z values.requires_grad = True ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:156: UserWarning: The PyTorch API of MaskedTensors is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.masked module for further information about the project.
2023-01-11T21:40:03.2989756Z   warnings.warn(("The PyTorch API of MaskedTensors is in prototype stage "
2023-01-11T21:40:03.2989990Z ok (0.005s)
2023-01-11T21:40:03.2990192Z   test_mean_grad_case_1d (__main__.TestReductions)
2023-01-11T21:40:03.2990425Z values.requires_grad = False ... ok (0.001s)
2023-01-11T21:40:03.2990648Z   test_mean_grad_case_1e (__main__.TestReductions)
2023-01-11T21:40:03.2991265Z values.requires_grad = True ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:161: UserWarning: It is not recommended to create a MaskedTensor with a tensor that requires_grad. To avoid this, you can use data.clone().detach()
2023-01-11T21:40:03.2991762Z   warnings.warn("It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
2023-01-11T21:40:03.2992004Z ok (0.005s)
2023-01-11T21:40:03.2992207Z   test_mean_grad_case_1f (__main__.TestReductions)
2023-01-11T21:40:03.2992995Z values.requires_grad = False ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:156: UserWarning: The PyTorch API of MaskedTensors is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.masked module for further information about the project.
2023-01-11T21:40:03.2993583Z   warnings.warn(("The PyTorch API of MaskedTensors is in prototype stage "
2023-01-11T21:40:03.2993803Z ok (0.002s)
2023-01-11T21:40:03.2994009Z   test_prod (__main__.TestReductions) ... ok (0.003s)
2023-01-11T21:40:03.2994644Z   test_prod_grad (__main__.TestReductions) ... /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py:161: UserWarning: It is not recommended to create a MaskedTensor with a tensor that requires_grad. To avoid this, you can use data.clone().detach()
2023-01-11T21:40:03.2995206Z   warnings.warn("It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
2023-01-11T21:40:03.2995460Z ok (0.003s)
2023-01-11T21:40:03.2995665Z   test_sum (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:03.2995901Z   test_sum_grad (__main__.TestReductions) ... ok (0.002s)
2023-01-11T21:40:03.2996171Z   test_inplace_unary_fn_aten_abs_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2996454Z   test_inplace_unary_fn_aten_absolute_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2996727Z   test_inplace_unary_fn_aten_acos_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2997004Z   test_inplace_unary_fn_aten_acosh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2997320Z   test_inplace_unary_fn_aten_arccos_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2997600Z   test_inplace_unary_fn_aten_arccosh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2997869Z   test_inplace_unary_fn_aten_arcsin_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2998146Z   test_inplace_unary_fn_aten_arcsinh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2998427Z   test_inplace_unary_fn_aten_arctan_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2998693Z   test_inplace_unary_fn_aten_arctanh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2998965Z   test_inplace_unary_fn_aten_asin_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2999242Z   test_inplace_unary_fn_aten_asinh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2999534Z   test_inplace_unary_fn_aten_atan_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.2999810Z   test_inplace_unary_fn_aten_atanh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3000097Z   test_inplace_unary_fn_aten_bitwise_not_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3000380Z   test_inplace_unary_fn_aten_ceil_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3000642Z   test_inplace_unary_fn_aten_clamp_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3000922Z   test_inplace_unary_fn_aten_clip_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3001207Z   test_inplace_unary_fn_aten_conj_physical_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3001479Z   test_inplace_unary_fn_aten_cos_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3001750Z   test_inplace_unary_fn_aten_cosh_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3002026Z   test_inplace_unary_fn_aten_deg2rad_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3002311Z   test_inplace_unary_fn_aten_digamma_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3002571Z   test_inplace_unary_fn_aten_erf_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3002843Z   test_inplace_unary_fn_aten_erfc_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3003117Z   test_inplace_unary_fn_aten_erfinv_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3003378Z   test_inplace_unary_fn_aten_exp2_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3003650Z   test_inplace_unary_fn_aten_exp_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3003926Z   test_inplace_unary_fn_aten_expm1_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3004202Z   test_inplace_unary_fn_aten_fix_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3004463Z   test_inplace_unary_fn_aten_floor_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3004738Z   test_inplace_unary_fn_aten_frac_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3005010Z   test_inplace_unary_fn_aten_i0_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3005275Z   test_inplace_unary_fn_aten_lgamma_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3005553Z   test_inplace_unary_fn_aten_log10_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3005826Z   test_inplace_unary_fn_aten_log1p_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3006086Z   test_inplace_unary_fn_aten_log2_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3006359Z   test_inplace_unary_fn_aten_log_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3006632Z   test_inplace_unary_fn_aten_logit_ (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3006911Z   test_inplace_unary_fn_aten_nan_to_num_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3007177Z   test_inplace_unary_fn_aten_neg_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3007455Z   test_inplace_unary_fn_aten_negative_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3007734Z   test_inplace_unary_fn_aten_pow_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3007998Z   test_inplace_unary_fn_aten_rad2deg_ (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3008321Z   test_inplace_unary_fn_aten_reciprocal_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3008605Z   test_inplace_unary_fn_aten_round_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3008881Z   test_inplace_unary_fn_aten_rsqrt_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3009368Z   test_inplace_unary_fn_aten_sgn_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3009648Z   test_inplace_unary_fn_aten_sigmoid_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3009929Z   test_inplace_unary_fn_aten_sign_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3010190Z   test_inplace_unary_fn_aten_sin_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3010465Z   test_inplace_unary_fn_aten_sinc_ (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3010810Z   test_inplace_unary_fn_aten_sinh_ (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3011086Z   test_inplace_unary_fn_aten_sqrt_ (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3011353Z   test_inplace_unary_fn_aten_square_ (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3011630Z   test_inplace_unary_fn_aten_tan_ (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3011902Z   test_inplace_unary_fn_aten_tanh_ (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3012164Z   test_inplace_unary_fn_aten_trunc_ (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3012432Z   test_unary_fn_aten_abs (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3012694Z   test_unary_fn_aten_absolute (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3012944Z   test_unary_fn_aten_acos (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3013205Z   test_unary_fn_aten_acosh (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3013466Z   test_unary_fn_aten_angle (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3013727Z   test_unary_fn_aten_arccos (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3013976Z   test_unary_fn_aten_arccosh (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3014240Z   test_unary_fn_aten_arcsin (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3014503Z   test_unary_fn_aten_arcsinh (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3014749Z   test_unary_fn_aten_arctan (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3015008Z   test_unary_fn_aten_arctanh (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3015268Z   test_unary_fn_aten_asin (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3015513Z   test_unary_fn_aten_asinh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3015769Z   test_unary_fn_aten_atan (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3016025Z   test_unary_fn_aten_atanh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3016294Z   test_unary_fn_aten_bitwise_not (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3016546Z   test_unary_fn_aten_ceil (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3016805Z   test_unary_fn_aten_clamp (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3017066Z   test_unary_fn_aten_clip (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3017321Z   test_unary_fn_aten_conj_physical (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3017591Z   test_unary_fn_aten_cos (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3017849Z   test_unary_fn_aten_cosh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3018111Z   test_unary_fn_aten_deg2rad (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3018362Z   test_unary_fn_aten_digamma (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3018623Z   test_unary_fn_aten_erf (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3018879Z   test_unary_fn_aten_erfc (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3019124Z   test_unary_fn_aten_erfinv (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3019382Z   test_unary_fn_aten_exp (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3019640Z   test_unary_fn_aten_exp2 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3019933Z   test_unary_fn_aten_expm1 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3020187Z   test_unary_fn_aten_fix (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3020443Z   test_unary_fn_aten_floor (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3020697Z   test_unary_fn_aten_frac (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3020935Z   test_unary_fn_aten_i0 (__main__.TestUnary) ... ok (0.002s)
2023-01-11T21:40:03.3021190Z   test_unary_fn_aten_isnan (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3021447Z   test_unary_fn_aten_lgamma (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3021691Z   test_unary_fn_aten_log (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3021947Z   test_unary_fn_aten_log10 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3022231Z   test_unary_fn_aten_log1p (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3022470Z   test_unary_fn_aten_log2 (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3022722Z   test_unary_fn_aten_logit (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3022982Z   test_unary_fn_aten_nan_to_num (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3023243Z   test_unary_fn_aten_neg (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3023543Z   test_unary_fn_aten_negative (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3023816Z   test_unary_fn_aten_positive (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3024076Z   test_unary_fn_aten_pow (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3024320Z   test_unary_fn_aten_rad2deg (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3024590Z   test_unary_fn_aten_reciprocal (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3024858Z   test_unary_fn_aten_round (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3025098Z   test_unary_fn_aten_rsqrt (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3025351Z   test_unary_fn_aten_sgn (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3025613Z   test_unary_fn_aten_sigmoid (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3025872Z   test_unary_fn_aten_sign (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3026116Z   test_unary_fn_aten_signbit (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3026370Z   test_unary_fn_aten_sin (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3026621Z   test_unary_fn_aten_sinc (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3026862Z   test_unary_fn_aten_sinh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3027111Z   test_unary_fn_aten_sqrt (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3027365Z   test_unary_fn_aten_square (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3027610Z   test_unary_fn_aten_tan (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3027860Z   test_unary_fn_aten_tanh (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3028112Z   test_unary_fn_aten_trunc (__main__.TestUnary) ... ok (0.001s)
2023-01-11T21:40:03.3028261Z 
2023-01-11T21:40:03.3028491Z ----------------------------------------------------------------------
2023-01-11T21:40:03.3028719Z Ran 207 tests in 0.461s
2023-01-11T21:40:03.3028833Z 
2023-01-11T21:40:03.3028896Z OK
2023-01-11T21:40:03.3028986Z 
2023-01-11T21:40:03.3029070Z Generating XML reports...
2023-01-11T21:40:03.3029460Z Generated XML report: test-reports/python-unittest/test_maskedtensor/TEST-TestBinary-20230111214002.xml
2023-01-11T21:40:03.3029962Z Generated XML report: test-reports/python-unittest/test_maskedtensor/TEST-TestReductions-20230111214002.xml
2023-01-11T21:40:03.3032439Z Generated XML report: test-reports/python-unittest/test_maskedtensor/TEST-TestUnary-20230111214002.xml
2023-01-11T21:40:03.3032763Z 
2023-01-11T21:40:03.3033064Z ##[endgroup]
2023-01-11T21:40:03.3033509Z FINISHED PRINTING LOG FILE of test_maskedtensor (/var/lib/jenkins/workspace/test/test-reports/test_maskedtensor_0qyzhzi2)
2023-01-11T21:40:03.3033950Z 
2023-01-11T21:40:05.1696220Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:05.2568397Z Ignoring disabled issues:  []
2023-01-11T21:40:05.2724914Z Running test_matmul_cuda ... [2023-01-11 21:40:05.272201]
2023-01-11T21:40:05.2727306Z Executing ['/opt/conda/bin/python', '-bb', 'test_matmul_cuda.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:05.272470]
2023-01-11T21:40:07.1211610Z 
2023-01-11T21:40:07.1212142Z Expand the folded group to see the log file of test_matmul_cuda
2023-01-11T21:40:07.1213053Z ##[group]PRINTING LOG FILE of test_matmul_cuda (/var/lib/jenkins/workspace/test/test-reports/test_matmul_cuda_9t_f89h0)
2023-01-11T21:40:07.1213350Z 
2023-01-11T21:40:07.1213454Z Running tests...
2023-01-11T21:40:07.1213979Z ----------------------------------------------------------------------
2023-01-11T21:40:07.1214389Z 
2023-01-11T21:40:07.1214597Z ----------------------------------------------------------------------
2023-01-11T21:40:07.1214871Z Ran 0 tests in 0.000s
2023-01-11T21:40:07.1215009Z 
2023-01-11T21:40:07.1215082Z OK
2023-01-11T21:40:07.1215219Z 
2023-01-11T21:40:07.1215376Z Generating XML reports...
2023-01-11T21:40:07.1215962Z Test results will be stored in test-reports/python-unittest/test_matmul_cuda
2023-01-11T21:40:07.1216143Z 
2023-01-11T21:40:07.1216377Z ##[endgroup]
2023-01-11T21:40:07.1216774Z FINISHED PRINTING LOG FILE of test_matmul_cuda (/var/lib/jenkins/workspace/test/test-reports/test_matmul_cuda_9t_f89h0)
2023-01-11T21:40:07.1216990Z 
2023-01-11T21:40:08.9781921Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:09.0431591Z Ignoring disabled issues:  []
2023-01-11T21:40:09.0587826Z Running test_mkl_verbose ... [2023-01-11 21:40:09.058414]
2023-01-11T21:40:09.0588992Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkl_verbose.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:09.058671]
2023-01-11T21:40:14.5763268Z 
2023-01-11T21:40:14.5763740Z Expand the folded group to see the log file of test_mkl_verbose
2023-01-11T21:40:14.5764832Z ##[group]PRINTING LOG FILE of test_mkl_verbose (/var/lib/jenkins/workspace/test/test-reports/test_mkl_verbose_rikpwh8j)
2023-01-11T21:40:14.5765235Z 
2023-01-11T21:40:14.5765368Z Running tests...
2023-01-11T21:40:14.5766000Z ----------------------------------------------------------------------
2023-01-11T21:40:14.5766640Z Test results will be stored in test-reports/python-unittest/test_mkl_verbose
2023-01-11T21:40:14.5767152Z   test_verbose_off (__main__.TestMKLVerbose) ... ok (1.610s)
2023-01-11T21:40:14.5767577Z   test_verbose_on (__main__.TestMKLVerbose) ... ok (2.242s)
2023-01-11T21:40:14.5767813Z 
2023-01-11T21:40:14.5768125Z ----------------------------------------------------------------------
2023-01-11T21:40:14.5768537Z Ran 2 tests in 3.852s
2023-01-11T21:40:14.5768722Z 
2023-01-11T21:40:14.5768833Z OK
2023-01-11T21:40:14.5768991Z 
2023-01-11T21:40:14.5769382Z Generating XML reports...
2023-01-11T21:40:14.5770114Z Generated XML report: test-reports/python-unittest/test_mkl_verbose/TEST-TestMKLVerbose-20230111214010.xml
2023-01-11T21:40:14.5770551Z 
2023-01-11T21:40:14.5771088Z ##[endgroup]
2023-01-11T21:40:14.5771772Z FINISHED PRINTING LOG FILE of test_mkl_verbose (/var/lib/jenkins/workspace/test/test-reports/test_mkl_verbose_rikpwh8j)
2023-01-11T21:40:14.5772108Z 
2023-01-11T21:40:16.3967072Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:40:16.4628309Z Ignoring disabled issues:  []
2023-01-11T21:40:16.4783747Z Running test_mkldnn ... [2023-01-11 21:40:16.478119]
2023-01-11T21:40:16.4786229Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkldnn.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:40:16.478370]
2023-01-11T21:40:59.9240476Z 
2023-01-11T21:40:59.9240983Z Expand the folded group to see the log file of test_mkldnn
2023-01-11T21:40:59.9241840Z ##[group]PRINTING LOG FILE of test_mkldnn (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_q_oe10ug)
2023-01-11T21:40:59.9289985Z 
2023-01-11T21:40:59.9290621Z Running tests...
2023-01-11T21:40:59.9291347Z ----------------------------------------------------------------------
2023-01-11T21:40:59.9291743Z Test results will be stored in test-reports/python-unittest/test_mkldnn
2023-01-11T21:40:59.9292158Z   test_0_dimension_tensor (__main__.TestMkldnn) ... ok (0.289s)
2023-01-11T21:40:59.9292635Z   test_adaptive_avg_pool2d (__main__.TestMkldnn) ... ok (0.112s)
2023-01-11T21:40:59.9293147Z   test_adaptive_avg_pool2d_bf16 (__main__.TestMkldnn) ... ok (0.031s)
2023-01-11T21:40:59.9293534Z   test_add (__main__.TestMkldnn) ... ok (0.059s)
2023-01-11T21:40:59.9293983Z   test_autograd_from_mkldnn (__main__.TestMkldnn) ... ok (0.006s)
2023-01-11T21:40:59.9294451Z   test_autograd_to_mkldnn (__main__.TestMkldnn) ... ok (0.009s)
2023-01-11T21:40:59.9294888Z   test_avg_pool2d (__main__.TestMkldnn) ... ok (0.014s)
2023-01-11T21:40:59.9306225Z   test_avg_pool2d_bf16 (__main__.TestMkldnn) ... ok (0.006s)
2023-01-11T21:40:59.9306618Z   test_avg_pool2d_stride_none (__main__.TestMkldnn) ... ok (0.005s)
2023-01-11T21:40:59.9307042Z   test_avg_pool3d (__main__.TestMkldnn) ... ok (1.191s)
2023-01-11T21:40:59.9307503Z   test_avg_pool3d_bf16 (__main__.TestMkldnn) ... ok (0.274s)
2023-01-11T21:40:59.9308732Z   test_batch_norm_2d (__main__.TestMkldnn) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py:780: UserWarning: The input to trace is already a ScriptModule, tracing it is a no-op. Returning the object as is.
2023-01-11T21:40:59.9309394Z   warnings.warn(
2023-01-11T21:40:59.9309683Z ok (0.098s)
2023-01-11T21:40:59.9309998Z   test_batch_norm_2d_bf16 (__main__.TestMkldnn) ... ok (0.018s)
2023-01-11T21:40:59.9310400Z   test_batch_norm_3d (__main__.TestMkldnn) ... ok (1.020s)
2023-01-11T21:40:59.9310808Z   test_batch_norm_3d_bf16 (__main__.TestMkldnn) ... ok (0.399s)
2023-01-11T21:40:59.9311219Z   test_clone (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:40:59.9311678Z   test_conv1d (__main__.TestMkldnn) ... ok (0.142s)
2023-01-11T21:40:59.9312116Z   test_conv1d_bf16 (__main__.TestMkldnn) ... ok (0.048s)
2023-01-11T21:40:59.9312607Z   test_conv1d_functional (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9312914Z   test_conv2d (__main__.TestMkldnn) ... ok (0.759s)
2023-01-11T21:40:59.9313145Z   test_conv2d_bf16 (__main__.TestMkldnn) ... ok (0.174s)
2023-01-11T21:40:59.9313391Z   test_conv2d_legacy_jit_model (__main__.TestMkldnn)
2023-01-11T21:40:59.9313686Z MKLDNN integration used to serialize models with 5d weight for grouped ... ok (0.010s)
2023-01-11T21:40:59.9313975Z   test_conv2d_nhwc (__main__.TestMkldnn) ... ok (0.833s)
2023-01-11T21:40:59.9314216Z   test_conv2d_nhwc_bf16 (__main__.TestMkldnn) ... ok (1.715s)
2023-01-11T21:40:59.9314460Z   test_conv3d (__main__.TestMkldnn) ... ok (2.053s)
2023-01-11T21:40:59.9314701Z   test_conv3d_bf16 (__main__.TestMkldnn) ... ok (0.297s)
2023-01-11T21:40:59.9314944Z   test_conversion (__main__.TestMkldnn) ... ok (0.018s)
2023-01-11T21:40:59.9315181Z   test_copy (__main__.TestMkldnn) ... ok (0.013s)
2023-01-11T21:40:59.9315421Z   test_detach (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9315642Z   test_empty (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9315874Z   test_gelu (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:40:59.9316108Z   test_gelu_bf16 (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:40:59.9316344Z   test_is_mkldnn (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9316579Z   test_is_mkldnn_jit (__main__.TestMkldnn) ... ok (0.004s)
2023-01-11T21:40:59.9317176Z   test_legacy_new_failure (__main__.TestMkldnn) ... /var/lib/jenkins/workspace/test/test_mkldnn.py:1222: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:40:59.9318126Z   self.assertRaises(RuntimeError, lambda: x_mkldnn.new(x.storage()))
2023-01-11T21:40:59.9318747Z ok (0.006s)
2023-01-11T21:40:59.9319128Z   test_linear (__main__.TestMkldnn) ... ok (0.023s)
2023-01-11T21:40:59.9319610Z   test_linear_backward (__main__.TestMkldnn) ... ok (0.007s)
2023-01-11T21:40:59.9320098Z   test_linear_bf16 (__main__.TestMkldnn) ... ok (0.012s)
2023-01-11T21:40:59.9320587Z   test_linear_non_contiguous_weight (__main__.TestMkldnn) ... ok (0.005s)
2023-01-11T21:40:59.9321003Z   test_max_pool2d (__main__.TestMkldnn) ... ok (0.094s)
2023-01-11T21:40:59.9321485Z   test_max_pool2d_bf16 (__main__.TestMkldnn) ... ok (0.040s)
2023-01-11T21:40:59.9321980Z   test_max_pool2d_stride_none (__main__.TestMkldnn) ... ok (0.009s)
2023-01-11T21:40:59.9322450Z   test_max_pool3d (__main__.TestMkldnn) ... ok (13.386s)
2023-01-11T21:40:59.9322923Z   test_max_pool3d_bf16 (__main__.TestMkldnn) ... ok (7.892s)
2023-01-11T21:40:59.9323369Z   test_max_pool_unsupported (__main__.TestMkldnn) ... ok (0.012s)
2023-01-11T21:40:59.9323652Z   test_mkldnn_conv_shapecheck (__main__.TestMkldnn) ... ok (0.035s)
2023-01-11T21:40:59.9323894Z   test_mul (__main__.TestMkldnn) ... ok (0.052s)
2023-01-11T21:40:59.9324195Z   test_prelu (__main__.TestMkldnn) ... ok (6.011s)
2023-01-11T21:40:59.9324590Z   test_prelu_bf16 (__main__.TestMkldnn) ... ok (3.265s)
2023-01-11T21:40:59.9325011Z   test_relu (__main__.TestMkldnn) ... ok (0.003s)
2023-01-11T21:40:59.9325466Z   test_relu_ (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:40:59.9325904Z   test_relu_bf16 (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9326370Z   test_relu_inplace_bf16 (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9326719Z   test_repr (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9326960Z   test_reshape (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:40:59.9327212Z   test_reshape_backward (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:40:59.9327469Z   test_reshape_blocked_format (__main__.TestMkldnn) ... ok (0.007s)
2023-01-11T21:40:59.9328175Z   test_resnet18 (__main__.TestMkldnn) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
2023-01-11T21:40:59.9328598Z   warnings.warn(
2023-01-11T21:40:59.9329422Z /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
2023-01-11T21:40:59.9329853Z   warnings.warn(msg)
2023-01-11T21:40:59.9330032Z ok (0.325s)
2023-01-11T21:40:59.9330249Z   test_resnext50_32x4d (__main__.TestMkldnn) ... ok (0.651s)
2023-01-11T21:40:59.9330505Z   test_set_data_tensorimpl_type (__main__.TestMkldnn) ... ok (0.003s)
2023-01-11T21:40:59.9330774Z   test_sigmoid (__main__.TestMkldnn) ... ok (0.002s)
2023-01-11T21:40:59.9331015Z   test_softmax (__main__.TestMkldnn) ... ok (0.003s)
2023-01-11T21:40:59.9331257Z   test_tanh (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9331483Z   test_transpose (__main__.TestMkldnn) ... ok (0.003s)
2023-01-11T21:40:59.9331744Z   test_transpose_invalid_dime (__main__.TestMkldnn) ... ok (0.008s)
2023-01-11T21:40:59.9332012Z   test_unsupported (__main__.TestMkldnn) ... ok (0.055s)
2023-01-11T21:40:59.9332237Z   test_view (__main__.TestMkldnn) ... ok (0.007s)
2023-01-11T21:40:59.9332466Z   test_zero_ (__main__.TestMkldnn) ... ok (0.001s)
2023-01-11T21:40:59.9332602Z 
2023-01-11T21:40:59.9332812Z ----------------------------------------------------------------------
2023-01-11T21:40:59.9333043Z Ran 68 tests in 41.538s
2023-01-11T21:40:59.9333159Z 
2023-01-11T21:40:59.9333218Z OK
2023-01-11T21:40:59.9333308Z 
2023-01-11T21:40:59.9333391Z Generating XML reports...
2023-01-11T21:40:59.9333782Z Generated XML report: test-reports/python-unittest/test_mkldnn/TEST-TestMkldnn-20230111214017.xml
2023-01-11T21:40:59.9333986Z 
2023-01-11T21:40:59.9334323Z ##[endgroup]
2023-01-11T21:40:59.9334794Z FINISHED PRINTING LOG FILE of test_mkldnn (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_q_oe10ug)
2023-01-11T21:40:59.9335007Z 
2023-01-11T21:41:01.8266934Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:01.8991206Z Ignoring disabled issues:  []
2023-01-11T21:41:01.9146540Z Running test_mkldnn_fusion ... [2023-01-11 21:41:01.914341]
2023-01-11T21:41:01.9147965Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkldnn_fusion.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:01.914572]
2023-01-11T21:41:26.0862589Z 
2023-01-11T21:41:26.0863049Z Expand the folded group to see the log file of inductor/test_torchinductor
2023-01-11T21:41:26.0865847Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_9_sfivr0)
2023-01-11T21:41:26.0899984Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:26.0900784Z 
2023-01-11T21:41:26.0901545Z Running tests...
2023-01-11T21:41:26.0902586Z ----------------------------------------------------------------------
2023-01-11T21:41:26.0903542Z Test results will be stored in test-reports/python-unittest/inductor.test_torchinductor
2023-01-11T21:41:26.0906740Z   test_auto_simd (__main__.CPUReproTests) ... ok (0.226s)
2023-01-11T21:41:26.0908515Z   test_complex_memory_overlap (__main__.CPUReproTests) ... ok (0.002s)
2023-01-11T21:41:26.0910527Z   test_conv_stride_constraints (__main__.CPUReproTests) ... [2023-01-11 21:23:34,815] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.0911325Z [2023-01-11 21:23:36,361] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.0912054Z [2023-01-11 21:23:36,382] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.0912715Z [2023-01-11 21:23:36,396] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.0913009Z 
2023-01-11T21:41:26.0994999Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.0995269Z import torch
2023-01-11T21:41:26.0996013Z import random
2023-01-11T21:41:26.0996305Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.0996655Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.0996856Z 
2023-01-11T21:41:26.0996945Z aten = torch.ops.aten
2023-01-11T21:41:26.0997273Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.0997606Z async_compile = AsyncCompile()
2023-01-11T21:41:26.0997776Z 
2023-01-11T21:41:26.0997782Z 
2023-01-11T21:41:26.0998005Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.0998447Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.0998915Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.0999247Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.0999488Z {
2023-01-11T21:41:26.0999736Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.0999991Z     {
2023-01-11T21:41:26.1000211Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.1000482Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.1000722Z         {
2023-01-11T21:41:26.1000934Z             for(long i1=0; i1<5; i1+=1)
2023-01-11T21:41:26.1001173Z             {
2023-01-11T21:41:26.1001408Z                 #pragma GCC ivdep
2023-01-11T21:41:26.1001674Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:41:26.1001902Z                 {
2023-01-11T21:41:26.1002124Z                     {
2023-01-11T21:41:26.1002348Z                         {
2023-01-11T21:41:26.1002614Z                             auto tmp0 = in_ptr0[i2 + (256*i1) + (1280*i0)];
2023-01-11T21:41:26.1002929Z                             out_ptr0[i1 + (5*i2) + (1280*i0)] = tmp0;
2023-01-11T21:41:26.1003197Z                         }
2023-01-11T21:41:26.1003568Z                     }
2023-01-11T21:41:26.1003789Z                 }
2023-01-11T21:41:26.1004005Z             }
2023-01-11T21:41:26.1004201Z         }
2023-01-11T21:41:26.1004400Z     }
2023-01-11T21:41:26.1004596Z }
2023-01-11T21:41:26.1004803Z ''')
2023-01-11T21:41:26.1004928Z 
2023-01-11T21:41:26.1004933Z 
2023-01-11T21:41:26.1005057Z async_compile.wait(globals())
2023-01-11T21:41:26.1005322Z del async_compile
2023-01-11T21:41:26.1005466Z 
2023-01-11T21:41:26.1005558Z def call(args):
2023-01-11T21:41:26.1005783Z     inp_1, weight_1 = args
2023-01-11T21:41:26.1006029Z     args.clear()
2023-01-11T21:41:26.1006467Z     buf0 = empty_strided((2, 5, 16, 16), (1280, 1, 80, 5), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1006841Z     kernel_cpp_0(c_void_p(inp_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1007143Z     del inp_1
2023-01-11T21:41:26.1007530Z     buf1 = aten.convolution(buf0, weight_1, None, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:26.1007877Z     assert_size_stride(buf1, (2, 6, 14, 14), (1176, 1, 84, 6))
2023-01-11T21:41:26.1008144Z     del buf0
2023-01-11T21:41:26.1008361Z     del weight_1
2023-01-11T21:41:26.1008580Z     return (buf1, )
2023-01-11T21:41:26.1008717Z 
2023-01-11T21:41:26.1008723Z 
2023-01-11T21:41:26.1008825Z if __name__ == "__main__":
2023-01-11T21:41:26.1009344Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1009700Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1010185Z     inp_1 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1010693Z     weight_1 = rand_strided((6, 5, 3, 3), (45, 1, 15, 5), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1011063Z     print_performance(lambda: call([inp_1, weight_1]))
2023-01-11T21:41:26.1011267Z 
2023-01-11T21:41:26.1011272Z 
2023-01-11T21:41:26.1011390Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1011635Z import torch
2023-01-11T21:41:26.1011862Z import random
2023-01-11T21:41:26.1012160Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1012499Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1012699Z 
2023-01-11T21:41:26.1012803Z aten = torch.ops.aten
2023-01-11T21:41:26.1013124Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1013441Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1013605Z 
2023-01-11T21:41:26.1013611Z 
2023-01-11T21:41:26.1013732Z async_compile.wait(globals())
2023-01-11T21:41:26.1013989Z del async_compile
2023-01-11T21:41:26.1014135Z 
2023-01-11T21:41:26.1014227Z def call(args):
2023-01-11T21:41:26.1014450Z     inp_1, weight_1 = args
2023-01-11T21:41:26.1014692Z     args.clear()
2023-01-11T21:41:26.1015008Z     buf0 = aten.convolution(inp_1, weight_1, None, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:26.1015355Z     assert_size_stride(buf0, (2, 6, 14, 14), (1176, 196, 14, 1))
2023-01-11T21:41:26.1015620Z     del inp_1
2023-01-11T21:41:26.1015841Z     del weight_1
2023-01-11T21:41:26.1016057Z     return (buf0, )
2023-01-11T21:41:26.1016199Z 
2023-01-11T21:41:26.1016205Z 
2023-01-11T21:41:26.1016303Z if __name__ == "__main__":
2023-01-11T21:41:26.1016591Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1016943Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1017448Z     inp_1 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1017950Z     weight_1 = rand_strided((6, 5, 3, 3), (45, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1018326Z     print_performance(lambda: call([inp_1, weight_1]))
2023-01-11T21:41:26.1018529Z 
2023-01-11T21:41:26.1018600Z ok (1.605s)
2023-01-11T21:41:26.1019206Z   test_cpp_kernel_profile (__main__.CPUReproTests) ... STAGE:2023-01-11 21:23:36 1448:1448 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:41:26.1019904Z [2023-01-11 21:23:36,410] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 0
2023-01-11T21:41:26.1020775Z [2023-01-11 21:23:40,171] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 0
2023-01-11T21:41:26.1021584Z STAGE:2023-01-11 21:23:40 1448:1448 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:41:26.1022431Z STAGE:2023-01-11 21:23:40 1448:1448 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:41:26.1022805Z 
2023-01-11T21:41:26.1022978Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1023399Z import torch
2023-01-11T21:41:26.1023689Z import random
2023-01-11T21:41:26.1024079Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1024564Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1024831Z 
2023-01-11T21:41:26.1024971Z aten = torch.ops.aten
2023-01-11T21:41:26.1025512Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1025971Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1026208Z 
2023-01-11T21:41:26.1026216Z 
2023-01-11T21:41:26.1026454Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1026855Z #include <ATen/record_function.h>
2023-01-11T21:41:26.1027462Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1028098Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1028531Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1028939Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1029272Z {
2023-01-11T21:41:26.1029639Z     RECORD_FUNCTION("graph_0_kernel_cpp_0", c10::ArrayRef<c10::IValue>({}));
2023-01-11T21:41:26.1030018Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1030291Z     {
2023-01-11T21:41:26.1030520Z         #pragma omp for 
2023-01-11T21:41:26.1030800Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.1031059Z         {
2023-01-11T21:41:26.1031377Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1031800Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1032146Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1032448Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1032700Z         }
2023-01-11T21:41:26.1032970Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1033274Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.1033518Z         {
2023-01-11T21:41:26.1033775Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1034069Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.1034346Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1034634Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.1034887Z         }
2023-01-11T21:41:26.1035101Z     }
2023-01-11T21:41:26.1035316Z }
2023-01-11T21:41:26.1035563Z ''')
2023-01-11T21:41:26.1035685Z 
2023-01-11T21:41:26.1035707Z 
2023-01-11T21:41:26.1035822Z async_compile.wait(globals())
2023-01-11T21:41:26.1036107Z del async_compile
2023-01-11T21:41:26.1036266Z 
2023-01-11T21:41:26.1036369Z def call(args):
2023-01-11T21:41:26.1036612Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.1036873Z     args.clear()
2023-01-11T21:41:26.1037315Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1037781Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1038131Z     del arg0_1
2023-01-11T21:41:26.1038370Z     del arg1_1
2023-01-11T21:41:26.1038615Z     return (buf0, )
2023-01-11T21:41:26.1038760Z 
2023-01-11T21:41:26.1038766Z 
2023-01-11T21:41:26.1038875Z if __name__ == "__main__":
2023-01-11T21:41:26.1039199Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1039589Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1040144Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1040717Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1041291Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.1041515Z 
2023-01-11T21:41:26.1041656Z ok (3.779s)
2023-01-11T21:41:26.1042031Z   test_cpu_vec_cosim (__main__.CPUReproTests) ... ok (0.001s)
2023-01-11T21:41:26.1042809Z   test_inplace_add_alpha (__main__.CPUReproTests) ... [2023-01-11 21:23:40,194] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.1043600Z [2023-01-11 21:23:41,693] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.1043903Z 
2023-01-11T21:41:26.1044049Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1044380Z import torch
2023-01-11T21:41:26.1044665Z import random
2023-01-11T21:41:26.1045114Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1045588Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1045808Z 
2023-01-11T21:41:26.1045913Z aten = torch.ops.aten
2023-01-11T21:41:26.1046223Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1046526Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1046690Z 
2023-01-11T21:41:26.1046695Z 
2023-01-11T21:41:26.1046881Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1047298Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1047715Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1048029Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1048317Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1048540Z {
2023-01-11T21:41:26.1048770Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1049013Z     {
2023-01-11T21:41:26.1052122Z         #pragma omp for 
2023-01-11T21:41:26.1052351Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.1052576Z         {
2023-01-11T21:41:26.1052870Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1053223Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1053585Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.55));
2023-01-11T21:41:26.1053890Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.1054131Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.1054390Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1054621Z         }
2023-01-11T21:41:26.1054843Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1055100Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.1055322Z         {
2023-01-11T21:41:26.1055528Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.1055783Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.1056061Z             auto tmp2 = static_cast<float>(0.55);
2023-01-11T21:41:26.1056313Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.1056565Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.1056812Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.1057031Z         }
2023-01-11T21:41:26.1057210Z     }
2023-01-11T21:41:26.1057399Z }
2023-01-11T21:41:26.1057613Z ''')
2023-01-11T21:41:26.1057715Z 
2023-01-11T21:41:26.1057720Z 
2023-01-11T21:41:26.1057833Z async_compile.wait(globals())
2023-01-11T21:41:26.1058076Z del async_compile
2023-01-11T21:41:26.1058213Z 
2023-01-11T21:41:26.1058303Z def call(args):
2023-01-11T21:41:26.1058502Z     x_1, y_1 = args
2023-01-11T21:41:26.1058716Z     args.clear()
2023-01-11T21:41:26.1059039Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(y_1.data_ptr()), c_void_p(x_1.data_ptr()))
2023-01-11T21:41:26.1059324Z     del y_1
2023-01-11T21:41:26.1059535Z     return (x_1, )
2023-01-11T21:41:26.1059673Z 
2023-01-11T21:41:26.1059678Z 
2023-01-11T21:41:26.1059776Z if __name__ == "__main__":
2023-01-11T21:41:26.1060043Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1067548Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1093081Z     x_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1093525Z     y_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1093848Z     print_performance(lambda: call([x_1, y_1]))
2023-01-11T21:41:26.1094029Z 
2023-01-11T21:41:26.1094114Z ok (1.517s)
2023-01-11T21:41:26.1094789Z   test_inplace_squeeze_needed (__main__.CPUReproTests) ... [2023-01-11 21:23:41,842] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 1
2023-01-11T21:41:26.1095535Z [2023-01-11 21:23:43,411] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 1
2023-01-11T21:41:26.1095846Z 
2023-01-11T21:41:26.1095992Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1096317Z import torch
2023-01-11T21:41:26.1096744Z import random
2023-01-11T21:41:26.1097076Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1097532Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1097787Z 
2023-01-11T21:41:26.1097916Z aten = torch.ops.aten
2023-01-11T21:41:26.1098332Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1098710Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1098868Z 
2023-01-11T21:41:26.1098874Z 
2023-01-11T21:41:26.1099078Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1099510Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1099993Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.1100399Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.1100633Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1100949Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.1101240Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:26.1101554Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1101840Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.1102136Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.1102448Z                        bool* __restrict__ out_ptr4)
2023-01-11T21:41:26.1102692Z {
2023-01-11T21:41:26.1102931Z     auto in_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.1103267Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:41:26.1103516Z     {
2023-01-11T21:41:26.1103931Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.1104385Z         float tmp3 = 0;
2023-01-11T21:41:26.1104746Z         auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.1105138Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1105458Z         {
2023-01-11T21:41:26.1105792Z             #pragma omp for reduction(+:tmp3_vec) 
2023-01-11T21:41:26.1106136Z             for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.1106414Z             {
2023-01-11T21:41:26.1106777Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1107170Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1107508Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1107781Z                 tmp3_vec += tmp2;
2023-01-11T21:41:26.1108020Z             }
2023-01-11T21:41:26.1108428Z             tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:26.1108887Z             #pragma omp for simd simdlen(4) reduction(+:tmp3) 
2023-01-11T21:41:26.1109232Z             for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.1109483Z             {
2023-01-11T21:41:26.1109751Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1110056Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.1110326Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1110730Z                 tmp3 += tmp2;
2023-01-11T21:41:26.1110971Z             }
2023-01-11T21:41:26.1111194Z         }
2023-01-11T21:41:26.1111442Z         out_ptr0[0] = tmp3;
2023-01-11T21:41:26.1111680Z     }
2023-01-11T21:41:26.1111881Z     {
2023-01-11T21:41:26.1112270Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.1112677Z         float tmp8 = 0;
2023-01-11T21:41:26.1113009Z         auto tmp8_vec = at::vec::Vectorized<float>(tmp8);
2023-01-11T21:41:26.1113277Z         float tmp9 = 0;
2023-01-11T21:41:26.1113609Z         auto tmp9_vec = at::vec::Vectorized<float>(tmp9);
2023-01-11T21:41:26.1113987Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1114259Z         {
2023-01-11T21:41:26.1114614Z             #pragma omp for reduction(+:tmp8_vec) reduction(+:tmp9_vec) 
2023-01-11T21:41:26.1115092Z             for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.1115329Z             {
2023-01-11T21:41:26.1115631Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1116088Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1116541Z                 auto tmp3 = at::vec::Vectorized<float>(out_ptr0[0]);
2023-01-11T21:41:26.1116923Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1117284Z                 auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:26.1117677Z                 auto tmp5 = tmp3 / tmp4;
2023-01-11T21:41:26.1118036Z                 auto tmp6 = tmp2 - tmp5;
2023-01-11T21:41:26.1118353Z                 auto tmp7 = tmp6.pow(2);
2023-01-11T21:41:26.1118647Z                 tmp8_vec += tmp7;
2023-01-11T21:41:26.1118912Z                 tmp9_vec += tmp2;
2023-01-11T21:41:26.1119170Z             }
2023-01-11T21:41:26.1119627Z             tmp8 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp8_vec);
2023-01-11T21:41:26.1120185Z             tmp9 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp9_vec);
2023-01-11T21:41:26.1120684Z             #pragma omp for simd simdlen(4) reduction(+:tmp8) reduction(+:tmp9) 
2023-01-11T21:41:26.1121042Z             for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.1121317Z             {
2023-01-11T21:41:26.1121590Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1121917Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.1122891Z                 auto tmp3 = out_ptr0[0];
2023-01-11T21:41:26.1123200Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1123553Z                 auto tmp4 = static_cast<float>(10);
2023-01-11T21:41:26.1123851Z                 auto tmp5 = tmp3 / tmp4;
2023-01-11T21:41:26.1124256Z                 auto tmp6 = tmp2 - tmp5;
2023-01-11T21:41:26.1124552Z                 auto tmp7 = tmp6 * tmp6;
2023-01-11T21:41:26.1124797Z                 tmp8 += tmp7;
2023-01-11T21:41:26.1125074Z                 tmp9 += tmp2;
2023-01-11T21:41:26.1125323Z             }
2023-01-11T21:41:26.1125541Z         }
2023-01-11T21:41:26.1125753Z         out_ptr1[0] = tmp8;
2023-01-11T21:41:26.1126010Z         out_ptr2[0] = tmp9;
2023-01-11T21:41:26.1126250Z     }
2023-01-11T21:41:26.1126510Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1126778Z     {
2023-01-11T21:41:26.1127012Z         #pragma omp for 
2023-01-11T21:41:26.1127281Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.1127523Z         {
2023-01-11T21:41:26.1127755Z             {
2023-01-11T21:41:26.1127984Z                 {
2023-01-11T21:41:26.1128290Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.1128638Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.1128972Z                     auto tmp3 = out_ptr2[0];
2023-01-11T21:41:26.1129410Z                     auto tmp7 = out_ptr1[0];
2023-01-11T21:41:26.1129704Z                     auto tmp13 = in_ptr2[i0];
2023-01-11T21:41:26.1130174Z                     auto tmp15 = in_ptr3[i0];
2023-01-11T21:41:26.1130503Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1130887Z                     auto tmp4 = static_cast<float>(10);
2023-01-11T21:41:26.1131259Z                     auto tmp5 = tmp3 / tmp4;
2023-01-11T21:41:26.1131675Z                     auto tmp6 = tmp2 - tmp5;
2023-01-11T21:41:26.1132030Z                     auto tmp8 = tmp7 / tmp4;
2023-01-11T21:41:26.1132505Z                     auto tmp9 = static_cast<float>(1e-05);
2023-01-11T21:41:26.1133860Z                     auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.1134181Z                     auto tmp11 = 1 / std::sqrt(tmp10);
2023-01-11T21:41:26.1134485Z                     auto tmp12 = tmp6 * tmp11;
2023-01-11T21:41:26.1134789Z                     auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:26.1135159Z                     auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.1135477Z                     auto tmp17 = tmp16 * (tmp16>0);
2023-01-11T21:41:26.1135833Z                     auto tmp18 = static_cast<float>(0);
2023-01-11T21:41:26.1136196Z                     auto tmp19 = tmp17 <= tmp18;
2023-01-11T21:41:26.1136546Z                     in_out_ptr0[i0] = tmp12;
2023-01-11T21:41:26.1136870Z                     out_ptr3[i0] = tmp17;
2023-01-11T21:41:26.1137170Z                     out_ptr4[i0] = tmp19;
2023-01-11T21:41:26.1137460Z                 }
2023-01-11T21:41:26.1137728Z             }
2023-01-11T21:41:26.1137977Z         }
2023-01-11T21:41:26.1138224Z         #pragma omp single
2023-01-11T21:41:26.1138458Z         {
2023-01-11T21:41:26.1138677Z             {
2023-01-11T21:41:26.1138880Z                 {
2023-01-11T21:41:26.1139137Z                     auto tmp0 = out_ptr1[0];
2023-01-11T21:41:26.1139394Z                     auto tmp1 = static_cast<float>(10);
2023-01-11T21:41:26.1139737Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.1140150Z                     auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:41:26.1140469Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.1140847Z                     auto tmp5 = 1 / std::sqrt(tmp4);
2023-01-11T21:41:26.1141197Z                     auto tmp6 = tmp5 / tmp1;
2023-01-11T21:41:26.1141530Z                     in_out_ptr1[0] = tmp6;
2023-01-11T21:41:26.1141804Z                 }
2023-01-11T21:41:26.1142071Z             }
2023-01-11T21:41:26.1142334Z         }
2023-01-11T21:41:26.1142565Z     }
2023-01-11T21:41:26.1142811Z }
2023-01-11T21:41:26.1143090Z ''')
2023-01-11T21:41:26.1143303Z 
2023-01-11T21:41:26.1143310Z 
2023-01-11T21:41:26.1143464Z async_compile.wait(globals())
2023-01-11T21:41:26.1143791Z del async_compile
2023-01-11T21:41:26.1143975Z 
2023-01-11T21:41:26.1144092Z def call(args):
2023-01-11T21:41:26.1144468Z     primals_1, primals_2, primals_3, primals_4, primals_5 = args
2023-01-11T21:41:26.1144829Z     args.clear()
2023-01-11T21:41:26.1145354Z     buf0 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1145862Z     aten.mm.out(as_strided(primals_5, (1, 10), (10, 1)), as_strided(primals_1, (10, 10), (1, 10)), out=buf0)
2023-01-11T21:41:26.1146274Z     del primals_1
2023-01-11T21:41:26.1146790Z     buf1 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1147380Z     buf2 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1147942Z     buf3 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1148379Z     buf4 = as_strided(buf0, (10, ), (1, )); del buf0  # reuse
2023-01-11T21:41:26.1148914Z     buf5 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1149455Z     buf6 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1149857Z     buf7 = buf2; del buf2  # reuse
2023-01-11T21:41:26.1150581Z     kernel_cpp_0(c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:41:26.1151350Z     del buf1
2023-01-11T21:41:26.1151591Z     del buf3
2023-01-11T21:41:26.1151848Z     del primals_2
2023-01-11T21:41:26.1152121Z     del primals_4
2023-01-11T21:41:26.1152473Z     return (buf5, primals_3, as_strided(primals_5, (1, 10), (10, 1)), buf4, buf6, buf7, )
2023-01-11T21:41:26.1152745Z 
2023-01-11T21:41:26.1152752Z 
2023-01-11T21:41:26.1152878Z if __name__ == "__main__":
2023-01-11T21:41:26.1153230Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1153614Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1154165Z     primals_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1154744Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1155347Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1155883Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1156512Z     primals_5 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1156984Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]))
2023-01-11T21:41:26.1157265Z 
2023-01-11T21:41:26.1157378Z ok (1.828s)
2023-01-11T21:41:26.1158111Z   test_load_same_bool_tensor_twice (__main__.CPUReproTests) ... [2023-01-11 21:23:43,540] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 2
2023-01-11T21:41:26.1158912Z [2023-01-11 21:23:45,252] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 2
2023-01-11T21:41:26.1159231Z 
2023-01-11T21:41:26.1159370Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1159638Z import torch
2023-01-11T21:41:26.1159862Z import random
2023-01-11T21:41:26.1160151Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1160507Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1160676Z 
2023-01-11T21:41:26.1160783Z aten = torch.ops.aten
2023-01-11T21:41:26.1161108Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1161472Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1161654Z 
2023-01-11T21:41:26.1161663Z 
2023-01-11T21:41:26.1161826Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1162299Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1162724Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.1163023Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1163341Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1163690Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.1163988Z {
2023-01-11T21:41:26.1164265Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1164565Z     {
2023-01-11T21:41:26.1164846Z         #pragma omp for 
2023-01-11T21:41:26.1165154Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.1165450Z         {
2023-01-11T21:41:26.1165758Z             float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:26.1166134Z             flag_to_float(in_ptr0 + 8*i0, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:26.1166627Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:26.1167113Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1167478Z             flag_to_float(in_ptr0 + 8*i0, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:26.1168038Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:41:26.1168423Z             auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:41:26.1168763Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1174522Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.1178247Z         }
2023-01-11T21:41:26.1178632Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1179770Z         for(long i0=32; i0<34; i0+=1)
2023-01-11T21:41:26.1180010Z         {
2023-01-11T21:41:26.1180257Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1180506Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.1180909Z             auto tmp1 = static_cast<float>(-33.0);
2023-01-11T21:41:26.1181232Z             auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:41:26.1181502Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.1181771Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.1182024Z         }
2023-01-11T21:41:26.1182182Z     }
2023-01-11T21:41:26.1182390Z }
2023-01-11T21:41:26.1182611Z ''')
2023-01-11T21:41:26.1182746Z 
2023-01-11T21:41:26.1182752Z 
2023-01-11T21:41:26.1182888Z async_compile.wait(globals())
2023-01-11T21:41:26.1183236Z del async_compile
2023-01-11T21:41:26.1183514Z 
2023-01-11T21:41:26.1183635Z def call(args):
2023-01-11T21:41:26.1183925Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.1184219Z     args.clear()
2023-01-11T21:41:26.1184736Z     buf0 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1185318Z     buf1 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1185882Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1186331Z     del arg0_1
2023-01-11T21:41:26.1186588Z     del arg1_1
2023-01-11T21:41:26.1186837Z     return (buf0, buf1, )
2023-01-11T21:41:26.1186980Z 
2023-01-11T21:41:26.1186985Z 
2023-01-11T21:41:26.1187092Z if __name__ == "__main__":
2023-01-11T21:41:26.1187410Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1187800Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1188355Z     arg0_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1188963Z     arg1_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1189422Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.1189656Z 
2023-01-11T21:41:26.1189754Z ok (1.731s)
2023-01-11T21:41:26.1190466Z   test_masked_fill_softmax (__main__.CPUReproTests) ... [2023-01-11 21:23:45,308] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 3
2023-01-11T21:41:26.1191180Z [2023-01-11 21:23:47,098] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 3
2023-01-11T21:41:26.1191461Z 
2023-01-11T21:41:26.1191597Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1191854Z import torch
2023-01-11T21:41:26.1192107Z import random
2023-01-11T21:41:26.1192409Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1192843Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1193080Z 
2023-01-11T21:41:26.1193213Z aten = torch.ops.aten
2023-01-11T21:41:26.1193561Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1193935Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1194122Z 
2023-01-11T21:41:26.1194129Z 
2023-01-11T21:41:26.1194334Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1194828Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1195293Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.1195651Z                        const unsigned char* __restrict__ in_ptr0,
2023-01-11T21:41:26.1195995Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1196319Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1196640Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.1196883Z {
2023-01-11T21:41:26.1197137Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:26.1197461Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1197729Z     {
2023-01-11T21:41:26.1197982Z         #pragma omp for 
2023-01-11T21:41:26.1198251Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.1198579Z         {
2023-01-11T21:41:26.1198801Z             {
2023-01-11T21:41:26.1199522Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:26.1200249Z                 float tmp5 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.1200626Z                 auto tmp5_vec = at::vec::Vectorized<float>(tmp5);
2023-01-11T21:41:26.1200979Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.1201263Z                 {
2023-01-11T21:41:26.1201542Z                     float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:26.1201950Z                     flag_to_float(in_ptr0 + (8*i1) + (17*i0), g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:26.1202530Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:26.1203040Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (17*i0));
2023-01-11T21:41:26.1203439Z                     auto tmp1 = (tmp0);
2023-01-11T21:41:26.1203960Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:41:26.1204464Z                     auto tmp4 = decltype(tmp2)::blendv(tmp3, tmp2, tmp1);
2023-01-11T21:41:26.1204896Z                     tmp5_vec = at::vec::maximum(tmp5_vec, tmp4);
2023-01-11T21:41:26.1205172Z                 }
2023-01-11T21:41:26.1205621Z                 tmp5 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp5_vec);
2023-01-11T21:41:26.1206096Z                 #pragma omp simd simdlen(4)  reduction(max:tmp5)
2023-01-11T21:41:26.1206396Z                 for(long i1=16; i1<17; i1+=1)
2023-01-11T21:41:26.1206686Z                 {
2023-01-11T21:41:26.1207009Z                     auto tmp0 = in_ptr0[i1 + (17*i0)];
2023-01-11T21:41:26.1207367Z                     auto tmp3 = in_ptr1[i1 + (17*i0)];
2023-01-11T21:41:26.1207677Z                     auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:41:26.1208108Z                     auto tmp2 = static_cast<float>(-33.0);
2023-01-11T21:41:26.1208472Z                     auto tmp4 = tmp1 ? tmp2 : tmp3;
2023-01-11T21:41:26.1208793Z                     tmp5 = std::max(tmp5, tmp4);
2023-01-11T21:41:26.1209184Z                 }
2023-01-11T21:41:26.1209469Z                 out_ptr0[i0] = tmp5;
2023-01-11T21:41:26.1209729Z             }
2023-01-11T21:41:26.1209974Z         }
2023-01-11T21:41:26.1210234Z         #pragma omp for 
2023-01-11T21:41:26.1210474Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.1210702Z         {
2023-01-11T21:41:26.1210943Z             {
2023-01-11T21:41:26.1211368Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.1211779Z                 float tmp8 = 0;
2023-01-11T21:41:26.1212106Z                 auto tmp8_vec = at::vec::Vectorized<float>(tmp8);
2023-01-11T21:41:26.1212447Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.1212675Z                 {
2023-01-11T21:41:26.1212971Z                     float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:26.1213329Z                     flag_to_float(in_ptr0 + (8*i1) + (17*i0), g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:26.1213708Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:26.1214103Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (17*i0));
2023-01-11T21:41:26.1214517Z                     auto tmp5 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.1214805Z                     auto tmp1 = (tmp0);
2023-01-11T21:41:26.1215287Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:41:26.1215706Z                     auto tmp4 = decltype(tmp2)::blendv(tmp3, tmp2, tmp1);
2023-01-11T21:41:26.1216289Z                     auto tmp6 = tmp4 - tmp5;
2023-01-11T21:41:26.1216567Z                     auto tmp7 = tmp6.exp();
2023-01-11T21:41:26.1216913Z                     tmp7.store(out_ptr1 + (8*i1) + (17*i0));
2023-01-11T21:41:26.1217232Z                     tmp8_vec += tmp7;
2023-01-11T21:41:26.1217468Z                 }
2023-01-11T21:41:26.1217895Z                 tmp8 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp8_vec);
2023-01-11T21:41:26.1218374Z                 #pragma omp simd simdlen(4)  reduction(+:tmp8)
2023-01-11T21:41:26.1218715Z                 for(long i1=16; i1<17; i1+=1)
2023-01-11T21:41:26.1218968Z                 {
2023-01-11T21:41:26.1219282Z                     auto tmp0 = in_ptr0[i1 + (17*i0)];
2023-01-11T21:41:26.1219724Z                     auto tmp3 = in_ptr1[i1 + (17*i0)];
2023-01-11T21:41:26.1220075Z                     auto tmp5 = out_ptr0[i0];
2023-01-11T21:41:26.1220403Z                     auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:41:26.1220885Z                     auto tmp2 = static_cast<float>(-33.0);
2023-01-11T21:41:26.1221184Z                     auto tmp4 = tmp1 ? tmp2 : tmp3;
2023-01-11T21:41:26.1221552Z                     auto tmp6 = tmp4 - tmp5;
2023-01-11T21:41:26.1221858Z                     auto tmp7 = std::exp(tmp6);
2023-01-11T21:41:26.1222152Z                     out_ptr1[i1 + (17*i0)] = tmp7;
2023-01-11T21:41:26.1222354Z                     tmp8 += tmp7;
2023-01-11T21:41:26.1222615Z                 }
2023-01-11T21:41:26.1222867Z                 out_ptr2[i0] = tmp8;
2023-01-11T21:41:26.1223106Z             }
2023-01-11T21:41:26.1223416Z         }
2023-01-11T21:41:26.1223661Z         #pragma omp for 
2023-01-11T21:41:26.1223924Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.1224176Z         {
2023-01-11T21:41:26.1224444Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.1224688Z             {
2023-01-11T21:41:26.1225033Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (8*i1) + (17*i0));
2023-01-11T21:41:26.1225453Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:26.1225783Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.1226096Z                 tmp2.store(in_out_ptr0 + (8*i1) + (17*i0));
2023-01-11T21:41:26.1226428Z             }
2023-01-11T21:41:26.1226727Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.1227010Z             for(long i1=16; i1<17; i1+=1)
2023-01-11T21:41:26.1227292Z             {
2023-01-11T21:41:26.1227590Z                 auto tmp0 = out_ptr1[i1 + (17*i0)];
2023-01-11T21:41:26.1227908Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:26.1228244Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.1228569Z                 in_out_ptr0[i1 + (17*i0)] = tmp2;
2023-01-11T21:41:26.1228843Z             }
2023-01-11T21:41:26.1229096Z         }
2023-01-11T21:41:26.1229310Z     }
2023-01-11T21:41:26.1229514Z }
2023-01-11T21:41:26.1229786Z ''')
2023-01-11T21:41:26.1229942Z 
2023-01-11T21:41:26.1229951Z 
2023-01-11T21:41:26.1230101Z async_compile.wait(globals())
2023-01-11T21:41:26.1230401Z del async_compile
2023-01-11T21:41:26.1230557Z 
2023-01-11T21:41:26.1230664Z def call(args):
2023-01-11T21:41:26.1230947Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.1231220Z     args.clear()
2023-01-11T21:41:26.1231635Z     buf0 = empty_strided((2, 1), (1, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1232166Z     buf1 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1232653Z     buf2 = empty_strided((2, 1), (1, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1232975Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:26.1233438Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.1233852Z     del arg0_1
2023-01-11T21:41:26.1234085Z     del arg1_1
2023-01-11T21:41:26.1234324Z     return (buf3, )
2023-01-11T21:41:26.1234555Z 
2023-01-11T21:41:26.1234561Z 
2023-01-11T21:41:26.1234664Z if __name__ == "__main__":
2023-01-11T21:41:26.1234927Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1235286Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1235798Z     arg0_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1236271Z     arg1_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.uint8)
2023-01-11T21:41:26.1236688Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.1236928Z 
2023-01-11T21:41:26.1237038Z ok (1.847s)
2023-01-11T21:41:26.1237799Z   test_new_vec_op_cpu_only (__main__.CPUReproTests) ... [2023-01-11 21:23:47,137] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.1238741Z [2023-01-11 21:23:49,073] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.1239072Z 
2023-01-11T21:41:26.1239223Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1239546Z import torch
2023-01-11T21:41:26.1239830Z import random
2023-01-11T21:41:26.1240186Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1240639Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1240907Z 
2023-01-11T21:41:26.1241036Z aten = torch.ops.aten
2023-01-11T21:41:26.1241415Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1241826Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1242030Z 
2023-01-11T21:41:26.1242037Z 
2023-01-11T21:41:26.1242271Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1242824Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1243398Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1243808Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1244125Z {
2023-01-11T21:41:26.1244410Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1244728Z     {
2023-01-11T21:41:26.1245006Z         #pragma omp for 
2023-01-11T21:41:26.1245293Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.1245584Z         {
2023-01-11T21:41:26.1245953Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1246291Z             auto tmp1 = tmp0.erf();
2023-01-11T21:41:26.1246614Z             auto tmp2 = tmp1.expm1();
2023-01-11T21:41:26.1246916Z             auto tmp3 = tmp2.log1p();
2023-01-11T21:41:26.1247263Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1247556Z         }
2023-01-11T21:41:26.1247884Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1248201Z         for(long i0=16; i0<18; i0+=1)
2023-01-11T21:41:26.1248511Z         {
2023-01-11T21:41:26.1248805Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1249640Z             auto tmp1 = std::erf(tmp0);
2023-01-11T21:41:26.1251136Z             auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:26.1251514Z             auto tmp3 = std::log1p(tmp2);
2023-01-11T21:41:26.1251870Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.1252140Z         }
2023-01-11T21:41:26.1252403Z     }
2023-01-11T21:41:26.1252658Z }
2023-01-11T21:41:26.1252944Z ''')
2023-01-11T21:41:26.1253107Z 
2023-01-11T21:41:26.1253114Z 
2023-01-11T21:41:26.1253256Z async_compile.wait(globals())
2023-01-11T21:41:26.1253587Z del async_compile
2023-01-11T21:41:26.1253770Z 
2023-01-11T21:41:26.1253881Z def call(args):
2023-01-11T21:41:26.1254166Z     x_1, = args
2023-01-11T21:41:26.1254449Z     args.clear()
2023-01-11T21:41:26.1254964Z     buf0 = empty_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1255467Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1255852Z     del x_1
2023-01-11T21:41:26.1256122Z     return (buf0, )
2023-01-11T21:41:26.1256304Z 
2023-01-11T21:41:26.1256309Z 
2023-01-11T21:41:26.1256427Z if __name__ == "__main__":
2023-01-11T21:41:26.1256887Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1257226Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1257700Z     x_1 = rand_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1258070Z     print_performance(lambda: call([x_1]))
2023-01-11T21:41:26.1258278Z 
2023-01-11T21:41:26.1258388Z ok (1.973s)
2023-01-11T21:41:26.1259096Z   test_no_op_squeeze (__main__.CPUReproTests) ... [2023-01-11 21:23:49,100] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 4
2023-01-11T21:41:26.1259871Z [2023-01-11 21:23:49,103] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 4
2023-01-11T21:41:26.1260186Z 
2023-01-11T21:41:26.1260351Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1260721Z import torch
2023-01-11T21:41:26.1260983Z import random
2023-01-11T21:41:26.1261375Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1261847Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1262112Z 
2023-01-11T21:41:26.1262254Z aten = torch.ops.aten
2023-01-11T21:41:26.1262685Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1263197Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1263423Z 
2023-01-11T21:41:26.1263432Z 
2023-01-11T21:41:26.1263571Z async_compile.wait(globals())
2023-01-11T21:41:26.1263908Z del async_compile
2023-01-11T21:41:26.1264100Z 
2023-01-11T21:41:26.1264229Z def call(args):
2023-01-11T21:41:26.1264532Z     arg0_1, = args
2023-01-11T21:41:26.1264820Z     args.clear()
2023-01-11T21:41:26.1265131Z     return (arg0_1, )
2023-01-11T21:41:26.1265318Z 
2023-01-11T21:41:26.1265328Z 
2023-01-11T21:41:26.1265449Z if __name__ == "__main__":
2023-01-11T21:41:26.1265800Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1266255Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1266873Z     arg0_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1267330Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1267568Z 
2023-01-11T21:41:26.1267679Z ok (0.031s)
2023-01-11T21:41:26.1268453Z   test_parallel_num_threads (__main__.CPUReproTests) ... [2023-01-11 21:23:49,146] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 5
2023-01-11T21:41:26.1269312Z [2023-01-11 21:23:50,688] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 5
2023-01-11T21:41:26.1269628Z 
2023-01-11T21:41:26.1269792Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1270114Z import torch
2023-01-11T21:41:26.1270398Z import random
2023-01-11T21:41:26.1270742Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1271167Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1271444Z 
2023-01-11T21:41:26.1271570Z aten = torch.ops.aten
2023-01-11T21:41:26.1271967Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1272394Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1272613Z 
2023-01-11T21:41:26.1272620Z 
2023-01-11T21:41:26.1272864Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1273454Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1274009Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1274415Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1274803Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1277392Z {
2023-01-11T21:41:26.1277662Z     for(long i0=0; i0<25; i0+=1)
2023-01-11T21:41:26.1277947Z     {
2023-01-11T21:41:26.1278290Z         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1278759Z         auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1279148Z         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1279473Z         tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1279854Z     }
2023-01-11T21:41:26.1280134Z     #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.1280437Z     for(long i0=200; i0<200; i0+=1)
2023-01-11T21:41:26.1280663Z     {
2023-01-11T21:41:26.1280856Z         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1281110Z         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.1281371Z         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1281619Z         out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.1281848Z     }
2023-01-11T21:41:26.1282025Z }
2023-01-11T21:41:26.1282265Z ''')
2023-01-11T21:41:26.1282369Z 
2023-01-11T21:41:26.1282375Z 
2023-01-11T21:41:26.1282483Z async_compile.wait(globals())
2023-01-11T21:41:26.1282718Z del async_compile
2023-01-11T21:41:26.1282874Z 
2023-01-11T21:41:26.1282973Z def call(args):
2023-01-11T21:41:26.1283188Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.1283433Z     args.clear()
2023-01-11T21:41:26.1283935Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1284398Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1284743Z     del arg0_1
2023-01-11T21:41:26.1285011Z     del arg1_1
2023-01-11T21:41:26.1285251Z     return (buf0, )
2023-01-11T21:41:26.1285359Z 
2023-01-11T21:41:26.1285363Z 
2023-01-11T21:41:26.1285483Z if __name__ == "__main__":
2023-01-11T21:41:26.1285826Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1286172Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1286759Z     arg0_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1287352Z     arg1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1287808Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.1287995Z 
2023-01-11T21:41:26.1288107Z ok (1.586s)
2023-01-11T21:41:26.1288842Z   test_sign_cpu_only (__main__.CPUReproTests) ... [2023-01-11 21:23:50,706] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.1289560Z [2023-01-11 21:23:52,256] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.1289787Z 
2023-01-11T21:41:26.1289937Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1290219Z import torch
2023-01-11T21:41:26.1290423Z import random
2023-01-11T21:41:26.1290742Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1291041Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1291226Z 
2023-01-11T21:41:26.1291302Z aten = torch.ops.aten
2023-01-11T21:41:26.1291711Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1292068Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1292266Z 
2023-01-11T21:41:26.1292274Z 
2023-01-11T21:41:26.1292485Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1292859Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1293236Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1293547Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1293784Z {
2023-01-11T21:41:26.1294026Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1294263Z     {
2023-01-11T21:41:26.1294474Z         #pragma omp for 
2023-01-11T21:41:26.1294693Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.1294950Z         {
2023-01-11T21:41:26.1295275Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1295764Z             auto tmp1 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), decltype(tmp0)(0) < tmp0);
2023-01-11T21:41:26.1296340Z             auto tmp2 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), tmp0 < decltype(tmp0)(0));
2023-01-11T21:41:26.1296846Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:26.1297149Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1297573Z         }
2023-01-11T21:41:26.1297863Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1298128Z         for(long i0=16; i0<18; i0+=1)
2023-01-11T21:41:26.1298391Z         {
2023-01-11T21:41:26.1298676Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1298992Z             auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:41:26.1299298Z             auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:41:26.1299676Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:26.1299960Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.1300225Z         }
2023-01-11T21:41:26.1300455Z     }
2023-01-11T21:41:26.1300666Z }
2023-01-11T21:41:26.1300919Z ''')
2023-01-11T21:41:26.1301070Z 
2023-01-11T21:41:26.1301076Z 
2023-01-11T21:41:26.1301214Z async_compile.wait(globals())
2023-01-11T21:41:26.1301525Z del async_compile
2023-01-11T21:41:26.1301668Z 
2023-01-11T21:41:26.1301861Z def call(args):
2023-01-11T21:41:26.1302054Z     x_1, = args
2023-01-11T21:41:26.1302316Z     args.clear()
2023-01-11T21:41:26.1302771Z     buf0 = empty_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1303246Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1303548Z     del x_1
2023-01-11T21:41:26.1303758Z     return (buf0, )
2023-01-11T21:41:26.1303902Z 
2023-01-11T21:41:26.1303909Z 
2023-01-11T21:41:26.1304019Z if __name__ == "__main__":
2023-01-11T21:41:26.1304329Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1304648Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1305117Z     x_1 = rand_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1305483Z     print_performance(lambda: call([x_1]))
2023-01-11T21:41:26.1305659Z 
2023-01-11T21:41:26.1305750Z ok (1.566s)
2023-01-11T21:41:26.1306043Z   test_timed_cpu_only (__main__.CPUReproTests) ... ok (0.003s)
2023-01-11T21:41:26.1306730Z   test_vec_dynamic_shapes (__main__.CPUReproTests) ... [2023-01-11 21:23:52,423] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 6
2023-01-11T21:41:26.1307488Z [2023-01-11 21:23:54,171] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 6
2023-01-11T21:41:26.1307767Z 
2023-01-11T21:41:26.1307896Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1308197Z import torch
2023-01-11T21:41:26.1308443Z import random
2023-01-11T21:41:26.1308658Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1308948Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1309163Z 
2023-01-11T21:41:26.1309270Z aten = torch.ops.aten
2023-01-11T21:41:26.1309611Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1309925Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1310080Z 
2023-01-11T21:41:26.1310085Z 
2023-01-11T21:41:26.1310280Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1310736Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1311198Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.1311538Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1311854Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1312180Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.1312447Z                        const long ks0,
2023-01-11T21:41:26.1312710Z                        const long ks1)
2023-01-11T21:41:26.1312949Z {
2023-01-11T21:41:26.1313166Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:26.1313465Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1313677Z     {
2023-01-11T21:41:26.1313829Z         #pragma omp for 
2023-01-11T21:41:26.1314017Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:41:26.1314194Z         {
2023-01-11T21:41:26.1314375Z             {
2023-01-11T21:41:26.1314596Z                 {
2023-01-11T21:41:26.1315058Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.1315439Z                     for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:41:26.1315706Z                     {
2023-01-11T21:41:26.1315917Z                         {
2023-01-11T21:41:26.1316201Z                             auto tmp0 = in_ptr0[i1 + (i0*ks1)];
2023-01-11T21:41:26.1316484Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:26.1316720Z                         }
2023-01-11T21:41:26.1316945Z                     }
2023-01-11T21:41:26.1317184Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.1317364Z                 }
2023-01-11T21:41:26.1317511Z             }
2023-01-11T21:41:26.1317663Z         }
2023-01-11T21:41:26.1317829Z         #pragma omp for 
2023-01-11T21:41:26.1318019Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:41:26.1318180Z         {
2023-01-11T21:41:26.1318379Z             {
2023-01-11T21:41:26.1318536Z                 {
2023-01-11T21:41:26.1318704Z                     float tmp4 = 0;
2023-01-11T21:41:26.1318983Z                     for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:41:26.1319234Z                     {
2023-01-11T21:41:26.1319442Z                         {
2023-01-11T21:41:26.1319686Z                             auto tmp0 = in_ptr0[i1 + (i0*ks1)];
2023-01-11T21:41:26.1319967Z                             auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:26.1320301Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.1320587Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:26.1320864Z                             out_ptr1[i1 + (i0*ks1)] = tmp3;
2023-01-11T21:41:26.1321164Z                             tmp4 += tmp3;
2023-01-11T21:41:26.1321434Z                         }
2023-01-11T21:41:26.1321697Z                     }
2023-01-11T21:41:26.1321990Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.1322268Z                 }
2023-01-11T21:41:26.1322466Z             }
2023-01-11T21:41:26.1322607Z         }
2023-01-11T21:41:26.1322861Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.1323120Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:41:26.1323362Z         {
2023-01-11T21:41:26.1323596Z             for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:41:26.1323843Z             {
2023-01-11T21:41:26.1324039Z                 {
2023-01-11T21:41:26.1324262Z                     {
2023-01-11T21:41:26.1324539Z                         auto tmp0 = out_ptr1[i1 + (i0*ks1)];
2023-01-11T21:41:26.1324854Z                         auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:26.1325110Z                         auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.1325410Z                         in_out_ptr0[i1 + (i0*ks1)] = tmp2;
2023-01-11T21:41:26.1325675Z                     }
2023-01-11T21:41:26.1325885Z                 }
2023-01-11T21:41:26.1326089Z             }
2023-01-11T21:41:26.1326296Z         }
2023-01-11T21:41:26.1326488Z     }
2023-01-11T21:41:26.1326654Z }
2023-01-11T21:41:26.1326869Z ''')
2023-01-11T21:41:26.1326967Z 
2023-01-11T21:41:26.1326973Z 
2023-01-11T21:41:26.1327103Z async_compile.wait(globals())
2023-01-11T21:41:26.1327386Z del async_compile
2023-01-11T21:41:26.1327568Z 
2023-01-11T21:41:26.1327669Z def call(args):
2023-01-11T21:41:26.1327857Z     arg0_1, = args
2023-01-11T21:41:26.1328086Z     args.clear()
2023-01-11T21:41:26.1328337Z     arg0_1_size = arg0_1.size()
2023-01-11T21:41:26.1328561Z     s0 = arg0_1_size[0]
2023-01-11T21:41:26.1328793Z     s1 = arg0_1_size[1]
2023-01-11T21:41:26.1329334Z     buf0 = empty_strided((s0, 1), (1, s0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1329887Z     buf1 = empty_strided((s0, s1), (s1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1330473Z     buf2 = empty_strided((s0, 1), (1, s0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1330863Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:26.1331361Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_long(s0), c_long(s1))
2023-01-11T21:41:26.1331889Z     del arg0_1
2023-01-11T21:41:26.1332084Z     return (buf3, )
2023-01-11T21:41:26.1332259Z 
2023-01-11T21:41:26.1332265Z 
2023-01-11T21:41:26.1332391Z if __name__ == "__main__":
2023-01-11T21:41:26.1332707Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1333070Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1333599Z     arg0_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1333929Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1334102Z 
2023-01-11T21:41:26.1334192Z ok (1.916s)
2023-01-11T21:41:26.1334815Z   test_vec_kernel_cpu_only (__main__.CPUReproTests) ... [2023-01-11 21:23:54,342] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.1335571Z [2023-01-11 21:23:55,058] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.1336232Z [2023-01-11 21:23:55,224] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.1336909Z [2023-01-11 21:23:56,776] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.1337616Z [2023-01-11 21:23:56,977] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.1390900Z 
2023-01-11T21:41:26.1391096Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1391395Z import torch
2023-01-11T21:41:26.1393699Z import random
2023-01-11T21:41:26.1393915Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1394162Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1394313Z 
2023-01-11T21:41:26.1394391Z aten = torch.ops.aten
2023-01-11T21:41:26.1394629Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1394867Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1394991Z 
2023-01-11T21:41:26.1394996Z 
2023-01-11T21:41:26.1395161Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1395483Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1395816Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1396044Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1396254Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1396425Z {
2023-01-11T21:41:26.1396588Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1396766Z     {
2023-01-11T21:41:26.1396916Z         #pragma omp for 
2023-01-11T21:41:26.1397085Z         for(long i0=0; i0<200; i0+=1)
2023-01-11T21:41:26.1397251Z         {
2023-01-11T21:41:26.1397388Z             {
2023-01-11T21:41:26.1397529Z                 {
2023-01-11T21:41:26.1397704Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1397904Z                     auto tmp12 = in_ptr1[i0];
2023-01-11T21:41:26.1398114Z                     auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:26.1398320Z                     auto tmp2 = std::sin(tmp1);
2023-01-11T21:41:26.1398553Z                     auto tmp3 = -tmp2;
2023-01-11T21:41:26.1398748Z                     auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:26.1398995Z                     auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:26.1399194Z                     auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:26.1399390Z                     auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:41:26.1399591Z                     auto tmp8 = std::cos(tmp7);
2023-01-11T21:41:26.1399793Z                     auto tmp9 = std::exp(tmp8);
2023-01-11T21:41:26.1399998Z                     auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:41:26.1400196Z                     auto tmp11 = tmp10 + tmp0;
2023-01-11T21:41:26.1400438Z                     auto tmp13 = tmp11 - tmp12;
2023-01-11T21:41:26.1400638Z                     auto tmp14 = tmp13 * tmp0;
2023-01-11T21:41:26.1400825Z                     auto tmp15 = tmp14 / tmp0;
2023-01-11T21:41:26.1401024Z                     auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:26.1401320Z                     auto tmp17 = tmp16 * tmp16;
2023-01-11T21:41:26.1401516Z                     auto tmp18 = tmp17 * tmp15;
2023-01-11T21:41:26.1401705Z                     auto tmp19 = tmp18 * tmp18;
2023-01-11T21:41:26.1401904Z                     auto tmp20 = std::log(tmp19);
2023-01-11T21:41:26.1402109Z                     auto tmp21 = std::floor(tmp20);
2023-01-11T21:41:26.1402317Z                     auto tmp22 = std::ceil(tmp21);
2023-01-11T21:41:26.1402523Z                     auto tmp23 = std::trunc(tmp22);
2023-01-11T21:41:26.1402738Z                     auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:41:26.1402952Z                     auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:41:26.1403156Z                     auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:41:26.1403402Z                     auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:41:26.1403646Z                     auto tmp28 = tmp26 - tmp27;
2023-01-11T21:41:26.1403850Z                     auto tmp29 = tmp28 + tmp12;
2023-01-11T21:41:26.1404037Z                     out_ptr0[i0] = tmp29;
2023-01-11T21:41:26.1404198Z                 }
2023-01-11T21:41:26.1404338Z             }
2023-01-11T21:41:26.1404484Z         }
2023-01-11T21:41:26.1404624Z     }
2023-01-11T21:41:26.1404749Z }
2023-01-11T21:41:26.1404901Z ''')
2023-01-11T21:41:26.1404987Z 
2023-01-11T21:41:26.1404992Z 
2023-01-11T21:41:26.1405071Z async_compile.wait(globals())
2023-01-11T21:41:26.1405242Z del async_compile
2023-01-11T21:41:26.1405343Z 
2023-01-11T21:41:26.1405404Z def call(args):
2023-01-11T21:41:26.1405556Z     x1_1, x2_1 = args
2023-01-11T21:41:26.1405706Z     args.clear()
2023-01-11T21:41:26.1405986Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1406287Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1406509Z     del x1_1
2023-01-11T21:41:26.1406654Z     del x2_1
2023-01-11T21:41:26.1406810Z     return (buf0, )
2023-01-11T21:41:26.1406911Z 
2023-01-11T21:41:26.1406915Z 
2023-01-11T21:41:26.1406984Z if __name__ == "__main__":
2023-01-11T21:41:26.1407185Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1407435Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1407761Z     x1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1408085Z     x2_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1408327Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:41:26.1408460Z 
2023-01-11T21:41:26.1408464Z 
2023-01-11T21:41:26.1408551Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1408724Z import torch
2023-01-11T21:41:26.1408883Z import random
2023-01-11T21:41:26.1409260Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1409650Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1409795Z 
2023-01-11T21:41:26.1409870Z aten = torch.ops.aten
2023-01-11T21:41:26.1410178Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1410510Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1410669Z 
2023-01-11T21:41:26.1410685Z 
2023-01-11T21:41:26.1410875Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1411322Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1411807Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1412141Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1412480Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1412764Z {
2023-01-11T21:41:26.1413035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1413292Z     {
2023-01-11T21:41:26.1413506Z         #pragma omp for 
2023-01-11T21:41:26.1413792Z         for(long i0=0; i0<25; i0+=1)
2023-01-11T21:41:26.1414022Z         {
2023-01-11T21:41:26.1414373Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1414947Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1415298Z             auto tmp1 = tmp0.abs();
2023-01-11T21:41:26.1415604Z             auto tmp2 = tmp1.sin();
2023-01-11T21:41:26.1415892Z             auto tmp3 = tmp2.neg();
2023-01-11T21:41:26.1416160Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:26.1416540Z             auto tmp5 = decltype(tmp4)(1)/(decltype(tmp4)(1) + tmp4.neg().exp());
2023-01-11T21:41:26.1416943Z             auto tmp6 = at::vec::clamp_min(tmp5, decltype(tmp5)(0));
2023-01-11T21:41:26.1417310Z             auto tmp7 = tmp6.cos();
2023-01-11T21:41:26.1417583Z             auto tmp8 = tmp7.exp();
2023-01-11T21:41:26.1417863Z             auto tmp9 = tmp8.sqrt();
2023-01-11T21:41:26.1418248Z             auto tmp10 = tmp9 + tmp0;
2023-01-11T21:41:26.1418646Z             auto tmp12 = tmp10 - tmp11;
2023-01-11T21:41:26.1418952Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:41:26.1419256Z             auto tmp14 = tmp13 / tmp0;
2023-01-11T21:41:26.1419525Z             auto tmp15 = tmp14 * tmp14;
2023-01-11T21:41:26.1419812Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:26.1420098Z             auto tmp17 = tmp16 * tmp14;
2023-01-11T21:41:26.1420384Z             auto tmp18 = tmp17 * tmp17;
2023-01-11T21:41:26.1420672Z             auto tmp19 = tmp18.log();
2023-01-11T21:41:26.1420991Z             auto tmp20 = tmp19.floor();
2023-01-11T21:41:26.1421303Z             auto tmp21 = tmp20.ceil();
2023-01-11T21:41:26.1421620Z             auto tmp22 = tmp21.trunc();
2023-01-11T21:41:26.1421940Z             auto tmp23 = tmp22.lgamma();
2023-01-11T21:41:26.1422271Z             auto tmp24 = tmp23.fmod(tmp11);
2023-01-11T21:41:26.1422776Z             auto tmp25 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), decltype(tmp24)(0) < tmp24);
2023-01-11T21:41:26.1423457Z             auto tmp26 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), tmp24 < decltype(tmp24)(0));
2023-01-11T21:41:26.1423979Z             auto tmp27 = tmp25 - tmp26;
2023-01-11T21:41:26.1424271Z             auto tmp28 = tmp27 + tmp11;
2023-01-11T21:41:26.1424586Z             tmp28.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1424861Z         }
2023-01-11T21:41:26.1425148Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1425483Z         for(long i0=200; i0<200; i0+=1)
2023-01-11T21:41:26.1425767Z         {
2023-01-11T21:41:26.1426045Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1426360Z             auto tmp12 = in_ptr1[i0];
2023-01-11T21:41:26.1426708Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:26.1427035Z             auto tmp2 = std::sin(tmp1);
2023-01-11T21:41:26.1427443Z             auto tmp3 = -tmp2;
2023-01-11T21:41:26.1427765Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:26.1428204Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:26.1428517Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:26.1428831Z             auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:41:26.1429117Z             auto tmp8 = std::cos(tmp7);
2023-01-11T21:41:26.1429360Z             auto tmp9 = std::exp(tmp8);
2023-01-11T21:41:26.1429557Z             auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:41:26.1429749Z             auto tmp11 = tmp10 + tmp0;
2023-01-11T21:41:26.1429985Z             auto tmp13 = tmp11 - tmp12;
2023-01-11T21:41:26.1430174Z             auto tmp14 = tmp13 * tmp0;
2023-01-11T21:41:26.1430362Z             auto tmp15 = tmp14 / tmp0;
2023-01-11T21:41:26.1430539Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:26.1430722Z             auto tmp17 = tmp16 * tmp16;
2023-01-11T21:41:26.1430904Z             auto tmp18 = tmp17 * tmp15;
2023-01-11T21:41:26.1431082Z             auto tmp19 = tmp18 * tmp18;
2023-01-11T21:41:26.1431275Z             auto tmp20 = std::log(tmp19);
2023-01-11T21:41:26.1431477Z             auto tmp21 = std::floor(tmp20);
2023-01-11T21:41:26.1431678Z             auto tmp22 = std::ceil(tmp21);
2023-01-11T21:41:26.1431963Z             auto tmp23 = std::trunc(tmp22);
2023-01-11T21:41:26.1432164Z             auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:41:26.1432378Z             auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:41:26.1432577Z             auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:41:26.1432765Z             auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:41:26.1433000Z             auto tmp28 = tmp26 - tmp27;
2023-01-11T21:41:26.1433183Z             auto tmp29 = tmp28 + tmp12;
2023-01-11T21:41:26.1433561Z             out_ptr0[i0] = tmp29;
2023-01-11T21:41:26.1433811Z         }
2023-01-11T21:41:26.1434035Z     }
2023-01-11T21:41:26.1434257Z }
2023-01-11T21:41:26.1434505Z ''')
2023-01-11T21:41:26.1434644Z 
2023-01-11T21:41:26.1434651Z 
2023-01-11T21:41:26.1434786Z async_compile.wait(globals())
2023-01-11T21:41:26.1435089Z del async_compile
2023-01-11T21:41:26.1435347Z 
2023-01-11T21:41:26.1435461Z def call(args):
2023-01-11T21:41:26.1435561Z     x1_1, x2_1 = args
2023-01-11T21:41:26.1435674Z     args.clear()
2023-01-11T21:41:26.1435990Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1436244Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1436350Z     del x1_1
2023-01-11T21:41:26.1436445Z     del x2_1
2023-01-11T21:41:26.1436558Z     return (buf0, )
2023-01-11T21:41:26.1436567Z 
2023-01-11T21:41:26.1436574Z 
2023-01-11T21:41:26.1436694Z if __name__ == "__main__":
2023-01-11T21:41:26.1436866Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1437036Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1437359Z     x1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1437685Z     x2_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1437857Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:41:26.1437866Z 
2023-01-11T21:41:26.1438349Z [2023-01-11 21:23:58,790] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.1438791Z [2023-01-11 21:23:58,950] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.1439261Z [2023-01-11 21:24:00,496] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.1439271Z 
2023-01-11T21:41:26.1439426Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1439533Z import torch
2023-01-11T21:41:26.1439632Z import random
2023-01-11T21:41:26.1439820Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1440024Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1440033Z 
2023-01-11T21:41:26.1440161Z aten = torch.ops.aten
2023-01-11T21:41:26.1440386Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1440532Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1440544Z 
2023-01-11T21:41:26.1440551Z 
2023-01-11T21:41:26.1440786Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1441099Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1441261Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1441528Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1441672Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1441759Z {
2023-01-11T21:41:26.1441894Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1441976Z     {
2023-01-11T21:41:26.1442091Z         #pragma omp for 
2023-01-11T21:41:26.1442201Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:26.1442292Z         {
2023-01-11T21:41:26.1442411Z             #pragma GCC ivdep
2023-01-11T21:41:26.1442541Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.1442634Z             {
2023-01-11T21:41:26.1442724Z                 {
2023-01-11T21:41:26.1442809Z                     {
2023-01-11T21:41:26.1443112Z                         auto tmp0 = in_ptr0[i0 + (20*i1)];
2023-01-11T21:41:26.1443268Z                         auto tmp12 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.1443422Z                         auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:26.1443562Z                         auto tmp2 = std::sin(tmp1);
2023-01-11T21:41:26.1443794Z                         auto tmp3 = -tmp2;
2023-01-11T21:41:26.1443934Z                         auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:26.1444180Z                         auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:26.1444309Z                         auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:26.1444446Z                         auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:41:26.1444601Z                         auto tmp8 = std::cos(tmp7);
2023-01-11T21:41:26.1444853Z                         auto tmp9 = std::exp(tmp8);
2023-01-11T21:41:26.1445009Z                         auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:41:26.1445148Z                         auto tmp11 = tmp10 + tmp0;
2023-01-11T21:41:26.1445380Z                         auto tmp13 = tmp11 - tmp12;
2023-01-11T21:41:26.1445497Z                         auto tmp14 = tmp13 * tmp0;
2023-01-11T21:41:26.1445628Z                         auto tmp15 = tmp14 / tmp0;
2023-01-11T21:41:26.1445760Z                         auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:26.1445897Z                         auto tmp17 = tmp16 * tmp16;
2023-01-11T21:41:26.1446040Z                         auto tmp18 = tmp17 * tmp15;
2023-01-11T21:41:26.1446179Z                         auto tmp19 = tmp18 * tmp18;
2023-01-11T21:41:26.1446338Z                         auto tmp20 = std::log(tmp19);
2023-01-11T21:41:26.1446494Z                         auto tmp21 = std::floor(tmp20);
2023-01-11T21:41:26.1446644Z                         auto tmp22 = std::ceil(tmp21);
2023-01-11T21:41:26.1446808Z                         auto tmp23 = std::trunc(tmp22);
2023-01-11T21:41:26.1446961Z                         auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:41:26.1447117Z                         auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:41:26.1447252Z                         auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:41:26.1447389Z                         auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:41:26.1447642Z                         auto tmp28 = tmp26 - tmp27;
2023-01-11T21:41:26.1449382Z                         auto tmp29 = tmp28 + tmp12;
2023-01-11T21:41:26.1449522Z                         out_ptr0[i0 + (20*i1)] = tmp29;
2023-01-11T21:41:26.1449618Z                     }
2023-01-11T21:41:26.1449717Z                 }
2023-01-11T21:41:26.1449813Z             }
2023-01-11T21:41:26.1449905Z         }
2023-01-11T21:41:26.1449997Z     }
2023-01-11T21:41:26.1450071Z }
2023-01-11T21:41:26.1450206Z ''')
2023-01-11T21:41:26.1450217Z 
2023-01-11T21:41:26.1450224Z 
2023-01-11T21:41:26.1450359Z async_compile.wait(globals())
2023-01-11T21:41:26.1450458Z del async_compile
2023-01-11T21:41:26.1450463Z 
2023-01-11T21:41:26.1450530Z def call(args):
2023-01-11T21:41:26.1450596Z     x1_1, x2_1 = args
2023-01-11T21:41:26.1450661Z     args.clear()
2023-01-11T21:41:26.1450866Z     buf0 = empty_strided((20, 10), (1, 20), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1451011Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1451072Z     del x1_1
2023-01-11T21:41:26.1451129Z     del x2_1
2023-01-11T21:41:26.1451192Z     return (buf0, )
2023-01-11T21:41:26.1451197Z 
2023-01-11T21:41:26.1451202Z 
2023-01-11T21:41:26.1451272Z if __name__ == "__main__":
2023-01-11T21:41:26.1451382Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1451497Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1451681Z     x1_1 = rand_strided((20, 10), (1, 20), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1451896Z     x2_1 = rand_strided((20, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1452060Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:41:26.1452197Z 
2023-01-11T21:41:26.1452206Z 
2023-01-11T21:41:26.1452375Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1452494Z import torch
2023-01-11T21:41:26.1452614Z import random
2023-01-11T21:41:26.1452818Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1452977Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1452982Z 
2023-01-11T21:41:26.1453047Z aten = torch.ops.aten
2023-01-11T21:41:26.1453172Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1453258Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1453263Z 
2023-01-11T21:41:26.1453268Z 
2023-01-11T21:41:26.1453402Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1453649Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1453765Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1453863Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1453969Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1454049Z {
2023-01-11T21:41:26.1454185Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1454275Z     {
2023-01-11T21:41:26.1455111Z         #pragma omp for 
2023-01-11T21:41:26.1455241Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1455341Z         {
2023-01-11T21:41:26.1455587Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1455812Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1455955Z             auto tmp1 = tmp0.abs();
2023-01-11T21:41:26.1456043Z             auto tmp2 = tmp1.sin();
2023-01-11T21:41:26.1456116Z             auto tmp3 = tmp2.neg();
2023-01-11T21:41:26.1456196Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:26.1456324Z             auto tmp5 = decltype(tmp4)(1)/(decltype(tmp4)(1) + tmp4.neg().exp());
2023-01-11T21:41:26.1456448Z             auto tmp6 = at::vec::clamp_min(tmp5, decltype(tmp5)(0));
2023-01-11T21:41:26.1456527Z             auto tmp7 = tmp6.cos();
2023-01-11T21:41:26.1456597Z             auto tmp8 = tmp7.exp();
2023-01-11T21:41:26.1456671Z             auto tmp9 = tmp8.sqrt();
2023-01-11T21:41:26.1456773Z             auto tmp10 = tmp9 + tmp0;
2023-01-11T21:41:26.1456964Z             auto tmp12 = tmp10 - tmp11;
2023-01-11T21:41:26.1457074Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:41:26.1457150Z             auto tmp14 = tmp13 / tmp0;
2023-01-11T21:41:26.1457226Z             auto tmp15 = tmp14 * tmp14;
2023-01-11T21:41:26.1457297Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:26.1457420Z             auto tmp17 = tmp16 * tmp14;
2023-01-11T21:41:26.1457561Z             auto tmp18 = tmp17 * tmp17;
2023-01-11T21:41:26.1457724Z             auto tmp19 = tmp18.log();
2023-01-11T21:41:26.1457893Z             auto tmp20 = tmp19.floor();
2023-01-11T21:41:26.1458058Z             auto tmp21 = tmp20.ceil();
2023-01-11T21:41:26.1458230Z             auto tmp22 = tmp21.trunc();
2023-01-11T21:41:26.1458394Z             auto tmp23 = tmp22.lgamma();
2023-01-11T21:41:26.1458579Z             auto tmp24 = tmp23.fmod(tmp11);
2023-01-11T21:41:26.1458940Z             auto tmp25 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), decltype(tmp24)(0) < tmp24);
2023-01-11T21:41:26.1459300Z             auto tmp26 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), tmp24 < decltype(tmp24)(0));
2023-01-11T21:41:26.1459569Z             auto tmp27 = tmp25 - tmp26;
2023-01-11T21:41:26.1459730Z             auto tmp28 = tmp27 + tmp11;
2023-01-11T21:41:26.1459909Z             tmp28.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1460029Z         }
2023-01-11T21:41:26.1460209Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1460365Z         for(long i0=64; i0<70; i0+=1)
2023-01-11T21:41:26.1460485Z         {
2023-01-11T21:41:26.1460651Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1460816Z             auto tmp12 = in_ptr1[i0];
2023-01-11T21:41:26.1461073Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:26.1461242Z             auto tmp2 = std::sin(tmp1);
2023-01-11T21:41:26.1461462Z             auto tmp3 = -tmp2;
2023-01-11T21:41:26.1461623Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:26.1461893Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:26.1462056Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:26.1462218Z             auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:41:26.1462388Z             auto tmp8 = std::cos(tmp7);
2023-01-11T21:41:26.1462557Z             auto tmp9 = std::exp(tmp8);
2023-01-11T21:41:26.1462730Z             auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:41:26.1462892Z             auto tmp11 = tmp10 + tmp0;
2023-01-11T21:41:26.1463220Z             auto tmp13 = tmp11 - tmp12;
2023-01-11T21:41:26.1463449Z             auto tmp14 = tmp13 * tmp0;
2023-01-11T21:41:26.1463614Z             auto tmp15 = tmp14 / tmp0;
2023-01-11T21:41:26.1463781Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:41:26.1463945Z             auto tmp17 = tmp16 * tmp16;
2023-01-11T21:41:26.1464098Z             auto tmp18 = tmp17 * tmp15;
2023-01-11T21:41:26.1464262Z             auto tmp19 = tmp18 * tmp18;
2023-01-11T21:41:26.1464446Z             auto tmp20 = std::log(tmp19);
2023-01-11T21:41:26.1464636Z             auto tmp21 = std::floor(tmp20);
2023-01-11T21:41:26.1464825Z             auto tmp22 = std::ceil(tmp21);
2023-01-11T21:41:26.1465019Z             auto tmp23 = std::trunc(tmp22);
2023-01-11T21:41:26.1465212Z             auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:41:26.1465419Z             auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:41:26.1465584Z             auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:41:26.1465757Z             auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:41:26.1466028Z             auto tmp28 = tmp26 - tmp27;
2023-01-11T21:41:26.1466195Z             auto tmp29 = tmp28 + tmp12;
2023-01-11T21:41:26.1466360Z             out_ptr0[i0] = tmp29;
2023-01-11T21:41:26.1466493Z         }
2023-01-11T21:41:26.1466599Z     }
2023-01-11T21:41:26.1466722Z }
2023-01-11T21:41:26.1466878Z ''')
2023-01-11T21:41:26.1466890Z 
2023-01-11T21:41:26.1466900Z 
2023-01-11T21:41:26.1467087Z async_compile.wait(globals())
2023-01-11T21:41:26.1467231Z del async_compile
2023-01-11T21:41:26.1467239Z 
2023-01-11T21:41:26.1467382Z def call(args):
2023-01-11T21:41:26.1467518Z     x1_1, x2_1 = args
2023-01-11T21:41:26.1467658Z     args.clear()
2023-01-11T21:41:26.1468060Z     buf0 = empty_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1468385Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1468518Z     del x1_1
2023-01-11T21:41:26.1468645Z     del x2_1
2023-01-11T21:41:26.1468788Z     return (buf0, )
2023-01-11T21:41:26.1468797Z 
2023-01-11T21:41:26.1468811Z 
2023-01-11T21:41:26.1468965Z if __name__ == "__main__":
2023-01-11T21:41:26.1469204Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1469452Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1469861Z     x1_1 = rand_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1470263Z     x2_1 = rand_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1470488Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:41:26.1470496Z 
2023-01-11T21:41:26.1470632Z ok (6.322s)
2023-01-11T21:41:26.1471662Z   test_abs_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1471920Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1472478Z [2023-01-11 21:24:00,518] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 7
2023-01-11T21:41:26.1473110Z [2023-01-11 21:24:02,037] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 7
2023-01-11T21:41:26.1473119Z 
2023-01-11T21:41:26.1473312Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1473442Z import torch
2023-01-11T21:41:26.1473584Z import random
2023-01-11T21:41:26.1473823Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1474077Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1474087Z 
2023-01-11T21:41:26.1474245Z aten = torch.ops.aten
2023-01-11T21:41:26.1474524Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1474714Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1474725Z 
2023-01-11T21:41:26.1474733Z 
2023-01-11T21:41:26.1475073Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1475501Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1475755Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1475962Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1476082Z {
2023-01-11T21:41:26.1476284Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1476410Z     {
2023-01-11T21:41:26.1476568Z         #pragma omp for 
2023-01-11T21:41:26.1476721Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.1476844Z         {
2023-01-11T21:41:26.1477123Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1477296Z             auto tmp1 = tmp0.abs();
2023-01-11T21:41:26.1477577Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.1477751Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.1477927Z             auto tmp4 = tmp0 / tmp3;
2023-01-11T21:41:26.1478098Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1478221Z         }
2023-01-11T21:41:26.1478420Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1478586Z         for(long i0=16; i0<17; i0+=1)
2023-01-11T21:41:26.1478709Z         {
2023-01-11T21:41:26.1478881Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1479065Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:41:26.1479252Z             auto tmp2 = static_cast<float>(1);
2023-01-11T21:41:26.1479421Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.1479587Z             auto tmp4 = tmp0 / tmp3;
2023-01-11T21:41:26.1479745Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.1479875Z         }
2023-01-11T21:41:26.1480000Z     }
2023-01-11T21:41:26.1480115Z }
2023-01-11T21:41:26.1480255Z ''')
2023-01-11T21:41:26.1480267Z 
2023-01-11T21:41:26.1480277Z 
2023-01-11T21:41:26.1480458Z async_compile.wait(globals())
2023-01-11T21:41:26.1480602Z del async_compile
2023-01-11T21:41:26.1480616Z 
2023-01-11T21:41:26.1480753Z def call(args):
2023-01-11T21:41:26.1480887Z     arg0_1, = args
2023-01-11T21:41:26.1481031Z     args.clear()
2023-01-11T21:41:26.1481439Z     buf0 = empty_strided((17, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1481701Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1481832Z     del arg0_1
2023-01-11T21:41:26.1481975Z     return (buf0, )
2023-01-11T21:41:26.1481984Z 
2023-01-11T21:41:26.1481992Z 
2023-01-11T21:41:26.1482144Z if __name__ == "__main__":
2023-01-11T21:41:26.1482383Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1482640Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1483052Z     arg0_1 = rand_strided((17, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1483272Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1483281Z 
2023-01-11T21:41:26.1483399Z ok (1.542s)
2023-01-11T21:41:26.1484475Z   test_adaptive_avg_pool2d1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1484801Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1485354Z [2023-01-11 21:24:02,068] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 8
2023-01-11T21:41:26.1485887Z [2023-01-11 21:24:02,077] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._adaptive_avg_pool2d
2023-01-11T21:41:26.1485897Z 
2023-01-11T21:41:26.1486095Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1486239Z import torch
2023-01-11T21:41:26.1486385Z import random
2023-01-11T21:41:26.1486687Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1486922Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1486932Z 
2023-01-11T21:41:26.1487096Z aten = torch.ops.aten
2023-01-11T21:41:26.1487375Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1487560Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1487571Z 
2023-01-11T21:41:26.1487579Z 
2023-01-11T21:41:26.1487864Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1488300Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1488548Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1488752Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1488939Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.1489177Z {
2023-01-11T21:41:26.1489375Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1489497Z     {
2023-01-11T21:41:26.1489622Z         #pragma omp for 
2023-01-11T21:41:26.1489770Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1489884Z         {
2023-01-11T21:41:26.1490015Z             #pragma GCC ivdep
2023-01-11T21:41:26.1490160Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:26.1490275Z             {
2023-01-11T21:41:26.1490398Z                 #pragma GCC ivdep
2023-01-11T21:41:26.1490485Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:41:26.1490549Z                 {
2023-01-11T21:41:26.1490613Z                     {
2023-01-11T21:41:26.1490668Z                         {
2023-01-11T21:41:26.1490781Z                             auto tmp0 = static_cast<long>(((8*i1) / 3));
2023-01-11T21:41:26.1490897Z                             auto tmp1 = static_cast<long>(((21 + (16*i1)) / 6));
2023-01-11T21:41:26.1490989Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.1491098Z                             auto tmp3 = static_cast<long>(((8*i2) / 3));
2023-01-11T21:41:26.1491213Z                             auto tmp4 = static_cast<long>(((21 + (16*i2)) / 6));
2023-01-11T21:41:26.1491310Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:26.1491426Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:26.1491561Z                             float tmp7 = 0.0;
2023-01-11T21:41:26.1491680Z                             if(tmp6)
2023-01-11T21:41:26.1491776Z                             {
2023-01-11T21:41:26.1491964Z                                 auto tmp8 = in_ptr0[(16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1492103Z                                 tmp7 = tmp8;
2023-01-11T21:41:26.1492203Z                             }
2023-01-11T21:41:26.1492395Z                             auto tmp9 = static_cast<long>(1 + (((8*i2) / 3)));
2023-01-11T21:41:26.1492533Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:26.1492685Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:26.1492830Z                             float tmp12 = 0.0;
2023-01-11T21:41:26.1492951Z                             if(tmp11)
2023-01-11T21:41:26.1493062Z                             {
2023-01-11T21:41:26.1493355Z                                 auto tmp13 = in_ptr0[1 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1493491Z                                 tmp12 = tmp13;
2023-01-11T21:41:26.1493584Z                             }
2023-01-11T21:41:26.1493735Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:26.1493923Z                             auto tmp15 = static_cast<long>(2 + (((8*i2) / 3)));
2023-01-11T21:41:26.1494080Z                             auto tmp16 = tmp15 < tmp4;
2023-01-11T21:41:26.1494230Z                             auto tmp17 = tmp2 & tmp16;
2023-01-11T21:41:26.1494361Z                             float tmp18 = 0.0;
2023-01-11T21:41:26.1494484Z                             if(tmp17)
2023-01-11T21:41:26.1494588Z                             {
2023-01-11T21:41:26.1494818Z                                 auto tmp19 = in_ptr0[2 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1494957Z                                 tmp18 = tmp19;
2023-01-11T21:41:26.1495073Z                             }
2023-01-11T21:41:26.1495213Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:26.1495402Z                             auto tmp21 = static_cast<long>(3 + (((8*i2) / 3)));
2023-01-11T21:41:26.1495559Z                             auto tmp22 = tmp21 < tmp4;
2023-01-11T21:41:26.1495698Z                             auto tmp23 = tmp2 & tmp22;
2023-01-11T21:41:26.1495825Z                             float tmp24 = 0.0;
2023-01-11T21:41:26.1495946Z                             if(tmp23)
2023-01-11T21:41:26.1496046Z                             {
2023-01-11T21:41:26.1496228Z                                 auto tmp25 = in_ptr0[3 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1496353Z                                 tmp24 = tmp25;
2023-01-11T21:41:26.1496454Z                             }
2023-01-11T21:41:26.1496596Z                             auto tmp26 = tmp24 + tmp20;
2023-01-11T21:41:26.1496760Z                             auto tmp27 = static_cast<long>(1 + (((8*i1) / 3)));
2023-01-11T21:41:26.1496894Z                             auto tmp28 = tmp27 < tmp1;
2023-01-11T21:41:26.1497037Z                             auto tmp29 = tmp28 & tmp5;
2023-01-11T21:41:26.1497171Z                             float tmp30 = 0.0;
2023-01-11T21:41:26.1497279Z                             if(tmp29)
2023-01-11T21:41:26.1497387Z                             {
2023-01-11T21:41:26.1497580Z                                 auto tmp31 = in_ptr0[16 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1497709Z                                 tmp30 = tmp31;
2023-01-11T21:41:26.1497796Z                             }
2023-01-11T21:41:26.1497946Z                             auto tmp32 = tmp30 + tmp26;
2023-01-11T21:41:26.1498095Z                             auto tmp33 = tmp28 & tmp10;
2023-01-11T21:41:26.1498231Z                             float tmp34 = 0.0;
2023-01-11T21:41:26.1498347Z                             if(tmp33)
2023-01-11T21:41:26.1498458Z                             {
2023-01-11T21:41:26.1498920Z                                 auto tmp35 = in_ptr0[17 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1499160Z                                 tmp34 = tmp35;
2023-01-11T21:41:26.1499294Z                             }
2023-01-11T21:41:26.1499586Z                             auto tmp36 = tmp34 + tmp32;
2023-01-11T21:41:26.1499791Z                             auto tmp37 = tmp28 & tmp16;
2023-01-11T21:41:26.1499975Z                             float tmp38 = 0.0;
2023-01-11T21:41:26.1500139Z                             if(tmp37)
2023-01-11T21:41:26.1500280Z                             {
2023-01-11T21:41:26.1500543Z                                 auto tmp39 = in_ptr0[18 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1500709Z                                 tmp38 = tmp39;
2023-01-11T21:41:26.1500857Z                             }
2023-01-11T21:41:26.1501056Z                             auto tmp40 = tmp38 + tmp36;
2023-01-11T21:41:26.1501377Z                             auto tmp41 = tmp28 & tmp22;
2023-01-11T21:41:26.1501556Z                             float tmp42 = 0.0;
2023-01-11T21:41:26.1501717Z                             if(tmp41)
2023-01-11T21:41:26.1501859Z                             {
2023-01-11T21:41:26.1502120Z                                 auto tmp43 = in_ptr0[19 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1502283Z                                 tmp42 = tmp43;
2023-01-11T21:41:26.1502427Z                             }
2023-01-11T21:41:26.1502623Z                             auto tmp44 = tmp42 + tmp40;
2023-01-11T21:41:26.1502875Z                             auto tmp45 = static_cast<long>(2 + (((8*i1) / 3)));
2023-01-11T21:41:26.1503074Z                             auto tmp46 = tmp45 < tmp1;
2023-01-11T21:41:26.1524353Z                             auto tmp47 = tmp46 & tmp5;
2023-01-11T21:41:26.1524552Z                             float tmp48 = 0.0;
2023-01-11T21:41:26.1524717Z                             if(tmp47)
2023-01-11T21:41:26.1524845Z                             {
2023-01-11T21:41:26.1525109Z                                 auto tmp49 = in_ptr0[32 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1525288Z                                 tmp48 = tmp49;
2023-01-11T21:41:26.1525433Z                             }
2023-01-11T21:41:26.1525630Z                             auto tmp50 = tmp48 + tmp44;
2023-01-11T21:41:26.1525829Z                             auto tmp51 = tmp46 & tmp10;
2023-01-11T21:41:26.1526018Z                             float tmp52 = 0.0;
2023-01-11T21:41:26.1526159Z                             if(tmp51)
2023-01-11T21:41:26.1526304Z                             {
2023-01-11T21:41:26.1526573Z                                 auto tmp53 = in_ptr0[33 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1526747Z                                 tmp52 = tmp53;
2023-01-11T21:41:26.1526902Z                             }
2023-01-11T21:41:26.1527107Z                             auto tmp54 = tmp52 + tmp50;
2023-01-11T21:41:26.1527307Z                             auto tmp55 = tmp46 & tmp16;
2023-01-11T21:41:26.1527492Z                             float tmp56 = 0.0;
2023-01-11T21:41:26.1527633Z                             if(tmp55)
2023-01-11T21:41:26.1527776Z                             {
2023-01-11T21:41:26.1528035Z                                 auto tmp57 = in_ptr0[34 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1528212Z                                 tmp56 = tmp57;
2023-01-11T21:41:26.1528357Z                             }
2023-01-11T21:41:26.1528562Z                             auto tmp58 = tmp56 + tmp54;
2023-01-11T21:41:26.1528763Z                             auto tmp59 = tmp46 & tmp22;
2023-01-11T21:41:26.1528932Z                             float tmp60 = 0.0;
2023-01-11T21:41:26.1613227Z                             if(tmp59)
2023-01-11T21:41:26.1613378Z                             {
2023-01-11T21:41:26.1613566Z                                 auto tmp61 = in_ptr0[35 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1613676Z                                 tmp60 = tmp61;
2023-01-11T21:41:26.1613766Z                             }
2023-01-11T21:41:26.1613892Z                             auto tmp62 = tmp60 + tmp58;
2023-01-11T21:41:26.1614045Z                             auto tmp63 = static_cast<long>(3 + (((8*i1) / 3)));
2023-01-11T21:41:26.1614157Z                             auto tmp64 = tmp63 < tmp1;
2023-01-11T21:41:26.1614275Z                             auto tmp65 = tmp64 & tmp5;
2023-01-11T21:41:26.1614383Z                             float tmp66 = 0.0;
2023-01-11T21:41:26.1614482Z                             if(tmp65)
2023-01-11T21:41:26.1614568Z                             {
2023-01-11T21:41:26.1614730Z                                 auto tmp67 = in_ptr0[48 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1614950Z                                 tmp66 = tmp67;
2023-01-11T21:41:26.1615028Z                             }
2023-01-11T21:41:26.1615149Z                             auto tmp68 = tmp66 + tmp62;
2023-01-11T21:41:26.1615269Z                             auto tmp69 = tmp64 & tmp10;
2023-01-11T21:41:26.1615384Z                             float tmp70 = 0.0;
2023-01-11T21:41:26.1615478Z                             if(tmp69)
2023-01-11T21:41:26.1615568Z                             {
2023-01-11T21:41:26.1615724Z                                 auto tmp71 = in_ptr0[49 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1615833Z                                 tmp70 = tmp71;
2023-01-11T21:41:26.1615911Z                             }
2023-01-11T21:41:26.1616033Z                             auto tmp72 = tmp70 + tmp68;
2023-01-11T21:41:26.1616201Z                             auto tmp73 = tmp64 & tmp16;
2023-01-11T21:41:26.1616314Z                             float tmp74 = 0.0;
2023-01-11T21:41:26.1616414Z                             if(tmp73)
2023-01-11T21:41:26.1616500Z                             {
2023-01-11T21:41:26.1616652Z                                 auto tmp75 = in_ptr0[50 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1616746Z                                 tmp74 = tmp75;
2023-01-11T21:41:26.1616838Z                             }
2023-01-11T21:41:26.1616956Z                             auto tmp76 = tmp74 + tmp72;
2023-01-11T21:41:26.1617072Z                             auto tmp77 = tmp64 & tmp22;
2023-01-11T21:41:26.1617184Z                             float tmp78 = 0.0;
2023-01-11T21:41:26.1617278Z                             if(tmp77)
2023-01-11T21:41:26.1617367Z                             {
2023-01-11T21:41:26.1617518Z                                 auto tmp79 = in_ptr0[51 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:41:26.1617618Z                                 tmp78 = tmp79;
2023-01-11T21:41:26.1617706Z                             }
2023-01-11T21:41:26.1617827Z                             auto tmp80 = tmp78 + tmp76;
2023-01-11T21:41:26.1617937Z                             float tmp81 = 0.0;
2023-01-11T21:41:26.1618030Z                             if(tmp6)
2023-01-11T21:41:26.1618115Z                             {
2023-01-11T21:41:26.1618254Z                                 auto tmp82 = static_cast<float>(1);
2023-01-11T21:41:26.1618348Z                                 tmp81 = tmp82;
2023-01-11T21:41:26.1618432Z                             }
2023-01-11T21:41:26.1618541Z                             float tmp83 = 0.0;
2023-01-11T21:41:26.1618635Z                             if(tmp11)
2023-01-11T21:41:26.1618719Z                             {
2023-01-11T21:41:26.1618858Z                                 auto tmp84 = static_cast<float>(1);
2023-01-11T21:41:26.1618964Z                                 tmp83 = tmp84;
2023-01-11T21:41:26.1619037Z                             }
2023-01-11T21:41:26.1619156Z                             auto tmp85 = tmp83 + tmp81;
2023-01-11T21:41:26.1619271Z                             float tmp86 = 0.0;
2023-01-11T21:41:26.1619366Z                             if(tmp17)
2023-01-11T21:41:26.1619455Z                             {
2023-01-11T21:41:26.1619592Z                                 auto tmp87 = static_cast<float>(1);
2023-01-11T21:41:26.1619697Z                                 tmp86 = tmp87;
2023-01-11T21:41:26.1619782Z                             }
2023-01-11T21:41:26.1619891Z                             auto tmp88 = tmp86 + tmp85;
2023-01-11T21:41:26.1620006Z                             float tmp89 = 0.0;
2023-01-11T21:41:26.1620104Z                             if(tmp23)
2023-01-11T21:41:26.1620186Z                             {
2023-01-11T21:41:26.1620325Z                                 auto tmp90 = static_cast<float>(1);
2023-01-11T21:41:26.1620428Z                                 tmp89 = tmp90;
2023-01-11T21:41:26.1620515Z                             }
2023-01-11T21:41:26.1620623Z                             auto tmp91 = tmp89 + tmp88;
2023-01-11T21:41:26.1620769Z                             float tmp92 = 0.0;
2023-01-11T21:41:26.1620861Z                             if(tmp29)
2023-01-11T21:41:26.1620951Z                             {
2023-01-11T21:41:26.1621091Z                                 auto tmp93 = static_cast<float>(1);
2023-01-11T21:41:26.1621195Z                                 tmp92 = tmp93;
2023-01-11T21:41:26.1621283Z                             }
2023-01-11T21:41:26.1621390Z                             auto tmp94 = tmp92 + tmp91;
2023-01-11T21:41:26.1621501Z                             float tmp95 = 0.0;
2023-01-11T21:41:26.1621594Z                             if(tmp33)
2023-01-11T21:41:26.1621684Z                             {
2023-01-11T21:41:26.1621821Z                                 auto tmp96 = static_cast<float>(1);
2023-01-11T21:41:26.1621968Z                                 tmp95 = tmp96;
2023-01-11T21:41:26.1622057Z                             }
2023-01-11T21:41:26.1622176Z                             auto tmp97 = tmp95 + tmp94;
2023-01-11T21:41:26.1622277Z                             float tmp98 = 0.0;
2023-01-11T21:41:26.1622374Z                             if(tmp37)
2023-01-11T21:41:26.1622463Z                             {
2023-01-11T21:41:26.1622599Z                                 auto tmp99 = static_cast<float>(1);
2023-01-11T21:41:26.1622701Z                                 tmp98 = tmp99;
2023-01-11T21:41:26.1622786Z                             }
2023-01-11T21:41:26.1622910Z                             auto tmp100 = tmp98 + tmp97;
2023-01-11T21:41:26.1623011Z                             float tmp101 = 0.0;
2023-01-11T21:41:26.1623104Z                             if(tmp41)
2023-01-11T21:41:26.1623249Z                             {
2023-01-11T21:41:26.1623393Z                                 auto tmp102 = static_cast<float>(1);
2023-01-11T21:41:26.1623506Z                                 tmp101 = tmp102;
2023-01-11T21:41:26.1623592Z                             }
2023-01-11T21:41:26.1623719Z                             auto tmp103 = tmp101 + tmp100;
2023-01-11T21:41:26.1623819Z                             float tmp104 = 0.0;
2023-01-11T21:41:26.1623914Z                             if(tmp47)
2023-01-11T21:41:26.1623996Z                             {
2023-01-11T21:41:26.1624133Z                                 auto tmp105 = static_cast<float>(1);
2023-01-11T21:41:26.1624239Z                                 tmp104 = tmp105;
2023-01-11T21:41:26.1624322Z                             }
2023-01-11T21:41:26.1624442Z                             auto tmp106 = tmp104 + tmp103;
2023-01-11T21:41:26.1624542Z                             float tmp107 = 0.0;
2023-01-11T21:41:26.1624635Z                             if(tmp51)
2023-01-11T21:41:26.1624719Z                             {
2023-01-11T21:41:26.1624861Z                                 auto tmp108 = static_cast<float>(1);
2023-01-11T21:41:26.1624970Z                                 tmp107 = tmp108;
2023-01-11T21:41:26.1625053Z                             }
2023-01-11T21:41:26.1625177Z                             auto tmp109 = tmp107 + tmp106;
2023-01-11T21:41:26.1625275Z                             float tmp110 = 0.0;
2023-01-11T21:41:26.1625366Z                             if(tmp55)
2023-01-11T21:41:26.1625450Z                             {
2023-01-11T21:41:26.1625588Z                                 auto tmp111 = static_cast<float>(1);
2023-01-11T21:41:26.1625694Z                                 tmp110 = tmp111;
2023-01-11T21:41:26.1625778Z                             }
2023-01-11T21:41:26.1625899Z                             auto tmp112 = tmp110 + tmp109;
2023-01-11T21:41:26.1626009Z                             float tmp113 = 0.0;
2023-01-11T21:41:26.1626094Z                             if(tmp59)
2023-01-11T21:41:26.1626179Z                             {
2023-01-11T21:41:26.1626316Z                                 auto tmp114 = static_cast<float>(1);
2023-01-11T21:41:26.1626424Z                                 tmp113 = tmp114;
2023-01-11T21:41:26.1626549Z                             }
2023-01-11T21:41:26.1626669Z                             auto tmp115 = tmp113 + tmp112;
2023-01-11T21:41:26.1626781Z                             float tmp116 = 0.0;
2023-01-11T21:41:26.1626865Z                             if(tmp65)
2023-01-11T21:41:26.1626950Z                             {
2023-01-11T21:41:26.1627088Z                                 auto tmp117 = static_cast<float>(1);
2023-01-11T21:41:26.1627197Z                                 tmp116 = tmp117;
2023-01-11T21:41:26.1627283Z                             }
2023-01-11T21:41:26.1627403Z                             auto tmp118 = tmp116 + tmp115;
2023-01-11T21:41:26.1627510Z                             float tmp119 = 0.0;
2023-01-11T21:41:26.1627592Z                             if(tmp69)
2023-01-11T21:41:26.1627674Z                             {
2023-01-11T21:41:26.1627844Z                                 auto tmp120 = static_cast<float>(1);
2023-01-11T21:41:26.1627954Z                                 tmp119 = tmp120;
2023-01-11T21:41:26.1628041Z                             }
2023-01-11T21:41:26.1628161Z                             auto tmp121 = tmp119 + tmp118;
2023-01-11T21:41:26.1628272Z                             float tmp122 = 0.0;
2023-01-11T21:41:26.1628355Z                             if(tmp73)
2023-01-11T21:41:26.1628439Z                             {
2023-01-11T21:41:26.1628579Z                                 auto tmp123 = static_cast<float>(1);
2023-01-11T21:41:26.1628689Z                                 tmp122 = tmp123;
2023-01-11T21:41:26.1628774Z                             }
2023-01-11T21:41:26.1628897Z                             auto tmp124 = tmp122 + tmp121;
2023-01-11T21:41:26.1629009Z                             float tmp125 = 0.0;
2023-01-11T21:41:26.1629091Z                             if(tmp77)
2023-01-11T21:41:26.1629178Z                             {
2023-01-11T21:41:26.1629317Z                                 auto tmp126 = static_cast<float>(1);
2023-01-11T21:41:26.1629423Z                                 tmp125 = tmp126;
2023-01-11T21:41:26.1629511Z                             }
2023-01-11T21:41:26.1629634Z                             auto tmp127 = tmp125 + tmp124;
2023-01-11T21:41:26.1629754Z                             auto tmp128 = tmp80 / tmp127;
2023-01-11T21:41:26.1629874Z                             out_ptr0[i2 + (6*i1) + (36*i0)] = tmp128;
2023-01-11T21:41:26.1629959Z                         }
2023-01-11T21:41:26.1630042Z                     }
2023-01-11T21:41:26.1630121Z                 }
2023-01-11T21:41:26.1630200Z             }
2023-01-11T21:41:26.1630278Z         }
2023-01-11T21:41:26.1630373Z         #pragma omp for 
2023-01-11T21:41:26.1630464Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.1630537Z         {
2023-01-11T21:41:26.1630712Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1630882Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.1630986Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1631102Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.1631179Z         }
2023-01-11T21:41:26.1631286Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1631390Z         for(long i0=2048; i0<2048; i0+=1)
2023-01-11T21:41:26.1631464Z         {
2023-01-11T21:41:26.1631566Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1631687Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.1631788Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1631887Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.1631953Z         }
2023-01-11T21:41:26.1632029Z     }
2023-01-11T21:41:26.1632099Z }
2023-01-11T21:41:26.1632239Z ''')
2023-01-11T21:41:26.1632247Z 
2023-01-11T21:41:26.1632252Z 
2023-01-11T21:41:26.1632363Z async_compile.wait(globals())
2023-01-11T21:41:26.1632452Z del async_compile
2023-01-11T21:41:26.1632462Z 
2023-01-11T21:41:26.1632547Z def call(args):
2023-01-11T21:41:26.1632629Z     arg0_1, = args
2023-01-11T21:41:26.1632706Z     args.clear()
2023-01-11T21:41:26.1633037Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1633323Z     buf1 = empty_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1633530Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1633615Z     del arg0_1
2023-01-11T21:41:26.1633746Z     buf2 = aten._adaptive_avg_pool2d(buf1, [2, 5])
2023-01-11T21:41:26.1633829Z     del buf1
2023-01-11T21:41:26.1633902Z     buf3 = buf2
2023-01-11T21:41:26.1634030Z     assert_size_stride(buf3, (2, 4, 2, 5), (40, 10, 5, 1))
2023-01-11T21:41:26.1634109Z     del buf2
2023-01-11T21:41:26.1634204Z     return (buf0, buf3, )
2023-01-11T21:41:26.1634211Z 
2023-01-11T21:41:26.1634216Z 
2023-01-11T21:41:26.1634343Z if __name__ == "__main__":
2023-01-11T21:41:26.1634487Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1634638Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1634923Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1635058Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1635407Z [2023-01-11 21:24:04,110] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 8
2023-01-11T21:41:26.1635966Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1636125Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1636469Z [2023-01-11 21:24:04,153] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 9
2023-01-11T21:41:26.1636478Z 
2023-01-11T21:41:26.1636483Z 
2023-01-11T21:41:26.1636602Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1636684Z import torch
2023-01-11T21:41:26.1636771Z import random
2023-01-11T21:41:26.1636917Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1637056Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1637062Z 
2023-01-11T21:41:26.1637155Z aten = torch.ops.aten
2023-01-11T21:41:26.1637322Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1637431Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1637437Z 
2023-01-11T21:41:26.1637442Z 
2023-01-11T21:41:26.1637609Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1637862Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1638018Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1638144Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1638230Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.1638290Z {
2023-01-11T21:41:26.1638384Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1638442Z     {
2023-01-11T21:41:26.1638515Z         #pragma omp for 
2023-01-11T21:41:26.1638599Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1638659Z         {
2023-01-11T21:41:26.1638725Z             #pragma GCC ivdep
2023-01-11T21:41:26.1638805Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:26.1638866Z             {
2023-01-11T21:41:26.1638946Z                 #pragma GCC ivdep
2023-01-11T21:41:26.1639032Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:41:26.1639091Z                 {
2023-01-11T21:41:26.1639145Z                     {
2023-01-11T21:41:26.1639211Z                         {
2023-01-11T21:41:26.1639323Z                             auto tmp0 = static_cast<long>((i1 / 2));
2023-01-11T21:41:26.1639437Z                             auto tmp1 = static_cast<long>(((8 + (3*i1)) / 6));
2023-01-11T21:41:26.1639566Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.1639676Z                             auto tmp3 = static_cast<long>((i2 / 2));
2023-01-11T21:41:26.1639789Z                             auto tmp4 = static_cast<long>(((8 + (3*i2)) / 6));
2023-01-11T21:41:26.1639880Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:26.1639958Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:26.1640045Z                             float tmp7 = 0.0;
2023-01-11T21:41:26.1640119Z                             if(tmp6)
2023-01-11T21:41:26.1640187Z                             {
2023-01-11T21:41:26.1640301Z                                 auto tmp8 = in_ptr0[(3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:41:26.1640384Z                                 tmp7 = tmp8;
2023-01-11T21:41:26.1640483Z                             }
2023-01-11T21:41:26.1640583Z                             auto tmp9 = static_cast<long>(1 + (i2 / 2));
2023-01-11T21:41:26.1640680Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:26.1640774Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:26.1640862Z                             float tmp12 = 0.0;
2023-01-11T21:41:26.1640936Z                             if(tmp11)
2023-01-11T21:41:26.1641006Z                             {
2023-01-11T21:41:26.1641121Z                                 auto tmp13 = in_ptr0[1 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:41:26.1641195Z                                 tmp12 = tmp13;
2023-01-11T21:41:26.1641262Z                             }
2023-01-11T21:41:26.1641355Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:26.1641465Z                             auto tmp15 = static_cast<long>(1 + (i1 / 2));
2023-01-11T21:41:26.1641560Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:41:26.1641656Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:41:26.1641742Z                             float tmp18 = 0.0;
2023-01-11T21:41:26.1641819Z                             if(tmp17)
2023-01-11T21:41:26.1641875Z                             {
2023-01-11T21:41:26.1641987Z                                 auto tmp19 = in_ptr0[3 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:41:26.1642071Z                                 tmp18 = tmp19;
2023-01-11T21:41:26.1642137Z                             }
2023-01-11T21:41:26.1642230Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:26.1642323Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:41:26.1642408Z                             float tmp22 = 0.0;
2023-01-11T21:41:26.1642471Z                             if(tmp21)
2023-01-11T21:41:26.1642537Z                             {
2023-01-11T21:41:26.1642650Z                                 auto tmp23 = in_ptr0[4 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:41:26.1642735Z                                 tmp22 = tmp23;
2023-01-11T21:41:26.1642800Z                             }
2023-01-11T21:41:26.1642897Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:41:26.1642983Z                             float tmp25 = 0.0;
2023-01-11T21:41:26.1643045Z                             if(tmp6)
2023-01-11T21:41:26.1643112Z                             {
2023-01-11T21:41:26.1643218Z                                 auto tmp26 = static_cast<float>(1);
2023-01-11T21:41:26.1643300Z                                 tmp25 = tmp26;
2023-01-11T21:41:26.1643366Z                             }
2023-01-11T21:41:26.1643449Z                             float tmp27 = 0.0;
2023-01-11T21:41:26.1643523Z                             if(tmp11)
2023-01-11T21:41:26.1643578Z                             {
2023-01-11T21:41:26.1643684Z                                 auto tmp28 = static_cast<float>(1);
2023-01-11T21:41:26.1643768Z                                 tmp27 = tmp28;
2023-01-11T21:41:26.1643834Z                             }
2023-01-11T21:41:26.1643927Z                             auto tmp29 = tmp27 + tmp25;
2023-01-11T21:41:26.1644044Z                             float tmp30 = 0.0;
2023-01-11T21:41:26.1644120Z                             if(tmp17)
2023-01-11T21:41:26.1644175Z                             {
2023-01-11T21:41:26.1644280Z                                 auto tmp31 = static_cast<float>(1);
2023-01-11T21:41:26.1644362Z                                 tmp30 = tmp31;
2023-01-11T21:41:26.1644428Z                             }
2023-01-11T21:41:26.1644522Z                             auto tmp32 = tmp30 + tmp29;
2023-01-11T21:41:26.1644608Z                             float tmp33 = 0.0;
2023-01-11T21:41:26.1644683Z                             if(tmp21)
2023-01-11T21:41:26.1644738Z                             {
2023-01-11T21:41:26.1644846Z                                 auto tmp34 = static_cast<float>(1);
2023-01-11T21:41:26.1644978Z                                 tmp33 = tmp34;
2023-01-11T21:41:26.1645046Z                             }
2023-01-11T21:41:26.1645139Z                             auto tmp35 = tmp33 + tmp32;
2023-01-11T21:41:26.1645237Z                             auto tmp36 = tmp24 / tmp35;
2023-01-11T21:41:26.1645339Z                             out_ptr0[i2 + (6*i1) + (36*i0)] = tmp36;
2023-01-11T21:41:26.1645408Z                         }
2023-01-11T21:41:26.1645461Z                     }
2023-01-11T21:41:26.1645524Z                 }
2023-01-11T21:41:26.1645585Z             }
2023-01-11T21:41:26.1645645Z         }
2023-01-11T21:41:26.1645720Z         #pragma omp for 
2023-01-11T21:41:26.1645800Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1645850Z         {
2023-01-11T21:41:26.1645927Z             #pragma GCC ivdep
2023-01-11T21:41:26.1646009Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.1646071Z             {
2023-01-11T21:41:26.1646150Z                 #pragma GCC ivdep
2023-01-11T21:41:26.1646237Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:41:26.1646302Z                 {
2023-01-11T21:41:26.1646355Z                     {
2023-01-11T21:41:26.1646427Z                         {
2023-01-11T21:41:26.1646539Z                             auto tmp0 = static_cast<long>(((3*i1) / 2));
2023-01-11T21:41:26.1646653Z                             auto tmp1 = static_cast<long>(2 + (((3*i1) / 2)));
2023-01-11T21:41:26.1646748Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.1646857Z                             auto tmp3 = static_cast<long>(((3*i2) / 5));
2023-01-11T21:41:26.1646966Z                             auto tmp4 = static_cast<long>(((7 + (3*i2)) / 5));
2023-01-11T21:41:26.1647058Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:26.1647136Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:26.1647223Z                             float tmp7 = 0.0;
2023-01-11T21:41:26.1647297Z                             if(tmp6)
2023-01-11T21:41:26.1647368Z                             {
2023-01-11T21:41:26.1647490Z                                 auto tmp8 = in_ptr0[(3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:41:26.1647599Z                                 auto tmp9 = static_cast<float>(1);
2023-01-11T21:41:26.1647692Z                                 auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.1647765Z                                 tmp7 = tmp10;
2023-01-11T21:41:26.1647833Z                             }
2023-01-11T21:41:26.1647944Z                             auto tmp11 = static_cast<long>(1 + (((3*i2) / 5)));
2023-01-11T21:41:26.1648038Z                             auto tmp12 = tmp11 < tmp4;
2023-01-11T21:41:26.1648133Z                             auto tmp13 = tmp2 & tmp12;
2023-01-11T21:41:26.1648222Z                             float tmp14 = 0.0;
2023-01-11T21:41:26.1648296Z                             if(tmp13)
2023-01-11T21:41:26.1648353Z                             {
2023-01-11T21:41:26.1648475Z                                 auto tmp15 = in_ptr0[1 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:41:26.1648582Z                                 auto tmp16 = static_cast<float>(1);
2023-01-11T21:41:26.1648711Z                                 auto tmp17 = tmp15 + tmp16;
2023-01-11T21:41:26.1648794Z                                 tmp14 = tmp17;
2023-01-11T21:41:26.1648862Z                             }
2023-01-11T21:41:26.1648956Z                             auto tmp18 = tmp14 + tmp7;
2023-01-11T21:41:26.1649210Z                             auto tmp19 = static_cast<long>(1 + (((3*i1) / 2)));
2023-01-11T21:41:26.1649299Z                             auto tmp20 = tmp19 < tmp1;
2023-01-11T21:41:26.1649393Z                             auto tmp21 = tmp20 & tmp5;
2023-01-11T21:41:26.1649480Z                             float tmp22 = 0.0;
2023-01-11T21:41:26.1649553Z                             if(tmp21)
2023-01-11T21:41:26.1649620Z                             {
2023-01-11T21:41:26.1649806Z                                 auto tmp23 = in_ptr0[3 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:41:26.1649916Z                                 auto tmp24 = static_cast<float>(1);
2023-01-11T21:41:26.1650003Z                                 auto tmp25 = tmp23 + tmp24;
2023-01-11T21:41:26.1650086Z                                 tmp22 = tmp25;
2023-01-11T21:41:26.1650152Z                             }
2023-01-11T21:41:26.1650246Z                             auto tmp26 = tmp22 + tmp18;
2023-01-11T21:41:26.1650339Z                             auto tmp27 = tmp20 & tmp12;
2023-01-11T21:41:26.1650424Z                             float tmp28 = 0.0;
2023-01-11T21:41:26.1650498Z                             if(tmp27)
2023-01-11T21:41:26.1650554Z                             {
2023-01-11T21:41:26.1650675Z                                 auto tmp29 = in_ptr0[4 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:41:26.1650782Z                                 auto tmp30 = static_cast<float>(1);
2023-01-11T21:41:26.1650882Z                                 auto tmp31 = tmp29 + tmp30;
2023-01-11T21:41:26.1650964Z                                 tmp28 = tmp31;
2023-01-11T21:41:26.1651031Z                             }
2023-01-11T21:41:26.1651126Z                             auto tmp32 = tmp28 + tmp26;
2023-01-11T21:41:26.1651211Z                             float tmp33 = 0.0;
2023-01-11T21:41:26.1651274Z                             if(tmp6)
2023-01-11T21:41:26.1651341Z                             {
2023-01-11T21:41:26.1651453Z                                 auto tmp34 = static_cast<float>(1);
2023-01-11T21:41:26.1651536Z                                 tmp33 = tmp34;
2023-01-11T21:41:26.1651601Z                             }
2023-01-11T21:41:26.1651681Z                             float tmp35 = 0.0;
2023-01-11T21:41:26.1651755Z                             if(tmp13)
2023-01-11T21:41:26.1651810Z                             {
2023-01-11T21:41:26.1651917Z                                 auto tmp36 = static_cast<float>(1);
2023-01-11T21:41:26.1652002Z                                 tmp35 = tmp36;
2023-01-11T21:41:26.1652068Z                             }
2023-01-11T21:41:26.1652159Z                             auto tmp37 = tmp35 + tmp33;
2023-01-11T21:41:26.1652246Z                             float tmp38 = 0.0;
2023-01-11T21:41:26.1652321Z                             if(tmp21)
2023-01-11T21:41:26.1652375Z                             {
2023-01-11T21:41:26.1652480Z                                 auto tmp39 = static_cast<float>(1);
2023-01-11T21:41:26.1652563Z                                 tmp38 = tmp39;
2023-01-11T21:41:26.1652627Z                             }
2023-01-11T21:41:26.1652720Z                             auto tmp40 = tmp38 + tmp37;
2023-01-11T21:41:26.1652804Z                             float tmp41 = 0.0;
2023-01-11T21:41:26.1652881Z                             if(tmp27)
2023-01-11T21:41:26.1652937Z                             {
2023-01-11T21:41:26.1653041Z                                 auto tmp42 = static_cast<float>(1);
2023-01-11T21:41:26.1653123Z                                 tmp41 = tmp42;
2023-01-11T21:41:26.1653190Z                             }
2023-01-11T21:41:26.1653324Z                             auto tmp43 = tmp41 + tmp40;
2023-01-11T21:41:26.1653416Z                             auto tmp44 = tmp32 / tmp43;
2023-01-11T21:41:26.1653519Z                             out_ptr1[i2 + (5*i1) + (10*i0)] = tmp44;
2023-01-11T21:41:26.1653574Z                         }
2023-01-11T21:41:26.1653639Z                     }
2023-01-11T21:41:26.1653699Z                 }
2023-01-11T21:41:26.1653760Z             }
2023-01-11T21:41:26.1653819Z         }
2023-01-11T21:41:26.1653877Z     }
2023-01-11T21:41:26.1653934Z }
2023-01-11T21:41:26.1654013Z ''')
2023-01-11T21:41:26.1654019Z 
2023-01-11T21:41:26.1654024Z 
2023-01-11T21:41:26.1654114Z async_compile.wait(globals())
2023-01-11T21:41:26.1654184Z del async_compile
2023-01-11T21:41:26.1654189Z 
2023-01-11T21:41:26.1654258Z def call(args):
2023-01-11T21:41:26.1654324Z     arg0_1, = args
2023-01-11T21:41:26.1654425Z     args.clear()
2023-01-11T21:41:26.1654639Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1654846Z     buf1 = empty_strided((2, 4, 2, 5), (40, 10, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1654997Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1655064Z     del arg0_1
2023-01-11T21:41:26.1655137Z     return (buf0, buf1, )
2023-01-11T21:41:26.1655142Z 
2023-01-11T21:41:26.1655147Z 
2023-01-11T21:41:26.1655220Z if __name__ == "__main__":
2023-01-11T21:41:26.1655332Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1655452Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1655660Z     arg0_1 = rand_strided((2, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1655769Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1656020Z [2023-01-11 21:24:05,861] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 9
2023-01-11T21:41:26.1656447Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1656576Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1656830Z [2023-01-11 21:24:05,892] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 10
2023-01-11T21:41:26.1656835Z 
2023-01-11T21:41:26.1656839Z 
2023-01-11T21:41:26.1656934Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1657002Z import torch
2023-01-11T21:41:26.1657070Z import random
2023-01-11T21:41:26.1657184Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1657304Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1657309Z 
2023-01-11T21:41:26.1657374Z aten = torch.ops.aten
2023-01-11T21:41:26.1657505Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1657593Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1657598Z 
2023-01-11T21:41:26.1657602Z 
2023-01-11T21:41:26.1657733Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1657936Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1658050Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1658148Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1658243Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.1658291Z {
2023-01-11T21:41:26.1658385Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1658445Z     {
2023-01-11T21:41:26.1658520Z         #pragma omp for 
2023-01-11T21:41:26.1658602Z         for(long i0=0; i0<36; i0+=1)
2023-01-11T21:41:26.1658662Z         {
2023-01-11T21:41:26.1658796Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1658908Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1658969Z         }
2023-01-11T21:41:26.1659061Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1659141Z         for(long i0=288; i0<288; i0+=1)
2023-01-11T21:41:26.1659201Z         {
2023-01-11T21:41:26.1659282Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1659363Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.1659411Z         }
2023-01-11T21:41:26.1659486Z         #pragma omp for 
2023-01-11T21:41:26.1659566Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1659624Z         {
2023-01-11T21:41:26.1659700Z             #pragma GCC ivdep
2023-01-11T21:41:26.1659779Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.1659830Z             {
2023-01-11T21:41:26.1659909Z                 #pragma GCC ivdep
2023-01-11T21:41:26.1660024Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:41:26.1660091Z                 {
2023-01-11T21:41:26.1660156Z                     {
2023-01-11T21:41:26.1660225Z                         {
2023-01-11T21:41:26.1660332Z                             auto tmp0 = static_cast<long>(3*i1);
2023-01-11T21:41:26.1660432Z                             auto tmp1 = static_cast<long>(3 + (3*i1));
2023-01-11T21:41:26.1660526Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.1660638Z                             auto tmp3 = static_cast<long>(((6*i2) / 5));
2023-01-11T21:41:26.1660752Z                             auto tmp4 = static_cast<long>(2 + (((6*i2) / 5)));
2023-01-11T21:41:26.1660843Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:26.1660933Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:26.1661019Z                             float tmp7 = 0.0;
2023-01-11T21:41:26.1661093Z                             if(tmp6)
2023-01-11T21:41:26.1661150Z                             {
2023-01-11T21:41:26.1661263Z                                 auto tmp8 = in_ptr0[(18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:26.1661373Z                                 auto tmp9 = static_cast<float>(1);
2023-01-11T21:41:26.1661469Z                                 auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.1661552Z                                 tmp7 = tmp10;
2023-01-11T21:41:26.1661619Z                             }
2023-01-11T21:41:26.1661734Z                             auto tmp11 = static_cast<long>(1 + (((6*i2) / 5)));
2023-01-11T21:41:26.1661818Z                             auto tmp12 = tmp11 < tmp4;
2023-01-11T21:41:26.1661912Z                             auto tmp13 = tmp2 & tmp12;
2023-01-11T21:41:26.1661997Z                             float tmp14 = 0.0;
2023-01-11T21:41:26.1662072Z                             if(tmp13)
2023-01-11T21:41:26.1662139Z                             {
2023-01-11T21:41:26.1662257Z                                 auto tmp15 = in_ptr0[1 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:26.1662364Z                                 auto tmp16 = static_cast<float>(1);
2023-01-11T21:41:26.1662451Z                                 auto tmp17 = tmp15 + tmp16;
2023-01-11T21:41:26.1662534Z                                 tmp14 = tmp17;
2023-01-11T21:41:26.1662604Z                             }
2023-01-11T21:41:26.1662699Z                             auto tmp18 = tmp14 + tmp7;
2023-01-11T21:41:26.1662808Z                             auto tmp19 = static_cast<long>(1 + (3*i1));
2023-01-11T21:41:26.1662902Z                             auto tmp20 = tmp19 < tmp1;
2023-01-11T21:41:26.1662995Z                             auto tmp21 = tmp20 & tmp5;
2023-01-11T21:41:26.1663080Z                             float tmp22 = 0.0;
2023-01-11T21:41:26.1663231Z                             if(tmp21)
2023-01-11T21:41:26.1663306Z                             {
2023-01-11T21:41:26.1663422Z                                 auto tmp23 = in_ptr0[6 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:26.1663532Z                                 auto tmp24 = static_cast<float>(1);
2023-01-11T21:41:26.1663671Z                                 auto tmp25 = tmp23 + tmp24;
2023-01-11T21:41:26.1663755Z                                 tmp22 = tmp25;
2023-01-11T21:41:26.1663823Z                             }
2023-01-11T21:41:26.1663905Z                             auto tmp26 = tmp22 + tmp18;
2023-01-11T21:41:26.1663996Z                             auto tmp27 = tmp20 & tmp12;
2023-01-11T21:41:26.1664087Z                             float tmp28 = 0.0;
2023-01-11T21:41:26.1664162Z                             if(tmp27)
2023-01-11T21:41:26.1664229Z                             {
2023-01-11T21:41:26.1664341Z                                 auto tmp29 = in_ptr0[7 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:26.1664446Z                                 auto tmp30 = static_cast<float>(1);
2023-01-11T21:41:26.1664561Z                                 auto tmp31 = tmp29 + tmp30;
2023-01-11T21:41:26.1664649Z                                 tmp28 = tmp31;
2023-01-11T21:41:26.1664714Z                             }
2023-01-11T21:41:26.1664813Z                             auto tmp32 = tmp28 + tmp26;
2023-01-11T21:41:26.1664921Z                             auto tmp33 = static_cast<long>(2 + (3*i1));
2023-01-11T21:41:26.1665015Z                             auto tmp34 = tmp33 < tmp1;
2023-01-11T21:41:26.1665107Z                             auto tmp35 = tmp34 & tmp5;
2023-01-11T21:41:26.1665193Z                             float tmp36 = 0.0;
2023-01-11T21:41:26.1665255Z                             if(tmp35)
2023-01-11T21:41:26.1665322Z                             {
2023-01-11T21:41:26.1665438Z                                 auto tmp37 = in_ptr0[12 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:26.1665544Z                                 auto tmp38 = static_cast<float>(1);
2023-01-11T21:41:26.1665644Z                                 auto tmp39 = tmp37 + tmp38;
2023-01-11T21:41:26.1665729Z                                 tmp36 = tmp39;
2023-01-11T21:41:26.1665798Z                             }
2023-01-11T21:41:26.1665882Z                             auto tmp40 = tmp36 + tmp32;
2023-01-11T21:41:26.1665975Z                             auto tmp41 = tmp34 & tmp12;
2023-01-11T21:41:26.1666060Z                             float tmp42 = 0.0;
2023-01-11T21:41:26.1666134Z                             if(tmp41)
2023-01-11T21:41:26.1666201Z                             {
2023-01-11T21:41:26.1666314Z                                 auto tmp43 = in_ptr0[13 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:26.1666419Z                                 auto tmp44 = static_cast<float>(1);
2023-01-11T21:41:26.1666515Z                                 auto tmp45 = tmp43 + tmp44;
2023-01-11T21:41:26.1666586Z                                 tmp42 = tmp45;
2023-01-11T21:41:26.1666652Z                             }
2023-01-11T21:41:26.1666745Z                             auto tmp46 = tmp42 + tmp40;
2023-01-11T21:41:26.1666840Z                             auto tmp47 = tmp1 < tmp1;
2023-01-11T21:41:26.1666933Z                             auto tmp48 = tmp47 & tmp5;
2023-01-11T21:41:26.1667020Z                             float tmp49 = 0.0;
2023-01-11T21:41:26.1667094Z                             if(tmp48)
2023-01-11T21:41:26.1667150Z                             {
2023-01-11T21:41:26.1667261Z                                 auto tmp50 = in_ptr0[18 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:26.1667366Z                                 auto tmp51 = static_cast<float>(1);
2023-01-11T21:41:26.1667462Z                                 auto tmp52 = tmp50 + tmp51;
2023-01-11T21:41:26.1667544Z                                 tmp49 = tmp52;
2023-01-11T21:41:26.1667612Z                             }
2023-01-11T21:41:26.1667704Z                             auto tmp53 = tmp49 + tmp46;
2023-01-11T21:41:26.1667785Z                             auto tmp54 = tmp47 & tmp12;
2023-01-11T21:41:26.1667872Z                             float tmp55 = 0.0;
2023-01-11T21:41:26.1667946Z                             if(tmp54)
2023-01-11T21:41:26.1668013Z                             {
2023-01-11T21:41:26.1668158Z                                 auto tmp56 = in_ptr0[19 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:41:26.1668264Z                                 auto tmp57 = static_cast<float>(1);
2023-01-11T21:41:26.1668359Z                                 auto tmp58 = tmp56 + tmp57;
2023-01-11T21:41:26.1668441Z                                 tmp55 = tmp58;
2023-01-11T21:41:26.1668496Z                             }
2023-01-11T21:41:26.1668588Z                             auto tmp59 = tmp55 + tmp53;
2023-01-11T21:41:26.1668676Z                             float tmp60 = 0.0;
2023-01-11T21:41:26.1668749Z                             if(tmp6)
2023-01-11T21:41:26.1668815Z                             {
2023-01-11T21:41:26.1668919Z                                 auto tmp61 = static_cast<float>(1);
2023-01-11T21:41:26.1669030Z                                 tmp60 = tmp61;
2023-01-11T21:41:26.1669086Z                             }
2023-01-11T21:41:26.1669170Z                             float tmp62 = 0.0;
2023-01-11T21:41:26.1669248Z                             if(tmp13)
2023-01-11T21:41:26.1669314Z                             {
2023-01-11T21:41:26.1669421Z                                 auto tmp63 = static_cast<float>(1);
2023-01-11T21:41:26.1669502Z                                 tmp62 = tmp63;
2023-01-11T21:41:26.1669568Z                             }
2023-01-11T21:41:26.1669648Z                             auto tmp64 = tmp62 + tmp60;
2023-01-11T21:41:26.1669751Z                             float tmp65 = 0.0;
2023-01-11T21:41:26.1669849Z                             if(tmp21)
2023-01-11T21:41:26.1669936Z                             {
2023-01-11T21:41:26.1670327Z                                 auto tmp66 = static_cast<float>(1);
2023-01-11T21:41:26.1670708Z                                 tmp65 = tmp66;
2023-01-11T21:41:26.1670887Z                             }
2023-01-11T21:41:26.1671170Z                             auto tmp67 = tmp65 + tmp64;
2023-01-11T21:41:26.1671358Z                             float tmp68 = 0.0;
2023-01-11T21:41:26.1671511Z                             if(tmp27)
2023-01-11T21:41:26.1671656Z                             {
2023-01-11T21:41:26.1671887Z                                 auto tmp69 = static_cast<float>(1);
2023-01-11T21:41:26.1672062Z                                 tmp68 = tmp69;
2023-01-11T21:41:26.1672210Z                             }
2023-01-11T21:41:26.1672397Z                             auto tmp70 = tmp68 + tmp67;
2023-01-11T21:41:26.1672578Z                             float tmp71 = 0.0;
2023-01-11T21:41:26.1672736Z                             if(tmp35)
2023-01-11T21:41:26.1672879Z                             {
2023-01-11T21:41:26.1673113Z                                 auto tmp72 = static_cast<float>(1);
2023-01-11T21:41:26.1673288Z                                 tmp71 = tmp72;
2023-01-11T21:41:26.1673437Z                             }
2023-01-11T21:41:26.1673627Z                             auto tmp73 = tmp71 + tmp70;
2023-01-11T21:41:26.1673813Z                             float tmp74 = 0.0;
2023-01-11T21:41:26.1673975Z                             if(tmp41)
2023-01-11T21:41:26.1674119Z                             {
2023-01-11T21:41:26.1674352Z                                 auto tmp75 = static_cast<float>(1);
2023-01-11T21:41:26.1674528Z                                 tmp74 = tmp75;
2023-01-11T21:41:26.1674669Z                             }
2023-01-11T21:41:26.1674856Z                             auto tmp76 = tmp74 + tmp73;
2023-01-11T21:41:26.1675037Z                             float tmp77 = 0.0;
2023-01-11T21:41:26.1675197Z                             if(tmp48)
2023-01-11T21:41:26.1675343Z                             {
2023-01-11T21:41:26.1675576Z                                 auto tmp78 = static_cast<float>(1);
2023-01-11T21:41:26.1675755Z                                 tmp77 = tmp78;
2023-01-11T21:41:26.1675902Z                             }
2023-01-11T21:41:26.1676103Z                             auto tmp79 = tmp77 + tmp76;
2023-01-11T21:41:26.1676366Z                             float tmp80 = 0.0;
2023-01-11T21:41:26.1676528Z                             if(tmp54)
2023-01-11T21:41:26.1676671Z                             {
2023-01-11T21:41:26.1676905Z                                 auto tmp81 = static_cast<float>(1);
2023-01-11T21:41:26.1677084Z                                 tmp80 = tmp81;
2023-01-11T21:41:26.1677225Z                             }
2023-01-11T21:41:26.1677432Z                             auto tmp82 = tmp80 + tmp79;
2023-01-11T21:41:26.1677623Z                             auto tmp83 = tmp59 / tmp82;
2023-01-11T21:41:26.1677843Z                             out_ptr1[i2 + (5*i1) + (10*i0)] = tmp83;
2023-01-11T21:41:26.1677983Z                         }
2023-01-11T21:41:26.1678120Z                     }
2023-01-11T21:41:26.1678255Z                 }
2023-01-11T21:41:26.1678460Z             }
2023-01-11T21:41:26.1678594Z         }
2023-01-11T21:41:26.1678704Z     }
2023-01-11T21:41:26.1678829Z }
2023-01-11T21:41:26.1679033Z ''')
2023-01-11T21:41:26.1679046Z 
2023-01-11T21:41:26.1679056Z 
2023-01-11T21:41:26.1679251Z async_compile.wait(globals())
2023-01-11T21:41:26.1679405Z del async_compile
2023-01-11T21:41:26.1679415Z 
2023-01-11T21:41:26.1679564Z def call(args):
2023-01-11T21:41:26.1679709Z     arg0_1, = args
2023-01-11T21:41:26.1679845Z     args.clear()
2023-01-11T21:41:26.1680337Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1680815Z     buf1 = empty_strided((2, 4, 2, 5), (40, 10, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1681180Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1681324Z     del arg0_1
2023-01-11T21:41:26.1681490Z     return (buf0, buf1, )
2023-01-11T21:41:26.1681500Z 
2023-01-11T21:41:26.1681510Z 
2023-01-11T21:41:26.1681677Z if __name__ == "__main__":
2023-01-11T21:41:26.1681921Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1682180Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1682667Z     arg0_1 = rand_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1682897Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1683510Z [2023-01-11 21:24:07,680] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 10
2023-01-11T21:41:26.1683520Z 
2023-01-11T21:41:26.1683658Z ok (5.643s)
2023-01-11T21:41:26.1684806Z   test_adaptive_avg_pool2d2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1685084Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1685680Z [2023-01-11 21:24:07,701] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 11
2023-01-11T21:41:26.1686249Z [2023-01-11 21:24:07,707] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._adaptive_avg_pool2d
2023-01-11T21:41:26.1686840Z [2023-01-11 21:24:07,711] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 11
2023-01-11T21:41:26.1686868Z 
2023-01-11T21:41:26.1687057Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1687205Z import torch
2023-01-11T21:41:26.1687349Z import random
2023-01-11T21:41:26.1687602Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1687869Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1687880Z 
2023-01-11T21:41:26.1688044Z aten = torch.ops.aten
2023-01-11T21:41:26.1688347Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1688534Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1688546Z 
2023-01-11T21:41:26.1688658Z 
2023-01-11T21:41:26.1688838Z async_compile.wait(globals())
2023-01-11T21:41:26.1688990Z del async_compile
2023-01-11T21:41:26.1689000Z 
2023-01-11T21:41:26.1689283Z def call(args):
2023-01-11T21:41:26.1689425Z     arg0_1, = args
2023-01-11T21:41:26.1689569Z     args.clear()
2023-01-11T21:41:26.1689807Z     buf0 = aten._adaptive_avg_pool2d(arg0_1, [4, 4])
2023-01-11T21:41:26.1689947Z     del arg0_1
2023-01-11T21:41:26.1690076Z     buf1 = buf0
2023-01-11T21:41:26.1690299Z     assert_size_stride(buf1, (2, 4, 4, 4), (64, 16, 4, 1))
2023-01-11T21:41:26.1690439Z     del buf0
2023-01-11T21:41:26.1690588Z     return (buf1, )
2023-01-11T21:41:26.1690599Z 
2023-01-11T21:41:26.1690609Z 
2023-01-11T21:41:26.1690766Z if __name__ == "__main__":
2023-01-11T21:41:26.1691012Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1691422Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1691926Z     arg0_1 = rand_strided((2, 4, 21, 21), (1764, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1692164Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1692176Z 
2023-01-11T21:41:26.1692314Z ok (0.030s)
2023-01-11T21:41:26.1693430Z   test_add_const_float_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1693708Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1694305Z [2023-01-11 21:24:07,723] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 12
2023-01-11T21:41:26.1694915Z [2023-01-11 21:24:09,267] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 12
2023-01-11T21:41:26.1694930Z 
2023-01-11T21:41:26.1695090Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1695202Z import torch
2023-01-11T21:41:26.1695303Z import random
2023-01-11T21:41:26.1695493Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1695707Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1695717Z 
2023-01-11T21:41:26.1695867Z aten = torch.ops.aten
2023-01-11T21:41:26.1696115Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1696293Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1696302Z 
2023-01-11T21:41:26.1696310Z 
2023-01-11T21:41:26.1696566Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1696945Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1697150Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1697343Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1697452Z {
2023-01-11T21:41:26.1697636Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1697742Z     {
2023-01-11T21:41:26.1697883Z         #pragma omp for 
2023-01-11T21:41:26.1698037Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.1698134Z         {
2023-01-11T21:41:26.1698398Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1698654Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1.5));
2023-01-11T21:41:26.1698814Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1698982Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1699097Z         }
2023-01-11T21:41:26.1699268Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1699419Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.1699515Z         {
2023-01-11T21:41:26.1699667Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1699867Z             auto tmp1 = static_cast<float>(1.5);
2023-01-11T21:41:26.1700025Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1700330Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.1700445Z         }
2023-01-11T21:41:26.1700543Z     }
2023-01-11T21:41:26.1700649Z }
2023-01-11T21:41:26.1700818Z ''')
2023-01-11T21:41:26.1700828Z 
2023-01-11T21:41:26.1700836Z 
2023-01-11T21:41:26.1701007Z async_compile.wait(globals())
2023-01-11T21:41:26.1701141Z del async_compile
2023-01-11T21:41:26.1701149Z 
2023-01-11T21:41:26.1701276Z def call(args):
2023-01-11T21:41:26.1701405Z     arg0_1, = args
2023-01-11T21:41:26.1701532Z     args.clear()
2023-01-11T21:41:26.1701955Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1702150Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1702249Z     del arg0_1
2023-01-11T21:41:26.1702352Z     return (buf0, )
2023-01-11T21:41:26.1702359Z 
2023-01-11T21:41:26.1702364Z 
2023-01-11T21:41:26.1702549Z if __name__ == "__main__":
2023-01-11T21:41:26.1702728Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1702912Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1703258Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1703417Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1703424Z 
2023-01-11T21:41:26.1703520Z ok (1.556s)
2023-01-11T21:41:26.1704207Z   test_add_const_int_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1704391Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1704771Z [2023-01-11 21:24:09,280] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 13
2023-01-11T21:41:26.1705128Z [2023-01-11 21:24:10,798] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 13
2023-01-11T21:41:26.1705136Z 
2023-01-11T21:41:26.1705252Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1705341Z import torch
2023-01-11T21:41:26.1705435Z import random
2023-01-11T21:41:26.1705574Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1705726Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1705731Z 
2023-01-11T21:41:26.1705829Z aten = torch.ops.aten
2023-01-11T21:41:26.1705995Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1706109Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1706115Z 
2023-01-11T21:41:26.1706121Z 
2023-01-11T21:41:26.1706293Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1706548Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1706699Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1706813Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1706888Z {
2023-01-11T21:41:26.1707010Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1707085Z     {
2023-01-11T21:41:26.1707180Z         #pragma omp for 
2023-01-11T21:41:26.1707282Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.1707349Z         {
2023-01-11T21:41:26.1707522Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1707689Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.1707795Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1707911Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1707990Z         }
2023-01-11T21:41:26.1708108Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1708214Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.1708278Z         {
2023-01-11T21:41:26.1708383Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1708582Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.1708686Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1708788Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.1708865Z         }
2023-01-11T21:41:26.1708944Z     }
2023-01-11T21:41:26.1709006Z }
2023-01-11T21:41:26.1709103Z ''')
2023-01-11T21:41:26.1709109Z 
2023-01-11T21:41:26.1709113Z 
2023-01-11T21:41:26.1709225Z async_compile.wait(globals())
2023-01-11T21:41:26.1709319Z del async_compile
2023-01-11T21:41:26.1709325Z 
2023-01-11T21:41:26.1709414Z def call(args):
2023-01-11T21:41:26.1709499Z     arg0_1, = args
2023-01-11T21:41:26.1709592Z     args.clear()
2023-01-11T21:41:26.1709832Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1709999Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1710145Z     del arg0_1
2023-01-11T21:41:26.1710239Z     return (buf0, )
2023-01-11T21:41:26.1710245Z 
2023-01-11T21:41:26.1710249Z 
2023-01-11T21:41:26.1710347Z if __name__ == "__main__":
2023-01-11T21:41:26.1710491Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1710646Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1710899Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1711020Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1711026Z 
2023-01-11T21:41:26.1711106Z ok (1.531s)
2023-01-11T21:41:26.1711734Z   test_add_inplace_permuted_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1711895Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1712243Z [2023-01-11 21:24:10,813] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 14
2023-01-11T21:41:26.1712588Z [2023-01-11 21:24:12,320] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 14
2023-01-11T21:41:26.1712593Z 
2023-01-11T21:41:26.1712710Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1712797Z import torch
2023-01-11T21:41:26.1712885Z import random
2023-01-11T21:41:26.1713017Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1713167Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1713173Z 
2023-01-11T21:41:26.1713271Z aten = torch.ops.aten
2023-01-11T21:41:26.1713437Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1713552Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1713557Z 
2023-01-11T21:41:26.1713566Z 
2023-01-11T21:41:26.1713738Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1713992Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1714144Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1714263Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1714387Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1714462Z {
2023-01-11T21:41:26.1714583Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1714659Z     {
2023-01-11T21:41:26.1714770Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.1714873Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.1714938Z         {
2023-01-11T21:41:26.1715044Z             for(long i1=0; i1<12; i1+=1)
2023-01-11T21:41:26.1715122Z             {
2023-01-11T21:41:26.1715233Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:26.1715315Z                 {
2023-01-11T21:41:26.1715510Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (221*i1) + (2652*i0));
2023-01-11T21:41:26.1715729Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i2) + (221*i0));
2023-01-11T21:41:26.1715832Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1715972Z                     tmp2.store(out_ptr0 + (8*i2) + (221*i1) + (2652*i0));
2023-01-11T21:41:26.1716051Z                 }
2023-01-11T21:41:26.1716173Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.1716286Z                 for(long i2=216; i2<221; i2+=1)
2023-01-11T21:41:26.1716366Z                 {
2023-01-11T21:41:26.1716502Z                     auto tmp0 = out_ptr0[i2 + (221*i1) + (2652*i0)];
2023-01-11T21:41:26.1716626Z                     auto tmp1 = in_ptr1[i2 + (221*i0)];
2023-01-11T21:41:26.1716725Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1716851Z                     out_ptr0[i2 + (221*i1) + (2652*i0)] = tmp2;
2023-01-11T21:41:26.1716964Z                 }
2023-01-11T21:41:26.1717044Z             }
2023-01-11T21:41:26.1717122Z         }
2023-01-11T21:41:26.1717202Z     }
2023-01-11T21:41:26.1717263Z }
2023-01-11T21:41:26.1717360Z ''')
2023-01-11T21:41:26.1717366Z 
2023-01-11T21:41:26.1717371Z 
2023-01-11T21:41:26.1717481Z async_compile.wait(globals())
2023-01-11T21:41:26.1717570Z del async_compile
2023-01-11T21:41:26.1717576Z 
2023-01-11T21:41:26.1717664Z def call(args):
2023-01-11T21:41:26.1717757Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.1717845Z     args.clear()
2023-01-11T21:41:26.1718051Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:26.1718123Z     del arg1_1
2023-01-11T21:41:26.1718215Z     return (arg0_1, )
2023-01-11T21:41:26.1718221Z 
2023-01-11T21:41:26.1718226Z 
2023-01-11T21:41:26.1718319Z if __name__ == "__main__":
2023-01-11T21:41:26.1718462Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1718621Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1718910Z     arg0_1 = rand_strided((2, 13, 12, 17), (2652, 17, 221, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1719195Z     arg1_1 = rand_strided((2, 13, 1, 17), (221, 17, 17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1719340Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.1719346Z 
2023-01-11T21:41:26.1719415Z ok (1.522s)
2023-01-11T21:41:26.1720017Z   test_addmm_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1720176Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1720514Z [2023-01-11 21:24:12,350] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 15
2023-01-11T21:41:26.1720859Z [2023-01-11 21:24:13,927] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 15
2023-01-11T21:41:26.1720869Z 
2023-01-11T21:41:26.1720986Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1721072Z import torch
2023-01-11T21:41:26.1721161Z import random
2023-01-11T21:41:26.1721305Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1721445Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1721451Z 
2023-01-11T21:41:26.1721550Z aten = torch.ops.aten
2023-01-11T21:41:26.1721716Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1721829Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1721835Z 
2023-01-11T21:41:26.1721839Z 
2023-01-11T21:41:26.1722013Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1722268Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1722418Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1722586Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1722704Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.1722825Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1722947Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.1723067Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.1723143Z {
2023-01-11T21:41:26.1723266Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1723343Z     {
2023-01-11T21:41:26.1723426Z         #pragma omp for 
2023-01-11T21:41:26.1723527Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1723604Z         {
2023-01-11T21:41:26.1723771Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1723974Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.1724084Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1724199Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1724268Z         }
2023-01-11T21:41:26.1724386Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1724489Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.1724567Z         {
2023-01-11T21:41:26.1724672Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1724797Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.1724904Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1724993Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.1725074Z         }
2023-01-11T21:41:26.1725169Z         #pragma omp for 
2023-01-11T21:41:26.1725270Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1725347Z         {
2023-01-11T21:41:26.1725511Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.1725680Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.1725773Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1725884Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.1725963Z         }
2023-01-11T21:41:26.1726079Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1726181Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.1726258Z         {
2023-01-11T21:41:26.1726360Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.1726472Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.1726578Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1726676Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.1726755Z         }
2023-01-11T21:41:26.1726849Z         #pragma omp for 
2023-01-11T21:41:26.1726950Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1727026Z         {
2023-01-11T21:41:26.1727176Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:26.1727344Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.1727449Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1727561Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.1727641Z         }
2023-01-11T21:41:26.1727759Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1727862Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.1727927Z         {
2023-01-11T21:41:26.1728030Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:41:26.1728152Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.1728257Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1728358Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.1728435Z         }
2023-01-11T21:41:26.1728513Z     }
2023-01-11T21:41:26.1728575Z }
2023-01-11T21:41:26.1728678Z ''')
2023-01-11T21:41:26.1728684Z 
2023-01-11T21:41:26.1728689Z 
2023-01-11T21:41:26.1728859Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.1729238Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1729386Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.1729461Z {
2023-01-11T21:41:26.1729655Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1729718Z     {
2023-01-11T21:41:26.1729814Z         #pragma omp for 
2023-01-11T21:41:26.1729914Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1729992Z         {
2023-01-11T21:41:26.1730165Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.1730340Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:41:26.1730445Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1730566Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.1730630Z         }
2023-01-11T21:41:26.1730750Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1730852Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.1730928Z         {
2023-01-11T21:41:26.1731038Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.1731203Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:26.1731308Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1731402Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.1731479Z         }
2023-01-11T21:41:26.1731556Z     }
2023-01-11T21:41:26.1731630Z }
2023-01-11T21:41:26.1731730Z ''')
2023-01-11T21:41:26.1731736Z 
2023-01-11T21:41:26.1731741Z 
2023-01-11T21:41:26.1731852Z async_compile.wait(globals())
2023-01-11T21:41:26.1731943Z del async_compile
2023-01-11T21:41:26.1731949Z 
2023-01-11T21:41:26.1732023Z def call(args):
2023-01-11T21:41:26.1732124Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.1732211Z     args.clear()
2023-01-11T21:41:26.1732465Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1732715Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1732962Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1733269Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.1733346Z     del arg0_1
2023-01-11T21:41:26.1733427Z     del arg1_1
2023-01-11T21:41:26.1733510Z     del arg2_1
2023-01-11T21:41:26.1733760Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1733915Z     aten.addmm.out(buf0, buf1, buf2, beta=1, alpha=1, out=buf3)
2023-01-11T21:41:26.1733997Z     del buf0
2023-01-11T21:41:26.1734076Z     del buf1
2023-01-11T21:41:26.1734145Z     del buf2
2023-01-11T21:41:26.1734249Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:41:26.1734377Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.1734465Z     return (buf4, )
2023-01-11T21:41:26.1734471Z 
2023-01-11T21:41:26.1734476Z 
2023-01-11T21:41:26.1734568Z if __name__ == "__main__":
2023-01-11T21:41:26.1734714Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1734868Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1735125Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1735371Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1735618Z     arg2_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1735774Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.1735780Z 
2023-01-11T21:41:26.1735861Z ok (1.607s)
2023-01-11T21:41:26.1736481Z   test_alexnet_prefix_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1736643Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1736984Z [2023-01-11 21:24:14,048] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 16
2023-01-11T21:41:26.1737301Z [2023-01-11 21:24:14,134] torch._inductor.scheduler: [DEBUG] removed dead node: buf2
2023-01-11T21:41:26.1737648Z [2023-01-11 21:24:15,789] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 16
2023-01-11T21:41:26.1737654Z 
2023-01-11T21:41:26.1737772Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1737847Z import torch
2023-01-11T21:41:26.1737933Z import random
2023-01-11T21:41:26.1738078Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1738230Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1738236Z 
2023-01-11T21:41:26.1738332Z aten = torch.ops.aten
2023-01-11T21:41:26.1738500Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1738649Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1738656Z 
2023-01-11T21:41:26.1738661Z 
2023-01-11T21:41:26.1738821Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1739078Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1739229Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1739352Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1739427Z {
2023-01-11T21:41:26.1739551Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1739628Z     {
2023-01-11T21:41:26.1739711Z         #pragma omp for 
2023-01-11T21:41:26.1739818Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.1739896Z         {
2023-01-11T21:41:26.1739995Z             #pragma GCC ivdep
2023-01-11T21:41:26.1740099Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:41:26.1740178Z             {
2023-01-11T21:41:26.1740278Z                 #pragma GCC ivdep
2023-01-11T21:41:26.1740379Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:26.1740460Z                 {
2023-01-11T21:41:26.1740543Z                     {
2023-01-11T21:41:26.1740632Z                         {
2023-01-11T21:41:26.1740773Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1740916Z                             auto tmp2 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1741055Z                             auto tmp5 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1741198Z                             auto tmp8 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1741330Z                             auto tmp11 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1741474Z                             auto tmp14 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1741617Z                             auto tmp17 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1741762Z                             auto tmp20 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1741902Z                             auto tmp23 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.1742030Z                             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.1742152Z                             auto tmp3 = tmp2 * (tmp2>0);
2023-01-11T21:41:26.1742316Z                             auto tmp4 = (tmp1 != tmp1) ? tmp1 : std::max(tmp3, tmp1);
2023-01-11T21:41:26.1742425Z                             auto tmp6 = tmp5 * (tmp5>0);
2023-01-11T21:41:26.1742585Z                             auto tmp7 = (tmp4 != tmp4) ? tmp4 : std::max(tmp6, tmp4);
2023-01-11T21:41:26.1742706Z                             auto tmp9 = tmp8 * (tmp8>0);
2023-01-11T21:41:26.1742864Z                             auto tmp10 = (tmp7 != tmp7) ? tmp7 : std::max(tmp9, tmp7);
2023-01-11T21:41:26.1742993Z                             auto tmp12 = tmp11 * (tmp11>0);
2023-01-11T21:41:26.1743230Z                             auto tmp13 = (tmp10 != tmp10) ? tmp10 : std::max(tmp12, tmp10);
2023-01-11T21:41:26.1743370Z                             auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:41:26.1743625Z                             auto tmp16 = (tmp13 != tmp13) ? tmp13 : std::max(tmp15, tmp13);
2023-01-11T21:41:26.1743758Z                             auto tmp18 = tmp17 * (tmp17>0);
2023-01-11T21:41:26.1743956Z                             auto tmp19 = (tmp16 != tmp16) ? tmp16 : std::max(tmp18, tmp16);
2023-01-11T21:41:26.1744116Z                             auto tmp21 = tmp20 * (tmp20>0);
2023-01-11T21:41:26.1744318Z                             auto tmp22 = (tmp19 != tmp19) ? tmp19 : std::max(tmp21, tmp19);
2023-01-11T21:41:26.1744474Z                             auto tmp24 = tmp23 * (tmp23>0);
2023-01-11T21:41:26.1744671Z                             auto tmp25 = (tmp22 != tmp22) ? tmp22 : std::max(tmp24, tmp22);
2023-01-11T21:41:26.1744838Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp25;
2023-01-11T21:41:26.1745161Z                         }
2023-01-11T21:41:26.1745269Z                     }
2023-01-11T21:41:26.1745382Z                 }
2023-01-11T21:41:26.1745553Z             }
2023-01-11T21:41:26.1745718Z         }
2023-01-11T21:41:26.1745871Z     }
2023-01-11T21:41:26.1746028Z }
2023-01-11T21:41:26.1746238Z ''')
2023-01-11T21:41:26.1746251Z 
2023-01-11T21:41:26.1746258Z 
2023-01-11T21:41:26.1746411Z async_compile.wait(globals())
2023-01-11T21:41:26.1746598Z del async_compile
2023-01-11T21:41:26.1746607Z 
2023-01-11T21:41:26.1746955Z def call(args):
2023-01-11T21:41:26.1747091Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.1747261Z     args.clear()
2023-01-11T21:41:26.1747546Z     buf0 = aten.convolution(arg2_1, arg1_1, arg0_1, (4, 4), (2, 2), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:26.1747786Z     assert_size_stride(buf0, (16, 64, 55, 55), (193600, 3025, 55, 1))
2023-01-11T21:41:26.1747980Z     del arg0_1
2023-01-11T21:41:26.1748144Z     del arg1_1
2023-01-11T21:41:26.1748304Z     del arg2_1
2023-01-11T21:41:26.1748732Z     buf1 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1749041Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1749297Z     return (buf1, )
2023-01-11T21:41:26.1749308Z 
2023-01-11T21:41:26.1749316Z 
2023-01-11T21:41:26.1749502Z if __name__ == "__main__":
2023-01-11T21:41:26.1749756Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1750024Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1750442Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1750887Z     arg1_1 = rand_strided((64, 3, 11, 11), (363, 121, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1751287Z     arg2_1 = rand_strided((16, 3, 224, 224), (150528, 50176, 224, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1751574Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.1751590Z 
2023-01-11T21:41:26.1751747Z ok (1.942s)
2023-01-11T21:41:26.1752731Z   test_any_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1753022Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1753569Z [2023-01-11 21:24:15,907] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 17
2023-01-11T21:41:26.1754150Z [2023-01-11 21:24:17,477] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 17
2023-01-11T21:41:26.1754992Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1755447Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1755916Z [2023-01-11 21:24:17,514] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 18
2023-01-11T21:41:26.1756383Z [2023-01-11 21:24:19,164] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 18
2023-01-11T21:41:26.1756395Z 
2023-01-11T21:41:26.1756527Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1756758Z import torch
2023-01-11T21:41:26.1756905Z import random
2023-01-11T21:41:26.1757120Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1757348Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1757356Z 
2023-01-11T21:41:26.1757609Z aten = torch.ops.aten
2023-01-11T21:41:26.1757867Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1757998Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1758069Z 
2023-01-11T21:41:26.1758172Z 
2023-01-11T21:41:26.1758417Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1758831Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1759146Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.1759365Z                        bool* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.1759640Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1759851Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:26.1760056Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:26.1760133Z {
2023-01-11T21:41:26.1760294Z     auto out_ptr2 = in_out_ptr0;
2023-01-11T21:41:26.1760461Z     auto out_ptr3 = in_out_ptr1;
2023-01-11T21:41:26.1760629Z     {
2023-01-11T21:41:26.1760845Z         {
2023-01-11T21:41:26.1761054Z             bool tmp2 = 0;
2023-01-11T21:41:26.1761238Z             bool tmp4 = 0;
2023-01-11T21:41:26.1761364Z             bool tmp8 = 0;
2023-01-11T21:41:26.1761542Z             bool tmp10 = 0;
2023-01-11T21:41:26.1761797Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1761969Z             {
2023-01-11T21:41:26.1762333Z                 #pragma omp for reduction(||:tmp2) reduction(||:tmp4) reduction(||:tmp8) reduction(||:tmp10) 
2023-01-11T21:41:26.1762547Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.1762784Z                 {
2023-01-11T21:41:26.1762899Z                     {
2023-01-11T21:41:26.1763153Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1763398Z                         auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:41:26.1763642Z                         auto tmp3 = std::isinf(tmp0);
2023-01-11T21:41:26.1763865Z                         auto tmp5 = tmp3 == 0;
2023-01-11T21:41:26.1764124Z                         auto tmp6 = static_cast<long>(tmp5);
2023-01-11T21:41:26.1764370Z                         auto tmp7 = static_cast<bool>(tmp6);
2023-01-11T21:41:26.1764527Z                         auto tmp9 = tmp5 == 0;
2023-01-11T21:41:26.1764845Z                         tmp2 = tmp2 || tmp1;
2023-01-11T21:41:26.1765086Z                         tmp4 = tmp4 || tmp3;
2023-01-11T21:41:26.1765274Z                         tmp8 = tmp8 || tmp7;
2023-01-11T21:41:26.1765459Z                         tmp10 = tmp10 || tmp9;
2023-01-11T21:41:26.1765610Z                     }
2023-01-11T21:41:26.1765760Z                 }
2023-01-11T21:41:26.1765853Z             }
2023-01-11T21:41:26.1766031Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.1766217Z             out_ptr1[0] = tmp4;
2023-01-11T21:41:26.1766381Z             out_ptr2[0] = tmp8;
2023-01-11T21:41:26.1766582Z             out_ptr3[0] = tmp10;
2023-01-11T21:41:26.1766749Z         }
2023-01-11T21:41:26.1766900Z     }
2023-01-11T21:41:26.1767095Z     {
2023-01-11T21:41:26.1767263Z         {
2023-01-11T21:41:26.1767472Z             auto tmp0 = out_ptr2[0];
2023-01-11T21:41:26.1767776Z             auto tmp1 = tmp0 == 0;
2023-01-11T21:41:26.1767979Z             in_out_ptr0[0] = tmp1;
2023-01-11T21:41:26.1768144Z         }
2023-01-11T21:41:26.1768241Z     }
2023-01-11T21:41:26.1768467Z     {
2023-01-11T21:41:26.1768623Z         {
2023-01-11T21:41:26.1768824Z             auto tmp0 = out_ptr3[0];
2023-01-11T21:41:26.1769162Z             auto tmp1 = tmp0 == 0;
2023-01-11T21:41:26.1769381Z             in_out_ptr1[0] = tmp1;
2023-01-11T21:41:26.1769546Z         }
2023-01-11T21:41:26.1769645Z     }
2023-01-11T21:41:26.1769796Z }
2023-01-11T21:41:26.1770026Z ''')
2023-01-11T21:41:26.1770036Z 
2023-01-11T21:41:26.1770043Z 
2023-01-11T21:41:26.1770325Z async_compile.wait(globals())
2023-01-11T21:41:26.1770512Z del async_compile
2023-01-11T21:41:26.1770521Z 
2023-01-11T21:41:26.1770702Z def call(args):
2023-01-11T21:41:26.1770883Z     arg0_1, = args
2023-01-11T21:41:26.1771120Z     args.clear()
2023-01-11T21:41:26.1771543Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1771937Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1772326Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1772701Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1772951Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:41:26.1773170Z     buf5 = buf3; del buf3  # reuse
2023-01-11T21:41:26.1773718Z     kernel_cpp_0(c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1773815Z     del arg0_1
2023-01-11T21:41:26.1774002Z     return (buf0, buf1, buf4, buf5, )
2023-01-11T21:41:26.1774012Z 
2023-01-11T21:41:26.1774018Z 
2023-01-11T21:41:26.1774187Z if __name__ == "__main__":
2023-01-11T21:41:26.1774420Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1774670Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1775069Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1775348Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1775356Z 
2023-01-11T21:41:26.1775364Z 
2023-01-11T21:41:26.1775562Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1775662Z import torch
2023-01-11T21:41:26.1775818Z import random
2023-01-11T21:41:26.1776042Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1776286Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1776294Z 
2023-01-11T21:41:26.1776464Z aten = torch.ops.aten
2023-01-11T21:41:26.1776714Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1776916Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1776925Z 
2023-01-11T21:41:26.1776931Z 
2023-01-11T21:41:26.1777223Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1777530Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1777756Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.1777964Z                        bool* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.1778174Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1778376Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:26.1778573Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:26.1778756Z {
2023-01-11T21:41:26.1778885Z     auto out_ptr3 = in_out_ptr0;
2023-01-11T21:41:26.1779071Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:41:26.1779309Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1779457Z     {
2023-01-11T21:41:26.1779613Z         #pragma omp for 
2023-01-11T21:41:26.1779791Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.1779938Z         {
2023-01-11T21:41:26.1780033Z             {
2023-01-11T21:41:26.1780196Z                 {
2023-01-11T21:41:26.1780367Z                     bool tmp2 = 0;
2023-01-11T21:41:26.1780555Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.1780855Z                     {
2023-01-11T21:41:26.1781033Z                         {
2023-01-11T21:41:26.1781243Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.1781401Z                             auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:41:26.1781593Z                             tmp2 = tmp2 || tmp1;
2023-01-11T21:41:26.1781761Z                         }
2023-01-11T21:41:26.1781949Z                     }
2023-01-11T21:41:26.1782124Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.1782276Z                 }
2023-01-11T21:41:26.1782547Z             }
2023-01-11T21:41:26.1782652Z         }
2023-01-11T21:41:26.1782811Z     }
2023-01-11T21:41:26.1782980Z     {
2023-01-11T21:41:26.1783231Z         {
2023-01-11T21:41:26.1783423Z             bool tmp2 = 0;
2023-01-11T21:41:26.1783678Z             bool tmp5 = 0;
2023-01-11T21:41:26.1783862Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1784032Z             {
2023-01-11T21:41:26.1784326Z                 #pragma omp for reduction(||:tmp2) reduction(||:tmp5) 
2023-01-11T21:41:26.1784576Z                 for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.1784762Z                 {
2023-01-11T21:41:26.1784930Z                     {
2023-01-11T21:41:26.1785154Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1785343Z                         auto tmp1 = std::isinf(tmp0);
2023-01-11T21:41:26.1785593Z                         auto tmp3 = tmp1 == 0;
2023-01-11T21:41:26.1785800Z                         auto tmp4 = tmp3 == 0;
2023-01-11T21:41:26.1786014Z                         tmp2 = tmp2 || tmp1;
2023-01-11T21:41:26.1786229Z                         tmp5 = tmp5 || tmp4;
2023-01-11T21:41:26.1786450Z                     }
2023-01-11T21:41:26.1786616Z                 }
2023-01-11T21:41:26.1786716Z             }
2023-01-11T21:41:26.1786912Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.1787106Z             out_ptr2[0] = tmp5;
2023-01-11T21:41:26.1787269Z         }
2023-01-11T21:41:26.1787430Z     }
2023-01-11T21:41:26.1787656Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1787824Z     {
2023-01-11T21:41:26.1787955Z         #pragma omp for 
2023-01-11T21:41:26.1788191Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1788352Z         {
2023-01-11T21:41:26.1788516Z             {
2023-01-11T21:41:26.1788686Z                 {
2023-01-11T21:41:26.1788907Z                     bool tmp5 = 0;
2023-01-11T21:41:26.1789142Z                     for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.1789304Z                     {
2023-01-11T21:41:26.1789468Z                         {
2023-01-11T21:41:26.1789683Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:26.1789927Z                             auto tmp1 = std::isinf(tmp0);
2023-01-11T21:41:26.1790131Z                             auto tmp2 = tmp1 == 0;
2023-01-11T21:41:26.1790352Z                             auto tmp3 = static_cast<long>(tmp2);
2023-01-11T21:41:26.1790577Z                             auto tmp4 = static_cast<bool>(tmp3);
2023-01-11T21:41:26.1790710Z                             tmp5 = tmp5 || tmp4;
2023-01-11T21:41:26.1790854Z                         }
2023-01-11T21:41:26.1791021Z                     }
2023-01-11T21:41:26.1791198Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:26.1791345Z                 }
2023-01-11T21:41:26.1791522Z             }
2023-01-11T21:41:26.1791665Z         }
2023-01-11T21:41:26.1791767Z         #pragma omp for 
2023-01-11T21:41:26.1791946Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1792128Z         {
2023-01-11T21:41:26.1792287Z             {
2023-01-11T21:41:26.1792437Z                 {
2023-01-11T21:41:26.1792629Z                     auto tmp0 = out_ptr3[i0];
2023-01-11T21:41:26.1792753Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:41:26.1792949Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.1793133Z                 }
2023-01-11T21:41:26.1793277Z             }
2023-01-11T21:41:26.1793504Z         }
2023-01-11T21:41:26.1793674Z         #pragma omp single
2023-01-11T21:41:26.1793819Z         {
2023-01-11T21:41:26.1793907Z             {
2023-01-11T21:41:26.1794064Z                 {
2023-01-11T21:41:26.1794254Z                     auto tmp0 = out_ptr2[0];
2023-01-11T21:41:26.1794538Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:41:26.1794776Z                     in_out_ptr1[0] = tmp1;
2023-01-11T21:41:26.1794948Z                 }
2023-01-11T21:41:26.1795178Z             }
2023-01-11T21:41:26.1795284Z         }
2023-01-11T21:41:26.1795441Z     }
2023-01-11T21:41:26.1795605Z }
2023-01-11T21:41:26.1795809Z ''')
2023-01-11T21:41:26.1795819Z 
2023-01-11T21:41:26.1795826Z 
2023-01-11T21:41:26.1796046Z async_compile.wait(globals())
2023-01-11T21:41:26.1796230Z del async_compile
2023-01-11T21:41:26.1796241Z 
2023-01-11T21:41:26.1796535Z def call(args):
2023-01-11T21:41:26.1796656Z     arg0_1, = args
2023-01-11T21:41:26.1796819Z     args.clear()
2023-01-11T21:41:26.1797240Z     buf0 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1797631Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1798015Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1798427Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.1798639Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:26.1798787Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:41:26.1799234Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1799453Z     del arg0_1
2023-01-11T21:41:26.1799711Z     return (buf0, buf1, buf3, buf5, )
2023-01-11T21:41:26.1799726Z 
2023-01-11T21:41:26.1799732Z 
2023-01-11T21:41:26.1799916Z if __name__ == "__main__":
2023-01-11T21:41:26.1800180Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1800468Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1800891Z     arg0_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1801164Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1801173Z 
2023-01-11T21:41:26.1801346Z ok (3.294s)
2023-01-11T21:41:26.1802135Z   test_arange1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1802420Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1802880Z [2023-01-11 21:24:19,221] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 19
2023-01-11T21:41:26.1803370Z [2023-01-11 21:24:20,939] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 19
2023-01-11T21:41:26.1803386Z 
2023-01-11T21:41:26.1803590Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1803750Z import torch
2023-01-11T21:41:26.1803876Z import random
2023-01-11T21:41:26.1900377Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1900751Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1900761Z 
2023-01-11T21:41:26.1900896Z aten = torch.ops.aten
2023-01-11T21:41:26.1901132Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1901289Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1901296Z 
2023-01-11T21:41:26.1901303Z 
2023-01-11T21:41:26.1901613Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1901969Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1902178Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1902651Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.1902814Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.1902917Z {
2023-01-11T21:41:26.1903089Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1903263Z     {
2023-01-11T21:41:26.1903381Z         #pragma omp for 
2023-01-11T21:41:26.1903519Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.1903626Z         {
2023-01-11T21:41:26.1903731Z             {
2023-01-11T21:41:26.1903843Z                 {
2023-01-11T21:41:26.1903993Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1904172Z                     auto tmp1 = static_cast<float>(i0);
2023-01-11T21:41:26.1904311Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.1904450Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.1904560Z                 }
2023-01-11T21:41:26.1904764Z             }
2023-01-11T21:41:26.1904869Z         }
2023-01-11T21:41:26.1904995Z         #pragma omp for 
2023-01-11T21:41:26.1905138Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1905226Z         {
2023-01-11T21:41:26.1905361Z             #pragma GCC ivdep
2023-01-11T21:41:26.1905500Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.1905603Z             {
2023-01-11T21:41:26.1905714Z                 {
2023-01-11T21:41:26.1905824Z                     {
2023-01-11T21:41:26.1905985Z                         auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.1906168Z                         auto tmp1 = static_cast<long>(10 + i1);
2023-01-11T21:41:26.1906352Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.1906505Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.1906662Z                         out_ptr1[i1 + (8*i0)] = tmp3;
2023-01-11T21:41:26.1906775Z                     }
2023-01-11T21:41:26.1906885Z                 }
2023-01-11T21:41:26.1906979Z             }
2023-01-11T21:41:26.1907083Z         }
2023-01-11T21:41:26.1907185Z     }
2023-01-11T21:41:26.1907290Z }
2023-01-11T21:41:26.1907444Z ''')
2023-01-11T21:41:26.1907452Z 
2023-01-11T21:41:26.1907459Z 
2023-01-11T21:41:26.1907613Z async_compile.wait(globals())
2023-01-11T21:41:26.1907733Z del async_compile
2023-01-11T21:41:26.1907741Z 
2023-01-11T21:41:26.1907860Z def call(args):
2023-01-11T21:41:26.1907961Z     arg0_1, = args
2023-01-11T21:41:26.1908084Z     args.clear()
2023-01-11T21:41:26.1908429Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1908768Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1909046Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1909159Z     del arg0_1
2023-01-11T21:41:26.1909281Z     return (buf0, buf1, )
2023-01-11T21:41:26.1909287Z 
2023-01-11T21:41:26.1909293Z 
2023-01-11T21:41:26.1909386Z if __name__ == "__main__":
2023-01-11T21:41:26.1909539Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1909703Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1909977Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1910115Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1910121Z 
2023-01-11T21:41:26.1910210Z ok (1.775s)
2023-01-11T21:41:26.1910819Z   test_arange2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1910986Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1911342Z [2023-01-11 21:24:20,972] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 20
2023-01-11T21:41:26.1911749Z [2023-01-11 21:24:22,505] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 20
2023-01-11T21:41:26.1911852Z 
2023-01-11T21:41:26.1912109Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1912254Z import torch
2023-01-11T21:41:26.1912421Z import random
2023-01-11T21:41:26.1912630Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1912865Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1912874Z 
2023-01-11T21:41:26.1913033Z aten = torch.ops.aten
2023-01-11T21:41:26.1913294Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1913447Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1913456Z 
2023-01-11T21:41:26.1913483Z 
2023-01-11T21:41:26.1913736Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1914190Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1914424Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.1914618Z                        long* __restrict__ out_ptr0)
2023-01-11T21:41:26.1914742Z {
2023-01-11T21:41:26.1914935Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1915067Z     {
2023-01-11T21:41:26.1915195Z         #pragma omp for 
2023-01-11T21:41:26.1915338Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.1915474Z         {
2023-01-11T21:41:26.1915638Z             #pragma GCC ivdep
2023-01-11T21:41:26.1915799Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.1915925Z             {
2023-01-11T21:41:26.1916037Z                 {
2023-01-11T21:41:26.1916158Z                     {
2023-01-11T21:41:26.1916355Z                         auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.1916565Z                         auto tmp1 = static_cast<long>(i1);
2023-01-11T21:41:26.1916749Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1916936Z                         out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:26.1917049Z                     }
2023-01-11T21:41:26.1917171Z                 }
2023-01-11T21:41:26.1917296Z             }
2023-01-11T21:41:26.1917420Z         }
2023-01-11T21:41:26.1917542Z     }
2023-01-11T21:41:26.1917666Z }
2023-01-11T21:41:26.1917839Z ''')
2023-01-11T21:41:26.1917847Z 
2023-01-11T21:41:26.1917854Z 
2023-01-11T21:41:26.1918029Z async_compile.wait(globals())
2023-01-11T21:41:26.1918151Z del async_compile
2023-01-11T21:41:26.1918181Z 
2023-01-11T21:41:26.1918299Z def call(args):
2023-01-11T21:41:26.1918441Z     arg0_1, = args
2023-01-11T21:41:26.1918587Z     args.clear()
2023-01-11T21:41:26.1918966Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.1919223Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1919365Z     del arg0_1
2023-01-11T21:41:26.1919504Z     return (buf0, )
2023-01-11T21:41:26.1919521Z 
2023-01-11T21:41:26.1919528Z 
2023-01-11T21:41:26.1919648Z if __name__ == "__main__":
2023-01-11T21:41:26.1919870Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1920108Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1920480Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.1920666Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1920676Z 
2023-01-11T21:41:26.1920812Z ok (1.566s)
2023-01-11T21:41:26.1921680Z   test_arange3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1921933Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1922438Z [2023-01-11 21:24:22,544] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 21
2023-01-11T21:41:26.1923042Z [2023-01-11 21:24:24,015] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 21
2023-01-11T21:41:26.1923065Z 
2023-01-11T21:41:26.1923231Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1923364Z import torch
2023-01-11T21:41:26.1923500Z import random
2023-01-11T21:41:26.1923733Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1923959Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1923966Z 
2023-01-11T21:41:26.1924124Z aten = torch.ops.aten
2023-01-11T21:41:26.1924373Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1924531Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1924539Z 
2023-01-11T21:41:26.1924548Z 
2023-01-11T21:41:26.1924797Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1925272Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1925496Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1925691Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1925807Z {
2023-01-11T21:41:26.1926001Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1926103Z     {
2023-01-11T21:41:26.1926249Z         #pragma omp for 
2023-01-11T21:41:26.1926416Z         for(long i0=0; i0<14; i0+=1)
2023-01-11T21:41:26.1926540Z         {
2023-01-11T21:41:26.1926659Z             {
2023-01-11T21:41:26.1926793Z                 {
2023-01-11T21:41:26.1926964Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1927144Z                     auto tmp1 = static_cast<long>(4*i0);
2023-01-11T21:41:26.1927356Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.1927519Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.1927687Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.1927814Z                 }
2023-01-11T21:41:26.1927940Z             }
2023-01-11T21:41:26.1928068Z         }
2023-01-11T21:41:26.1928167Z     }
2023-01-11T21:41:26.1928284Z }
2023-01-11T21:41:26.1928439Z ''')
2023-01-11T21:41:26.1928451Z 
2023-01-11T21:41:26.1928458Z 
2023-01-11T21:41:26.1928634Z async_compile.wait(globals())
2023-01-11T21:41:26.1928780Z del async_compile
2023-01-11T21:41:26.1928789Z 
2023-01-11T21:41:26.1928924Z def call(args):
2023-01-11T21:41:26.1929198Z     arg0_1, = args
2023-01-11T21:41:26.1929326Z     args.clear()
2023-01-11T21:41:26.1929716Z     buf0 = empty_strided((14, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1929963Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1930097Z     del arg0_1
2023-01-11T21:41:26.1930237Z     return (buf0, )
2023-01-11T21:41:26.1930246Z 
2023-01-11T21:41:26.1930253Z 
2023-01-11T21:41:26.1930395Z if __name__ == "__main__":
2023-01-11T21:41:26.1930613Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1930844Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1931210Z     arg0_1 = rand_strided((14, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1931401Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1931407Z 
2023-01-11T21:41:26.1931539Z ok (1.511s)
2023-01-11T21:41:26.1932394Z   test_arange4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1932642Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1933138Z [2023-01-11 21:24:24,058] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 22
2023-01-11T21:41:26.1933636Z [2023-01-11 21:24:25,581] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 22
2023-01-11T21:41:26.1933809Z 
2023-01-11T21:41:26.1933967Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1934110Z import torch
2023-01-11T21:41:26.1934228Z import random
2023-01-11T21:41:26.1934446Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1934681Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1934689Z 
2023-01-11T21:41:26.1934832Z aten = torch.ops.aten
2023-01-11T21:41:26.1935082Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1935261Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1935273Z 
2023-01-11T21:41:26.1935280Z 
2023-01-11T21:41:26.1935538Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1935900Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1936255Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1936457Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1936567Z {
2023-01-11T21:41:26.1936755Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1936883Z     {
2023-01-11T21:41:26.1937039Z         #pragma omp for 
2023-01-11T21:41:26.1937203Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.1937304Z         {
2023-01-11T21:41:26.1937414Z             {
2023-01-11T21:41:26.1937540Z                 {
2023-01-11T21:41:26.1937720Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1938072Z                     auto tmp1 = static_cast<long>(512 + ((-1)*i0));
2023-01-11T21:41:26.1938267Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.1938519Z                     auto tmp3 = tmp0 - tmp2;
2023-01-11T21:41:26.1938675Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.1938808Z                 }
2023-01-11T21:41:26.1938937Z             }
2023-01-11T21:41:26.1939063Z         }
2023-01-11T21:41:26.1939174Z     }
2023-01-11T21:41:26.1939286Z }
2023-01-11T21:41:26.1939455Z ''')
2023-01-11T21:41:26.1939465Z 
2023-01-11T21:41:26.1939472Z 
2023-01-11T21:41:26.1939626Z async_compile.wait(globals())
2023-01-11T21:41:26.1939766Z del async_compile
2023-01-11T21:41:26.1939775Z 
2023-01-11T21:41:26.1939919Z def call(args):
2023-01-11T21:41:26.1940052Z     arg0_1, = args
2023-01-11T21:41:26.1940176Z     args.clear()
2023-01-11T21:41:26.1940560Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1940809Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.1940923Z     del arg0_1
2023-01-11T21:41:26.1941046Z     return (buf0, )
2023-01-11T21:41:26.1941057Z 
2023-01-11T21:41:26.1941066Z 
2023-01-11T21:41:26.1941214Z if __name__ == "__main__":
2023-01-11T21:41:26.1941447Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1941688Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1942048Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1942259Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1942271Z 
2023-01-11T21:41:26.1942402Z ok (1.565s)
2023-01-11T21:41:26.1943367Z   test_argmax_argmin1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1943596Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1944086Z [2023-01-11 21:24:25,616] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 23
2023-01-11T21:41:26.1944603Z [2023-01-11 21:24:27,087] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 23
2023-01-11T21:41:26.1944611Z 
2023-01-11T21:41:26.1944904Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1945043Z import torch
2023-01-11T21:41:26.1945190Z import random
2023-01-11T21:41:26.1945415Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1945630Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1945640Z 
2023-01-11T21:41:26.1945782Z aten = torch.ops.aten
2023-01-11T21:41:26.1946043Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1946217Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1946227Z 
2023-01-11T21:41:26.1946234Z 
2023-01-11T21:41:26.1946487Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1946860Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1947092Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1947353Z                        long* __restrict__ out_ptr0,
2023-01-11T21:41:26.1947546Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.1947657Z {
2023-01-11T21:41:26.1947782Z     {
2023-01-11T21:41:26.1947902Z         {
2023-01-11T21:41:26.1948129Z             struct IndexValue_1 {size_t index; float value;};
2023-01-11T21:41:26.1948629Z             IndexValue_1 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.1948889Z             #pragma omp declare reduction(argmax : struct IndexValue_1 :\
2023-01-11T21:41:26.1949151Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.1949428Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.1949876Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.1950081Z             struct IndexValue_2 {size_t index; float value;};
2023-01-11T21:41:26.1950339Z             IndexValue_2 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.1950606Z             #pragma omp declare reduction(argmin : struct IndexValue_2 :\
2023-01-11T21:41:26.1950876Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.1951151Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.1951418Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.1951617Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1951707Z             {
2023-01-11T21:41:26.1951970Z                 #pragma omp for reduction(argmax:tmp1) reduction(argmin:tmp2) 
2023-01-11T21:41:26.1952148Z                 for(long i0=0; i0<524288; i0+=1)
2023-01-11T21:41:26.1952281Z                 {
2023-01-11T21:41:26.1952415Z                     {
2023-01-11T21:41:26.1952577Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1952759Z                         if (tmp1.value < tmp0) {
2023-01-11T21:41:26.1952950Z                             tmp1.index = i0; tmp1.value = tmp0;
2023-01-11T21:41:26.1953093Z                         }
2023-01-11T21:41:26.1953266Z                         if (tmp2.value > tmp0) {
2023-01-11T21:41:26.1953469Z                             tmp2.index = i0; tmp2.value = tmp0;
2023-01-11T21:41:26.1953604Z                         }
2023-01-11T21:41:26.1953739Z                     }
2023-01-11T21:41:26.1953869Z                 }
2023-01-11T21:41:26.1953981Z             }
2023-01-11T21:41:26.1954145Z             out_ptr0[0] = tmp1.index;
2023-01-11T21:41:26.1954306Z             out_ptr1[0] = tmp2.index;
2023-01-11T21:41:26.1954417Z         }
2023-01-11T21:41:26.1954537Z     }
2023-01-11T21:41:26.1954666Z }
2023-01-11T21:41:26.1954838Z ''')
2023-01-11T21:41:26.1954848Z 
2023-01-11T21:41:26.1954855Z 
2023-01-11T21:41:26.1955007Z async_compile.wait(globals())
2023-01-11T21:41:26.1955151Z del async_compile
2023-01-11T21:41:26.1955166Z 
2023-01-11T21:41:26.1955291Z def call(args):
2023-01-11T21:41:26.1955433Z     arg0_1, = args
2023-01-11T21:41:26.1955679Z     args.clear()
2023-01-11T21:41:26.1956042Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.1956380Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.1956694Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.1956817Z     del arg0_1
2023-01-11T21:41:26.1956961Z     return (buf0, buf1, )
2023-01-11T21:41:26.1956969Z 
2023-01-11T21:41:26.1956976Z 
2023-01-11T21:41:26.1957113Z if __name__ == "__main__":
2023-01-11T21:41:26.1957332Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1957564Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1957981Z     arg0_1 = rand_strided((8, 256, 256), (65536, 256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1958275Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1958285Z 
2023-01-11T21:41:26.1958416Z ok (1.511s)
2023-01-11T21:41:26.1959296Z   test_argmax_argmin2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1959535Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1960031Z [2023-01-11 21:24:27,138] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 24
2023-01-11T21:41:26.1960526Z [2023-01-11 21:24:28,628] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 24
2023-01-11T21:41:26.1960536Z 
2023-01-11T21:41:26.1960722Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1960872Z import torch
2023-01-11T21:41:26.1961012Z import random
2023-01-11T21:41:26.1961237Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1961456Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1961464Z 
2023-01-11T21:41:26.1961598Z aten = torch.ops.aten
2023-01-11T21:41:26.1961853Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1962028Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1962035Z 
2023-01-11T21:41:26.1962045Z 
2023-01-11T21:41:26.1962294Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1962666Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1962898Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1963074Z                        long* __restrict__ out_ptr0,
2023-01-11T21:41:26.1963259Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.1963431Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:26.1963604Z                        long* __restrict__ out_ptr3)
2023-01-11T21:41:26.1963722Z {
2023-01-11T21:41:26.1963912Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1964033Z     {
2023-01-11T21:41:26.1964195Z         #pragma omp for 
2023-01-11T21:41:26.1964368Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:41:26.1964474Z         {
2023-01-11T21:41:26.1964595Z             {
2023-01-11T21:41:26.1964719Z                 {
2023-01-11T21:41:26.1964938Z                     struct IndexValue_3 {size_t index; float value;};
2023-01-11T21:41:26.1965431Z                     IndexValue_3 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.1965688Z                     #pragma omp declare reduction(argmax : struct IndexValue_3 :\
2023-01-11T21:41:26.1965979Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.1966251Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.1966717Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.1967067Z                     struct IndexValue_4 {size_t index; float value;};
2023-01-11T21:41:26.1967310Z                     IndexValue_4 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.1967565Z                     #pragma omp declare reduction(argmin : struct IndexValue_4 :\
2023-01-11T21:41:26.1967844Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.1968117Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.1968362Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.1968541Z                     for(long i1=0; i1<144; i1+=1)
2023-01-11T21:41:26.1968652Z                     {
2023-01-11T21:41:26.1968855Z                         {
2023-01-11T21:41:26.1969182Z                             auto tmp0 = in_ptr0[i0 + (144*i1)];
2023-01-11T21:41:26.1969369Z                             if (tmp1.value < tmp0) {
2023-01-11T21:41:26.1969586Z                                 tmp1.index = i1; tmp1.value = tmp0;
2023-01-11T21:41:26.1969717Z                             }
2023-01-11T21:41:26.1969900Z                             if (tmp2.value > tmp0) {
2023-01-11T21:41:26.1970084Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:41:26.1970211Z                             }
2023-01-11T21:41:26.1970342Z                         }
2023-01-11T21:41:26.1970476Z                     }
2023-01-11T21:41:26.1970663Z                     out_ptr0[i0] = tmp1.index;
2023-01-11T21:41:26.1970836Z                     out_ptr1[i0] = tmp2.index;
2023-01-11T21:41:26.1970935Z                 }
2023-01-11T21:41:26.1971017Z             }
2023-01-11T21:41:26.1971108Z         }
2023-01-11T21:41:26.1971230Z         #pragma omp for 
2023-01-11T21:41:26.1971352Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:41:26.1971446Z         {
2023-01-11T21:41:26.1971550Z             {
2023-01-11T21:41:26.1971646Z                 {
2023-01-11T21:41:26.1971819Z                     struct IndexValue_5 {size_t index; float value;};
2023-01-11T21:41:26.1972211Z                     IndexValue_5 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.1972418Z                     #pragma omp declare reduction(argmax : struct IndexValue_5 :\
2023-01-11T21:41:26.1972648Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.1972857Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.1973260Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.1973485Z                     struct IndexValue_6 {size_t index; float value;};
2023-01-11T21:41:26.1973747Z                     IndexValue_6 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.1973994Z                     #pragma omp declare reduction(argmin : struct IndexValue_6 :\
2023-01-11T21:41:26.1974276Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.1974548Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.1974826Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.1974997Z                     for(long i1=0; i1<144; i1+=1)
2023-01-11T21:41:26.1975114Z                     {
2023-01-11T21:41:26.1975252Z                         {
2023-01-11T21:41:26.1975446Z                             auto tmp0 = in_ptr0[i1 + (144*i0)];
2023-01-11T21:41:26.1975619Z                             if (tmp1.value < tmp0) {
2023-01-11T21:41:26.1975847Z                                 tmp1.index = i1; tmp1.value = tmp0;
2023-01-11T21:41:26.1975967Z                             }
2023-01-11T21:41:26.1976150Z                             if (tmp2.value > tmp0) {
2023-01-11T21:41:26.1976532Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:41:26.1976675Z                             }
2023-01-11T21:41:26.1976804Z                         }
2023-01-11T21:41:26.1976907Z                     }
2023-01-11T21:41:26.1977091Z                     out_ptr2[i0] = tmp1.index;
2023-01-11T21:41:26.1977264Z                     out_ptr3[i0] = tmp2.index;
2023-01-11T21:41:26.1977388Z                 }
2023-01-11T21:41:26.1977517Z             }
2023-01-11T21:41:26.1977645Z         }
2023-01-11T21:41:26.1977837Z     }
2023-01-11T21:41:26.1977938Z }
2023-01-11T21:41:26.1978091Z ''')
2023-01-11T21:41:26.1978099Z 
2023-01-11T21:41:26.1978106Z 
2023-01-11T21:41:26.1978251Z async_compile.wait(globals())
2023-01-11T21:41:26.1978378Z del async_compile
2023-01-11T21:41:26.1978385Z 
2023-01-11T21:41:26.1978599Z def call(args):
2023-01-11T21:41:26.1978707Z     arg0_1, = args
2023-01-11T21:41:26.1978828Z     args.clear()
2023-01-11T21:41:26.1979150Z     buf0 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.1979445Z     buf1 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.1979810Z     buf2 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.1980129Z     buf3 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.1980473Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.1980599Z     del arg0_1
2023-01-11T21:41:26.1980751Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:26.1980761Z 
2023-01-11T21:41:26.1980769Z 
2023-01-11T21:41:26.1980904Z if __name__ == "__main__":
2023-01-11T21:41:26.1981084Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1981277Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1981624Z     arg0_1 = rand_strided((144, 144), (144, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1981809Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1981821Z 
2023-01-11T21:41:26.1981913Z ok (1.537s)
2023-01-11T21:41:26.1982107Z   test_argmax_argmin3_cpu (__main__.CpuTests) ... skip: 
2023-01-11T21:41:26.1982371Z         FIXME: In the case of having equally max/min elements, our implementation returns
2023-01-11T21:41:26.1982548Z         the last index instead of the first one
2023-01-11T21:41:26.1982647Z          (0.001s)
2023-01-11T21:41:26.1983475Z   test_as_strided_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1983680Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1984127Z [2023-01-11 21:24:28,663] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 25
2023-01-11T21:41:26.1984563Z [2023-01-11 21:24:30,154] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 25
2023-01-11T21:41:26.1984572Z 
2023-01-11T21:41:26.1984728Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1984852Z import torch
2023-01-11T21:41:26.1984973Z import random
2023-01-11T21:41:26.1985148Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1985333Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1985359Z 
2023-01-11T21:41:26.1985475Z aten = torch.ops.aten
2023-01-11T21:41:26.1985690Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1985851Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1985861Z 
2023-01-11T21:41:26.1985867Z 
2023-01-11T21:41:26.1986073Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1986505Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1986707Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.1986867Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:41:26.1986960Z {
2023-01-11T21:41:26.1987130Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1987238Z     {
2023-01-11T21:41:26.1987370Z         #pragma omp for 
2023-01-11T21:41:26.1987512Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:26.1987623Z         {
2023-01-11T21:41:26.1987851Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1988069Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.1988202Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1988502Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.1988651Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.1988830Z             tmp4.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.1988950Z         }
2023-01-11T21:41:26.1989117Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.1989260Z         for(long i0=4096; i0<4096; i0+=1)
2023-01-11T21:41:26.1989339Z         {
2023-01-11T21:41:26.1989477Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.1989642Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.1989783Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.1989951Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.1990096Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.1990215Z             in_out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.1990313Z         }
2023-01-11T21:41:26.1990424Z     }
2023-01-11T21:41:26.1990527Z }
2023-01-11T21:41:26.1990676Z ''')
2023-01-11T21:41:26.1990690Z 
2023-01-11T21:41:26.1990699Z 
2023-01-11T21:41:26.1990849Z async_compile.wait(globals())
2023-01-11T21:41:26.1990961Z del async_compile
2023-01-11T21:41:26.1990974Z 
2023-01-11T21:41:26.1991096Z def call(args):
2023-01-11T21:41:26.1991204Z     arg0_1, = args
2023-01-11T21:41:26.1991335Z     args.clear()
2023-01-11T21:41:26.1991676Z     buf0 = empty_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1991853Z     buf1 = as_strided(buf0, (8, 8, 64), (512, 64, 1)); del buf0  # reuse
2023-01-11T21:41:26.1992070Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:26.1992257Z     return (as_strided(arg0_1, (8, 8, 64), (512, 64, 1)), buf1, )
2023-01-11T21:41:26.1992265Z 
2023-01-11T21:41:26.1992271Z 
2023-01-11T21:41:26.1992398Z if __name__ == "__main__":
2023-01-11T21:41:26.1992576Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.1992762Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.1993100Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.1993274Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.1993289Z 
2023-01-11T21:41:26.1993389Z ok (1.525s)
2023-01-11T21:41:26.1994159Z   test_as_strided_scatter_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.1994353Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.1994784Z [2023-01-11 21:24:30,187] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 26
2023-01-11T21:41:26.1995216Z [2023-01-11 21:24:31,699] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 26
2023-01-11T21:41:26.1995224Z 
2023-01-11T21:41:26.1995383Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.1995586Z import torch
2023-01-11T21:41:26.1995712Z import random
2023-01-11T21:41:26.1995908Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.1996112Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.1996120Z 
2023-01-11T21:41:26.1996248Z aten = torch.ops.aten
2023-01-11T21:41:26.1996468Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.1996620Z async_compile = AsyncCompile()
2023-01-11T21:41:26.1996626Z 
2023-01-11T21:41:26.1996633Z 
2023-01-11T21:41:26.1996858Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.1997169Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.1997367Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.1997534Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.1997779Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.1997888Z {
2023-01-11T21:41:26.1998062Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.1998173Z     {
2023-01-11T21:41:26.1998271Z         #pragma omp for 
2023-01-11T21:41:26.1998408Z         for(long i0=0; i0<1280; i0+=1)
2023-01-11T21:41:26.1998517Z         {
2023-01-11T21:41:26.1998743Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.1998963Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:26.1999092Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.1999309Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:26.1999459Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.1999599Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.1999707Z         }
2023-01-11T21:41:26.1999868Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2000010Z         for(long i0=10240; i0<10240; i0+=1)
2023-01-11T21:41:26.2000119Z         {
2023-01-11T21:41:26.2000262Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2000425Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:26.2000549Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.2000712Z             auto tmp3 = static_cast<float>(10);
2023-01-11T21:41:26.2000839Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.2000976Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.2001088Z         }
2023-01-11T21:41:26.2001221Z         #pragma omp for 
2023-01-11T21:41:26.2001345Z         for(long i0=0; i0<5120; i0+=1)
2023-01-11T21:41:26.2001458Z         {
2023-01-11T21:41:26.2001557Z             {
2023-01-11T21:41:26.2001663Z                 {
2023-01-11T21:41:26.2001836Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.2002006Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.2002158Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.2002310Z                     auto tmp3 = static_cast<float>(4);
2023-01-11T21:41:26.2002557Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:26.2002706Z                     out_ptr0[2*i0] = tmp4;
2023-01-11T21:41:26.2002821Z                 }
2023-01-11T21:41:26.2002936Z             }
2023-01-11T21:41:26.2003047Z         }
2023-01-11T21:41:26.2003157Z     }
2023-01-11T21:41:26.2003231Z }
2023-01-11T21:41:26.2003374Z ''')
2023-01-11T21:41:26.2003384Z 
2023-01-11T21:41:26.2003392Z 
2023-01-11T21:41:26.2003551Z async_compile.wait(globals())
2023-01-11T21:41:26.2003678Z del async_compile
2023-01-11T21:41:26.2003685Z 
2023-01-11T21:41:26.2003805Z def call(args):
2023-01-11T21:41:26.2003939Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2004052Z     args.clear()
2023-01-11T21:41:26.2004379Z     buf0 = empty_strided((10, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2004653Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2004771Z     del arg0_1
2023-01-11T21:41:26.2004878Z     del arg1_1
2023-01-11T21:41:26.2004996Z     return (buf0, )
2023-01-11T21:41:26.2005102Z 
2023-01-11T21:41:26.2005108Z 
2023-01-11T21:41:26.2005242Z if __name__ == "__main__":
2023-01-11T21:41:26.2005448Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2005653Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2005984Z     arg0_1 = rand_strided((10, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2006312Z     arg1_1 = rand_strided((10, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2006492Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2006498Z 
2023-01-11T21:41:26.2006612Z ok (1.545s)
2023-01-11T21:41:26.2007444Z   test_avg_pool2d1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2007670Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2008109Z [2023-01-11 21:24:31,722] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 27
2023-01-11T21:41:26.2008554Z [2023-01-11 21:24:33,266] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 27
2023-01-11T21:41:26.2008564Z 
2023-01-11T21:41:26.2008720Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2008839Z import torch
2023-01-11T21:41:26.2008928Z import random
2023-01-11T21:41:26.2009276Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2009490Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2009497Z 
2023-01-11T21:41:26.2009641Z aten = torch.ops.aten
2023-01-11T21:41:26.2009841Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2009989Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2010006Z 
2023-01-11T21:41:26.2010013Z 
2023-01-11T21:41:26.2010245Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2010574Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2010740Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2010907Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2011010Z {
2023-01-11T21:41:26.2011168Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2011269Z     {
2023-01-11T21:41:26.2011408Z         #pragma omp for 
2023-01-11T21:41:26.2011530Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2011623Z         {
2023-01-11T21:41:26.2011752Z             #pragma GCC ivdep
2023-01-11T21:41:26.2011893Z             for(long i1=0; i1<7; i1+=1)
2023-01-11T21:41:26.2012005Z             {
2023-01-11T21:41:26.2012137Z                 #pragma GCC ivdep
2023-01-11T21:41:26.2012269Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:41:26.2012364Z                 {
2023-01-11T21:41:26.2012488Z                     {
2023-01-11T21:41:26.2012611Z                         {
2023-01-11T21:41:26.2012793Z                             auto tmp0 = in_ptr0[(2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2012987Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2013165Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2013364Z                             auto tmp5 = in_ptr0[16 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2013543Z                             auto tmp7 = in_ptr0[17 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2013706Z                             auto tmp9 = in_ptr0[18 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2013887Z                             auto tmp11 = in_ptr0[32 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2014072Z                             auto tmp13 = in_ptr0[33 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2014420Z                             auto tmp15 = in_ptr0[34 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.2014587Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:41:26.2014728Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:41:26.2014883Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:41:26.2015043Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:41:26.2015187Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:41:26.2015353Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:41:26.2015513Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:41:26.2015665Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:41:26.2015966Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:41:26.2016125Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:41:26.2016308Z                             out_ptr0[i2 + (7*i1) + (49*i0)] = tmp18;
2023-01-11T21:41:26.2016398Z                         }
2023-01-11T21:41:26.2016514Z                     }
2023-01-11T21:41:26.2016631Z                 }
2023-01-11T21:41:26.2016738Z             }
2023-01-11T21:41:26.2016846Z         }
2023-01-11T21:41:26.2016947Z     }
2023-01-11T21:41:26.2017054Z }
2023-01-11T21:41:26.2017191Z ''')
2023-01-11T21:41:26.2017198Z 
2023-01-11T21:41:26.2017203Z 
2023-01-11T21:41:26.2017353Z async_compile.wait(globals())
2023-01-11T21:41:26.2017483Z del async_compile
2023-01-11T21:41:26.2017491Z 
2023-01-11T21:41:26.2017614Z def call(args):
2023-01-11T21:41:26.2017732Z     arg0_1, = args
2023-01-11T21:41:26.2017852Z     args.clear()
2023-01-11T21:41:26.2018197Z     buf0 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2018406Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2018532Z     del arg0_1
2023-01-11T21:41:26.2018664Z     return (buf0, )
2023-01-11T21:41:26.2018674Z 
2023-01-11T21:41:26.2018683Z 
2023-01-11T21:41:26.2018813Z if __name__ == "__main__":
2023-01-11T21:41:26.2018997Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2019207Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2019582Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2019747Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2019754Z 
2023-01-11T21:41:26.2019853Z ok (1.566s)
2023-01-11T21:41:26.2020633Z   test_avg_pool2d2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2020861Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2021284Z [2023-01-11 21:24:33,309] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 28
2023-01-11T21:41:26.2021722Z [2023-01-11 21:24:34,833] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 28
2023-01-11T21:41:26.2021731Z 
2023-01-11T21:41:26.2021887Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2022000Z import torch
2023-01-11T21:41:26.2022124Z import random
2023-01-11T21:41:26.2022325Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2022511Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2022534Z 
2023-01-11T21:41:26.2022657Z aten = torch.ops.aten
2023-01-11T21:41:26.2022876Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2023041Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2023050Z 
2023-01-11T21:41:26.2023055Z 
2023-01-11T21:41:26.2023481Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2023802Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2023997Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2024167Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2024260Z {
2023-01-11T21:41:26.2024419Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2024524Z     {
2023-01-11T21:41:26.2024656Z         #pragma omp for 
2023-01-11T21:41:26.2024805Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.2024912Z         {
2023-01-11T21:41:26.2025050Z             #pragma GCC ivdep
2023-01-11T21:41:26.2025176Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:41:26.2025276Z             {
2023-01-11T21:41:26.2025409Z                 #pragma GCC ivdep
2023-01-11T21:41:26.2025636Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:26.2025755Z                 {
2023-01-11T21:41:26.2025879Z                     {
2023-01-11T21:41:26.2025998Z                         {
2023-01-11T21:41:26.2026157Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2026348Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2026536Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2026728Z                             auto tmp5 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2026892Z                             auto tmp7 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2027075Z                             auto tmp9 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2027277Z                             auto tmp11 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2027473Z                             auto tmp13 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2027640Z                             auto tmp15 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.2027808Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:41:26.2027978Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:41:26.2028134Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:41:26.2028265Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:41:26.2028393Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:41:26.2028523Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:41:26.2028636Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:41:26.2028764Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:41:26.2028919Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:41:26.2029051Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:41:26.2029191Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp18;
2023-01-11T21:41:26.2029282Z                         }
2023-01-11T21:41:26.2029372Z                     }
2023-01-11T21:41:26.2029463Z                 }
2023-01-11T21:41:26.2029534Z             }
2023-01-11T21:41:26.2029615Z         }
2023-01-11T21:41:26.2029691Z     }
2023-01-11T21:41:26.2029772Z }
2023-01-11T21:41:26.2029886Z ''')
2023-01-11T21:41:26.2029892Z 
2023-01-11T21:41:26.2029897Z 
2023-01-11T21:41:26.2030021Z async_compile.wait(globals())
2023-01-11T21:41:26.2030119Z del async_compile
2023-01-11T21:41:26.2030125Z 
2023-01-11T21:41:26.2030203Z def call(args):
2023-01-11T21:41:26.2030290Z     arg0_1, = args
2023-01-11T21:41:26.2030378Z     args.clear()
2023-01-11T21:41:26.2030709Z     buf0 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2030920Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2031146Z     del arg0_1
2023-01-11T21:41:26.2031327Z     return (buf0, )
2023-01-11T21:41:26.2031420Z 
2023-01-11T21:41:26.2031427Z 
2023-01-11T21:41:26.2031600Z if __name__ == "__main__":
2023-01-11T21:41:26.2031817Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2032045Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2032473Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2032674Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2032687Z 
2023-01-11T21:41:26.2032797Z ok (1.634s)
2023-01-11T21:41:26.2033750Z   test_avg_pool2d3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2034000Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2034502Z [2023-01-11 21:24:34,921] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 29
2023-01-11T21:41:26.2035006Z [2023-01-11 21:24:36,487] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 29
2023-01-11T21:41:26.2035015Z 
2023-01-11T21:41:26.2035173Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2035307Z import torch
2023-01-11T21:41:26.2035429Z import random
2023-01-11T21:41:26.2035650Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2035876Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2035886Z 
2023-01-11T21:41:26.2036038Z aten = torch.ops.aten
2023-01-11T21:41:26.2036281Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2036447Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2036477Z 
2023-01-11T21:41:26.2036486Z 
2023-01-11T21:41:26.2036729Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2037107Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2037327Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2037519Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2037641Z {
2023-01-11T21:41:26.2037826Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2037943Z     {
2023-01-11T21:41:26.2038070Z         #pragma omp for 
2023-01-11T21:41:26.2038221Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.2038357Z         {
2023-01-11T21:41:26.2038515Z             #pragma GCC ivdep
2023-01-11T21:41:26.2038667Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.2038787Z             {
2023-01-11T21:41:26.2038915Z                 {
2023-01-11T21:41:26.2039021Z                     {
2023-01-11T21:41:26.2039361Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:41:26.2039560Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.2039745Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.2039925Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:26.2040102Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.2040286Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:26.2040602Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:41:26.2040767Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:26.2040949Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:41:26.2041126Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:41:26.2041310Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:41:26.2041471Z                         float tmp11 = 0.0;
2023-01-11T21:41:26.2041611Z                         if(tmp10)
2023-01-11T21:41:26.2041728Z                         {
2023-01-11T21:41:26.2042067Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2042368Z                             tmp11 = tmp12;
2023-01-11T21:41:26.2042491Z                         }
2023-01-11T21:41:26.2042698Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:41:26.2042884Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:41:26.2043061Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:41:26.2043225Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:41:26.2043396Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:41:26.2043562Z                         float tmp18 = 0.0;
2023-01-11T21:41:26.2043713Z                         if(tmp17)
2023-01-11T21:41:26.2043845Z                         {
2023-01-11T21:41:26.2044177Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2044402Z                             tmp18 = tmp19;
2023-01-11T21:41:26.2044517Z                         }
2023-01-11T21:41:26.2044698Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:41:26.2044911Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:41:26.2045095Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:41:26.2045262Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:41:26.2045447Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:41:26.2045627Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:41:26.2045792Z                         float tmp26 = 0.0;
2023-01-11T21:41:26.2045921Z                         if(tmp25)
2023-01-11T21:41:26.2046049Z                         {
2023-01-11T21:41:26.2046389Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2046548Z                             tmp26 = tmp27;
2023-01-11T21:41:26.2046692Z                         }
2023-01-11T21:41:26.2046873Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:41:26.2047075Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:41:26.2047244Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:41:26.2047414Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:41:26.2047587Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:41:26.2047765Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:41:26.2047915Z                         float tmp34 = 0.0;
2023-01-11T21:41:26.2048069Z                         if(tmp33)
2023-01-11T21:41:26.2048200Z                         {
2023-01-11T21:41:26.2048513Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2048670Z                             tmp34 = tmp35;
2023-01-11T21:41:26.2048789Z                         }
2023-01-11T21:41:26.2048964Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:41:26.2049284Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:41:26.2049452Z                         float tmp38 = 0.0;
2023-01-11T21:41:26.2049604Z                         if(tmp37)
2023-01-11T21:41:26.2049724Z                         {
2023-01-11T21:41:26.2049918Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:26.2050075Z                             tmp38 = tmp39;
2023-01-11T21:41:26.2050212Z                         }
2023-01-11T21:41:26.2050393Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:41:26.2050571Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:41:26.2050723Z                         float tmp42 = 0.0;
2023-01-11T21:41:26.2050853Z                         if(tmp41)
2023-01-11T21:41:26.2050981Z                         {
2023-01-11T21:41:26.2051186Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2051340Z                             tmp42 = tmp43;
2023-01-11T21:41:26.2051477Z                         }
2023-01-11T21:41:26.2051645Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:41:26.2052024Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:41:26.2052185Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:41:26.2052363Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:41:26.2052527Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:41:26.2052696Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:41:26.2052861Z                         float tmp50 = 0.0;
2023-01-11T21:41:26.2053007Z                         if(tmp49)
2023-01-11T21:41:26.2053142Z                         {
2023-01-11T21:41:26.2053351Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2053485Z                             tmp50 = tmp51;
2023-01-11T21:41:26.2053618Z                         }
2023-01-11T21:41:26.2053890Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:41:26.2054071Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:41:26.2054239Z                         float tmp54 = 0.0;
2023-01-11T21:41:26.2054378Z                         if(tmp53)
2023-01-11T21:41:26.2054505Z                         {
2023-01-11T21:41:26.2054687Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2054851Z                             tmp54 = tmp55;
2023-01-11T21:41:26.2054987Z                         }
2023-01-11T21:41:26.2055173Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:41:26.2055330Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:41:26.2055498Z                         float tmp58 = 0.0;
2023-01-11T21:41:26.2055646Z                         if(tmp57)
2023-01-11T21:41:26.2055757Z                         {
2023-01-11T21:41:26.2055970Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2056133Z                             tmp58 = tmp59;
2023-01-11T21:41:26.2056248Z                         }
2023-01-11T21:41:26.2056436Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:41:26.2056671Z                         auto tmp61 = static_cast<float>(0.1111111111111111);
2023-01-11T21:41:26.2056849Z                         auto tmp62 = tmp60 * tmp61;
2023-01-11T21:41:26.2057010Z                         out_ptr0[i1 + (4*i0)] = tmp62;
2023-01-11T21:41:26.2057128Z                     }
2023-01-11T21:41:26.2057255Z                 }
2023-01-11T21:41:26.2057388Z             }
2023-01-11T21:41:26.2057517Z         }
2023-01-11T21:41:26.2057636Z     }
2023-01-11T21:41:26.2057767Z }
2023-01-11T21:41:26.2057930Z ''')
2023-01-11T21:41:26.2057939Z 
2023-01-11T21:41:26.2057945Z 
2023-01-11T21:41:26.2058108Z async_compile.wait(globals())
2023-01-11T21:41:26.2058262Z del async_compile
2023-01-11T21:41:26.2058271Z 
2023-01-11T21:41:26.2058414Z def call(args):
2023-01-11T21:41:26.2058552Z     arg0_1, = args
2023-01-11T21:41:26.2058693Z     args.clear()
2023-01-11T21:41:26.2059098Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2059336Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2059467Z     del arg0_1
2023-01-11T21:41:26.2059606Z     return (buf0, )
2023-01-11T21:41:26.2059614Z 
2023-01-11T21:41:26.2059619Z 
2023-01-11T21:41:26.2059756Z if __name__ == "__main__":
2023-01-11T21:41:26.2059968Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2060206Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2060620Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2060808Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2060822Z 
2023-01-11T21:41:26.2060936Z ok (1.589s)
2023-01-11T21:41:26.2061821Z   test_avg_pool2d4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2062160Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2062662Z [2023-01-11 21:24:36,522] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 30
2023-01-11T21:41:26.2063253Z [2023-01-11 21:24:38,083] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 30
2023-01-11T21:41:26.2063266Z 
2023-01-11T21:41:26.2063435Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2063576Z import torch
2023-01-11T21:41:26.2063719Z import random
2023-01-11T21:41:26.2063943Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2064153Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2064248Z 
2023-01-11T21:41:26.2064399Z aten = torch.ops.aten
2023-01-11T21:41:26.2064652Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2064826Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2064837Z 
2023-01-11T21:41:26.2064844Z 
2023-01-11T21:41:26.2065109Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2065479Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2065702Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2065887Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2065995Z {
2023-01-11T21:41:26.2066174Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2066293Z     {
2023-01-11T21:41:26.2066446Z         #pragma omp for 
2023-01-11T21:41:26.2066609Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.2066730Z         {
2023-01-11T21:41:26.2066882Z             #pragma GCC ivdep
2023-01-11T21:41:26.2067020Z             for(long i1=0; i1<55; i1+=1)
2023-01-11T21:41:26.2067141Z             {
2023-01-11T21:41:26.2067303Z                 #pragma GCC ivdep
2023-01-11T21:41:26.2067485Z                 for(long i2=0; i2<55; i2+=1)
2023-01-11T21:41:26.2067618Z                 {
2023-01-11T21:41:26.2067747Z                     {
2023-01-11T21:41:26.2067876Z                         {
2023-01-11T21:41:26.2068076Z                             auto tmp0 = in_ptr0[(2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2068297Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2068504Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2068717Z                             auto tmp5 = in_ptr0[111 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2068930Z                             auto tmp7 = in_ptr0[112 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2069148Z                             auto tmp9 = in_ptr0[113 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2069365Z                             auto tmp11 = in_ptr0[222 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2069580Z                             auto tmp13 = in_ptr0[223 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2069761Z                             auto tmp15 = in_ptr0[224 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.2069947Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:41:26.2070130Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:41:26.2070303Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:41:26.2070470Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:41:26.2070648Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:41:26.2070836Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:41:26.2071006Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:41:26.2071195Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:41:26.2071415Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:41:26.2071674Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:41:26.2071873Z                             out_ptr0[i2 + (55*i1) + (3025*i0)] = tmp18;
2023-01-11T21:41:26.2072010Z                         }
2023-01-11T21:41:26.2072136Z                     }
2023-01-11T21:41:26.2072266Z                 }
2023-01-11T21:41:26.2072363Z             }
2023-01-11T21:41:26.2072475Z         }
2023-01-11T21:41:26.2072599Z     }
2023-01-11T21:41:26.2072721Z }
2023-01-11T21:41:26.2072892Z ''')
2023-01-11T21:41:26.2072906Z 
2023-01-11T21:41:26.2072913Z 
2023-01-11T21:41:26.2073094Z async_compile.wait(globals())
2023-01-11T21:41:26.2073233Z del async_compile
2023-01-11T21:41:26.2073244Z 
2023-01-11T21:41:26.2073347Z def call(args):
2023-01-11T21:41:26.2073479Z     arg0_1, = args
2023-01-11T21:41:26.2073620Z     args.clear()
2023-01-11T21:41:26.2074141Z     buf0 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2074392Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2074529Z     del arg0_1
2023-01-11T21:41:26.2074671Z     return (buf0, )
2023-01-11T21:41:26.2074679Z 
2023-01-11T21:41:26.2074686Z 
2023-01-11T21:41:26.2074820Z if __name__ == "__main__":
2023-01-11T21:41:26.2075038Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2075264Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2075700Z     arg0_1 = rand_strided((2, 8, 111, 111), (98568, 12321, 111, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2075920Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2075927Z 
2023-01-11T21:41:26.2076044Z ok (1.601s)
2023-01-11T21:41:26.2076921Z   test_avg_pool2d5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2077179Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2077682Z [2023-01-11 21:24:38,113] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 31
2023-01-11T21:41:26.2077690Z 
2023-01-11T21:41:26.2077855Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2077973Z import torch
2023-01-11T21:41:26.2078104Z import random
2023-01-11T21:41:26.2078319Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2078556Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2078570Z 
2023-01-11T21:41:26.2078705Z aten = torch.ops.aten
2023-01-11T21:41:26.2078958Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2079144Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2079155Z 
2023-01-11T21:41:26.2079162Z 
2023-01-11T21:41:26.2079399Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2079781Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2080006Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2080194Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2080314Z {
2023-01-11T21:41:26.2080487Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2080608Z     {
2023-01-11T21:41:26.2080743Z         #pragma omp for 
2023-01-11T21:41:26.2080903Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.2081017Z         {
2023-01-11T21:41:26.2081169Z             #pragma GCC ivdep
2023-01-11T21:41:26.2081314Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.2081447Z             {
2023-01-11T21:41:26.2081576Z                 {
2023-01-11T21:41:26.2081684Z                     {
2023-01-11T21:41:26.2082030Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:41:26.2082214Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.2082499Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.2082705Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:26.2082891Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.2083064Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:26.2083386Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:41:26.2083563Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:26.2083740Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:41:26.2083905Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:41:26.2084081Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:41:26.2084257Z                         float tmp11 = 0.0;
2023-01-11T21:41:26.2084480Z                         if(tmp10)
2023-01-11T21:41:26.2084618Z                         {
2023-01-11T21:41:26.2084934Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2085107Z                             tmp11 = tmp12;
2023-01-11T21:41:26.2085246Z                         }
2023-01-11T21:41:26.2085458Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:41:26.2085636Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:41:26.2085811Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:41:26.2085993Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:41:26.2086149Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:41:26.2086325Z                         float tmp18 = 0.0;
2023-01-11T21:41:26.2086479Z                         if(tmp17)
2023-01-11T21:41:26.2086606Z                         {
2023-01-11T21:41:26.2086952Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2087118Z                             tmp18 = tmp19;
2023-01-11T21:41:26.2087255Z                         }
2023-01-11T21:41:26.2087417Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:41:26.2087620Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:41:26.2087808Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:41:26.2087987Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:41:26.2088152Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:41:26.2088323Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:41:26.2088482Z                         float tmp26 = 0.0;
2023-01-11T21:41:26.2088608Z                         if(tmp25)
2023-01-11T21:41:26.2088762Z                         {
2023-01-11T21:41:26.2089224Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2089384Z                             tmp26 = tmp27;
2023-01-11T21:41:26.2089523Z                         }
2023-01-11T21:41:26.2089708Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:41:26.2089912Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:41:26.2090082Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:41:26.2090250Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:41:26.2090431Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:41:26.2090614Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:41:26.2090753Z                         float tmp34 = 0.0;
2023-01-11T21:41:26.2090866Z                         if(tmp33)
2023-01-11T21:41:26.2090970Z                         {
2023-01-11T21:41:26.2091242Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2091352Z                             tmp34 = tmp35;
2023-01-11T21:41:26.2091456Z                         }
2023-01-11T21:41:26.2091594Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:41:26.2091737Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:41:26.2091868Z                         float tmp38 = 0.0;
2023-01-11T21:41:26.2092115Z                         if(tmp37)
2023-01-11T21:41:26.2092218Z                         {
2023-01-11T21:41:26.2092365Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:26.2092490Z                             tmp38 = tmp39;
2023-01-11T21:41:26.2092595Z                         }
2023-01-11T21:41:26.2092730Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:41:26.2092863Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:41:26.2092989Z                         float tmp42 = 0.0;
2023-01-11T21:41:26.2093099Z                         if(tmp41)
2023-01-11T21:41:26.2093184Z                         {
2023-01-11T21:41:26.2093343Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2093462Z                             tmp42 = tmp43;
2023-01-11T21:41:26.2093614Z                         }
2023-01-11T21:41:26.2093751Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:41:26.2093919Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:41:26.2094058Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:41:26.2094179Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:41:26.2094312Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:41:26.2094448Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:41:26.2094576Z                         float tmp50 = 0.0;
2023-01-11T21:41:26.2094685Z                         if(tmp49)
2023-01-11T21:41:26.2094787Z                         {
2023-01-11T21:41:26.2094946Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2095052Z                             tmp50 = tmp51;
2023-01-11T21:41:26.2095148Z                         }
2023-01-11T21:41:26.2095289Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:41:26.2095423Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:41:26.2095546Z                         float tmp54 = 0.0;
2023-01-11T21:41:26.2095661Z                         if(tmp53)
2023-01-11T21:41:26.2095761Z                         {
2023-01-11T21:41:26.2095902Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2096023Z                             tmp54 = tmp55;
2023-01-11T21:41:26.2096120Z                         }
2023-01-11T21:41:26.2096255Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:41:26.2096390Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:41:26.2096515Z                         float tmp58 = 0.0;
2023-01-11T21:41:26.2096627Z                         if(tmp57)
2023-01-11T21:41:26.2096710Z                         {
2023-01-11T21:41:26.2096867Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2096990Z                             tmp58 = tmp59;
2023-01-11T21:41:26.2097090Z                         }
2023-01-11T21:41:26.2097226Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:41:26.2097353Z                         float tmp61 = 0.0;
2023-01-11T21:41:26.2097461Z                         if(tmp10)
2023-01-11T21:41:26.2097639Z                         {
2023-01-11T21:41:26.2097779Z                             auto tmp62 = static_cast<float>(1);
2023-01-11T21:41:26.2097884Z                             tmp61 = tmp62;
2023-01-11T21:41:26.2097974Z                         }
2023-01-11T21:41:26.2098083Z                         float tmp63 = 0.0;
2023-01-11T21:41:26.2098181Z                         if(tmp17)
2023-01-11T21:41:26.2098270Z                         {
2023-01-11T21:41:26.2098410Z                             auto tmp64 = static_cast<float>(1);
2023-01-11T21:41:26.2098500Z                             tmp63 = tmp64;
2023-01-11T21:41:26.2098585Z                         }
2023-01-11T21:41:26.2098714Z                         auto tmp65 = tmp63 + tmp61;
2023-01-11T21:41:26.2098824Z                         float tmp66 = 0.0;
2023-01-11T21:41:26.2098954Z                         if(tmp25)
2023-01-11T21:41:26.2099042Z                         {
2023-01-11T21:41:26.2099181Z                             auto tmp67 = static_cast<float>(1);
2023-01-11T21:41:26.2099270Z                             tmp66 = tmp67;
2023-01-11T21:41:26.2099357Z                         }
2023-01-11T21:41:26.2099475Z                         auto tmp68 = tmp66 + tmp65;
2023-01-11T21:41:26.2099581Z                         float tmp69 = 0.0;
2023-01-11T21:41:26.2099677Z                         if(tmp33)
2023-01-11T21:41:26.2099765Z                         {
2023-01-11T21:41:26.2099903Z                             auto tmp70 = static_cast<float>(1);
2023-01-11T21:41:26.2099993Z                             tmp69 = tmp70;
2023-01-11T21:41:26.2100079Z                         }
2023-01-11T21:41:26.2100199Z                         auto tmp71 = tmp69 + tmp68;
2023-01-11T21:41:26.2100340Z                         float tmp72 = 0.0;
2023-01-11T21:41:26.2100438Z                         if(tmp37)
2023-01-11T21:41:26.2100531Z                         {
2023-01-11T21:41:26.2100671Z                             auto tmp73 = static_cast<float>(1);
2023-01-11T21:41:26.2100760Z                             tmp72 = tmp73;
2023-01-11T21:41:26.2100845Z                         }
2023-01-11T21:41:26.2100963Z                         auto tmp74 = tmp72 + tmp71;
2023-01-11T21:41:26.2101069Z                         float tmp75 = 0.0;
2023-01-11T21:41:26.2101162Z                         if(tmp41)
2023-01-11T21:41:26.2101251Z                         {
2023-01-11T21:41:26.2101388Z                             auto tmp76 = static_cast<float>(1);
2023-01-11T21:41:26.2101475Z                             tmp75 = tmp76;
2023-01-11T21:41:26.2101562Z                         }
2023-01-11T21:41:26.2101681Z                         auto tmp77 = tmp75 + tmp74;
2023-01-11T21:41:26.2101791Z                         float tmp78 = 0.0;
2023-01-11T21:41:26.2101888Z                         if(tmp49)
2023-01-11T21:41:26.2101975Z                         {
2023-01-11T21:41:26.2102117Z                             auto tmp79 = static_cast<float>(1);
2023-01-11T21:41:26.2102204Z                             tmp78 = tmp79;
2023-01-11T21:41:26.2102290Z                         }
2023-01-11T21:41:26.2102408Z                         auto tmp80 = tmp78 + tmp77;
2023-01-11T21:41:26.2102515Z                         float tmp81 = 0.0;
2023-01-11T21:41:26.2102611Z                         if(tmp53)
2023-01-11T21:41:26.2102697Z                         {
2023-01-11T21:41:26.2102831Z                             auto tmp82 = static_cast<float>(1);
2023-01-11T21:41:26.2102919Z                             tmp81 = tmp82;
2023-01-11T21:41:26.2103005Z                         }
2023-01-11T21:41:26.2103187Z                         auto tmp83 = tmp81 + tmp80;
2023-01-11T21:41:26.2103296Z                         float tmp84 = 0.0;
2023-01-11T21:41:26.2103396Z                         if(tmp57)
2023-01-11T21:41:26.2103482Z                         {
2023-01-11T21:41:26.2103618Z                             auto tmp85 = static_cast<float>(1);
2023-01-11T21:41:26.2103711Z                             tmp84 = tmp85;
2023-01-11T21:41:26.2103799Z                         }
2023-01-11T21:41:26.2103917Z                         auto tmp86 = tmp84 + tmp83;
2023-01-11T21:41:26.2104035Z                         auto tmp87 = tmp60 / tmp86;
2023-01-11T21:41:26.2104156Z                         out_ptr0[i1 + (4*i0)] = tmp87;
2023-01-11T21:41:26.2104242Z                     }
2023-01-11T21:41:26.2104328Z                 }
2023-01-11T21:41:26.2104397Z             }
2023-01-11T21:41:26.2104478Z         }
2023-01-11T21:41:26.2104557Z     }
2023-01-11T21:41:26.2104640Z }
2023-01-11T21:41:26.2104746Z ''')
2023-01-11T21:41:26.2104752Z 
2023-01-11T21:41:26.2104758Z 
2023-01-11T21:41:26.2104878Z async_compile.wait(globals())
2023-01-11T21:41:26.2104967Z del async_compile
2023-01-11T21:41:26.2104976Z 
2023-01-11T21:41:26.2105054Z def call(args):
2023-01-11T21:41:26.2105142Z     arg0_1, = args
2023-01-11T21:41:26.2105289Z     args.clear()
2023-01-11T21:41:26.2105573Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2105747Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2105836Z     del arg0_1
2023-01-11T21:41:26.2105927Z     return (buf0, )
2023-01-11T21:41:26.2105932Z 
2023-01-11T21:41:26.2105937Z 
2023-01-11T21:41:26.2106032Z if __name__ == "__main__":
2023-01-11T21:41:26.2106165Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2106323Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2106606Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2106746Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2107139Z [2023-01-11 21:24:39,689] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 31
2023-01-11T21:41:26.2107146Z 
2023-01-11T21:41:26.2107231Z ok (1.598s)
2023-01-11T21:41:26.2107873Z   test_avg_pool2d6_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2108038Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2108378Z [2023-01-11 21:24:39,711] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 32
2023-01-11T21:41:26.2108711Z [2023-01-11 21:24:41,258] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 32
2023-01-11T21:41:26.2108728Z 
2023-01-11T21:41:26.2108837Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2108924Z import torch
2023-01-11T21:41:26.2109014Z import random
2023-01-11T21:41:26.2109166Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2109318Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2109324Z 
2023-01-11T21:41:26.2109420Z aten = torch.ops.aten
2023-01-11T21:41:26.2109589Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2109692Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2109698Z 
2023-01-11T21:41:26.2109703Z 
2023-01-11T21:41:26.2109878Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2110135Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2110287Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2110411Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2110488Z {
2023-01-11T21:41:26.2110611Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2110679Z     {
2023-01-11T21:41:26.2110777Z         #pragma omp for 
2023-01-11T21:41:26.2110888Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.2110971Z         {
2023-01-11T21:41:26.2111073Z             #pragma GCC ivdep
2023-01-11T21:41:26.2111182Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.2111265Z             {
2023-01-11T21:41:26.2111335Z                 {
2023-01-11T21:41:26.2111416Z                     {
2023-01-11T21:41:26.2111644Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:41:26.2111774Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.2111894Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.2112028Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:26.2112151Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.2112257Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:26.2112485Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:41:26.2112604Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:26.2112758Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:41:26.2112872Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:41:26.2112986Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:41:26.2113092Z                         float tmp11 = 0.0;
2023-01-11T21:41:26.2113176Z                         if(tmp10)
2023-01-11T21:41:26.2113261Z                         {
2023-01-11T21:41:26.2113489Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2113592Z                             tmp11 = tmp12;
2023-01-11T21:41:26.2113680Z                         }
2023-01-11T21:41:26.2113816Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:41:26.2113934Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:41:26.2114087Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:41:26.2114194Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:41:26.2114310Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:41:26.2114420Z                         float tmp18 = 0.0;
2023-01-11T21:41:26.2114512Z                         if(tmp17)
2023-01-11T21:41:26.2114598Z                         {
2023-01-11T21:41:26.2114820Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2114921Z                             tmp18 = tmp19;
2023-01-11T21:41:26.2114996Z                         }
2023-01-11T21:41:26.2115117Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:41:26.2115255Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:41:26.2115371Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:41:26.2115487Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:41:26.2115601Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:41:26.2115721Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:41:26.2115818Z                         float tmp26 = 0.0;
2023-01-11T21:41:26.2115913Z                         if(tmp25)
2023-01-11T21:41:26.2115996Z                         {
2023-01-11T21:41:26.2116221Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2116327Z                             tmp26 = tmp27;
2023-01-11T21:41:26.2116416Z                         }
2023-01-11T21:41:26.2116535Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:41:26.2116660Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:41:26.2116777Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:41:26.2116893Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:41:26.2117009Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:41:26.2117123Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:41:26.2117235Z                         float tmp34 = 0.0;
2023-01-11T21:41:26.2117326Z                         if(tmp33)
2023-01-11T21:41:26.2117399Z                         {
2023-01-11T21:41:26.2117626Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2117727Z                             tmp34 = tmp35;
2023-01-11T21:41:26.2117810Z                         }
2023-01-11T21:41:26.2117929Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:41:26.2118044Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:41:26.2118151Z                         float tmp38 = 0.0;
2023-01-11T21:41:26.2118247Z                         if(tmp37)
2023-01-11T21:41:26.2118320Z                         {
2023-01-11T21:41:26.2118451Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:26.2118555Z                             tmp38 = tmp39;
2023-01-11T21:41:26.2118638Z                         }
2023-01-11T21:41:26.2118757Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:41:26.2118875Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:41:26.2118984Z                         float tmp42 = 0.0;
2023-01-11T21:41:26.2119100Z                         if(tmp41)
2023-01-11T21:41:26.2119187Z                         {
2023-01-11T21:41:26.2119320Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2119426Z                             tmp42 = tmp43;
2023-01-11T21:41:26.2119513Z                         }
2023-01-11T21:41:26.2119628Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:41:26.2119766Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:41:26.2119870Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:41:26.2119983Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:41:26.2120101Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:41:26.2120216Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:41:26.2120354Z                         float tmp50 = 0.0;
2023-01-11T21:41:26.2120450Z                         if(tmp49)
2023-01-11T21:41:26.2120538Z                         {
2023-01-11T21:41:26.2120662Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2120763Z                             tmp50 = tmp51;
2023-01-11T21:41:26.2120848Z                         }
2023-01-11T21:41:26.2120967Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:41:26.2121084Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:41:26.2121189Z                         float tmp54 = 0.0;
2023-01-11T21:41:26.2121283Z                         if(tmp53)
2023-01-11T21:41:26.2121356Z                         {
2023-01-11T21:41:26.2121489Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2121590Z                             tmp54 = tmp55;
2023-01-11T21:41:26.2121676Z                         }
2023-01-11T21:41:26.2121794Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:41:26.2121907Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:41:26.2122016Z                         float tmp58 = 0.0;
2023-01-11T21:41:26.2122096Z                         if(tmp57)
2023-01-11T21:41:26.2122180Z                         {
2023-01-11T21:41:26.2122315Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.2122419Z                             tmp58 = tmp59;
2023-01-11T21:41:26.2122505Z                         }
2023-01-11T21:41:26.2122625Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:41:26.2122775Z                         auto tmp61 = static_cast<float>(0.3333333333333333);
2023-01-11T21:41:26.2122881Z                         auto tmp62 = tmp60 * tmp61;
2023-01-11T21:41:26.2123002Z                         out_ptr0[i1 + (4*i0)] = tmp62;
2023-01-11T21:41:26.2123089Z                     }
2023-01-11T21:41:26.2123176Z                 }
2023-01-11T21:41:26.2123262Z             }
2023-01-11T21:41:26.2123343Z         }
2023-01-11T21:41:26.2123422Z     }
2023-01-11T21:41:26.2123488Z }
2023-01-11T21:41:26.2123593Z ''')
2023-01-11T21:41:26.2123599Z 
2023-01-11T21:41:26.2123604Z 
2023-01-11T21:41:26.2123717Z async_compile.wait(globals())
2023-01-11T21:41:26.2123811Z del async_compile
2023-01-11T21:41:26.2123816Z 
2023-01-11T21:41:26.2123911Z def call(args):
2023-01-11T21:41:26.2124002Z     arg0_1, = args
2023-01-11T21:41:26.2124096Z     args.clear()
2023-01-11T21:41:26.2124367Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2124540Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2124626Z     del arg0_1
2023-01-11T21:41:26.2124719Z     return (buf0, )
2023-01-11T21:41:26.2124724Z 
2023-01-11T21:41:26.2124729Z 
2023-01-11T21:41:26.2124825Z if __name__ == "__main__":
2023-01-11T21:41:26.2124969Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2125132Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2125414Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2125573Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2125591Z 
2023-01-11T21:41:26.2125661Z ok (1.570s)
2023-01-11T21:41:26.2126312Z   test_avg_pool2d7_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2126511Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2126915Z [2023-01-11 21:24:41,287] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 33
2023-01-11T21:41:26.2127494Z [2023-01-11 21:24:41,294] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.avg_pool2d
2023-01-11T21:41:26.2127985Z [2023-01-11 21:24:41,298] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 33
2023-01-11T21:41:26.2128003Z 
2023-01-11T21:41:26.2128181Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2128331Z import torch
2023-01-11T21:41:26.2128470Z import random
2023-01-11T21:41:26.2128671Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2128897Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2128908Z 
2023-01-11T21:41:26.2129185Z aten = torch.ops.aten
2023-01-11T21:41:26.2129435Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2129610Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2129620Z 
2023-01-11T21:41:26.2129627Z 
2023-01-11T21:41:26.2129793Z async_compile.wait(globals())
2023-01-11T21:41:26.2129938Z del async_compile
2023-01-11T21:41:26.2129948Z 
2023-01-11T21:41:26.2130089Z def call(args):
2023-01-11T21:41:26.2130208Z     arg0_1, = args
2023-01-11T21:41:26.2130343Z     args.clear()
2023-01-11T21:41:26.2130723Z     buf0 = aten.avg_pool2d(arg0_1, [13, 13], [1, 1], [0, 0], False, True, None)
2023-01-11T21:41:26.2130853Z     del arg0_1
2023-01-11T21:41:26.2130983Z     buf1 = buf0
2023-01-11T21:41:26.2131196Z     assert_size_stride(buf1, (1, 1, 12, 12), (144, 144, 12, 1))
2023-01-11T21:41:26.2131326Z     del buf0
2023-01-11T21:41:26.2131446Z     return (buf1, )
2023-01-11T21:41:26.2131454Z 
2023-01-11T21:41:26.2131460Z 
2023-01-11T21:41:26.2131610Z if __name__ == "__main__":
2023-01-11T21:41:26.2131826Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2132052Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2132498Z     arg0_1 = rand_strided((1, 1, 24, 24), (576, 576, 24, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2132702Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2132709Z 
2023-01-11T21:41:26.2132845Z ok (0.039s)
2023-01-11T21:41:26.2133727Z   test_avg_pool2d_backward2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2133983Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2134466Z [2023-01-11 21:24:41,320] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 34
2023-01-11T21:41:26.2134493Z 
2023-01-11T21:41:26.2134652Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2134780Z import torch
2023-01-11T21:41:26.2134919Z import random
2023-01-11T21:41:26.2135140Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2135378Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2135384Z 
2023-01-11T21:41:26.2135527Z aten = torch.ops.aten
2023-01-11T21:41:26.2135778Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2136084Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2136092Z 
2023-01-11T21:41:26.2136099Z 
2023-01-11T21:41:26.2136373Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2136749Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2136970Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2137168Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2137281Z {
2023-01-11T21:41:26.2137467Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2137565Z     {
2023-01-11T21:41:26.2137718Z         #pragma omp for 
2023-01-11T21:41:26.2137878Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:26.2138009Z         {
2023-01-11T21:41:26.2138276Z             #pragma GCC ivdep
2023-01-11T21:41:26.2138445Z             for(long i1=0; i1<15; i1+=1)
2023-01-11T21:41:26.2138574Z             {
2023-01-11T21:41:26.2138692Z                 {
2023-01-11T21:41:26.2138824Z                     {
2023-01-11T21:41:26.2139160Z                         auto tmp0 = static_cast<int>((-1) + i0);
2023-01-11T21:41:26.2139471Z                         auto tmp1 = static_cast<int>((-1) + i1);
2023-01-11T21:41:26.2139669Z                         auto tmp2 = static_cast<int>(2 + i0);
2023-01-11T21:41:26.2139869Z                         auto tmp3 = static_cast<int>(2 + i1);
2023-01-11T21:41:26.2140064Z                         auto tmp4 = static_cast<int>(0);
2023-01-11T21:41:26.2140298Z                         auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:41:26.2140536Z                         auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:41:26.2140728Z                         auto tmp7 = static_cast<int>(20);
2023-01-11T21:41:26.2140981Z                         auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:41:26.2141176Z                         auto tmp9 = static_cast<int>(15);
2023-01-11T21:41:26.2141425Z                         auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp3, tmp9);
2023-01-11T21:41:26.2141606Z                         auto tmp11 = tmp5 + tmp4;
2023-01-11T21:41:26.2141788Z                         auto tmp12 = tmp6 + tmp4;
2023-01-11T21:41:26.2141985Z                         auto tmp13 = static_cast<int>(1);
2023-01-11T21:41:26.2142160Z                         auto tmp14 = static_cast<int>(3);
2023-01-11T21:41:26.2142336Z                         auto tmp15 = tmp11 * tmp13;
2023-01-11T21:41:26.2142618Z                         auto tmp16 = tmp15 - tmp13;
2023-01-11T21:41:26.2142798Z                         auto tmp17 = tmp12 * tmp13;
2023-01-11T21:41:26.2143065Z                         auto tmp18 = tmp17 - tmp13;
2023-01-11T21:41:26.2143324Z                         auto tmp19 = tmp16 + tmp14;
2023-01-11T21:41:26.2143507Z                         auto tmp20 = tmp7 + tmp13;
2023-01-11T21:41:26.2143742Z                         auto tmp21 = (tmp20 != tmp20) ? tmp20 : std::min(tmp19, tmp20);
2023-01-11T21:41:26.2143921Z                         auto tmp22 = tmp18 + tmp14;
2023-01-11T21:41:26.2144095Z                         auto tmp23 = tmp9 + tmp13;
2023-01-11T21:41:26.2144333Z                         auto tmp24 = (tmp23 != tmp23) ? tmp23 : std::min(tmp22, tmp23);
2023-01-11T21:41:26.2144583Z                         auto tmp25 = (tmp4 != tmp4) ? tmp4 : std::max(tmp16, tmp4);
2023-01-11T21:41:26.2144826Z                         auto tmp26 = (tmp4 != tmp4) ? tmp4 : std::max(tmp18, tmp4);
2023-01-11T21:41:26.2145056Z                         auto tmp27 = (tmp7 != tmp7) ? tmp7 : std::min(tmp21, tmp7);
2023-01-11T21:41:26.2145288Z                         auto tmp28 = (tmp9 != tmp9) ? tmp9 : std::min(tmp24, tmp9);
2023-01-11T21:41:26.2145572Z                         auto tmp29 = tmp27 - tmp25;
2023-01-11T21:41:26.2145834Z                         auto tmp30 = tmp28 - tmp26;
2023-01-11T21:41:26.2146008Z                         auto tmp31 = tmp29 * tmp30;
2023-01-11T21:41:26.2146395Z                         auto tmp32 = tmp8 - tmp13;
2023-01-11T21:41:26.2146649Z                         auto tmp33 = (tmp32 != tmp32) ? tmp32 : std::min(tmp11, tmp32);
2023-01-11T21:41:26.2146912Z                         auto tmp34 = tmp10 - tmp13;
2023-01-11T21:41:26.2147152Z                         auto tmp35 = (tmp34 != tmp34) ? tmp34 : std::min(tmp12, tmp34);
2023-01-11T21:41:26.2147360Z                         auto tmp36 = in_ptr0[tmp35 + (15*tmp33)];
2023-01-11T21:41:26.2147534Z                         auto tmp37 = tmp36 / tmp31;
2023-01-11T21:41:26.2147698Z                         auto tmp38 = tmp11 < tmp8;
2023-01-11T21:41:26.2147876Z                         auto tmp39 = tmp12 < tmp10;
2023-01-11T21:41:26.2148043Z                         auto tmp40 = tmp38 & tmp39;
2023-01-11T21:41:26.2148338Z                         auto tmp41 = static_cast<float>(0.0);
2023-01-11T21:41:26.2148534Z                         auto tmp42 = tmp40 ? tmp37 : tmp41;
2023-01-11T21:41:26.2148711Z                         auto tmp43 = tmp6 + tmp13;
2023-01-11T21:41:26.2148894Z                         auto tmp44 = tmp43 * tmp13;
2023-01-11T21:41:26.2149161Z                         auto tmp45 = tmp44 - tmp13;
2023-01-11T21:41:26.2149335Z                         auto tmp46 = tmp45 + tmp14;
2023-01-11T21:41:26.2149578Z                         auto tmp47 = (tmp23 != tmp23) ? tmp23 : std::min(tmp46, tmp23);
2023-01-11T21:41:26.2149820Z                         auto tmp48 = (tmp4 != tmp4) ? tmp4 : std::max(tmp45, tmp4);
2023-01-11T21:41:26.2150054Z                         auto tmp49 = (tmp9 != tmp9) ? tmp9 : std::min(tmp47, tmp9);
2023-01-11T21:41:26.2150349Z                         auto tmp50 = tmp49 - tmp48;
2023-01-11T21:41:26.2150523Z                         auto tmp51 = tmp29 * tmp50;
2023-01-11T21:41:26.2150772Z                         auto tmp52 = (tmp34 != tmp34) ? tmp34 : std::min(tmp43, tmp34);
2023-01-11T21:41:26.2150971Z                         auto tmp53 = in_ptr0[tmp52 + (15*tmp33)];
2023-01-11T21:41:26.2151148Z                         auto tmp54 = tmp53 / tmp51;
2023-01-11T21:41:26.2151327Z                         auto tmp55 = tmp43 < tmp10;
2023-01-11T21:41:26.2151503Z                         auto tmp56 = tmp38 & tmp55;
2023-01-11T21:41:26.2151675Z                         auto tmp57 = tmp42 + tmp54;
2023-01-11T21:41:26.2151863Z                         auto tmp58 = tmp56 ? tmp57 : tmp42;
2023-01-11T21:41:26.2152056Z                         auto tmp59 = static_cast<int>(2);
2023-01-11T21:41:26.2152249Z                         auto tmp60 = tmp6 + tmp59;
2023-01-11T21:41:26.2152406Z                         auto tmp61 = tmp60 * tmp13;
2023-01-11T21:41:26.2152685Z                         auto tmp62 = tmp61 - tmp13;
2023-01-11T21:41:26.2152855Z                         auto tmp63 = tmp62 + tmp14;
2023-01-11T21:41:26.2153110Z                         auto tmp64 = (tmp23 != tmp23) ? tmp23 : std::min(tmp63, tmp23);
2023-01-11T21:41:26.2153348Z                         auto tmp65 = (tmp4 != tmp4) ? tmp4 : std::max(tmp62, tmp4);
2023-01-11T21:41:26.2153646Z                         auto tmp66 = (tmp9 != tmp9) ? tmp9 : std::min(tmp64, tmp9);
2023-01-11T21:41:26.2153918Z                         auto tmp67 = tmp66 - tmp65;
2023-01-11T21:41:26.2154093Z                         auto tmp68 = tmp29 * tmp67;
2023-01-11T21:41:26.2154320Z                         auto tmp69 = (tmp34 != tmp34) ? tmp34 : std::min(tmp60, tmp34);
2023-01-11T21:41:26.2154522Z                         auto tmp70 = in_ptr0[tmp69 + (15*tmp33)];
2023-01-11T21:41:26.2154696Z                         auto tmp71 = tmp70 / tmp68;
2023-01-11T21:41:26.2154871Z                         auto tmp72 = tmp60 < tmp10;
2023-01-11T21:41:26.2155047Z                         auto tmp73 = tmp38 & tmp72;
2023-01-11T21:41:26.2155218Z                         auto tmp74 = tmp58 + tmp71;
2023-01-11T21:41:26.2155417Z                         auto tmp75 = tmp73 ? tmp74 : tmp58;
2023-01-11T21:41:26.2155578Z                         auto tmp76 = tmp5 + tmp13;
2023-01-11T21:41:26.2155745Z                         auto tmp77 = tmp76 * tmp13;
2023-01-11T21:41:26.2156144Z                         auto tmp78 = tmp77 - tmp13;
2023-01-11T21:41:26.2156322Z                         auto tmp79 = tmp78 + tmp14;
2023-01-11T21:41:26.2156595Z                         auto tmp80 = (tmp20 != tmp20) ? tmp20 : std::min(tmp79, tmp20);
2023-01-11T21:41:26.2156832Z                         auto tmp81 = (tmp4 != tmp4) ? tmp4 : std::max(tmp78, tmp4);
2023-01-11T21:41:26.2157068Z                         auto tmp82 = (tmp7 != tmp7) ? tmp7 : std::min(tmp80, tmp7);
2023-01-11T21:41:26.2157340Z                         auto tmp83 = tmp82 - tmp81;
2023-01-11T21:41:26.2157491Z                         auto tmp84 = tmp83 * tmp30;
2023-01-11T21:41:26.2157726Z                         auto tmp85 = (tmp32 != tmp32) ? tmp32 : std::min(tmp76, tmp32);
2023-01-11T21:41:26.2158014Z                         auto tmp86 = in_ptr0[tmp35 + (15*tmp85)];
2023-01-11T21:41:26.2158194Z                         auto tmp87 = tmp86 / tmp84;
2023-01-11T21:41:26.2158379Z                         auto tmp88 = tmp76 < tmp8;
2023-01-11T21:41:26.2158546Z                         auto tmp89 = tmp88 & tmp39;
2023-01-11T21:41:26.2158716Z                         auto tmp90 = tmp75 + tmp87;
2023-01-11T21:41:26.2158915Z                         auto tmp91 = tmp89 ? tmp90 : tmp75;
2023-01-11T21:41:26.2159077Z                         auto tmp92 = tmp83 * tmp50;
2023-01-11T21:41:26.2159282Z                         auto tmp93 = in_ptr0[tmp52 + (15*tmp85)];
2023-01-11T21:41:26.2159449Z                         auto tmp94 = tmp93 / tmp92;
2023-01-11T21:41:26.2159624Z                         auto tmp95 = tmp88 & tmp55;
2023-01-11T21:41:26.2159790Z                         auto tmp96 = tmp91 + tmp94;
2023-01-11T21:41:26.2159985Z                         auto tmp97 = tmp95 ? tmp96 : tmp91;
2023-01-11T21:41:26.2160168Z                         auto tmp98 = tmp83 * tmp67;
2023-01-11T21:41:26.2160349Z                         auto tmp99 = in_ptr0[tmp69 + (15*tmp85)];
2023-01-11T21:41:26.2160524Z                         auto tmp100 = tmp99 / tmp98;
2023-01-11T21:41:26.2160712Z                         auto tmp101 = tmp88 & tmp72;
2023-01-11T21:41:26.2160892Z                         auto tmp102 = tmp97 + tmp100;
2023-01-11T21:41:26.2161092Z                         auto tmp103 = tmp101 ? tmp102 : tmp97;
2023-01-11T21:41:26.2161265Z                         auto tmp104 = tmp5 + tmp59;
2023-01-11T21:41:26.2161434Z                         auto tmp105 = tmp104 * tmp13;
2023-01-11T21:41:26.2161730Z                         auto tmp106 = tmp105 - tmp13;
2023-01-11T21:41:26.2161890Z                         auto tmp107 = tmp106 + tmp14;
2023-01-11T21:41:26.2162145Z                         auto tmp108 = (tmp20 != tmp20) ? tmp20 : std::min(tmp107, tmp20);
2023-01-11T21:41:26.2162382Z                         auto tmp109 = (tmp4 != tmp4) ? tmp4 : std::max(tmp106, tmp4);
2023-01-11T21:41:26.2162633Z                         auto tmp110 = (tmp7 != tmp7) ? tmp7 : std::min(tmp108, tmp7);
2023-01-11T21:41:26.2162923Z                         auto tmp111 = tmp110 - tmp109;
2023-01-11T21:41:26.2163102Z                         auto tmp112 = tmp111 * tmp30;
2023-01-11T21:41:26.2163345Z                         auto tmp113 = (tmp32 != tmp32) ? tmp32 : std::min(tmp104, tmp32);
2023-01-11T21:41:26.2163555Z                         auto tmp114 = in_ptr0[tmp35 + (15*tmp113)];
2023-01-11T21:41:26.2163720Z                         auto tmp115 = tmp114 / tmp112;
2023-01-11T21:41:26.2163908Z                         auto tmp116 = tmp104 < tmp8;
2023-01-11T21:41:26.2164087Z                         auto tmp117 = tmp116 & tmp39;
2023-01-11T21:41:26.2164268Z                         auto tmp118 = tmp103 + tmp115;
2023-01-11T21:41:26.2164462Z                         auto tmp119 = tmp117 ? tmp118 : tmp103;
2023-01-11T21:41:26.2164645Z                         auto tmp120 = tmp111 * tmp50;
2023-01-11T21:41:26.2164851Z                         auto tmp121 = in_ptr0[tmp52 + (15*tmp113)];
2023-01-11T21:41:26.2165033Z                         auto tmp122 = tmp121 / tmp120;
2023-01-11T21:41:26.2165313Z                         auto tmp123 = tmp116 & tmp55;
2023-01-11T21:41:26.2165491Z                         auto tmp124 = tmp119 + tmp122;
2023-01-11T21:41:26.2165698Z                         auto tmp125 = tmp123 ? tmp124 : tmp119;
2023-01-11T21:41:26.2165878Z                         auto tmp126 = tmp111 * tmp67;
2023-01-11T21:41:26.2166081Z                         auto tmp127 = in_ptr0[tmp69 + (15*tmp113)];
2023-01-11T21:41:26.2166249Z                         auto tmp128 = tmp127 / tmp126;
2023-01-11T21:41:26.2166422Z                         auto tmp129 = tmp116 & tmp72;
2023-01-11T21:41:26.2166583Z                         auto tmp130 = tmp125 + tmp128;
2023-01-11T21:41:26.2166786Z                         auto tmp131 = tmp129 ? tmp130 : tmp125;
2023-01-11T21:41:26.2166972Z                         out_ptr0[i1 + (15*i0)] = tmp131;
2023-01-11T21:41:26.2167182Z                     }
2023-01-11T21:41:26.2167314Z                 }
2023-01-11T21:41:26.2167439Z             }
2023-01-11T21:41:26.2167569Z         }
2023-01-11T21:41:26.2167673Z     }
2023-01-11T21:41:26.2167794Z }
2023-01-11T21:41:26.2167960Z ''')
2023-01-11T21:41:26.2167971Z 
2023-01-11T21:41:26.2167980Z 
2023-01-11T21:41:26.2168140Z async_compile.wait(globals())
2023-01-11T21:41:26.2168275Z del async_compile
2023-01-11T21:41:26.2168287Z 
2023-01-11T21:41:26.2168423Z def call(args):
2023-01-11T21:41:26.2168571Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2168697Z     args.clear()
2023-01-11T21:41:26.2169403Z     buf0 = empty_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2169669Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2169804Z     del arg0_1
2023-01-11T21:41:26.2169948Z     return (buf0, )
2023-01-11T21:41:26.2169957Z 
2023-01-11T21:41:26.2169965Z 
2023-01-11T21:41:26.2170117Z if __name__ == "__main__":
2023-01-11T21:41:26.2170326Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2170564Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2170970Z     arg0_1 = rand_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2171367Z     arg1_1 = rand_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2171591Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2172097Z [2023-01-11 21:24:42,936] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 34
2023-01-11T21:41:26.2172105Z 
2023-01-11T21:41:26.2172227Z ok (1.638s)
2023-01-11T21:41:26.2173110Z   test_avg_pool2d_backward3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2173351Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2173846Z [2023-01-11 21:24:42,967] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 35
2023-01-11T21:41:26.2174332Z [2023-01-11 21:24:44,699] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 35
2023-01-11T21:41:26.2174342Z 
2023-01-11T21:41:26.2174513Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2174630Z import torch
2023-01-11T21:41:26.2174772Z import random
2023-01-11T21:41:26.2174981Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2175197Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2175208Z 
2023-01-11T21:41:26.2175356Z aten = torch.ops.aten
2023-01-11T21:41:26.2175610Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2175793Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2175801Z 
2023-01-11T21:41:26.2175953Z 
2023-01-11T21:41:26.2176221Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2176576Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2176799Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2176980Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2177100Z {
2023-01-11T21:41:26.2177289Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2177407Z     {
2023-01-11T21:41:26.2177554Z         #pragma omp for 
2023-01-11T21:41:26.2177702Z         for(long i0=0; i0<2016; i0+=1)
2023-01-11T21:41:26.2177820Z         {
2023-01-11T21:41:26.2177972Z             #pragma GCC ivdep
2023-01-11T21:41:26.2178133Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:41:26.2178263Z             {
2023-01-11T21:41:26.2178541Z                 #pragma GCC ivdep
2023-01-11T21:41:26.2178719Z                 for(long i2=0; i2<21; i2+=1)
2023-01-11T21:41:26.2178818Z                 {
2023-01-11T21:41:26.2178955Z                     {
2023-01-11T21:41:26.2179090Z                         {
2023-01-11T21:41:26.2179306Z                             auto tmp0 = static_cast<int>(((1 + i1) / 2));
2023-01-11T21:41:26.2179510Z                             auto tmp1 = static_cast<int>(((1 + i2) / 2));
2023-01-11T21:41:26.2179717Z                             auto tmp2 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:41:26.2179922Z                             auto tmp3 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:41:26.2180105Z                             auto tmp4 = static_cast<int>(0);
2023-01-11T21:41:26.2180341Z                             auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:41:26.2180580Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:41:26.2180788Z                             auto tmp7 = static_cast<int>(11);
2023-01-11T21:41:26.2181022Z                             auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:41:26.2181262Z                             auto tmp9 = (tmp7 != tmp7) ? tmp7 : std::min(tmp3, tmp7);
2023-01-11T21:41:26.2181448Z                             auto tmp10 = tmp5 + tmp4;
2023-01-11T21:41:26.2181622Z                             auto tmp11 = tmp6 + tmp4;
2023-01-11T21:41:26.2181799Z                             auto tmp12 = static_cast<int>(1);
2023-01-11T21:41:26.2182109Z                             auto tmp13 = tmp8 - tmp12;
2023-01-11T21:41:26.2182351Z                             auto tmp14 = (tmp13 != tmp13) ? tmp13 : std::min(tmp10, tmp13);
2023-01-11T21:41:26.2182631Z                             auto tmp15 = tmp9 - tmp12;
2023-01-11T21:41:26.2182890Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp11, tmp15);
2023-01-11T21:41:26.2183114Z                             auto tmp17 = in_ptr0[tmp16 + (11*tmp14) + (121*i0)];
2023-01-11T21:41:26.2183371Z                             auto tmp18 = tmp17 / 1;
2023-01-11T21:41:26.2183552Z                             auto tmp19 = tmp10 < tmp8;
2023-01-11T21:41:26.2183721Z                             auto tmp20 = tmp11 < tmp9;
2023-01-11T21:41:26.2183905Z                             auto tmp21 = tmp19 & tmp20;
2023-01-11T21:41:26.2184104Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:26.2184299Z                             auto tmp23 = tmp21 ? tmp18 : tmp22;
2023-01-11T21:41:26.2184495Z                             out_ptr0[i2 + (21*i1) + (441*i0)] = tmp23;
2023-01-11T21:41:26.2184624Z                         }
2023-01-11T21:41:26.2184755Z                     }
2023-01-11T21:41:26.2184878Z                 }
2023-01-11T21:41:26.2184986Z             }
2023-01-11T21:41:26.2185112Z         }
2023-01-11T21:41:26.2185239Z     }
2023-01-11T21:41:26.2185368Z }
2023-01-11T21:41:26.2185520Z ''')
2023-01-11T21:41:26.2185535Z 
2023-01-11T21:41:26.2185547Z 
2023-01-11T21:41:26.2185724Z async_compile.wait(globals())
2023-01-11T21:41:26.2185867Z del async_compile
2023-01-11T21:41:26.2185977Z 
2023-01-11T21:41:26.2186103Z def call(args):
2023-01-11T21:41:26.2186244Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2186406Z     args.clear()
2023-01-11T21:41:26.2186849Z     buf0 = empty_strided((1, 2016, 21, 21), (889056, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2187104Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2187242Z     del arg0_1
2023-01-11T21:41:26.2187373Z     return (buf0, )
2023-01-11T21:41:26.2187381Z 
2023-01-11T21:41:26.2187387Z 
2023-01-11T21:41:26.2187519Z if __name__ == "__main__":
2023-01-11T21:41:26.2187739Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2187971Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2188500Z     arg0_1 = rand_strided((1, 2016, 11, 11), (243936, 121, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2188928Z     arg1_1 = rand_strided((1, 2016, 21, 21), (889056, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2189152Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2189160Z 
2023-01-11T21:41:26.2189295Z ok (1.791s)
2023-01-11T21:41:26.2190211Z   test_avg_pool2d_backward4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2190446Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2190850Z [2023-01-11 21:24:44,750] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 36
2023-01-11T21:41:26.2191301Z [2023-01-11 21:24:44,761] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.avg_pool2d_backward
2023-01-11T21:41:26.2191804Z [2023-01-11 21:24:44,763] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 36
2023-01-11T21:41:26.2191822Z 
2023-01-11T21:41:26.2192007Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2192146Z import torch
2023-01-11T21:41:26.2192273Z import random
2023-01-11T21:41:26.2192504Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2192739Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2192750Z 
2023-01-11T21:41:26.2192906Z aten = torch.ops.aten
2023-01-11T21:41:26.2193136Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2193315Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2193329Z 
2023-01-11T21:41:26.2193336Z 
2023-01-11T21:41:26.2193501Z async_compile.wait(globals())
2023-01-11T21:41:26.2193651Z del async_compile
2023-01-11T21:41:26.2193659Z 
2023-01-11T21:41:26.2193803Z def call(args):
2023-01-11T21:41:26.2193936Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2194070Z     args.clear()
2023-01-11T21:41:26.2194325Z     buf0 = aten.avg_pool2d_backward(arg0_1, arg1_1, [13, 13], [1, 1], [0, 0], True, False, None)
2023-01-11T21:41:26.2194455Z     del arg0_1
2023-01-11T21:41:26.2194592Z     del arg1_1
2023-01-11T21:41:26.2194722Z     buf1 = buf0
2023-01-11T21:41:26.2194928Z     assert_size_stride(buf1, (1, 16, 24, 24), (9216, 576, 24, 1))
2023-01-11T21:41:26.2195060Z     del buf0
2023-01-11T21:41:26.2195208Z     return (buf1, )
2023-01-11T21:41:26.2195215Z 
2023-01-11T21:41:26.2195222Z 
2023-01-11T21:41:26.2195370Z if __name__ == "__main__":
2023-01-11T21:41:26.2195568Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2195791Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2196299Z     arg0_1 = rand_strided((1, 16, 12, 12), (2304, 144, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2196658Z     arg1_1 = rand_strided((1, 16, 24, 24), (9216, 576, 24, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2196841Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2196960Z 
2023-01-11T21:41:26.2197078Z ok (0.036s)
2023-01-11T21:41:26.2197857Z   test_avg_pool2d_backward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2198053Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2198477Z [2023-01-11 21:24:44,785] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 37
2023-01-11T21:41:26.2198965Z [2023-01-11 21:24:46,434] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 37
2023-01-11T21:41:26.2199001Z 
2023-01-11T21:41:26.2199146Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2199268Z import torch
2023-01-11T21:41:26.2199388Z import random
2023-01-11T21:41:26.2199576Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2199774Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2199780Z 
2023-01-11T21:41:26.2199912Z aten = torch.ops.aten
2023-01-11T21:41:26.2200130Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2200266Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2200274Z 
2023-01-11T21:41:26.2200300Z 
2023-01-11T21:41:26.2200502Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2200832Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2201033Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2201197Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2201301Z {
2023-01-11T21:41:26.2201465Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2201573Z     {
2023-01-11T21:41:26.2201690Z         #pragma omp for 
2023-01-11T21:41:26.2201826Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2201936Z         {
2023-01-11T21:41:26.2202071Z             #pragma GCC ivdep
2023-01-11T21:41:26.2202210Z             for(long i1=0; i1<14; i1+=1)
2023-01-11T21:41:26.2202320Z             {
2023-01-11T21:41:26.2202445Z                 #pragma GCC ivdep
2023-01-11T21:41:26.2202597Z                 for(long i2=0; i2<14; i2+=1)
2023-01-11T21:41:26.2202715Z                 {
2023-01-11T21:41:26.2202822Z                     {
2023-01-11T21:41:26.2202934Z                         {
2023-01-11T21:41:26.2203123Z                             auto tmp0 = static_cast<int>((i1 / 2));
2023-01-11T21:41:26.2203297Z                             auto tmp1 = static_cast<int>((i2 / 2));
2023-01-11T21:41:26.2203473Z                             auto tmp2 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:41:26.2203653Z                             auto tmp3 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:41:26.2203834Z                             auto tmp4 = static_cast<int>(0);
2023-01-11T21:41:26.2204053Z                             auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:41:26.2204269Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:41:26.2204431Z                             auto tmp7 = static_cast<int>(7);
2023-01-11T21:41:26.2204644Z                             auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:41:26.2204852Z                             auto tmp9 = (tmp7 != tmp7) ? tmp7 : std::min(tmp3, tmp7);
2023-01-11T21:41:26.2205012Z                             auto tmp10 = tmp5 + tmp4;
2023-01-11T21:41:26.2205144Z                             auto tmp11 = tmp6 + tmp4;
2023-01-11T21:41:26.2205318Z                             auto tmp12 = static_cast<int>(1);
2023-01-11T21:41:26.2205593Z                             auto tmp13 = tmp8 - tmp12;
2023-01-11T21:41:26.2205811Z                             auto tmp14 = (tmp13 != tmp13) ? tmp13 : std::min(tmp10, tmp13);
2023-01-11T21:41:26.2206147Z                             auto tmp15 = tmp9 - tmp12;
2023-01-11T21:41:26.2206364Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp11, tmp15);
2023-01-11T21:41:26.2206559Z                             auto tmp17 = in_ptr0[tmp16 + (7*tmp14) + (49*i0)];
2023-01-11T21:41:26.2206714Z                             auto tmp18 = tmp17 / 4;
2023-01-11T21:41:26.2206845Z                             auto tmp19 = tmp10 < tmp8;
2023-01-11T21:41:26.2207004Z                             auto tmp20 = tmp11 < tmp9;
2023-01-11T21:41:26.2207166Z                             auto tmp21 = tmp19 & tmp20;
2023-01-11T21:41:26.2207356Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:26.2207592Z                             auto tmp23 = tmp21 ? tmp18 : tmp22;
2023-01-11T21:41:26.2207775Z                             out_ptr0[i2 + (14*i1) + (196*i0)] = tmp23;
2023-01-11T21:41:26.2207905Z                         }
2023-01-11T21:41:26.2208002Z                     }
2023-01-11T21:41:26.2208115Z                 }
2023-01-11T21:41:26.2208230Z             }
2023-01-11T21:41:26.2208332Z         }
2023-01-11T21:41:26.2208437Z     }
2023-01-11T21:41:26.2208541Z }
2023-01-11T21:41:26.2208683Z ''')
2023-01-11T21:41:26.2208691Z 
2023-01-11T21:41:26.2208697Z 
2023-01-11T21:41:26.2208830Z async_compile.wait(globals())
2023-01-11T21:41:26.2208953Z del async_compile
2023-01-11T21:41:26.2208960Z 
2023-01-11T21:41:26.2209202Z def call(args):
2023-01-11T21:41:26.2209327Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2209453Z     args.clear()
2023-01-11T21:41:26.2209824Z     buf0 = empty_strided((2, 4, 14, 14), (784, 196, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2210045Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2210135Z     del arg0_1
2023-01-11T21:41:26.2210255Z     return (buf0, )
2023-01-11T21:41:26.2210265Z 
2023-01-11T21:41:26.2210276Z 
2023-01-11T21:41:26.2210406Z if __name__ == "__main__":
2023-01-11T21:41:26.2210593Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2210794Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2211144Z     arg0_1 = rand_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2211508Z     arg1_1 = rand_strided((2, 4, 14, 14), (784, 196, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2211688Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2211694Z 
2023-01-11T21:41:26.2211802Z ok (1.699s)
2023-01-11T21:41:26.2212544Z   test_baddbmm_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2212759Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2213199Z [2023-01-11 21:24:46,521] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 38
2023-01-11T21:41:26.2213639Z [2023-01-11 21:24:48,229] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 38
2023-01-11T21:41:26.2213647Z 
2023-01-11T21:41:26.2213808Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2213933Z import torch
2023-01-11T21:41:26.2214049Z import random
2023-01-11T21:41:26.2214241Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2214444Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2214450Z 
2023-01-11T21:41:26.2214569Z aten = torch.ops.aten
2023-01-11T21:41:26.2214793Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2214938Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2214944Z 
2023-01-11T21:41:26.2215089Z 
2023-01-11T21:41:26.2215318Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2215645Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2215824Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.2216005Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:41:26.2216111Z {
2023-01-11T21:41:26.2216258Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2216358Z     {
2023-01-11T21:41:26.2216504Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2216640Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.2216750Z         {
2023-01-11T21:41:26.2216895Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.2216991Z             {
2023-01-11T21:41:26.2217143Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:41:26.2217337Z                 {
2023-01-11T21:41:26.2217602Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (100*i0));
2023-01-11T21:41:26.2217862Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + (8*i2) + (100*i1) + (12800*i0));
2023-01-11T21:41:26.2218018Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2218194Z                     tmp2.store(in_out_ptr0 + (8*i2) + (100*i1) + (12800*i0));
2023-01-11T21:41:26.2218318Z                 }
2023-01-11T21:41:26.2218468Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.2218624Z                 for(long i2=96; i2<100; i2+=1)
2023-01-11T21:41:26.2218736Z                 {
2023-01-11T21:41:26.2218897Z                     auto tmp0 = in_ptr0[i2 + (100*i0)];
2023-01-11T21:41:26.2219070Z                     auto tmp1 = in_out_ptr0[i2 + (100*i1) + (12800*i0)];
2023-01-11T21:41:26.2219232Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2219412Z                     in_out_ptr0[i2 + (100*i1) + (12800*i0)] = tmp2;
2023-01-11T21:41:26.2219508Z                 }
2023-01-11T21:41:26.2219618Z             }
2023-01-11T21:41:26.2219724Z         }
2023-01-11T21:41:26.2219822Z     }
2023-01-11T21:41:26.2219930Z }
2023-01-11T21:41:26.2220075Z ''')
2023-01-11T21:41:26.2220083Z 
2023-01-11T21:41:26.2220089Z 
2023-01-11T21:41:26.2220239Z async_compile.wait(globals())
2023-01-11T21:41:26.2220346Z del async_compile
2023-01-11T21:41:26.2220371Z 
2023-01-11T21:41:26.2220471Z def call(args):
2023-01-11T21:41:26.2220606Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.2220732Z     args.clear()
2023-01-11T21:41:26.2221087Z     buf0 = empty_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2221248Z     aten.bmm.out(arg1_1, arg2_1, out=buf0)
2023-01-11T21:41:26.2221364Z     del arg1_1
2023-01-11T21:41:26.2221441Z     del arg2_1
2023-01-11T21:41:26.2221583Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.2221810Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:26.2221931Z     del arg0_1
2023-01-11T21:41:26.2222057Z     return (buf1, )
2023-01-11T21:41:26.2222068Z 
2023-01-11T21:41:26.2222074Z 
2023-01-11T21:41:26.2222203Z if __name__ == "__main__":
2023-01-11T21:41:26.2222385Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2222590Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2222924Z     arg0_1 = rand_strided((6, 1, 100), (100, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2223341Z     arg1_1 = rand_strided((6, 128, 64), (8192, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2223697Z     arg2_1 = rand_strided((6, 64, 100), (6400, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2223884Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.2223893Z 
2023-01-11T21:41:26.2224003Z ok (1.771s)
2023-01-11T21:41:26.2224760Z   test_batch_norm_2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2225075Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2225517Z [2023-01-11 21:24:48,543] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 39
2023-01-11T21:41:26.2225953Z [2023-01-11 21:24:50,318] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 39
2023-01-11T21:41:26.2226757Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2226973Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2227387Z [2023-01-11 21:24:50,608] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 40
2023-01-11T21:41:26.2227394Z 
2023-01-11T21:41:26.2227550Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2227673Z import torch
2023-01-11T21:41:26.2227791Z import random
2023-01-11T21:41:26.2227975Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2228181Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2228188Z 
2023-01-11T21:41:26.2228327Z aten = torch.ops.aten
2023-01-11T21:41:26.2228528Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2228689Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2228695Z 
2023-01-11T21:41:26.2228702Z 
2023-01-11T21:41:26.2228933Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2229274Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2229465Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2229643Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2229807Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.2229977Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:26.2230147Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:41:26.2230282Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2230444Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.2230610Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.2230769Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:41:26.2230876Z {
2023-01-11T21:41:26.2231034Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2231151Z     {
2023-01-11T21:41:26.2231267Z         #pragma omp for 
2023-01-11T21:41:26.2231402Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.2231512Z         {
2023-01-11T21:41:26.2231739Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2231891Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2232001Z         }
2023-01-11T21:41:26.2232151Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2232292Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.2232392Z         {
2023-01-11T21:41:26.2232528Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2232660Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.2232765Z         }
2023-01-11T21:41:26.2232900Z         #pragma omp for 
2023-01-11T21:41:26.2233019Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.2233129Z         {
2023-01-11T21:41:26.2233356Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.2233500Z             tmp0.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.2233609Z         }
2023-01-11T21:41:26.2233778Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2234027Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.2234115Z         {
2023-01-11T21:41:26.2234250Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.2234375Z             out_ptr1[i0] = tmp0;
2023-01-11T21:41:26.2234489Z         }
2023-01-11T21:41:26.2234648Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2234785Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.2234891Z         {
2023-01-11T21:41:26.2235016Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.2235119Z             {
2023-01-11T21:41:26.2235273Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:26.2235393Z                 {
2023-01-11T21:41:26.2235645Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + (8*i2) + (64*i1) + (640*i0));
2023-01-11T21:41:26.2235919Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i1]);
2023-01-11T21:41:26.2236140Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr1[i1]);
2023-01-11T21:41:26.2236355Z                     auto tmp11 = at::vec::Vectorized<float>(in_ptr3[i1]);
2023-01-11T21:41:26.2236547Z                     auto tmp13 = at::vec::Vectorized<float>(in_ptr4[i1]);
2023-01-11T21:41:26.2236794Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.2237157Z                     auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:41:26.2237317Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.2237460Z                     auto tmp6 = tmp5.sqrt();
2023-01-11T21:41:26.2237632Z                     auto tmp7 = tmp6.reciprocal();
2023-01-11T21:41:26.2237861Z                     auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.2238016Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.2238163Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:41:26.2238317Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:41:26.2238477Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.2238699Z                     auto tmp15 = at::vec::clamp_min(tmp14, decltype(tmp14)(0));
2023-01-11T21:41:26.2238888Z                     tmp15.store(out_ptr2 + (8*i2) + (64*i1) + (640*i0));
2023-01-11T21:41:26.2239004Z                 }
2023-01-11T21:41:26.2239161Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.2239295Z                 for(long i2=64; i2<64; i2+=1)
2023-01-11T21:41:26.2239407Z                 {
2023-01-11T21:41:26.2239581Z                     auto tmp0 = in_ptr2[i2 + (64*i1) + (640*i0)];
2023-01-11T21:41:26.2239737Z                     auto tmp1 = out_ptr0[i1];
2023-01-11T21:41:26.2239883Z                     auto tmp3 = out_ptr1[i1];
2023-01-11T21:41:26.2240038Z                     auto tmp11 = in_ptr3[i1];
2023-01-11T21:41:26.2240187Z                     auto tmp13 = in_ptr4[i1];
2023-01-11T21:41:26.2240414Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.2240668Z                     auto tmp4 = static_cast<float>(1e-05);
2023-01-11T21:41:26.2240822Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.2240999Z                     auto tmp6 = std::sqrt(tmp5);
2023-01-11T21:41:26.2241154Z                     auto tmp7 = 1 / tmp6;
2023-01-11T21:41:26.2241332Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:41:26.2241477Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.2241632Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:41:26.2241777Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:41:26.2241929Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.2242096Z                     auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:41:26.2242262Z                     out_ptr2[i2 + (64*i1) + (640*i0)] = tmp15;
2023-01-11T21:41:26.2242381Z                 }
2023-01-11T21:41:26.2242496Z             }
2023-01-11T21:41:26.2242609Z         }
2023-01-11T21:41:26.2242729Z         #pragma omp for 
2023-01-11T21:41:26.2242867Z         for(long i0=0; i0<1280; i0+=1)
2023-01-11T21:41:26.2243064Z         {
2023-01-11T21:41:26.2243175Z             {
2023-01-11T21:41:26.2243290Z                 {
2023-01-11T21:41:26.2243450Z                     auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:26.2243611Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:26.2243767Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:41:26.2243898Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.2244006Z                 }
2023-01-11T21:41:26.2244118Z             }
2023-01-11T21:41:26.2244232Z         }
2023-01-11T21:41:26.2244340Z     }
2023-01-11T21:41:26.2244423Z }
2023-01-11T21:41:26.2244568Z ''')
2023-01-11T21:41:26.2244576Z 
2023-01-11T21:41:26.2244582Z 
2023-01-11T21:41:26.2244730Z async_compile.wait(globals())
2023-01-11T21:41:26.2244863Z del async_compile
2023-01-11T21:41:26.2244871Z 
2023-01-11T21:41:26.2245023Z def call(args):
2023-01-11T21:41:26.2245211Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
2023-01-11T21:41:26.2245308Z     args.clear()
2023-01-11T21:41:26.2245564Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2245815Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2246103Z     buf2 = empty_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2246375Z     buf3 = empty_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2246811Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(primals_6.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.2246907Z     del primals_1
2023-01-11T21:41:26.2247001Z     del primals_2
2023-01-11T21:41:26.2247093Z     del primals_3
2023-01-11T21:41:26.2247185Z     del primals_4
2023-01-11T21:41:26.2247322Z     return (buf0, buf1, buf2, primals_6, buf0, buf1, buf3, )
2023-01-11T21:41:26.2247328Z 
2023-01-11T21:41:26.2247333Z 
2023-01-11T21:41:26.2247429Z if __name__ == "__main__":
2023-01-11T21:41:26.2247574Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2247731Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2247994Z     primals_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2248257Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2248524Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2248786Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2249131Z     primals_5 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2249424Z     primals_6 = rand_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2249651Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]))
2023-01-11T21:41:26.2249657Z 
2023-01-11T21:41:26.2250012Z [2023-01-11 21:24:52,269] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 40
2023-01-11T21:41:26.2250019Z 
2023-01-11T21:41:26.2250139Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2250226Z import torch
2023-01-11T21:41:26.2250320Z import random
2023-01-11T21:41:26.2250467Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2250607Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2250613Z 
2023-01-11T21:41:26.2250713Z aten = torch.ops.aten
2023-01-11T21:41:26.2250879Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2250996Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2251005Z 
2023-01-11T21:41:26.2251011Z 
2023-01-11T21:41:26.2251188Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2251515Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2251668Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2251804Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2251923Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.2252055Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:26.2252186Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:41:26.2252313Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2252438Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.2252561Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.2252684Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:41:26.2252763Z {
2023-01-11T21:41:26.2252921Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2253001Z     {
2023-01-11T21:41:26.2253100Z         #pragma omp for 
2023-01-11T21:41:26.2253209Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.2253293Z         {
2023-01-11T21:41:26.2253472Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2253580Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2253661Z         }
2023-01-11T21:41:26.2253784Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2253892Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.2253974Z         {
2023-01-11T21:41:26.2254081Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2254187Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.2254252Z         }
2023-01-11T21:41:26.2254349Z         #pragma omp for 
2023-01-11T21:41:26.2254454Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.2254536Z         {
2023-01-11T21:41:26.2254708Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.2254823Z             tmp0.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.2254909Z         }
2023-01-11T21:41:26.2255016Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2255120Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.2255199Z         {
2023-01-11T21:41:26.2255303Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.2255402Z             out_ptr1[i0] = tmp0;
2023-01-11T21:41:26.2255483Z         }
2023-01-11T21:41:26.2255596Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2255685Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2255763Z         {
2023-01-11T21:41:26.2255869Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.2255952Z             {
2023-01-11T21:41:26.2256063Z                 for(long i2=0; i2<32; i2+=1)
2023-01-11T21:41:26.2256147Z                 {
2023-01-11T21:41:26.2256344Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + (8*i2) + (256*i1) + (2560*i0));
2023-01-11T21:41:26.2256494Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i1]);
2023-01-11T21:41:26.2256659Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr1[i1]);
2023-01-11T21:41:26.2256819Z                     auto tmp11 = at::vec::Vectorized<float>(in_ptr3[i1]);
2023-01-11T21:41:26.2256979Z                     auto tmp13 = at::vec::Vectorized<float>(in_ptr4[i1]);
2023-01-11T21:41:26.2257157Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.2257439Z                     auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:41:26.2257559Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.2257678Z                     auto tmp6 = tmp5.sqrt();
2023-01-11T21:41:26.2257795Z                     auto tmp7 = tmp6.reciprocal();
2023-01-11T21:41:26.2257970Z                     auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.2258089Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.2258205Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:41:26.2258323Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:41:26.2258480Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.2258652Z                     auto tmp15 = at::vec::clamp_min(tmp14, decltype(tmp14)(0));
2023-01-11T21:41:26.2258797Z                     tmp15.store(out_ptr2 + (8*i2) + (256*i1) + (2560*i0));
2023-01-11T21:41:26.2258867Z                 }
2023-01-11T21:41:26.2258989Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.2259103Z                 for(long i2=256; i2<256; i2+=1)
2023-01-11T21:41:26.2259189Z                 {
2023-01-11T21:41:26.2259325Z                     auto tmp0 = in_ptr2[i2 + (256*i1) + (2560*i0)];
2023-01-11T21:41:26.2259445Z                     auto tmp1 = out_ptr0[i1];
2023-01-11T21:41:26.2259566Z                     auto tmp3 = out_ptr1[i1];
2023-01-11T21:41:26.2259667Z                     auto tmp11 = in_ptr3[i1];
2023-01-11T21:41:26.2259813Z                     auto tmp13 = in_ptr4[i1];
2023-01-11T21:41:26.2259987Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.2260202Z                     auto tmp4 = static_cast<float>(1e-05);
2023-01-11T21:41:26.2260319Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.2260447Z                     auto tmp6 = std::sqrt(tmp5);
2023-01-11T21:41:26.2260555Z                     auto tmp7 = 1 / tmp6;
2023-01-11T21:41:26.2260673Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:41:26.2260789Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.2260904Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:41:26.2261020Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:41:26.2261139Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.2261263Z                     auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:41:26.2261395Z                     out_ptr2[i2 + (256*i1) + (2560*i0)] = tmp15;
2023-01-11T21:41:26.2261483Z                 }
2023-01-11T21:41:26.2261550Z             }
2023-01-11T21:41:26.2261629Z         }
2023-01-11T21:41:26.2261734Z         #pragma omp for 
2023-01-11T21:41:26.2261841Z         for(long i0=0; i0<7680; i0+=1)
2023-01-11T21:41:26.2261922Z         {
2023-01-11T21:41:26.2262003Z             {
2023-01-11T21:41:26.2262072Z                 {
2023-01-11T21:41:26.2262211Z                     auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:26.2262373Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:26.2262534Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:41:26.2262676Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.2262766Z                 }
2023-01-11T21:41:26.2262878Z             }
2023-01-11T21:41:26.2262969Z         }
2023-01-11T21:41:26.2263068Z     }
2023-01-11T21:41:26.2263321Z }
2023-01-11T21:41:26.2263458Z ''')
2023-01-11T21:41:26.2263468Z 
2023-01-11T21:41:26.2263475Z 
2023-01-11T21:41:26.2263626Z async_compile.wait(globals())
2023-01-11T21:41:26.2263738Z del async_compile
2023-01-11T21:41:26.2263746Z 
2023-01-11T21:41:26.2263868Z def call(args):
2023-01-11T21:41:26.2264082Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
2023-01-11T21:41:26.2264204Z     args.clear()
2023-01-11T21:41:26.2264528Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2264843Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2265199Z     buf2 = empty_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2265551Z     buf3 = empty_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2266078Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(primals_6.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.2266205Z     del primals_1
2023-01-11T21:41:26.2266329Z     del primals_2
2023-01-11T21:41:26.2266518Z     del primals_3
2023-01-11T21:41:26.2266641Z     del primals_4
2023-01-11T21:41:26.2266824Z     return (buf0, buf1, buf2, primals_6, buf0, buf1, buf3, )
2023-01-11T21:41:26.2266831Z 
2023-01-11T21:41:26.2266837Z 
2023-01-11T21:41:26.2266971Z if __name__ == "__main__":
2023-01-11T21:41:26.2267162Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2267367Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2267700Z     primals_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2268019Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2268345Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2268680Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2269065Z     primals_5 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2269453Z     primals_6 = rand_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2269737Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]))
2023-01-11T21:41:26.2269744Z 
2023-01-11T21:41:26.2269856Z ok (4.036s)
2023-01-11T21:41:26.2270618Z   test_bernoulli1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2270823Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2271260Z [2023-01-11 21:24:52,308] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 41
2023-01-11T21:41:26.2271689Z [2023-01-11 21:24:53,808] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 41
2023-01-11T21:41:26.2271720Z 
2023-01-11T21:41:26.2271868Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2271980Z import torch
2023-01-11T21:41:26.2272100Z import random
2023-01-11T21:41:26.2272294Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2272496Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2272506Z 
2023-01-11T21:41:26.2272635Z aten = torch.ops.aten
2023-01-11T21:41:26.2272853Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2272988Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2272999Z 
2023-01-11T21:41:26.2273005Z 
2023-01-11T21:41:26.2273230Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2273566Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2273753Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2273868Z {
2023-01-11T21:41:26.2274035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2274142Z     {
2023-01-11T21:41:26.2274252Z         #pragma omp for 
2023-01-11T21:41:26.2274391Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.2274493Z         {
2023-01-11T21:41:26.2274728Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:26.2274882Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2274989Z         }
2023-01-11T21:41:26.2275143Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2275265Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.2275370Z         {
2023-01-11T21:41:26.2275533Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:26.2275676Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.2275782Z         }
2023-01-11T21:41:26.2275888Z     }
2023-01-11T21:41:26.2275998Z }
2023-01-11T21:41:26.2276115Z ''')
2023-01-11T21:41:26.2276122Z 
2023-01-11T21:41:26.2276129Z 
2023-01-11T21:41:26.2276279Z async_compile.wait(globals())
2023-01-11T21:41:26.2276495Z del async_compile
2023-01-11T21:41:26.2276501Z 
2023-01-11T21:41:26.2276624Z def call(args):
2023-01-11T21:41:26.2276745Z     arg0_1, = args
2023-01-11T21:41:26.2276862Z     args.clear()
2023-01-11T21:41:26.2277192Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2277340Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2277481Z     aten.bernoulli_(buf0, )
2023-01-11T21:41:26.2277608Z     return (buf0, buf0, )
2023-01-11T21:41:26.2277615Z 
2023-01-11T21:41:26.2277622Z 
2023-01-11T21:41:26.2277753Z if __name__ == "__main__":
2023-01-11T21:41:26.2277943Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2278144Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2278534Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2278715Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2278723Z 
2023-01-11T21:41:26.2278820Z ok (1.538s)
2023-01-11T21:41:26.2279589Z   test_bernoulli2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2279796Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2280229Z [2023-01-11 21:24:53,833] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 42
2023-01-11T21:41:26.2280660Z [2023-01-11 21:24:53,833] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.2281092Z [2023-01-11 21:24:55,452] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 42
2023-01-11T21:41:26.2281100Z 
2023-01-11T21:41:26.2281260Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2281382Z import torch
2023-01-11T21:41:26.2281496Z import random
2023-01-11T21:41:26.2281691Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2281872Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2281884Z 
2023-01-11T21:41:26.2282001Z aten = torch.ops.aten
2023-01-11T21:41:26.2282222Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2282384Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2282639Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.2282647Z 
2023-01-11T21:41:26.2282654Z 
2023-01-11T21:41:26.2282882Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2283214Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2283403Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:26.2283569Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2283741Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:26.2283844Z {
2023-01-11T21:41:26.2284013Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2284111Z     {
2023-01-11T21:41:26.2284243Z         #pragma omp for 
2023-01-11T21:41:26.2284384Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2284473Z         {
2023-01-11T21:41:26.2284576Z             {
2023-01-11T21:41:26.2284685Z                 {
2023-01-11T21:41:26.2284828Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.2284978Z                     auto tmp5 = in_ptr1[i0];
2023-01-11T21:41:26.2285154Z                     auto tmp1 = static_cast<int>(65535);
2023-01-11T21:41:26.2285310Z                     auto tmp2 = tmp0 ^ tmp1;
2023-01-11T21:41:26.2285463Z                     auto tmp3 = static_cast<int>(i0);
2023-01-11T21:41:26.2285692Z                     auto tmp4 = static_cast<float>(normalized_rand_cpu(tmp2, tmp3));;
2023-01-11T21:41:26.2285936Z                     auto tmp6 = tmp4 < tmp5;
2023-01-11T21:41:26.2286073Z                     out_ptr0[i0] = tmp6;
2023-01-11T21:41:26.2286185Z                 }
2023-01-11T21:41:26.2286298Z             }
2023-01-11T21:41:26.2286406Z         }
2023-01-11T21:41:26.2286497Z     }
2023-01-11T21:41:26.2286603Z }
2023-01-11T21:41:26.2286747Z ''')
2023-01-11T21:41:26.2286754Z 
2023-01-11T21:41:26.2286760Z 
2023-01-11T21:41:26.2286911Z async_compile.wait(globals())
2023-01-11T21:41:26.2287037Z del async_compile
2023-01-11T21:41:26.2287046Z 
2023-01-11T21:41:26.2287163Z def call(args):
2023-01-11T21:41:26.2287280Z     arg0_1, = args
2023-01-11T21:41:26.2287382Z     args.clear()
2023-01-11T21:41:26.2287601Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.2287979Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2288270Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2288394Z     del arg0_1
2023-01-11T21:41:26.2288521Z     return (buf0, )
2023-01-11T21:41:26.2288528Z 
2023-01-11T21:41:26.2288535Z 
2023-01-11T21:41:26.2288664Z if __name__ == "__main__":
2023-01-11T21:41:26.2288852Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2289158Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2289504Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2289825Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2290013Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2290022Z 
2023-01-11T21:41:26.2290133Z ok (1.643s)
2023-01-11T21:41:26.2290893Z   test_bitwise2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2291111Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2291544Z [2023-01-11 21:24:55,476] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 43
2023-01-11T21:41:26.2291974Z [2023-01-11 21:24:56,955] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 43
2023-01-11T21:41:26.2291981Z 
2023-01-11T21:41:26.2292122Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2292242Z import torch
2023-01-11T21:41:26.2292365Z import random
2023-01-11T21:41:26.2292550Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2292748Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2292759Z 
2023-01-11T21:41:26.2292892Z aten = torch.ops.aten
2023-01-11T21:41:26.2293117Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2293260Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2293287Z 
2023-01-11T21:41:26.2293294Z 
2023-01-11T21:41:26.2293501Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2293825Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2294024Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.2294196Z                        const bool* __restrict__ in_ptr1,
2023-01-11T21:41:26.2294350Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:26.2294514Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:41:26.2294669Z                        bool* __restrict__ out_ptr2,
2023-01-11T21:41:26.2294808Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:41:26.2294919Z {
2023-01-11T21:41:26.2295085Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2295187Z     {
2023-01-11T21:41:26.2295323Z         #pragma omp for 
2023-01-11T21:41:26.2295616Z         for(long i0=0; i0<40; i0+=1)
2023-01-11T21:41:26.2295729Z         {
2023-01-11T21:41:26.2295819Z             {
2023-01-11T21:41:26.2295927Z                 {
2023-01-11T21:41:26.2296084Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2296236Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.2296384Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:41:26.2296538Z                     auto tmp3 = tmp0 | tmp2;
2023-01-11T21:41:26.2296682Z                     auto tmp4 = tmp0 ^ tmp2;
2023-01-11T21:41:26.2296813Z                     auto tmp5 = tmp0 & tmp2;
2023-01-11T21:41:26.2296956Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.2297095Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.2297239Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.2297448Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:26.2297562Z                 }
2023-01-11T21:41:26.2297672Z             }
2023-01-11T21:41:26.2297766Z         }
2023-01-11T21:41:26.2297864Z     }
2023-01-11T21:41:26.2297968Z }
2023-01-11T21:41:26.2298119Z ''')
2023-01-11T21:41:26.2298129Z 
2023-01-11T21:41:26.2298135Z 
2023-01-11T21:41:26.2298281Z async_compile.wait(globals())
2023-01-11T21:41:26.2298406Z del async_compile
2023-01-11T21:41:26.2298412Z 
2023-01-11T21:41:26.2298533Z def call(args):
2023-01-11T21:41:26.2298644Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2298772Z     args.clear()
2023-01-11T21:41:26.2299096Z     buf0 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2299411Z     buf1 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2299724Z     buf2 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2300041Z     buf3 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2300433Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.2300549Z     del arg0_1
2023-01-11T21:41:26.2300649Z     del arg1_1
2023-01-11T21:41:26.2300794Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:26.2300802Z 
2023-01-11T21:41:26.2300807Z 
2023-01-11T21:41:26.2300928Z if __name__ == "__main__":
2023-01-11T21:41:26.2301117Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2301318Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2301639Z     arg0_1 = rand_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2301952Z     arg1_1 = rand_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2302138Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2302146Z 
2023-01-11T21:41:26.2302241Z ok (1.503s)
2023-01-11T21:41:26.2302999Z   test_bitwise_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2303310Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2303753Z [2023-01-11 21:24:56,977] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 44
2023-01-11T21:41:26.2304189Z [2023-01-11 21:24:58,656] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 44
2023-01-11T21:41:26.2304197Z 
2023-01-11T21:41:26.2304355Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2304477Z import torch
2023-01-11T21:41:26.2304597Z import random
2023-01-11T21:41:26.2304794Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2304977Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2304984Z 
2023-01-11T21:41:26.2305209Z aten = torch.ops.aten
2023-01-11T21:41:26.2305428Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2305585Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2305595Z 
2023-01-11T21:41:26.2305602Z 
2023-01-11T21:41:26.2305827Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2306151Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2306350Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.2306532Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.2306674Z                        int* __restrict__ out_ptr0,
2023-01-11T21:41:26.2306835Z                        int* __restrict__ out_ptr1,
2023-01-11T21:41:26.2306982Z                        int* __restrict__ out_ptr2,
2023-01-11T21:41:26.2307200Z                        int* __restrict__ out_ptr3)
2023-01-11T21:41:26.2307303Z {
2023-01-11T21:41:26.2307475Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2307576Z     {
2023-01-11T21:41:26.2307687Z         #pragma omp for 
2023-01-11T21:41:26.2307818Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.2307930Z         {
2023-01-11T21:41:26.2308041Z             {
2023-01-11T21:41:26.2308156Z                 {
2023-01-11T21:41:26.2308316Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2308461Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.2308583Z                     auto tmp1 = ~tmp0;
2023-01-11T21:41:26.2308736Z                     auto tmp3 = tmp0 | tmp2;
2023-01-11T21:41:26.2308889Z                     auto tmp4 = tmp0 ^ tmp2;
2023-01-11T21:41:26.2309045Z                     auto tmp5 = tmp0 & tmp2;
2023-01-11T21:41:26.2309183Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.2309323Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.2309467Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.2309587Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:26.2309704Z                 }
2023-01-11T21:41:26.2309813Z             }
2023-01-11T21:41:26.2309918Z         }
2023-01-11T21:41:26.2310014Z     }
2023-01-11T21:41:26.2310121Z }
2023-01-11T21:41:26.2310242Z ''')
2023-01-11T21:41:26.2310255Z 
2023-01-11T21:41:26.2310279Z 
2023-01-11T21:41:26.2310416Z async_compile.wait(globals())
2023-01-11T21:41:26.2310546Z del async_compile
2023-01-11T21:41:26.2310554Z 
2023-01-11T21:41:26.2310676Z def call(args):
2023-01-11T21:41:26.2310795Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2310916Z     args.clear()
2023-01-11T21:41:26.2311243Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.2311551Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.2311848Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.2312173Z     buf3 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.2312550Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.2312673Z     del arg0_1
2023-01-11T21:41:26.2312787Z     del arg1_1
2023-01-11T21:41:26.2312939Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:26.2312947Z 
2023-01-11T21:41:26.2312954Z 
2023-01-11T21:41:26.2313078Z if __name__ == "__main__":
2023-01-11T21:41:26.2313268Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2313458Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2313773Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.2314058Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.2314215Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2314222Z 
2023-01-11T21:41:26.2314312Z ok (1.701s)
2023-01-11T21:41:26.2314943Z   test_bmm1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2315198Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2315554Z [2023-01-11 21:24:58,680] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 45
2023-01-11T21:41:26.2315922Z [2023-01-11 21:25:00,364] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 45
2023-01-11T21:41:26.2316560Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2316735Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2317072Z [2023-01-11 21:25:00,388] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 46
2023-01-11T21:41:26.2317433Z [2023-01-11 21:25:01,942] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 46
2023-01-11T21:41:26.2317439Z 
2023-01-11T21:41:26.2317565Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2317664Z import torch
2023-01-11T21:41:26.2317758Z import random
2023-01-11T21:41:26.2317909Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2318070Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2318076Z 
2023-01-11T21:41:26.2318185Z aten = torch.ops.aten
2023-01-11T21:41:26.2318350Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2318475Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2318481Z 
2023-01-11T21:41:26.2318487Z 
2023-01-11T21:41:26.2318669Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2318933Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2319091Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2319230Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2319361Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2319484Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2319546Z {
2023-01-11T21:41:26.2319670Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2319761Z     {
2023-01-11T21:41:26.2319887Z         #pragma omp for 
2023-01-11T21:41:26.2320016Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.2320125Z         {
2023-01-11T21:41:26.2320342Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2320575Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.2320719Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2320876Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2320981Z         }
2023-01-11T21:41:26.2321140Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2321281Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.2321386Z         {
2023-01-11T21:41:26.2321515Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2321748Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.2321931Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2322088Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.2322384Z         }
2023-01-11T21:41:26.2322574Z         #pragma omp for 
2023-01-11T21:41:26.2322768Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.2322912Z         {
2023-01-11T21:41:26.2323128Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.2323466Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.2323643Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2323827Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.2323966Z         }
2023-01-11T21:41:26.2324218Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2324393Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.2324485Z         {
2023-01-11T21:41:26.2324662Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.2324870Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.2325044Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2325216Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.2325353Z         }
2023-01-11T21:41:26.2325509Z     }
2023-01-11T21:41:26.2325593Z }
2023-01-11T21:41:26.2325818Z ''')
2023-01-11T21:41:26.2325890Z 
2023-01-11T21:41:26.2325900Z 
2023-01-11T21:41:26.2326204Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2326609Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2326840Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.2326971Z {
2023-01-11T21:41:26.2327186Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2327274Z     {
2023-01-11T21:41:26.2327438Z         #pragma omp for 
2023-01-11T21:41:26.2327611Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.2327814Z         {
2023-01-11T21:41:26.2328095Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.2328359Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.2328539Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2328752Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.2328844Z         }
2023-01-11T21:41:26.2329163Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2329329Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.2329465Z         {
2023-01-11T21:41:26.2329721Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.2329906Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.2330017Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2330176Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.2330312Z         }
2023-01-11T21:41:26.2330441Z     }
2023-01-11T21:41:26.2330570Z }
2023-01-11T21:41:26.2330738Z ''')
2023-01-11T21:41:26.2330746Z 
2023-01-11T21:41:26.2330752Z 
2023-01-11T21:41:26.2330922Z async_compile.wait(globals())
2023-01-11T21:41:26.2331017Z del async_compile
2023-01-11T21:41:26.2331112Z 
2023-01-11T21:41:26.2331206Z def call(args):
2023-01-11T21:41:26.2331353Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2331509Z     args.clear()
2023-01-11T21:41:26.2331884Z     buf0 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2332064Z     aten.bmm.out(arg0_1, arg1_1, out=buf0)
2023-01-11T21:41:26.2332418Z     buf1 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2332776Z     buf2 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2333057Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2333196Z     del arg0_1
2023-01-11T21:41:26.2333407Z     del arg1_1
2023-01-11T21:41:26.2333775Z     buf3 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2333948Z     aten.bmm.out(buf1, buf2, out=buf3)
2023-01-11T21:41:26.2334079Z     del buf1
2023-01-11T21:41:26.2334210Z     del buf2
2023-01-11T21:41:26.2334325Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:41:26.2334512Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.2334664Z     return (buf0, buf4, )
2023-01-11T21:41:26.2334677Z 
2023-01-11T21:41:26.2334684Z 
2023-01-11T21:41:26.2334831Z if __name__ == "__main__":
2023-01-11T21:41:26.2335097Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2335433Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2335805Z     arg0_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2336159Z     arg1_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2336319Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2336328Z 
2023-01-11T21:41:26.2336383Z 
2023-01-11T21:41:26.2336511Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2336647Z import torch
2023-01-11T21:41:26.2336790Z import random
2023-01-11T21:41:26.2337041Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2337308Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2337316Z 
2023-01-11T21:41:26.2337468Z aten = torch.ops.aten
2023-01-11T21:41:26.2337789Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2337913Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2337924Z 
2023-01-11T21:41:26.2337929Z 
2023-01-11T21:41:26.2338189Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2338541Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2338768Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2338965Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2339146Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2339378Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2339506Z {
2023-01-11T21:41:26.2339644Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2339768Z     {
2023-01-11T21:41:26.2339920Z         #pragma omp for 
2023-01-11T21:41:26.2340085Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.2340223Z         {
2023-01-11T21:41:26.2340498Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2340742Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.2340854Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2341071Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2341203Z         }
2023-01-11T21:41:26.2341381Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2341553Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.2341681Z         {
2023-01-11T21:41:26.2341838Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2341978Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.2342140Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2342295Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.2342464Z         }
2023-01-11T21:41:26.2342619Z         #pragma omp for 
2023-01-11T21:41:26.2342787Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.2342872Z         {
2023-01-11T21:41:26.2343113Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.2343421Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.2343611Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2343786Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.2343913Z         }
2023-01-11T21:41:26.2344135Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2344307Z         for(long i0=80; i0<80; i0+=1)
2023-01-11T21:41:26.2344388Z         {
2023-01-11T21:41:26.2344549Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.2344731Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.2344889Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2345039Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.2345173Z         }
2023-01-11T21:41:26.2345251Z     }
2023-01-11T21:41:26.2345376Z }
2023-01-11T21:41:26.2345596Z ''')
2023-01-11T21:41:26.2345604Z 
2023-01-11T21:41:26.2345615Z 
2023-01-11T21:41:26.2345867Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2346226Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2346520Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.2346674Z {
2023-01-11T21:41:26.2346860Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2346936Z     {
2023-01-11T21:41:26.2347090Z         #pragma omp for 
2023-01-11T21:41:26.2347257Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:26.2347452Z         {
2023-01-11T21:41:26.2347703Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.2347943Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.2348104Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2348233Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.2348359Z         }
2023-01-11T21:41:26.2348620Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2348789Z         for(long i0=160; i0<160; i0+=1)
2023-01-11T21:41:26.2348919Z         {
2023-01-11T21:41:26.2349134Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.2349322Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.2349435Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2349595Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.2349752Z         }
2023-01-11T21:41:26.2349888Z     }
2023-01-11T21:41:26.2350014Z }
2023-01-11T21:41:26.2350179Z ''')
2023-01-11T21:41:26.2350188Z 
2023-01-11T21:41:26.2350195Z 
2023-01-11T21:41:26.2350370Z async_compile.wait(globals())
2023-01-11T21:41:26.2350467Z del async_compile
2023-01-11T21:41:26.2350475Z 
2023-01-11T21:41:26.2350660Z def call(args):
2023-01-11T21:41:26.2350808Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2350952Z     args.clear()
2023-01-11T21:41:26.2351341Z     buf0 = empty_strided((1, 16, 10), (160, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2351635Z     aten.mm.out(as_strided(arg0_1, (16, 8), (8, 1)), as_strided(arg1_1, (8, 10), (10, 1)), out=as_strided(buf0, (16, 10), (10, 1)))
2023-01-11T21:41:26.2352001Z     buf1 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2352359Z     buf2 = empty_strided((1, 8, 10), (80, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2352640Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2352782Z     del arg0_1
2023-01-11T21:41:26.2352961Z     del arg1_1
2023-01-11T21:41:26.2353328Z     buf3 = empty_strided((1, 16, 10), (160, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2353624Z     aten.mm.out(as_strided(buf1, (16, 8), (8, 1)), as_strided(buf2, (8, 10), (10, 1)), out=as_strided(buf3, (16, 10), (10, 1)))
2023-01-11T21:41:26.2353782Z     del buf1
2023-01-11T21:41:26.2353914Z     del buf2
2023-01-11T21:41:26.2354033Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:41:26.2354225Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.2354377Z     return (buf0, buf4, )
2023-01-11T21:41:26.2354390Z 
2023-01-11T21:41:26.2354396Z 
2023-01-11T21:41:26.2354547Z if __name__ == "__main__":
2023-01-11T21:41:26.2354798Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2355039Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2355411Z     arg0_1 = rand_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2355728Z     arg1_1 = rand_strided((1, 8, 10), (80, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2355891Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2355948Z 
2023-01-11T21:41:26.2356035Z ok (3.286s)
2023-01-11T21:41:26.2356821Z   test_bmm2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2357126Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2357592Z [2023-01-11 21:25:01,960] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 47
2023-01-11T21:41:26.2358121Z [2023-01-11 21:25:01,963] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 47
2023-01-11T21:41:26.2358130Z 
2023-01-11T21:41:26.2358313Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2358484Z import torch
2023-01-11T21:41:26.2358626Z import random
2023-01-11T21:41:26.2358793Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2359018Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2359025Z 
2023-01-11T21:41:26.2359186Z aten = torch.ops.aten
2023-01-11T21:41:26.2359501Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2359682Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2359693Z 
2023-01-11T21:41:26.2359700Z 
2023-01-11T21:41:26.2359907Z async_compile.wait(globals())
2023-01-11T21:41:26.2360053Z del async_compile
2023-01-11T21:41:26.2360060Z 
2023-01-11T21:41:26.2360201Z def call(args):
2023-01-11T21:41:26.2360302Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2360447Z     args.clear()
2023-01-11T21:41:26.2360818Z     buf0 = empty_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2361115Z     aten.mm.out(as_strided(arg0_1, (8, 8), (1, 8)), as_strided(arg1_1, (8, 8), (8, 1)), out=as_strided(buf0, (8, 8), (8, 1)))
2023-01-11T21:41:26.2361253Z     del arg0_1
2023-01-11T21:41:26.2361389Z     del arg1_1
2023-01-11T21:41:26.2361562Z     return (buf0, )
2023-01-11T21:41:26.2361570Z 
2023-01-11T21:41:26.2361576Z 
2023-01-11T21:41:26.2361751Z if __name__ == "__main__":
2023-01-11T21:41:26.2361920Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2362145Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2362515Z     arg0_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2362884Z     arg1_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2363096Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2363104Z 
2023-01-11T21:41:26.2363241Z ok (0.019s)
2023-01-11T21:41:26.2364023Z   test_bool_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2364295Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2364751Z [2023-01-11 21:25:01,994] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 48
2023-01-11T21:41:26.2365169Z [2023-01-11 21:25:03,657] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 48
2023-01-11T21:41:26.2365226Z 
2023-01-11T21:41:26.2365357Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2365508Z import torch
2023-01-11T21:41:26.2365650Z import random
2023-01-11T21:41:26.2365863Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2366084Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2366092Z 
2023-01-11T21:41:26.2366247Z aten = torch.ops.aten
2023-01-11T21:41:26.2366556Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2366685Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2366691Z 
2023-01-11T21:41:26.2366746Z 
2023-01-11T21:41:26.2366947Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2367312Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2367601Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.2367794Z                        const bool* __restrict__ in_ptr1,
2023-01-11T21:41:26.2367975Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:26.2368156Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:41:26.2368337Z                        bool* __restrict__ out_ptr2,
2023-01-11T21:41:26.2368468Z                        bool* __restrict__ out_ptr3,
2023-01-11T21:41:26.2368675Z                        bool* __restrict__ out_ptr4,
2023-01-11T21:41:26.2368866Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:41:26.2369163Z                        bool* __restrict__ out_ptr6,
2023-01-11T21:41:26.2369340Z                        bool* __restrict__ out_ptr7,
2023-01-11T21:41:26.2369517Z                        bool* __restrict__ out_ptr8)
2023-01-11T21:41:26.2369728Z {
2023-01-11T21:41:26.2369870Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2370000Z     {
2023-01-11T21:41:26.2370199Z         #pragma omp for 
2023-01-11T21:41:26.2370394Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.2370524Z         {
2023-01-11T21:41:26.2370660Z             {
2023-01-11T21:41:26.2370795Z                 {
2023-01-11T21:41:26.2370923Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2371095Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.2371270Z                     auto tmp2 = tmp0 || tmp1;
2023-01-11T21:41:26.2371450Z                     auto tmp3 = tmp0 && tmp1;
2023-01-11T21:41:26.2371593Z                     auto tmp4 = tmp0 & tmp1;
2023-01-11T21:41:26.2371759Z                     auto tmp5 = tmp0 | tmp1;
2023-01-11T21:41:26.2371906Z                     auto tmp6 = tmp0 ^ tmp1;
2023-01-11T21:41:26.2372004Z                     auto tmp7 = tmp0 == 0;
2023-01-11T21:41:26.2372145Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.2372280Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.2372413Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.2372559Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:26.2372713Z                     out_ptr4[i0] = tmp6;
2023-01-11T21:41:26.2372845Z                     out_ptr5[i0] = tmp3;
2023-01-11T21:41:26.2372937Z                     out_ptr6[i0] = tmp2;
2023-01-11T21:41:26.2373092Z                     out_ptr7[i0] = tmp7;
2023-01-11T21:41:26.2373224Z                     out_ptr8[i0] = tmp1;
2023-01-11T21:41:26.2373337Z                 }
2023-01-11T21:41:26.2373448Z             }
2023-01-11T21:41:26.2373564Z         }
2023-01-11T21:41:26.2373630Z     }
2023-01-11T21:41:26.2373734Z }
2023-01-11T21:41:26.2373880Z ''')
2023-01-11T21:41:26.2373886Z 
2023-01-11T21:41:26.2373891Z 
2023-01-11T21:41:26.2374036Z async_compile.wait(globals())
2023-01-11T21:41:26.2374180Z del async_compile
2023-01-11T21:41:26.2374186Z 
2023-01-11T21:41:26.2374310Z def call(args):
2023-01-11T21:41:26.2374435Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2374564Z     args.clear()
2023-01-11T21:41:26.2374807Z     buf0 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2375078Z     buf1 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2375344Z     buf2 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2375638Z     buf3 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2375905Z     buf4 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2376197Z     buf5 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2376467Z     buf6 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2376693Z     buf7 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2376970Z     buf8 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2377450Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:41:26.2377642Z     del arg0_1
2023-01-11T21:41:26.2377758Z     del arg1_1
2023-01-11T21:41:26.2377938Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, )
2023-01-11T21:41:26.2377944Z 
2023-01-11T21:41:26.2377949Z 
2023-01-11T21:41:26.2378077Z if __name__ == "__main__":
2023-01-11T21:41:26.2378276Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2378472Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2378704Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2378979Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.2379197Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2379204Z 
2023-01-11T21:41:26.2379371Z ok (1.696s)
2023-01-11T21:41:26.2379850Z   test_both_scalars_cpu (__main__.CpuTests) ... [2023-01-11 21:25:03,720] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 49
2023-01-11T21:41:26.2380229Z [2023-01-11 21:25:05,338] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 49
2023-01-11T21:41:26.2380236Z 
2023-01-11T21:41:26.2380394Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2380538Z import torch
2023-01-11T21:41:26.2380615Z import random
2023-01-11T21:41:26.2380794Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2380979Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2380985Z 
2023-01-11T21:41:26.2381111Z aten = torch.ops.aten
2023-01-11T21:41:26.2381310Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2381456Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2381462Z 
2023-01-11T21:41:26.2381468Z 
2023-01-11T21:41:26.2381678Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2381966Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2382162Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2382273Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.2382422Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.2382568Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.2382715Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.2382902Z                        float* __restrict__ out_ptr5)
2023-01-11T21:41:26.2383219Z {
2023-01-11T21:41:26.2383326Z     {
2023-01-11T21:41:26.2383393Z         {
2023-01-11T21:41:26.2383545Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:41:26.2383725Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:41:26.2383864Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2383992Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.2384103Z         }
2023-01-11T21:41:26.2384169Z     }
2023-01-11T21:41:26.2384278Z     {
2023-01-11T21:41:26.2384381Z         {
2023-01-11T21:41:26.2384534Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:41:26.2384683Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:26.2384836Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2384961Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.2385027Z         }
2023-01-11T21:41:26.2385129Z     }
2023-01-11T21:41:26.2385280Z     {
2023-01-11T21:41:26.2385382Z         {
2023-01-11T21:41:26.2385531Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:41:26.2385680Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:41:26.2385867Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.2385952Z             out_ptr2[0] = tmp2;
2023-01-11T21:41:26.2386076Z         }
2023-01-11T21:41:26.2386182Z     }
2023-01-11T21:41:26.2386292Z     {
2023-01-11T21:41:26.2386395Z         {
2023-01-11T21:41:26.2386565Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:41:26.2386752Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:26.2386997Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.2387153Z             out_ptr3[0] = tmp2;
2023-01-11T21:41:26.2387287Z         }
2023-01-11T21:41:26.2387457Z     }
2023-01-11T21:41:26.2387598Z     {
2023-01-11T21:41:26.2387751Z         {
2023-01-11T21:41:26.2387894Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:41:26.2388075Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:41:26.2388235Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.2388388Z             out_ptr4[0] = tmp2;
2023-01-11T21:41:26.2388523Z         }
2023-01-11T21:41:26.2388643Z     }
2023-01-11T21:41:26.2388832Z     {
2023-01-11T21:41:26.2388919Z         {
2023-01-11T21:41:26.2389182Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:41:26.2389366Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:26.2389525Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.2389671Z             out_ptr5[0] = tmp2;
2023-01-11T21:41:26.2389795Z         }
2023-01-11T21:41:26.2389873Z     }
2023-01-11T21:41:26.2390009Z }
2023-01-11T21:41:26.2390179Z ''')
2023-01-11T21:41:26.2390186Z 
2023-01-11T21:41:26.2390193Z 
2023-01-11T21:41:26.2390415Z async_compile.wait(globals())
2023-01-11T21:41:26.2390580Z del async_compile
2023-01-11T21:41:26.2390588Z 
2023-01-11T21:41:26.2390729Z def call(args):
2023-01-11T21:41:26.2391069Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2391388Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2391668Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2392002Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2392334Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2392652Z     buf5 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2393110Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:26.2393305Z     return (buf0, buf1, buf2, buf3, buf4, buf5, )
2023-01-11T21:41:26.2393314Z 
2023-01-11T21:41:26.2393320Z 
2023-01-11T21:41:26.2450829Z if __name__ == "__main__":
2023-01-11T21:41:26.2451275Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2451455Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2451630Z     print_performance(lambda: call([]))
2023-01-11T21:41:26.2451640Z 
2023-01-11T21:41:26.2451755Z ok (1.681s)
2023-01-11T21:41:26.2452394Z   test_cat_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2452574Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2452992Z [2023-01-11 21:25:05,391] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 50
2023-01-11T21:41:26.2453365Z [2023-01-11 21:25:06,995] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 50
2023-01-11T21:41:26.2453941Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2454116Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2454624Z [2023-01-11 21:25:07,045] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 51
2023-01-11T21:41:26.2454986Z [2023-01-11 21:25:08,715] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 51
2023-01-11T21:41:26.2454994Z 
2023-01-11T21:41:26.2455131Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2455232Z import torch
2023-01-11T21:41:26.2455336Z import random
2023-01-11T21:41:26.2455497Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2455664Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2455672Z 
2023-01-11T21:41:26.2455783Z aten = torch.ops.aten
2023-01-11T21:41:26.2455952Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2456083Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2456091Z 
2023-01-11T21:41:26.2456156Z 
2023-01-11T21:41:26.2456355Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2456628Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2456797Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2456937Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2457074Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.2457227Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.2457346Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.2457485Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.2457625Z                        double* __restrict__ out_ptr5,
2023-01-11T21:41:26.2457758Z                        double* __restrict__ out_ptr6)
2023-01-11T21:41:26.2457845Z {
2023-01-11T21:41:26.2457982Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2458070Z     {
2023-01-11T21:41:26.2458169Z         #pragma omp for 
2023-01-11T21:41:26.2458284Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2458375Z         {
2023-01-11T21:41:26.2458496Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.2458587Z             {
2023-01-11T21:41:26.2458797Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (16*i0));
2023-01-11T21:41:26.2458986Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.2459130Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2459309Z                 tmp0.store(out_ptr0 + (8*i1) + (36*i0));
2023-01-11T21:41:26.2459453Z                 tmp2.store(out_ptr1 + (8*i1) + (36*i0));
2023-01-11T21:41:26.2459566Z             }
2023-01-11T21:41:26.2459698Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.2459824Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:41:26.2459920Z             {
2023-01-11T21:41:26.2460041Z                 auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:26.2460183Z                 auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.2460310Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2460445Z                 out_ptr0[i1 + (36*i0)] = tmp0;
2023-01-11T21:41:26.2460572Z                 out_ptr1[i1 + (36*i0)] = tmp2;
2023-01-11T21:41:26.2460663Z             }
2023-01-11T21:41:26.2460772Z         }
2023-01-11T21:41:26.2460866Z         #pragma omp for 
2023-01-11T21:41:26.2460982Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2461073Z         {
2023-01-11T21:41:26.2461190Z             #pragma GCC ivdep
2023-01-11T21:41:26.2461326Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.2461439Z             {
2023-01-11T21:41:26.2461532Z                 {
2023-01-11T21:41:26.2461612Z                     {
2023-01-11T21:41:26.2461750Z                         auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:26.2461897Z                         auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.2462029Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2462168Z                         out_ptr2[i1 + (36*i0)] = tmp2;
2023-01-11T21:41:26.2462311Z                     }
2023-01-11T21:41:26.2462407Z                 }
2023-01-11T21:41:26.2462482Z             }
2023-01-11T21:41:26.2462574Z         }
2023-01-11T21:41:26.2462685Z         #pragma omp for 
2023-01-11T21:41:26.2462802Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.2462895Z         {
2023-01-11T21:41:26.2462988Z             {
2023-01-11T21:41:26.2463065Z                 {
2023-01-11T21:41:26.2463270Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2463412Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.2463543Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.2463695Z                     auto tmp3 = static_cast<double>(tmp2);
2023-01-11T21:41:26.2463819Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.2463940Z                     out_ptr4[i0] = tmp2;
2023-01-11T21:41:26.2464083Z                     out_ptr5[i0] = tmp3;
2023-01-11T21:41:26.2464208Z                     out_ptr6[i0] = tmp3;
2023-01-11T21:41:26.2464310Z                 }
2023-01-11T21:41:26.2464401Z             }
2023-01-11T21:41:26.2464492Z         }
2023-01-11T21:41:26.2464582Z     }
2023-01-11T21:41:26.2464670Z }
2023-01-11T21:41:26.2464774Z ''')
2023-01-11T21:41:26.2464780Z 
2023-01-11T21:41:26.2464786Z 
2023-01-11T21:41:26.2464934Z async_compile.wait(globals())
2023-01-11T21:41:26.2465036Z del async_compile
2023-01-11T21:41:26.2465043Z 
2023-01-11T21:41:26.2465165Z def call(args):
2023-01-11T21:41:26.2465267Z     arg0_1, = args
2023-01-11T21:41:26.2465373Z     args.clear()
2023-01-11T21:41:26.2465661Z     buf3 = empty_strided((8, 36), (36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2465807Z     buf0 = as_strided(buf3, (8, 16), (36, 1))  # alias
2023-01-11T21:41:26.2465934Z     buf2 = as_strided(buf3, (8, 16), (36, 1), 20)  # alias
2023-01-11T21:41:26.2466077Z     buf1 = as_strided(buf3, (8, 4), (36, 1), 16)  # alias
2023-01-11T21:41:26.2466367Z     buf6 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2466510Z     buf4 = as_strided(buf6, (8, 16), (16, 1))  # alias
2023-01-11T21:41:26.2466656Z     buf5 = as_strided(buf6, (8, 16), (16, 1), 128)  # alias
2023-01-11T21:41:26.2466934Z     buf9 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.2467074Z     buf7 = as_strided(buf9, (8, 16), (16, 1))  # alias
2023-01-11T21:41:26.2467203Z     buf8 = as_strided(buf9, (8, 16), (16, 1), 128)  # alias
2023-01-11T21:41:26.2467583Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:41:26.2467683Z     del arg0_1
2023-01-11T21:41:26.2467806Z     return (buf3, buf6, buf9, )
2023-01-11T21:41:26.2467813Z 
2023-01-11T21:41:26.2467818Z 
2023-01-11T21:41:26.2467932Z if __name__ == "__main__":
2023-01-11T21:41:26.2468097Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2468275Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2468554Z     arg0_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2468709Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2468717Z 
2023-01-11T21:41:26.2468721Z 
2023-01-11T21:41:26.2468836Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2468952Z import torch
2023-01-11T21:41:26.2469051Z import random
2023-01-11T21:41:26.2469214Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2469383Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2469390Z 
2023-01-11T21:41:26.2469500Z aten = torch.ops.aten
2023-01-11T21:41:26.2469687Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2469798Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2469827Z 
2023-01-11T21:41:26.2469833Z 
2023-01-11T21:41:26.2470007Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2470276Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2470492Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2470640Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2470785Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.2470935Z                        const double* __restrict__ in_ptr3,
2023-01-11T21:41:26.2471076Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2471194Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.2471330Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.2471459Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.2471592Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.2471764Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:26.2471909Z                        double* __restrict__ out_ptr6,
2023-01-11T21:41:26.2472052Z                        double* __restrict__ out_ptr7,
2023-01-11T21:41:26.2472192Z                        float* __restrict__ out_ptr8,
2023-01-11T21:41:26.2472313Z                        double* __restrict__ out_ptr9)
2023-01-11T21:41:26.2472405Z {
2023-01-11T21:41:26.2472542Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2472636Z     {
2023-01-11T21:41:26.2472770Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2472887Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2472979Z         {
2023-01-11T21:41:26.2473089Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.2473202Z             {
2023-01-11T21:41:26.2473351Z                 #pragma GCC ivdep
2023-01-11T21:41:26.2473512Z                 for(long i2=0; i2<16; i2+=1)
2023-01-11T21:41:26.2473631Z                 {
2023-01-11T21:41:26.2473758Z                     {
2023-01-11T21:41:26.2473869Z                         {
2023-01-11T21:41:26.2474065Z                             auto tmp0 = in_ptr0[i0 + (3*i2) + (48*i1)];
2023-01-11T21:41:26.2474266Z                             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.2474440Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2474635Z                             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.2474808Z                             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.2474997Z                             out_ptr0[i2 + (48*i1) + (144*i0)] = tmp0;
2023-01-11T21:41:26.2475183Z                             out_ptr1[i2 + (48*i1) + (144*i0)] = tmp2;
2023-01-11T21:41:26.2475346Z                             out_ptr2[i2 + (48*i1) + (144*i0)] = tmp4;
2023-01-11T21:41:26.2475470Z                         }
2023-01-11T21:41:26.2496261Z                     }
2023-01-11T21:41:26.2496402Z                 }
2023-01-11T21:41:26.2496483Z             }
2023-01-11T21:41:26.2496564Z         }
2023-01-11T21:41:26.2496695Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2496791Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2496874Z         {
2023-01-11T21:41:26.2496981Z             for(long i1=0; i1<144; i1+=1)
2023-01-11T21:41:26.2497059Z             {
2023-01-11T21:41:26.2497145Z                 {
2023-01-11T21:41:26.2497218Z                     {
2023-01-11T21:41:26.2497347Z                         auto tmp0 = in_ptr1[i1 + (144*i0)];
2023-01-11T21:41:26.2497465Z                         out_ptr3[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.2497547Z                     }
2023-01-11T21:41:26.2497624Z                 }
2023-01-11T21:41:26.2497698Z             }
2023-01-11T21:41:26.2497773Z         }
2023-01-11T21:41:26.2497879Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2497980Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2498055Z         {
2023-01-11T21:41:26.2498158Z             for(long i1=0; i1<48; i1+=1)
2023-01-11T21:41:26.2498237Z             {
2023-01-11T21:41:26.2498316Z                 {
2023-01-11T21:41:26.2498390Z                     {
2023-01-11T21:41:26.2498639Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.2498769Z                         auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.2498885Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.2499004Z                         out_ptr4[i1 + (48*i0)] = tmp2;
2023-01-11T21:41:26.2499084Z                     }
2023-01-11T21:41:26.2499165Z                 }
2023-01-11T21:41:26.2499233Z             }
2023-01-11T21:41:26.2499308Z         }
2023-01-11T21:41:26.2499404Z         #pragma omp for 
2023-01-11T21:41:26.2499505Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:41:26.2499580Z         {
2023-01-11T21:41:26.2499656Z             {
2023-01-11T21:41:26.2499733Z                 {
2023-01-11T21:41:26.2499842Z                     auto tmp0 = out_ptr4[i0];
2023-01-11T21:41:26.2500030Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.2500141Z                     out_ptr5[i0] = tmp0;
2023-01-11T21:41:26.2500245Z                     out_ptr6[i0] = tmp1;
2023-01-11T21:41:26.2500354Z                     out_ptr7[i0] = tmp1;
2023-01-11T21:41:26.2500435Z                 }
2023-01-11T21:41:26.2500511Z             }
2023-01-11T21:41:26.2500578Z         }
2023-01-11T21:41:26.2500693Z         #pragma omp for  collapse(3)
2023-01-11T21:41:26.2500790Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.2500865Z         {
2023-01-11T21:41:26.2500969Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.2501047Z             {
2023-01-11T21:41:26.2501156Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:41:26.2501228Z                 {
2023-01-11T21:41:26.2501306Z                     {
2023-01-11T21:41:26.2501392Z                         {
2023-01-11T21:41:26.2501530Z                             auto tmp0 = in_ptr2[i2 + (48*i1) + (144*i0)];
2023-01-11T21:41:26.2501667Z                             out_ptr8[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:41:26.2501754Z                         }
2023-01-11T21:41:26.2501835Z                     }
2023-01-11T21:41:26.2501912Z                 }
2023-01-11T21:41:26.2501989Z             }
2023-01-11T21:41:26.2502065Z         }
2023-01-11T21:41:26.2502178Z         #pragma omp for  collapse(3)
2023-01-11T21:41:26.2502278Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.2502353Z         {
2023-01-11T21:41:26.2502448Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.2502523Z             {
2023-01-11T21:41:26.2502632Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:41:26.2502710Z                 {
2023-01-11T21:41:26.2502790Z                     {
2023-01-11T21:41:26.2502872Z                         {
2023-01-11T21:41:26.2503008Z                             auto tmp0 = in_ptr3[i2 + (48*i1) + (144*i0)];
2023-01-11T21:41:26.2503192Z                             out_ptr9[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:41:26.2503281Z                         }
2023-01-11T21:41:26.2503362Z                     }
2023-01-11T21:41:26.2503439Z                 }
2023-01-11T21:41:26.2503519Z             }
2023-01-11T21:41:26.2503594Z         }
2023-01-11T21:41:26.2503669Z     }
2023-01-11T21:41:26.2503735Z }
2023-01-11T21:41:26.2503869Z ''')
2023-01-11T21:41:26.2503878Z 
2023-01-11T21:41:26.2503883Z 
2023-01-11T21:41:26.2503996Z async_compile.wait(globals())
2023-01-11T21:41:26.2504087Z del async_compile
2023-01-11T21:41:26.2504093Z 
2023-01-11T21:41:26.2504179Z def call(args):
2023-01-11T21:41:26.2504265Z     arg0_1, = args
2023-01-11T21:41:26.2504353Z     args.clear()
2023-01-11T21:41:26.2504640Z     buf3 = empty_strided((1, 3, 3, 48), (432, 144, 48, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2504780Z     buf0 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1))  # alias
2023-01-11T21:41:26.2504918Z     buf1 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1), 16)  # alias
2023-01-11T21:41:26.2505058Z     buf2 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1), 32)  # alias
2023-01-11T21:41:26.2505344Z     buf4 = empty_strided((1, 3, 3, 48), (432, 1, 144, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2505675Z     buf7 = empty_strided((2, 3, 3, 16), (144, 48, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2505812Z     buf5 = as_strided(buf7, (1, 3, 3, 16), (144, 48, 16, 1))  # alias
2023-01-11T21:41:26.2505949Z     buf6 = as_strided(buf7, (1, 3, 3, 16), (144, 48, 16, 1), 144)  # alias
2023-01-11T21:41:26.2506224Z     buf11 = empty_strided((2, 3, 3, 16), (144, 48, 16, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.2506362Z     buf9 = as_strided(buf11, (1, 3, 3, 16), (144, 48, 16, 1))  # alias
2023-01-11T21:41:26.2506501Z     buf10 = as_strided(buf11, (1, 3, 3, 16), (144, 48, 16, 1), 144)  # alias
2023-01-11T21:41:26.2506778Z     buf8 = empty_strided((2, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2507100Z     buf12 = empty_strided((2, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.2507648Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf12.data_ptr()))
2023-01-11T21:41:26.2507739Z     del arg0_1
2023-01-11T21:41:26.2507820Z     del buf0
2023-01-11T21:41:26.2507902Z     del buf1
2023-01-11T21:41:26.2507976Z     del buf10
2023-01-11T21:41:26.2508055Z     del buf11
2023-01-11T21:41:26.2508135Z     del buf2
2023-01-11T21:41:26.2508215Z     del buf3
2023-01-11T21:41:26.2508296Z     del buf5
2023-01-11T21:41:26.2508374Z     del buf6
2023-01-11T21:41:26.2508444Z     del buf7
2023-01-11T21:41:26.2508521Z     del buf9
2023-01-11T21:41:26.2508628Z     return (buf4, buf8, buf12, )
2023-01-11T21:41:26.2508634Z 
2023-01-11T21:41:26.2508642Z 
2023-01-11T21:41:26.2508738Z if __name__ == "__main__":
2023-01-11T21:41:26.2508881Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2509040Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2509325Z     arg0_1 = rand_strided((1, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2509461Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2509468Z 
2023-01-11T21:41:26.2509541Z ok (3.376s)
2023-01-11T21:41:26.2510164Z   test_cat_extern_kernel_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2510329Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2510679Z [2023-01-11 21:25:08,760] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 52
2023-01-11T21:41:26.2511045Z [2023-01-11 21:25:10,266] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 52
2023-01-11T21:41:26.2511052Z 
2023-01-11T21:41:26.2511170Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2511257Z import torch
2023-01-11T21:41:26.2511344Z import random
2023-01-11T21:41:26.2511490Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2511634Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2511641Z 
2023-01-11T21:41:26.2511738Z aten = torch.ops.aten
2023-01-11T21:41:26.2511905Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2512018Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2512025Z 
2023-01-11T21:41:26.2512030Z 
2023-01-11T21:41:26.2512205Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2512464Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2512661Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2512785Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2512852Z {
2023-01-11T21:41:26.2512973Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2513045Z     {
2023-01-11T21:41:26.2513140Z         #pragma omp for 
2023-01-11T21:41:26.2513243Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.2513319Z         {
2023-01-11T21:41:26.2513425Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:26.2513495Z             {
2023-01-11T21:41:26.2513688Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (256*i0));
2023-01-11T21:41:26.2513816Z                 tmp0.store(out_ptr0 + (8*i1) + (512*i0));
2023-01-11T21:41:26.2513893Z             }
2023-01-11T21:41:26.2514010Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.2514179Z             for(long i1=256; i1<256; i1+=1)
2023-01-11T21:41:26.2514260Z             {
2023-01-11T21:41:26.2514374Z                 auto tmp0 = in_ptr0[i1 + (256*i0)];
2023-01-11T21:41:26.2514491Z                 out_ptr0[i1 + (512*i0)] = tmp0;
2023-01-11T21:41:26.2514567Z             }
2023-01-11T21:41:26.2514644Z         }
2023-01-11T21:41:26.2514718Z     }
2023-01-11T21:41:26.2514794Z }
2023-01-11T21:41:26.2514895Z ''')
2023-01-11T21:41:26.2514902Z 
2023-01-11T21:41:26.2514908Z 
2023-01-11T21:41:26.2515010Z async_compile.wait(globals())
2023-01-11T21:41:26.2515101Z del async_compile
2023-01-11T21:41:26.2515107Z 
2023-01-11T21:41:26.2515191Z def call(args):
2023-01-11T21:41:26.2515302Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.2515389Z     args.clear()
2023-01-11T21:41:26.2515663Z     buf0 = empty_strided((256, 1600), (1600, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2515781Z     aten.mm.out(arg1_1, arg2_1, out=buf0)
2023-01-11T21:41:26.2515881Z     del arg1_1
2023-01-11T21:41:26.2515975Z     del arg2_1
2023-01-11T21:41:26.2516251Z     buf3 = empty_strided((256, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2516395Z     buf1 = as_strided(buf3, (256, 256), (512, 1))  # alias
2023-01-11T21:41:26.2516539Z     aten.mm.out(as_strided(buf0, (256, 100), (1600, 1)), arg3_1, out=buf1)
2023-01-11T21:41:26.2516630Z     del arg3_1
2023-01-11T21:41:26.2516717Z     del buf0
2023-01-11T21:41:26.2516860Z     buf2 = as_strided(buf3, (256, 256), (512, 1), 256)  # alias
2023-01-11T21:41:26.2517039Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2517136Z     del arg0_1
2023-01-11T21:41:26.2517235Z     return (buf3, )
2023-01-11T21:41:26.2517242Z 
2023-01-11T21:41:26.2517247Z 
2023-01-11T21:41:26.2517332Z if __name__ == "__main__":
2023-01-11T21:41:26.2517487Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2517655Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2517940Z     arg0_1 = rand_strided((256, 256), (256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2518222Z     arg1_1 = rand_strided((256, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2518510Z     arg2_1 = rand_strided((1024, 1600), (1600, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2518787Z     arg3_1 = rand_strided((100, 256), (256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2518961Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.2518968Z 
2023-01-11T21:41:26.2519043Z ok (1.588s)
2023-01-11T21:41:26.2519671Z   test_cat_upcasting_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2519841Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2520233Z [2023-01-11 21:25:10,326] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 53
2023-01-11T21:41:26.2520590Z [2023-01-11 21:25:11,839] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 53
2023-01-11T21:41:26.2520597Z 
2023-01-11T21:41:26.2520728Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2520829Z import torch
2023-01-11T21:41:26.2520925Z import random
2023-01-11T21:41:26.2521083Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2521227Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2521250Z 
2023-01-11T21:41:26.2521342Z aten = torch.ops.aten
2023-01-11T21:41:26.2521518Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2521644Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2521651Z 
2023-01-11T21:41:26.2521693Z 
2023-01-11T21:41:26.2521882Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2522150Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2522313Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2522458Z                        const half* __restrict__ in_ptr1,
2023-01-11T21:41:26.2522574Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2522703Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2522786Z {
2023-01-11T21:41:26.2522918Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2523004Z     {
2023-01-11T21:41:26.2523111Z         #pragma omp for 
2023-01-11T21:41:26.2523223Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2523294Z         {
2023-01-11T21:41:26.2523405Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.2523491Z             {
2023-01-11T21:41:26.2523685Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (16*i0));
2023-01-11T21:41:26.2523825Z                 tmp0.store(out_ptr0 + (8*i1) + (36*i0));
2023-01-11T21:41:26.2523915Z             }
2023-01-11T21:41:26.2524043Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.2524142Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:41:26.2524227Z             {
2023-01-11T21:41:26.2524356Z                 auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:26.2524480Z                 out_ptr0[i1 + (36*i0)] = tmp0;
2023-01-11T21:41:26.2524567Z             }
2023-01-11T21:41:26.2524653Z         }
2023-01-11T21:41:26.2524759Z         #pragma omp for 
2023-01-11T21:41:26.2524851Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2524938Z         {
2023-01-11T21:41:26.2525046Z             #pragma GCC ivdep
2023-01-11T21:41:26.2525159Z             for(long i1=0; i1<20; i1+=1)
2023-01-11T21:41:26.2525245Z             {
2023-01-11T21:41:26.2525332Z                 {
2023-01-11T21:41:26.2525423Z                     {
2023-01-11T21:41:26.2525575Z                         auto tmp0 = static_cast<float>(in_ptr1[i1 + (20*i0)]);
2023-01-11T21:41:26.2525721Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.2525852Z                         out_ptr1[i1 + (36*i0)] = tmp1;
2023-01-11T21:41:26.2525942Z                     }
2023-01-11T21:41:26.2526029Z                 }
2023-01-11T21:41:26.2526114Z             }
2023-01-11T21:41:26.2526199Z         }
2023-01-11T21:41:26.2526267Z     }
2023-01-11T21:41:26.2526348Z }
2023-01-11T21:41:26.2526454Z ''')
2023-01-11T21:41:26.2526462Z 
2023-01-11T21:41:26.2526467Z 
2023-01-11T21:41:26.2526584Z async_compile.wait(globals())
2023-01-11T21:41:26.2526684Z del async_compile
2023-01-11T21:41:26.2526691Z 
2023-01-11T21:41:26.2526786Z def call(args):
2023-01-11T21:41:26.2526885Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2526971Z     args.clear()
2023-01-11T21:41:26.2527221Z     buf2 = empty_strided((8, 36), (36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2527325Z     buf0 = as_strided(buf2, (8, 16), (36, 1))  # alias
2023-01-11T21:41:26.2527427Z     buf1 = as_strided(buf2, (8, 20), (36, 1), 16)  # alias
2023-01-11T21:41:26.2527654Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.2527722Z     del arg0_1
2023-01-11T21:41:26.2527788Z     del arg1_1
2023-01-11T21:41:26.2527847Z     return (buf2, )
2023-01-11T21:41:26.2527863Z 
2023-01-11T21:41:26.2527868Z 
2023-01-11T21:41:26.2527930Z if __name__ == "__main__":
2023-01-11T21:41:26.2528041Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2528164Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2528365Z     arg0_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2528561Z     arg1_1 = rand_strided((8, 20), (20, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.2528709Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2528714Z 
2023-01-11T21:41:26.2528781Z ok (1.536s)
2023-01-11T21:41:26.2529521Z   test_cauchy_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2529655Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2529907Z [2023-01-11 21:25:11,863] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 54
2023-01-11T21:41:26.2530168Z [2023-01-11 21:25:13,344] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 54
2023-01-11T21:41:26.2530174Z 
2023-01-11T21:41:26.2530272Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2530342Z import torch
2023-01-11T21:41:26.2530414Z import random
2023-01-11T21:41:26.2530527Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2530649Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2530655Z 
2023-01-11T21:41:26.2530731Z aten = torch.ops.aten
2023-01-11T21:41:26.2530852Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2530942Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2530948Z 
2023-01-11T21:41:26.2530952Z 
2023-01-11T21:41:26.2531085Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2531290Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2531407Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2531510Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2531607Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2531665Z {
2023-01-11T21:41:26.2531712Z     {
2023-01-11T21:41:26.2531776Z         {
2023-01-11T21:41:26.2531852Z             float tmp6 = 0;
2023-01-11T21:41:26.2531956Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2532020Z             {
2023-01-11T21:41:26.2532123Z                 #pragma omp for reduction(+:tmp6) 
2023-01-11T21:41:26.2532201Z                 for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.2532263Z                 {
2023-01-11T21:41:26.2532352Z                     for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:26.2532418Z                     {
2023-01-11T21:41:26.2532484Z                         {
2023-01-11T21:41:26.2532578Z                             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2532672Z                             auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.2532801Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.2532890Z                             auto tmp3 = 1 / tmp2;
2023-01-11T21:41:26.2532996Z                             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.2533092Z                             auto tmp5 = tmp3 * tmp4;
2023-01-11T21:41:26.2533173Z                             tmp6 += tmp5;
2023-01-11T21:41:26.2533300Z                         }
2023-01-11T21:41:26.2533367Z                     }
2023-01-11T21:41:26.2533417Z                 }
2023-01-11T21:41:26.2533479Z             }
2023-01-11T21:41:26.2533557Z             out_ptr0[0] = tmp6;
2023-01-11T21:41:26.2533622Z         }
2023-01-11T21:41:26.2533686Z     }
2023-01-11T21:41:26.2533746Z }
2023-01-11T21:41:26.2533825Z ''')
2023-01-11T21:41:26.2533831Z 
2023-01-11T21:41:26.2533835Z 
2023-01-11T21:41:26.2533909Z async_compile.wait(globals())
2023-01-11T21:41:26.2533980Z del async_compile
2023-01-11T21:41:26.2533985Z 
2023-01-11T21:41:26.2534054Z def call(args):
2023-01-11T21:41:26.2534129Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2534201Z     args.clear()
2023-01-11T21:41:26.2534385Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2534588Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2534645Z     del arg0_1
2023-01-11T21:41:26.2534716Z     del arg1_1
2023-01-11T21:41:26.2534786Z     return (buf0, )
2023-01-11T21:41:26.2534791Z 
2023-01-11T21:41:26.2534795Z 
2023-01-11T21:41:26.2534868Z if __name__ == "__main__":
2023-01-11T21:41:26.2534980Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2535104Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2535299Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2535491Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2535593Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2535610Z 
2023-01-11T21:41:26.2535663Z ok (1.505s)
2023-01-11T21:41:26.2536127Z   test_clamp_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2536255Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2536541Z [2023-01-11 21:25:13,377] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 55
2023-01-11T21:41:26.2537022Z [2023-01-11 21:25:14,977] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 55
2023-01-11T21:41:26.2537031Z 
2023-01-11T21:41:26.2537198Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2537323Z import torch
2023-01-11T21:41:26.2537449Z import random
2023-01-11T21:41:26.2537643Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2537869Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2537879Z 
2023-01-11T21:41:26.2538021Z aten = torch.ops.aten
2023-01-11T21:41:26.2538269Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2538443Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2538453Z 
2023-01-11T21:41:26.2538460Z 
2023-01-11T21:41:26.2538718Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2539099Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2539317Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2539459Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2539576Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2539699Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.2539827Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.2539907Z {
2023-01-11T21:41:26.2540039Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2540121Z     {
2023-01-11T21:41:26.2540213Z         #pragma omp for 
2023-01-11T21:41:26.2540322Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2540408Z         {
2023-01-11T21:41:26.2540669Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2540847Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.2541158Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-0.10000000149011612));
2023-01-11T21:41:26.2541308Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:41:26.2541495Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.10000000149011612));
2023-01-11T21:41:26.2541642Z             auto tmp4 = at::vec::minimum(tmp2, tmp3);
2023-01-11T21:41:26.2541806Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:41:26.2541953Z             auto tmp7 = at::vec::maximum(tmp5, tmp6);
2023-01-11T21:41:26.2542071Z             auto tmp8 = tmp0 + tmp5;
2023-01-11T21:41:26.2542262Z             auto tmp9 = at::vec::minimum(tmp8, tmp6);
2023-01-11T21:41:26.2542391Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2542517Z             tmp7.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.2542641Z             tmp9.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.2542711Z         }
2023-01-11T21:41:26.2542835Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2542949Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.2543038Z         {
2023-01-11T21:41:26.2543222Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2543334Z             auto tmp5 = in_ptr1[i0];
2023-01-11T21:41:26.2543574Z             auto tmp1 = static_cast<float>(-0.10000000149011612);
2023-01-11T21:41:26.2543727Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:26.2543890Z             auto tmp3 = static_cast<float>(0.10000000149011612);
2023-01-11T21:41:26.2544089Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:41:26.2544259Z             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:26.2544458Z             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:41:26.2544610Z             auto tmp8 = tmp0 + tmp5;
2023-01-11T21:41:26.2544812Z             auto tmp9 = (tmp6 != tmp6) ? tmp6 : std::min(tmp8, tmp6);
2023-01-11T21:41:26.2544951Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.2545070Z             out_ptr1[i0] = tmp7;
2023-01-11T21:41:26.2545212Z             out_ptr2[i0] = tmp9;
2023-01-11T21:41:26.2545321Z         }
2023-01-11T21:41:26.2545429Z     }
2023-01-11T21:41:26.2545533Z }
2023-01-11T21:41:26.2545680Z ''')
2023-01-11T21:41:26.2545688Z 
2023-01-11T21:41:26.2545693Z 
2023-01-11T21:41:26.2545819Z async_compile.wait(globals())
2023-01-11T21:41:26.2545900Z del async_compile
2023-01-11T21:41:26.2545923Z 
2023-01-11T21:41:26.2546021Z def call(args):
2023-01-11T21:41:26.2546168Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.2546313Z     args.clear()
2023-01-11T21:41:26.2546712Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2547034Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2547367Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2547717Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2547830Z     del arg0_1
2023-01-11T21:41:26.2547972Z     del arg1_1
2023-01-11T21:41:26.2548143Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.2548151Z 
2023-01-11T21:41:26.2548156Z 
2023-01-11T21:41:26.2548286Z if __name__ == "__main__":
2023-01-11T21:41:26.2548504Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2548731Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2549100Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2549453Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2549661Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.2549809Z 
2023-01-11T21:41:26.2549950Z ok (1.633s)
2023-01-11T21:41:26.2550632Z   test_clone_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2550833Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2551273Z [2023-01-11 21:25:15,009] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 56
2023-01-11T21:41:26.2551787Z [2023-01-11 21:25:16,523] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 56
2023-01-11T21:41:26.2551809Z 
2023-01-11T21:41:26.2552020Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2552178Z import torch
2023-01-11T21:41:26.2552336Z import random
2023-01-11T21:41:26.2552513Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2552672Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2552679Z 
2023-01-11T21:41:26.2552782Z aten = torch.ops.aten
2023-01-11T21:41:26.2552961Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2553086Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2553093Z 
2023-01-11T21:41:26.2553098Z 
2023-01-11T21:41:26.2553291Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2553555Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2553716Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2553838Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2553975Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2554062Z {
2023-01-11T21:41:26.2554197Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2554280Z     {
2023-01-11T21:41:26.2554385Z         #pragma omp for 
2023-01-11T21:41:26.2554499Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.2554569Z         {
2023-01-11T21:41:26.2554753Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2554932Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.2555049Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2555224Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.2555339Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.2555464Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2555572Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.2555657Z         }
2023-01-11T21:41:26.2555787Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2555900Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.2555989Z         {
2023-01-11T21:41:26.2556103Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2556238Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.2556337Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.2556474Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:26.2556585Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.2556696Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.2556803Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.2556893Z         }
2023-01-11T21:41:26.2556977Z     }
2023-01-11T21:41:26.2557042Z }
2023-01-11T21:41:26.2557149Z ''')
2023-01-11T21:41:26.2557155Z 
2023-01-11T21:41:26.2557160Z 
2023-01-11T21:41:26.2557279Z async_compile.wait(globals())
2023-01-11T21:41:26.2557381Z del async_compile
2023-01-11T21:41:26.2557389Z 
2023-01-11T21:41:26.2557485Z def call(args):
2023-01-11T21:41:26.2557585Z     arg0_1, = args
2023-01-11T21:41:26.2557682Z     args.clear()
2023-01-11T21:41:26.2557944Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2558292Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2558506Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.2558601Z     del arg0_1
2023-01-11T21:41:26.2558708Z     return (buf0, buf1, )
2023-01-11T21:41:26.2558715Z 
2023-01-11T21:41:26.2558720Z 
2023-01-11T21:41:26.2558822Z if __name__ == "__main__":
2023-01-11T21:41:26.2558974Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2559139Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2559394Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2559540Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2559547Z 
2023-01-11T21:41:26.2559676Z ok (1.545s)
2023-01-11T21:41:26.2560306Z   test_constant_pad_1d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2560480Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2560837Z [2023-01-11 21:25:16,553] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 57
2023-01-11T21:41:26.2561196Z [2023-01-11 21:25:18,046] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 57
2023-01-11T21:41:26.2561203Z 
2023-01-11T21:41:26.2561330Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2561429Z import torch
2023-01-11T21:41:26.2561532Z import random
2023-01-11T21:41:26.2561669Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2561828Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2561838Z 
2023-01-11T21:41:26.2561943Z aten = torch.ops.aten
2023-01-11T21:41:26.2562123Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2562247Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2562254Z 
2023-01-11T21:41:26.2562260Z 
2023-01-11T21:41:26.2562443Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2562708Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2562869Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2562987Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2563116Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2563201Z {
2023-01-11T21:41:26.2563335Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2563423Z     {
2023-01-11T21:41:26.2563532Z         #pragma omp for 
2023-01-11T21:41:26.2563642Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.2563718Z         {
2023-01-11T21:41:26.2563827Z             #pragma GCC ivdep
2023-01-11T21:41:26.2563941Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:26.2564031Z             {
2023-01-11T21:41:26.2564122Z                 {
2023-01-11T21:41:26.2564214Z                     {
2023-01-11T21:41:26.2564341Z                         auto tmp0 = static_cast<long>(i1);
2023-01-11T21:41:26.2564479Z                         auto tmp1 = static_cast<long>(31);
2023-01-11T21:41:26.2564602Z                         auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.2564715Z                         float tmp3 = 6.0;
2023-01-11T21:41:26.2564823Z                         if(tmp2)
2023-01-11T21:41:26.2564920Z                         {
2023-01-11T21:41:26.2565060Z                             auto tmp4 = in_ptr0[i1 + (31*i0)];
2023-01-11T21:41:26.2565156Z                             tmp3 = tmp4;
2023-01-11T21:41:26.2565249Z                         }
2023-01-11T21:41:26.2565376Z                         out_ptr0[i1 + (32*i0)] = tmp3;
2023-01-11T21:41:26.2565509Z                     }
2023-01-11T21:41:26.2565599Z                 }
2023-01-11T21:41:26.2565687Z             }
2023-01-11T21:41:26.2565774Z         }
2023-01-11T21:41:26.2565866Z         #pragma omp for 
2023-01-11T21:41:26.2565979Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.2566061Z         {
2023-01-11T21:41:26.2566170Z             #pragma GCC ivdep
2023-01-11T21:41:26.2566285Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:41:26.2566375Z             {
2023-01-11T21:41:26.2566464Z                 {
2023-01-11T21:41:26.2566541Z                     {
2023-01-11T21:41:26.2566769Z                         auto tmp0 = static_cast<long>((-2) + i1);
2023-01-11T21:41:26.2566910Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.2567096Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.2567242Z                         auto tmp3 = static_cast<long>(31);
2023-01-11T21:41:26.2567365Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.2567494Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:26.2567597Z                         float tmp6 = 99.0;
2023-01-11T21:41:26.2567699Z                         if(tmp5)
2023-01-11T21:41:26.2567790Z                         {
2023-01-11T21:41:26.2568026Z                             auto tmp7 = in_ptr0[(-2) + i1 + (31*i0)];
2023-01-11T21:41:26.2568139Z                             tmp6 = tmp7;
2023-01-11T21:41:26.2568234Z                         }
2023-01-11T21:41:26.2568357Z                         out_ptr1[i1 + (36*i0)] = tmp6;
2023-01-11T21:41:26.2568431Z                     }
2023-01-11T21:41:26.2568519Z                 }
2023-01-11T21:41:26.2568603Z             }
2023-01-11T21:41:26.2568687Z         }
2023-01-11T21:41:26.2568769Z     }
2023-01-11T21:41:26.2568851Z }
2023-01-11T21:41:26.2568963Z ''')
2023-01-11T21:41:26.2568970Z 
2023-01-11T21:41:26.2568975Z 
2023-01-11T21:41:26.2569300Z async_compile.wait(globals())
2023-01-11T21:41:26.2569405Z del async_compile
2023-01-11T21:41:26.2569412Z 
2023-01-11T21:41:26.2569504Z def call(args):
2023-01-11T21:41:26.2569596Z     arg0_1, = args
2023-01-11T21:41:26.2569689Z     args.clear()
2023-01-11T21:41:26.2569980Z     buf0 = empty_strided((2, 16, 32), (512, 32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2570265Z     buf1 = empty_strided((2, 16, 36), (576, 36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2570464Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.2570557Z     del arg0_1
2023-01-11T21:41:26.2570656Z     return (buf0, buf1, )
2023-01-11T21:41:26.2570663Z 
2023-01-11T21:41:26.2570668Z 
2023-01-11T21:41:26.2570769Z if __name__ == "__main__":
2023-01-11T21:41:26.2570922Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2571088Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2571373Z     arg0_1 = rand_strided((2, 16, 31), (496, 31, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2571525Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2571532Z 
2023-01-11T21:41:26.2571620Z ok (1.522s)
2023-01-11T21:41:26.2572230Z   test_constant_pad_2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2572399Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2572747Z [2023-01-11 21:25:18,076] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 58
2023-01-11T21:41:26.2573108Z [2023-01-11 21:25:19,605] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 58
2023-01-11T21:41:26.2573187Z 
2023-01-11T21:41:26.2573314Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2573409Z import torch
2023-01-11T21:41:26.2573505Z import random
2023-01-11T21:41:26.2573658Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2573817Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2573825Z 
2023-01-11T21:41:26.2573914Z aten = torch.ops.aten
2023-01-11T21:41:26.2574088Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2574208Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2574215Z 
2023-01-11T21:41:26.2574221Z 
2023-01-11T21:41:26.2574407Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2574672Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2574828Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2575016Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2575147Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2575219Z {
2023-01-11T21:41:26.2575351Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2575433Z     {
2023-01-11T21:41:26.2575535Z         #pragma omp for 
2023-01-11T21:41:26.2575647Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.2575735Z         {
2023-01-11T21:41:26.2575827Z             #pragma GCC ivdep
2023-01-11T21:41:26.2575939Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.2576024Z             {
2023-01-11T21:41:26.2576111Z                 {
2023-01-11T21:41:26.2576202Z                     {
2023-01-11T21:41:26.2576431Z                         auto tmp0 = static_cast<long>((-1) + i0);
2023-01-11T21:41:26.2576567Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.2576677Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.2576813Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:26.2576935Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.2577168Z                         auto tmp5 = static_cast<long>((-1) + i1);
2023-01-11T21:41:26.2577297Z                         auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:41:26.2577421Z                         auto tmp7 = tmp5 < tmp3;
2023-01-11T21:41:26.2577540Z                         auto tmp8 = tmp2 & tmp4;
2023-01-11T21:41:26.2577660Z                         auto tmp9 = tmp8 & tmp6;
2023-01-11T21:41:26.2577769Z                         auto tmp10 = tmp9 & tmp7;
2023-01-11T21:41:26.2577882Z                         float tmp11 = 6.0;
2023-01-11T21:41:26.2577985Z                         if(tmp10)
2023-01-11T21:41:26.2578076Z                         {
2023-01-11T21:41:26.2578307Z                             auto tmp12 = in_ptr0[(-9) + i1 + (8*i0)];
2023-01-11T21:41:26.2578417Z                             tmp11 = tmp12;
2023-01-11T21:41:26.2578513Z                         }
2023-01-11T21:41:26.2578625Z                         out_ptr0[i1 + (10*i0)] = tmp11;
2023-01-11T21:41:26.2578714Z                     }
2023-01-11T21:41:26.2578804Z                 }
2023-01-11T21:41:26.2578889Z             }
2023-01-11T21:41:26.2578974Z         }
2023-01-11T21:41:26.2579077Z         #pragma omp for 
2023-01-11T21:41:26.2579170Z         for(long i0=0; i0<15; i0+=1)
2023-01-11T21:41:26.2579253Z         {
2023-01-11T21:41:26.2579360Z             #pragma GCC ivdep
2023-01-11T21:41:26.2579475Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:41:26.2579564Z             {
2023-01-11T21:41:26.2579648Z                 {
2023-01-11T21:41:26.2579734Z                     {
2023-01-11T21:41:26.2579947Z                         auto tmp0 = static_cast<long>((-3) + i0);
2023-01-11T21:41:26.2580079Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.2580207Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.2580344Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:26.2580467Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.2580695Z                         auto tmp5 = static_cast<long>((-1) + i1);
2023-01-11T21:41:26.2580861Z                         auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:41:26.2580972Z                         auto tmp7 = tmp5 < tmp3;
2023-01-11T21:41:26.2581090Z                         auto tmp8 = tmp2 & tmp4;
2023-01-11T21:41:26.2581209Z                         auto tmp9 = tmp8 & tmp6;
2023-01-11T21:41:26.2581330Z                         auto tmp10 = tmp9 & tmp7;
2023-01-11T21:41:26.2581446Z                         float tmp11 = 99.0;
2023-01-11T21:41:26.2581548Z                         if(tmp10)
2023-01-11T21:41:26.2581639Z                         {
2023-01-11T21:41:26.2581875Z                             auto tmp12 = in_ptr0[(-25) + i1 + (8*i0)];
2023-01-11T21:41:26.2581973Z                             tmp11 = tmp12;
2023-01-11T21:41:26.2582062Z                         }
2023-01-11T21:41:26.2582227Z                         out_ptr1[i1 + (11*i0)] = tmp11;
2023-01-11T21:41:26.2582318Z                     }
2023-01-11T21:41:26.2582410Z                 }
2023-01-11T21:41:26.2582499Z             }
2023-01-11T21:41:26.2582570Z         }
2023-01-11T21:41:26.2582649Z     }
2023-01-11T21:41:26.2582726Z }
2023-01-11T21:41:26.2582835Z ''')
2023-01-11T21:41:26.2582842Z 
2023-01-11T21:41:26.2582846Z 
2023-01-11T21:41:26.2582965Z async_compile.wait(globals())
2023-01-11T21:41:26.2583062Z del async_compile
2023-01-11T21:41:26.2583069Z 
2023-01-11T21:41:26.2583215Z def call(args):
2023-01-11T21:41:26.2583309Z     arg0_1, = args
2023-01-11T21:41:26.2583390Z     args.clear()
2023-01-11T21:41:26.2583690Z     buf0 = empty_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2583982Z     buf1 = empty_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2584201Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.2584295Z     del arg0_1
2023-01-11T21:41:26.2584400Z     return (buf0, buf1, )
2023-01-11T21:41:26.2584410Z 
2023-01-11T21:41:26.2584415Z 
2023-01-11T21:41:26.2584515Z if __name__ == "__main__":
2023-01-11T21:41:26.2584668Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2584816Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2585103Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2585249Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2585256Z 
2023-01-11T21:41:26.2585344Z ok (1.559s)
2023-01-11T21:41:26.2585969Z   test_constant_pad_3d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2586139Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2586498Z [2023-01-11 21:25:19,635] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 59
2023-01-11T21:41:26.2586859Z [2023-01-11 21:25:21,181] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 59
2023-01-11T21:41:26.2586867Z 
2023-01-11T21:41:26.2586994Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2587073Z import torch
2023-01-11T21:41:26.2587169Z import random
2023-01-11T21:41:26.2587321Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2587484Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2587491Z 
2023-01-11T21:41:26.2587600Z aten = torch.ops.aten
2023-01-11T21:41:26.2587779Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2587904Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2587912Z 
2023-01-11T21:41:26.2587917Z 
2023-01-11T21:41:26.2588104Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2588398Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2588556Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2588686Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2588818Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2588902Z {
2023-01-11T21:41:26.2589034Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2589120Z     {
2023-01-11T21:41:26.2589227Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2589335Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.2589420Z         {
2023-01-11T21:41:26.2589531Z             for(long i1=0; i1<15; i1+=1)
2023-01-11T21:41:26.2589618Z             {
2023-01-11T21:41:26.2589728Z                 #pragma GCC ivdep
2023-01-11T21:41:26.2589891Z                 for(long i2=0; i2<11; i2+=1)
2023-01-11T21:41:26.2589967Z                 {
2023-01-11T21:41:26.2590082Z                     #pragma GCC ivdep
2023-01-11T21:41:26.2590205Z                     for(long i3=0; i3<7; i3+=1)
2023-01-11T21:41:26.2590298Z                     {
2023-01-11T21:41:26.2590395Z                         {
2023-01-11T21:41:26.2590493Z                             {
2023-01-11T21:41:26.2590736Z                                 auto tmp0 = static_cast<long>((-5) + i1);
2023-01-11T21:41:26.2590865Z                                 auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.2590995Z                                 auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.2591138Z                                 auto tmp3 = static_cast<long>(4);
2023-01-11T21:41:26.2591271Z                                 auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.2591510Z                                 auto tmp5 = static_cast<long>((-3) + i2);
2023-01-11T21:41:26.2591645Z                                 auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:41:26.2591778Z                                 auto tmp7 = tmp5 < tmp3;
2023-01-11T21:41:26.2592010Z                                 auto tmp8 = static_cast<long>((-1) + i3);
2023-01-11T21:41:26.2592141Z                                 auto tmp9 = tmp8 >= tmp1;
2023-01-11T21:41:26.2592269Z                                 auto tmp10 = tmp8 < tmp3;
2023-01-11T21:41:26.2592401Z                                 auto tmp11 = tmp2 & tmp4;
2023-01-11T21:41:26.2592536Z                                 auto tmp12 = tmp11 & tmp6;
2023-01-11T21:41:26.2592670Z                                 auto tmp13 = tmp12 & tmp7;
2023-01-11T21:41:26.2592802Z                                 auto tmp14 = tmp13 & tmp9;
2023-01-11T21:41:26.2592941Z                                 auto tmp15 = tmp14 & tmp10;
2023-01-11T21:41:26.2593046Z                                 float tmp16 = 6.0;
2023-01-11T21:41:26.2593155Z                                 if(tmp15)
2023-01-11T21:41:26.2593259Z                                 {
2023-01-11T21:41:26.2593534Z                                     auto tmp17 = in_ptr0[(-93) + i3 + (4*i2) + (16*i1) + (64*i0)];
2023-01-11T21:41:26.2593660Z                                     tmp16 = tmp17;
2023-01-11T21:41:26.2593760Z                                 }
2023-01-11T21:41:26.2593913Z                                 out_ptr0[i3 + (7*i2) + (77*i1) + (1155*i0)] = tmp16;
2023-01-11T21:41:26.2593991Z                             }
2023-01-11T21:41:26.2594085Z                         }
2023-01-11T21:41:26.2594177Z                     }
2023-01-11T21:41:26.2594266Z                 }
2023-01-11T21:41:26.2594352Z             }
2023-01-11T21:41:26.2594439Z         }
2023-01-11T21:41:26.2594547Z         #pragma omp for 
2023-01-11T21:41:26.2594644Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.2594730Z         {
2023-01-11T21:41:26.2594837Z             #pragma GCC ivdep
2023-01-11T21:41:26.2594948Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:41:26.2595042Z             {
2023-01-11T21:41:26.2595158Z                 #pragma GCC ivdep
2023-01-11T21:41:26.2595275Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:26.2595407Z                 {
2023-01-11T21:41:26.2595493Z                     {
2023-01-11T21:41:26.2595584Z                         {
2023-01-11T21:41:26.2595823Z                             auto tmp0 = static_cast<long>((-3) + i1);
2023-01-11T21:41:26.2595964Z                             auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.2596094Z                             auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.2596236Z                             auto tmp3 = static_cast<long>(4);
2023-01-11T21:41:26.2596348Z                             auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.2596473Z                             auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:26.2596590Z                             float tmp6 = 6.0;
2023-01-11T21:41:26.2596693Z                             if(tmp5)
2023-01-11T21:41:26.2596829Z                             {
2023-01-11T21:41:26.2597089Z                                 auto tmp7 = in_ptr0[(-12) + i2 + (4*i1) + (16*i0)];
2023-01-11T21:41:26.2597214Z                                 tmp6 = tmp7;
2023-01-11T21:41:26.2597293Z                             }
2023-01-11T21:41:26.2597403Z                             out_ptr1[i2 + (4*i1) + (44*i0)] = tmp6;
2023-01-11T21:41:26.2597470Z                         }
2023-01-11T21:41:26.2597535Z                     }
2023-01-11T21:41:26.2597599Z                 }
2023-01-11T21:41:26.2597661Z             }
2023-01-11T21:41:26.2597720Z         }
2023-01-11T21:41:26.2597769Z     }
2023-01-11T21:41:26.2597829Z }
2023-01-11T21:41:26.2597908Z ''')
2023-01-11T21:41:26.2597914Z 
2023-01-11T21:41:26.2597918Z 
2023-01-11T21:41:26.2598006Z async_compile.wait(globals())
2023-01-11T21:41:26.2598079Z del async_compile
2023-01-11T21:41:26.2598084Z 
2023-01-11T21:41:26.2598153Z def call(args):
2023-01-11T21:41:26.2598221Z     arg0_1, = args
2023-01-11T21:41:26.2598282Z     args.clear()
2023-01-11T21:41:26.2598501Z     buf0 = empty_strided((2, 15, 11, 7), (1155, 77, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2598717Z     buf1 = empty_strided((2, 4, 11, 4), (176, 44, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2598884Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.2598954Z     del arg0_1
2023-01-11T21:41:26.2599029Z     return (buf0, buf1, )
2023-01-11T21:41:26.2599034Z 
2023-01-11T21:41:26.2599039Z 
2023-01-11T21:41:26.2599114Z if __name__ == "__main__":
2023-01-11T21:41:26.2599230Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2599340Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2599551Z     arg0_1 = rand_strided((2, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2599658Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.2599663Z 
2023-01-11T21:41:26.2599730Z ok (1.576s)
2023-01-11T21:41:26.2600218Z   test_conv2d_backward_channels_last_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2600346Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2600605Z [2023-01-11 21:25:21,224] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 60
2023-01-11T21:41:26.2600865Z [2023-01-11 21:25:21,243] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 60
2023-01-11T21:41:26.2600871Z 
2023-01-11T21:41:26.2600964Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2601033Z import torch
2023-01-11T21:41:26.2601090Z import random
2023-01-11T21:41:26.2601205Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2601324Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2601364Z 
2023-01-11T21:41:26.2601443Z aten = torch.ops.aten
2023-01-11T21:41:26.2601575Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2601664Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2601669Z 
2023-01-11T21:41:26.2601674Z 
2023-01-11T21:41:26.2601759Z async_compile.wait(globals())
2023-01-11T21:41:26.2601819Z del async_compile
2023-01-11T21:41:26.2601835Z 
2023-01-11T21:41:26.2601893Z def call(args):
2023-01-11T21:41:26.2601975Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.2602046Z     args.clear()
2023-01-11T21:41:26.2602209Z     buf0 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [320], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, True, True])
2023-01-11T21:41:26.2602278Z     del arg0_1
2023-01-11T21:41:26.2602344Z     del arg1_1
2023-01-11T21:41:26.2602439Z     del arg2_1
2023-01-11T21:41:26.2602495Z     buf1 = buf0[0]
2023-01-11T21:41:26.2602607Z     assert_size_stride(buf1, (2, 2048, 8, 8), (131072, 1, 16384, 2048))
2023-01-11T21:41:26.2602675Z     buf2 = buf0[1]
2023-01-11T21:41:26.2602785Z     assert_size_stride(buf2, (320, 2048, 1, 1), (2048, 1, 2048, 2048))
2023-01-11T21:41:26.2602852Z     buf3 = buf0[2]
2023-01-11T21:41:26.2602947Z     assert_size_stride(buf3, (320, ), (1, ))
2023-01-11T21:41:26.2603011Z     del buf0
2023-01-11T21:41:26.2603080Z     return (buf1, buf2, buf3, )
2023-01-11T21:41:26.2603085Z 
2023-01-11T21:41:26.2603089Z 
2023-01-11T21:41:26.2603162Z if __name__ == "__main__":
2023-01-11T21:41:26.2603273Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2603393Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2603612Z     arg0_1 = rand_strided((2, 320, 8, 8), (20480, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2603834Z     arg1_1 = rand_strided((2, 2048, 8, 8), (131072, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2604063Z     arg2_1 = rand_strided((320, 2048, 1, 1), (2048, 1, 2048, 2048), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2604187Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.2604192Z 
2023-01-11T21:41:26.2604245Z ok (0.087s)
2023-01-11T21:41:26.2604436Z   test_conv2d_binary_cpu (__main__.CpuTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:41:26.2604909Z   test_conv2d_channels_last_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2605032Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2605290Z [2023-01-11 21:25:21,343] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 61
2023-01-11T21:41:26.2605555Z [2023-01-11 21:25:22,841] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 61
2023-01-11T21:41:26.2605980Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2606103Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2606359Z [2023-01-11 21:25:22,911] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 62
2023-01-11T21:41:26.2606619Z [2023-01-11 21:25:22,933] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 62
2023-01-11T21:41:26.2607042Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2607206Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2607445Z [2023-01-11 21:25:23,002] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 63
2023-01-11T21:41:26.2607705Z [2023-01-11 21:25:23,024] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 63
2023-01-11T21:41:26.2607710Z 
2023-01-11T21:41:26.2607803Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2607871Z import torch
2023-01-11T21:41:26.2607940Z import random
2023-01-11T21:41:26.2608083Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2608205Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2608213Z 
2023-01-11T21:41:26.2608290Z aten = torch.ops.aten
2023-01-11T21:41:26.2608410Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2608500Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2608505Z 
2023-01-11T21:41:26.2608509Z 
2023-01-11T21:41:26.2608643Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2608846Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2608965Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2609215Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2609304Z {
2023-01-11T21:41:26.2609428Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2609479Z     {
2023-01-11T21:41:26.2609568Z         #pragma omp for  collapse(3)
2023-01-11T21:41:26.2609656Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.2609718Z         {
2023-01-11T21:41:26.2609800Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.2609865Z             {
2023-01-11T21:41:26.2609941Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:41:26.2610005Z                 {
2023-01-11T21:41:26.2610071Z                     {
2023-01-11T21:41:26.2610139Z                         {
2023-01-11T21:41:26.2610250Z                             auto tmp0 = in_ptr0[i2 + (256*i1) + (768*i0)];
2023-01-11T21:41:26.2610358Z                             out_ptr0[i1 + (3*i2) + (768*i0)] = tmp0;
2023-01-11T21:41:26.2610426Z                         }
2023-01-11T21:41:26.2610479Z                     }
2023-01-11T21:41:26.2610544Z                 }
2023-01-11T21:41:26.2610607Z             }
2023-01-11T21:41:26.2610669Z         }
2023-01-11T21:41:26.2610730Z     }
2023-01-11T21:41:26.2610790Z }
2023-01-11T21:41:26.2610871Z ''')
2023-01-11T21:41:26.2610877Z 
2023-01-11T21:41:26.2610881Z 
2023-01-11T21:41:26.2610958Z async_compile.wait(globals())
2023-01-11T21:41:26.2611030Z del async_compile
2023-01-11T21:41:26.2611035Z 
2023-01-11T21:41:26.2611104Z def call(args):
2023-01-11T21:41:26.2611206Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.2611275Z     args.clear()
2023-01-11T21:41:26.2611491Z     buf0 = empty_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2611629Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2611774Z     buf1 = aten.convolution(buf0, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:26.2611867Z     assert_size_stride(buf1, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:41:26.2611931Z     del buf0
2023-01-11T21:41:26.2612001Z     del primals_2
2023-01-11T21:41:26.2612098Z     return (buf1, primals_1, primals_3, )
2023-01-11T21:41:26.2612103Z 
2023-01-11T21:41:26.2612107Z 
2023-01-11T21:41:26.2612181Z if __name__ == "__main__":
2023-01-11T21:41:26.2612293Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2612414Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2612689Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2612874Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2613092Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2613229Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.2613234Z 
2023-01-11T21:41:26.2613239Z 
2023-01-11T21:41:26.2613332Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2613402Z import torch
2023-01-11T21:41:26.2613473Z import random
2023-01-11T21:41:26.2613586Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2613706Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2613710Z 
2023-01-11T21:41:26.2613831Z aten = torch.ops.aten
2023-01-11T21:41:26.2613965Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2614055Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2614062Z 
2023-01-11T21:41:26.2614066Z 
2023-01-11T21:41:26.2614153Z async_compile.wait(globals())
2023-01-11T21:41:26.2614222Z del async_compile
2023-01-11T21:41:26.2614227Z 
2023-01-11T21:41:26.2614295Z def call(args):
2023-01-11T21:41:26.2614396Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.2614467Z     args.clear()
2023-01-11T21:41:26.2614606Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:26.2614711Z     assert_size_stride(buf0, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:41:26.2614781Z     del primals_2
2023-01-11T21:41:26.2614879Z     return (buf0, primals_1, primals_3, )
2023-01-11T21:41:26.2614883Z 
2023-01-11T21:41:26.2614887Z 
2023-01-11T21:41:26.2614961Z if __name__ == "__main__":
2023-01-11T21:41:26.2615078Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2615198Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2615400Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2615598Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2615815Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2615951Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.2615956Z 
2023-01-11T21:41:26.2615960Z 
2023-01-11T21:41:26.2616054Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2616123Z import torch
2023-01-11T21:41:26.2616191Z import random
2023-01-11T21:41:26.2616302Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2616408Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2616424Z 
2023-01-11T21:41:26.2616488Z aten = torch.ops.aten
2023-01-11T21:41:26.2616622Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2616711Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2616718Z 
2023-01-11T21:41:26.2616722Z 
2023-01-11T21:41:26.2616807Z async_compile.wait(globals())
2023-01-11T21:41:26.2616878Z del async_compile
2023-01-11T21:41:26.2616883Z 
2023-01-11T21:41:26.2616952Z def call(args):
2023-01-11T21:41:26.2617051Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.2617108Z     args.clear()
2023-01-11T21:41:26.2617260Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:26.2617364Z     assert_size_stride(buf0, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:41:26.2617436Z     del primals_2
2023-01-11T21:41:26.2617533Z     return (buf0, primals_1, primals_3, )
2023-01-11T21:41:26.2617538Z 
2023-01-11T21:41:26.2617542Z 
2023-01-11T21:41:26.2617617Z if __name__ == "__main__":
2023-01-11T21:41:26.2617731Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2617850Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2618050Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2618279Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2618500Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2618638Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.2618643Z 
2023-01-11T21:41:26.2618708Z ok (1.753s)
2023-01-11T21:41:26.2619207Z   test_conv2d_packed_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2619334Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2619595Z [2023-01-11 21:25:23,074] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 64
2023-01-11T21:41:26.2619856Z [2023-01-11 21:25:24,629] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 64
2023-01-11T21:41:26.2619861Z 
2023-01-11T21:41:26.2619955Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2620012Z import torch
2023-01-11T21:41:26.2620080Z import random
2023-01-11T21:41:26.2620192Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2620310Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2620315Z 
2023-01-11T21:41:26.2620391Z aten = torch.ops.aten
2023-01-11T21:41:26.2620523Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2620611Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2620619Z 
2023-01-11T21:41:26.2620623Z 
2023-01-11T21:41:26.2620755Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2620946Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2621064Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2621164Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2621224Z {
2023-01-11T21:41:26.2621320Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2621380Z     {
2023-01-11T21:41:26.2621468Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2621536Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2621597Z         {
2023-01-11T21:41:26.2621683Z             for(long i1=0; i1<3136; i1+=1)
2023-01-11T21:41:26.2621749Z             {
2023-01-11T21:41:26.2621812Z                 {
2023-01-11T21:41:26.2621878Z                     {
2023-01-11T21:41:26.2621980Z                         auto tmp0 = in_ptr0[i1 + (3136*i0)];
2023-01-11T21:41:26.2622064Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.2622131Z                     }
2023-01-11T21:41:26.2622200Z                 }
2023-01-11T21:41:26.2622265Z             }
2023-01-11T21:41:26.2622327Z         }
2023-01-11T21:41:26.2622388Z     }
2023-01-11T21:41:26.2622434Z }
2023-01-11T21:41:26.2622514Z ''')
2023-01-11T21:41:26.2622519Z 
2023-01-11T21:41:26.2622524Z 
2023-01-11T21:41:26.2622657Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2622860Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2622978Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2623080Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2623220Z {
2023-01-11T21:41:26.2623326Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2623374Z     {
2023-01-11T21:41:26.2623451Z         #pragma omp for 
2023-01-11T21:41:26.2623533Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.2623600Z         {
2023-01-11T21:41:26.2623682Z             #pragma GCC ivdep
2023-01-11T21:41:26.2623771Z             for(long i1=0; i1<324; i1+=1)
2023-01-11T21:41:26.2623875Z             {
2023-01-11T21:41:26.2623927Z                 {
2023-01-11T21:41:26.2623994Z                     {
2023-01-11T21:41:26.2624099Z                         auto tmp0 = in_ptr0[i0 + (64*i1)];
2023-01-11T21:41:26.2624198Z                         out_ptr0[i1 + (324*i0)] = tmp0;
2023-01-11T21:41:26.2624265Z                     }
2023-01-11T21:41:26.2624330Z                 }
2023-01-11T21:41:26.2624381Z             }
2023-01-11T21:41:26.2624444Z         }
2023-01-11T21:41:26.2624506Z     }
2023-01-11T21:41:26.2624568Z }
2023-01-11T21:41:26.2624650Z ''')
2023-01-11T21:41:26.2624655Z 
2023-01-11T21:41:26.2624660Z 
2023-01-11T21:41:26.2624750Z async_compile.wait(globals())
2023-01-11T21:41:26.2624825Z del async_compile
2023-01-11T21:41:26.2624830Z 
2023-01-11T21:41:26.2624900Z def call(args):
2023-01-11T21:41:26.2625001Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.2625075Z     args.clear()
2023-01-11T21:41:26.2625292Z     buf0 = empty_strided((1, 3, 56, 56), (9408, 1, 168, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2625429Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2625499Z     del arg2_1
2023-01-11T21:41:26.2625752Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (3, 3), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.2625866Z     assert_size_stride(buf1, (1, 64, 18, 18), (20736, 1, 1152, 64))
2023-01-11T21:41:26.2625921Z     del arg0_1
2023-01-11T21:41:26.2625991Z     del arg1_1
2023-01-11T21:41:26.2626057Z     del buf0
2023-01-11T21:41:26.2626279Z     buf2 = empty_strided((1, 64, 18, 18), (20736, 324, 18, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2626411Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2626483Z     return (buf2, )
2023-01-11T21:41:26.2626491Z 
2023-01-11T21:41:26.2626495Z 
2023-01-11T21:41:26.2626572Z if __name__ == "__main__":
2023-01-11T21:41:26.2626686Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2626797Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2627006Z     arg0_1 = rand_strided((64, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2627200Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2627418Z     arg2_1 = rand_strided((1, 3, 56, 56), (9408, 3136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2627540Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.2627545Z 
2023-01-11T21:41:26.2627614Z ok (1.607s)
2023-01-11T21:41:26.2627802Z   test_conv2d_unary_cpu (__main__.CpuTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:41:26.2628292Z   test_conv3d_channels_last_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2628421Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2628664Z [2023-01-11 21:25:24,704] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 65
2023-01-11T21:41:26.2628924Z [2023-01-11 21:25:26,389] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 65
2023-01-11T21:41:26.2629350Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2629475Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2629767Z [2023-01-11 21:25:26,459] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 66
2023-01-11T21:41:26.2630025Z [2023-01-11 21:25:28,004] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 66
2023-01-11T21:41:26.2630448Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2630570Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2630849Z [2023-01-11 21:25:28,076] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 67
2023-01-11T21:41:26.2631110Z [2023-01-11 21:25:28,108] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 67
2023-01-11T21:41:26.2631119Z 
2023-01-11T21:41:26.2631212Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2631269Z import torch
2023-01-11T21:41:26.2631338Z import random
2023-01-11T21:41:26.2631450Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2631570Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2631575Z 
2023-01-11T21:41:26.2631651Z aten = torch.ops.aten
2023-01-11T21:41:26.2631783Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2631872Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2631878Z 
2023-01-11T21:41:26.2631882Z 
2023-01-11T21:41:26.2632015Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2632209Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2632324Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2632422Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2632482Z {
2023-01-11T21:41:26.2632580Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2632642Z     {
2023-01-11T21:41:26.2632717Z         #pragma omp for 
2023-01-11T21:41:26.2632785Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.2632847Z         {
2023-01-11T21:41:26.2632985Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2633079Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2633142Z         }
2023-01-11T21:41:26.2633233Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2633311Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:26.2633361Z         {
2023-01-11T21:41:26.2633443Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2633523Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.2633584Z         }
2023-01-11T21:41:26.2633648Z     }
2023-01-11T21:41:26.2633706Z }
2023-01-11T21:41:26.2633783Z ''')
2023-01-11T21:41:26.2633788Z 
2023-01-11T21:41:26.2633799Z 
2023-01-11T21:41:26.2633875Z async_compile.wait(globals())
2023-01-11T21:41:26.2633944Z del async_compile
2023-01-11T21:41:26.2633949Z 
2023-01-11T21:41:26.2634018Z def call(args):
2023-01-11T21:41:26.2634117Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.2634187Z     args.clear()
2023-01-11T21:41:26.2634405Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2634544Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2634694Z     buf1 = aten.convolution(primals_3, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.2634793Z     assert_size_stride(buf1, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:41:26.2634857Z     del buf0
2023-01-11T21:41:26.2634928Z     del primals_2
2023-01-11T21:41:26.2635029Z     return (buf1, primals_1, primals_3, )
2023-01-11T21:41:26.2635035Z 
2023-01-11T21:41:26.2635039Z 
2023-01-11T21:41:26.2635149Z if __name__ == "__main__":
2023-01-11T21:41:26.2635260Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2635381Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2635591Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2635788Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2636022Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2636158Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.2636164Z 
2023-01-11T21:41:26.2636168Z 
2023-01-11T21:41:26.2636259Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2636329Z import torch
2023-01-11T21:41:26.2636431Z import random
2023-01-11T21:41:26.2636548Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2636656Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2636675Z 
2023-01-11T21:41:26.2636739Z aten = torch.ops.aten
2023-01-11T21:41:26.2636870Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2636959Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2636965Z 
2023-01-11T21:41:26.2636969Z 
2023-01-11T21:41:26.2637101Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2637305Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2637423Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2637527Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2637625Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2637708Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2637768Z {
2023-01-11T21:41:26.2637867Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2637927Z     {
2023-01-11T21:41:26.2638001Z         #pragma omp for 
2023-01-11T21:41:26.2638082Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.2638133Z         {
2023-01-11T21:41:26.2638265Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2638355Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2638420Z         }
2023-01-11T21:41:26.2638518Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2638598Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:26.2638665Z         {
2023-01-11T21:41:26.2638736Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2638814Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.2638874Z         }
2023-01-11T21:41:26.2638961Z         #pragma omp for  collapse(3)
2023-01-11T21:41:26.2639041Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.2639099Z         {
2023-01-11T21:41:26.2639182Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.2639233Z             {
2023-01-11T21:41:26.2639322Z                 for(long i2=0; i2<4096; i2+=1)
2023-01-11T21:41:26.2639388Z                 {
2023-01-11T21:41:26.2639454Z                     {
2023-01-11T21:41:26.2639520Z                         {
2023-01-11T21:41:26.2639627Z                             auto tmp0 = in_ptr1[i1 + (3*i2) + (12288*i0)];
2023-01-11T21:41:26.2639731Z                             out_ptr1[i2 + (4096*i1) + (12288*i0)] = tmp0;
2023-01-11T21:41:26.2639785Z                         }
2023-01-11T21:41:26.2639849Z                     }
2023-01-11T21:41:26.2639912Z                 }
2023-01-11T21:41:26.2639974Z             }
2023-01-11T21:41:26.2640034Z         }
2023-01-11T21:41:26.2640094Z     }
2023-01-11T21:41:26.2640150Z }
2023-01-11T21:41:26.2640218Z ''')
2023-01-11T21:41:26.2640223Z 
2023-01-11T21:41:26.2640228Z 
2023-01-11T21:41:26.2640316Z async_compile.wait(globals())
2023-01-11T21:41:26.2640387Z del async_compile
2023-01-11T21:41:26.2640396Z 
2023-01-11T21:41:26.2640467Z def call(args):
2023-01-11T21:41:26.2640567Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.2640688Z     args.clear()
2023-01-11T21:41:26.2640904Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2641122Z     buf1 = empty_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2641323Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.2641467Z     buf2 = aten.convolution(buf1, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.2641580Z     assert_size_stride(buf2, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:41:26.2641647Z     del buf0
2023-01-11T21:41:26.2641713Z     del buf1
2023-01-11T21:41:26.2641785Z     del primals_2
2023-01-11T21:41:26.2641914Z     return (buf2, primals_1, primals_3, )
2023-01-11T21:41:26.2641920Z 
2023-01-11T21:41:26.2641924Z 
2023-01-11T21:41:26.2641988Z if __name__ == "__main__":
2023-01-11T21:41:26.2642102Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2642223Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2642444Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2642642Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2642872Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 1, 768, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2643009Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.2643014Z 
2023-01-11T21:41:26.2643018Z 
2023-01-11T21:41:26.2643111Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2643178Z import torch
2023-01-11T21:41:26.2643234Z import random
2023-01-11T21:41:26.2643350Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2643468Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2643475Z 
2023-01-11T21:41:26.2643551Z aten = torch.ops.aten
2023-01-11T21:41:26.2643681Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2643770Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2643776Z 
2023-01-11T21:41:26.2643780Z 
2023-01-11T21:41:26.2643912Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2644117Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2644222Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2644323Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.2644419Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.2644513Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.2644573Z {
2023-01-11T21:41:26.2644671Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2644731Z     {
2023-01-11T21:41:26.2644794Z         #pragma omp for 
2023-01-11T21:41:26.2644875Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.2644938Z         {
2023-01-11T21:41:26.2645073Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.2645162Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.2645224Z         }
2023-01-11T21:41:26.2645316Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.2645384Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:26.2645445Z         {
2023-01-11T21:41:26.2645528Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.2645605Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.2645665Z         }
2023-01-11T21:41:26.2645751Z         #pragma omp for  collapse(3)
2023-01-11T21:41:26.2645816Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.2645876Z         {
2023-01-11T21:41:26.2645956Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.2646020Z             {
2023-01-11T21:41:26.2646110Z                 for(long i2=0; i2<4096; i2+=1)
2023-01-11T21:41:26.2646174Z                 {
2023-01-11T21:41:26.2646270Z                     {
2023-01-11T21:41:26.2646325Z                         {
2023-01-11T21:41:26.2646434Z                             auto tmp0 = in_ptr1[i1 + (3*i2) + (12288*i0)];
2023-01-11T21:41:26.2646538Z                             out_ptr1[i2 + (4096*i1) + (12288*i0)] = tmp0;
2023-01-11T21:41:26.2646604Z                         }
2023-01-11T21:41:26.2646667Z                     }
2023-01-11T21:41:26.2646730Z                 }
2023-01-11T21:41:26.2646793Z             }
2023-01-11T21:41:26.2646842Z         }
2023-01-11T21:41:26.2646901Z     }
2023-01-11T21:41:26.2646958Z }
2023-01-11T21:41:26.2647038Z ''')
2023-01-11T21:41:26.2647043Z 
2023-01-11T21:41:26.2647047Z 
2023-01-11T21:41:26.2647135Z async_compile.wait(globals())
2023-01-11T21:41:26.2647205Z del async_compile
2023-01-11T21:41:26.2647210Z 
2023-01-11T21:41:26.2647311Z def call(args):
2023-01-11T21:41:26.2647401Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.2647472Z     args.clear()
2023-01-11T21:41:26.2647688Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2647918Z     buf1 = empty_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2648114Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.2648260Z     buf2 = aten.convolution(buf1, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.2648373Z     assert_size_stride(buf2, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:41:26.2648439Z     del buf0
2023-01-11T21:41:26.2648491Z     del buf1
2023-01-11T21:41:26.2648561Z     del primals_2
2023-01-11T21:41:26.2648662Z     return (buf2, primals_1, primals_3, )
2023-01-11T21:41:26.2648667Z 
2023-01-11T21:41:26.2648674Z 
2023-01-11T21:41:26.2648750Z if __name__ == "__main__":
2023-01-11T21:41:26.2648864Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2648987Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2649408Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2649612Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2649830Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 1, 768, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2649969Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.2649975Z 
2023-01-11T21:41:26.2650040Z ok (3.477s)
2023-01-11T21:41:26.2650178Z   test_conv_autotune_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:26.2650515Z   test_conv_backward_cpu (__main__.CpuTests) ... [2023-01-11 21:25:28,187] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 68
2023-01-11T21:41:26.2650777Z [2023-01-11 21:25:28,257] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 68
2023-01-11T21:41:26.2650785Z 
2023-01-11T21:41:26.2650878Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2650947Z import torch
2023-01-11T21:41:26.2651019Z import random
2023-01-11T21:41:26.2651121Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2651240Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2651245Z 
2023-01-11T21:41:26.2651325Z aten = torch.ops.aten
2023-01-11T21:41:26.2651461Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2651551Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2651556Z 
2023-01-11T21:41:26.2651561Z 
2023-01-11T21:41:26.2651646Z async_compile.wait(globals())
2023-01-11T21:41:26.2651717Z del async_compile
2023-01-11T21:41:26.2651722Z 
2023-01-11T21:41:26.2651792Z def call(args):
2023-01-11T21:41:26.2651906Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1 = args
2023-01-11T21:41:26.2652038Z     args.clear()
2023-01-11T21:41:26.2652200Z     buf0 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [4], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, True, True])
2023-01-11T21:41:26.2652269Z     buf1 = buf0[0]
2023-01-11T21:41:26.2652375Z     assert_size_stride(buf1, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:41:26.2652443Z     buf2 = buf0[1]
2023-01-11T21:41:26.2652547Z     assert_size_stride(buf2, (4, 4, 3, 3), (36, 9, 3, 1))
2023-01-11T21:41:26.2652601Z     buf3 = buf0[2]
2023-01-11T21:41:26.2652694Z     assert_size_stride(buf3, (4, ), (1, ))
2023-01-11T21:41:26.2652760Z     del buf0
2023-01-11T21:41:26.2652925Z     buf4 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [4], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, False, False])
2023-01-11T21:41:26.2652992Z     del arg0_1
2023-01-11T21:41:26.2653059Z     del arg1_1
2023-01-11T21:41:26.2653198Z     del arg2_1
2023-01-11T21:41:26.2653256Z     buf5 = buf4[0]
2023-01-11T21:41:26.2653361Z     assert_size_stride(buf5, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:41:26.2653426Z     del buf4
2023-01-11T21:41:26.2653584Z     buf6 = aten.convolution_backward(arg3_1, arg4_1, arg5_1, [4], [1], [0], [1], False, [0], 1, [True, True, True])
2023-01-11T21:41:26.2653652Z     del arg3_1
2023-01-11T21:41:26.2653717Z     del arg4_1
2023-01-11T21:41:26.2653780Z     del arg5_1
2023-01-11T21:41:26.2653835Z     buf7 = buf6[0]
2023-01-11T21:41:26.2653939Z     assert_size_stride(buf7, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:41:26.2654009Z     buf8 = buf6[1]
2023-01-11T21:41:26.2654109Z     assert_size_stride(buf8, (4, 4, 3, 3), (36, 9, 3, 1))
2023-01-11T21:41:26.2654177Z     buf9 = buf6[2]
2023-01-11T21:41:26.2654272Z     assert_size_stride(buf9, (4, ), (1, ))
2023-01-11T21:41:26.2654340Z     del buf6
2023-01-11T21:41:26.2654498Z     buf10 = aten.convolution_backward(arg6_1, arg7_1, arg8_1, [4], [1, 1, 1], [0, 0, 0], [1, 1, 1], False, [0, 0, 0], 1, [True, True, True])
2023-01-11T21:41:26.2654567Z     del arg6_1
2023-01-11T21:41:26.2654636Z     del arg7_1
2023-01-11T21:41:26.2654707Z     del arg8_1
2023-01-11T21:41:26.2654777Z     buf11 = buf10[0]
2023-01-11T21:41:26.2654891Z     assert_size_stride(buf11, (3, 4, 5, 5, 5), (500, 125, 25, 5, 1))
2023-01-11T21:41:26.2654966Z     buf12 = buf10[1]
2023-01-11T21:41:26.2655062Z     assert_size_stride(buf12, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
2023-01-11T21:41:26.2655131Z     buf13 = buf10[2]
2023-01-11T21:41:26.2655227Z     assert_size_stride(buf13, (4, ), (1, ))
2023-01-11T21:41:26.2655295Z     del buf10
2023-01-11T21:41:26.2655425Z     return (buf1, buf2, buf3, buf5, buf7, buf8, buf9, buf11, buf12, buf13, )
2023-01-11T21:41:26.2655430Z 
2023-01-11T21:41:26.2655435Z 
2023-01-11T21:41:26.2655512Z if __name__ == "__main__":
2023-01-11T21:41:26.2655628Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2655741Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2655957Z     arg0_1 = rand_strided((3, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2656173Z     arg1_1 = rand_strided((3, 4, 5, 5), (100, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2656384Z     arg2_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2656587Z     arg3_1 = rand_strided((3, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2656794Z     arg4_1 = rand_strided((3, 4, 5, 5), (100, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2657002Z     arg5_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2657222Z     arg6_1 = rand_strided((3, 4, 3, 3, 3), (108, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2657444Z     arg7_1 = rand_strided((3, 4, 5, 5, 5), (500, 125, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2657648Z     arg8_1 = rand_strided((4, 4, 3, 3, 3), (108, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2657812Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1]))
2023-01-11T21:41:26.2657850Z 
2023-01-11T21:41:26.2657920Z ok (0.150s)
2023-01-11T21:41:26.2658393Z   test_conv_bn_fuse_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2658522Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2658786Z [2023-01-11 21:25:28,489] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 69
2023-01-11T21:41:26.2659076Z [2023-01-11 21:25:28,511] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 69
2023-01-11T21:41:26.2659508Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2659637Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2659896Z [2023-01-11 21:25:28,658] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 70
2023-01-11T21:41:26.2660160Z [2023-01-11 21:25:28,681] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 70
2023-01-11T21:41:26.2660589Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2660705Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2660962Z [2023-01-11 21:25:28,790] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 71
2023-01-11T21:41:26.2661216Z [2023-01-11 21:25:28,812] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 71
2023-01-11T21:41:26.2661641Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2661764Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2662012Z [2023-01-11 21:25:28,921] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 72
2023-01-11T21:41:26.2662273Z [2023-01-11 21:25:28,943] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 72
2023-01-11T21:41:26.2662698Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2662822Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2663073Z [2023-01-11 21:25:29,053] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 73
2023-01-11T21:41:26.2663447Z [2023-01-11 21:25:29,076] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 73
2023-01-11T21:41:26.2663867Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2664022Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2664278Z [2023-01-11 21:25:29,187] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 74
2023-01-11T21:41:26.2664283Z 
2023-01-11T21:41:26.2664377Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2664450Z import torch
2023-01-11T21:41:26.2664519Z import random
2023-01-11T21:41:26.2664635Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2664782Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2664789Z 
2023-01-11T21:41:26.2664870Z aten = torch.ops.aten
2023-01-11T21:41:26.2664990Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2665082Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2665087Z 
2023-01-11T21:41:26.2665091Z 
2023-01-11T21:41:26.2665178Z async_compile.wait(globals())
2023-01-11T21:41:26.2665252Z del async_compile
2023-01-11T21:41:26.2665257Z 
2023-01-11T21:41:26.2665325Z def call(args):
2023-01-11T21:41:26.2665445Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2665521Z     args.clear()
2023-01-11T21:41:26.2665651Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:41:26.2665743Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:41:26.2665812Z     del arg0_1
2023-01-11T21:41:26.2665876Z     del arg1_1
2023-01-11T21:41:26.2665942Z     del arg7_1
2023-01-11T21:41:26.2666017Z     return (buf0, )
2023-01-11T21:41:26.2666022Z 
2023-01-11T21:41:26.2666026Z 
2023-01-11T21:41:26.2666100Z if __name__ == "__main__":
2023-01-11T21:41:26.2666213Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2666324Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2666528Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2666717Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2666906Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2667090Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2667278Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2667464Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2667643Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2667836Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2667992Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2667998Z 
2023-01-11T21:41:26.2668002Z 
2023-01-11T21:41:26.2668095Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2668164Z import torch
2023-01-11T21:41:26.2668237Z import random
2023-01-11T21:41:26.2668350Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2668468Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2668473Z 
2023-01-11T21:41:26.2668549Z aten = torch.ops.aten
2023-01-11T21:41:26.2668668Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2668758Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2668763Z 
2023-01-11T21:41:26.2668767Z 
2023-01-11T21:41:26.2668852Z async_compile.wait(globals())
2023-01-11T21:41:26.2668922Z del async_compile
2023-01-11T21:41:26.2668929Z 
2023-01-11T21:41:26.2668998Z def call(args):
2023-01-11T21:41:26.2669113Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2669221Z     args.clear()
2023-01-11T21:41:26.2669352Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:41:26.2669446Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:41:26.2669511Z     del arg0_1
2023-01-11T21:41:26.2669576Z     del arg1_1
2023-01-11T21:41:26.2669641Z     del arg7_1
2023-01-11T21:41:26.2669709Z     return (buf0, )
2023-01-11T21:41:26.2669715Z 
2023-01-11T21:41:26.2669719Z 
2023-01-11T21:41:26.2669792Z if __name__ == "__main__":
2023-01-11T21:41:26.2669904Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2670024Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2670219Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2670438Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2670632Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2670824Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2671012Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2671198Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2671375Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2671585Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2671726Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2671731Z 
2023-01-11T21:41:26.2671747Z 
2023-01-11T21:41:26.2671828Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2671896Z import torch
2023-01-11T21:41:26.2671967Z import random
2023-01-11T21:41:26.2672080Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2672201Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2672207Z 
2023-01-11T21:41:26.2672282Z aten = torch.ops.aten
2023-01-11T21:41:26.2672414Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2672491Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2672497Z 
2023-01-11T21:41:26.2672501Z 
2023-01-11T21:41:26.2672587Z async_compile.wait(globals())
2023-01-11T21:41:26.2672657Z del async_compile
2023-01-11T21:41:26.2672662Z 
2023-01-11T21:41:26.2672733Z def call(args):
2023-01-11T21:41:26.2672850Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2672921Z     args.clear()
2023-01-11T21:41:26.2673049Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:41:26.2673154Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:41:26.2673212Z     del arg0_1
2023-01-11T21:41:26.2673278Z     del arg1_1
2023-01-11T21:41:26.2673343Z     del arg7_1
2023-01-11T21:41:26.2673416Z     return (buf0, )
2023-01-11T21:41:26.2673421Z 
2023-01-11T21:41:26.2673425Z 
2023-01-11T21:41:26.2673498Z if __name__ == "__main__":
2023-01-11T21:41:26.2673607Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2673728Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2673919Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2674108Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2674295Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2674484Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2674673Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2674861Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2675041Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2675283Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2675423Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2675439Z 
2023-01-11T21:41:26.2675443Z 
2023-01-11T21:41:26.2675524Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2675592Z import torch
2023-01-11T21:41:26.2675661Z import random
2023-01-11T21:41:26.2675774Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2675893Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2675899Z 
2023-01-11T21:41:26.2675974Z aten = torch.ops.aten
2023-01-11T21:41:26.2676104Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2676182Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2676216Z 
2023-01-11T21:41:26.2676232Z 
2023-01-11T21:41:26.2676308Z async_compile.wait(globals())
2023-01-11T21:41:26.2676380Z del async_compile
2023-01-11T21:41:26.2676385Z 
2023-01-11T21:41:26.2676453Z def call(args):
2023-01-11T21:41:26.2676567Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2676637Z     args.clear()
2023-01-11T21:41:26.2676765Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:41:26.2676868Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:41:26.2676924Z     del arg0_1
2023-01-11T21:41:26.2676990Z     del arg1_1
2023-01-11T21:41:26.2677053Z     del arg7_1
2023-01-11T21:41:26.2677124Z     return (buf0, )
2023-01-11T21:41:26.2677129Z 
2023-01-11T21:41:26.2677133Z 
2023-01-11T21:41:26.2677208Z if __name__ == "__main__":
2023-01-11T21:41:26.2677321Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2677443Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2677636Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2677833Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2678021Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2678209Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2678397Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2678581Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2678754Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2678964Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2679106Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2679128Z 
2023-01-11T21:41:26.2679132Z 
2023-01-11T21:41:26.2679213Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2679282Z import torch
2023-01-11T21:41:26.2679350Z import random
2023-01-11T21:41:26.2679463Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2679582Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2679587Z 
2023-01-11T21:41:26.2679663Z aten = torch.ops.aten
2023-01-11T21:41:26.2679795Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2679873Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2679889Z 
2023-01-11T21:41:26.2679894Z 
2023-01-11T21:41:26.2679968Z async_compile.wait(globals())
2023-01-11T21:41:26.2680039Z del async_compile
2023-01-11T21:41:26.2680044Z 
2023-01-11T21:41:26.2680113Z def call(args):
2023-01-11T21:41:26.2680228Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2680297Z     args.clear()
2023-01-11T21:41:26.2680427Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:41:26.2680536Z     assert_size_stride(buf0, (1, 32, 110), (3520, 110, 1))
2023-01-11T21:41:26.2680627Z     del arg0_1
2023-01-11T21:41:26.2680691Z     del arg1_1
2023-01-11T21:41:26.2680757Z     del arg7_1
2023-01-11T21:41:26.2680827Z     return (buf0, )
2023-01-11T21:41:26.2680833Z 
2023-01-11T21:41:26.2680837Z 
2023-01-11T21:41:26.2680913Z if __name__ == "__main__":
2023-01-11T21:41:26.2681025Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2681146Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2681352Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2681529Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2681715Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2681955Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2682145Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2682332Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2682511Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2682716Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2682871Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2682877Z 
2023-01-11T21:41:26.2683131Z [2023-01-11 21:25:29,209] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 74
2023-01-11T21:41:26.2683564Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2683693Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2683951Z [2023-01-11 21:25:29,322] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 75
2023-01-11T21:41:26.2684213Z [2023-01-11 21:25:29,345] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 75
2023-01-11T21:41:26.2684638Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2684764Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2685021Z [2023-01-11 21:25:29,456] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 76
2023-01-11T21:41:26.2685282Z [2023-01-11 21:25:29,479] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 76
2023-01-11T21:41:26.2685703Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2685826Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2686077Z [2023-01-11 21:25:29,590] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 77
2023-01-11T21:41:26.2686321Z [2023-01-11 21:25:29,612] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 77
2023-01-11T21:41:26.2686747Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2686915Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2687166Z [2023-01-11 21:25:29,731] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 78
2023-01-11T21:41:26.2687423Z [2023-01-11 21:25:29,753] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 78
2023-01-11T21:41:26.2687874Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2688003Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2688254Z [2023-01-11 21:25:29,865] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 79
2023-01-11T21:41:26.2688259Z 
2023-01-11T21:41:26.2688353Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2688421Z import torch
2023-01-11T21:41:26.2688491Z import random
2023-01-11T21:41:26.2688593Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2688710Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2688715Z 
2023-01-11T21:41:26.2688796Z aten = torch.ops.aten
2023-01-11T21:41:26.2688927Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2689141Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2689150Z 
2023-01-11T21:41:26.2689156Z 
2023-01-11T21:41:26.2689297Z async_compile.wait(globals())
2023-01-11T21:41:26.2689385Z del async_compile
2023-01-11T21:41:26.2689391Z 
2023-01-11T21:41:26.2689467Z def call(args):
2023-01-11T21:41:26.2689574Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2689645Z     args.clear()
2023-01-11T21:41:26.2689778Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:41:26.2689884Z     assert_size_stride(buf0, (1, 128, 110), (14080, 110, 1))
2023-01-11T21:41:26.2689953Z     del arg0_1
2023-01-11T21:41:26.2690021Z     del arg1_1
2023-01-11T21:41:26.2690088Z     del arg7_1
2023-01-11T21:41:26.2690146Z     return (buf0, )
2023-01-11T21:41:26.2690150Z 
2023-01-11T21:41:26.2690155Z 
2023-01-11T21:41:26.2690229Z if __name__ == "__main__":
2023-01-11T21:41:26.2690343Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2690468Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2690685Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2690879Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2691073Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2691264Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2691439Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2691625Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2691803Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2692016Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2692171Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2692177Z 
2023-01-11T21:41:26.2692181Z 
2023-01-11T21:41:26.2692277Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2692346Z import torch
2023-01-11T21:41:26.2692414Z import random
2023-01-11T21:41:26.2692580Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2692699Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2692704Z 
2023-01-11T21:41:26.2692781Z aten = torch.ops.aten
2023-01-11T21:41:26.2692914Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2693004Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2693009Z 
2023-01-11T21:41:26.2693014Z 
2023-01-11T21:41:26.2693102Z async_compile.wait(globals())
2023-01-11T21:41:26.2693178Z del async_compile
2023-01-11T21:41:26.2693183Z 
2023-01-11T21:41:26.2693256Z def call(args):
2023-01-11T21:41:26.2693360Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2693430Z     args.clear()
2023-01-11T21:41:26.2693608Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:41:26.2693714Z     assert_size_stride(buf0, (1, 32, 108), (3456, 108, 1))
2023-01-11T21:41:26.2693780Z     del arg0_1
2023-01-11T21:41:26.2693847Z     del arg1_1
2023-01-11T21:41:26.2693913Z     del arg7_1
2023-01-11T21:41:26.2693971Z     return (buf0, )
2023-01-11T21:41:26.2693977Z 
2023-01-11T21:41:26.2693992Z 
2023-01-11T21:41:26.2694054Z if __name__ == "__main__":
2023-01-11T21:41:26.2694164Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2694283Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2694489Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2694681Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2694871Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2695065Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2695243Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2695427Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2695606Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2695814Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2695968Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2695974Z 
2023-01-11T21:41:26.2695978Z 
2023-01-11T21:41:26.2696073Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2696144Z import torch
2023-01-11T21:41:26.2696218Z import random
2023-01-11T21:41:26.2696320Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2696440Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2696446Z 
2023-01-11T21:41:26.2696524Z aten = torch.ops.aten
2023-01-11T21:41:26.2696661Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2696755Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2696760Z 
2023-01-11T21:41:26.2696766Z 
2023-01-11T21:41:26.2696856Z async_compile.wait(globals())
2023-01-11T21:41:26.2696928Z del async_compile
2023-01-11T21:41:26.2696933Z 
2023-01-11T21:41:26.2697006Z def call(args):
2023-01-11T21:41:26.2697110Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2697183Z     args.clear()
2023-01-11T21:41:26.2697313Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:41:26.2697422Z     assert_size_stride(buf0, (1, 128, 108), (13824, 108, 1))
2023-01-11T21:41:26.2697493Z     del arg0_1
2023-01-11T21:41:26.2697565Z     del arg1_1
2023-01-11T21:41:26.2697631Z     del arg7_1
2023-01-11T21:41:26.2697691Z     return (buf0, )
2023-01-11T21:41:26.2697710Z 
2023-01-11T21:41:26.2697715Z 
2023-01-11T21:41:26.2697778Z if __name__ == "__main__":
2023-01-11T21:41:26.2697894Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2698018Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2698269Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2698467Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2698664Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2698854Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2699033Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2699227Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2699404Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2699616Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2699804Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2699810Z 
2023-01-11T21:41:26.2699815Z 
2023-01-11T21:41:26.2699911Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2699981Z import torch
2023-01-11T21:41:26.2700056Z import random
2023-01-11T21:41:26.2700173Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2700279Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2700284Z 
2023-01-11T21:41:26.2700363Z aten = torch.ops.aten
2023-01-11T21:41:26.2700495Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2700589Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2700594Z 
2023-01-11T21:41:26.2700598Z 
2023-01-11T21:41:26.2700691Z async_compile.wait(globals())
2023-01-11T21:41:26.2700766Z del async_compile
2023-01-11T21:41:26.2700771Z 
2023-01-11T21:41:26.2700842Z def call(args):
2023-01-11T21:41:26.2700961Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2701020Z     args.clear()
2023-01-11T21:41:26.2701150Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:41:26.2701262Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:41:26.2701332Z     del arg0_1
2023-01-11T21:41:26.2701396Z     del arg1_1
2023-01-11T21:41:26.2701463Z     del arg7_1
2023-01-11T21:41:26.2701536Z     return (buf0, )
2023-01-11T21:41:26.2701541Z 
2023-01-11T21:41:26.2701545Z 
2023-01-11T21:41:26.2701607Z if __name__ == "__main__":
2023-01-11T21:41:26.2701720Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2701843Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2702053Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2702245Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2702441Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2702632Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2702818Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2702990Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2703246Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2703461Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2703615Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2703620Z 
2023-01-11T21:41:26.2703624Z 
2023-01-11T21:41:26.2703720Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2703793Z import torch
2023-01-11T21:41:26.2703864Z import random
2023-01-11T21:41:26.2703979Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2704089Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2704094Z 
2023-01-11T21:41:26.2704172Z aten = torch.ops.aten
2023-01-11T21:41:26.2704351Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2704442Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2704447Z 
2023-01-11T21:41:26.2704452Z 
2023-01-11T21:41:26.2704539Z async_compile.wait(globals())
2023-01-11T21:41:26.2704611Z del async_compile
2023-01-11T21:41:26.2704617Z 
2023-01-11T21:41:26.2704685Z def call(args):
2023-01-11T21:41:26.2704802Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2704860Z     args.clear()
2023-01-11T21:41:26.2704987Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:41:26.2705091Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:41:26.2705156Z     del arg0_1
2023-01-11T21:41:26.2705223Z     del arg1_1
2023-01-11T21:41:26.2705290Z     del arg7_1
2023-01-11T21:41:26.2705394Z     return (buf0, )
2023-01-11T21:41:26.2705399Z 
2023-01-11T21:41:26.2705403Z 
2023-01-11T21:41:26.2705465Z if __name__ == "__main__":
2023-01-11T21:41:26.2705578Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2705698Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2705906Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2706097Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2706291Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2706477Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2706669Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2706846Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2707028Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2707237Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2707393Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2707398Z 
2023-01-11T21:41:26.2707711Z [2023-01-11 21:25:29,887] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 79
2023-01-11T21:41:26.2709095Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2709394Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2710006Z [2023-01-11 21:25:29,997] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 80
2023-01-11T21:41:26.2710622Z [2023-01-11 21:25:30,019] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 80
2023-01-11T21:41:26.2711646Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2711931Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2712508Z [2023-01-11 21:25:30,129] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 81
2023-01-11T21:41:26.2713116Z [2023-01-11 21:25:30,151] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 81
2023-01-11T21:41:26.2714147Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2714579Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2715177Z [2023-01-11 21:25:30,261] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 82
2023-01-11T21:41:26.2715798Z [2023-01-11 21:25:30,282] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 82
2023-01-11T21:41:26.2716906Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2717193Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2717800Z [2023-01-11 21:25:30,391] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 83
2023-01-11T21:41:26.2718416Z [2023-01-11 21:25:30,412] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 83
2023-01-11T21:41:26.2719433Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2719712Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2720287Z [2023-01-11 21:25:30,522] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 84
2023-01-11T21:41:26.2720330Z 
2023-01-11T21:41:26.2720519Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2720689Z import torch
2023-01-11T21:41:26.2720848Z import random
2023-01-11T21:41:26.2721111Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2721386Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2721398Z 
2023-01-11T21:41:26.2721576Z aten = torch.ops.aten
2023-01-11T21:41:26.2721884Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2722067Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2722079Z 
2023-01-11T21:41:26.2722108Z 
2023-01-11T21:41:26.2722293Z async_compile.wait(globals())
2023-01-11T21:41:26.2722456Z del async_compile
2023-01-11T21:41:26.2722469Z 
2023-01-11T21:41:26.2722631Z def call(args):
2023-01-11T21:41:26.2722900Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2723060Z     args.clear()
2023-01-11T21:41:26.2723364Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:41:26.2723596Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:41:26.2723734Z     del arg0_1
2023-01-11T21:41:26.2723883Z     del arg1_1
2023-01-11T21:41:26.2724031Z     del arg7_1
2023-01-11T21:41:26.2724191Z     return (buf0, )
2023-01-11T21:41:26.2724205Z 
2023-01-11T21:41:26.2724213Z 
2023-01-11T21:41:26.2724387Z if __name__ == "__main__":
2023-01-11T21:41:26.2724650Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2724934Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2725403Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2725856Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2726302Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2726744Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2727184Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2727748Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2728163Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2728637Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2728970Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2729175Z 
2023-01-11T21:41:26.2729184Z 
2023-01-11T21:41:26.2729377Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2729536Z import torch
2023-01-11T21:41:26.2729692Z import random
2023-01-11T21:41:26.2729954Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2730233Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2730246Z 
2023-01-11T21:41:26.2730549Z aten = torch.ops.aten
2023-01-11T21:41:26.2730866Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2731064Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2731100Z 
2023-01-11T21:41:26.2731112Z 
2023-01-11T21:41:26.2731291Z async_compile.wait(globals())
2023-01-11T21:41:26.2731459Z del async_compile
2023-01-11T21:41:26.2731470Z 
2023-01-11T21:41:26.2731628Z def call(args):
2023-01-11T21:41:26.2731886Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2732042Z     args.clear()
2023-01-11T21:41:26.2732333Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:41:26.2732571Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:41:26.2732706Z     del arg0_1
2023-01-11T21:41:26.2732854Z     del arg1_1
2023-01-11T21:41:26.2732999Z     del arg7_1
2023-01-11T21:41:26.2733159Z     return (buf0, )
2023-01-11T21:41:26.2733171Z 
2023-01-11T21:41:26.2733181Z 
2023-01-11T21:41:26.2733357Z if __name__ == "__main__":
2023-01-11T21:41:26.2733613Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2733904Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2734403Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2734828Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2735278Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2735718Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2736164Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2736600Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2737016Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2737501Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2737859Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2737883Z 
2023-01-11T21:41:26.2737892Z 
2023-01-11T21:41:26.2738090Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2738244Z import torch
2023-01-11T21:41:26.2738399Z import random
2023-01-11T21:41:26.2738665Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2738943Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2738955Z 
2023-01-11T21:41:26.2739129Z aten = torch.ops.aten
2023-01-11T21:41:26.2739437Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2739639Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2739653Z 
2023-01-11T21:41:26.2739662Z 
2023-01-11T21:41:26.2739842Z async_compile.wait(globals())
2023-01-11T21:41:26.2740012Z del async_compile
2023-01-11T21:41:26.2740023Z 
2023-01-11T21:41:26.2740185Z def call(args):
2023-01-11T21:41:26.2740456Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2740756Z     args.clear()
2023-01-11T21:41:26.2741052Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:41:26.2741288Z     assert_size_stride(buf0, (1, 32, 110), (3520, 110, 1))
2023-01-11T21:41:26.2741418Z     del arg0_1
2023-01-11T21:41:26.2741569Z     del arg1_1
2023-01-11T21:41:26.2741718Z     del arg7_1
2023-01-11T21:41:26.2741882Z     return (buf0, )
2023-01-11T21:41:26.2741892Z 
2023-01-11T21:41:26.2741900Z 
2023-01-11T21:41:26.2742068Z if __name__ == "__main__":
2023-01-11T21:41:26.2742324Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2742607Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2743102Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2743707Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2744156Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2744599Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2745030Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2745457Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2745869Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2746338Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2746706Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2746721Z 
2023-01-11T21:41:26.2746730Z 
2023-01-11T21:41:26.2746948Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2747082Z import torch
2023-01-11T21:41:26.2747240Z import random
2023-01-11T21:41:26.2747511Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2747786Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2747807Z 
2023-01-11T21:41:26.2747983Z aten = torch.ops.aten
2023-01-11T21:41:26.2748295Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2748500Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2748511Z 
2023-01-11T21:41:26.2748521Z 
2023-01-11T21:41:26.2748705Z async_compile.wait(globals())
2023-01-11T21:41:26.2748864Z del async_compile
2023-01-11T21:41:26.2748875Z 
2023-01-11T21:41:26.2749034Z def call(args):
2023-01-11T21:41:26.2749298Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2749457Z     args.clear()
2023-01-11T21:41:26.2749755Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:41:26.2749994Z     assert_size_stride(buf0, (1, 128, 110), (14080, 110, 1))
2023-01-11T21:41:26.2750150Z     del arg0_1
2023-01-11T21:41:26.2750286Z     del arg1_1
2023-01-11T21:41:26.2750434Z     del arg7_1
2023-01-11T21:41:26.2750594Z     return (buf0, )
2023-01-11T21:41:26.2750616Z 
2023-01-11T21:41:26.2750625Z 
2023-01-11T21:41:26.2750793Z if __name__ == "__main__":
2023-01-11T21:41:26.2751056Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2751338Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2751829Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2752255Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2752702Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2753145Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2753585Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2754025Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2754447Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2755038Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2755386Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2755398Z 
2023-01-11T21:41:26.2755406Z 
2023-01-11T21:41:26.2755576Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2755682Z import torch
2023-01-11T21:41:26.2755801Z import random
2023-01-11T21:41:26.2756001Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2756208Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2756217Z 
2023-01-11T21:41:26.2756350Z aten = torch.ops.aten
2023-01-11T21:41:26.2756579Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2756737Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2756746Z 
2023-01-11T21:41:26.2756753Z 
2023-01-11T21:41:26.2756970Z async_compile.wait(globals())
2023-01-11T21:41:26.2757083Z del async_compile
2023-01-11T21:41:26.2757091Z 
2023-01-11T21:41:26.2757216Z def call(args):
2023-01-11T21:41:26.2757418Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2757542Z     args.clear()
2023-01-11T21:41:26.2757768Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:41:26.2757950Z     assert_size_stride(buf0, (1, 32, 108), (3456, 108, 1))
2023-01-11T21:41:26.2758069Z     del arg0_1
2023-01-11T21:41:26.2758168Z     del arg1_1
2023-01-11T21:41:26.2758279Z     del arg7_1
2023-01-11T21:41:26.2758403Z     return (buf0, )
2023-01-11T21:41:26.2758412Z 
2023-01-11T21:41:26.2758418Z 
2023-01-11T21:41:26.2758547Z if __name__ == "__main__":
2023-01-11T21:41:26.2758742Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2758951Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2759326Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2759674Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2760007Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2760347Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2760687Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2761024Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2761347Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2761718Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2761991Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2762002Z 
2023-01-11T21:41:26.2762537Z [2023-01-11 21:25:30,543] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 84
2023-01-11T21:41:26.2763103Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2763261Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2763610Z [2023-01-11 21:25:30,697] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 85
2023-01-11T21:41:26.2763971Z [2023-01-11 21:25:32,293] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 85
2023-01-11T21:41:26.2764539Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2764771Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2765117Z [2023-01-11 21:25:32,407] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 86
2023-01-11T21:41:26.2765472Z [2023-01-11 21:25:32,430] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 86
2023-01-11T21:41:26.2766031Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2766247Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2766596Z [2023-01-11 21:25:32,549] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 87
2023-01-11T21:41:26.2766606Z 
2023-01-11T21:41:26.2766883Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2766961Z import torch
2023-01-11T21:41:26.2767054Z import random
2023-01-11T21:41:26.2767206Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2767362Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2767369Z 
2023-01-11T21:41:26.2767470Z aten = torch.ops.aten
2023-01-11T21:41:26.2767645Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2767770Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2767778Z 
2023-01-11T21:41:26.2767782Z 
2023-01-11T21:41:26.2767901Z async_compile.wait(globals())
2023-01-11T21:41:26.2767981Z del async_compile
2023-01-11T21:41:26.2767988Z 
2023-01-11T21:41:26.2768086Z def call(args):
2023-01-11T21:41:26.2768241Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2768340Z     args.clear()
2023-01-11T21:41:26.2768513Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:41:26.2768656Z     assert_size_stride(buf0, (1, 128, 108), (13824, 108, 1))
2023-01-11T21:41:26.2768750Z     del arg0_1
2023-01-11T21:41:26.2768825Z     del arg1_1
2023-01-11T21:41:26.2768914Z     del arg7_1
2023-01-11T21:41:26.2769122Z     return (buf0, )
2023-01-11T21:41:26.2769131Z 
2023-01-11T21:41:26.2769137Z 
2023-01-11T21:41:26.2769238Z if __name__ == "__main__":
2023-01-11T21:41:26.2769393Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2769558Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2769848Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2770120Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2770372Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2770638Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2770901Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2771165Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2771413Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2771702Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2771909Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2771916Z 
2023-01-11T21:41:26.2771921Z 
2023-01-11T21:41:26.2772049Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2772145Z import torch
2023-01-11T21:41:26.2772226Z import random
2023-01-11T21:41:26.2772384Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2772549Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2772616Z 
2023-01-11T21:41:26.2772721Z aten = torch.ops.aten
2023-01-11T21:41:26.2772898Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2773022Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2773030Z 
2023-01-11T21:41:26.2773035Z 
2023-01-11T21:41:26.2773222Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2773487Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2773630Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2773762Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2773845Z {
2023-01-11T21:41:26.2773978Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2774063Z     {
2023-01-11T21:41:26.2774186Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2774322Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2774415Z         {
2023-01-11T21:41:26.2774533Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2774624Z             {
2023-01-11T21:41:26.2774713Z                 {
2023-01-11T21:41:26.2774808Z                     {
2023-01-11T21:41:26.2774956Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.2775067Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.2775158Z                     }
2023-01-11T21:41:26.2775246Z                 }
2023-01-11T21:41:26.2775339Z             }
2023-01-11T21:41:26.2775428Z         }
2023-01-11T21:41:26.2775514Z     }
2023-01-11T21:41:26.2775596Z }
2023-01-11T21:41:26.2775692Z ''')
2023-01-11T21:41:26.2775698Z 
2023-01-11T21:41:26.2775704Z 
2023-01-11T21:41:26.2775888Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2776155Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2776319Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2776453Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2776538Z {
2023-01-11T21:41:26.2776673Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2776846Z     {
2023-01-11T21:41:26.2776950Z         #pragma omp for 
2023-01-11T21:41:26.2777060Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.2777147Z         {
2023-01-11T21:41:26.2777255Z             #pragma GCC ivdep
2023-01-11T21:41:26.2777373Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2777459Z             {
2023-01-11T21:41:26.2777531Z                 {
2023-01-11T21:41:26.2777623Z                     {
2023-01-11T21:41:26.2777760Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.2777891Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:26.2777983Z                     }
2023-01-11T21:41:26.2778070Z                 }
2023-01-11T21:41:26.2778154Z             }
2023-01-11T21:41:26.2778226Z         }
2023-01-11T21:41:26.2778331Z     }
2023-01-11T21:41:26.2778434Z }
2023-01-11T21:41:26.2778624Z ''')
2023-01-11T21:41:26.2778638Z 
2023-01-11T21:41:26.2778644Z 
2023-01-11T21:41:26.2778916Z async_compile.wait(globals())
2023-01-11T21:41:26.2779059Z del async_compile
2023-01-11T21:41:26.2779127Z 
2023-01-11T21:41:26.2779285Z def call(args):
2023-01-11T21:41:26.2779497Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2779642Z     args.clear()
2023-01-11T21:41:26.2780099Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2780371Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2780522Z     del arg7_1
2023-01-11T21:41:26.2781026Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.2781263Z     assert_size_stride(buf1, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:26.2781406Z     del arg0_1
2023-01-11T21:41:26.2781517Z     del arg1_1
2023-01-11T21:41:26.2781653Z     del buf0
2023-01-11T21:41:26.2782203Z     buf2 = empty_strided((1, 32, 112, 112), (401408, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2782467Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2782609Z     return (buf2, )
2023-01-11T21:41:26.2782619Z 
2023-01-11T21:41:26.2782625Z 
2023-01-11T21:41:26.2782783Z if __name__ == "__main__":
2023-01-11T21:41:26.2783026Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2783365Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2783803Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2784195Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2784670Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2785066Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2785471Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2785848Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2786225Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2786656Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2786976Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2786988Z 
2023-01-11T21:41:26.2786995Z 
2023-01-11T21:41:26.2787189Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2787348Z import torch
2023-01-11T21:41:26.2787487Z import random
2023-01-11T21:41:26.2787721Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2787977Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2787987Z 
2023-01-11T21:41:26.2788152Z aten = torch.ops.aten
2023-01-11T21:41:26.2788403Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2788591Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2788601Z 
2023-01-11T21:41:26.2788609Z 
2023-01-11T21:41:26.2788796Z async_compile.wait(globals())
2023-01-11T21:41:26.2788942Z del async_compile
2023-01-11T21:41:26.2788951Z 
2023-01-11T21:41:26.2789099Z def call(args):
2023-01-11T21:41:26.2789341Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2789474Z     args.clear()
2023-01-11T21:41:26.2790004Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.2790218Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:26.2790366Z     del arg0_1
2023-01-11T21:41:26.2790502Z     del arg1_1
2023-01-11T21:41:26.2790649Z     del arg7_1
2023-01-11T21:41:26.2790801Z     return (buf0, )
2023-01-11T21:41:26.2790811Z 
2023-01-11T21:41:26.2790817Z 
2023-01-11T21:41:26.2790984Z if __name__ == "__main__":
2023-01-11T21:41:26.2791213Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2791452Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2791875Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2792271Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2792653Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2793054Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2793434Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2793818Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2794192Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2794627Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2795033Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2795064Z 
2023-01-11T21:41:26.2795578Z [2023-01-11 21:25:34,136] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 87
2023-01-11T21:41:26.2796425Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2796681Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2797254Z [2023-01-11 21:25:34,279] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 88
2023-01-11T21:41:26.2797801Z [2023-01-11 21:25:34,302] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 88
2023-01-11T21:41:26.2798642Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2798898Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2799411Z [2023-01-11 21:25:34,442] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 89
2023-01-11T21:41:26.2799425Z 
2023-01-11T21:41:26.2799612Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2799773Z import torch
2023-01-11T21:41:26.2799906Z import random
2023-01-11T21:41:26.2800140Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2800395Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2800411Z 
2023-01-11T21:41:26.2800582Z aten = torch.ops.aten
2023-01-11T21:41:26.2800854Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2801044Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2801053Z 
2023-01-11T21:41:26.2801058Z 
2023-01-11T21:41:26.2801334Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2801735Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2801966Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2802156Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2802287Z {
2023-01-11T21:41:26.2802496Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2802626Z     {
2023-01-11T21:41:26.2802785Z         #pragma omp for 
2023-01-11T21:41:26.2802965Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.2803066Z         {
2023-01-11T21:41:26.2803234Z             #pragma GCC ivdep
2023-01-11T21:41:26.2803420Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2803564Z             {
2023-01-11T21:41:26.2803704Z                 {
2023-01-11T21:41:26.2803850Z                     {
2023-01-11T21:41:26.2804050Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.2804228Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:26.2804371Z                     }
2023-01-11T21:41:26.2804508Z                 }
2023-01-11T21:41:26.2804641Z             }
2023-01-11T21:41:26.2804773Z         }
2023-01-11T21:41:26.2804907Z     }
2023-01-11T21:41:26.2805008Z }
2023-01-11T21:41:26.2805176Z ''')
2023-01-11T21:41:26.2805188Z 
2023-01-11T21:41:26.2805195Z 
2023-01-11T21:41:26.2805487Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2805908Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2806138Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2806439Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2806564Z {
2023-01-11T21:41:26.2806773Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2806883Z     {
2023-01-11T21:41:26.2807032Z         #pragma omp for 
2023-01-11T21:41:26.2807205Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.2807341Z         {
2023-01-11T21:41:26.2807512Z             #pragma GCC ivdep
2023-01-11T21:41:26.2807701Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2807812Z             {
2023-01-11T21:41:26.2807933Z                 {
2023-01-11T21:41:26.2808064Z                     {
2023-01-11T21:41:26.2808284Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:26.2808490Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:26.2808627Z                     }
2023-01-11T21:41:26.2808834Z                 }
2023-01-11T21:41:26.2808935Z             }
2023-01-11T21:41:26.2809201Z         }
2023-01-11T21:41:26.2809344Z     }
2023-01-11T21:41:26.2809470Z }
2023-01-11T21:41:26.2809672Z ''')
2023-01-11T21:41:26.2809685Z 
2023-01-11T21:41:26.2809692Z 
2023-01-11T21:41:26.2809877Z async_compile.wait(globals())
2023-01-11T21:41:26.2810020Z del async_compile
2023-01-11T21:41:26.2810028Z 
2023-01-11T21:41:26.2810150Z def call(args):
2023-01-11T21:41:26.2810395Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2810547Z     args.clear()
2023-01-11T21:41:26.2811011Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2811283Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2811409Z     del arg7_1
2023-01-11T21:41:26.2811808Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:26.2811982Z     assert_size_stride(buf1, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:26.2812073Z     del arg0_1
2023-01-11T21:41:26.2812175Z     del arg1_1
2023-01-11T21:41:26.2812275Z     del buf0
2023-01-11T21:41:26.2812644Z     buf2 = empty_strided((1, 128, 112, 112), (1605632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2812847Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2812956Z     return (buf2, )
2023-01-11T21:41:26.2812964Z 
2023-01-11T21:41:26.2812969Z 
2023-01-11T21:41:26.2813103Z if __name__ == "__main__":
2023-01-11T21:41:26.2813314Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2813537Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2813977Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2814397Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2814808Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2815176Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2815481Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2815783Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2816107Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2816883Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2817234Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2817247Z 
2023-01-11T21:41:26.2817257Z 
2023-01-11T21:41:26.2817471Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2817634Z import torch
2023-01-11T21:41:26.2817799Z import random
2023-01-11T21:41:26.2818070Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2818348Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2818501Z 
2023-01-11T21:41:26.2818680Z aten = torch.ops.aten
2023-01-11T21:41:26.2818967Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2819176Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2819188Z 
2023-01-11T21:41:26.2819197Z 
2023-01-11T21:41:26.2819393Z async_compile.wait(globals())
2023-01-11T21:41:26.2819559Z del async_compile
2023-01-11T21:41:26.2819570Z 
2023-01-11T21:41:26.2819734Z def call(args):
2023-01-11T21:41:26.2820001Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2820157Z     args.clear()
2023-01-11T21:41:26.2820768Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:26.2821008Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:26.2821254Z     del arg0_1
2023-01-11T21:41:26.2821410Z     del arg1_1
2023-01-11T21:41:26.2821559Z     del arg7_1
2023-01-11T21:41:26.2821724Z     return (buf0, )
2023-01-11T21:41:26.2821736Z 
2023-01-11T21:41:26.2821745Z 
2023-01-11T21:41:26.2821922Z if __name__ == "__main__":
2023-01-11T21:41:26.2822180Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2822443Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2822941Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2823496Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2823956Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2824412Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2824863Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2825318Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2825742Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2826259Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2826614Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2826627Z 
2023-01-11T21:41:26.2827243Z [2023-01-11 21:25:34,476] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 89
2023-01-11T21:41:26.2828263Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2828554Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2829163Z [2023-01-11 21:25:34,586] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 90
2023-01-11T21:41:26.2829794Z [2023-01-11 21:25:34,609] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 90
2023-01-11T21:41:26.2830824Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2831107Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2831721Z [2023-01-11 21:25:34,719] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 91
2023-01-11T21:41:26.2832339Z [2023-01-11 21:25:34,752] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 91
2023-01-11T21:41:26.2833360Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2833733Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2834333Z [2023-01-11 21:25:34,887] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 92
2023-01-11T21:41:26.2834346Z 
2023-01-11T21:41:26.2834561Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2834721Z import torch
2023-01-11T21:41:26.2834880Z import random
2023-01-11T21:41:26.2835148Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2835499Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2835515Z 
2023-01-11T21:41:26.2835693Z aten = torch.ops.aten
2023-01-11T21:41:26.2835991Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2836198Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2836210Z 
2023-01-11T21:41:26.2836220Z 
2023-01-11T21:41:26.2836547Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2837026Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2837301Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2837525Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2837659Z {
2023-01-11T21:41:26.2837880Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2837995Z     {
2023-01-11T21:41:26.2838201Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2838384Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2838527Z         {
2023-01-11T21:41:26.2838727Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2838867Z             {
2023-01-11T21:41:26.2839008Z                 {
2023-01-11T21:41:26.2839145Z                     {
2023-01-11T21:41:26.2839375Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.2839581Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.2839730Z                     }
2023-01-11T21:41:26.2839867Z                 }
2023-01-11T21:41:26.2840011Z             }
2023-01-11T21:41:26.2840175Z         }
2023-01-11T21:41:26.2840266Z     }
2023-01-11T21:41:26.2840368Z }
2023-01-11T21:41:26.2840524Z ''')
2023-01-11T21:41:26.2840534Z 
2023-01-11T21:41:26.2840541Z 
2023-01-11T21:41:26.2840789Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2841185Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2841421Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2841627Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2841731Z {
2023-01-11T21:41:26.2841929Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2842066Z     {
2023-01-11T21:41:26.2842225Z         #pragma omp for 
2023-01-11T21:41:26.2842391Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.2842515Z         {
2023-01-11T21:41:26.2842680Z             #pragma GCC ivdep
2023-01-11T21:41:26.2842842Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2842982Z             {
2023-01-11T21:41:26.2843106Z                 {
2023-01-11T21:41:26.2843240Z                     {
2023-01-11T21:41:26.2843452Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.2843649Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:26.2843786Z                     }
2023-01-11T21:41:26.2843899Z                 }
2023-01-11T21:41:26.2844029Z             }
2023-01-11T21:41:26.2844155Z         }
2023-01-11T21:41:26.2844279Z     }
2023-01-11T21:41:26.2844407Z }
2023-01-11T21:41:26.2844606Z ''')
2023-01-11T21:41:26.2844617Z 
2023-01-11T21:41:26.2844624Z 
2023-01-11T21:41:26.2844816Z async_compile.wait(globals())
2023-01-11T21:41:26.2845122Z del async_compile
2023-01-11T21:41:26.2845133Z 
2023-01-11T21:41:26.2845280Z def call(args):
2023-01-11T21:41:26.2845519Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2845667Z     args.clear()
2023-01-11T21:41:26.2846154Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2846424Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2846571Z     del arg7_1
2023-01-11T21:41:26.2847105Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:26.2847270Z     assert_size_stride(buf1, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:26.2847371Z     del arg0_1
2023-01-11T21:41:26.2847595Z     del arg1_1
2023-01-11T21:41:26.2847718Z     del buf0
2023-01-11T21:41:26.2848092Z     buf2 = empty_strided((1, 32, 112, 112), (401408, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2848293Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2848384Z     return (buf2, )
2023-01-11T21:41:26.2848415Z 
2023-01-11T21:41:26.2848421Z 
2023-01-11T21:41:26.2848521Z if __name__ == "__main__":
2023-01-11T21:41:26.2848684Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2848855Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2849311Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2849596Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2849870Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2850144Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2850419Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2850672Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2850918Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2851237Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2851448Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2851456Z 
2023-01-11T21:41:26.2851461Z 
2023-01-11T21:41:26.2851588Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2851688Z import torch
2023-01-11T21:41:26.2851787Z import random
2023-01-11T21:41:26.2851942Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2852086Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2852092Z 
2023-01-11T21:41:26.2852200Z aten = torch.ops.aten
2023-01-11T21:41:26.2852378Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2852506Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2852513Z 
2023-01-11T21:41:26.2852518Z 
2023-01-11T21:41:26.2852638Z async_compile.wait(globals())
2023-01-11T21:41:26.2852737Z del async_compile
2023-01-11T21:41:26.2852744Z 
2023-01-11T21:41:26.2852839Z def call(args):
2023-01-11T21:41:26.2852999Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2853082Z     args.clear()
2023-01-11T21:41:26.2853422Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:26.2853572Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:26.2853668Z     del arg0_1
2023-01-11T21:41:26.2853763Z     del arg1_1
2023-01-11T21:41:26.2853854Z     del arg7_1
2023-01-11T21:41:26.2853955Z     return (buf0, )
2023-01-11T21:41:26.2853961Z 
2023-01-11T21:41:26.2853969Z 
2023-01-11T21:41:26.2854057Z if __name__ == "__main__":
2023-01-11T21:41:26.2854244Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2854572Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2854926Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2855253Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2855590Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2855907Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2856355Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2856719Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2857068Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2857588Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2857888Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2857904Z 
2023-01-11T21:41:26.2857910Z 
2023-01-11T21:41:26.2858099Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2858254Z import torch
2023-01-11T21:41:26.2858408Z import random
2023-01-11T21:41:26.2858646Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2858860Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2858891Z 
2023-01-11T21:41:26.2859024Z aten = torch.ops.aten
2023-01-11T21:41:26.2859281Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2859466Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2859476Z 
2023-01-11T21:41:26.2859483Z 
2023-01-11T21:41:26.2859760Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2860150Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2860387Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2860590Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2860692Z {
2023-01-11T21:41:26.2860876Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2860998Z     {
2023-01-11T21:41:26.2861155Z         #pragma omp for 
2023-01-11T21:41:26.2861327Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.2861459Z         {
2023-01-11T21:41:26.2861620Z             #pragma GCC ivdep
2023-01-11T21:41:26.2861780Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2861899Z             {
2023-01-11T21:41:26.2862033Z                 {
2023-01-11T21:41:26.2862167Z                     {
2023-01-11T21:41:26.2862372Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.2862569Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:26.2862709Z                     }
2023-01-11T21:41:26.2862911Z                 }
2023-01-11T21:41:26.2863014Z             }
2023-01-11T21:41:26.2863197Z         }
2023-01-11T21:41:26.2863320Z     }
2023-01-11T21:41:26.2863429Z }
2023-01-11T21:41:26.2863576Z ''')
2023-01-11T21:41:26.2863588Z 
2023-01-11T21:41:26.2863594Z 
2023-01-11T21:41:26.2863845Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2864160Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2864371Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2864538Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2864650Z {
2023-01-11T21:41:26.2864824Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2864925Z     {
2023-01-11T21:41:26.2865063Z         #pragma omp for 
2023-01-11T21:41:26.2865194Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.2865304Z         {
2023-01-11T21:41:26.2865445Z             #pragma GCC ivdep
2023-01-11T21:41:26.2865605Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2865726Z             {
2023-01-11T21:41:26.2865838Z                 {
2023-01-11T21:41:26.2866048Z                     {
2023-01-11T21:41:26.2866207Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:26.2866377Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:26.2866498Z                     }
2023-01-11T21:41:26.2866616Z                 }
2023-01-11T21:41:26.2866735Z             }
2023-01-11T21:41:26.2866837Z         }
2023-01-11T21:41:26.2866925Z     }
2023-01-11T21:41:26.2867034Z }
2023-01-11T21:41:26.2867186Z ''')
2023-01-11T21:41:26.2867194Z 
2023-01-11T21:41:26.2867199Z 
2023-01-11T21:41:26.2867352Z async_compile.wait(globals())
2023-01-11T21:41:26.2867484Z del async_compile
2023-01-11T21:41:26.2867493Z 
2023-01-11T21:41:26.2867722Z def call(args):
2023-01-11T21:41:26.2867948Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2868166Z     args.clear()
2023-01-11T21:41:26.2868605Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2868865Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2869001Z     del arg7_1
2023-01-11T21:41:26.2869502Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:26.2869725Z     assert_size_stride(buf1, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:26.2869869Z     del arg0_1
2023-01-11T21:41:26.2870008Z     del arg1_1
2023-01-11T21:41:26.2870122Z     del buf0
2023-01-11T21:41:26.2870576Z     buf2 = empty_strided((1, 128, 112, 112), (1605632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2870837Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2870971Z     return (buf2, )
2023-01-11T21:41:26.2870983Z 
2023-01-11T21:41:26.2870991Z 
2023-01-11T21:41:26.2871152Z if __name__ == "__main__":
2023-01-11T21:41:26.2871389Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2871644Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2872054Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2872426Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2872811Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2873177Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2873560Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2873938Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2874391Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2874789Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2875047Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2875058Z 
2023-01-11T21:41:26.2875514Z [2023-01-11 21:25:34,910] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 92
2023-01-11T21:41:26.2876189Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2876404Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2876852Z [2023-01-11 21:25:35,031] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 93
2023-01-11T21:41:26.2877298Z [2023-01-11 21:25:36,581] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 93
2023-01-11T21:41:26.2878082Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2878297Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2878730Z [2023-01-11 21:25:36,688] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 94
2023-01-11T21:41:26.2879176Z [2023-01-11 21:25:36,710] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 94
2023-01-11T21:41:26.2879928Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2880148Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2880583Z [2023-01-11 21:25:36,855] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 95
2023-01-11T21:41:26.2880597Z 
2023-01-11T21:41:26.2880758Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2880865Z import torch
2023-01-11T21:41:26.2880988Z import random
2023-01-11T21:41:26.2881193Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2881395Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2881403Z 
2023-01-11T21:41:26.2881534Z aten = torch.ops.aten
2023-01-11T21:41:26.2881756Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2881923Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2881931Z 
2023-01-11T21:41:26.2881939Z 
2023-01-11T21:41:26.2882096Z async_compile.wait(globals())
2023-01-11T21:41:26.2882209Z del async_compile
2023-01-11T21:41:26.2882223Z 
2023-01-11T21:41:26.2882357Z def call(args):
2023-01-11T21:41:26.2882551Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2882680Z     args.clear()
2023-01-11T21:41:26.2883120Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:26.2883307Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:26.2883415Z     del arg0_1
2023-01-11T21:41:26.2883517Z     del arg1_1
2023-01-11T21:41:26.2883641Z     del arg7_1
2023-01-11T21:41:26.2883761Z     return (buf0, )
2023-01-11T21:41:26.2883775Z 
2023-01-11T21:41:26.2883780Z 
2023-01-11T21:41:26.2883921Z if __name__ == "__main__":
2023-01-11T21:41:26.2884125Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2884322Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2884698Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2885034Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2885346Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2885682Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2886022Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2886336Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2886644Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2887032Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2887295Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2887306Z 
2023-01-11T21:41:26.2887394Z 
2023-01-11T21:41:26.2887555Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2887660Z import torch
2023-01-11T21:41:26.2887787Z import random
2023-01-11T21:41:26.2887987Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2888184Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2888194Z 
2023-01-11T21:41:26.2888334Z aten = torch.ops.aten
2023-01-11T21:41:26.2888568Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2888728Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2888736Z 
2023-01-11T21:41:26.2888741Z 
2023-01-11T21:41:26.2888969Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2889413Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2889738Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2889915Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2890006Z {
2023-01-11T21:41:26.2890185Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2890304Z     {
2023-01-11T21:41:26.2890477Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2890606Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2890718Z         {
2023-01-11T21:41:26.2890881Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2890987Z             {
2023-01-11T21:41:26.2891103Z                 {
2023-01-11T21:41:26.2891219Z                     {
2023-01-11T21:41:26.2891401Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.2891549Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.2891670Z                     }
2023-01-11T21:41:26.2891787Z                 }
2023-01-11T21:41:26.2891889Z             }
2023-01-11T21:41:26.2891995Z         }
2023-01-11T21:41:26.2892108Z     }
2023-01-11T21:41:26.2892223Z }
2023-01-11T21:41:26.2892361Z ''')
2023-01-11T21:41:26.2892379Z 
2023-01-11T21:41:26.2892385Z 
2023-01-11T21:41:26.2892625Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2892963Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2893174Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2893356Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2893459Z {
2023-01-11T21:41:26.2893634Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2893722Z     {
2023-01-11T21:41:26.2893850Z         #pragma omp for 
2023-01-11T21:41:26.2893998Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.2894116Z         {
2023-01-11T21:41:26.2894264Z             #pragma GCC ivdep
2023-01-11T21:41:26.2894417Z             for(long i1=0; i1<12100; i1+=1)
2023-01-11T21:41:26.2894536Z             {
2023-01-11T21:41:26.2894638Z                 {
2023-01-11T21:41:26.2894748Z                     {
2023-01-11T21:41:26.2894930Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.2895108Z                         out_ptr0[i1 + (12100*i0)] = tmp0;
2023-01-11T21:41:26.2895238Z                     }
2023-01-11T21:41:26.2895456Z                 }
2023-01-11T21:41:26.2895587Z             }
2023-01-11T21:41:26.2895698Z         }
2023-01-11T21:41:26.2895813Z     }
2023-01-11T21:41:26.2895937Z }
2023-01-11T21:41:26.2896114Z ''')
2023-01-11T21:41:26.2896124Z 
2023-01-11T21:41:26.2896133Z 
2023-01-11T21:41:26.2896310Z async_compile.wait(globals())
2023-01-11T21:41:26.2896464Z del async_compile
2023-01-11T21:41:26.2896475Z 
2023-01-11T21:41:26.2896621Z def call(args):
2023-01-11T21:41:26.2896822Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2896974Z     args.clear()
2023-01-11T21:41:26.2897409Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2897685Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2897814Z     del arg7_1
2023-01-11T21:41:26.2898310Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.2898661Z     assert_size_stride(buf1, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:41:26.2898791Z     del arg0_1
2023-01-11T21:41:26.2898910Z     del arg1_1
2023-01-11T21:41:26.2899049Z     del buf0
2023-01-11T21:41:26.2899514Z     buf2 = empty_strided((1, 32, 110, 110), (387200, 12100, 110, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2899772Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2899913Z     return (buf2, )
2023-01-11T21:41:26.2899925Z 
2023-01-11T21:41:26.2899935Z 
2023-01-11T21:41:26.2900092Z if __name__ == "__main__":
2023-01-11T21:41:26.2900316Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2900609Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2901028Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2901423Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2901802Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2902248Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2902578Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2902897Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2903297Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2903660Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2903922Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2903936Z 
2023-01-11T21:41:26.2903942Z 
2023-01-11T21:41:26.2904103Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2904240Z import torch
2023-01-11T21:41:26.2904369Z import random
2023-01-11T21:41:26.2904576Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2904781Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2904789Z 
2023-01-11T21:41:26.2904919Z aten = torch.ops.aten
2023-01-11T21:41:26.2905126Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2905307Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2905317Z 
2023-01-11T21:41:26.2905323Z 
2023-01-11T21:41:26.2905483Z async_compile.wait(globals())
2023-01-11T21:41:26.2905615Z del async_compile
2023-01-11T21:41:26.2905624Z 
2023-01-11T21:41:26.2905738Z def call(args):
2023-01-11T21:41:26.2905943Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2906073Z     args.clear()
2023-01-11T21:41:26.2906517Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.2906690Z     assert_size_stride(buf0, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:41:26.2906815Z     del arg0_1
2023-01-11T21:41:26.2906939Z     del arg1_1
2023-01-11T21:41:26.2907064Z     del arg7_1
2023-01-11T21:41:26.2907200Z     return (buf0, )
2023-01-11T21:41:26.2907214Z 
2023-01-11T21:41:26.2907220Z 
2023-01-11T21:41:26.2907359Z if __name__ == "__main__":
2023-01-11T21:41:26.2907556Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2907771Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2908116Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2908438Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2908778Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2909120Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2909433Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2909858Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2910181Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2910560Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2910805Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2910814Z 
2023-01-11T21:41:26.2911262Z [2023-01-11 21:25:38,531] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 95
2023-01-11T21:41:26.2912039Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2912262Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2912723Z [2023-01-11 21:25:38,669] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 96
2023-01-11T21:41:26.2913170Z [2023-01-11 21:25:38,691] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 96
2023-01-11T21:41:26.2913868Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2914091Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2914539Z [2023-01-11 21:25:38,817] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 97
2023-01-11T21:41:26.2914555Z 
2023-01-11T21:41:26.2914719Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2914835Z import torch
2023-01-11T21:41:26.2914943Z import random
2023-01-11T21:41:26.2915147Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2915357Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2915366Z 
2023-01-11T21:41:26.2915514Z aten = torch.ops.aten
2023-01-11T21:41:26.2915732Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2915904Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2915913Z 
2023-01-11T21:41:26.2915920Z 
2023-01-11T21:41:26.2916157Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2916505Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2916701Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2916869Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2916986Z {
2023-01-11T21:41:26.2917158Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2917378Z     {
2023-01-11T21:41:26.2917532Z         #pragma omp for 
2023-01-11T21:41:26.2917694Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.2917803Z         {
2023-01-11T21:41:26.2917970Z             #pragma GCC ivdep
2023-01-11T21:41:26.2918158Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2918290Z             {
2023-01-11T21:41:26.2918421Z                 {
2023-01-11T21:41:26.2918551Z                     {
2023-01-11T21:41:26.2918732Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.2918884Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:26.2918986Z                     }
2023-01-11T21:41:26.2919090Z                 }
2023-01-11T21:41:26.2919199Z             }
2023-01-11T21:41:26.2919330Z         }
2023-01-11T21:41:26.2919452Z     }
2023-01-11T21:41:26.2933900Z }
2023-01-11T21:41:26.2934162Z ''')
2023-01-11T21:41:26.2934327Z 
2023-01-11T21:41:26.2934332Z 
2023-01-11T21:41:26.2934618Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2934964Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2935171Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2935334Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2935427Z {
2023-01-11T21:41:26.2935593Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2935707Z     {
2023-01-11T21:41:26.2935843Z         #pragma omp for 
2023-01-11T21:41:26.2935990Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.2936110Z         {
2023-01-11T21:41:26.2936242Z             #pragma GCC ivdep
2023-01-11T21:41:26.2936380Z             for(long i1=0; i1<12100; i1+=1)
2023-01-11T21:41:26.2936493Z             {
2023-01-11T21:41:26.2936721Z                 {
2023-01-11T21:41:26.2936845Z                     {
2023-01-11T21:41:26.2937026Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:26.2937192Z                         out_ptr0[i1 + (12100*i0)] = tmp0;
2023-01-11T21:41:26.2937310Z                     }
2023-01-11T21:41:26.2937411Z                 }
2023-01-11T21:41:26.2937526Z             }
2023-01-11T21:41:26.2937645Z         }
2023-01-11T21:41:26.2937755Z     }
2023-01-11T21:41:26.2937863Z }
2023-01-11T21:41:26.2938019Z ''')
2023-01-11T21:41:26.2938029Z 
2023-01-11T21:41:26.2938035Z 
2023-01-11T21:41:26.2938162Z async_compile.wait(globals())
2023-01-11T21:41:26.2938296Z del async_compile
2023-01-11T21:41:26.2938307Z 
2023-01-11T21:41:26.2938434Z def call(args):
2023-01-11T21:41:26.2938638Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2938762Z     args.clear()
2023-01-11T21:41:26.2939163Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2939394Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2939523Z     del arg7_1
2023-01-11T21:41:26.2939939Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:26.2940128Z     assert_size_stride(buf1, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:41:26.2940254Z     del arg0_1
2023-01-11T21:41:26.2940369Z     del arg1_1
2023-01-11T21:41:26.2940491Z     del buf0
2023-01-11T21:41:26.2940883Z     buf2 = empty_strided((1, 128, 110, 110), (1548800, 12100, 110, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2941114Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2941223Z     return (buf2, )
2023-01-11T21:41:26.2941253Z 
2023-01-11T21:41:26.2941260Z 
2023-01-11T21:41:26.2941379Z if __name__ == "__main__":
2023-01-11T21:41:26.2941583Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2941790Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2942153Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2942488Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2942813Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2943224Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2943564Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2943869Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2944187Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2944581Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2944841Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2944854Z 
2023-01-11T21:41:26.2944952Z 
2023-01-11T21:41:26.2945118Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2945240Z import torch
2023-01-11T21:41:26.2945370Z import random
2023-01-11T21:41:26.2945563Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2945740Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2945750Z 
2023-01-11T21:41:26.2945889Z aten = torch.ops.aten
2023-01-11T21:41:26.2946121Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2946287Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2946296Z 
2023-01-11T21:41:26.2946304Z 
2023-01-11T21:41:26.2946459Z async_compile.wait(globals())
2023-01-11T21:41:26.2946576Z del async_compile
2023-01-11T21:41:26.2946585Z 
2023-01-11T21:41:26.2946709Z def call(args):
2023-01-11T21:41:26.2946971Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2947086Z     args.clear()
2023-01-11T21:41:26.2947529Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:26.2947729Z     assert_size_stride(buf0, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:41:26.2947851Z     del arg0_1
2023-01-11T21:41:26.2947972Z     del arg1_1
2023-01-11T21:41:26.2948097Z     del arg7_1
2023-01-11T21:41:26.2948220Z     return (buf0, )
2023-01-11T21:41:26.2948230Z 
2023-01-11T21:41:26.2948236Z 
2023-01-11T21:41:26.2948352Z if __name__ == "__main__":
2023-01-11T21:41:26.2948543Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2948753Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2949120Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2949440Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2949778Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2950113Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2950443Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2950760Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2951072Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2951441Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2951702Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2951717Z 
2023-01-11T21:41:26.2952169Z [2023-01-11 21:25:40,417] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 97
2023-01-11T21:41:26.2952896Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2953109Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2953562Z [2023-01-11 21:25:40,523] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 98
2023-01-11T21:41:26.2954001Z [2023-01-11 21:25:40,546] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 98
2023-01-11T21:41:26.2954713Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2954921Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2955441Z [2023-01-11 21:25:40,656] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 99
2023-01-11T21:41:26.2955884Z [2023-01-11 21:25:42,191] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 99
2023-01-11T21:41:26.2956592Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2956787Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2957286Z [2023-01-11 21:25:42,323] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 100
2023-01-11T21:41:26.2957303Z 
2023-01-11T21:41:26.2957475Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2957600Z import torch
2023-01-11T21:41:26.2957723Z import random
2023-01-11T21:41:26.2957923Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2958114Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2958146Z 
2023-01-11T21:41:26.2958380Z aten = torch.ops.aten
2023-01-11T21:41:26.2958636Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2958812Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2958822Z 
2023-01-11T21:41:26.2958829Z 
2023-01-11T21:41:26.2959107Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2959493Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2959714Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2959919Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2960025Z {
2023-01-11T21:41:26.2960220Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2960345Z     {
2023-01-11T21:41:26.2960537Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.2960691Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.2960816Z         {
2023-01-11T21:41:26.2960995Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2961111Z             {
2023-01-11T21:41:26.2961242Z                 {
2023-01-11T21:41:26.2961379Z                     {
2023-01-11T21:41:26.2961582Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.2961762Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.2961896Z                     }
2023-01-11T21:41:26.2962030Z                 }
2023-01-11T21:41:26.2962140Z             }
2023-01-11T21:41:26.2962268Z         }
2023-01-11T21:41:26.2962390Z     }
2023-01-11T21:41:26.2962510Z }
2023-01-11T21:41:26.2962687Z ''')
2023-01-11T21:41:26.2962700Z 
2023-01-11T21:41:26.2962712Z 
2023-01-11T21:41:26.2962977Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2963366Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2963584Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2963774Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2963896Z {
2023-01-11T21:41:26.2964096Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2964222Z     {
2023-01-11T21:41:26.2964379Z         #pragma omp for 
2023-01-11T21:41:26.2964546Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.2964657Z         {
2023-01-11T21:41:26.2964890Z             #pragma GCC ivdep
2023-01-11T21:41:26.2965057Z             for(long i1=0; i1<11664; i1+=1)
2023-01-11T21:41:26.2965169Z             {
2023-01-11T21:41:26.2965288Z                 {
2023-01-11T21:41:26.2965406Z                     {
2023-01-11T21:41:26.2965584Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.2965737Z                         out_ptr0[i1 + (11664*i0)] = tmp0;
2023-01-11T21:41:26.2965842Z                     }
2023-01-11T21:41:26.2966051Z                 }
2023-01-11T21:41:26.2966164Z             }
2023-01-11T21:41:26.2966279Z         }
2023-01-11T21:41:26.2966385Z     }
2023-01-11T21:41:26.2966475Z }
2023-01-11T21:41:26.2966632Z ''')
2023-01-11T21:41:26.2966641Z 
2023-01-11T21:41:26.2966647Z 
2023-01-11T21:41:26.2966788Z async_compile.wait(globals())
2023-01-11T21:41:26.2966913Z del async_compile
2023-01-11T21:41:26.2966925Z 
2023-01-11T21:41:26.2967048Z def call(args):
2023-01-11T21:41:26.2967242Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2967365Z     args.clear()
2023-01-11T21:41:26.2967743Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2967946Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2968136Z     del arg7_1
2023-01-11T21:41:26.2968578Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:26.2968767Z     assert_size_stride(buf1, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:41:26.2968894Z     del arg0_1
2023-01-11T21:41:26.2969143Z     del arg1_1
2023-01-11T21:41:26.2969265Z     del buf0
2023-01-11T21:41:26.2969652Z     buf2 = empty_strided((1, 32, 108, 108), (373248, 11664, 108, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2969861Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2969990Z     return (buf2, )
2023-01-11T21:41:26.2969998Z 
2023-01-11T21:41:26.2970005Z 
2023-01-11T21:41:26.2970139Z if __name__ == "__main__":
2023-01-11T21:41:26.2970338Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2970551Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2970907Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2971241Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2971546Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2971879Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2972203Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2972522Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2972847Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2973240Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2973497Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2973505Z 
2023-01-11T21:41:26.2973511Z 
2023-01-11T21:41:26.2973680Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2973803Z import torch
2023-01-11T21:41:26.2973917Z import random
2023-01-11T21:41:26.2974128Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2974324Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2974333Z 
2023-01-11T21:41:26.2974469Z aten = torch.ops.aten
2023-01-11T21:41:26.2974695Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2974857Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2974865Z 
2023-01-11T21:41:26.2974872Z 
2023-01-11T21:41:26.2975027Z async_compile.wait(globals())
2023-01-11T21:41:26.2975163Z del async_compile
2023-01-11T21:41:26.2975170Z 
2023-01-11T21:41:26.2975265Z def call(args):
2023-01-11T21:41:26.2975465Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2975589Z     args.clear()
2023-01-11T21:41:26.2976035Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:26.2976221Z     assert_size_stride(buf0, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:41:26.2976470Z     del arg0_1
2023-01-11T21:41:26.2976589Z     del arg1_1
2023-01-11T21:41:26.2976697Z     del arg7_1
2023-01-11T21:41:26.2976825Z     return (buf0, )
2023-01-11T21:41:26.2976837Z 
2023-01-11T21:41:26.2976843Z 
2023-01-11T21:41:26.2976970Z if __name__ == "__main__":
2023-01-11T21:41:26.2977284Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2977526Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2977946Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2978318Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2978700Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2979068Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2979541Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2979932Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2980295Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2980740Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2981050Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2981066Z 
2023-01-11T21:41:26.2981073Z 
2023-01-11T21:41:26.2981248Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.2981393Z import torch
2023-01-11T21:41:26.2981522Z import random
2023-01-11T21:41:26.2981752Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.2981989Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.2981999Z 
2023-01-11T21:41:26.2982148Z aten = torch.ops.aten
2023-01-11T21:41:26.2982412Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.2982598Z async_compile = AsyncCompile()
2023-01-11T21:41:26.2982617Z 
2023-01-11T21:41:26.2982624Z 
2023-01-11T21:41:26.2982901Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.2983354Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2983537Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2983675Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2983771Z {
2023-01-11T21:41:26.2983945Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2984057Z     {
2023-01-11T21:41:26.2984199Z         #pragma omp for 
2023-01-11T21:41:26.2984350Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.2984450Z         {
2023-01-11T21:41:26.2984597Z             #pragma GCC ivdep
2023-01-11T21:41:26.2984756Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.2984883Z             {
2023-01-11T21:41:26.2985005Z                 {
2023-01-11T21:41:26.2985127Z                     {
2023-01-11T21:41:26.2985318Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.2985481Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:26.2985604Z                     }
2023-01-11T21:41:26.2985724Z                 }
2023-01-11T21:41:26.2985838Z             }
2023-01-11T21:41:26.2985954Z         }
2023-01-11T21:41:26.2986067Z     }
2023-01-11T21:41:26.2986175Z }
2023-01-11T21:41:26.2986339Z ''')
2023-01-11T21:41:26.2986351Z 
2023-01-11T21:41:26.2986357Z 
2023-01-11T21:41:26.2986629Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.2987009Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.2987229Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.2987409Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.2987522Z {
2023-01-11T21:41:26.2987713Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.2987812Z     {
2023-01-11T21:41:26.2987952Z         #pragma omp for 
2023-01-11T21:41:26.2988243Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.2988359Z         {
2023-01-11T21:41:26.2988505Z             #pragma GCC ivdep
2023-01-11T21:41:26.2988669Z             for(long i1=0; i1<11664; i1+=1)
2023-01-11T21:41:26.2988786Z             {
2023-01-11T21:41:26.2988888Z                 {
2023-01-11T21:41:26.2989008Z                     {
2023-01-11T21:41:26.2989198Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:26.2989377Z                         out_ptr0[i1 + (11664*i0)] = tmp0;
2023-01-11T21:41:26.2989500Z                     }
2023-01-11T21:41:26.2989619Z                 }
2023-01-11T21:41:26.2989736Z             }
2023-01-11T21:41:26.2989834Z         }
2023-01-11T21:41:26.2989944Z     }
2023-01-11T21:41:26.2990050Z }
2023-01-11T21:41:26.2990223Z ''')
2023-01-11T21:41:26.2990236Z 
2023-01-11T21:41:26.2990328Z 
2023-01-11T21:41:26.2990518Z async_compile.wait(globals())
2023-01-11T21:41:26.2990653Z del async_compile
2023-01-11T21:41:26.2990670Z 
2023-01-11T21:41:26.2990800Z def call(args):
2023-01-11T21:41:26.2991004Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.2991135Z     args.clear()
2023-01-11T21:41:26.2991594Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2991845Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.2991965Z     del arg7_1
2023-01-11T21:41:26.2992456Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:26.2992663Z     assert_size_stride(buf1, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:41:26.2992770Z     del arg0_1
2023-01-11T21:41:26.2992895Z     del arg1_1
2023-01-11T21:41:26.2993011Z     del buf0
2023-01-11T21:41:26.2993471Z     buf2 = empty_strided((1, 128, 108, 108), (1492992, 11664, 108, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2993720Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.2993854Z     return (buf2, )
2023-01-11T21:41:26.2993867Z 
2023-01-11T21:41:26.2993874Z 
2023-01-11T21:41:26.2994009Z if __name__ == "__main__":
2023-01-11T21:41:26.2994222Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.2994434Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.2994864Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2995248Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2995624Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2995993Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2996369Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2996820Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2997122Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.2997473Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.2997721Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.2997732Z 
2023-01-11T21:41:26.2998182Z [2023-01-11 21:25:42,345] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 100
2023-01-11T21:41:26.2998885Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.2999100Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.2999659Z [2023-01-11 21:25:42,465] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 101
2023-01-11T21:41:26.3000116Z [2023-01-11 21:25:42,496] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 101
2023-01-11T21:41:26.3000814Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3001018Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3001546Z [2023-01-11 21:25:42,601] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 102
2023-01-11T21:41:26.3002015Z [2023-01-11 21:25:42,634] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 102
2023-01-11T21:41:26.3002713Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3002914Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3003347Z [2023-01-11 21:25:42,759] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 103
2023-01-11T21:41:26.3003359Z 
2023-01-11T21:41:26.3003515Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3003623Z import torch
2023-01-11T21:41:26.3003740Z import random
2023-01-11T21:41:26.3003928Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3004126Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3004141Z 
2023-01-11T21:41:26.3004269Z aten = torch.ops.aten
2023-01-11T21:41:26.3004463Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3004592Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3004599Z 
2023-01-11T21:41:26.3004604Z 
2023-01-11T21:41:26.3004736Z async_compile.wait(globals())
2023-01-11T21:41:26.3004845Z del async_compile
2023-01-11T21:41:26.3004853Z 
2023-01-11T21:41:26.3004961Z def call(args):
2023-01-11T21:41:26.3005149Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3005262Z     args.clear()
2023-01-11T21:41:26.3005703Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:26.3005865Z     assert_size_stride(buf0, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:41:26.3005979Z     del arg0_1
2023-01-11T21:41:26.3006082Z     del arg1_1
2023-01-11T21:41:26.3006185Z     del arg7_1
2023-01-11T21:41:26.3006301Z     return (buf0, )
2023-01-11T21:41:26.3006310Z 
2023-01-11T21:41:26.3006316Z 
2023-01-11T21:41:26.3006433Z if __name__ == "__main__":
2023-01-11T21:41:26.3006613Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3006805Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3007156Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3007478Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3007796Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3008112Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3008427Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3008745Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3009180Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3009674Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3009907Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3009917Z 
2023-01-11T21:41:26.3009939Z 
2023-01-11T21:41:26.3010073Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3010182Z import torch
2023-01-11T21:41:26.3010295Z import random
2023-01-11T21:41:26.3010473Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3010663Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3010671Z 
2023-01-11T21:41:26.3010792Z aten = torch.ops.aten
2023-01-11T21:41:26.3011000Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3011245Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3011257Z 
2023-01-11T21:41:26.3011281Z 
2023-01-11T21:41:26.3011513Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3011843Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3012029Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3012183Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3012281Z {
2023-01-11T21:41:26.3012437Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3012532Z     {
2023-01-11T21:41:26.3012661Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3012789Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3012887Z         {
2023-01-11T21:41:26.3013026Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3013123Z             {
2023-01-11T21:41:26.3013222Z                 {
2023-01-11T21:41:26.3013311Z                     {
2023-01-11T21:41:26.3013479Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.3013624Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.3013730Z                     }
2023-01-11T21:41:26.3013837Z                 }
2023-01-11T21:41:26.3013934Z             }
2023-01-11T21:41:26.3014031Z         }
2023-01-11T21:41:26.3014113Z     }
2023-01-11T21:41:26.3014207Z }
2023-01-11T21:41:26.3014351Z ''')
2023-01-11T21:41:26.3014360Z 
2023-01-11T21:41:26.3014365Z 
2023-01-11T21:41:26.3014588Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3014915Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3015105Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3015259Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3015336Z {
2023-01-11T21:41:26.3015491Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3015584Z     {
2023-01-11T21:41:26.3015703Z         #pragma omp for 
2023-01-11T21:41:26.3015840Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.3015944Z         {
2023-01-11T21:41:26.3016071Z             #pragma GCC ivdep
2023-01-11T21:41:26.3016199Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3016297Z             {
2023-01-11T21:41:26.3016392Z                 {
2023-01-11T21:41:26.3016495Z                     {
2023-01-11T21:41:26.3016653Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.3016803Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:26.3016907Z                     }
2023-01-11T21:41:26.3016993Z                 }
2023-01-11T21:41:26.3017091Z             }
2023-01-11T21:41:26.3017186Z         }
2023-01-11T21:41:26.3017279Z     }
2023-01-11T21:41:26.3017372Z }
2023-01-11T21:41:26.3017522Z ''')
2023-01-11T21:41:26.3017532Z 
2023-01-11T21:41:26.3017538Z 
2023-01-11T21:41:26.3017684Z async_compile.wait(globals())
2023-01-11T21:41:26.3017782Z del async_compile
2023-01-11T21:41:26.3017790Z 
2023-01-11T21:41:26.3017906Z def call(args):
2023-01-11T21:41:26.3018095Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3018299Z     args.clear()
2023-01-11T21:41:26.3018675Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3018887Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3018994Z     del arg7_1
2023-01-11T21:41:26.3019399Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.3019573Z     assert_size_stride(buf1, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:26.3019680Z     del arg0_1
2023-01-11T21:41:26.3019785Z     del arg1_1
2023-01-11T21:41:26.3019890Z     del buf0
2023-01-11T21:41:26.3020264Z     buf2 = empty_strided((1, 32, 112, 112), (401408, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3020599Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3020722Z     return (buf2, )
2023-01-11T21:41:26.3020732Z 
2023-01-11T21:41:26.3020738Z 
2023-01-11T21:41:26.3020849Z if __name__ == "__main__":
2023-01-11T21:41:26.3021031Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3021224Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3021581Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3021892Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3022180Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3022482Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3022796Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3023093Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3023467Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3023831Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3024083Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3024094Z 
2023-01-11T21:41:26.3024100Z 
2023-01-11T21:41:26.3024251Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3024361Z import torch
2023-01-11T21:41:26.3024472Z import random
2023-01-11T21:41:26.3024655Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3024827Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3024851Z 
2023-01-11T21:41:26.3024959Z aten = torch.ops.aten
2023-01-11T21:41:26.3025170Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3025319Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3025327Z 
2023-01-11T21:41:26.3025333Z 
2023-01-11T21:41:26.3025477Z async_compile.wait(globals())
2023-01-11T21:41:26.3025594Z del async_compile
2023-01-11T21:41:26.3025601Z 
2023-01-11T21:41:26.3025718Z def call(args):
2023-01-11T21:41:26.3025901Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3025997Z     args.clear()
2023-01-11T21:41:26.3026438Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.3026611Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:26.3026716Z     del arg0_1
2023-01-11T21:41:26.3026820Z     del arg1_1
2023-01-11T21:41:26.3026926Z     del arg7_1
2023-01-11T21:41:26.3027038Z     return (buf0, )
2023-01-11T21:41:26.3027047Z 
2023-01-11T21:41:26.3027053Z 
2023-01-11T21:41:26.3027171Z if __name__ == "__main__":
2023-01-11T21:41:26.3027335Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3027529Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3027896Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3028221Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3028629Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3028936Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3029241Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3029544Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3029825Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3030182Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3030432Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3030441Z 
2023-01-11T21:41:26.3030966Z [2023-01-11 21:25:42,791] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 103
2023-01-11T21:41:26.3031675Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3031872Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3032307Z [2023-01-11 21:25:42,925] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 104
2023-01-11T21:41:26.3032757Z [2023-01-11 21:25:42,947] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 104
2023-01-11T21:41:26.3033455Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3033658Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3034091Z [2023-01-11 21:25:43,074] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 105
2023-01-11T21:41:26.3034101Z 
2023-01-11T21:41:26.3034233Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3034346Z import torch
2023-01-11T21:41:26.3034456Z import random
2023-01-11T21:41:26.3034640Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3034832Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3034841Z 
2023-01-11T21:41:26.3034963Z aten = torch.ops.aten
2023-01-11T21:41:26.3035176Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3035309Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3035335Z 
2023-01-11T21:41:26.3035341Z 
2023-01-11T21:41:26.3035569Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3035897Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3036088Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3036243Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3036340Z {
2023-01-11T21:41:26.3036495Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3036589Z     {
2023-01-11T21:41:26.3036693Z         #pragma omp for 
2023-01-11T21:41:26.3036822Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3036920Z         {
2023-01-11T21:41:26.3037049Z             #pragma GCC ivdep
2023-01-11T21:41:26.3037188Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3037287Z             {
2023-01-11T21:41:26.3037388Z                 {
2023-01-11T21:41:26.3037484Z                     {
2023-01-11T21:41:26.3037652Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.3037892Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:26.3037996Z                     }
2023-01-11T21:41:26.3038096Z                 }
2023-01-11T21:41:26.3038194Z             }
2023-01-11T21:41:26.3038277Z         }
2023-01-11T21:41:26.3038371Z     }
2023-01-11T21:41:26.3038466Z }
2023-01-11T21:41:26.3038610Z ''')
2023-01-11T21:41:26.3038619Z 
2023-01-11T21:41:26.3038625Z 
2023-01-11T21:41:26.3038852Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3039153Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3039363Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3039536Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3039628Z {
2023-01-11T21:41:26.3039803Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3039976Z     {
2023-01-11T21:41:26.3040126Z         #pragma omp for 
2023-01-11T21:41:26.3040274Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.3040393Z         {
2023-01-11T21:41:26.3040536Z             #pragma GCC ivdep
2023-01-11T21:41:26.3040678Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3040793Z             {
2023-01-11T21:41:26.3040909Z                 {
2023-01-11T21:41:26.3041029Z                     {
2023-01-11T21:41:26.3041213Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:26.3041382Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:26.3041485Z                     }
2023-01-11T21:41:26.3041600Z                 }
2023-01-11T21:41:26.3041716Z             }
2023-01-11T21:41:26.3041826Z         }
2023-01-11T21:41:26.3041936Z     }
2023-01-11T21:41:26.3042043Z }
2023-01-11T21:41:26.3042190Z ''')
2023-01-11T21:41:26.3042199Z 
2023-01-11T21:41:26.3042206Z 
2023-01-11T21:41:26.3042352Z async_compile.wait(globals())
2023-01-11T21:41:26.3042486Z del async_compile
2023-01-11T21:41:26.3042495Z 
2023-01-11T21:41:26.3042618Z def call(args):
2023-01-11T21:41:26.3042835Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3042967Z     args.clear()
2023-01-11T21:41:26.3043388Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3043626Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3043748Z     del arg7_1
2023-01-11T21:41:26.3044207Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:26.3044412Z     assert_size_stride(buf1, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:26.3044536Z     del arg0_1
2023-01-11T21:41:26.3044655Z     del arg1_1
2023-01-11T21:41:26.3044775Z     del buf0
2023-01-11T21:41:26.3045207Z     buf2 = empty_strided((1, 128, 112, 112), (1605632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3045446Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3045565Z     return (buf2, )
2023-01-11T21:41:26.3045590Z 
2023-01-11T21:41:26.3045598Z 
2023-01-11T21:41:26.3045716Z if __name__ == "__main__":
2023-01-11T21:41:26.3045925Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3046147Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3046543Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3046913Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3047276Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3047632Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3047974Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3048344Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3048680Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3049301Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3049555Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3049564Z 
2023-01-11T21:41:26.3049571Z 
2023-01-11T21:41:26.3049724Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3049837Z import torch
2023-01-11T21:41:26.3049949Z import random
2023-01-11T21:41:26.3050133Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3050311Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3050320Z 
2023-01-11T21:41:26.3050443Z aten = torch.ops.aten
2023-01-11T21:41:26.3050656Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3050900Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3050911Z 
2023-01-11T21:41:26.3050917Z 
2023-01-11T21:41:26.3051062Z async_compile.wait(globals())
2023-01-11T21:41:26.3051187Z del async_compile
2023-01-11T21:41:26.3051194Z 
2023-01-11T21:41:26.3051306Z def call(args):
2023-01-11T21:41:26.3051493Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3051590Z     args.clear()
2023-01-11T21:41:26.3052021Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:26.3052201Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:26.3052310Z     del arg0_1
2023-01-11T21:41:26.3052416Z     del arg1_1
2023-01-11T21:41:26.3052523Z     del arg7_1
2023-01-11T21:41:26.3052637Z     return (buf0, )
2023-01-11T21:41:26.3052645Z 
2023-01-11T21:41:26.3052650Z 
2023-01-11T21:41:26.3052753Z if __name__ == "__main__":
2023-01-11T21:41:26.3052941Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3053138Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3053489Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3053818Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3054136Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3054456Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3054767Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3055062Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3055356Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3055673Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3055892Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3055899Z 
2023-01-11T21:41:26.3056269Z [2023-01-11 21:25:43,107] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 105
2023-01-11T21:41:26.3056858Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3057029Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3057387Z [2023-01-11 21:25:43,217] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 106
2023-01-11T21:41:26.3057765Z [2023-01-11 21:25:43,239] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 106
2023-01-11T21:41:26.3058354Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3058603Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3058942Z [2023-01-11 21:25:43,351] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 107
2023-01-11T21:41:26.3059332Z [2023-01-11 21:25:43,382] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 107
2023-01-11T21:41:26.3060134Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3060359Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3060824Z [2023-01-11 21:25:43,518] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 108
2023-01-11T21:41:26.3060832Z 
2023-01-11T21:41:26.3060996Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3061121Z import torch
2023-01-11T21:41:26.3061246Z import random
2023-01-11T21:41:26.3061449Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3061645Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3061669Z 
2023-01-11T21:41:26.3061789Z aten = torch.ops.aten
2023-01-11T21:41:26.3062026Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3062186Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3062194Z 
2023-01-11T21:41:26.3062201Z 
2023-01-11T21:41:26.3062451Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3062815Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3063027Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3063270Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3063364Z {
2023-01-11T21:41:26.3063533Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3063639Z     {
2023-01-11T21:41:26.3063794Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3063929Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3064042Z         {
2023-01-11T21:41:26.3064192Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3064287Z             {
2023-01-11T21:41:26.3064396Z                 {
2023-01-11T21:41:26.3064511Z                     {
2023-01-11T21:41:26.3064689Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.3064855Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.3064971Z                     }
2023-01-11T21:41:26.3065087Z                 }
2023-01-11T21:41:26.3065183Z             }
2023-01-11T21:41:26.3065290Z         }
2023-01-11T21:41:26.3065395Z     }
2023-01-11T21:41:26.3065499Z }
2023-01-11T21:41:26.3065649Z ''')
2023-01-11T21:41:26.3065657Z 
2023-01-11T21:41:26.3065663Z 
2023-01-11T21:41:26.3065909Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3066268Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3066457Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3066629Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3066734Z {
2023-01-11T21:41:26.3066907Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3067016Z     {
2023-01-11T21:41:26.3067155Z         #pragma omp for 
2023-01-11T21:41:26.3067297Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.3067392Z         {
2023-01-11T21:41:26.3067537Z             #pragma GCC ivdep
2023-01-11T21:41:26.3067691Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3067880Z             {
2023-01-11T21:41:26.3067993Z                 {
2023-01-11T21:41:26.3068107Z                     {
2023-01-11T21:41:26.3068285Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.3068438Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:26.3068552Z                     }
2023-01-11T21:41:26.3068662Z                 }
2023-01-11T21:41:26.3068777Z             }
2023-01-11T21:41:26.3068886Z         }
2023-01-11T21:41:26.3068989Z     }
2023-01-11T21:41:26.3069076Z }
2023-01-11T21:41:26.3069228Z ''')
2023-01-11T21:41:26.3069237Z 
2023-01-11T21:41:26.3069244Z 
2023-01-11T21:41:26.3069399Z async_compile.wait(globals())
2023-01-11T21:41:26.3069526Z del async_compile
2023-01-11T21:41:26.3069534Z 
2023-01-11T21:41:26.3069658Z def call(args):
2023-01-11T21:41:26.3069917Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3070050Z     args.clear()
2023-01-11T21:41:26.3070453Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3070675Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3070791Z     del arg7_1
2023-01-11T21:41:26.3071246Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:26.3071437Z     assert_size_stride(buf1, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:26.3071556Z     del arg0_1
2023-01-11T21:41:26.3071669Z     del arg1_1
2023-01-11T21:41:26.3071761Z     del buf0
2023-01-11T21:41:26.3072067Z     buf2 = empty_strided((1, 32, 112, 112), (401408, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3072249Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3072348Z     return (buf2, )
2023-01-11T21:41:26.3072359Z 
2023-01-11T21:41:26.3072365Z 
2023-01-11T21:41:26.3072467Z if __name__ == "__main__":
2023-01-11T21:41:26.3072620Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3072786Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3073126Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3073587Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3073980Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3074368Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3074758Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3075128Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3075498Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3075953Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3076273Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3076284Z 
2023-01-11T21:41:26.3076293Z 
2023-01-11T21:41:26.3076485Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3076617Z import torch
2023-01-11T21:41:26.3076740Z import random
2023-01-11T21:41:26.3076978Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3077222Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3077236Z 
2023-01-11T21:41:26.3077387Z aten = torch.ops.aten
2023-01-11T21:41:26.3077648Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3077840Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3077852Z 
2023-01-11T21:41:26.3077859Z 
2023-01-11T21:41:26.3078044Z async_compile.wait(globals())
2023-01-11T21:41:26.3078195Z del async_compile
2023-01-11T21:41:26.3078213Z 
2023-01-11T21:41:26.3078324Z def call(args):
2023-01-11T21:41:26.3078561Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3078821Z     args.clear()
2023-01-11T21:41:26.3079333Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:26.3079559Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:41:26.3079695Z     del arg0_1
2023-01-11T21:41:26.3079838Z     del arg1_1
2023-01-11T21:41:26.3079954Z     del arg7_1
2023-01-11T21:41:26.3080101Z     return (buf0, )
2023-01-11T21:41:26.3080112Z 
2023-01-11T21:41:26.3080120Z 
2023-01-11T21:41:26.3080280Z if __name__ == "__main__":
2023-01-11T21:41:26.3080510Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3080756Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3081283Z     arg0_1 = rand_strided((32, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3081685Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3082066Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3082426Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3082797Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3083193Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3083556Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3083998Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3084312Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3084323Z 
2023-01-11T21:41:26.3084329Z 
2023-01-11T21:41:26.3084510Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3084658Z import torch
2023-01-11T21:41:26.3084792Z import random
2023-01-11T21:41:26.3085021Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3085270Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3085282Z 
2023-01-11T21:41:26.3085435Z aten = torch.ops.aten
2023-01-11T21:41:26.3085701Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3085888Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3085897Z 
2023-01-11T21:41:26.3085907Z 
2023-01-11T21:41:26.3086183Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3086564Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3086786Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3086985Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3087109Z {
2023-01-11T21:41:26.3087296Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3087434Z     {
2023-01-11T21:41:26.3087599Z         #pragma omp for 
2023-01-11T21:41:26.3087768Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3087886Z         {
2023-01-11T21:41:26.3088036Z             #pragma GCC ivdep
2023-01-11T21:41:26.3088221Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3088355Z             {
2023-01-11T21:41:26.3088491Z                 {
2023-01-11T21:41:26.3088632Z                     {
2023-01-11T21:41:26.3088838Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.3088990Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:26.3089279Z                     }
2023-01-11T21:41:26.3089415Z                 }
2023-01-11T21:41:26.3089552Z             }
2023-01-11T21:41:26.3089687Z         }
2023-01-11T21:41:26.3089809Z     }
2023-01-11T21:41:26.3089912Z }
2023-01-11T21:41:26.3090091Z ''')
2023-01-11T21:41:26.3090102Z 
2023-01-11T21:41:26.3090111Z 
2023-01-11T21:41:26.3090402Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3090812Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3091183Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3091390Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3091521Z {
2023-01-11T21:41:26.3091715Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3091806Z     {
2023-01-11T21:41:26.3091962Z         #pragma omp for 
2023-01-11T21:41:26.3092132Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.3092264Z         {
2023-01-11T21:41:26.3092426Z             #pragma GCC ivdep
2023-01-11T21:41:26.3092601Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3092718Z             {
2023-01-11T21:41:26.3092834Z                 {
2023-01-11T21:41:26.3092971Z                     {
2023-01-11T21:41:26.3093181Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:26.3093477Z                         out_ptr0[i1 + (12544*i0)] = tmp0;
2023-01-11T21:41:26.3093611Z                     }
2023-01-11T21:41:26.3093748Z                 }
2023-01-11T21:41:26.3093867Z             }
2023-01-11T21:41:26.3093997Z         }
2023-01-11T21:41:26.3094127Z     }
2023-01-11T21:41:26.3094251Z }
2023-01-11T21:41:26.3094437Z ''')
2023-01-11T21:41:26.3094446Z 
2023-01-11T21:41:26.3094453Z 
2023-01-11T21:41:26.3094618Z async_compile.wait(globals())
2023-01-11T21:41:26.3094777Z del async_compile
2023-01-11T21:41:26.3094786Z 
2023-01-11T21:41:26.3094935Z def call(args):
2023-01-11T21:41:26.3095147Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3095290Z     args.clear()
2023-01-11T21:41:26.3095750Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3096019Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3096158Z     del arg7_1
2023-01-11T21:41:26.3096642Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:26.3096869Z     assert_size_stride(buf1, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:26.3096998Z     del arg0_1
2023-01-11T21:41:26.3097140Z     del arg1_1
2023-01-11T21:41:26.3097269Z     del buf0
2023-01-11T21:41:26.3097738Z     buf2 = empty_strided((1, 128, 112, 112), (1605632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3098008Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3098141Z     return (buf2, )
2023-01-11T21:41:26.3098150Z 
2023-01-11T21:41:26.3098156Z 
2023-01-11T21:41:26.3098314Z if __name__ == "__main__":
2023-01-11T21:41:26.3098547Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3098769Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3099197Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3099592Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3099975Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3100376Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3100738Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3101125Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3101495Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3101909Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3102216Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3102230Z 
2023-01-11T21:41:26.3102757Z [2023-01-11 21:25:43,540] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 108
2023-01-11T21:41:26.3103643Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3104026Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3104546Z [2023-01-11 21:25:43,664] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 109
2023-01-11T21:41:26.3105088Z [2023-01-11 21:25:43,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 109
2023-01-11T21:41:26.3105982Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3106231Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3106756Z [2023-01-11 21:25:43,800] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 110
2023-01-11T21:41:26.3107265Z [2023-01-11 21:25:43,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 110
2023-01-11T21:41:26.3108067Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3108301Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3108821Z [2023-01-11 21:25:43,934] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 111
2023-01-11T21:41:26.3108837Z 
2023-01-11T21:41:26.3109024Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3109175Z import torch
2023-01-11T21:41:26.3109331Z import random
2023-01-11T21:41:26.3109562Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3109804Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3109816Z 
2023-01-11T21:41:26.3109974Z aten = torch.ops.aten
2023-01-11T21:41:26.3110223Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3110415Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3110425Z 
2023-01-11T21:41:26.3110436Z 
2023-01-11T21:41:26.3110611Z async_compile.wait(globals())
2023-01-11T21:41:26.3110751Z del async_compile
2023-01-11T21:41:26.3110761Z 
2023-01-11T21:41:26.3110909Z def call(args):
2023-01-11T21:41:26.3111156Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3111309Z     args.clear()
2023-01-11T21:41:26.3111825Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:26.3112047Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:41:26.3112190Z     del arg0_1
2023-01-11T21:41:26.3112337Z     del arg1_1
2023-01-11T21:41:26.3112459Z     del arg7_1
2023-01-11T21:41:26.3112610Z     return (buf0, )
2023-01-11T21:41:26.3112620Z 
2023-01-11T21:41:26.3112627Z 
2023-01-11T21:41:26.3112783Z if __name__ == "__main__":
2023-01-11T21:41:26.3113011Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3113256Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3113664Z     arg0_1 = rand_strided((128, 3, 1, 1), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3114067Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3114461Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3114841Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3115327Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3115714Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3116075Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3116528Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3116805Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3116818Z 
2023-01-11T21:41:26.3116848Z 
2023-01-11T21:41:26.3117020Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3117161Z import torch
2023-01-11T21:41:26.3117323Z import random
2023-01-11T21:41:26.3117630Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3117862Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3117882Z 
2023-01-11T21:41:26.3118036Z aten = torch.ops.aten
2023-01-11T21:41:26.3118300Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3118468Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3118480Z 
2023-01-11T21:41:26.3118487Z 
2023-01-11T21:41:26.3118757Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3119141Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3119380Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3119571Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3119706Z {
2023-01-11T21:41:26.3119905Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3120010Z     {
2023-01-11T21:41:26.3120188Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3120350Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3120475Z         {
2023-01-11T21:41:26.3120667Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3120799Z             {
2023-01-11T21:41:26.3120934Z                 {
2023-01-11T21:41:26.3121052Z                     {
2023-01-11T21:41:26.3121263Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.3121442Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.3121581Z                     }
2023-01-11T21:41:26.3121719Z                 }
2023-01-11T21:41:26.3121847Z             }
2023-01-11T21:41:26.3121974Z         }
2023-01-11T21:41:26.3122084Z     }
2023-01-11T21:41:26.3122199Z }
2023-01-11T21:41:26.3122374Z ''')
2023-01-11T21:41:26.3122387Z 
2023-01-11T21:41:26.3122395Z 
2023-01-11T21:41:26.3122673Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3123066Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3123300Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3123505Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3123617Z {
2023-01-11T21:41:26.3123820Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3123939Z     {
2023-01-11T21:41:26.3124089Z         #pragma omp for 
2023-01-11T21:41:26.3124258Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.3124391Z         {
2023-01-11T21:41:26.3124561Z             #pragma GCC ivdep
2023-01-11T21:41:26.3124721Z             for(long i1=0; i1<12100; i1+=1)
2023-01-11T21:41:26.3124841Z             {
2023-01-11T21:41:26.3124975Z                 {
2023-01-11T21:41:26.3125117Z                     {
2023-01-11T21:41:26.3125326Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.3125529Z                         out_ptr0[i1 + (12100*i0)] = tmp0;
2023-01-11T21:41:26.3125663Z                     }
2023-01-11T21:41:26.3125762Z                 }
2023-01-11T21:41:26.3125892Z             }
2023-01-11T21:41:26.3126029Z         }
2023-01-11T21:41:26.3126167Z     }
2023-01-11T21:41:26.3126289Z }
2023-01-11T21:41:26.3126467Z ''')
2023-01-11T21:41:26.3126584Z 
2023-01-11T21:41:26.3126592Z 
2023-01-11T21:41:26.3126774Z async_compile.wait(globals())
2023-01-11T21:41:26.3126912Z del async_compile
2023-01-11T21:41:26.3126921Z 
2023-01-11T21:41:26.3127069Z def call(args):
2023-01-11T21:41:26.3127311Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3127462Z     args.clear()
2023-01-11T21:41:26.3127913Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3128188Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3128328Z     del arg7_1
2023-01-11T21:41:26.3128802Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.3129340Z     assert_size_stride(buf1, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:41:26.3129508Z     del arg0_1
2023-01-11T21:41:26.3129641Z     del arg1_1
2023-01-11T21:41:26.3129780Z     del buf0
2023-01-11T21:41:26.3130262Z     buf2 = empty_strided((1, 32, 110, 110), (387200, 12100, 110, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3130517Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3130668Z     return (buf2, )
2023-01-11T21:41:26.3130679Z 
2023-01-11T21:41:26.3130685Z 
2023-01-11T21:41:26.3130819Z if __name__ == "__main__":
2023-01-11T21:41:26.3131042Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3131276Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3131714Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3132092Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3132489Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3132874Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3133192Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3133488Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3133782Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3134135Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3134385Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3134395Z 
2023-01-11T21:41:26.3134402Z 
2023-01-11T21:41:26.3134596Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3134745Z import torch
2023-01-11T21:41:26.3134895Z import random
2023-01-11T21:41:26.3135133Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3135352Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3135362Z 
2023-01-11T21:41:26.3135522Z aten = torch.ops.aten
2023-01-11T21:41:26.3135807Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3135999Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3136011Z 
2023-01-11T21:41:26.3136018Z 
2023-01-11T21:41:26.3136188Z async_compile.wait(globals())
2023-01-11T21:41:26.3136341Z del async_compile
2023-01-11T21:41:26.3136351Z 
2023-01-11T21:41:26.3136501Z def call(args):
2023-01-11T21:41:26.3136743Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3136867Z     args.clear()
2023-01-11T21:41:26.3137370Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.3137589Z     assert_size_stride(buf0, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:41:26.3137726Z     del arg0_1
2023-01-11T21:41:26.3137872Z     del arg1_1
2023-01-11T21:41:26.3138001Z     del arg7_1
2023-01-11T21:41:26.3138151Z     return (buf0, )
2023-01-11T21:41:26.3138164Z 
2023-01-11T21:41:26.3138171Z 
2023-01-11T21:41:26.3138465Z if __name__ == "__main__":
2023-01-11T21:41:26.3138675Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3138900Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3139344Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3139839Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3140179Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3140517Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3140843Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3141166Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3141557Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3141952Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3142233Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3142244Z 
2023-01-11T21:41:26.3142709Z [2023-01-11 21:25:43,966] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 111
2023-01-11T21:41:26.3143492Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3143716Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3144175Z [2023-01-11 21:25:44,104] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 112
2023-01-11T21:41:26.3144630Z [2023-01-11 21:25:44,126] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 112
2023-01-11T21:41:26.3145330Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3145553Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3146000Z [2023-01-11 21:25:44,257] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 113
2023-01-11T21:41:26.3146014Z 
2023-01-11T21:41:26.3146166Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3146297Z import torch
2023-01-11T21:41:26.3146423Z import random
2023-01-11T21:41:26.3146621Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3146831Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3146846Z 
2023-01-11T21:41:26.3146985Z aten = torch.ops.aten
2023-01-11T21:41:26.3147228Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3147358Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3147388Z 
2023-01-11T21:41:26.3147395Z 
2023-01-11T21:41:26.3147624Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3147962Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3148153Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3148339Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3148452Z {
2023-01-11T21:41:26.3148629Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3148738Z     {
2023-01-11T21:41:26.3148858Z         #pragma omp for 
2023-01-11T21:41:26.3148997Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3149117Z         {
2023-01-11T21:41:26.3149265Z             #pragma GCC ivdep
2023-01-11T21:41:26.3149535Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3149661Z             {
2023-01-11T21:41:26.3149767Z                 {
2023-01-11T21:41:26.3149873Z                     {
2023-01-11T21:41:26.3150065Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.3150241Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:26.3150365Z                     }
2023-01-11T21:41:26.3150483Z                 }
2023-01-11T21:41:26.3150602Z             }
2023-01-11T21:41:26.3150685Z         }
2023-01-11T21:41:26.3150804Z     }
2023-01-11T21:41:26.3150919Z }
2023-01-11T21:41:26.3151082Z ''')
2023-01-11T21:41:26.3151091Z 
2023-01-11T21:41:26.3151096Z 
2023-01-11T21:41:26.3151344Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3151743Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3151960Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3152143Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3152233Z {
2023-01-11T21:41:26.3152400Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3152512Z     {
2023-01-11T21:41:26.3152652Z         #pragma omp for 
2023-01-11T21:41:26.3152809Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.3152924Z         {
2023-01-11T21:41:26.3153055Z             #pragma GCC ivdep
2023-01-11T21:41:26.3153195Z             for(long i1=0; i1<12100; i1+=1)
2023-01-11T21:41:26.3153311Z             {
2023-01-11T21:41:26.3153437Z                 {
2023-01-11T21:41:26.3153566Z                     {
2023-01-11T21:41:26.3153756Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:26.3153926Z                         out_ptr0[i1 + (12100*i0)] = tmp0;
2023-01-11T21:41:26.3154015Z                     }
2023-01-11T21:41:26.3154129Z                 }
2023-01-11T21:41:26.3154250Z             }
2023-01-11T21:41:26.3154376Z         }
2023-01-11T21:41:26.3154489Z     }
2023-01-11T21:41:26.3154599Z }
2023-01-11T21:41:26.3154759Z ''')
2023-01-11T21:41:26.3154768Z 
2023-01-11T21:41:26.3154774Z 
2023-01-11T21:41:26.3154894Z async_compile.wait(globals())
2023-01-11T21:41:26.3155023Z del async_compile
2023-01-11T21:41:26.3155034Z 
2023-01-11T21:41:26.3155163Z def call(args):
2023-01-11T21:41:26.3155371Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3155506Z     args.clear()
2023-01-11T21:41:26.3155893Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3156125Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3156256Z     del arg7_1
2023-01-11T21:41:26.3156674Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:26.3156871Z     assert_size_stride(buf1, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:41:26.3157009Z     del arg0_1
2023-01-11T21:41:26.3157133Z     del arg1_1
2023-01-11T21:41:26.3157248Z     del buf0
2023-01-11T21:41:26.3157646Z     buf2 = empty_strided((1, 128, 110, 110), (1548800, 12100, 110, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3157876Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3157990Z     return (buf2, )
2023-01-11T21:41:26.3158003Z 
2023-01-11T21:41:26.3158027Z 
2023-01-11T21:41:26.3158135Z if __name__ == "__main__":
2023-01-11T21:41:26.3158334Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3158544Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3158915Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3159259Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3159598Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3160031Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3160345Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3160676Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3161001Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3161386Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3161664Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3161674Z 
2023-01-11T21:41:26.3161680Z 
2023-01-11T21:41:26.3161851Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3161988Z import torch
2023-01-11T21:41:26.3162111Z import random
2023-01-11T21:41:26.3162389Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3162610Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3162629Z 
2023-01-11T21:41:26.3162773Z aten = torch.ops.aten
2023-01-11T21:41:26.3162990Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3163147Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3163155Z 
2023-01-11T21:41:26.3163160Z 
2023-01-11T21:41:26.3163324Z async_compile.wait(globals())
2023-01-11T21:41:26.3163459Z del async_compile
2023-01-11T21:41:26.3163469Z 
2023-01-11T21:41:26.3163595Z def call(args):
2023-01-11T21:41:26.3163780Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3163907Z     args.clear()
2023-01-11T21:41:26.3164349Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (1, 1), 4, 'none', [], '')
2023-01-11T21:41:26.3164543Z     assert_size_stride(buf0, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:41:26.3164674Z     del arg0_1
2023-01-11T21:41:26.3164799Z     del arg1_1
2023-01-11T21:41:26.3164925Z     del arg7_1
2023-01-11T21:41:26.3165056Z     return (buf0, )
2023-01-11T21:41:26.3165067Z 
2023-01-11T21:41:26.3165075Z 
2023-01-11T21:41:26.3165198Z if __name__ == "__main__":
2023-01-11T21:41:26.3165381Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3165590Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3165964Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3166295Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3166631Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3166962Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3167297Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3167617Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3167930Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3168327Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3168597Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3168611Z 
2023-01-11T21:41:26.3169204Z [2023-01-11 21:25:44,289] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 113
2023-01-11T21:41:26.3169914Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3170138Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3170584Z [2023-01-11 21:25:44,396] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 114
2023-01-11T21:41:26.3171165Z [2023-01-11 21:25:44,417] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 114
2023-01-11T21:41:26.3171868Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3172091Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3172532Z [2023-01-11 21:25:44,529] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 115
2023-01-11T21:41:26.3173063Z [2023-01-11 21:25:44,561] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 115
2023-01-11T21:41:26.3173784Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3174005Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3174445Z [2023-01-11 21:25:44,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 116
2023-01-11T21:41:26.3174457Z 
2023-01-11T21:41:26.3174633Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3174761Z import torch
2023-01-11T21:41:26.3174891Z import random
2023-01-11T21:41:26.3175085Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3175283Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3175293Z 
2023-01-11T21:41:26.3175444Z aten = torch.ops.aten
2023-01-11T21:41:26.3175682Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3175845Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3175854Z 
2023-01-11T21:41:26.3175859Z 
2023-01-11T21:41:26.3176101Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3176443Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3176646Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3176810Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3176900Z {
2023-01-11T21:41:26.3177073Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3177187Z     {
2023-01-11T21:41:26.3177349Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3177494Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3177613Z         {
2023-01-11T21:41:26.3177780Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3177881Z             {
2023-01-11T21:41:26.3178001Z                 {
2023-01-11T21:41:26.3178127Z                     {
2023-01-11T21:41:26.3178307Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.3178469Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.3178592Z                     }
2023-01-11T21:41:26.3178710Z                 }
2023-01-11T21:41:26.3178814Z             }
2023-01-11T21:41:26.3178924Z         }
2023-01-11T21:41:26.3179034Z     }
2023-01-11T21:41:26.3179141Z }
2023-01-11T21:41:26.3179304Z ''')
2023-01-11T21:41:26.3179316Z 
2023-01-11T21:41:26.3179322Z 
2023-01-11T21:41:26.3179579Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3179919Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3180114Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3180294Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3180407Z {
2023-01-11T21:41:26.3180591Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3180791Z     {
2023-01-11T21:41:26.3180938Z         #pragma omp for 
2023-01-11T21:41:26.3181097Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.3181194Z         {
2023-01-11T21:41:26.3181340Z             #pragma GCC ivdep
2023-01-11T21:41:26.3181494Z             for(long i1=0; i1<11664; i1+=1)
2023-01-11T21:41:26.3181607Z             {
2023-01-11T21:41:26.3181725Z                 {
2023-01-11T21:41:26.3181846Z                     {
2023-01-11T21:41:26.3182002Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.3182168Z                         out_ptr0[i1 + (11664*i0)] = tmp0;
2023-01-11T21:41:26.3182293Z                     }
2023-01-11T21:41:26.3182401Z                 }
2023-01-11T21:41:26.3182518Z             }
2023-01-11T21:41:26.3182633Z         }
2023-01-11T21:41:26.3182744Z     }
2023-01-11T21:41:26.3182898Z }
2023-01-11T21:41:26.3183078Z ''')
2023-01-11T21:41:26.3183085Z 
2023-01-11T21:41:26.3183092Z 
2023-01-11T21:41:26.3183322Z async_compile.wait(globals())
2023-01-11T21:41:26.3183466Z del async_compile
2023-01-11T21:41:26.3183478Z 
2023-01-11T21:41:26.3183604Z def call(args):
2023-01-11T21:41:26.3183810Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3183946Z     args.clear()
2023-01-11T21:41:26.3184349Z     buf0 = empty_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3184569Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3184699Z     del arg7_1
2023-01-11T21:41:26.3185130Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:26.3185326Z     assert_size_stride(buf1, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:41:26.3185454Z     del arg0_1
2023-01-11T21:41:26.3185583Z     del arg1_1
2023-01-11T21:41:26.3185696Z     del buf0
2023-01-11T21:41:26.3186066Z     buf2 = empty_strided((1, 32, 108, 108), (373248, 11664, 108, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3186305Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3186431Z     return (buf2, )
2023-01-11T21:41:26.3186439Z 
2023-01-11T21:41:26.3186446Z 
2023-01-11T21:41:26.3186582Z if __name__ == "__main__":
2023-01-11T21:41:26.3186787Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3187002Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3187362Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3187709Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3188025Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3188371Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3188700Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3189030Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3189356Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3189679Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3189891Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3189899Z 
2023-01-11T21:41:26.3189904Z 
2023-01-11T21:41:26.3190035Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3190132Z import torch
2023-01-11T21:41:26.3190216Z import random
2023-01-11T21:41:26.3190373Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3190536Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3190543Z 
2023-01-11T21:41:26.3190653Z aten = torch.ops.aten
2023-01-11T21:41:26.3190835Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3191063Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3191071Z 
2023-01-11T21:41:26.3191076Z 
2023-01-11T21:41:26.3191194Z async_compile.wait(globals())
2023-01-11T21:41:26.3191276Z del async_compile
2023-01-11T21:41:26.3191298Z 
2023-01-11T21:41:26.3191375Z def call(args):
2023-01-11T21:41:26.3191563Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3191689Z     args.clear()
2023-01-11T21:41:26.3192252Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 1, 'none', [], '')
2023-01-11T21:41:26.3192473Z     assert_size_stride(buf0, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:41:26.3192609Z     del arg0_1
2023-01-11T21:41:26.3192744Z     del arg1_1
2023-01-11T21:41:26.3192862Z     del arg7_1
2023-01-11T21:41:26.3193015Z     return (buf0, )
2023-01-11T21:41:26.3193025Z 
2023-01-11T21:41:26.3193102Z 
2023-01-11T21:41:26.3193265Z if __name__ == "__main__":
2023-01-11T21:41:26.3193492Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3193731Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3194158Z     arg0_1 = rand_strided((32, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3194537Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3194918Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3195272Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3195655Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3196031Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3196385Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3196831Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3197135Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3197154Z 
2023-01-11T21:41:26.3197160Z 
2023-01-11T21:41:26.3197346Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3197491Z import torch
2023-01-11T21:41:26.3197614Z import random
2023-01-11T21:41:26.3197842Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3198076Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3198086Z 
2023-01-11T21:41:26.3198246Z aten = torch.ops.aten
2023-01-11T21:41:26.3198502Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3198689Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3198699Z 
2023-01-11T21:41:26.3198704Z 
2023-01-11T21:41:26.3198972Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3199362Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3199582Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3199789Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3199919Z {
2023-01-11T21:41:26.3200110Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3200235Z     {
2023-01-11T21:41:26.3200394Z         #pragma omp for 
2023-01-11T21:41:26.3200558Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3200652Z         {
2023-01-11T21:41:26.3200822Z             #pragma GCC ivdep
2023-01-11T21:41:26.3201000Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.3201134Z             {
2023-01-11T21:41:26.3201265Z                 {
2023-01-11T21:41:26.3201398Z                     {
2023-01-11T21:41:26.3201602Z                         auto tmp0 = in_ptr0[i1 + (12544*i0)];
2023-01-11T21:41:26.3201768Z                         out_ptr0[i0 + (12*i1)] = tmp0;
2023-01-11T21:41:26.3201908Z                     }
2023-01-11T21:41:26.3202041Z                 }
2023-01-11T21:41:26.3202175Z             }
2023-01-11T21:41:26.3202313Z         }
2023-01-11T21:41:26.3202543Z     }
2023-01-11T21:41:26.3202652Z }
2023-01-11T21:41:26.3202830Z ''')
2023-01-11T21:41:26.3202840Z 
2023-01-11T21:41:26.3202848Z 
2023-01-11T21:41:26.3203128Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3203528Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3203759Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3203957Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3204083Z {
2023-01-11T21:41:26.3204277Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3204383Z     {
2023-01-11T21:41:26.3204549Z         #pragma omp for 
2023-01-11T21:41:26.3204713Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.3204842Z         {
2023-01-11T21:41:26.3205005Z             #pragma GCC ivdep
2023-01-11T21:41:26.3205266Z             for(long i1=0; i1<11664; i1+=1)
2023-01-11T21:41:26.3205401Z             {
2023-01-11T21:41:26.3205518Z                 {
2023-01-11T21:41:26.3205657Z                     {
2023-01-11T21:41:26.3205865Z                         auto tmp0 = in_ptr0[i0 + (128*i1)];
2023-01-11T21:41:26.3206051Z                         out_ptr0[i1 + (11664*i0)] = tmp0;
2023-01-11T21:41:26.3206186Z                     }
2023-01-11T21:41:26.3206320Z                 }
2023-01-11T21:41:26.3206436Z             }
2023-01-11T21:41:26.3206565Z         }
2023-01-11T21:41:26.3206691Z     }
2023-01-11T21:41:26.3206817Z }
2023-01-11T21:41:26.3206994Z ''')
2023-01-11T21:41:26.3207004Z 
2023-01-11T21:41:26.3207013Z 
2023-01-11T21:41:26.3207189Z async_compile.wait(globals())
2023-01-11T21:41:26.3207338Z del async_compile
2023-01-11T21:41:26.3207348Z 
2023-01-11T21:41:26.3207498Z def call(args):
2023-01-11T21:41:26.3207717Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3207862Z     args.clear()
2023-01-11T21:41:26.3208341Z     buf0 = empty_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3208609Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3208732Z     del arg7_1
2023-01-11T21:41:26.3209373Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:26.3209603Z     assert_size_stride(buf1, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:41:26.3209719Z     del arg0_1
2023-01-11T21:41:26.3209856Z     del arg1_1
2023-01-11T21:41:26.3210001Z     del buf0
2023-01-11T21:41:26.3210463Z     buf2 = empty_strided((1, 128, 108, 108), (1492992, 11664, 108, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3210718Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3210861Z     return (buf2, )
2023-01-11T21:41:26.3210875Z 
2023-01-11T21:41:26.3210881Z 
2023-01-11T21:41:26.3211042Z if __name__ == "__main__":
2023-01-11T21:41:26.3211271Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3211499Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3211925Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3212308Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3212693Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3213081Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3213472Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3213855Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3214217Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3214660Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3214967Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3215113Z 
2023-01-11T21:41:26.3215670Z [2023-01-11 21:25:44,717] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 116
2023-01-11T21:41:26.3216490Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3216730Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3217243Z [2023-01-11 21:25:44,858] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 117
2023-01-11T21:41:26.3217863Z [2023-01-11 21:25:44,881] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 117
2023-01-11T21:41:26.3218709Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3218948Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3219454Z [2023-01-11 21:25:45,121] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 118
2023-01-11T21:41:26.3219974Z [2023-01-11 21:25:46,869] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 118
2023-01-11T21:41:26.3220791Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3221015Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3221521Z [2023-01-11 21:25:47,184] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 119
2023-01-11T21:41:26.3222031Z [2023-01-11 21:25:47,207] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 119
2023-01-11T21:41:26.3222839Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3223089Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3223692Z [2023-01-11 21:25:47,904] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 120
2023-01-11T21:41:26.3223709Z 
2023-01-11T21:41:26.3223906Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3224054Z import torch
2023-01-11T21:41:26.3224196Z import random
2023-01-11T21:41:26.3224409Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3224640Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3224655Z 
2023-01-11T21:41:26.3224806Z aten = torch.ops.aten
2023-01-11T21:41:26.3225077Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3225262Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3225276Z 
2023-01-11T21:41:26.3225283Z 
2023-01-11T21:41:26.3225457Z async_compile.wait(globals())
2023-01-11T21:41:26.3225603Z del async_compile
2023-01-11T21:41:26.3225612Z 
2023-01-11T21:41:26.3225764Z def call(args):
2023-01-11T21:41:26.3225982Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3226236Z     args.clear()
2023-01-11T21:41:26.3226755Z     buf0 = torch.ops.mkldnn._convolution_pointwise(arg7_1, arg0_1, arg1_1, (0, 0), (1, 1), (2, 2), 4, 'none', [], '')
2023-01-11T21:41:26.3226975Z     assert_size_stride(buf0, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:41:26.3227117Z     del arg0_1
2023-01-11T21:41:26.3227252Z     del arg1_1
2023-01-11T21:41:26.3227387Z     del arg7_1
2023-01-11T21:41:26.3227538Z     return (buf0, )
2023-01-11T21:41:26.3227551Z 
2023-01-11T21:41:26.3227558Z 
2023-01-11T21:41:26.3227686Z if __name__ == "__main__":
2023-01-11T21:41:26.3227911Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3228145Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3228580Z     arg0_1 = rand_strided((128, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3229043Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3229441Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3229836Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3230223Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3230599Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3230953Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3231399Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3231700Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3231715Z 
2023-01-11T21:41:26.3231723Z 
2023-01-11T21:41:26.3231911Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3232062Z import torch
2023-01-11T21:41:26.3232216Z import random
2023-01-11T21:41:26.3232453Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3232665Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3232678Z 
2023-01-11T21:41:26.3232835Z aten = torch.ops.aten
2023-01-11T21:41:26.3233095Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3233281Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3233290Z 
2023-01-11T21:41:26.3233296Z 
2023-01-11T21:41:26.3233472Z async_compile.wait(globals())
2023-01-11T21:41:26.3233616Z del async_compile
2023-01-11T21:41:26.3233627Z 
2023-01-11T21:41:26.3233771Z def call(args):
2023-01-11T21:41:26.3233995Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3234120Z     args.clear()
2023-01-11T21:41:26.3234389Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3234632Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3234777Z     del arg0_1
2023-01-11T21:41:26.3234912Z     del arg1_1
2023-01-11T21:41:26.3235061Z     del arg7_1
2023-01-11T21:41:26.3235196Z     return (buf0, )
2023-01-11T21:41:26.3235208Z 
2023-01-11T21:41:26.3235216Z 
2023-01-11T21:41:26.3235370Z if __name__ == "__main__":
2023-01-11T21:41:26.3235581Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3235815Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3236265Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3236654Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3237040Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3237416Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3237793Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3238155Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3238624Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3239094Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3239400Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3239413Z 
2023-01-11T21:41:26.3239422Z 
2023-01-11T21:41:26.3239607Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3239760Z import torch
2023-01-11T21:41:26.3239907Z import random
2023-01-11T21:41:26.3240140Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3240362Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3240390Z 
2023-01-11T21:41:26.3240525Z aten = torch.ops.aten
2023-01-11T21:41:26.3240883Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3241075Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3241090Z 
2023-01-11T21:41:26.3241103Z 
2023-01-11T21:41:26.3241374Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3241768Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3242000Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3242191Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3242315Z {
2023-01-11T21:41:26.3242496Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3242626Z     {
2023-01-11T21:41:26.3242820Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3242990Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3243113Z         {
2023-01-11T21:41:26.3243302Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3243419Z             {
2023-01-11T21:41:26.3243553Z                 {
2023-01-11T21:41:26.3243697Z                     {
2023-01-11T21:41:26.3243893Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3244097Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3244248Z                     }
2023-01-11T21:41:26.3244378Z                 }
2023-01-11T21:41:26.3244492Z             }
2023-01-11T21:41:26.3244626Z         }
2023-01-11T21:41:26.3244746Z     }
2023-01-11T21:41:26.3244863Z }
2023-01-11T21:41:26.3245050Z ''')
2023-01-11T21:41:26.3245061Z 
2023-01-11T21:41:26.3245069Z 
2023-01-11T21:41:26.3245241Z async_compile.wait(globals())
2023-01-11T21:41:26.3245393Z del async_compile
2023-01-11T21:41:26.3245403Z 
2023-01-11T21:41:26.3245525Z def call(args):
2023-01-11T21:41:26.3245758Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3245902Z     args.clear()
2023-01-11T21:41:26.3246390Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3246659Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3246804Z     del arg7_1
2023-01-11T21:41:26.3247091Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3247324Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3247432Z     del arg0_1
2023-01-11T21:41:26.3247567Z     del arg1_1
2023-01-11T21:41:26.3247714Z     return (buf1, )
2023-01-11T21:41:26.3247723Z 
2023-01-11T21:41:26.3247730Z 
2023-01-11T21:41:26.3247894Z if __name__ == "__main__":
2023-01-11T21:41:26.3248122Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3248368Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3248817Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3249341Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3249725Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3250098Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3250624Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3250940Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3251230Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3251614Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3251862Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3251871Z 
2023-01-11T21:41:26.3251878Z 
2023-01-11T21:41:26.3252026Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3252123Z import torch
2023-01-11T21:41:26.3252235Z import random
2023-01-11T21:41:26.3252466Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3252655Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3252667Z 
2023-01-11T21:41:26.3252785Z aten = torch.ops.aten
2023-01-11T21:41:26.3252989Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3253127Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3253135Z 
2023-01-11T21:41:26.3253140Z 
2023-01-11T21:41:26.3253274Z async_compile.wait(globals())
2023-01-11T21:41:26.3253369Z del async_compile
2023-01-11T21:41:26.3253376Z 
2023-01-11T21:41:26.3253485Z def call(args):
2023-01-11T21:41:26.3253662Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3253770Z     args.clear()
2023-01-11T21:41:26.3253984Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3254162Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3254271Z     del arg0_1
2023-01-11T21:41:26.3254359Z     del arg1_1
2023-01-11T21:41:26.3254462Z     del arg7_1
2023-01-11T21:41:26.3254571Z     return (buf0, )
2023-01-11T21:41:26.3254583Z 
2023-01-11T21:41:26.3254589Z 
2023-01-11T21:41:26.3254704Z if __name__ == "__main__":
2023-01-11T21:41:26.3254879Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3255064Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3255410Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3255722Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3256010Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3256313Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3256610Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3256917Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3257202Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3257673Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3257887Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3257893Z 
2023-01-11T21:41:26.3258264Z [2023-01-11 21:25:49,549] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 120
2023-01-11T21:41:26.3258833Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3259004Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3259344Z [2023-01-11 21:25:50,097] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 121
2023-01-11T21:41:26.3259741Z [2023-01-11 21:25:50,120] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 121
2023-01-11T21:41:26.3260304Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3260466Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3260815Z [2023-01-11 21:25:50,338] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 122
2023-01-11T21:41:26.3261206Z [2023-01-11 21:25:50,367] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 122
2023-01-11T21:41:26.3261771Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3261938Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3262290Z [2023-01-11 21:25:50,670] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 123
2023-01-11T21:41:26.3262646Z [2023-01-11 21:25:50,694] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 123
2023-01-11T21:41:26.3263267Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3263437Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3263771Z [2023-01-11 21:25:51,340] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 124
2023-01-11T21:41:26.3263794Z 
2023-01-11T21:41:26.3263903Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3263995Z import torch
2023-01-11T21:41:26.3264092Z import random
2023-01-11T21:41:26.3264245Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3264403Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3264411Z 
2023-01-11T21:41:26.3264514Z aten = torch.ops.aten
2023-01-11T21:41:26.3264686Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3264796Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3264803Z 
2023-01-11T21:41:26.3264808Z 
2023-01-11T21:41:26.3264991Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3265255Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3265415Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3265547Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3265628Z {
2023-01-11T21:41:26.3265758Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3265824Z     {
2023-01-11T21:41:26.3265927Z         #pragma omp for 
2023-01-11T21:41:26.3266037Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3266121Z         {
2023-01-11T21:41:26.3266226Z             #pragma GCC ivdep
2023-01-11T21:41:26.3266344Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3266430Z             {
2023-01-11T21:41:26.3266502Z                 {
2023-01-11T21:41:26.3266588Z                     {
2023-01-11T21:41:26.3266728Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:26.3266857Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3266984Z                     }
2023-01-11T21:41:26.3267070Z                 }
2023-01-11T21:41:26.3267153Z             }
2023-01-11T21:41:26.3267220Z         }
2023-01-11T21:41:26.3267300Z     }
2023-01-11T21:41:26.3267378Z }
2023-01-11T21:41:26.3267485Z ''')
2023-01-11T21:41:26.3267492Z 
2023-01-11T21:41:26.3267497Z 
2023-01-11T21:41:26.3267615Z async_compile.wait(globals())
2023-01-11T21:41:26.3267712Z del async_compile
2023-01-11T21:41:26.3267719Z 
2023-01-11T21:41:26.3267812Z def call(args):
2023-01-11T21:41:26.3267953Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3268048Z     args.clear()
2023-01-11T21:41:26.3268371Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3268588Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3268683Z     del arg7_1
2023-01-11T21:41:26.3268869Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3269026Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3269117Z     del arg0_1
2023-01-11T21:41:26.3269192Z     del arg1_1
2023-01-11T21:41:26.3269287Z     return (buf1, )
2023-01-11T21:41:26.3269294Z 
2023-01-11T21:41:26.3269299Z 
2023-01-11T21:41:26.3269398Z if __name__ == "__main__":
2023-01-11T21:41:26.3269547Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3269709Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3270007Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3270270Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3270538Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3270788Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3271049Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3271308Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3271555Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3271879Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3272084Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3272091Z 
2023-01-11T21:41:26.3272096Z 
2023-01-11T21:41:26.3272220Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3272315Z import torch
2023-01-11T21:41:26.3272395Z import random
2023-01-11T21:41:26.3272551Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3272710Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3272717Z 
2023-01-11T21:41:26.3272822Z aten = torch.ops.aten
2023-01-11T21:41:26.3272997Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3273117Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3273124Z 
2023-01-11T21:41:26.3273129Z 
2023-01-11T21:41:26.3273246Z async_compile.wait(globals())
2023-01-11T21:41:26.3273345Z del async_compile
2023-01-11T21:41:26.3273351Z 
2023-01-11T21:41:26.3273430Z def call(args):
2023-01-11T21:41:26.3273584Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3273678Z     args.clear()
2023-01-11T21:41:26.3273865Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3274019Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3274109Z     del arg0_1
2023-01-11T21:41:26.3274204Z     del arg1_1
2023-01-11T21:41:26.3274279Z     del arg7_1
2023-01-11T21:41:26.3274373Z     return (buf0, )
2023-01-11T21:41:26.3274415Z 
2023-01-11T21:41:26.3274420Z 
2023-01-11T21:41:26.3274522Z if __name__ == "__main__":
2023-01-11T21:41:26.3274672Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3274835Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3275129Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3275396Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3275657Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3275902Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3276165Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3276453Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3276702Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3277026Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3277232Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3277239Z 
2023-01-11T21:41:26.3277243Z 
2023-01-11T21:41:26.3277370Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3277463Z import torch
2023-01-11T21:41:26.3277557Z import random
2023-01-11T21:41:26.3277696Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3277854Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3277861Z 
2023-01-11T21:41:26.3277964Z aten = torch.ops.aten
2023-01-11T21:41:26.3278136Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3278257Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3278267Z 
2023-01-11T21:41:26.3278272Z 
2023-01-11T21:41:26.3278456Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3278722Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3278879Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3278996Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3279079Z {
2023-01-11T21:41:26.3279209Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3279291Z     {
2023-01-11T21:41:26.3279411Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3279521Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3279604Z         {
2023-01-11T21:41:26.3279710Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3279793Z             {
2023-01-11T21:41:26.3279879Z                 {
2023-01-11T21:41:26.3279968Z                     {
2023-01-11T21:41:26.3280102Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3280233Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3280310Z                     }
2023-01-11T21:41:26.3280399Z                 }
2023-01-11T21:41:26.3280482Z             }
2023-01-11T21:41:26.3280565Z         }
2023-01-11T21:41:26.3280645Z     }
2023-01-11T21:41:26.3280723Z }
2023-01-11T21:41:26.3280830Z ''')
2023-01-11T21:41:26.3280837Z 
2023-01-11T21:41:26.3280842Z 
2023-01-11T21:41:26.3280945Z async_compile.wait(globals())
2023-01-11T21:41:26.3281041Z del async_compile
2023-01-11T21:41:26.3281048Z 
2023-01-11T21:41:26.3281139Z def call(args):
2023-01-11T21:41:26.3281295Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3281390Z     args.clear()
2023-01-11T21:41:26.3281711Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3281885Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3281980Z     del arg7_1
2023-01-11T21:41:26.3282149Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3282341Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3282432Z     del arg0_1
2023-01-11T21:41:26.3282521Z     del arg1_1
2023-01-11T21:41:26.3282614Z     return (buf1, )
2023-01-11T21:41:26.3282620Z 
2023-01-11T21:41:26.3282625Z 
2023-01-11T21:41:26.3282724Z if __name__ == "__main__":
2023-01-11T21:41:26.3282875Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3283037Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3283318Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3283581Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3283843Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3284136Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3284398Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3284659Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3284907Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3285276Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3285520Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3285528Z 
2023-01-11T21:41:26.3285554Z 
2023-01-11T21:41:26.3285724Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3285854Z import torch
2023-01-11T21:41:26.3286028Z import random
2023-01-11T21:41:26.3286242Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3286445Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3286455Z 
2023-01-11T21:41:26.3286597Z aten = torch.ops.aten
2023-01-11T21:41:26.3286828Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3286984Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3286995Z 
2023-01-11T21:41:26.3287000Z 
2023-01-11T21:41:26.3287157Z async_compile.wait(globals())
2023-01-11T21:41:26.3287288Z del async_compile
2023-01-11T21:41:26.3287298Z 
2023-01-11T21:41:26.3287425Z def call(args):
2023-01-11T21:41:26.3287625Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3287755Z     args.clear()
2023-01-11T21:41:26.3287984Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3288177Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3288287Z     del arg0_1
2023-01-11T21:41:26.3288408Z     del arg1_1
2023-01-11T21:41:26.3288537Z     del arg7_1
2023-01-11T21:41:26.3288777Z     return (buf0, )
2023-01-11T21:41:26.3288787Z 
2023-01-11T21:41:26.3288794Z 
2023-01-11T21:41:26.3288958Z if __name__ == "__main__":
2023-01-11T21:41:26.3289331Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3289582Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3290036Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3290436Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3290829Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3291217Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3291610Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3291983Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3292353Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3292830Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3293277Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3293309Z 
2023-01-11T21:41:26.3293823Z [2023-01-11 21:25:51,368] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 124
2023-01-11T21:41:26.3294643Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3294890Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3295521Z [2023-01-11 21:25:51,954] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 125
2023-01-11T21:41:26.3296057Z [2023-01-11 21:25:51,979] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 125
2023-01-11T21:41:26.3296883Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3297132Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3297654Z [2023-01-11 21:25:52,200] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 126
2023-01-11T21:41:26.3298185Z [2023-01-11 21:25:52,230] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 126
2023-01-11T21:41:26.3298996Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3299250Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3299758Z [2023-01-11 21:25:52,558] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 127
2023-01-11T21:41:26.3300270Z [2023-01-11 21:25:52,586] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 127
2023-01-11T21:41:26.3301088Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3301345Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3301855Z [2023-01-11 21:25:53,274] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 128
2023-01-11T21:41:26.3301869Z 
2023-01-11T21:41:26.3302064Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3302214Z import torch
2023-01-11T21:41:26.3302368Z import random
2023-01-11T21:41:26.3302599Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3302828Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3302843Z 
2023-01-11T21:41:26.3302983Z aten = torch.ops.aten
2023-01-11T21:41:26.3303332Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3303527Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3303539Z 
2023-01-11T21:41:26.3303550Z 
2023-01-11T21:41:26.3303845Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3304245Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3304595Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3304789Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3304924Z {
2023-01-11T21:41:26.3305115Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3305242Z     {
2023-01-11T21:41:26.3305408Z         #pragma omp for 
2023-01-11T21:41:26.3305584Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3305719Z         {
2023-01-11T21:41:26.3305883Z             #pragma GCC ivdep
2023-01-11T21:41:26.3306054Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3306190Z             {
2023-01-11T21:41:26.3306328Z                 {
2023-01-11T21:41:26.3306468Z                     {
2023-01-11T21:41:26.3306676Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:26.3306974Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3307120Z                     }
2023-01-11T21:41:26.3307236Z                 }
2023-01-11T21:41:26.3307382Z             }
2023-01-11T21:41:26.3307510Z         }
2023-01-11T21:41:26.3307646Z     }
2023-01-11T21:41:26.3307755Z }
2023-01-11T21:41:26.3307944Z ''')
2023-01-11T21:41:26.3307954Z 
2023-01-11T21:41:26.3307961Z 
2023-01-11T21:41:26.3308144Z async_compile.wait(globals())
2023-01-11T21:41:26.3308269Z del async_compile
2023-01-11T21:41:26.3308299Z 
2023-01-11T21:41:26.3308425Z def call(args):
2023-01-11T21:41:26.3308658Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3308802Z     args.clear()
2023-01-11T21:41:26.3309297Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3309566Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3309699Z     del arg7_1
2023-01-11T21:41:26.3309976Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3310192Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3310336Z     del arg0_1
2023-01-11T21:41:26.3310479Z     del arg1_1
2023-01-11T21:41:26.3310620Z     return (buf1, )
2023-01-11T21:41:26.3310629Z 
2023-01-11T21:41:26.3310636Z 
2023-01-11T21:41:26.3310792Z if __name__ == "__main__":
2023-01-11T21:41:26.3311015Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3311264Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3311703Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3312084Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3312480Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3312880Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3313267Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3313646Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3314013Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3314464Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3314765Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3314782Z 
2023-01-11T21:41:26.3314790Z 
2023-01-11T21:41:26.3314972Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3315115Z import torch
2023-01-11T21:41:26.3315266Z import random
2023-01-11T21:41:26.3315482Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3315726Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3315743Z 
2023-01-11T21:41:26.3315911Z aten = torch.ops.aten
2023-01-11T21:41:26.3316170Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3316468Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3316480Z 
2023-01-11T21:41:26.3316486Z 
2023-01-11T21:41:26.3316647Z async_compile.wait(globals())
2023-01-11T21:41:26.3316809Z del async_compile
2023-01-11T21:41:26.3316819Z 
2023-01-11T21:41:26.3316965Z def call(args):
2023-01-11T21:41:26.3317205Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3317354Z     args.clear()
2023-01-11T21:41:26.3317612Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3317840Z     assert_size_stride(buf0, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:41:26.3317984Z     del arg0_1
2023-01-11T21:41:26.3318101Z     del arg1_1
2023-01-11T21:41:26.3318248Z     del arg7_1
2023-01-11T21:41:26.3318470Z     return (buf0, )
2023-01-11T21:41:26.3318487Z 
2023-01-11T21:41:26.3318493Z 
2023-01-11T21:41:26.3318648Z if __name__ == "__main__":
2023-01-11T21:41:26.3318884Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3319128Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3319591Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3319959Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3320337Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3320721Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3321108Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3321477Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3321845Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3322327Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3322637Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3322653Z 
2023-01-11T21:41:26.3322660Z 
2023-01-11T21:41:26.3322857Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3322982Z import torch
2023-01-11T21:41:26.3323130Z import random
2023-01-11T21:41:26.3323355Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3323591Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3323604Z 
2023-01-11T21:41:26.3323764Z aten = torch.ops.aten
2023-01-11T21:41:26.3324026Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3324216Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3324228Z 
2023-01-11T21:41:26.3324235Z 
2023-01-11T21:41:26.3324521Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3324899Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3325139Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3325339Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3325470Z {
2023-01-11T21:41:26.3325670Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3325810Z     {
2023-01-11T21:41:26.3326001Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3326141Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3326273Z         {
2023-01-11T21:41:26.3326452Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3326589Z             {
2023-01-11T21:41:26.3326722Z                 {
2023-01-11T21:41:26.3326866Z                     {
2023-01-11T21:41:26.3327060Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3327238Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3327390Z                     }
2023-01-11T21:41:26.3327533Z                 }
2023-01-11T21:41:26.3327663Z             }
2023-01-11T21:41:26.3327799Z         }
2023-01-11T21:41:26.3328032Z     }
2023-01-11T21:41:26.3328143Z }
2023-01-11T21:41:26.3328333Z ''')
2023-01-11T21:41:26.3328347Z 
2023-01-11T21:41:26.3328353Z 
2023-01-11T21:41:26.3328536Z async_compile.wait(globals())
2023-01-11T21:41:26.3328691Z del async_compile
2023-01-11T21:41:26.3328700Z 
2023-01-11T21:41:26.3328850Z def call(args):
2023-01-11T21:41:26.3329221Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3329375Z     args.clear()
2023-01-11T21:41:26.3329869Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3330116Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3330266Z     del arg7_1
2023-01-11T21:41:26.3330658Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3330899Z     assert_size_stride(buf1, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:41:26.3331041Z     del arg0_1
2023-01-11T21:41:26.3331189Z     del arg1_1
2023-01-11T21:41:26.3331338Z     return (buf1, )
2023-01-11T21:41:26.3331350Z 
2023-01-11T21:41:26.3331356Z 
2023-01-11T21:41:26.3331510Z if __name__ == "__main__":
2023-01-11T21:41:26.3331723Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3331961Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3332422Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3332819Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3333201Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3333578Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3333960Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3334352Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3334699Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3335147Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3335449Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3335461Z 
2023-01-11T21:41:26.3335469Z 
2023-01-11T21:41:26.3335664Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3335813Z import torch
2023-01-11T21:41:26.3335947Z import random
2023-01-11T21:41:26.3336180Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3336428Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3336440Z 
2023-01-11T21:41:26.3336578Z aten = torch.ops.aten
2023-01-11T21:41:26.3336833Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3337019Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3337036Z 
2023-01-11T21:41:26.3337046Z 
2023-01-11T21:41:26.3337234Z async_compile.wait(globals())
2023-01-11T21:41:26.3337384Z del async_compile
2023-01-11T21:41:26.3337393Z 
2023-01-11T21:41:26.3337543Z def call(args):
2023-01-11T21:41:26.3337783Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3337929Z     args.clear()
2023-01-11T21:41:26.3338169Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3338406Z     assert_size_stride(buf0, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:41:26.3338552Z     del arg0_1
2023-01-11T21:41:26.3338692Z     del arg1_1
2023-01-11T21:41:26.3338818Z     del arg7_1
2023-01-11T21:41:26.3338964Z     return (buf0, )
2023-01-11T21:41:26.3338977Z 
2023-01-11T21:41:26.3338983Z 
2023-01-11T21:41:26.3339146Z if __name__ == "__main__":
2023-01-11T21:41:26.3339356Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3339743Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3340206Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3340608Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3341004Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3341390Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3341762Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3342147Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3342491Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3343059Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3343471Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3343492Z 
2023-01-11T21:41:26.3344035Z [2023-01-11 21:25:53,303] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 128
2023-01-11T21:41:26.3344860Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3345117Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3345622Z [2023-01-11 21:25:53,937] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 129
2023-01-11T21:41:26.3346154Z [2023-01-11 21:25:53,960] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 129
2023-01-11T21:41:26.3346968Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3347216Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3347728Z [2023-01-11 21:25:54,281] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 130
2023-01-11T21:41:26.3348271Z [2023-01-11 21:25:54,311] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 130
2023-01-11T21:41:26.3349068Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3349327Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3349832Z [2023-01-11 21:25:54,685] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 131
2023-01-11T21:41:26.3350270Z [2023-01-11 21:25:54,708] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 131
2023-01-11T21:41:26.3351091Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3351341Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3351849Z [2023-01-11 21:25:55,319] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 132
2023-01-11T21:41:26.3351972Z 
2023-01-11T21:41:26.3352179Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3352319Z import torch
2023-01-11T21:41:26.3352461Z import random
2023-01-11T21:41:26.3352672Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3352917Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3352928Z 
2023-01-11T21:41:26.3353087Z aten = torch.ops.aten
2023-01-11T21:41:26.3353352Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3353546Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3353557Z 
2023-01-11T21:41:26.3353563Z 
2023-01-11T21:41:26.3353840Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3354344Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3354593Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3354777Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3354897Z {
2023-01-11T21:41:26.3355086Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3355218Z     {
2023-01-11T21:41:26.3355380Z         #pragma omp for 
2023-01-11T21:41:26.3355549Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3355679Z         {
2023-01-11T21:41:26.3355819Z             #pragma GCC ivdep
2023-01-11T21:41:26.3356001Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3356168Z             {
2023-01-11T21:41:26.3356306Z                 {
2023-01-11T21:41:26.3356445Z                     {
2023-01-11T21:41:26.3356641Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:26.3356837Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3356957Z                     }
2023-01-11T21:41:26.3357095Z                 }
2023-01-11T21:41:26.3357230Z             }
2023-01-11T21:41:26.3357367Z         }
2023-01-11T21:41:26.3357499Z     }
2023-01-11T21:41:26.3357617Z }
2023-01-11T21:41:26.3357778Z ''')
2023-01-11T21:41:26.3357790Z 
2023-01-11T21:41:26.3357817Z 
2023-01-11T21:41:26.3357980Z async_compile.wait(globals())
2023-01-11T21:41:26.3358125Z del async_compile
2023-01-11T21:41:26.3358138Z 
2023-01-11T21:41:26.3358285Z def call(args):
2023-01-11T21:41:26.3358516Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3358672Z     args.clear()
2023-01-11T21:41:26.3359154Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3359416Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3359544Z     del arg7_1
2023-01-11T21:41:26.3359861Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3360092Z     assert_size_stride(buf1, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:41:26.3360232Z     del arg0_1
2023-01-11T21:41:26.3360372Z     del arg1_1
2023-01-11T21:41:26.3360519Z     return (buf1, )
2023-01-11T21:41:26.3360529Z 
2023-01-11T21:41:26.3360536Z 
2023-01-11T21:41:26.3360694Z if __name__ == "__main__":
2023-01-11T21:41:26.3360918Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3361138Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3361591Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3361984Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3362368Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3362752Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3363142Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3363524Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3363986Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3364437Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3364750Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3364766Z 
2023-01-11T21:41:26.3364773Z 
2023-01-11T21:41:26.3364961Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3365115Z import torch
2023-01-11T21:41:26.3365262Z import random
2023-01-11T21:41:26.3365496Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3365735Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3365747Z 
2023-01-11T21:41:26.3365903Z aten = torch.ops.aten
2023-01-11T21:41:26.3366223Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3366424Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3366440Z 
2023-01-11T21:41:26.3366449Z 
2023-01-11T21:41:26.3366630Z async_compile.wait(globals())
2023-01-11T21:41:26.3366779Z del async_compile
2023-01-11T21:41:26.3366791Z 
2023-01-11T21:41:26.3366927Z def call(args):
2023-01-11T21:41:26.3367159Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3367313Z     args.clear()
2023-01-11T21:41:26.3367588Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3367801Z     assert_size_stride(buf0, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:41:26.3367937Z     del arg0_1
2023-01-11T21:41:26.3368073Z     del arg1_1
2023-01-11T21:41:26.3368214Z     del arg7_1
2023-01-11T21:41:26.3368357Z     return (buf0, )
2023-01-11T21:41:26.3368368Z 
2023-01-11T21:41:26.3368375Z 
2023-01-11T21:41:26.3368537Z if __name__ == "__main__":
2023-01-11T21:41:26.3368757Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3368982Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3369741Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3370087Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3370419Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3370758Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3371100Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3371435Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3371748Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3372144Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3372416Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3372432Z 
2023-01-11T21:41:26.3372440Z 
2023-01-11T21:41:26.3372604Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3372724Z import torch
2023-01-11T21:41:26.3372852Z import random
2023-01-11T21:41:26.3373045Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3373255Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3373263Z 
2023-01-11T21:41:26.3373397Z aten = torch.ops.aten
2023-01-11T21:41:26.3373620Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3373760Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3373768Z 
2023-01-11T21:41:26.3373777Z 
2023-01-11T21:41:26.3374025Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3374368Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3374577Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3374888Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3375001Z {
2023-01-11T21:41:26.3375174Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3375266Z     {
2023-01-11T21:41:26.3375426Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3375570Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3375686Z         {
2023-01-11T21:41:26.3375848Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3375962Z             {
2023-01-11T21:41:26.3376075Z                 {
2023-01-11T21:41:26.3376184Z                     {
2023-01-11T21:41:26.3376356Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3376537Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3376662Z                     }
2023-01-11T21:41:26.3376783Z                 }
2023-01-11T21:41:26.3376895Z             }
2023-01-11T21:41:26.3377106Z         }
2023-01-11T21:41:26.3377208Z     }
2023-01-11T21:41:26.3377315Z }
2023-01-11T21:41:26.3377477Z ''')
2023-01-11T21:41:26.3377494Z 
2023-01-11T21:41:26.3377500Z 
2023-01-11T21:41:26.3377651Z async_compile.wait(globals())
2023-01-11T21:41:26.3377782Z del async_compile
2023-01-11T21:41:26.3377794Z 
2023-01-11T21:41:26.3377922Z def call(args):
2023-01-11T21:41:26.3378130Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3378245Z     args.clear()
2023-01-11T21:41:26.3378650Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3378879Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3379005Z     del arg7_1
2023-01-11T21:41:26.3379245Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3379450Z     assert_size_stride(buf1, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:41:26.3379582Z     del arg0_1
2023-01-11T21:41:26.3379702Z     del arg1_1
2023-01-11T21:41:26.3379812Z     return (buf1, )
2023-01-11T21:41:26.3379822Z 
2023-01-11T21:41:26.3379828Z 
2023-01-11T21:41:26.3379958Z if __name__ == "__main__":
2023-01-11T21:41:26.3380157Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3380376Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3380765Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3381106Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3381443Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3381772Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3382088Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3382417Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3382734Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3383255Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3383526Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3383537Z 
2023-01-11T21:41:26.3383543Z 
2023-01-11T21:41:26.3383718Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3383842Z import torch
2023-01-11T21:41:26.3383975Z import random
2023-01-11T21:41:26.3384151Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3384363Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3384376Z 
2023-01-11T21:41:26.3384513Z aten = torch.ops.aten
2023-01-11T21:41:26.3384735Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3384899Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3384909Z 
2023-01-11T21:41:26.3384916Z 
2023-01-11T21:41:26.3385072Z async_compile.wait(globals())
2023-01-11T21:41:26.3385311Z del async_compile
2023-01-11T21:41:26.3385323Z 
2023-01-11T21:41:26.3385454Z def call(args):
2023-01-11T21:41:26.3385628Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3385755Z     args.clear()
2023-01-11T21:41:26.3385988Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3386187Z     assert_size_stride(buf0, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:41:26.3386318Z     del arg0_1
2023-01-11T21:41:26.3386437Z     del arg1_1
2023-01-11T21:41:26.3386558Z     del arg7_1
2023-01-11T21:41:26.3386666Z     return (buf0, )
2023-01-11T21:41:26.3386677Z 
2023-01-11T21:41:26.3386709Z 
2023-01-11T21:41:26.3386831Z if __name__ == "__main__":
2023-01-11T21:41:26.3387112Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3387322Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3387733Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3388073Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3388411Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3388736Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3389051Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3389379Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3389697Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3390104Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3390376Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3390394Z 
2023-01-11T21:41:26.3390851Z [2023-01-11 21:25:55,348] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 132
2023-01-11T21:41:26.3391565Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3391789Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3392236Z [2023-01-11 21:25:55,850] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 133
2023-01-11T21:41:26.3392682Z [2023-01-11 21:25:55,872] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 133
2023-01-11T21:41:26.3393382Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3393594Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3394044Z [2023-01-11 21:25:56,081] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 134
2023-01-11T21:41:26.3394494Z [2023-01-11 21:25:56,109] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 134
2023-01-11T21:41:26.3395202Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3395510Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3395963Z [2023-01-11 21:25:56,389] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 135
2023-01-11T21:41:26.3396420Z [2023-01-11 21:25:56,411] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 135
2023-01-11T21:41:26.3397124Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3397337Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3397852Z [2023-01-11 21:25:57,045] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 136
2023-01-11T21:41:26.3397872Z 
2023-01-11T21:41:26.3398040Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3398151Z import torch
2023-01-11T21:41:26.3398285Z import random
2023-01-11T21:41:26.3398480Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3398690Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3398698Z 
2023-01-11T21:41:26.3398834Z aten = torch.ops.aten
2023-01-11T21:41:26.3399065Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3399229Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3399239Z 
2023-01-11T21:41:26.3399245Z 
2023-01-11T21:41:26.3399485Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3399816Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3400027Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3400202Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3400307Z {
2023-01-11T21:41:26.3400482Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3400593Z     {
2023-01-11T21:41:26.3400735Z         #pragma omp for 
2023-01-11T21:41:26.3400863Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3400978Z         {
2023-01-11T21:41:26.3401117Z             #pragma GCC ivdep
2023-01-11T21:41:26.3401270Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3401386Z             {
2023-01-11T21:41:26.3401508Z                 {
2023-01-11T21:41:26.3401627Z                     {
2023-01-11T21:41:26.3401796Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:26.3401961Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3402086Z                     }
2023-01-11T21:41:26.3402199Z                 }
2023-01-11T21:41:26.3402318Z             }
2023-01-11T21:41:26.3402443Z         }
2023-01-11T21:41:26.3402557Z     }
2023-01-11T21:41:26.3402641Z }
2023-01-11T21:41:26.3402800Z ''')
2023-01-11T21:41:26.3402818Z 
2023-01-11T21:41:26.3402824Z 
2023-01-11T21:41:26.3402983Z async_compile.wait(globals())
2023-01-11T21:41:26.3403114Z del async_compile
2023-01-11T21:41:26.3403122Z 
2023-01-11T21:41:26.3403255Z def call(args):
2023-01-11T21:41:26.3403447Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3403579Z     args.clear()
2023-01-11T21:41:26.3403989Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3404219Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3404348Z     del arg7_1
2023-01-11T21:41:26.3404580Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3404739Z     assert_size_stride(buf1, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:41:26.3404831Z     del arg0_1
2023-01-11T21:41:26.3404920Z     del arg1_1
2023-01-11T21:41:26.3405000Z     return (buf1, )
2023-01-11T21:41:26.3405087Z 
2023-01-11T21:41:26.3405092Z 
2023-01-11T21:41:26.3405179Z if __name__ == "__main__":
2023-01-11T21:41:26.3405329Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3405491Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3405793Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3406062Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3406325Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3406590Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3406853Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3407135Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3407382Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3407706Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3407910Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3407917Z 
2023-01-11T21:41:26.3407922Z 
2023-01-11T21:41:26.3408046Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3408142Z import torch
2023-01-11T21:41:26.3408235Z import random
2023-01-11T21:41:26.3408388Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3408531Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3408537Z 
2023-01-11T21:41:26.3408640Z aten = torch.ops.aten
2023-01-11T21:41:26.3408816Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3408940Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3408947Z 
2023-01-11T21:41:26.3408952Z 
2023-01-11T21:41:26.3409243Z async_compile.wait(globals())
2023-01-11T21:41:26.3409345Z del async_compile
2023-01-11T21:41:26.3409352Z 
2023-01-11T21:41:26.3409444Z def call(args):
2023-01-11T21:41:26.3409603Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3409684Z     args.clear()
2023-01-11T21:41:26.3409868Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3410020Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3410111Z     del arg0_1
2023-01-11T21:41:26.3410201Z     del arg1_1
2023-01-11T21:41:26.3410290Z     del arg7_1
2023-01-11T21:41:26.3410384Z     return (buf0, )
2023-01-11T21:41:26.3410391Z 
2023-01-11T21:41:26.3410396Z 
2023-01-11T21:41:26.3410495Z if __name__ == "__main__":
2023-01-11T21:41:26.3410633Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3410791Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3411089Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3411356Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3411615Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3411875Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3412135Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3412378Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3412625Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3412950Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3413159Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3413166Z 
2023-01-11T21:41:26.3413234Z 
2023-01-11T21:41:26.3413363Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3413455Z import torch
2023-01-11T21:41:26.3413549Z import random
2023-01-11T21:41:26.3413703Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3413847Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3413868Z 
2023-01-11T21:41:26.3413957Z aten = torch.ops.aten
2023-01-11T21:41:26.3414131Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3414251Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3414258Z 
2023-01-11T21:41:26.3414264Z 
2023-01-11T21:41:26.3414450Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3414710Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3414865Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3415037Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3415122Z {
2023-01-11T21:41:26.3415240Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3415322Z     {
2023-01-11T21:41:26.3415445Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3415554Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3415640Z         {
2023-01-11T21:41:26.3415759Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3415831Z             {
2023-01-11T21:41:26.3415918Z                 {
2023-01-11T21:41:26.3416007Z                     {
2023-01-11T21:41:26.3416146Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3416275Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3416362Z                     }
2023-01-11T21:41:26.3416448Z                 }
2023-01-11T21:41:26.3416516Z             }
2023-01-11T21:41:26.3416599Z         }
2023-01-11T21:41:26.3416679Z     }
2023-01-11T21:41:26.3416757Z }
2023-01-11T21:41:26.3416868Z ''')
2023-01-11T21:41:26.3416875Z 
2023-01-11T21:41:26.3416879Z 
2023-01-11T21:41:26.3416998Z async_compile.wait(globals())
2023-01-11T21:41:26.3417102Z del async_compile
2023-01-11T21:41:26.3417109Z 
2023-01-11T21:41:26.3417187Z def call(args):
2023-01-11T21:41:26.3417341Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3417434Z     args.clear()
2023-01-11T21:41:26.3417756Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3417932Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3418022Z     del arg7_1
2023-01-11T21:41:26.3418205Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3418357Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3418433Z     del arg0_1
2023-01-11T21:41:26.3418525Z     del arg1_1
2023-01-11T21:41:26.3418619Z     return (buf1, )
2023-01-11T21:41:26.3418626Z 
2023-01-11T21:41:26.3418631Z 
2023-01-11T21:41:26.3418734Z if __name__ == "__main__":
2023-01-11T21:41:26.3418882Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3419044Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3419336Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3419598Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3419858Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3420195Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3420516Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3420842Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3421149Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3421554Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3421871Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3421880Z 
2023-01-11T21:41:26.3421885Z 
2023-01-11T21:41:26.3422053Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3422156Z import torch
2023-01-11T21:41:26.3422279Z import random
2023-01-11T21:41:26.3422479Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3422680Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3422689Z 
2023-01-11T21:41:26.3422831Z aten = torch.ops.aten
2023-01-11T21:41:26.3423057Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3423292Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3423305Z 
2023-01-11T21:41:26.3423311Z 
2023-01-11T21:41:26.3423528Z async_compile.wait(globals())
2023-01-11T21:41:26.3423644Z del async_compile
2023-01-11T21:41:26.3423652Z 
2023-01-11T21:41:26.3423787Z def call(args):
2023-01-11T21:41:26.3423987Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3424114Z     args.clear()
2023-01-11T21:41:26.3424347Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3424540Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3424657Z     del arg0_1
2023-01-11T21:41:26.3424765Z     del arg1_1
2023-01-11T21:41:26.3424883Z     del arg7_1
2023-01-11T21:41:26.3425010Z     return (buf0, )
2023-01-11T21:41:26.3425018Z 
2023-01-11T21:41:26.3425025Z 
2023-01-11T21:41:26.3425151Z if __name__ == "__main__":
2023-01-11T21:41:26.3425349Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3425564Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3425961Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3426300Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3426611Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3426948Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3427281Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3427610Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3427921Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3428331Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3428588Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3428604Z 
2023-01-11T21:41:26.3429071Z [2023-01-11 21:25:57,072] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 136
2023-01-11T21:41:26.3429791Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3430003Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3430435Z [2023-01-11 21:25:57,624] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 137
2023-01-11T21:41:26.3430889Z [2023-01-11 21:25:57,646] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 137
2023-01-11T21:41:26.3431587Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3431900Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3432335Z [2023-01-11 21:25:57,851] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 138
2023-01-11T21:41:26.3432794Z [2023-01-11 21:25:57,880] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 138
2023-01-11T21:41:26.3433564Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3433790Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3434234Z [2023-01-11 21:25:58,155] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 139
2023-01-11T21:41:26.3434705Z [2023-01-11 21:25:58,178] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 139
2023-01-11T21:41:26.3435423Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3435637Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3436060Z [2023-01-11 21:25:58,810] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 140
2023-01-11T21:41:26.3436094Z 
2023-01-11T21:41:26.3436243Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3436367Z import torch
2023-01-11T21:41:26.3436493Z import random
2023-01-11T21:41:26.3436689Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3436899Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3436910Z 
2023-01-11T21:41:26.3437044Z aten = torch.ops.aten
2023-01-11T21:41:26.3437276Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3437406Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3437416Z 
2023-01-11T21:41:26.3437422Z 
2023-01-11T21:41:26.3437672Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3438010Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3438213Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3438386Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3438510Z {
2023-01-11T21:41:26.3438685Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3438776Z     {
2023-01-11T21:41:26.3438913Z         #pragma omp for 
2023-01-11T21:41:26.3439059Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3439175Z         {
2023-01-11T21:41:26.3439323Z             #pragma GCC ivdep
2023-01-11T21:41:26.3439478Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3439595Z             {
2023-01-11T21:41:26.3439691Z                 {
2023-01-11T21:41:26.3439810Z                     {
2023-01-11T21:41:26.3439992Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:26.3440159Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3440277Z                     }
2023-01-11T21:41:26.3440392Z                 }
2023-01-11T21:41:26.3440495Z             }
2023-01-11T21:41:26.3440590Z         }
2023-01-11T21:41:26.3440697Z     }
2023-01-11T21:41:26.3440803Z }
2023-01-11T21:41:26.3440958Z ''')
2023-01-11T21:41:26.3440975Z 
2023-01-11T21:41:26.3440982Z 
2023-01-11T21:41:26.3441142Z async_compile.wait(globals())
2023-01-11T21:41:26.3441349Z del async_compile
2023-01-11T21:41:26.3441361Z 
2023-01-11T21:41:26.3441490Z def call(args):
2023-01-11T21:41:26.3441678Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3441815Z     args.clear()
2023-01-11T21:41:26.3442231Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3442466Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3442586Z     del arg7_1
2023-01-11T21:41:26.3442822Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3443023Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3443141Z     del arg0_1
2023-01-11T21:41:26.3443249Z     del arg1_1
2023-01-11T21:41:26.3443448Z     return (buf1, )
2023-01-11T21:41:26.3443459Z 
2023-01-11T21:41:26.3443465Z 
2023-01-11T21:41:26.3443600Z if __name__ == "__main__":
2023-01-11T21:41:26.3443805Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3444022Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3444397Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3444738Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3445088Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3445405Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3445733Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3446061Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3446379Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3446775Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3447040Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3447049Z 
2023-01-11T21:41:26.3447054Z 
2023-01-11T21:41:26.3447215Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3447344Z import torch
2023-01-11T21:41:26.3447453Z import random
2023-01-11T21:41:26.3447644Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3447851Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3447859Z 
2023-01-11T21:41:26.3447990Z aten = torch.ops.aten
2023-01-11T21:41:26.3448216Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3448366Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3448374Z 
2023-01-11T21:41:26.3448382Z 
2023-01-11T21:41:26.3448541Z async_compile.wait(globals())
2023-01-11T21:41:26.3448675Z del async_compile
2023-01-11T21:41:26.3448685Z 
2023-01-11T21:41:26.3448797Z def call(args):
2023-01-11T21:41:26.3449126Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3449265Z     args.clear()
2023-01-11T21:41:26.3449507Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3449714Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3449842Z     del arg0_1
2023-01-11T21:41:26.3449960Z     del arg1_1
2023-01-11T21:41:26.3450069Z     del arg7_1
2023-01-11T21:41:26.3450203Z     return (buf0, )
2023-01-11T21:41:26.3450212Z 
2023-01-11T21:41:26.3450217Z 
2023-01-11T21:41:26.3450349Z if __name__ == "__main__":
2023-01-11T21:41:26.3450535Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3450742Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3451149Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3451485Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3451945Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3452262Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3452587Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3452916Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3453225Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3453618Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3453884Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3453895Z 
2023-01-11T21:41:26.3454006Z 
2023-01-11T21:41:26.3454182Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3454315Z import torch
2023-01-11T21:41:26.3454451Z import random
2023-01-11T21:41:26.3454627Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3454835Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3454844Z 
2023-01-11T21:41:26.3454977Z aten = torch.ops.aten
2023-01-11T21:41:26.3455208Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3455369Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3455379Z 
2023-01-11T21:41:26.3455384Z 
2023-01-11T21:41:26.3455626Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3455971Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3456181Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3456334Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3456453Z {
2023-01-11T21:41:26.3456631Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3456749Z     {
2023-01-11T21:41:26.3456912Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3457061Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3457181Z         {
2023-01-11T21:41:26.3457321Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3457430Z             {
2023-01-11T21:41:26.3457554Z                 {
2023-01-11T21:41:26.3457674Z                     {
2023-01-11T21:41:26.3457852Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3458021Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3458123Z                     }
2023-01-11T21:41:26.3458238Z                 }
2023-01-11T21:41:26.3458354Z             }
2023-01-11T21:41:26.3458470Z         }
2023-01-11T21:41:26.3458578Z     }
2023-01-11T21:41:26.3458685Z }
2023-01-11T21:41:26.3458840Z ''')
2023-01-11T21:41:26.3458849Z 
2023-01-11T21:41:26.3458854Z 
2023-01-11T21:41:26.3458996Z async_compile.wait(globals())
2023-01-11T21:41:26.3459129Z del async_compile
2023-01-11T21:41:26.3459139Z 
2023-01-11T21:41:26.3459263Z def call(args):
2023-01-11T21:41:26.3459462Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3459597Z     args.clear()
2023-01-11T21:41:26.3460009Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3460240Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3460361Z     del arg7_1
2023-01-11T21:41:26.3460579Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3460785Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3460916Z     del arg0_1
2023-01-11T21:41:26.3461037Z     del arg1_1
2023-01-11T21:41:26.3461166Z     return (buf1, )
2023-01-11T21:41:26.3461177Z 
2023-01-11T21:41:26.3461191Z 
2023-01-11T21:41:26.3461322Z if __name__ == "__main__":
2023-01-11T21:41:26.3461514Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3461832Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3462208Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3462534Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3462869Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3463270Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3463604Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3463934Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3464255Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3464730Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3464984Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3465002Z 
2023-01-11T21:41:26.3465031Z 
2023-01-11T21:41:26.3465181Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3465307Z import torch
2023-01-11T21:41:26.3465434Z import random
2023-01-11T21:41:26.3465628Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3465836Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3465847Z 
2023-01-11T21:41:26.3465986Z aten = torch.ops.aten
2023-01-11T21:41:26.3466215Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3466353Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3466360Z 
2023-01-11T21:41:26.3466365Z 
2023-01-11T21:41:26.3466527Z async_compile.wait(globals())
2023-01-11T21:41:26.3466662Z del async_compile
2023-01-11T21:41:26.3466670Z 
2023-01-11T21:41:26.3466802Z def call(args):
2023-01-11T21:41:26.3467013Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3467144Z     args.clear()
2023-01-11T21:41:26.3467385Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3467588Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3467702Z     del arg0_1
2023-01-11T21:41:26.3467825Z     del arg1_1
2023-01-11T21:41:26.3467937Z     del arg7_1
2023-01-11T21:41:26.3468070Z     return (buf0, )
2023-01-11T21:41:26.3468079Z 
2023-01-11T21:41:26.3468086Z 
2023-01-11T21:41:26.3468218Z if __name__ == "__main__":
2023-01-11T21:41:26.3468413Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3468627Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3469010Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3469354Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3469692Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3470034Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3470369Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3470705Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3471017Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3471426Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3471668Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3471697Z 
2023-01-11T21:41:26.3472115Z [2023-01-11 21:25:58,838] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 140
2023-01-11T21:41:26.3472700Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3472961Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3473374Z [2023-01-11 21:25:59,443] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 141
2023-01-11T21:41:26.3473810Z [2023-01-11 21:25:59,465] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 141
2023-01-11T21:41:26.3474557Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3474773Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3475194Z [2023-01-11 21:25:59,699] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 142
2023-01-11T21:41:26.3475623Z [2023-01-11 21:25:59,727] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 142
2023-01-11T21:41:26.3476310Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3476514Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3476942Z [2023-01-11 21:26:00,070] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 143
2023-01-11T21:41:26.3477372Z [2023-01-11 21:26:00,092] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 143
2023-01-11T21:41:26.3478060Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3478256Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3478684Z [2023-01-11 21:26:00,817] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 144
2023-01-11T21:41:26.3478694Z 
2023-01-11T21:41:26.3478849Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3478959Z import torch
2023-01-11T21:41:26.3479069Z import random
2023-01-11T21:41:26.3479250Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3479447Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3479457Z 
2023-01-11T21:41:26.3479563Z aten = torch.ops.aten
2023-01-11T21:41:26.3479770Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3479912Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3479920Z 
2023-01-11T21:41:26.3479927Z 
2023-01-11T21:41:26.3480161Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3480487Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3480675Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3480829Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3480925Z {
2023-01-11T21:41:26.3481065Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3481167Z     {
2023-01-11T21:41:26.3481290Z         #pragma omp for 
2023-01-11T21:41:26.3481418Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3481613Z         {
2023-01-11T21:41:26.3481739Z             #pragma GCC ivdep
2023-01-11T21:41:26.3481863Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3481963Z             {
2023-01-11T21:41:26.3482062Z                 {
2023-01-11T21:41:26.3482164Z                     {
2023-01-11T21:41:26.3482323Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:26.3482473Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3482575Z                     }
2023-01-11T21:41:26.3482657Z                 }
2023-01-11T21:41:26.3482760Z             }
2023-01-11T21:41:26.3482856Z         }
2023-01-11T21:41:26.3482948Z     }
2023-01-11T21:41:26.3483041Z }
2023-01-11T21:41:26.3483185Z ''')
2023-01-11T21:41:26.3483195Z 
2023-01-11T21:41:26.3483200Z 
2023-01-11T21:41:26.3483409Z async_compile.wait(globals())
2023-01-11T21:41:26.3483520Z del async_compile
2023-01-11T21:41:26.3483546Z 
2023-01-11T21:41:26.3483638Z def call(args):
2023-01-11T21:41:26.3483833Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3483942Z     args.clear()
2023-01-11T21:41:26.3484350Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3484560Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3484667Z     del arg7_1
2023-01-11T21:41:26.3484885Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3485052Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:41:26.3485157Z     del arg0_1
2023-01-11T21:41:26.3485261Z     del arg1_1
2023-01-11T21:41:26.3485373Z     return (buf1, )
2023-01-11T21:41:26.3485382Z 
2023-01-11T21:41:26.3485387Z 
2023-01-11T21:41:26.3485511Z if __name__ == "__main__":
2023-01-11T21:41:26.3485694Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3485896Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3486276Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3486588Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3486909Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3487229Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3487540Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3487848Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3488144Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3488539Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3488787Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3488803Z 
2023-01-11T21:41:26.3488809Z 
2023-01-11T21:41:26.3488942Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3489184Z import torch
2023-01-11T21:41:26.3489299Z import random
2023-01-11T21:41:26.3489477Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3489673Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3489682Z 
2023-01-11T21:41:26.3489801Z aten = torch.ops.aten
2023-01-11T21:41:26.3490017Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3490142Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3490149Z 
2023-01-11T21:41:26.3490155Z 
2023-01-11T21:41:26.3490265Z async_compile.wait(globals())
2023-01-11T21:41:26.3490385Z del async_compile
2023-01-11T21:41:26.3490392Z 
2023-01-11T21:41:26.3490510Z def call(args):
2023-01-11T21:41:26.3490704Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3490938Z     args.clear()
2023-01-11T21:41:26.3491181Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3491380Z     assert_size_stride(buf0, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:41:26.3491498Z     del arg0_1
2023-01-11T21:41:26.3491595Z     del arg1_1
2023-01-11T21:41:26.3491708Z     del arg7_1
2023-01-11T21:41:26.3491827Z     return (buf0, )
2023-01-11T21:41:26.3491835Z 
2023-01-11T21:41:26.3491842Z 
2023-01-11T21:41:26.3491968Z if __name__ == "__main__":
2023-01-11T21:41:26.3492168Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3492388Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3492817Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3493250Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3493614Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3493973Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3494317Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3494670Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3494996Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3495430Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3495702Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3495713Z 
2023-01-11T21:41:26.3495719Z 
2023-01-11T21:41:26.3495883Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3495993Z import torch
2023-01-11T21:41:26.3496116Z import random
2023-01-11T21:41:26.3496316Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3496537Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3496546Z 
2023-01-11T21:41:26.3496683Z aten = torch.ops.aten
2023-01-11T21:41:26.3496919Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3497080Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3497089Z 
2023-01-11T21:41:26.3497096Z 
2023-01-11T21:41:26.3497357Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3497706Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3497915Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3498091Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3498198Z {
2023-01-11T21:41:26.3498371Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3498479Z     {
2023-01-11T21:41:26.3498641Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3498765Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3498882Z         {
2023-01-11T21:41:26.3499034Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3499141Z             {
2023-01-11T21:41:26.3499255Z                 {
2023-01-11T21:41:26.3499370Z                     {
2023-01-11T21:41:26.3499543Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3499699Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3499811Z                     }
2023-01-11T21:41:26.3499923Z                 }
2023-01-11T21:41:26.3500034Z             }
2023-01-11T21:41:26.3500145Z         }
2023-01-11T21:41:26.3500246Z     }
2023-01-11T21:41:26.3500335Z }
2023-01-11T21:41:26.3500494Z ''')
2023-01-11T21:41:26.3500504Z 
2023-01-11T21:41:26.3500511Z 
2023-01-11T21:41:26.3500668Z async_compile.wait(globals())
2023-01-11T21:41:26.3500792Z del async_compile
2023-01-11T21:41:26.3500807Z 
2023-01-11T21:41:26.3500932Z def call(args):
2023-01-11T21:41:26.3501141Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3501365Z     args.clear()
2023-01-11T21:41:26.3501808Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3502026Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3502144Z     del arg7_1
2023-01-11T21:41:26.3502387Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3502590Z     assert_size_stride(buf1, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:41:26.3502708Z     del arg0_1
2023-01-11T21:41:26.3502827Z     del arg1_1
2023-01-11T21:41:26.3502951Z     return (buf1, )
2023-01-11T21:41:26.3502960Z 
2023-01-11T21:41:26.3502966Z 
2023-01-11T21:41:26.3503097Z if __name__ == "__main__":
2023-01-11T21:41:26.3503403Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3503612Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3503991Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3504303Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3504607Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3504911Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3505216Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3505527Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3505811Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3506189Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3506443Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3506459Z 
2023-01-11T21:41:26.3506465Z 
2023-01-11T21:41:26.3506613Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3506720Z import torch
2023-01-11T21:41:26.3506831Z import random
2023-01-11T21:41:26.3507014Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3507201Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3507210Z 
2023-01-11T21:41:26.3507317Z aten = torch.ops.aten
2023-01-11T21:41:26.3507526Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3507671Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3507680Z 
2023-01-11T21:41:26.3507686Z 
2023-01-11T21:41:26.3507826Z async_compile.wait(globals())
2023-01-11T21:41:26.3507939Z del async_compile
2023-01-11T21:41:26.3507947Z 
2023-01-11T21:41:26.3508058Z def call(args):
2023-01-11T21:41:26.3508244Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3508360Z     args.clear()
2023-01-11T21:41:26.3508565Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3508742Z     assert_size_stride(buf0, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:41:26.3508842Z     del arg0_1
2023-01-11T21:41:26.3508950Z     del arg1_1
2023-01-11T21:41:26.3509055Z     del arg7_1
2023-01-11T21:41:26.3509168Z     return (buf0, )
2023-01-11T21:41:26.3509176Z 
2023-01-11T21:41:26.3509182Z 
2023-01-11T21:41:26.3509302Z if __name__ == "__main__":
2023-01-11T21:41:26.3509470Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3509668Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3510055Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3510387Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3510710Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3511147Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3511464Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3511778Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3512062Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3512456Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3512709Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3512718Z 
2023-01-11T21:41:26.3513166Z [2023-01-11 21:26:00,845] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 144
2023-01-11T21:41:26.3513932Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3514149Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3514585Z [2023-01-11 21:26:01,488] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 145
2023-01-11T21:41:26.3515027Z [2023-01-11 21:26:01,510] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 145
2023-01-11T21:41:26.3515734Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3515936Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3516373Z [2023-01-11 21:26:01,748] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 146
2023-01-11T21:41:26.3516812Z [2023-01-11 21:26:01,776] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 146
2023-01-11T21:41:26.3517504Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3517704Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3518140Z [2023-01-11 21:26:02,089] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 147
2023-01-11T21:41:26.3518582Z [2023-01-11 21:26:02,111] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 147
2023-01-11T21:41:26.3519286Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3519483Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3519916Z [2023-01-11 21:26:02,768] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 148
2023-01-11T21:41:26.3519926Z 
2023-01-11T21:41:26.3520078Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3520189Z import torch
2023-01-11T21:41:26.3520302Z import random
2023-01-11T21:41:26.3520479Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3520676Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3520760Z 
2023-01-11T21:41:26.3520898Z aten = torch.ops.aten
2023-01-11T21:41:26.3521115Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3521263Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3521271Z 
2023-01-11T21:41:26.3521277Z 
2023-01-11T21:41:26.3521504Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3521835Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3522026Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3522170Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3522267Z {
2023-01-11T21:41:26.3522426Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3522526Z     {
2023-01-11T21:41:26.3522651Z         #pragma omp for 
2023-01-11T21:41:26.3522863Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3522975Z         {
2023-01-11T21:41:26.3523085Z             #pragma GCC ivdep
2023-01-11T21:41:26.3523233Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3523337Z             {
2023-01-11T21:41:26.3530083Z                 {
2023-01-11T21:41:26.3530203Z                     {
2023-01-11T21:41:26.3530354Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:26.3530487Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3530562Z                     }
2023-01-11T21:41:26.3530648Z                 }
2023-01-11T21:41:26.3530735Z             }
2023-01-11T21:41:26.3530817Z         }
2023-01-11T21:41:26.3530898Z     }
2023-01-11T21:41:26.3530980Z }
2023-01-11T21:41:26.3531103Z ''')
2023-01-11T21:41:26.3531111Z 
2023-01-11T21:41:26.3531131Z 
2023-01-11T21:41:26.3531239Z async_compile.wait(globals())
2023-01-11T21:41:26.3531336Z del async_compile
2023-01-11T21:41:26.3531343Z 
2023-01-11T21:41:26.3531443Z def call(args):
2023-01-11T21:41:26.3531605Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3531708Z     args.clear()
2023-01-11T21:41:26.3532047Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3532226Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3532303Z     del arg7_1
2023-01-11T21:41:26.3532488Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3532641Z     assert_size_stride(buf1, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:41:26.3532732Z     del arg0_1
2023-01-11T21:41:26.3532818Z     del arg1_1
2023-01-11T21:41:26.3532911Z     return (buf1, )
2023-01-11T21:41:26.3532919Z 
2023-01-11T21:41:26.3532924Z 
2023-01-11T21:41:26.3533025Z if __name__ == "__main__":
2023-01-11T21:41:26.3533182Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3533333Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3533637Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3533912Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3534176Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3534440Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3534704Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3534963Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3535209Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3535518Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3535730Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3535737Z 
2023-01-11T21:41:26.3535858Z 
2023-01-11T21:41:26.3535985Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3536078Z import torch
2023-01-11T21:41:26.3536174Z import random
2023-01-11T21:41:26.3536330Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3536488Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3536494Z 
2023-01-11T21:41:26.3536597Z aten = torch.ops.aten
2023-01-11T21:41:26.3536757Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3536877Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3536884Z 
2023-01-11T21:41:26.3536889Z 
2023-01-11T21:41:26.3537006Z async_compile.wait(globals())
2023-01-11T21:41:26.3537103Z del async_compile
2023-01-11T21:41:26.3537111Z 
2023-01-11T21:41:26.3537204Z def call(args):
2023-01-11T21:41:26.3537410Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3537508Z     args.clear()
2023-01-11T21:41:26.3537693Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3537835Z     assert_size_stride(buf0, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:41:26.3537924Z     del arg0_1
2023-01-11T21:41:26.3538012Z     del arg1_1
2023-01-11T21:41:26.3538099Z     del arg7_1
2023-01-11T21:41:26.3538194Z     return (buf0, )
2023-01-11T21:41:26.3538201Z 
2023-01-11T21:41:26.3538206Z 
2023-01-11T21:41:26.3538306Z if __name__ == "__main__":
2023-01-11T21:41:26.3538456Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3538604Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3538943Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3539255Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3539578Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3539886Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3540211Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3540526Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3540823Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3541191Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3541444Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3541456Z 
2023-01-11T21:41:26.3541462Z 
2023-01-11T21:41:26.3541619Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3541736Z import torch
2023-01-11T21:41:26.3541853Z import random
2023-01-11T21:41:26.3542043Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3542239Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3542254Z 
2023-01-11T21:41:26.3542377Z aten = torch.ops.aten
2023-01-11T21:41:26.3542578Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3542725Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3542734Z 
2023-01-11T21:41:26.3542739Z 
2023-01-11T21:41:26.3542987Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3543399Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3543593Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3543752Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3543852Z {
2023-01-11T21:41:26.3544008Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3544094Z     {
2023-01-11T21:41:26.3544238Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3544378Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3544479Z         {
2023-01-11T21:41:26.3544623Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3544844Z             {
2023-01-11T21:41:26.3544950Z                 {
2023-01-11T21:41:26.3545041Z                     {
2023-01-11T21:41:26.3545209Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3545366Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3545476Z                     }
2023-01-11T21:41:26.3545578Z                 }
2023-01-11T21:41:26.3545682Z             }
2023-01-11T21:41:26.3545786Z         }
2023-01-11T21:41:26.3545865Z     }
2023-01-11T21:41:26.3545957Z }
2023-01-11T21:41:26.3546114Z ''')
2023-01-11T21:41:26.3546125Z 
2023-01-11T21:41:26.3546130Z 
2023-01-11T21:41:26.3546282Z async_compile.wait(globals())
2023-01-11T21:41:26.3546402Z del async_compile
2023-01-11T21:41:26.3546409Z 
2023-01-11T21:41:26.3546523Z def call(args):
2023-01-11T21:41:26.3546794Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3546911Z     args.clear()
2023-01-11T21:41:26.3547323Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3547542Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3547654Z     del arg7_1
2023-01-11T21:41:26.3547873Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:41:26.3548055Z     assert_size_stride(buf1, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:41:26.3548166Z     del arg0_1
2023-01-11T21:41:26.3548275Z     del arg1_1
2023-01-11T21:41:26.3548372Z     return (buf1, )
2023-01-11T21:41:26.3548382Z 
2023-01-11T21:41:26.3548387Z 
2023-01-11T21:41:26.3548510Z if __name__ == "__main__":
2023-01-11T21:41:26.3548693Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3548895Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3549289Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3549631Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3549957Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3550277Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3550576Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3550885Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3551186Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3551566Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3551819Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3551829Z 
2023-01-11T21:41:26.3551835Z 
2023-01-11T21:41:26.3551987Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3552108Z import torch
2023-01-11T21:41:26.3552222Z import random
2023-01-11T21:41:26.3552388Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3552582Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3552590Z 
2023-01-11T21:41:26.3552714Z aten = torch.ops.aten
2023-01-11T21:41:26.3552930Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3553075Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3553083Z 
2023-01-11T21:41:26.3553089Z 
2023-01-11T21:41:26.3553227Z async_compile.wait(globals())
2023-01-11T21:41:26.3553345Z del async_compile
2023-01-11T21:41:26.3553353Z 
2023-01-11T21:41:26.3553467Z def call(args):
2023-01-11T21:41:26.3553635Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3553748Z     args.clear()
2023-01-11T21:41:26.3553978Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3554275Z     assert_size_stride(buf0, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:41:26.3554383Z     del arg0_1
2023-01-11T21:41:26.3554489Z     del arg1_1
2023-01-11T21:41:26.3554596Z     del arg7_1
2023-01-11T21:41:26.3554693Z     return (buf0, )
2023-01-11T21:41:26.3554703Z 
2023-01-11T21:41:26.3554724Z 
2023-01-11T21:41:26.3554828Z if __name__ == "__main__":
2023-01-11T21:41:26.3555012Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3555206Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3555601Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3555938Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3556336Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3556672Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3556971Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3557273Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3557561Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3557946Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3558170Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3558180Z 
2023-01-11T21:41:26.3558638Z [2023-01-11 21:26:02,796] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 148
2023-01-11T21:41:26.3558647Z 
2023-01-11T21:41:26.3558799Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3558910Z import torch
2023-01-11T21:41:26.3559031Z import random
2023-01-11T21:41:26.3559202Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3559400Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3559408Z 
2023-01-11T21:41:26.3559531Z aten = torch.ops.aten
2023-01-11T21:41:26.3559748Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3559896Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3559904Z 
2023-01-11T21:41:26.3559910Z 
2023-01-11T21:41:26.3560133Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3560460Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3560651Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3560793Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3560890Z {
2023-01-11T21:41:26.3561047Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3561146Z     {
2023-01-11T21:41:26.3561274Z         #pragma omp for 
2023-01-11T21:41:26.3561407Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3561511Z         {
2023-01-11T21:41:26.3561627Z             #pragma GCC ivdep
2023-01-11T21:41:26.3561770Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:41:26.3561874Z             {
2023-01-11T21:41:26.3561980Z                 {
2023-01-11T21:41:26.3562085Z                     {
2023-01-11T21:41:26.3562247Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:41:26.3562385Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:41:26.3562492Z                     }
2023-01-11T21:41:26.3562594Z                 }
2023-01-11T21:41:26.3562697Z             }
2023-01-11T21:41:26.3562796Z         }
2023-01-11T21:41:26.3562897Z     }
2023-01-11T21:41:26.3562992Z }
2023-01-11T21:41:26.3563106Z ''')
2023-01-11T21:41:26.3563114Z 
2023-01-11T21:41:26.3563120Z 
2023-01-11T21:41:26.3563265Z async_compile.wait(globals())
2023-01-11T21:41:26.3563379Z del async_compile
2023-01-11T21:41:26.3563392Z 
2023-01-11T21:41:26.3563505Z def call(args):
2023-01-11T21:41:26.3563695Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:41:26.3563889Z     args.clear()
2023-01-11T21:41:26.3564286Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3564497Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3564590Z     del arg7_1
2023-01-11T21:41:26.3564810Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:41:26.3565000Z     assert_size_stride(buf1, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:41:26.3565111Z     del arg0_1
2023-01-11T21:41:26.3565218Z     del arg1_1
2023-01-11T21:41:26.3565331Z     return (buf1, )
2023-01-11T21:41:26.3565340Z 
2023-01-11T21:41:26.3565346Z 
2023-01-11T21:41:26.3565467Z if __name__ == "__main__":
2023-01-11T21:41:26.3565685Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3565891Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3566264Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3566585Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3566904Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3567219Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3567529Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3567846Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3568126Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3568519Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3568771Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:41:26.3568785Z 
2023-01-11T21:41:26.3568890Z ok (34.968s)
2023-01-11T21:41:26.3569821Z   test_conv_functional_bn_fuse_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3570027Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3570466Z [2023-01-11 21:26:03,341] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 149
2023-01-11T21:41:26.3570914Z [2023-01-11 21:26:04,875] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 149
2023-01-11T21:41:26.3570923Z 
2023-01-11T21:41:26.3571079Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3571198Z import torch
2023-01-11T21:41:26.3571297Z import random
2023-01-11T21:41:26.3571481Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3571676Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3571684Z 
2023-01-11T21:41:26.3571809Z aten = torch.ops.aten
2023-01-11T21:41:26.3572023Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3572172Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3572180Z 
2023-01-11T21:41:26.3572186Z 
2023-01-11T21:41:26.3572414Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3572744Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3572918Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3573076Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3573178Z {
2023-01-11T21:41:26.3573339Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3573436Z     {
2023-01-11T21:41:26.3573683Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3573824Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3573917Z         {
2023-01-11T21:41:26.3574067Z             for(long i1=0; i1<31136; i1+=1)
2023-01-11T21:41:26.3574176Z             {
2023-01-11T21:41:26.3574290Z                 {
2023-01-11T21:41:26.3574406Z                     {
2023-01-11T21:41:26.3574583Z                         auto tmp0 = in_ptr0[i1 + (31136*i0)];
2023-01-11T21:41:26.3574746Z                         out_ptr0[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.3574846Z                     }
2023-01-11T21:41:26.3574963Z                 }
2023-01-11T21:41:26.3575073Z             }
2023-01-11T21:41:26.3575183Z         }
2023-01-11T21:41:26.3575290Z     }
2023-01-11T21:41:26.3575393Z }
2023-01-11T21:41:26.3575525Z ''')
2023-01-11T21:41:26.3575550Z 
2023-01-11T21:41:26.3575557Z 
2023-01-11T21:41:26.3575851Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3576217Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3576432Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3576605Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3576711Z {
2023-01-11T21:41:26.3576881Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3576986Z     {
2023-01-11T21:41:26.3577103Z         #pragma omp for 
2023-01-11T21:41:26.3577247Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.3577359Z         {
2023-01-11T21:41:26.3577500Z             #pragma GCC ivdep
2023-01-11T21:41:26.3577652Z             for(long i1=0; i1<29916; i1+=1)
2023-01-11T21:41:26.3577766Z             {
2023-01-11T21:41:26.3577875Z                 {
2023-01-11T21:41:26.3577975Z                     {
2023-01-11T21:41:26.3578152Z                         auto tmp0 = in_ptr0[i0 + (64*i1)];
2023-01-11T21:41:26.3578323Z                         out_ptr0[i1 + (29916*i0)] = tmp0;
2023-01-11T21:41:26.3578439Z                     }
2023-01-11T21:41:26.3578558Z                 }
2023-01-11T21:41:26.3578669Z             }
2023-01-11T21:41:26.3578763Z         }
2023-01-11T21:41:26.3578869Z     }
2023-01-11T21:41:26.3578974Z }
2023-01-11T21:41:26.3579125Z ''')
2023-01-11T21:41:26.3579134Z 
2023-01-11T21:41:26.3579140Z 
2023-01-11T21:41:26.3579298Z async_compile.wait(globals())
2023-01-11T21:41:26.3579425Z del async_compile
2023-01-11T21:41:26.3579433Z 
2023-01-11T21:41:26.3579558Z def call(args):
2023-01-11T21:41:26.3579754Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1 = args
2023-01-11T21:41:26.3579863Z     args.clear()
2023-01-11T21:41:26.3580262Z     buf0 = empty_strided((1, 3, 556, 56), (93408, 1, 168, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3580499Z     kernel_cpp_0(c_void_p(arg6_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3580621Z     del arg6_1
2023-01-11T21:41:26.3581082Z     buf1 = torch.ops.mkldnn._convolution_pointwise(buf0, arg2_1, arg3_1, (0, 0), (1, 1), (1, 1), 1, 'none', [], '')
2023-01-11T21:41:26.3581279Z     assert_size_stride(buf1, (1, 64, 554, 54), (1914624, 1, 3456, 64))
2023-01-11T21:41:26.3581400Z     del arg2_1
2023-01-11T21:41:26.3581500Z     del arg3_1
2023-01-11T21:41:26.3581610Z     del buf0
2023-01-11T21:41:26.3582027Z     buf2 = empty_strided((1, 64, 554, 54), (1914624, 29916, 54, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3582259Z     kernel_cpp_1(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3582387Z     return (buf2, )
2023-01-11T21:41:26.3582397Z 
2023-01-11T21:41:26.3582404Z 
2023-01-11T21:41:26.3582533Z if __name__ == "__main__":
2023-01-11T21:41:26.3582735Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3582952Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3583362Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3583715Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3584091Z     arg2_1 = rand_strided((64, 3, 3, 3), (1, 0, 0, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3584517Z     arg3_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3584860Z     arg4_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3585198Z     arg5_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3585601Z     arg6_1 = rand_strided((1, 3, 556, 56), (93408, 31136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3585860Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1]))
2023-01-11T21:41:26.3585869Z 
2023-01-11T21:41:26.3585969Z ok (1.669s)
2023-01-11T21:41:26.3586649Z   test_convolution1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3586825Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3587181Z [2023-01-11 21:26:04,984] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 150
2023-01-11T21:41:26.3587542Z [2023-01-11 21:26:06,493] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 150
2023-01-11T21:41:26.3587549Z 
2023-01-11T21:41:26.3587674Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3587768Z import torch
2023-01-11T21:41:26.3587864Z import random
2023-01-11T21:41:26.3588017Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3588162Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3588188Z 
2023-01-11T21:41:26.3588277Z aten = torch.ops.aten
2023-01-11T21:41:26.3588455Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3588582Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3588589Z 
2023-01-11T21:41:26.3588594Z 
2023-01-11T21:41:26.3588777Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3589041Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3589194Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.3589323Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:26.3589390Z {
2023-01-11T21:41:26.3589519Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3589601Z     {
2023-01-11T21:41:26.3589703Z         #pragma omp for 
2023-01-11T21:41:26.3589815Z         for(long i0=0; i0<2352; i0+=1)
2023-01-11T21:41:26.3589900Z         {
2023-01-11T21:41:26.3589985Z             {
2023-01-11T21:41:26.3590056Z                 {
2023-01-11T21:41:26.3590186Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.3590312Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.3590453Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:41:26.3590575Z                     auto tmp3 = tmp1 <= tmp2;
2023-01-11T21:41:26.3590694Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.3590808Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.3590879Z                 }
2023-01-11T21:41:26.3590963Z             }
2023-01-11T21:41:26.3591045Z         }
2023-01-11T21:41:26.3591129Z     }
2023-01-11T21:41:26.3591211Z }
2023-01-11T21:41:26.3591317Z ''')
2023-01-11T21:41:26.3591324Z 
2023-01-11T21:41:26.3591329Z 
2023-01-11T21:41:26.3591449Z async_compile.wait(globals())
2023-01-11T21:41:26.3591531Z del async_compile
2023-01-11T21:41:26.3591538Z 
2023-01-11T21:41:26.3591631Z def call(args):
2023-01-11T21:41:26.3591769Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.3591869Z     args.clear()
2023-01-11T21:41:26.3592071Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:41:26.3592258Z     assert_size_stride(buf0, (2, 6, 14, 14), (1176, 196, 14, 1))
2023-01-11T21:41:26.3592355Z     del primals_2
2023-01-11T21:41:26.3592497Z     buf1 = as_strided(buf0, (2, 6, 14, 14), (1176, 196, 14, 1)); del buf0  # reuse
2023-01-11T21:41:26.3592788Z     buf2 = empty_strided((2, 6, 14, 14), (1176, 196, 14, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.3592966Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.3593105Z     return (buf1, primals_1, primals_3, buf2, )
2023-01-11T21:41:26.3593112Z 
2023-01-11T21:41:26.3593117Z 
2023-01-11T21:41:26.3593216Z if __name__ == "__main__":
2023-01-11T21:41:26.3593365Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3593527Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3593935Z     primals_1 = rand_strided((6, 5, 3, 3), (45, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3594212Z     primals_2 = rand_strided((6, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3594506Z     primals_3 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3594691Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.3594698Z 
2023-01-11T21:41:26.3594789Z ok (1.595s)
2023-01-11T21:41:26.3595407Z   test_convolution2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3595572Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3595931Z [2023-01-11 21:26:06,536] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 151
2023-01-11T21:41:26.3596297Z [2023-01-11 21:26:06,558] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 151
2023-01-11T21:41:26.3596304Z 
2023-01-11T21:41:26.3596428Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3596522Z import torch
2023-01-11T21:41:26.3596603Z import random
2023-01-11T21:41:26.3596753Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3596911Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3596918Z 
2023-01-11T21:41:26.3597021Z aten = torch.ops.aten
2023-01-11T21:41:26.3597196Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3597318Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3597325Z 
2023-01-11T21:41:26.3597330Z 
2023-01-11T21:41:26.3597449Z async_compile.wait(globals())
2023-01-11T21:41:26.3597544Z del async_compile
2023-01-11T21:41:26.3597555Z 
2023-01-11T21:41:26.3597633Z def call(args):
2023-01-11T21:41:26.3597742Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.3597839Z     args.clear()
2023-01-11T21:41:26.3598011Z     buf0 = aten.convolution(arg0_1, arg1_1, arg2_1, (4,), (0,), (1,), True, (0,), 1)
2023-01-11T21:41:26.3598151Z     assert_size_stride(buf0, (2, 16, 364), (5824, 364, 1))
2023-01-11T21:41:26.3598243Z     del arg0_1
2023-01-11T21:41:26.3598331Z     del arg1_1
2023-01-11T21:41:26.3598404Z     del arg2_1
2023-01-11T21:41:26.3598499Z     return (buf0, )
2023-01-11T21:41:26.3598506Z 
2023-01-11T21:41:26.3598511Z 
2023-01-11T21:41:26.3598610Z if __name__ == "__main__":
2023-01-11T21:41:26.3598760Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3598921Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3599209Z     arg0_1 = rand_strided((2, 32, 90), (2880, 90, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3599495Z     arg1_1 = rand_strided((32, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3599759Z     arg2_1 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3599945Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.3599967Z 
2023-01-11T21:41:26.3600041Z ok (0.065s)
2023-01-11T21:41:26.3600647Z   test_cos_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3600810Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3601159Z [2023-01-11 21:26:06,590] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 152
2023-01-11T21:41:26.3601558Z [2023-01-11 21:26:08,068] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 152
2023-01-11T21:41:26.3601567Z 
2023-01-11T21:41:26.3601698Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3601791Z import torch
2023-01-11T21:41:26.3601886Z import random
2023-01-11T21:41:26.3602023Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3602182Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3602190Z 
2023-01-11T21:41:26.3602293Z aten = torch.ops.aten
2023-01-11T21:41:26.3602468Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3602591Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3602597Z 
2023-01-11T21:41:26.3602602Z 
2023-01-11T21:41:26.3602785Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3603049Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3603206Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3603338Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.3603454Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.3603541Z {
2023-01-11T21:41:26.3603671Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3603753Z     {
2023-01-11T21:41:26.3603856Z         #pragma omp for 
2023-01-11T21:41:26.3603968Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.3604036Z         {
2023-01-11T21:41:26.3604238Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3604350Z             auto tmp1 = tmp0.cos();
2023-01-11T21:41:26.3604527Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.3604640Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.3604812Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.3604925Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.3605036Z             auto tmp6 = tmp5.cos();
2023-01-11T21:41:26.3605145Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.3605265Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.3605355Z         }
2023-01-11T21:41:26.3605482Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3605592Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.3605675Z         {
2023-01-11T21:41:26.3605786Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3605889Z             auto tmp1 = std::cos(tmp0);
2023-01-11T21:41:26.3606019Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:26.3606134Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.3606265Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.3606377Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.3606496Z             auto tmp6 = std::cos(tmp5);
2023-01-11T21:41:26.3606601Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.3606692Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.3606773Z         }
2023-01-11T21:41:26.3606857Z     }
2023-01-11T21:41:26.3606941Z }
2023-01-11T21:41:26.3607048Z ''')
2023-01-11T21:41:26.3607055Z 
2023-01-11T21:41:26.3607097Z 
2023-01-11T21:41:26.3607216Z async_compile.wait(globals())
2023-01-11T21:41:26.3607315Z del async_compile
2023-01-11T21:41:26.3607322Z 
2023-01-11T21:41:26.3607401Z def call(args):
2023-01-11T21:41:26.3607494Z     arg0_1, = args
2023-01-11T21:41:26.3607588Z     args.clear()
2023-01-11T21:41:26.3607861Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3608132Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3608346Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.3608437Z     del arg0_1
2023-01-11T21:41:26.3608524Z     return (buf0, buf1, )
2023-01-11T21:41:26.3608546Z 
2023-01-11T21:41:26.3608551Z 
2023-01-11T21:41:26.3608635Z if __name__ == "__main__":
2023-01-11T21:41:26.3608818Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3608984Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3609388Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3609532Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3609538Z 
2023-01-11T21:41:26.3609626Z ok (1.510s)
2023-01-11T21:41:26.3610245Z   test_cpp_wrapper_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3610413Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3610770Z [2023-01-11 21:26:08,101] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 153
2023-01-11T21:41:26.3610779Z 
2023-01-11T21:41:26.3610886Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3610985Z import torch
2023-01-11T21:41:26.3611078Z import random
2023-01-11T21:41:26.3611230Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3611386Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3611392Z 
2023-01-11T21:41:26.3611495Z aten = torch.ops.aten
2023-01-11T21:41:26.3611670Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3611776Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3611798Z 
2023-01-11T21:41:26.3611803Z 
2023-01-11T21:41:26.3611968Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3612230Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3612385Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.3612525Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:41:26.3612610Z {
2023-01-11T21:41:26.3612739Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3612821Z     {
2023-01-11T21:41:26.3612913Z         #pragma omp for 
2023-01-11T21:41:26.3613022Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:26.3613104Z         {
2023-01-11T21:41:26.3613280Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3613456Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.3613571Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3613743Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.3613858Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.3613973Z             tmp4.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3614055Z         }
2023-01-11T21:41:26.3614184Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3614299Z         for(long i0=4096; i0<4096; i0+=1)
2023-01-11T21:41:26.3614382Z         {
2023-01-11T21:41:26.3614498Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3614615Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.3614793Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3614923Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.3615035Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.3615145Z             in_out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.3615229Z         }
2023-01-11T21:41:26.3615311Z     }
2023-01-11T21:41:26.3615376Z }
2023-01-11T21:41:26.3615487Z ''')
2023-01-11T21:41:26.3615493Z 
2023-01-11T21:41:26.3615613Z async_compile.wait(globals())
2023-01-11T21:41:26.3615712Z del async_compile
2023-01-11T21:41:26.3615868Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3615964Z wrapper = (
2023-01-11T21:41:26.3616069Z '''
2023-01-11T21:41:26.3616153Z #include <dlfcn.h>
2023-01-11T21:41:26.3616250Z #include <assert.h>
2023-01-11T21:41:26.3616466Z     std::vector<at::Tensor> call_0(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3616574Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3616670Z     arg0_1 = args[0];
2023-01-11T21:41:26.3616852Z     auto buf0 = at::empty_strided({64, 64}, {64, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3617026Z     auto buf1 = at::as_strided(buf0, {8, 8, 64}, {512, 64, 1}); buf0.reset();  // reuse
2023-01-11T21:41:26.3617353Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/7t/c7teerg2txdzjxddrmv3s7fxnncw2uo4ovbnvjzi3xo2blybtvpk.so", RTLD_NOW);
2023-01-11T21:41:26.3617467Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3617611Z     void (*kernel_cpp_0)(float*,const float*);
2023-01-11T21:41:26.3617822Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3618047Z     kernel_cpp_0((float*)(buf1.data_ptr()), (float*)(arg0_1.data_ptr()));
2023-01-11T21:41:26.3618455Z     return std::vector<at::Tensor>({at::as_strided(arg0_1, {8, 8, 64}, {512, 64, 1}), buf1}); }''' )
2023-01-11T21:41:26.3618464Z 
2023-01-11T21:41:26.3618598Z module = load_inline(
2023-01-11T21:41:26.3619075Z     name='inline_extension_cvr6w7wpa3pkttzmjiie5bttvbnlvilrhuls2ejiiqdffyagh3el',
2023-01-11T21:41:26.3619227Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3619409Z     functions=['call_0'],
2023-01-11T21:41:26.3620075Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3620339Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3621475Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3621486Z 
2023-01-11T21:41:26.3621612Z def _wrap_func(f):
2023-01-11T21:41:26.3621730Z     def g(args):
2023-01-11T21:41:26.3621860Z         return f(args)
2023-01-11T21:41:26.3621974Z     return g
2023-01-11T21:41:26.3622117Z call = _wrap_func(module.call_0)
2023-01-11T21:41:26.3622146Z 
2023-01-11T21:41:26.3622154Z 
2023-01-11T21:41:26.3622269Z if __name__ == "__main__":
2023-01-11T21:41:26.3622472Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3622688Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3623049Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3623297Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3623308Z 
2023-01-11T21:41:26.3623784Z [2023-01-11 21:26:26,886] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 153
2023-01-11T21:41:26.3624563Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3624845Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3625321Z [2023-01-11 21:26:26,907] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 154
2023-01-11T21:41:26.3625331Z 
2023-01-11T21:41:26.3625479Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3625602Z import torch
2023-01-11T21:41:26.3625725Z import random
2023-01-11T21:41:26.3625928Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3626141Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3626151Z 
2023-01-11T21:41:26.3626286Z aten = torch.ops.aten
2023-01-11T21:41:26.3626522Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3626667Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3626693Z 
2023-01-11T21:41:26.3626700Z 
2023-01-11T21:41:26.3626973Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3627347Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3627560Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.3627737Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.3627905Z                        int* __restrict__ out_ptr0,
2023-01-11T21:41:26.3628067Z                        int* __restrict__ out_ptr1,
2023-01-11T21:41:26.3628226Z                        int* __restrict__ out_ptr2,
2023-01-11T21:41:26.3628370Z                        int* __restrict__ out_ptr3)
2023-01-11T21:41:26.3628487Z {
2023-01-11T21:41:26.3628660Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3628766Z     {
2023-01-11T21:41:26.3628899Z         #pragma omp for 
2023-01-11T21:41:26.3629039Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.3629148Z         {
2023-01-11T21:41:26.3629241Z             {
2023-01-11T21:41:26.3629355Z                 {
2023-01-11T21:41:26.3629513Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3629672Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.3629818Z                     auto tmp1 = ~tmp0;
2023-01-11T21:41:26.3629975Z                     auto tmp3 = tmp0 | tmp2;
2023-01-11T21:41:26.3630130Z                     auto tmp4 = tmp0 ^ tmp2;
2023-01-11T21:41:26.3630269Z                     auto tmp5 = tmp0 & tmp2;
2023-01-11T21:41:26.3630410Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.3630553Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.3630697Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.3630841Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:41:26.3630946Z                 }
2023-01-11T21:41:26.3631054Z             }
2023-01-11T21:41:26.3631122Z         }
2023-01-11T21:41:26.3631204Z     }
2023-01-11T21:41:26.3631283Z }
2023-01-11T21:41:26.3631396Z ''')
2023-01-11T21:41:26.3631403Z 
2023-01-11T21:41:26.3631525Z async_compile.wait(globals())
2023-01-11T21:41:26.3631624Z del async_compile
2023-01-11T21:41:26.3631782Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3631867Z wrapper = (
2023-01-11T21:41:26.3631977Z '''
2023-01-11T21:41:26.3632076Z #include <dlfcn.h>
2023-01-11T21:41:26.3632175Z #include <assert.h>
2023-01-11T21:41:26.3632351Z     std::vector<at::Tensor> call_1(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3632464Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:26.3632544Z     arg0_1 = args[0];
2023-01-11T21:41:26.3632638Z     arg1_1 = args[1];
2023-01-11T21:41:26.3632810Z     auto buf0 = at::empty_strided({64, }, {1, }, at::ScalarType::Int); 
2023-01-11T21:41:26.3632980Z     auto buf1 = at::empty_strided({64, }, {1, }, at::ScalarType::Int); 
2023-01-11T21:41:26.3633147Z     auto buf2 = at::empty_strided({64, }, {1, }, at::ScalarType::Int); 
2023-01-11T21:41:26.3633312Z     auto buf3 = at::empty_strided({64, }, {1, }, at::ScalarType::Int); 
2023-01-11T21:41:26.3633643Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/rv/crvxoskxizzxrfommbf7bg5tkfmti76e7o2dzmn36stpkt3uvwnx.so", RTLD_NOW);
2023-01-11T21:41:26.3633823Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3633990Z     void (*kernel_cpp_0)(const int*,const int*,int*,int*,int*,int*);
2023-01-11T21:41:26.3634135Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3634417Z     kernel_cpp_0((int*)(arg0_1.data_ptr()), (int*)(arg1_1.data_ptr()), (int*)(buf0.data_ptr()), (int*)(buf1.data_ptr()), (int*)(buf2.data_ptr()), (int*)(buf3.data_ptr()));
2023-01-11T21:41:26.3634511Z     arg0_1.reset();
2023-01-11T21:41:26.3634603Z     arg1_1.reset();
2023-01-11T21:41:26.3634857Z     return std::vector<at::Tensor>({buf0, buf1, buf2, buf3}); }''' )
2023-01-11T21:41:26.3634865Z 
2023-01-11T21:41:26.3634965Z module = load_inline(
2023-01-11T21:41:26.3635309Z     name='inline_extension_cuyauoq6gdklrmicb22pr47u4ph7mongsidlmteo5v4kpvsi5a4d',
2023-01-11T21:41:26.3635467Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3635611Z     functions=['call_1'],
2023-01-11T21:41:26.3636099Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3636303Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3637107Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3637114Z 
2023-01-11T21:41:26.3637212Z def _wrap_func(f):
2023-01-11T21:41:26.3637305Z     def g(args):
2023-01-11T21:41:26.3637402Z         return f(args)
2023-01-11T21:41:26.3637489Z     return g
2023-01-11T21:41:26.3637595Z call = _wrap_func(module.call_1)
2023-01-11T21:41:26.3637602Z 
2023-01-11T21:41:26.3637622Z 
2023-01-11T21:41:26.3637710Z if __name__ == "__main__":
2023-01-11T21:41:26.3637859Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3638025Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3638289Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.3638548Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.3638699Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.3638706Z 
2023-01-11T21:41:26.3639067Z [2023-01-11 21:26:46,297] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 154
2023-01-11T21:41:26.3639636Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3639804Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3640142Z [2023-01-11 21:26:46,321] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 155
2023-01-11T21:41:26.3640150Z 
2023-01-11T21:41:26.3640274Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3640365Z import torch
2023-01-11T21:41:26.3640460Z import random
2023-01-11T21:41:26.3640612Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3640772Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3640779Z 
2023-01-11T21:41:26.3640882Z aten = torch.ops.aten
2023-01-11T21:41:26.3641042Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3641163Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3641170Z 
2023-01-11T21:41:26.3641175Z 
2023-01-11T21:41:26.3641359Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3641625Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3641781Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3641960Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3642092Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.3642218Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.3642283Z {
2023-01-11T21:41:26.3642414Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3642494Z     {
2023-01-11T21:41:26.3642597Z         #pragma omp for 
2023-01-11T21:41:26.3642707Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.3642791Z         {
2023-01-11T21:41:26.3642968Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3643133Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.3643246Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3643398Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.3643490Z         }
2023-01-11T21:41:26.3643617Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3643730Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.3643815Z         {
2023-01-11T21:41:26.3643912Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3644043Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.3644155Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3644261Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3644345Z         }
2023-01-11T21:41:26.3644448Z         #pragma omp for 
2023-01-11T21:41:26.3644553Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.3644621Z         {
2023-01-11T21:41:26.3644794Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.3644969Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.3645084Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3645207Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.3645290Z         }
2023-01-11T21:41:26.3645415Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3645514Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.3645597Z         {
2023-01-11T21:41:26.3645709Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.3645839Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.3645949Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3646056Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.3646140Z         }
2023-01-11T21:41:26.3646205Z     }
2023-01-11T21:41:26.3646284Z }
2023-01-11T21:41:26.3646393Z ''')
2023-01-11T21:41:26.3646400Z 
2023-01-11T21:41:26.3646405Z 
2023-01-11T21:41:26.3646589Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3646851Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3647005Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.3647086Z {
2023-01-11T21:41:26.3647216Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3647284Z     {
2023-01-11T21:41:26.3647386Z         #pragma omp for 
2023-01-11T21:41:26.3647497Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.3647581Z         {
2023-01-11T21:41:26.3647762Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3647939Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.3648051Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3648164Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3648248Z         }
2023-01-11T21:41:26.3648373Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3648482Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.3648566Z         {
2023-01-11T21:41:26.3648685Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.3648815Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.3648918Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3649156Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3649244Z         }
2023-01-11T21:41:26.3649396Z     }
2023-01-11T21:41:26.3649476Z }
2023-01-11T21:41:26.3649586Z ''')
2023-01-11T21:41:26.3649593Z 
2023-01-11T21:41:26.3649699Z async_compile.wait(globals())
2023-01-11T21:41:26.3649797Z del async_compile
2023-01-11T21:41:26.3649951Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3650047Z wrapper = (
2023-01-11T21:41:26.3650155Z '''
2023-01-11T21:41:26.3650253Z #include <dlfcn.h>
2023-01-11T21:41:26.3650352Z #include <assert.h>
2023-01-11T21:41:26.3650505Z     std::vector<at::Tensor> call_2(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3650617Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:26.3650711Z     arg0_1 = args[0];
2023-01-11T21:41:26.3650805Z     arg1_1 = args[1];
2023-01-11T21:41:26.3650982Z     auto buf0 = at::empty_strided({2, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3651197Z     at::bmm_out(buf0, arg0_1, arg1_1);
2023-01-11T21:41:26.3651375Z     auto buf1 = at::empty_strided({2, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3651553Z     auto buf2 = at::empty_strided({2, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3651869Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/pc/cpc65tdxpyhvrndvlki6qhxomio7koznfdtyy2it7cwxowywkzk6.so", RTLD_NOW);
2023-01-11T21:41:26.3652000Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3652163Z     void (*kernel_cpp_0)(const float*,const float*,float*,float*);
2023-01-11T21:41:26.3652324Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3652563Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(arg1_1.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf2.data_ptr()));
2023-01-11T21:41:26.3652660Z     arg0_1.reset();
2023-01-11T21:41:26.3652753Z     arg1_1.reset();
2023-01-11T21:41:26.3652933Z     auto buf3 = at::empty_strided({2, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3653037Z     at::bmm_out(buf3, buf1, buf2);
2023-01-11T21:41:26.3653133Z     buf1.reset();
2023-01-11T21:41:26.3653229Z     buf2.reset();
2023-01-11T21:41:26.3653363Z     auto buf4 = buf3; buf3.reset();  // reuse
2023-01-11T21:41:26.3653691Z     auto kernel_cpp_1_lib = dlopen("/tmp/torchinductor_jenkins/2i/c2iaojccekodbchj66prruw6huckuqrbwyh2qznbbrfrnmn36tp4.so", RTLD_NOW);
2023-01-11T21:41:26.3653817Z     assert(kernel_cpp_1_lib != nullptr);
2023-01-11T21:41:26.3653930Z     void (*kernel_cpp_1)(float*);
2023-01-11T21:41:26.3654077Z     *(void **) (&kernel_cpp_1) = dlsym(kernel_cpp_1_lib, "kernel");
2023-01-11T21:41:26.3654210Z     kernel_cpp_1((float*)(buf4.data_ptr()));
2023-01-11T21:41:26.3654439Z     return std::vector<at::Tensor>({buf0, buf4}); }''' )
2023-01-11T21:41:26.3654447Z 
2023-01-11T21:41:26.3654549Z module = load_inline(
2023-01-11T21:41:26.3654892Z     name='inline_extension_chfnadliirwsn3ujofkzbsnpr6hburp722b4pdqce6kt2trz36ns',
2023-01-11T21:41:26.3655012Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3655166Z     functions=['call_2'],
2023-01-11T21:41:26.3655657Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3655844Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3656646Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3656670Z 
2023-01-11T21:41:26.3656751Z def _wrap_func(f):
2023-01-11T21:41:26.3656842Z     def g(args):
2023-01-11T21:41:26.3656940Z         return f(args)
2023-01-11T21:41:26.3657027Z     return g
2023-01-11T21:41:26.3657148Z call = _wrap_func(module.call_2)
2023-01-11T21:41:26.3657155Z 
2023-01-11T21:41:26.3657160Z 
2023-01-11T21:41:26.3657264Z if __name__ == "__main__":
2023-01-11T21:41:26.3657413Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3657597Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3657878Z     arg0_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3658149Z     arg1_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3658300Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.3658307Z 
2023-01-11T21:41:26.3658671Z [2023-01-11 21:27:05,496] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 155
2023-01-11T21:41:26.3659268Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3659438Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3659798Z [2023-01-11 21:27:05,519] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 156
2023-01-11T21:41:26.3659804Z 
2023-01-11T21:41:26.3659927Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3660018Z import torch
2023-01-11T21:41:26.3660096Z import random
2023-01-11T21:41:26.3660247Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3660410Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3660417Z 
2023-01-11T21:41:26.3660518Z aten = torch.ops.aten
2023-01-11T21:41:26.3660693Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3660814Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3660821Z 
2023-01-11T21:41:26.3660826Z 
2023-01-11T21:41:26.3661010Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3661257Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3661413Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3661554Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3661686Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.3661814Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.3661895Z {
2023-01-11T21:41:26.3662023Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3662104Z     {
2023-01-11T21:41:26.3662193Z         #pragma omp for 
2023-01-11T21:41:26.3662300Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.3662384Z         {
2023-01-11T21:41:26.3662562Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3662738Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.3662851Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3662973Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.3663043Z         }
2023-01-11T21:41:26.3663237Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3663355Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.3663439Z         {
2023-01-11T21:41:26.3663552Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3663683Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.3663792Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3663884Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3663967Z         }
2023-01-11T21:41:26.3664069Z         #pragma omp for 
2023-01-11T21:41:26.3664175Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.3664260Z         {
2023-01-11T21:41:26.3664436Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.3664613Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.3664709Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3664833Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.3664919Z         }
2023-01-11T21:41:26.3665043Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3665192Z         for(long i0=80; i0<80; i0+=1)
2023-01-11T21:41:26.3665276Z         {
2023-01-11T21:41:26.3665385Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.3665501Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.3665610Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3665716Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.3665801Z         }
2023-01-11T21:41:26.3665881Z     }
2023-01-11T21:41:26.3665961Z }
2023-01-11T21:41:26.3666055Z ''')
2023-01-11T21:41:26.3666064Z 
2023-01-11T21:41:26.3666084Z 
2023-01-11T21:41:26.3666252Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3666513Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3666668Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.3666787Z {
2023-01-11T21:41:26.3666920Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3667002Z     {
2023-01-11T21:41:26.3667107Z         #pragma omp for 
2023-01-11T21:41:26.3667201Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:26.3667286Z         {
2023-01-11T21:41:26.3667466Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3667642Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.3667754Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3667881Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3667965Z         }
2023-01-11T21:41:26.3668075Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3668184Z         for(long i0=160; i0<160; i0+=1)
2023-01-11T21:41:26.3668267Z         {
2023-01-11T21:41:26.3668385Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.3668517Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.3668633Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3668745Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3668818Z         }
2023-01-11T21:41:26.3668898Z     }
2023-01-11T21:41:26.3668977Z }
2023-01-11T21:41:26.3669084Z ''')
2023-01-11T21:41:26.3669090Z 
2023-01-11T21:41:26.3669208Z async_compile.wait(globals())
2023-01-11T21:41:26.3669306Z del async_compile
2023-01-11T21:41:26.3669461Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3669540Z wrapper = (
2023-01-11T21:41:26.3669648Z '''
2023-01-11T21:41:26.3669748Z #include <dlfcn.h>
2023-01-11T21:41:26.3669847Z #include <assert.h>
2023-01-11T21:41:26.3670016Z     std::vector<at::Tensor> call_3(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3670128Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:26.3670222Z     arg0_1 = args[0];
2023-01-11T21:41:26.3670301Z     arg1_1 = args[1];
2023-01-11T21:41:26.3670482Z     auto buf0 = at::empty_strided({1, 16, 10}, {160, 10, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3670648Z     auto buf0_as_strided = at::as_strided(buf0, {16, 10}, {10, 1});
2023-01-11T21:41:26.3670858Z     at::mm_out(buf0_as_strided, at::as_strided(arg0_1, {16, 8}, {8, 1}), at::as_strided(arg1_1, {8, 10}, {10, 1}));
2023-01-11T21:41:26.3671041Z     auto buf1 = at::empty_strided({1, 16, 8}, {128, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3671212Z     auto buf2 = at::empty_strided({1, 8, 10}, {80, 10, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3671536Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/ra/cralzjg7p7jfrh7lq7f5aawuy5ztcxsmezjq7ltkvv7n75g5gmjr.so", RTLD_NOW);
2023-01-11T21:41:26.3671663Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3671813Z     void (*kernel_cpp_0)(const float*,const float*,float*,float*);
2023-01-11T21:41:26.3671974Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3672214Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(arg1_1.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf2.data_ptr()));
2023-01-11T21:41:26.3672309Z     arg0_1.reset();
2023-01-11T21:41:26.3672401Z     arg1_1.reset();
2023-01-11T21:41:26.3672620Z     auto buf3 = at::empty_strided({1, 16, 10}, {160, 10, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3672782Z     auto buf3_as_strided = at::as_strided(buf3, {16, 10}, {10, 1});
2023-01-11T21:41:26.3672987Z     at::mm_out(buf3_as_strided, at::as_strided(buf1, {16, 8}, {8, 1}), at::as_strided(buf2, {8, 10}, {10, 1}));
2023-01-11T21:41:26.3673080Z     buf1.reset();
2023-01-11T21:41:26.3673158Z     buf2.reset();
2023-01-11T21:41:26.3673287Z     auto buf4 = buf3; buf3.reset();  // reuse
2023-01-11T21:41:26.3673601Z     auto kernel_cpp_1_lib = dlopen("/tmp/torchinductor_jenkins/yc/cycbydl6b3o6njowlq7op2e3gon6ehl777xoi27mhfawtegw4ri4.so", RTLD_NOW);
2023-01-11T21:41:26.3673729Z     assert(kernel_cpp_1_lib != nullptr);
2023-01-11T21:41:26.3673842Z     void (*kernel_cpp_1)(float*);
2023-01-11T21:41:26.3674046Z     *(void **) (&kernel_cpp_1) = dlsym(kernel_cpp_1_lib, "kernel");
2023-01-11T21:41:26.3674186Z     kernel_cpp_1((float*)(buf4.data_ptr()));
2023-01-11T21:41:26.3674400Z     return std::vector<at::Tensor>({buf0, buf4}); }''' )
2023-01-11T21:41:26.3674426Z 
2023-01-11T21:41:26.3674512Z module = load_inline(
2023-01-11T21:41:26.3674856Z     name='inline_extension_cfk34ucpswp45ihunrnj5h3vvmenylbjrdehpsrzmjuafwmv7mzt',
2023-01-11T21:41:26.3674967Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3675118Z     functions=['call_3'],
2023-01-11T21:41:26.3675608Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3675831Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3676439Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3676446Z 
2023-01-11T21:41:26.3676519Z def _wrap_func(f):
2023-01-11T21:41:26.3676577Z     def g(args):
2023-01-11T21:41:26.3676649Z         return f(args)
2023-01-11T21:41:26.3676715Z     return g
2023-01-11T21:41:26.3676804Z call = _wrap_func(module.call_3)
2023-01-11T21:41:26.3676809Z 
2023-01-11T21:41:26.3676814Z 
2023-01-11T21:41:26.3676890Z if __name__ == "__main__":
2023-01-11T21:41:26.3677001Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3677122Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3677329Z     arg0_1 = rand_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3677516Z     arg1_1 = rand_strided((1, 8, 10), (80, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3677633Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.3677638Z 
2023-01-11T21:41:26.3677908Z [2023-01-11 21:27:24,602] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 156
2023-01-11T21:41:26.3678338Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3678467Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3678725Z [2023-01-11 21:27:24,619] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 157
2023-01-11T21:41:26.3678730Z 
2023-01-11T21:41:26.3678823Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3678893Z import torch
2023-01-11T21:41:26.3678963Z import random
2023-01-11T21:41:26.3679065Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3679185Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3679190Z 
2023-01-11T21:41:26.3679269Z aten = torch.ops.aten
2023-01-11T21:41:26.3679404Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3679533Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3679538Z 
2023-01-11T21:41:26.3679626Z async_compile.wait(globals())
2023-01-11T21:41:26.3679699Z del async_compile
2023-01-11T21:41:26.3679815Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3679872Z wrapper = (
2023-01-11T21:41:26.3679953Z '''
2023-01-11T21:41:26.3680028Z #include <dlfcn.h>
2023-01-11T21:41:26.3680102Z #include <assert.h>
2023-01-11T21:41:26.3680215Z     at::Tensor call_4(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3680298Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:26.3680355Z     arg0_1 = args[0];
2023-01-11T21:41:26.3680425Z     arg1_1 = args[1];
2023-01-11T21:41:26.3680557Z     auto buf0 = at::empty_strided({1, 8, 8}, {64, 8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3680707Z     auto buf0_as_strided = at::as_strided(buf0, {8, 8}, {8, 1});
2023-01-11T21:41:26.3680867Z     at::mm_out(buf0_as_strided, at::as_strided(arg0_1, {8, 8}, {1, 8}), at::as_strided(arg1_1, {8, 8}, {8, 1}));
2023-01-11T21:41:26.3680941Z     arg0_1.reset();
2023-01-11T21:41:26.3681009Z     arg1_1.reset();
2023-01-11T21:41:26.3681118Z     return buf0; }''' )
2023-01-11T21:41:26.3681123Z 
2023-01-11T21:41:26.3681184Z module = load_inline(
2023-01-11T21:41:26.3681440Z     name='inline_extension_chbemg2ahqboherkh3dfy5k24qgsw5gtkd5ehfcdmncijlslqgpn',
2023-01-11T21:41:26.3681524Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3681633Z     functions=['call_4'],
2023-01-11T21:41:26.3681987Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3682135Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3682731Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3682739Z 
2023-01-11T21:41:26.3682812Z def _wrap_func(f):
2023-01-11T21:41:26.3682880Z     def g(args):
2023-01-11T21:41:26.3682939Z         return f(args)
2023-01-11T21:41:26.3683007Z     return g
2023-01-11T21:41:26.3683101Z call = _wrap_func(module.call_4)
2023-01-11T21:41:26.3683106Z 
2023-01-11T21:41:26.3683111Z 
2023-01-11T21:41:26.3683188Z if __name__ == "__main__":
2023-01-11T21:41:26.3683302Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3683424Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3683630Z     arg0_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3683834Z     arg1_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3683936Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.3683941Z 
2023-01-11T21:41:26.3684209Z [2023-01-11 21:27:43,519] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 157
2023-01-11T21:41:26.3684639Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3684769Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3685029Z [2023-01-11 21:27:43,569] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 158
2023-01-11T21:41:26.3685034Z 
2023-01-11T21:41:26.3685128Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3685198Z import torch
2023-01-11T21:41:26.3685272Z import random
2023-01-11T21:41:26.3685386Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3685522Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3685526Z 
2023-01-11T21:41:26.3685605Z aten = torch.ops.aten
2023-01-11T21:41:26.3685737Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3685828Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3685833Z 
2023-01-11T21:41:26.3685838Z 
2023-01-11T21:41:26.3685972Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3686176Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3686295Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3686397Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.3686479Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.3686573Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.3686699Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.3686797Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.3686896Z                        double* __restrict__ out_ptr5,
2023-01-11T21:41:26.3686991Z                        double* __restrict__ out_ptr6)
2023-01-11T21:41:26.3687051Z {
2023-01-11T21:41:26.3687134Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3687194Z     {
2023-01-11T21:41:26.3687271Z         #pragma omp for 
2023-01-11T21:41:26.3687354Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.3687417Z         {
2023-01-11T21:41:26.3687498Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.3687561Z             {
2023-01-11T21:41:26.3687689Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (16*i0));
2023-01-11T21:41:26.3687824Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.3687915Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3688017Z                 tmp0.store(out_ptr0 + (8*i1) + (36*i0));
2023-01-11T21:41:26.3688117Z                 tmp2.store(out_ptr1 + (8*i1) + (36*i0));
2023-01-11T21:41:26.3688182Z             }
2023-01-11T21:41:26.3688273Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.3688345Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:41:26.3688408Z             {
2023-01-11T21:41:26.3688504Z                 auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:26.3688602Z                 auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.3688687Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3688776Z                 out_ptr0[i1 + (36*i0)] = tmp0;
2023-01-11T21:41:26.3688865Z                 out_ptr1[i1 + (36*i0)] = tmp2;
2023-01-11T21:41:26.3688915Z             }
2023-01-11T21:41:26.3688977Z         }
2023-01-11T21:41:26.3689198Z         #pragma omp for 
2023-01-11T21:41:26.3689326Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.3689422Z         {
2023-01-11T21:41:26.3689526Z             #pragma GCC ivdep
2023-01-11T21:41:26.3689611Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.3689664Z             {
2023-01-11T21:41:26.3689727Z                 {
2023-01-11T21:41:26.3689793Z                     {
2023-01-11T21:41:26.3689894Z                         auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:41:26.3689997Z                         auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.3690089Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3690182Z                         out_ptr2[i1 + (36*i0)] = tmp2;
2023-01-11T21:41:26.3690235Z                     }
2023-01-11T21:41:26.3690299Z                 }
2023-01-11T21:41:26.3690361Z             }
2023-01-11T21:41:26.3690423Z         }
2023-01-11T21:41:26.3690498Z         #pragma omp for 
2023-01-11T21:41:26.3690579Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.3690644Z         {
2023-01-11T21:41:26.3690694Z             {
2023-01-11T21:41:26.3690757Z                 {
2023-01-11T21:41:26.3690850Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3690950Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.3691099Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3691205Z                     auto tmp3 = static_cast<double>(tmp2);
2023-01-11T21:41:26.3691290Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.3691360Z                     out_ptr4[i0] = tmp2;
2023-01-11T21:41:26.3691441Z                     out_ptr5[i0] = tmp3;
2023-01-11T21:41:26.3691523Z                     out_ptr6[i0] = tmp3;
2023-01-11T21:41:26.3691587Z                 }
2023-01-11T21:41:26.3691649Z             }
2023-01-11T21:41:26.3691710Z         }
2023-01-11T21:41:26.3691759Z     }
2023-01-11T21:41:26.3691818Z }
2023-01-11T21:41:26.3691903Z ''')
2023-01-11T21:41:26.3691909Z 
2023-01-11T21:41:26.3691997Z async_compile.wait(globals())
2023-01-11T21:41:26.3692070Z del async_compile
2023-01-11T21:41:26.3692227Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3692300Z wrapper = (
2023-01-11T21:41:26.3692365Z '''
2023-01-11T21:41:26.3692441Z #include <dlfcn.h>
2023-01-11T21:41:26.3692512Z #include <assert.h>
2023-01-11T21:41:26.3692642Z     std::vector<at::Tensor> call_5(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3692718Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3692790Z     arg0_1 = args[0];
2023-01-11T21:41:26.3692922Z     auto buf3 = at::empty_strided({8, 36}, {36, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3693034Z     auto buf0 = at::as_strided(buf3, {8, 16}, {36, 1});  // alias
2023-01-11T21:41:26.3693137Z     auto buf2 = at::as_strided(buf3, {8, 16}, {36, 1}, 20);  // alias
2023-01-11T21:41:26.3693247Z     auto buf1 = at::as_strided(buf3, {8, 4}, {36, 1}, 16);  // alias
2023-01-11T21:41:26.3693376Z     auto buf6 = at::empty_strided({16, 16}, {16, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3693486Z     auto buf4 = at::as_strided(buf6, {8, 16}, {16, 1});  // alias
2023-01-11T21:41:26.3693601Z     auto buf5 = at::as_strided(buf6, {8, 16}, {16, 1}, 128);  // alias
2023-01-11T21:41:26.3693731Z     auto buf9 = at::empty_strided({16, 16}, {16, 1}, at::ScalarType::Double); 
2023-01-11T21:41:26.3693842Z     auto buf7 = at::as_strided(buf9, {8, 16}, {16, 1});  // alias
2023-01-11T21:41:26.3693955Z     auto buf8 = at::as_strided(buf9, {8, 16}, {16, 1}, 128);  // alias
2023-01-11T21:41:26.3694216Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/xx/cxxvcduhvysrcz4iolqfqobmycduemx7yqmtsfsczg53dn526b2j.so", RTLD_NOW);
2023-01-11T21:41:26.3694298Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3694445Z     void (*kernel_cpp_0)(const float*,float*,float*,float*,float*,float*,double*,double*);
2023-01-11T21:41:26.3694565Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3694839Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf2.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf4.data_ptr()), (float*)(buf5.data_ptr()), (double*)(buf7.data_ptr()), (double*)(buf8.data_ptr()));
2023-01-11T21:41:26.3694910Z     arg0_1.reset();
2023-01-11T21:41:26.3695091Z     return std::vector<at::Tensor>({buf3, buf6, buf9}); }''' )
2023-01-11T21:41:26.3695096Z 
2023-01-11T21:41:26.3695171Z module = load_inline(
2023-01-11T21:41:26.3695428Z     name='inline_extension_cji7g5r4jyhbjebn7otwyol5fhfvue57jqht5etdlccgyremizfh',
2023-01-11T21:41:26.3695499Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3695610Z     functions=['call_5'],
2023-01-11T21:41:26.3695968Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3696117Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3696718Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3696724Z 
2023-01-11T21:41:26.3696828Z def _wrap_func(f):
2023-01-11T21:41:26.3696897Z     def g(args):
2023-01-11T21:41:26.3696968Z         return f(args)
2023-01-11T21:41:26.3697033Z     return g
2023-01-11T21:41:26.3697110Z call = _wrap_func(module.call_5)
2023-01-11T21:41:26.3697115Z 
2023-01-11T21:41:26.3697120Z 
2023-01-11T21:41:26.3697196Z if __name__ == "__main__":
2023-01-11T21:41:26.3697310Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3697432Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3697632Z     arg0_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3697739Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3697744Z 
2023-01-11T21:41:26.3698052Z [2023-01-11 21:28:02,476] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 158
2023-01-11T21:41:26.3698481Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3698610Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3698855Z [2023-01-11 21:28:02,537] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 159
2023-01-11T21:41:26.3698873Z 
2023-01-11T21:41:26.3698955Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3699027Z import torch
2023-01-11T21:41:26.3699095Z import random
2023-01-11T21:41:26.3699207Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3699325Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3699330Z 
2023-01-11T21:41:26.3699409Z aten = torch.ops.aten
2023-01-11T21:41:26.3699541Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3699620Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3699625Z 
2023-01-11T21:41:26.3699629Z 
2023-01-11T21:41:26.3699764Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3699968Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3700086Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3700188Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3700292Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.3700397Z                        const double* __restrict__ in_ptr3,
2023-01-11T21:41:26.3700494Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.3700576Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.3700669Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.3700764Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.3700859Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.3700953Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:26.3701051Z                        double* __restrict__ out_ptr6,
2023-01-11T21:41:26.3701147Z                        double* __restrict__ out_ptr7,
2023-01-11T21:41:26.3701228Z                        float* __restrict__ out_ptr8,
2023-01-11T21:41:26.3701323Z                        double* __restrict__ out_ptr9)
2023-01-11T21:41:26.3701384Z {
2023-01-11T21:41:26.3701482Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3701542Z     {
2023-01-11T21:41:26.3701632Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3701713Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3701763Z         {
2023-01-11T21:41:26.3701844Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.3701909Z             {
2023-01-11T21:41:26.3701992Z                 #pragma GCC ivdep
2023-01-11T21:41:26.3702080Z                 for(long i2=0; i2<16; i2+=1)
2023-01-11T21:41:26.3702144Z                 {
2023-01-11T21:41:26.3702240Z                     {
2023-01-11T21:41:26.3702295Z                         {
2023-01-11T21:41:26.3702405Z                             auto tmp0 = in_ptr0[i0 + (3*i2) + (48*i1)];
2023-01-11T21:41:26.3702512Z                             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.3702608Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3702714Z                             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.3702808Z                             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.3702914Z                             out_ptr0[i2 + (48*i1) + (144*i0)] = tmp0;
2023-01-11T21:41:26.3703002Z                             out_ptr1[i2 + (48*i1) + (144*i0)] = tmp2;
2023-01-11T21:41:26.3703098Z                             out_ptr2[i2 + (48*i1) + (144*i0)] = tmp4;
2023-01-11T21:41:26.3703284Z                         }
2023-01-11T21:41:26.3703360Z                     }
2023-01-11T21:41:26.3703424Z                 }
2023-01-11T21:41:26.3703488Z             }
2023-01-11T21:41:26.3703549Z         }
2023-01-11T21:41:26.3703627Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3703710Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3703775Z         {
2023-01-11T21:41:26.3703861Z             for(long i1=0; i1<144; i1+=1)
2023-01-11T21:41:26.3703925Z             {
2023-01-11T21:41:26.3703988Z                 {
2023-01-11T21:41:26.3704052Z                     {
2023-01-11T21:41:26.3704143Z                         auto tmp0 = in_ptr1[i1 + (144*i0)];
2023-01-11T21:41:26.3704235Z                         out_ptr3[i0 + (3*i1)] = tmp0;
2023-01-11T21:41:26.3704300Z                     }
2023-01-11T21:41:26.3704363Z                 }
2023-01-11T21:41:26.3704425Z             }
2023-01-11T21:41:26.3704486Z         }
2023-01-11T21:41:26.3704574Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.3704643Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3704704Z         {
2023-01-11T21:41:26.3704787Z             for(long i1=0; i1<48; i1+=1)
2023-01-11T21:41:26.3704851Z             {
2023-01-11T21:41:26.3704915Z                 {
2023-01-11T21:41:26.3704979Z                     {
2023-01-11T21:41:26.3705067Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:41:26.3705173Z                         auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.3705264Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3705359Z                         out_ptr4[i1 + (48*i0)] = tmp2;
2023-01-11T21:41:26.3705423Z                     }
2023-01-11T21:41:26.3705485Z                 }
2023-01-11T21:41:26.3705546Z             }
2023-01-11T21:41:26.3705595Z         }
2023-01-11T21:41:26.3705669Z         #pragma omp for 
2023-01-11T21:41:26.3705749Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:41:26.3705811Z         {
2023-01-11T21:41:26.3705877Z             {
2023-01-11T21:41:26.3705939Z                 {
2023-01-11T21:41:26.3706031Z                     auto tmp0 = out_ptr4[i0];
2023-01-11T21:41:26.3706127Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.3706212Z                     out_ptr5[i0] = tmp0;
2023-01-11T21:41:26.3706297Z                     out_ptr6[i0] = tmp1;
2023-01-11T21:41:26.3706378Z                     out_ptr7[i0] = tmp1;
2023-01-11T21:41:26.3706442Z                 }
2023-01-11T21:41:26.3706505Z             }
2023-01-11T21:41:26.3706566Z         }
2023-01-11T21:41:26.3706643Z         #pragma omp for  collapse(3)
2023-01-11T21:41:26.3706723Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.3706785Z         {
2023-01-11T21:41:26.3706866Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.3706929Z             {
2023-01-11T21:41:26.3707015Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:41:26.3707066Z                 {
2023-01-11T21:41:26.3707130Z                     {
2023-01-11T21:41:26.3707199Z                         {
2023-01-11T21:41:26.3707308Z                             auto tmp0 = in_ptr2[i2 + (48*i1) + (144*i0)];
2023-01-11T21:41:26.3707443Z                             out_ptr8[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:41:26.3707511Z                         }
2023-01-11T21:41:26.3707576Z                     }
2023-01-11T21:41:26.3707626Z                 }
2023-01-11T21:41:26.3707689Z             }
2023-01-11T21:41:26.3707749Z         }
2023-01-11T21:41:26.3707842Z         #pragma omp for  collapse(3)
2023-01-11T21:41:26.3707922Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.3707983Z         {
2023-01-11T21:41:26.3708062Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.3708112Z             {
2023-01-11T21:41:26.3708201Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:41:26.3708264Z                 {
2023-01-11T21:41:26.3708328Z                     {
2023-01-11T21:41:26.3708395Z                         {
2023-01-11T21:41:26.3708532Z                             auto tmp0 = in_ptr3[i2 + (48*i1) + (144*i0)];
2023-01-11T21:41:26.3708638Z                             out_ptr9[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:41:26.3708695Z                         }
2023-01-11T21:41:26.3708760Z                     }
2023-01-11T21:41:26.3708823Z                 }
2023-01-11T21:41:26.3708885Z             }
2023-01-11T21:41:26.3708946Z         }
2023-01-11T21:41:26.3709006Z     }
2023-01-11T21:41:26.3709052Z }
2023-01-11T21:41:26.3709137Z ''')
2023-01-11T21:41:26.3709143Z 
2023-01-11T21:41:26.3709235Z async_compile.wait(globals())
2023-01-11T21:41:26.3709307Z del async_compile
2023-01-11T21:41:26.3709422Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3709491Z wrapper = (
2023-01-11T21:41:26.3709569Z '''
2023-01-11T21:41:26.3709629Z #include <dlfcn.h>
2023-01-11T21:41:26.3709701Z #include <assert.h>
2023-01-11T21:41:26.3709831Z     std::vector<at::Tensor> call_6(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3709911Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3709983Z     arg0_1 = args[0];
2023-01-11T21:41:26.3710121Z     auto buf3 = at::empty_strided({1, 3, 3, 48}, {432, 144, 48, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3710245Z     auto buf0 = at::as_strided(buf3, {1, 3, 3, 16}, {432, 144, 48, 1});  // alias
2023-01-11T21:41:26.3710369Z     auto buf1 = at::as_strided(buf3, {1, 3, 3, 16}, {432, 144, 48, 1}, 16);  // alias
2023-01-11T21:41:26.3710486Z     auto buf2 = at::as_strided(buf3, {1, 3, 3, 16}, {432, 144, 48, 1}, 32);  // alias
2023-01-11T21:41:26.3710608Z     auto buf4 = at::empty_strided({1, 3, 3, 48}, {432, 1, 144, 3}, at::ScalarType::Float); 
2023-01-11T21:41:26.3710741Z     auto buf7 = at::empty_strided({2, 3, 3, 16}, {144, 48, 16, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3710861Z     auto buf5 = at::as_strided(buf7, {1, 3, 3, 16}, {144, 48, 16, 1});  // alias
2023-01-11T21:41:26.3710983Z     auto buf6 = at::as_strided(buf7, {1, 3, 3, 16}, {144, 48, 16, 1}, 144);  // alias
2023-01-11T21:41:26.3711121Z     auto buf11 = at::empty_strided({2, 3, 3, 16}, {144, 48, 16, 1}, at::ScalarType::Double); 
2023-01-11T21:41:26.3711239Z     auto buf9 = at::as_strided(buf11, {1, 3, 3, 16}, {144, 48, 16, 1});  // alias
2023-01-11T21:41:26.3711366Z     auto buf10 = at::as_strided(buf11, {1, 3, 3, 16}, {144, 48, 16, 1}, 144);  // alias
2023-01-11T21:41:26.3711498Z     auto buf8 = at::empty_strided({2, 3, 3, 16}, {144, 1, 48, 3}, at::ScalarType::Float); 
2023-01-11T21:41:26.3711636Z     auto buf12 = at::empty_strided({2, 3, 3, 16}, {144, 1, 48, 3}, at::ScalarType::Double); 
2023-01-11T21:41:26.3711893Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/tf/ctf2pptj3ayxmvzwwc77hmhshbex7tnnbmizfndel6malv4tyfpu.so", RTLD_NOW);
2023-01-11T21:41:26.3711978Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3712191Z     void (*kernel_cpp_0)(const float*,const float*,const float*,const double*,float*,float*,float*,float*,float*,float*,double*,double*,float*,double*);
2023-01-11T21:41:26.3712315Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3712732Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(buf3.data_ptr()), (float*)(buf7.data_ptr()), (double*)(buf11.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf2.data_ptr()), (float*)(buf4.data_ptr()), (float*)(buf5.data_ptr()), (float*)(buf6.data_ptr()), (double*)(buf9.data_ptr()), (double*)(buf10.data_ptr()), (float*)(buf8.data_ptr()), (double*)(buf12.data_ptr()));
2023-01-11T21:41:26.3712836Z     arg0_1.reset();
2023-01-11T21:41:26.3712905Z     buf0.reset();
2023-01-11T21:41:26.3712973Z     buf1.reset();
2023-01-11T21:41:26.3713044Z     buf10.reset();
2023-01-11T21:41:26.3713100Z     buf11.reset();
2023-01-11T21:41:26.3713168Z     buf2.reset();
2023-01-11T21:41:26.3713237Z     buf3.reset();
2023-01-11T21:41:26.3713304Z     buf5.reset();
2023-01-11T21:41:26.3713372Z     buf6.reset();
2023-01-11T21:41:26.3713438Z     buf7.reset();
2023-01-11T21:41:26.3713505Z     buf9.reset();
2023-01-11T21:41:26.3713706Z     return std::vector<at::Tensor>({buf4, buf8, buf12}); }''' )
2023-01-11T21:41:26.3713712Z 
2023-01-11T21:41:26.3713790Z module = load_inline(
2023-01-11T21:41:26.3714056Z     name='inline_extension_cwwd52gevofbwzpjosptdssanute3m5y5v4upouhvxxh6jtkg7ny',
2023-01-11T21:41:26.3714142Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3714251Z     functions=['call_6'],
2023-01-11T21:41:26.3714612Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3714760Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3715356Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3715361Z 
2023-01-11T21:41:26.3715438Z def _wrap_func(f):
2023-01-11T21:41:26.3715493Z     def g(args):
2023-01-11T21:41:26.3715565Z         return f(args)
2023-01-11T21:41:26.3715631Z     return g
2023-01-11T21:41:26.3715720Z call = _wrap_func(module.call_6)
2023-01-11T21:41:26.3715725Z 
2023-01-11T21:41:26.3715729Z 
2023-01-11T21:41:26.3715804Z if __name__ == "__main__":
2023-01-11T21:41:26.3715916Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3716039Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3716238Z     arg0_1 = rand_strided((1, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3716345Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3716350Z 
2023-01-11T21:41:26.3716615Z [2023-01-11 21:28:20,791] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 159
2023-01-11T21:41:26.3717043Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3717175Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3717431Z [2023-01-11 21:28:20,844] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 160
2023-01-11T21:41:26.3717437Z 
2023-01-11T21:41:26.3717530Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3717600Z import torch
2023-01-11T21:41:26.3717670Z import random
2023-01-11T21:41:26.3717784Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3717891Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3717897Z 
2023-01-11T21:41:26.3717977Z aten = torch.ops.aten
2023-01-11T21:41:26.3718109Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3718203Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3718209Z 
2023-01-11T21:41:26.3718213Z 
2023-01-11T21:41:26.3718347Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3718591Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3718705Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.3718766Z {
2023-01-11T21:41:26.3718849Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3718910Z     {
2023-01-11T21:41:26.3718988Z         #pragma omp for 
2023-01-11T21:41:26.3719073Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.3719135Z         {
2023-01-11T21:41:26.3719274Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3719409Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:26.3719491Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3719553Z         }
2023-01-11T21:41:26.3719675Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3719760Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.3719825Z         {
2023-01-11T21:41:26.3719913Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.3720050Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:26.3720119Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:26.3720202Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3720263Z         }
2023-01-11T21:41:26.3720323Z     }
2023-01-11T21:41:26.3720382Z }
2023-01-11T21:41:26.3720459Z ''')
2023-01-11T21:41:26.3720464Z 
2023-01-11T21:41:26.3720551Z async_compile.wait(globals())
2023-01-11T21:41:26.3720610Z del async_compile
2023-01-11T21:41:26.3720725Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3720795Z wrapper = (
2023-01-11T21:41:26.3720873Z '''
2023-01-11T21:41:26.3720945Z #include <dlfcn.h>
2023-01-11T21:41:26.3721018Z #include <assert.h>
2023-01-11T21:41:26.3721134Z     std::vector<at::Tensor> call_7(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3721244Z     at::Tensor primals_1, primals_2, primals_3;
2023-01-11T21:41:26.3721320Z     primals_1 = args[0];
2023-01-11T21:41:26.3721398Z     primals_2 = args[1];
2023-01-11T21:41:26.3721471Z     primals_3 = args[2];
2023-01-11T21:41:26.3721602Z     auto buf0 = at::empty_strided({2, 16}, {16, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3721751Z     at::addmm_out(buf0, primals_2, primals_3, at::as_strided(primals_1, {8, 16}, {1, 8}), 1, 1);
2023-01-11T21:41:26.3721826Z     primals_1.reset();
2023-01-11T21:41:26.3721886Z     primals_2.reset();
2023-01-11T21:41:26.3721983Z     auto buf1 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:26.3722233Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/iw/ciwiq5etlmci4iuytny63x3zhsv2pxo7evslwt4lbr6dhqxlpznj.so", RTLD_NOW);
2023-01-11T21:41:26.3722333Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3722418Z     void (*kernel_cpp_0)(float*);
2023-01-11T21:41:26.3722543Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3722649Z     kernel_cpp_0((float*)(buf1.data_ptr()));
2023-01-11T21:41:26.3722824Z     return std::vector<at::Tensor>({buf1, primals_3, buf1}); }''' )
2023-01-11T21:41:26.3722844Z 
2023-01-11T21:41:26.3722906Z module = load_inline(
2023-01-11T21:41:26.3723159Z     name='inline_extension_cfo4kg3ofnmy3tbyyrnmb4ko7l4ctd5apfqvog3nnpfe7zmqeqge',
2023-01-11T21:41:26.3723242Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3723351Z     functions=['call_7'],
2023-01-11T21:41:26.3723710Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3723859Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3724450Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3724456Z 
2023-01-11T21:41:26.3724560Z def _wrap_func(f):
2023-01-11T21:41:26.3724617Z     def g(args):
2023-01-11T21:41:26.3724689Z         return f(args)
2023-01-11T21:41:26.3724754Z     return g
2023-01-11T21:41:26.3724845Z call = _wrap_func(module.call_7)
2023-01-11T21:41:26.3724849Z 
2023-01-11T21:41:26.3724854Z 
2023-01-11T21:41:26.3724929Z if __name__ == "__main__":
2023-01-11T21:41:26.3725041Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3725162Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3725368Z     primals_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3725555Z     primals_2 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3725755Z     primals_3 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3725917Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.3725923Z 
2023-01-11T21:41:26.3726192Z [2023-01-11 21:28:41,541] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 160
2023-01-11T21:41:26.3726624Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3726750Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3727007Z [2023-01-11 21:28:41,725] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 161
2023-01-11T21:41:26.3727012Z 
2023-01-11T21:41:26.3727104Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3727173Z import torch
2023-01-11T21:41:26.3727231Z import random
2023-01-11T21:41:26.3727345Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3727463Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3727470Z 
2023-01-11T21:41:26.3727547Z aten = torch.ops.aten
2023-01-11T21:41:26.3727680Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3727769Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3727774Z 
2023-01-11T21:41:26.3727778Z 
2023-01-11T21:41:26.3727912Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3728114Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3728228Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.3728275Z {
2023-01-11T21:41:26.3728368Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3728432Z     {
2023-01-11T21:41:26.3728508Z         #pragma omp for 
2023-01-11T21:41:26.3728588Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.3728652Z         {
2023-01-11T21:41:26.3728776Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3728906Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.3729000Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3729226Z         }
2023-01-11T21:41:26.3729369Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3729481Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.3729546Z         {
2023-01-11T21:41:26.3729624Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.3729712Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.3729794Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.3729857Z         }
2023-01-11T21:41:26.3729918Z     }
2023-01-11T21:41:26.3729978Z }
2023-01-11T21:41:26.3730059Z ''')
2023-01-11T21:41:26.3730064Z 
2023-01-11T21:41:26.3730068Z 
2023-01-11T21:41:26.3730200Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.3730396Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3730514Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.3730631Z {
2023-01-11T21:41:26.3730728Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3730789Z     {
2023-01-11T21:41:26.3730864Z         #pragma omp for 
2023-01-11T21:41:26.3730945Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.3730993Z         {
2023-01-11T21:41:26.3731130Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3731258Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.3731353Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3731414Z         }
2023-01-11T21:41:26.3731507Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3731588Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.3731637Z         {
2023-01-11T21:41:26.3731723Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.3731843Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.3731930Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.3731994Z         }
2023-01-11T21:41:26.3732054Z     }
2023-01-11T21:41:26.3732099Z }
2023-01-11T21:41:26.3732179Z ''')
2023-01-11T21:41:26.3732185Z 
2023-01-11T21:41:26.3732189Z 
2023-01-11T21:41:26.3732320Z kernel_cpp_2 = async_compile.cpp('''
2023-01-11T21:41:26.3732522Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3732636Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.3732695Z {
2023-01-11T21:41:26.3732789Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3732848Z     {
2023-01-11T21:41:26.3732910Z         #pragma omp for 
2023-01-11T21:41:26.3732988Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.3733048Z         {
2023-01-11T21:41:26.3733183Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3733311Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.3733405Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.3733468Z         }
2023-01-11T21:41:26.3733550Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3733631Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.3733693Z         {
2023-01-11T21:41:26.3733780Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.3733865Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.3733946Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.3734007Z         }
2023-01-11T21:41:26.3734054Z     }
2023-01-11T21:41:26.3734113Z }
2023-01-11T21:41:26.3734193Z ''')
2023-01-11T21:41:26.3734198Z 
2023-01-11T21:41:26.3734202Z 
2023-01-11T21:41:26.3734336Z kernel_cpp_3 = async_compile.cpp('''
2023-01-11T21:41:26.3734540Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3734655Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.3734752Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:26.3734798Z {
2023-01-11T21:41:26.3734895Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3734954Z     {
2023-01-11T21:41:26.3735031Z         #pragma omp for 
2023-01-11T21:41:26.3735112Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.3735173Z         {
2023-01-11T21:41:26.3735236Z             {
2023-01-11T21:41:26.3735287Z                 {
2023-01-11T21:41:26.3735383Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.3735479Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.3735583Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:41:26.3735674Z                     auto tmp3 = tmp1 <= tmp2;
2023-01-11T21:41:26.3735761Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.3735844Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.3735895Z                 }
2023-01-11T21:41:26.3735959Z             }
2023-01-11T21:41:26.3736018Z         }
2023-01-11T21:41:26.3736078Z     }
2023-01-11T21:41:26.3736136Z }
2023-01-11T21:41:26.3736245Z ''')
2023-01-11T21:41:26.3736250Z 
2023-01-11T21:41:26.3736340Z async_compile.wait(globals())
2023-01-11T21:41:26.3736400Z del async_compile
2023-01-11T21:41:26.3736515Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3736585Z wrapper = (
2023-01-11T21:41:26.3736663Z '''
2023-01-11T21:41:26.3736735Z #include <dlfcn.h>
2023-01-11T21:41:26.3736808Z #include <assert.h>
2023-01-11T21:41:26.3736922Z     std::vector<at::Tensor> call_8(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3737099Z     at::Tensor primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9;
2023-01-11T21:41:26.3737175Z     primals_1 = args[0];
2023-01-11T21:41:26.3737258Z     primals_2 = args[1];
2023-01-11T21:41:26.3737330Z     primals_3 = args[2];
2023-01-11T21:41:26.3737404Z     primals_4 = args[3];
2023-01-11T21:41:26.3737521Z     primals_5 = args[4];
2023-01-11T21:41:26.3737597Z     primals_6 = args[5];
2023-01-11T21:41:26.3737658Z     primals_7 = args[6];
2023-01-11T21:41:26.3737733Z     primals_8 = args[7];
2023-01-11T21:41:26.3737806Z     primals_9 = args[8];
2023-01-11T21:41:26.3737936Z     auto buf0 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3738082Z     at::addmm_out(buf0, primals_2, primals_9, at::as_strided(primals_1, {8, 8}, {1, 8}), 1, 1);
2023-01-11T21:41:26.3738157Z     primals_1.reset();
2023-01-11T21:41:26.3738231Z     primals_2.reset();
2023-01-11T21:41:26.3738316Z     auto buf1 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:26.3738576Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/qf/cqfoag4edkqhhwar6dcfekr7xrl4rhfowlfpqtdov7pzkh5vony6.so", RTLD_NOW);
2023-01-11T21:41:26.3738674Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3738760Z     void (*kernel_cpp_0)(float*);
2023-01-11T21:41:26.3738885Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3738987Z     kernel_cpp_0((float*)(buf1.data_ptr()));
2023-01-11T21:41:26.3739117Z     auto buf2 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3739262Z     at::addmm_out(buf2, primals_4, buf1, at::as_strided(primals_3, {8, 8}, {1, 8}), 1, 1);
2023-01-11T21:41:26.3739324Z     primals_4.reset();
2023-01-11T21:41:26.3739420Z     auto buf3 = buf2; buf2.reset();  // reuse
2023-01-11T21:41:26.3739676Z     auto kernel_cpp_1_lib = dlopen("/tmp/torchinductor_jenkins/qf/cqfoag4edkqhhwar6dcfekr7xrl4rhfowlfpqtdov7pzkh5vony6.so", RTLD_NOW);
2023-01-11T21:41:26.3739771Z     assert(kernel_cpp_1_lib != nullptr);
2023-01-11T21:41:26.3739857Z     void (*kernel_cpp_1)(float*);
2023-01-11T21:41:26.3739978Z     *(void **) (&kernel_cpp_1) = dlsym(kernel_cpp_1_lib, "kernel");
2023-01-11T21:41:26.3740079Z     kernel_cpp_1((float*)(buf3.data_ptr()));
2023-01-11T21:41:26.3740207Z     auto buf4 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3740339Z     at::addmm_out(buf4, primals_6, buf3, at::as_strided(primals_5, {8, 8}, {1, 8}), 1, 1);
2023-01-11T21:41:26.3740418Z     primals_6.reset();
2023-01-11T21:41:26.3740513Z     auto buf5 = buf4; buf4.reset();  // reuse
2023-01-11T21:41:26.3740766Z     auto kernel_cpp_2_lib = dlopen("/tmp/torchinductor_jenkins/qf/cqfoag4edkqhhwar6dcfekr7xrl4rhfowlfpqtdov7pzkh5vony6.so", RTLD_NOW);
2023-01-11T21:41:26.3740860Z     assert(kernel_cpp_2_lib != nullptr);
2023-01-11T21:41:26.3740945Z     void (*kernel_cpp_2)(float*);
2023-01-11T21:41:26.3741066Z     *(void **) (&kernel_cpp_2) = dlsym(kernel_cpp_2_lib, "kernel");
2023-01-11T21:41:26.3741154Z     kernel_cpp_2((float*)(buf5.data_ptr()));
2023-01-11T21:41:26.3741282Z     auto buf6 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3741420Z     at::addmm_out(buf6, primals_8, buf5, at::as_strided(primals_7, {8, 8}, {1, 8}), 1, 1);
2023-01-11T21:41:26.3741495Z     primals_8.reset();
2023-01-11T21:41:26.3741593Z     auto buf7 = buf6; buf6.reset();  // reuse
2023-01-11T21:41:26.3741718Z     auto buf8 = at::empty_strided({2, 8}, {8, 1}, at::ScalarType::Bool); 
2023-01-11T21:41:26.3741989Z     auto kernel_cpp_3_lib = dlopen("/tmp/torchinductor_jenkins/by/cby2ob4hd4n2ixdn4t25zfpvb6vyfd66nfdv33d5vsx36mxcwhy4.so", RTLD_NOW);
2023-01-11T21:41:26.3742085Z     assert(kernel_cpp_3_lib != nullptr);
2023-01-11T21:41:26.3742176Z     void (*kernel_cpp_3)(float*,bool*);
2023-01-11T21:41:26.3742284Z     *(void **) (&kernel_cpp_3) = dlsym(kernel_cpp_3_lib, "kernel");
2023-01-11T21:41:26.3742410Z     kernel_cpp_3((float*)(buf7.data_ptr()), (bool*)(buf8.data_ptr()));
2023-01-11T21:41:26.3742803Z     return std::vector<at::Tensor>({buf7, primals_9, buf1, buf3, buf5, buf8, at::as_strided(primals_7, {8, 8}, {8, 1}), at::as_strided(primals_5, {8, 8}, {8, 1}), at::as_strided(primals_3, {8, 8}, {8, 1})}); }''' )
2023-01-11T21:41:26.3742810Z 
2023-01-11T21:41:26.3742887Z module = load_inline(
2023-01-11T21:41:26.3743258Z     name='inline_extension_csdpljql3gdtwyefwna55hislgjyk4wkihbuoidk4i5vw3bqzfra',
2023-01-11T21:41:26.3743353Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3743469Z     functions=['call_8'],
2023-01-11T21:41:26.3743840Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3743975Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3744570Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3744590Z 
2023-01-11T21:41:26.3744650Z def _wrap_func(f):
2023-01-11T21:41:26.3744721Z     def g(args):
2023-01-11T21:41:26.3744796Z         return f(args)
2023-01-11T21:41:26.3744863Z     return g
2023-01-11T21:41:26.3744955Z call = _wrap_func(module.call_8)
2023-01-11T21:41:26.3744960Z 
2023-01-11T21:41:26.3744965Z 
2023-01-11T21:41:26.3745041Z if __name__ == "__main__":
2023-01-11T21:41:26.3745156Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3745267Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3745471Z     primals_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3745668Z     primals_2 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3745868Z     primals_3 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3746060Z     primals_4 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3746255Z     primals_5 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3746450Z     primals_6 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3746651Z     primals_7 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3746834Z     primals_8 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3747034Z     primals_9 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3747238Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]))
2023-01-11T21:41:26.3747244Z 
2023-01-11T21:41:26.3747510Z [2023-01-11 21:29:02,667] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 161
2023-01-11T21:41:26.3747766Z [2023-01-11 21:29:02,690] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 162
2023-01-11T21:41:26.3747772Z 
2023-01-11T21:41:26.3747865Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3747934Z import torch
2023-01-11T21:41:26.3748003Z import random
2023-01-11T21:41:26.3748120Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3748229Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3748235Z 
2023-01-11T21:41:26.3748312Z aten = torch.ops.aten
2023-01-11T21:41:26.3748497Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3748588Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3748593Z 
2023-01-11T21:41:26.3748598Z 
2023-01-11T21:41:26.3748733Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3748936Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3749055Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3749164Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3749251Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3749311Z {
2023-01-11T21:41:26.3749408Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3749468Z     {
2023-01-11T21:41:26.3749544Z         #pragma omp for 
2023-01-11T21:41:26.3749653Z         for(long i0=0; i0<12500; i0+=1)
2023-01-11T21:41:26.3749705Z         {
2023-01-11T21:41:26.3749839Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3749970Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.3750056Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3750145Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.3750208Z         }
2023-01-11T21:41:26.3750302Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3750391Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:41:26.3750441Z         {
2023-01-11T21:41:26.3750526Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3750608Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.3750689Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3750767Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3750828Z         }
2023-01-11T21:41:26.3750888Z     }
2023-01-11T21:41:26.3750934Z }
2023-01-11T21:41:26.3751014Z ''')
2023-01-11T21:41:26.3751020Z 
2023-01-11T21:41:26.3751108Z async_compile.wait(globals())
2023-01-11T21:41:26.3751178Z del async_compile
2023-01-11T21:41:26.3751299Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3751368Z wrapper = (
2023-01-11T21:41:26.3751433Z '''
2023-01-11T21:41:26.3751506Z #include <dlfcn.h>
2023-01-11T21:41:26.3751578Z #include <assert.h>
2023-01-11T21:41:26.3751709Z     std::vector<at::Tensor> call_9(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3751800Z     at::Tensor primals_1, primals_2;
2023-01-11T21:41:26.3751878Z     primals_1 = args[0];
2023-01-11T21:41:26.3751954Z     primals_2 = args[1];
2023-01-11T21:41:26.3752074Z     auto buf0 = at::empty_strided({100000, }, {1, }, at::ScalarType::Float); 
2023-01-11T21:41:26.3752312Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/76/c76av2fn5v7sv2ienoi5x7je4ut64tlvvozup7k5kastmftp5d3z.so", RTLD_NOW);
2023-01-11T21:41:26.3752407Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3752526Z     void (*kernel_cpp_0)(const float*,const float*,float*);
2023-01-11T21:41:26.3752648Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3752818Z     kernel_cpp_0((float*)(primals_1.data_ptr()), (float*)(primals_2.data_ptr()), (float*)(buf0.data_ptr()));
2023-01-11T21:41:26.3752894Z     primals_2.reset();
2023-01-11T21:41:26.3753072Z     return std::vector<at::Tensor>({buf0, primals_1}); }''' )
2023-01-11T21:41:26.3753078Z 
2023-01-11T21:41:26.3753153Z module = load_inline(
2023-01-11T21:41:26.3753394Z     name='inline_extension_c5mwnoxgxnxcege5ed3o2rm3o6dlemfn2y763tsgjbijuxitr5bs',
2023-01-11T21:41:26.3753477Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3753589Z     functions=['call_9'],
2023-01-11T21:41:26.3753948Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3754096Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3754698Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3754734Z 
2023-01-11T21:41:26.3754809Z def _wrap_func(f):
2023-01-11T21:41:26.3754878Z     def g(args):
2023-01-11T21:41:26.3754939Z         return f(args)
2023-01-11T21:41:26.3755005Z     return g
2023-01-11T21:41:26.3755094Z call = _wrap_func(module.call_9)
2023-01-11T21:41:26.3755100Z 
2023-01-11T21:41:26.3755104Z 
2023-01-11T21:41:26.3755179Z if __name__ == "__main__":
2023-01-11T21:41:26.3755293Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3755412Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3755621Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3755853Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3755969Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:26.3755976Z 
2023-01-11T21:41:26.3756243Z [2023-01-11 21:29:23,682] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 162
2023-01-11T21:41:26.3756503Z [2023-01-11 21:29:23,684] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 162
2023-01-11T21:41:26.3756508Z 
2023-01-11T21:41:26.3756602Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3756674Z import torch
2023-01-11T21:41:26.3756744Z import random
2023-01-11T21:41:26.3756857Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3756976Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3756981Z 
2023-01-11T21:41:26.3757046Z aten = torch.ops.aten
2023-01-11T21:41:26.3757181Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3757274Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3757279Z 
2023-01-11T21:41:26.3757284Z 
2023-01-11T21:41:26.3757425Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3757629Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3757747Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3757850Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3757949Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3757997Z {
2023-01-11T21:41:26.3758094Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3758155Z     {
2023-01-11T21:41:26.3758230Z         #pragma omp for 
2023-01-11T21:41:26.3758313Z         for(long i0=0; i0<12500; i0+=1)
2023-01-11T21:41:26.3758375Z         {
2023-01-11T21:41:26.3758514Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3758634Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.3758720Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3758809Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.3758875Z         }
2023-01-11T21:41:26.3758969Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3759057Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:41:26.3759119Z         {
2023-01-11T21:41:26.3759190Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3759271Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.3759354Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3759432Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3759492Z         }
2023-01-11T21:41:26.3759552Z     }
2023-01-11T21:41:26.3759609Z }
2023-01-11T21:41:26.3759675Z ''')
2023-01-11T21:41:26.3759680Z 
2023-01-11T21:41:26.3759769Z async_compile.wait(globals())
2023-01-11T21:41:26.3759840Z del async_compile
2023-01-11T21:41:26.3759957Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3760028Z wrapper = (
2023-01-11T21:41:26.3760105Z '''
2023-01-11T21:41:26.3760179Z #include <dlfcn.h>
2023-01-11T21:41:26.3760273Z #include <assert.h>
2023-01-11T21:41:26.3760400Z     std::vector<at::Tensor> call_10(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3760495Z     at::Tensor primals_1, tangents_1;
2023-01-11T21:41:26.3760572Z     primals_1 = args[0];
2023-01-11T21:41:26.3760648Z     tangents_1 = args[1];
2023-01-11T21:41:26.3760781Z     auto buf0 = at::empty_strided({100000, }, {1, }, at::ScalarType::Float); 
2023-01-11T21:41:26.3761019Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/76/c76av2fn5v7sv2ienoi5x7je4ut64tlvvozup7k5kastmftp5d3z.so", RTLD_NOW);
2023-01-11T21:41:26.3761104Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3761222Z     void (*kernel_cpp_0)(const float*,const float*,float*);
2023-01-11T21:41:26.3761345Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3761539Z     kernel_cpp_0((float*)(tangents_1.data_ptr()), (float*)(primals_1.data_ptr()), (float*)(buf0.data_ptr()));
2023-01-11T21:41:26.3761617Z     primals_1.reset();
2023-01-11T21:41:26.3761694Z     tangents_1.reset();
2023-01-11T21:41:26.3761877Z     return std::vector<at::Tensor>({at::Tensor(), buf0}); }''' )
2023-01-11T21:41:26.3761883Z 
2023-01-11T21:41:26.3761958Z module = load_inline(
2023-01-11T21:41:26.3762201Z     name='inline_extension_cw4gwewvco6iido3xvjhqkdjwb76c4nhgeylgbiva5xor4layxbv',
2023-01-11T21:41:26.3762283Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3762395Z     functions=['call_10'],
2023-01-11T21:41:26.3762755Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3762902Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3763500Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3763507Z 
2023-01-11T21:41:26.3763583Z def _wrap_func(f):
2023-01-11T21:41:26.3763652Z     def g(args):
2023-01-11T21:41:26.3763725Z         return f(args)
2023-01-11T21:41:26.3763778Z     return g
2023-01-11T21:41:26.3763869Z call = _wrap_func(module.call_10)
2023-01-11T21:41:26.3763874Z 
2023-01-11T21:41:26.3763879Z 
2023-01-11T21:41:26.3763955Z if __name__ == "__main__":
2023-01-11T21:41:26.3764066Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3764187Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3764395Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3764601Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3764715Z     print_performance(lambda: call([primals_1, tangents_1]))
2023-01-11T21:41:26.3764734Z 
2023-01-11T21:41:26.3764988Z [2023-01-11 21:29:42,426] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 162
2023-01-11T21:41:26.3765244Z [2023-01-11 21:29:42,545] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 163
2023-01-11T21:41:26.3765496Z [2023-01-11 21:29:42,546] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.3765740Z [2023-01-11 21:29:42,548] torch._inductor.graph: [DEBUG] Set _can_use_cpp_wrapper to False due to Constants
2023-01-11T21:41:26.3765998Z [2023-01-11 21:29:44,064] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 163
2023-01-11T21:41:26.3766249Z [2023-01-11 21:29:44,067] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 163
2023-01-11T21:41:26.3766255Z 
2023-01-11T21:41:26.3766347Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3766419Z import torch
2023-01-11T21:41:26.3766476Z import random
2023-01-11T21:41:26.3766590Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3766753Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3766758Z 
2023-01-11T21:41:26.3766836Z aten = torch.ops.aten
2023-01-11T21:41:26.3766968Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3767059Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3767219Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.3767225Z 
2023-01-11T21:41:26.3767230Z 
2023-01-11T21:41:26.3767365Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3767568Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3767669Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:26.3767772Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3767906Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.3768005Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3768068Z {
2023-01-11T21:41:26.3768164Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3768224Z     {
2023-01-11T21:41:26.3768287Z         #pragma omp for 
2023-01-11T21:41:26.3768371Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:41:26.3768435Z         {
2023-01-11T21:41:26.3768498Z             {
2023-01-11T21:41:26.3768563Z                 {
2023-01-11T21:41:26.3768650Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.3768729Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.3768819Z                     auto tmp7 = in_ptr2[i0];
2023-01-11T21:41:26.3768918Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.3769310Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.3769454Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:41:26.3769553Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.3769656Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.3769748Z                     auto tmp8 = tmp6 * tmp7;
2023-01-11T21:41:26.3769823Z                     auto tmp9 = tmp5 * tmp8;
2023-01-11T21:41:26.3769937Z                     auto tmp10 = static_cast<float>(1.492537313432836);
2023-01-11T21:41:26.3770028Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:26.3770112Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:26.3770177Z                 }
2023-01-11T21:41:26.3770241Z             }
2023-01-11T21:41:26.3770302Z         }
2023-01-11T21:41:26.3770350Z     }
2023-01-11T21:41:26.3770408Z }
2023-01-11T21:41:26.3770493Z ''')
2023-01-11T21:41:26.3770497Z 
2023-01-11T21:41:26.3770502Z 
2023-01-11T21:41:26.3770590Z async_compile.wait(globals())
2023-01-11T21:41:26.3770663Z del async_compile
2023-01-11T21:41:26.3770668Z 
2023-01-11T21:41:26.3770740Z def call(args):
2023-01-11T21:41:26.3770827Z     primals_1, primals_2 = args
2023-01-11T21:41:26.3770885Z     args.clear()
2023-01-11T21:41:26.3771020Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.3771221Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3771431Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3771504Z     del primals_2
2023-01-11T21:41:26.3771616Z     return (buf0, primals_1, seed_cpu_None.clone(), )
2023-01-11T21:41:26.3771621Z 
2023-01-11T21:41:26.3771625Z 
2023-01-11T21:41:26.3771700Z if __name__ == "__main__":
2023-01-11T21:41:26.3771811Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3771919Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3772116Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3772326Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3772527Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3772718Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:26.3772724Z 
2023-01-11T21:41:26.3772728Z 
2023-01-11T21:41:26.3772821Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3772891Z import torch
2023-01-11T21:41:26.3772960Z import random
2023-01-11T21:41:26.3773060Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3773178Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3773183Z 
2023-01-11T21:41:26.3773260Z aten = torch.ops.aten
2023-01-11T21:41:26.3773392Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3773481Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3773486Z 
2023-01-11T21:41:26.3773490Z 
2023-01-11T21:41:26.3773660Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3773867Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3773986Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.3774078Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3774183Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.3774279Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3774341Z {
2023-01-11T21:41:26.3774436Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3774495Z     {
2023-01-11T21:41:26.3774574Z         #pragma omp for 
2023-01-11T21:41:26.3774647Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:41:26.3774709Z         {
2023-01-11T21:41:26.3774774Z             {
2023-01-11T21:41:26.3774842Z                 {
2023-01-11T21:41:26.3774932Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.3775022Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.3775115Z                     auto tmp10 = in_ptr2[i0];
2023-01-11T21:41:26.3775203Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.3775341Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.3775445Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:41:26.3775535Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.3775639Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.3775730Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.3775841Z                     auto tmp8 = static_cast<float>(1.492537313432836);
2023-01-11T21:41:26.3775920Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.3776011Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:26.3776094Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:26.3776159Z                 }
2023-01-11T21:41:26.3776221Z             }
2023-01-11T21:41:26.3776286Z         }
2023-01-11T21:41:26.3776349Z     }
2023-01-11T21:41:26.3776396Z }
2023-01-11T21:41:26.3776476Z ''')
2023-01-11T21:41:26.3776481Z 
2023-01-11T21:41:26.3776571Z async_compile.wait(globals())
2023-01-11T21:41:26.3776643Z del async_compile
2023-01-11T21:41:26.3776760Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3776829Z wrapper = (
2023-01-11T21:41:26.3776906Z '''
2023-01-11T21:41:26.3776965Z #include <dlfcn.h>
2023-01-11T21:41:26.3777041Z #include <assert.h>
2023-01-11T21:41:26.3777174Z     std::vector<at::Tensor> call_11(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3777292Z     at::Tensor primals_1, philox_seed_like, tangents_1;
2023-01-11T21:41:26.3777368Z     primals_1 = args[0];
2023-01-11T21:41:26.3777453Z     philox_seed_like = args[1];
2023-01-11T21:41:26.3777532Z     tangents_1 = args[2];
2023-01-11T21:41:26.3777652Z     auto buf0 = at::empty_strided({100000, }, {1, }, at::ScalarType::Float); 
2023-01-11T21:41:26.3777915Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/lq/clqibsqxpldu4echxzc3z6phb5szhugslpae44irpgfrltvzbhob.so", RTLD_NOW);
2023-01-11T21:41:26.3778011Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3778180Z     void (*kernel_cpp_0)(const long*,const float*,const float*,float*);
2023-01-11T21:41:26.3778303Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3778563Z     kernel_cpp_0((long*)(philox_seed_like.data_ptr()), (float*)(tangents_1.data_ptr()), (float*)(primals_1.data_ptr()), (float*)(buf0.data_ptr()));
2023-01-11T21:41:26.3778681Z     philox_seed_like.reset();
2023-01-11T21:41:26.3778784Z     primals_1.reset();
2023-01-11T21:41:26.3778876Z     tangents_1.reset();
2023-01-11T21:41:26.3779122Z     return std::vector<at::Tensor>({at::Tensor(), buf0}); }''' )
2023-01-11T21:41:26.3779130Z 
2023-01-11T21:41:26.3779232Z module = load_inline(
2023-01-11T21:41:26.3779748Z     name='inline_extension_c7pb2bael7vzlyb6qu32uoilu6pwiy3hiolshidh4zeg5hqyeqvp',
2023-01-11T21:41:26.3780090Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3780384Z     functions=['call_11'],
2023-01-11T21:41:26.3781315Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3781690Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3782866Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3782896Z 
2023-01-11T21:41:26.3783009Z def _wrap_func(f):
2023-01-11T21:41:26.3783208Z     def g(args):
2023-01-11T21:41:26.3783346Z         return f(args)
2023-01-11T21:41:26.3783509Z     return g
2023-01-11T21:41:26.3783731Z call = _wrap_func(module.call_11)
2023-01-11T21:41:26.3783742Z 
2023-01-11T21:41:26.3783750Z 
2023-01-11T21:41:26.3783935Z if __name__ == "__main__":
2023-01-11T21:41:26.3784210Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3784519Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3785003Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3785417Z     philox_seed_like = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.3785805Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3786071Z     print_performance(lambda: call([primals_1, philox_seed_like, tangents_1]))
2023-01-11T21:41:26.3786081Z 
2023-01-11T21:41:26.3786607Z [2023-01-11 21:30:04,139] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 163
2023-01-11T21:41:26.3787626Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3787926Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3788485Z [2023-01-11 21:30:04,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 164
2023-01-11T21:41:26.3788496Z 
2023-01-11T21:41:26.3788668Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3788776Z import torch
2023-01-11T21:41:26.3788902Z import random
2023-01-11T21:41:26.3789109Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3789321Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3789331Z 
2023-01-11T21:41:26.3789467Z aten = torch.ops.aten
2023-01-11T21:41:26.3789758Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3789978Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3789997Z 
2023-01-11T21:41:26.3790220Z async_compile.wait(globals())
2023-01-11T21:41:26.3790367Z del async_compile
2023-01-11T21:41:26.3790763Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3790927Z wrapper = (
2023-01-11T21:41:26.3791132Z '''
2023-01-11T21:41:26.3791311Z #include <dlfcn.h>
2023-01-11T21:41:26.3791468Z #include <assert.h>
2023-01-11T21:41:26.3791685Z     at::Tensor call_12(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3791823Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:26.3791956Z     arg0_1 = args[0];
2023-01-11T21:41:26.3792084Z     arg1_1 = args[1];
2023-01-11T21:41:26.3792322Z     auto buf0 = at::empty_strided({32, 32}, {32, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3792547Z     at::mm_out(buf0, arg0_1, at::as_strided(arg1_1, {32, 32}, {32, 1}));
2023-01-11T21:41:26.3792675Z     arg0_1.reset();
2023-01-11T21:41:26.3792834Z     arg1_1.reset();
2023-01-11T21:41:26.3793076Z     return buf0; }''' )
2023-01-11T21:41:26.3793089Z 
2023-01-11T21:41:26.3793350Z module = load_inline(
2023-01-11T21:41:26.3793960Z     name='inline_extension_cbk5vdefanataz2xcfa5gu6egrblcazbommb7bzzso26owifwayb',
2023-01-11T21:41:26.3794166Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3794437Z     functions=['call_12'],
2023-01-11T21:41:26.3795124Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3795402Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3796700Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3796721Z 
2023-01-11T21:41:26.3796898Z def _wrap_func(f):
2023-01-11T21:41:26.3797040Z     def g(args):
2023-01-11T21:41:26.3797218Z         return f(args)
2023-01-11T21:41:26.3797374Z     return g
2023-01-11T21:41:26.3797594Z call = _wrap_func(module.call_12)
2023-01-11T21:41:26.3797614Z 
2023-01-11T21:41:26.3797623Z 
2023-01-11T21:41:26.3797781Z if __name__ == "__main__":
2023-01-11T21:41:26.3797989Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3798211Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3798574Z     arg0_1 = rand_strided((32, 32), (1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3798948Z     arg1_1 = rand_strided((32, 1, 32), (32, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3799201Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.3799214Z 
2023-01-11T21:41:26.3799844Z [2023-01-11 21:30:22,669] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 164
2023-01-11T21:41:26.3800443Z STAGE:2023-01-11 21:30:22 1448:1448 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:41:26.3801006Z [2023-01-11 21:30:22,682] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 165
2023-01-11T21:41:26.3801506Z [2023-01-11 21:30:22,682] torch._inductor.graph: [DEBUG] Set _can_use_cpp_wrapper to False due to profiler not supported
2023-01-11T21:41:26.3802006Z [2023-01-11 21:30:24,357] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 165
2023-01-11T21:41:26.3802584Z STAGE:2023-01-11 21:30:24 1448:1448 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:41:26.3803179Z STAGE:2023-01-11 21:30:24 1448:1448 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:41:26.3804119Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3804355Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3804925Z [2023-01-11 21:30:24,388] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 166
2023-01-11T21:41:26.3804937Z 
2023-01-11T21:41:26.3805114Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3805289Z import torch
2023-01-11T21:41:26.3805459Z import random
2023-01-11T21:41:26.3805733Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3806006Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3806023Z 
2023-01-11T21:41:26.3806191Z aten = torch.ops.aten
2023-01-11T21:41:26.3806511Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3806725Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3806737Z 
2023-01-11T21:41:26.3806749Z 
2023-01-11T21:41:26.3807051Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3807480Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3807702Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3807897Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3808079Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3808197Z {
2023-01-11T21:41:26.3808422Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3808573Z     {
2023-01-11T21:41:26.3808760Z         #pragma omp for 
2023-01-11T21:41:26.3808950Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.3809229Z         {
2023-01-11T21:41:26.3809557Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3809842Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.3810042Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3810233Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.3810355Z         }
2023-01-11T21:41:26.3810535Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3810686Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.3810805Z         {
2023-01-11T21:41:26.3810941Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3811096Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.3811243Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3811400Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3811552Z         }
2023-01-11T21:41:26.3811688Z     }
2023-01-11T21:41:26.3811828Z }
2023-01-11T21:41:26.3812027Z ''')
2023-01-11T21:41:26.3812039Z 
2023-01-11T21:41:26.3812049Z 
2023-01-11T21:41:26.3812257Z async_compile.wait(globals())
2023-01-11T21:41:26.3812429Z del async_compile
2023-01-11T21:41:26.3812441Z 
2023-01-11T21:41:26.3812609Z def call(args):
2023-01-11T21:41:26.3812787Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.3812956Z     args.clear()
2023-01-11T21:41:26.3813207Z     from torch.profiler import record_function
2023-01-11T21:41:26.3813511Z     with record_function('inductor_wrapper_call'):
2023-01-11T21:41:26.3813889Z         buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3814178Z         kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.3814307Z         del arg0_1
2023-01-11T21:41:26.3814458Z         del arg1_1
2023-01-11T21:41:26.3814630Z         return (buf0, )
2023-01-11T21:41:26.3814647Z 
2023-01-11T21:41:26.3814656Z 
2023-01-11T21:41:26.3814844Z if __name__ == "__main__":
2023-01-11T21:41:26.3815103Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3815373Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3815840Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3816285Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3816492Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.3816507Z 
2023-01-11T21:41:26.3816514Z 
2023-01-11T21:41:26.3816686Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3816933Z import torch
2023-01-11T21:41:26.3817064Z import random
2023-01-11T21:41:26.3817270Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3817523Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3817535Z 
2023-01-11T21:41:26.3817722Z aten = torch.ops.aten
2023-01-11T21:41:26.3818030Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3818247Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3818260Z 
2023-01-11T21:41:26.3818269Z 
2023-01-11T21:41:26.3818598Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3819073Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3819308Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3819485Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.3819717Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.3819896Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.3820073Z                        long* __restrict__ out_ptr3,
2023-01-11T21:41:26.3820245Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.3820383Z {
2023-01-11T21:41:26.3820530Z     {
2023-01-11T21:41:26.3820680Z         {
2023-01-11T21:41:26.3820835Z             float tmp1 = 0;
2023-01-11T21:41:26.3821376Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.3821665Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.3821931Z             struct IndexValue_7 {size_t index; float value;};
2023-01-11T21:41:26.3822389Z             IndexValue_7 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.3822641Z             #pragma omp declare reduction(argmax : struct IndexValue_7 :\
2023-01-11T21:41:26.3822915Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.3823270Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.3823737Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.3824005Z             struct IndexValue_8 {size_t index; float value;};
2023-01-11T21:41:26.3824316Z             IndexValue_8 tmp5{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.3824639Z             #pragma omp declare reduction(argmin : struct IndexValue_8 :\
2023-01-11T21:41:26.3824984Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.3825305Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.3825581Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.3825736Z             for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.3825842Z             {
2023-01-11T21:41:26.3825959Z                 {
2023-01-11T21:41:26.3826123Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3826272Z                     tmp1 += tmp0;
2023-01-11T21:41:26.3826455Z                     tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:41:26.3826636Z                     tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:41:26.3826851Z                     if (tmp4.value < tmp0) {
2023-01-11T21:41:26.3827099Z                         tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:41:26.3827248Z                     }
2023-01-11T21:41:26.3827462Z                     if (tmp5.value > tmp0) {
2023-01-11T21:41:26.3827706Z                         tmp5.index = i0; tmp5.value = tmp0;
2023-01-11T21:41:26.3827852Z                     }
2023-01-11T21:41:26.3828007Z                 }
2023-01-11T21:41:26.3828154Z             }
2023-01-11T21:41:26.3828318Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:26.3828487Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.3828640Z             out_ptr2[0] = tmp3;
2023-01-11T21:41:26.3828797Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:41:26.3829035Z             out_ptr4[0] = tmp5.index;
2023-01-11T21:41:26.3829150Z         }
2023-01-11T21:41:26.3829260Z     }
2023-01-11T21:41:26.3829351Z }
2023-01-11T21:41:26.3829507Z ''')
2023-01-11T21:41:26.3829518Z 
2023-01-11T21:41:26.3829680Z async_compile.wait(globals())
2023-01-11T21:41:26.3829842Z del async_compile
2023-01-11T21:41:26.3830113Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3830279Z wrapper = (
2023-01-11T21:41:26.3830467Z '''
2023-01-11T21:41:26.3830618Z #include <dlfcn.h>
2023-01-11T21:41:26.3830793Z #include <assert.h>
2023-01-11T21:41:26.3831102Z     std::vector<at::Tensor> call_13(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3831288Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3831463Z     arg0_1 = args[0];
2023-01-11T21:41:26.3831717Z     auto buf0 = at::empty_strided({}, {}, at::ScalarType::Float); 
2023-01-11T21:41:26.3832005Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Float); 
2023-01-11T21:41:26.3832220Z     auto buf2 = at::empty_strided({}, {}, at::ScalarType::Float); 
2023-01-11T21:41:26.3832431Z     auto buf3 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:26.3832643Z     auto buf4 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:26.3833197Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/5l/c5l5a67pncykxc4k6lcjmgu7elavbipctmegtckslekz4senmd6h.so", RTLD_NOW);
2023-01-11T21:41:26.3833417Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3833724Z     void (*kernel_cpp_0)(const float*,float*,float*,float*,long*,long*);
2023-01-11T21:41:26.3834004Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3834526Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf1.data_ptr()), (float*)(buf2.data_ptr()), (long*)(buf3.data_ptr()), (long*)(buf4.data_ptr()));
2023-01-11T21:41:26.3834674Z     arg0_1.reset();
2023-01-11T21:41:26.3835042Z     return std::vector<at::Tensor>({buf0, buf1, buf2, buf3, buf4}); }''' )
2023-01-11T21:41:26.3835075Z 
2023-01-11T21:41:26.3835196Z module = load_inline(
2023-01-11T21:41:26.3835663Z     name='inline_extension_cnves6oopp5lj6abdi7pghxpzsce42tujpsuwbgsp347d4fyecyk',
2023-01-11T21:41:26.3835826Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3836107Z     functions=['call_13'],
2023-01-11T21:41:26.3836955Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3837309Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3838519Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3838531Z 
2023-01-11T21:41:26.3838663Z def _wrap_func(f):
2023-01-11T21:41:26.3838769Z     def g(args):
2023-01-11T21:41:26.3838909Z         return f(args)
2023-01-11T21:41:26.3839055Z     return g
2023-01-11T21:41:26.3839278Z call = _wrap_func(module.call_13)
2023-01-11T21:41:26.3839292Z 
2023-01-11T21:41:26.3839301Z 
2023-01-11T21:41:26.3839487Z if __name__ == "__main__":
2023-01-11T21:41:26.3839752Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3840052Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3840510Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3840737Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3840747Z 
2023-01-11T21:41:26.3841259Z [2023-01-11 21:30:44,275] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 166
2023-01-11T21:41:26.3842090Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3842489Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3843099Z [2023-01-11 21:30:44,309] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 167
2023-01-11T21:41:26.3843111Z 
2023-01-11T21:41:26.3843339Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3843507Z import torch
2023-01-11T21:41:26.3843674Z import random
2023-01-11T21:41:26.3843921Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3844122Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3844132Z 
2023-01-11T21:41:26.3844271Z aten = torch.ops.aten
2023-01-11T21:41:26.3844572Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3844745Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3844755Z 
2023-01-11T21:41:26.3844766Z 
2023-01-11T21:41:26.3845037Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3845511Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3845780Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3846025Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3846240Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.3846471Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.3846621Z {
2023-01-11T21:41:26.3846833Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3846953Z     {
2023-01-11T21:41:26.3847095Z         #pragma omp for 
2023-01-11T21:41:26.3847241Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.3847338Z         {
2023-01-11T21:41:26.3847590Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3847832Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.3848129Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.3848335Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.3848620Z             auto tmp4 = at::vec::clamp_min(tmp3, decltype(tmp3)(0));
2023-01-11T21:41:26.3848945Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:26.3849283Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:41:26.3849480Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.3849699Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.3849840Z         }
2023-01-11T21:41:26.3850027Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3850180Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.3850294Z         {
2023-01-11T21:41:26.3850444Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3850582Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.3850740Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.3850892Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.3851063Z             auto tmp4 = tmp3 * (tmp3>0);
2023-01-11T21:41:26.3851300Z             auto tmp5 = static_cast<float>(10);
2023-01-11T21:41:26.3851497Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:41:26.3851694Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.3851858Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.3852007Z         }
2023-01-11T21:41:26.3852154Z     }
2023-01-11T21:41:26.3852295Z }
2023-01-11T21:41:26.3852506Z ''')
2023-01-11T21:41:26.3852517Z 
2023-01-11T21:41:26.3852721Z async_compile.wait(globals())
2023-01-11T21:41:26.3852865Z del async_compile
2023-01-11T21:41:26.3853100Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3853225Z wrapper = (
2023-01-11T21:41:26.3853374Z '''
2023-01-11T21:41:26.3853505Z #include <dlfcn.h>
2023-01-11T21:41:26.3853641Z #include <assert.h>
2023-01-11T21:41:26.3853882Z     std::vector<at::Tensor> call_14(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3854141Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:26.3854314Z     arg0_1 = args[0];
2023-01-11T21:41:26.3854487Z     arg1_1 = args[1];
2023-01-11T21:41:26.3854796Z     auto buf0 = at::empty_strided({8, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3855104Z     auto buf1 = at::empty_strided({8, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3855698Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/7x/c7xp2x2aeezqsrsqkwdk4izewjw4zicynazuew4aormeadx7we4w.so", RTLD_NOW);
2023-01-11T21:41:26.3855922Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3856158Z     void (*kernel_cpp_0)(const float*,const float*,float*,float*);
2023-01-11T21:41:26.3856361Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3856756Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(arg1_1.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf1.data_ptr()));
2023-01-11T21:41:26.3856889Z     arg0_1.reset();
2023-01-11T21:41:26.3857014Z     arg1_1.reset();
2023-01-11T21:41:26.3857418Z     return std::vector<at::Tensor>({buf0, buf1}); }''' )
2023-01-11T21:41:26.3857430Z 
2023-01-11T21:41:26.3857609Z module = load_inline(
2023-01-11T21:41:26.3858232Z     name='inline_extension_calgsbsip6x4xs4ezhgcztxahbh3ege57zlzguw75tt24wn43c6r',
2023-01-11T21:41:26.3858433Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3858689Z     functions=['call_14'],
2023-01-11T21:41:26.3859425Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3859694Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3860983Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3861003Z 
2023-01-11T21:41:26.3861185Z def _wrap_func(f):
2023-01-11T21:41:26.3861349Z     def g(args):
2023-01-11T21:41:26.3861525Z         return f(args)
2023-01-11T21:41:26.3861683Z     return g
2023-01-11T21:41:26.3861883Z call = _wrap_func(module.call_14)
2023-01-11T21:41:26.3861918Z 
2023-01-11T21:41:26.3861932Z 
2023-01-11T21:41:26.3862077Z if __name__ == "__main__":
2023-01-11T21:41:26.3862293Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3862519Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3862898Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3863349Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3863597Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.3863607Z 
2023-01-11T21:41:26.3864246Z [2023-01-11 21:31:04,272] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 167
2023-01-11T21:41:26.3865247Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3865484Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3865962Z [2023-01-11 21:31:04,293] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 168
2023-01-11T21:41:26.3865972Z 
2023-01-11T21:41:26.3866141Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3866261Z import torch
2023-01-11T21:41:26.3866389Z import random
2023-01-11T21:41:26.3866644Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3866934Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3866946Z 
2023-01-11T21:41:26.3867128Z aten = torch.ops.aten
2023-01-11T21:41:26.3867545Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3867741Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3867753Z 
2023-01-11T21:41:26.3867762Z 
2023-01-11T21:41:26.3868096Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3868495Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3868711Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3868887Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.3868997Z {
2023-01-11T21:41:26.3869174Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3869261Z     {
2023-01-11T21:41:26.3869406Z         #pragma omp for 
2023-01-11T21:41:26.3869597Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.3869750Z         {
2023-01-11T21:41:26.3870151Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.3870482Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:26.3870690Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3870880Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.3871031Z         }
2023-01-11T21:41:26.3871239Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3871403Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.3871518Z         {
2023-01-11T21:41:26.3871670Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3871939Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:26.3872070Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:26.3872222Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:26.3872365Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.3872497Z         }
2023-01-11T21:41:26.3872645Z     }
2023-01-11T21:41:26.3872789Z }
2023-01-11T21:41:26.3872974Z ''')
2023-01-11T21:41:26.3872989Z 
2023-01-11T21:41:26.3873173Z async_compile.wait(globals())
2023-01-11T21:41:26.3873347Z del async_compile
2023-01-11T21:41:26.3873642Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3873808Z wrapper = (
2023-01-11T21:41:26.3873996Z '''
2023-01-11T21:41:26.3874176Z #include <dlfcn.h>
2023-01-11T21:41:26.3874329Z #include <assert.h>
2023-01-11T21:41:26.3874536Z     at::Tensor call_15(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3874674Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3874796Z     arg0_1 = args[0];
2023-01-11T21:41:26.3875034Z     auto buf0 = at::empty_strided({8, 8}, {8, 1}, at::ScalarType::Float); 
2023-01-11T21:41:26.3875467Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/35/c35igtmj5ncrr7wfhg5mi3p27d5wx6jrpmulflkkuspmmqmzjoe5.so", RTLD_NOW);
2023-01-11T21:41:26.3875692Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3875926Z     void (*kernel_cpp_0)(const float*,float*);
2023-01-11T21:41:26.3876204Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3876512Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(buf0.data_ptr()));
2023-01-11T21:41:26.3876690Z     arg0_1.reset();
2023-01-11T21:41:26.3876963Z     return buf0; }''' )
2023-01-11T21:41:26.3876977Z 
2023-01-11T21:41:26.3877148Z module = load_inline(
2023-01-11T21:41:26.3877663Z     name='inline_extension_cjm5ytgzhvhh3tc653lkgnoh5sagh4gzhwzzhzx6xddloix3de7v',
2023-01-11T21:41:26.3877818Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3878025Z     functions=['call_15'],
2023-01-11T21:41:26.3878714Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3879071Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3880488Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3880582Z 
2023-01-11T21:41:26.3880716Z def _wrap_func(f):
2023-01-11T21:41:26.3880841Z     def g(args):
2023-01-11T21:41:26.3880971Z         return f(args)
2023-01-11T21:41:26.3881091Z     return g
2023-01-11T21:41:26.3881257Z call = _wrap_func(module.call_15)
2023-01-11T21:41:26.3881267Z 
2023-01-11T21:41:26.3881274Z 
2023-01-11T21:41:26.3881409Z if __name__ == "__main__":
2023-01-11T21:41:26.3881634Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3881914Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3882383Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3882638Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3882653Z 
2023-01-11T21:41:26.3883367Z [2023-01-11 21:31:24,171] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 168
2023-01-11T21:41:26.3884169Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3884403Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3884950Z [2023-01-11 21:31:24,192] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 169
2023-01-11T21:41:26.3884960Z 
2023-01-11T21:41:26.3885185Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3885337Z import torch
2023-01-11T21:41:26.3885508Z import random
2023-01-11T21:41:26.3885777Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3886065Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3886083Z 
2023-01-11T21:41:26.3886280Z aten = torch.ops.aten
2023-01-11T21:41:26.3886569Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3886746Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3886755Z 
2023-01-11T21:41:26.3886763Z 
2023-01-11T21:41:26.3887017Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3887367Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3887584Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3887782Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:26.3888021Z                        double* __restrict__ out_ptr1,
2023-01-11T21:41:26.3888250Z                        double* __restrict__ out_ptr2)
2023-01-11T21:41:26.3888403Z {
2023-01-11T21:41:26.3888638Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3888762Z     {
2023-01-11T21:41:26.3888943Z         #pragma omp for 
2023-01-11T21:41:26.3889280Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.3889433Z         {
2023-01-11T21:41:26.3889587Z             {
2023-01-11T21:41:26.3889731Z                 {
2023-01-11T21:41:26.3889895Z                     double tmp2 = 0;
2023-01-11T21:41:26.3890044Z                     for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:26.3890165Z                     {
2023-01-11T21:41:26.3890287Z                         {
2023-01-11T21:41:26.3890474Z                             auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.3890678Z                             auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.3890826Z                             tmp2 += tmp1;
2023-01-11T21:41:26.3890970Z                         }
2023-01-11T21:41:26.3891107Z                     }
2023-01-11T21:41:26.3891304Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.3891465Z                 }
2023-01-11T21:41:26.3891627Z             }
2023-01-11T21:41:26.3891775Z         }
2023-01-11T21:41:26.3891914Z     }
2023-01-11T21:41:26.3892036Z     {
2023-01-11T21:41:26.3892187Z         {
2023-01-11T21:41:26.3892380Z             double tmp2 = 0;
2023-01-11T21:41:26.3892753Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3892884Z             {
2023-01-11T21:41:26.3893073Z                 #pragma omp for reduction(+:tmp2) 
2023-01-11T21:41:26.3893236Z                 for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.3893339Z                 {
2023-01-11T21:41:26.3893459Z                     {
2023-01-11T21:41:26.3893630Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3893829Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.3893976Z                         tmp2 += tmp1;
2023-01-11T21:41:26.3894132Z                     }
2023-01-11T21:41:26.3894289Z                 }
2023-01-11T21:41:26.3894425Z             }
2023-01-11T21:41:26.3894603Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.3894751Z         }
2023-01-11T21:41:26.3894890Z     }
2023-01-11T21:41:26.3895204Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3895355Z     {
2023-01-11T21:41:26.3895536Z         #pragma omp for 
2023-01-11T21:41:26.3895716Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.3895851Z         {
2023-01-11T21:41:26.3896016Z             #pragma GCC ivdep
2023-01-11T21:41:26.3896172Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:26.3896286Z             {
2023-01-11T21:41:26.3896402Z                 {
2023-01-11T21:41:26.3896506Z                     {
2023-01-11T21:41:26.3896689Z                         auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.3896861Z                         auto tmp2 = out_ptr0[i1];
2023-01-11T21:41:26.3897030Z                         auto tmp4 = out_ptr1[0];
2023-01-11T21:41:26.3897273Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.3897490Z                         auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.3897722Z                         auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.3897918Z                         out_ptr2[i1 + (32*i0)] = tmp5;
2023-01-11T21:41:26.3898072Z                     }
2023-01-11T21:41:26.3898232Z                 }
2023-01-11T21:41:26.3898385Z             }
2023-01-11T21:41:26.3898530Z         }
2023-01-11T21:41:26.3898677Z     }
2023-01-11T21:41:26.3898817Z }
2023-01-11T21:41:26.3898995Z ''')
2023-01-11T21:41:26.3899007Z 
2023-01-11T21:41:26.3899185Z async_compile.wait(globals())
2023-01-11T21:41:26.3899316Z del async_compile
2023-01-11T21:41:26.3899529Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3899656Z wrapper = (
2023-01-11T21:41:26.3899802Z '''
2023-01-11T21:41:26.3899938Z #include <dlfcn.h>
2023-01-11T21:41:26.3900054Z #include <assert.h>
2023-01-11T21:41:26.3900311Z     at::Tensor call_16(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3900499Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3900672Z     arg0_1 = args[0];
2023-01-11T21:41:26.3900990Z     auto buf0 = at::empty_strided({32, }, {1, }, at::ScalarType::Double); 
2023-01-11T21:41:26.3901288Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Double); 
2023-01-11T21:41:26.3901596Z     auto buf2 = at::empty_strided({32, 32}, {32, 1}, at::ScalarType::Double); 
2023-01-11T21:41:26.3902123Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/zh/czhbish5ijarri5zlmhjqoss3r374biyjp3mnifm6ga2y5j5hx55.so", RTLD_NOW);
2023-01-11T21:41:26.3902281Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3902503Z     void (*kernel_cpp_0)(const float*,double*,double*,double*);
2023-01-11T21:41:26.3902728Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3903070Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (double*)(buf0.data_ptr()), (double*)(buf1.data_ptr()), (double*)(buf2.data_ptr()));
2023-01-11T21:41:26.3903307Z     arg0_1.reset();
2023-01-11T21:41:26.3903579Z     return buf2; }''' )
2023-01-11T21:41:26.3903594Z 
2023-01-11T21:41:26.3903777Z module = load_inline(
2023-01-11T21:41:26.3904393Z     name='inline_extension_cch76d6suiocuagkhrlcimmdoy5nhjvwl4xulurcswqzhcc7kzny',
2023-01-11T21:41:26.3904577Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3904944Z     functions=['call_16'],
2023-01-11T21:41:26.3905664Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3905936Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3907219Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3907233Z 
2023-01-11T21:41:26.3907408Z def _wrap_func(f):
2023-01-11T21:41:26.3907580Z     def g(args):
2023-01-11T21:41:26.3907754Z         return f(args)
2023-01-11T21:41:26.3907894Z     return g
2023-01-11T21:41:26.3908234Z call = _wrap_func(module.call_16)
2023-01-11T21:41:26.3908248Z 
2023-01-11T21:41:26.3908256Z 
2023-01-11T21:41:26.3908420Z if __name__ == "__main__":
2023-01-11T21:41:26.3908626Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3908847Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3909222Z     arg0_1 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.3909415Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3909424Z 
2023-01-11T21:41:26.3909989Z [2023-01-11 21:31:44,909] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 169
2023-01-11T21:41:26.3910995Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3911273Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3911815Z [2023-01-11 21:31:44,930] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 170
2023-01-11T21:41:26.3911826Z 
2023-01-11T21:41:26.3911994Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3912119Z import torch
2023-01-11T21:41:26.3912246Z import random
2023-01-11T21:41:26.3912454Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3912666Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3912676Z 
2023-01-11T21:41:26.3912816Z aten = torch.ops.aten
2023-01-11T21:41:26.3913052Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3913277Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3913290Z 
2023-01-11T21:41:26.3913299Z 
2023-01-11T21:41:26.3913631Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3914114Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3914371Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.3914621Z                        const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.3914835Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.3914959Z {
2023-01-11T21:41:26.3915100Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.3915211Z     {
2023-01-11T21:41:26.3915322Z         {
2023-01-11T21:41:26.3915461Z             long tmp2 = 0;
2023-01-11T21:41:26.3915600Z             long tmp3 = 0;
2023-01-11T21:41:26.3915792Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3915912Z             {
2023-01-11T21:41:26.3916143Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:26.3916353Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.3916505Z                 {
2023-01-11T21:41:26.3916672Z                     {
2023-01-11T21:41:26.3916903Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3917134Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:26.3917409Z                         tmp2 += tmp1;
2023-01-11T21:41:26.3917570Z                         tmp3 += tmp1;
2023-01-11T21:41:26.3917732Z                     }
2023-01-11T21:41:26.3917884Z                 }
2023-01-11T21:41:26.3918013Z             }
2023-01-11T21:41:26.3918149Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.3918292Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:26.3918392Z         }
2023-01-11T21:41:26.3918502Z     }
2023-01-11T21:41:26.3918609Z     {
2023-01-11T21:41:26.3918722Z         {
2023-01-11T21:41:26.3918877Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.3919028Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:26.3919213Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:26.3919397Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3919668Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.3919863Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:26.3920016Z         }
2023-01-11T21:41:26.3920166Z     }
2023-01-11T21:41:26.3920310Z }
2023-01-11T21:41:26.3920520Z ''')
2023-01-11T21:41:26.3920538Z 
2023-01-11T21:41:26.3920730Z async_compile.wait(globals())
2023-01-11T21:41:26.3920904Z del async_compile
2023-01-11T21:41:26.3921138Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3921266Z wrapper = (
2023-01-11T21:41:26.3921418Z '''
2023-01-11T21:41:26.3921556Z #include <dlfcn.h>
2023-01-11T21:41:26.3921676Z #include <assert.h>
2023-01-11T21:41:26.3921887Z     at::Tensor call_17(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3922026Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3922157Z     arg0_1 = args[0];
2023-01-11T21:41:26.3922401Z     auto buf0 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:26.3922687Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:26.3922916Z     auto buf2 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:26.3923507Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/7i/c7ikfrx6rninixt746snmnigf7nld7jz6nxxc6lhqudu4kpzv6cu.so", RTLD_NOW);
2023-01-11T21:41:26.3923724Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3923981Z     void (*kernel_cpp_0)(long*,const bool*,long*);
2023-01-11T21:41:26.3924252Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3924529Z     kernel_cpp_0((long*)(buf2.data_ptr()), (bool*)(arg0_1.data_ptr()), (long*)(buf1.data_ptr()));
2023-01-11T21:41:26.3924657Z     arg0_1.reset();
2023-01-11T21:41:26.3924862Z     return buf2; }''' )
2023-01-11T21:41:26.3924872Z 
2023-01-11T21:41:26.3925008Z module = load_inline(
2023-01-11T21:41:26.3925481Z     name='inline_extension_cilgouchaeqshp6duams4dexs6tbjv33w7cmpjeqyecqjxdnhm6c',
2023-01-11T21:41:26.3925617Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3925838Z     functions=['call_17'],
2023-01-11T21:41:26.3926701Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3927057Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3928276Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3928288Z 
2023-01-11T21:41:26.3928423Z def _wrap_func(f):
2023-01-11T21:41:26.3928546Z     def g(args):
2023-01-11T21:41:26.3928679Z         return f(args)
2023-01-11T21:41:26.3928773Z     return g
2023-01-11T21:41:26.3928980Z call = _wrap_func(module.call_17)
2023-01-11T21:41:26.3928992Z 
2023-01-11T21:41:26.3929000Z 
2023-01-11T21:41:26.3929360Z if __name__ == "__main__":
2023-01-11T21:41:26.3929638Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3929924Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3930392Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.3930772Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3930783Z 
2023-01-11T21:41:26.3931302Z [2023-01-11 21:32:05,720] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 170
2023-01-11T21:41:26.3932102Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3932391Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3933074Z [2023-01-11 21:32:05,738] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 171
2023-01-11T21:41:26.3933090Z 
2023-01-11T21:41:26.3933316Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3933494Z import torch
2023-01-11T21:41:26.3933668Z import random
2023-01-11T21:41:26.3933927Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3934150Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3934160Z 
2023-01-11T21:41:26.3934295Z aten = torch.ops.aten
2023-01-11T21:41:26.3934518Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3934685Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3934694Z 
2023-01-11T21:41:26.3934701Z 
2023-01-11T21:41:26.3934954Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3935314Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3935526Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.3935789Z                        const unsigned char* __restrict__ in_ptr0,
2023-01-11T21:41:26.3936024Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.3936173Z {
2023-01-11T21:41:26.3936349Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.3936489Z     {
2023-01-11T21:41:26.3936634Z         {
2023-01-11T21:41:26.3936796Z             long tmp2 = 0;
2023-01-11T21:41:26.3936945Z             long tmp3 = 0;
2023-01-11T21:41:26.3937135Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3937252Z             {
2023-01-11T21:41:26.3937463Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:26.3937623Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.3937737Z                 {
2023-01-11T21:41:26.3937859Z                     {
2023-01-11T21:41:26.3938046Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3938292Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:26.3938480Z                         tmp2 += tmp1;
2023-01-11T21:41:26.3938652Z                         tmp3 += tmp1;
2023-01-11T21:41:26.3938811Z                     }
2023-01-11T21:41:26.3938959Z                 }
2023-01-11T21:41:26.3939109Z             }
2023-01-11T21:41:26.3939293Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.3939475Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:26.3939625Z         }
2023-01-11T21:41:26.3939747Z     }
2023-01-11T21:41:26.3939889Z     {
2023-01-11T21:41:26.3940030Z         {
2023-01-11T21:41:26.3940200Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.3940351Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:26.3940531Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:26.3940668Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3940809Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.3940956Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:26.3941072Z         }
2023-01-11T21:41:26.3941181Z     }
2023-01-11T21:41:26.3941291Z }
2023-01-11T21:41:26.3941496Z ''')
2023-01-11T21:41:26.3941515Z 
2023-01-11T21:41:26.3941697Z async_compile.wait(globals())
2023-01-11T21:41:26.3941868Z del async_compile
2023-01-11T21:41:26.3942246Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3942416Z wrapper = (
2023-01-11T21:41:26.3942607Z '''
2023-01-11T21:41:26.3942779Z #include <dlfcn.h>
2023-01-11T21:41:26.3942959Z #include <assert.h>
2023-01-11T21:41:26.3943277Z     at::Tensor call_18(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3943428Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3943556Z     arg0_1 = args[0];
2023-01-11T21:41:26.3943783Z     auto buf0 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:26.3944004Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:26.3944179Z     auto buf2 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:26.3944615Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/tb/ctbh34bqrqepgalem35dqwj3753otv7p7fp3h5othhadkxb5qbmw.so", RTLD_NOW);
2023-01-11T21:41:26.3944896Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3945176Z     void (*kernel_cpp_0)(long*,const unsigned char*,long*);
2023-01-11T21:41:26.3945466Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3945849Z     kernel_cpp_0((long*)(buf2.data_ptr()), (unsigned char*)(arg0_1.data_ptr()), (long*)(buf1.data_ptr()));
2023-01-11T21:41:26.3946015Z     arg0_1.reset();
2023-01-11T21:41:26.3946275Z     return buf2; }''' )
2023-01-11T21:41:26.3946294Z 
2023-01-11T21:41:26.3946469Z module = load_inline(
2023-01-11T21:41:26.3946951Z     name='inline_extension_cd35e6efhhtvu2vyxgwximssv66dj3ogpttagtwmu3k3432jc22z',
2023-01-11T21:41:26.3947084Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3947289Z     functions=['call_18'],
2023-01-11T21:41:26.3948023Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3948389Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3949742Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3949759Z 
2023-01-11T21:41:26.3949892Z def _wrap_func(f):
2023-01-11T21:41:26.3950017Z     def g(args):
2023-01-11T21:41:26.3950151Z         return f(args)
2023-01-11T21:41:26.3950254Z     return g
2023-01-11T21:41:26.3950414Z call = _wrap_func(module.call_18)
2023-01-11T21:41:26.3950424Z 
2023-01-11T21:41:26.3950431Z 
2023-01-11T21:41:26.3950565Z if __name__ == "__main__":
2023-01-11T21:41:26.3950769Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3950989Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3951343Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.uint8)
2023-01-11T21:41:26.3951583Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3951600Z 
2023-01-11T21:41:26.3952207Z [2023-01-11 21:32:25,897] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 171
2023-01-11T21:41:26.3953215Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3953455Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3953923Z [2023-01-11 21:32:25,916] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 172
2023-01-11T21:41:26.3953949Z 
2023-01-11T21:41:26.3954103Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3954227Z import torch
2023-01-11T21:41:26.3954359Z import random
2023-01-11T21:41:26.3954568Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3954787Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3954864Z 
2023-01-11T21:41:26.3955012Z aten = torch.ops.aten
2023-01-11T21:41:26.3955316Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3955490Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3955507Z 
2023-01-11T21:41:26.3955516Z 
2023-01-11T21:41:26.3955837Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3956310Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3956570Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.3956810Z                        const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.3957006Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.3957118Z {
2023-01-11T21:41:26.3957258Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.3957453Z     {
2023-01-11T21:41:26.3957565Z         {
2023-01-11T21:41:26.3957703Z             long tmp2 = 0;
2023-01-11T21:41:26.3957841Z             long tmp3 = 0;
2023-01-11T21:41:26.3958030Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3958143Z             {
2023-01-11T21:41:26.3958352Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:26.3958519Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.3958663Z                 {
2023-01-11T21:41:26.3958823Z                     {
2023-01-11T21:41:26.3959050Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.3959297Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:26.3959485Z                         tmp2 += tmp1;
2023-01-11T21:41:26.3959649Z                         tmp3 += tmp1;
2023-01-11T21:41:26.3959801Z                     }
2023-01-11T21:41:26.3959956Z                 }
2023-01-11T21:41:26.3960098Z             }
2023-01-11T21:41:26.3960292Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.3960444Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:26.3960563Z         }
2023-01-11T21:41:26.3960663Z     }
2023-01-11T21:41:26.3960770Z     {
2023-01-11T21:41:26.3960886Z         {
2023-01-11T21:41:26.3961039Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.3961185Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:26.3961362Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:26.3961495Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3961633Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.3961779Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:26.3961893Z         }
2023-01-11T21:41:26.3962003Z     }
2023-01-11T21:41:26.3962109Z }
2023-01-11T21:41:26.3962283Z ''')
2023-01-11T21:41:26.3962297Z 
2023-01-11T21:41:26.3962492Z async_compile.wait(globals())
2023-01-11T21:41:26.3962664Z del async_compile
2023-01-11T21:41:26.3962948Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3963120Z wrapper = (
2023-01-11T21:41:26.3963310Z '''
2023-01-11T21:41:26.3963478Z #include <dlfcn.h>
2023-01-11T21:41:26.3963654Z #include <assert.h>
2023-01-11T21:41:26.3963908Z     at::Tensor call_19(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3964076Z     at::Tensor arg0_1;
2023-01-11T21:41:26.3964209Z     arg0_1 = args[0];
2023-01-11T21:41:26.3964429Z     auto buf0 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:26.3964655Z     auto buf1 = at::empty_strided({}, {}, at::ScalarType::Long); 
2023-01-11T21:41:26.3964832Z     auto buf2 = buf0; buf0.reset();  // reuse
2023-01-11T21:41:26.3965283Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/3h/c3h2ropcb3kmljlkoaniup2ktv5vwaget4hkir3pmqeix6qdyoww.so", RTLD_NOW);
2023-01-11T21:41:26.3965463Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3965638Z     void (*kernel_cpp_0)(long*,const int*,long*);
2023-01-11T21:41:26.3965857Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3966154Z     kernel_cpp_0((long*)(buf2.data_ptr()), (int*)(arg0_1.data_ptr()), (long*)(buf1.data_ptr()));
2023-01-11T21:41:26.3966423Z     arg0_1.reset();
2023-01-11T21:41:26.3966683Z     return buf2; }''' )
2023-01-11T21:41:26.3966695Z 
2023-01-11T21:41:26.3966852Z module = load_inline(
2023-01-11T21:41:26.3967478Z     name='inline_extension_cnlotoitsz6eiybqu2bw3lgfkvhrhrmri7y2brdfjlaghnyjsytr',
2023-01-11T21:41:26.3967655Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.3967924Z     functions=['call_19'],
2023-01-11T21:41:26.3968600Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.3968877Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.3970284Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.3970305Z 
2023-01-11T21:41:26.3970494Z def _wrap_func(f):
2023-01-11T21:41:26.3970654Z     def g(args):
2023-01-11T21:41:26.3970829Z         return f(args)
2023-01-11T21:41:26.3970981Z     return g
2023-01-11T21:41:26.3971182Z call = _wrap_func(module.call_19)
2023-01-11T21:41:26.3971194Z 
2023-01-11T21:41:26.3971203Z 
2023-01-11T21:41:26.3971380Z if __name__ == "__main__":
2023-01-11T21:41:26.3971654Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.3971936Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.3972304Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.3972497Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.3972506Z 
2023-01-11T21:41:26.3973006Z [2023-01-11 21:32:46,022] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 172
2023-01-11T21:41:26.3973800Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.3974073Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.3974646Z [2023-01-11 21:32:46,045] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 173
2023-01-11T21:41:26.3974682Z 
2023-01-11T21:41:26.3974884Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.3975052Z import torch
2023-01-11T21:41:26.3975210Z import random
2023-01-11T21:41:26.3975470Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.3975752Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.3975764Z 
2023-01-11T21:41:26.3975923Z aten = torch.ops.aten
2023-01-11T21:41:26.3976166Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.3976321Z async_compile = AsyncCompile()
2023-01-11T21:41:26.3976331Z 
2023-01-11T21:41:26.3976338Z 
2023-01-11T21:41:26.3976596Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.3976959Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.3977173Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.3977364Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.3977541Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.3977712Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.3977830Z {
2023-01-11T21:41:26.3978035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.3978187Z     {
2023-01-11T21:41:26.3978377Z         #pragma omp for 
2023-01-11T21:41:26.3978576Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.3978736Z         {
2023-01-11T21:41:26.3978915Z             #pragma GCC ivdep
2023-01-11T21:41:26.3979090Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.3979348Z             {
2023-01-11T21:41:26.3979504Z                 {
2023-01-11T21:41:26.3979652Z                     {
2023-01-11T21:41:26.3979845Z                         auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:26.3980022Z                         auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.3980192Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.3980346Z                         out_ptr0[i0 + (8*i1)] = tmp2;
2023-01-11T21:41:26.3980463Z                     }
2023-01-11T21:41:26.3980577Z                 }
2023-01-11T21:41:26.3980692Z             }
2023-01-11T21:41:26.3980802Z         }
2023-01-11T21:41:26.3980941Z         #pragma omp for 
2023-01-11T21:41:26.3981085Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.3981180Z         {
2023-01-11T21:41:26.3981479Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.3981759Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.3981972Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3982271Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:26.3982470Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.3982679Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.3982827Z         }
2023-01-11T21:41:26.3983033Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.3983343Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.3983496Z         {
2023-01-11T21:41:26.3983670Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.3983848Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.3983999Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.3984160Z             auto tmp3 = static_cast<float>(10);
2023-01-11T21:41:26.3984308Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.3984449Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.3984566Z         }
2023-01-11T21:41:26.3984681Z     }
2023-01-11T21:41:26.3984793Z }
2023-01-11T21:41:26.3984959Z ''')
2023-01-11T21:41:26.3984971Z 
2023-01-11T21:41:26.3985115Z async_compile.wait(globals())
2023-01-11T21:41:26.3985247Z del async_compile
2023-01-11T21:41:26.3985459Z from torch.utils.cpp_extension import load_inline
2023-01-11T21:41:26.3985593Z wrapper = (
2023-01-11T21:41:26.3985783Z '''
2023-01-11T21:41:26.3985947Z #include <dlfcn.h>
2023-01-11T21:41:26.3986121Z #include <assert.h>
2023-01-11T21:41:26.3986401Z     std::vector<at::Tensor> call_20(std::vector<at::Tensor> args) {
2023-01-11T21:41:26.3986606Z     at::Tensor arg0_1, arg1_1;
2023-01-11T21:41:26.3986777Z     arg0_1 = args[0];
2023-01-11T21:41:26.3986944Z     arg1_1 = args[1];
2023-01-11T21:41:26.3987244Z     auto buf0 = at::empty_strided({8, 8}, {1, 8}, at::ScalarType::Float); 
2023-01-11T21:41:26.3987532Z     auto buf1 = at::empty_strided({8, 8}, {1, 8}, at::ScalarType::Float); 
2023-01-11T21:41:26.3987969Z     auto kernel_cpp_0_lib = dlopen("/tmp/torchinductor_jenkins/ue/cueituey67usa3z6yqp6aokcyslwfb22e2edl237c4xqodiu4ah2.so", RTLD_NOW);
2023-01-11T21:41:26.3988148Z     assert(kernel_cpp_0_lib != nullptr);
2023-01-11T21:41:26.3988356Z     void (*kernel_cpp_0)(const float*,const float*,float*,float*);
2023-01-11T21:41:26.3988576Z     *(void **) (&kernel_cpp_0) = dlsym(kernel_cpp_0_lib, "kernel");
2023-01-11T21:41:26.3988906Z     kernel_cpp_0((float*)(arg0_1.data_ptr()), (float*)(arg1_1.data_ptr()), (float*)(buf0.data_ptr()), (float*)(buf1.data_ptr()));
2023-01-11T21:41:26.3989035Z     arg0_1.reset();
2023-01-11T21:41:26.3989162Z     arg1_1.reset();
2023-01-11T21:41:26.3989514Z     return std::vector<at::Tensor>({buf0, buf1}); }''' )
2023-01-11T21:41:26.3989531Z 
2023-01-11T21:41:26.3989719Z module = load_inline(
2023-01-11T21:41:26.3990321Z     name='inline_extension_cjhlg7jjaalr6ljwfilqinh666vxmfig6blf4xoze25v3hhrk5f6',
2023-01-11T21:41:26.3990510Z     cpp_sources=[wrapper],
2023-01-11T21:41:26.4000229Z     functions=['call_20'],
2023-01-11T21:41:26.4001220Z     extra_cflags=['-std=c++17 -Wno-unused-variable -march=native -O3 -ffast-math -fno-finite-math-only -fopenmp -Wall  -D C10_USING_CUSTOM_GENERATED_MACROS'],
2023-01-11T21:41:26.4001720Z     extra_ldflags=['-shared -fPIC  -lgomp'],
2023-01-11T21:41:26.4002887Z     extra_include_paths=['-I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10'])
2023-01-11T21:41:26.4002903Z 
2023-01-11T21:41:26.4003083Z def _wrap_func(f):
2023-01-11T21:41:26.4003252Z     def g(args):
2023-01-11T21:41:26.4003426Z         return f(args)
2023-01-11T21:41:26.4003561Z     return g
2023-01-11T21:41:26.4003776Z call = _wrap_func(module.call_20)
2023-01-11T21:41:26.4003789Z 
2023-01-11T21:41:26.4003869Z 
2023-01-11T21:41:26.4004048Z if __name__ == "__main__":
2023-01-11T21:41:26.4004317Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4004621Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4005026Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4005389Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4005597Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4005608Z 
2023-01-11T21:41:26.4006108Z [2023-01-11 21:33:06,966] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 173
2023-01-11T21:41:26.4006272Z ok (418.898s)
2023-01-11T21:41:26.4006592Z   test_cudnn_rnn_cpu (__main__.CpuTests) ... skip: requires CUDA (0.002s)
2023-01-11T21:41:26.4007050Z   test_dense_mask_index_cpu (__main__.CpuTests) ... skip: https://github.com/pytorch/torchdynamo/issues/1697 (0.001s)
2023-01-11T21:41:26.4008096Z   test_div1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4008335Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4008824Z [2023-01-11 21:33:07,026] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 174
2023-01-11T21:41:26.4009573Z [2023-01-11 21:33:08,694] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 174
2023-01-11T21:41:26.4009586Z 
2023-01-11T21:41:26.4009805Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4009976Z import torch
2023-01-11T21:41:26.4010126Z import random
2023-01-11T21:41:26.4010411Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4010697Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4010709Z 
2023-01-11T21:41:26.4010900Z aten = torch.ops.aten
2023-01-11T21:41:26.4011195Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4011364Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4011373Z 
2023-01-11T21:41:26.4011380Z 
2023-01-11T21:41:26.4011639Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4012003Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4012204Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4012393Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4012612Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4012843Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4013065Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.4013295Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4013513Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.4013765Z {
2023-01-11T21:41:26.4013980Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4014125Z     {
2023-01-11T21:41:26.4014285Z         #pragma omp for 
2023-01-11T21:41:26.4014440Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4014555Z         {
2023-01-11T21:41:26.4014807Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4015029Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.4015180Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4015333Z             auto tmp3 = tmp2.floor();
2023-01-11T21:41:26.4015506Z             auto tmp4 = tmp2.trunc();
2023-01-11T21:41:26.4015723Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4015940Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4016241Z             tmp4.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4016426Z             tmp2.store(out_ptr3 + 8*i0);
2023-01-11T21:41:26.4016634Z             tmp3.store(out_ptr4 + 8*i0);
2023-01-11T21:41:26.4016792Z         }
2023-01-11T21:41:26.4017021Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4017215Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.4017339Z         {
2023-01-11T21:41:26.4017504Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4017639Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4017788Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4017962Z             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:26.4018126Z             auto tmp4 = std::trunc(tmp2);
2023-01-11T21:41:26.4018270Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4018413Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4018554Z             out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.4018708Z             out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.4018902Z             out_ptr4[i0] = tmp3;
2023-01-11T21:41:26.4019052Z         }
2023-01-11T21:41:26.4019203Z     }
2023-01-11T21:41:26.4019325Z }
2023-01-11T21:41:26.4019537Z ''')
2023-01-11T21:41:26.4019551Z 
2023-01-11T21:41:26.4019561Z 
2023-01-11T21:41:26.4019776Z async_compile.wait(globals())
2023-01-11T21:41:26.4019933Z del async_compile
2023-01-11T21:41:26.4019945Z 
2023-01-11T21:41:26.4020104Z def call(args):
2023-01-11T21:41:26.4020278Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4020442Z     args.clear()
2023-01-11T21:41:26.4020839Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4021199Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4021554Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4021971Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4022396Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4023005Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4023291Z     del arg0_1
2023-01-11T21:41:26.4023455Z     del arg1_1
2023-01-11T21:41:26.4023657Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4023669Z 
2023-01-11T21:41:26.4023678Z 
2023-01-11T21:41:26.4023823Z if __name__ == "__main__":
2023-01-11T21:41:26.4024030Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4024250Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4024599Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4024979Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4025238Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4025251Z 
2023-01-11T21:41:26.4025424Z ok (1.726s)
2023-01-11T21:41:26.4026515Z   test_div2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4026902Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4027395Z [2023-01-11 21:33:08,761] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 175
2023-01-11T21:41:26.4027892Z [2023-01-11 21:33:10,462] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 175
2023-01-11T21:41:26.4027902Z 
2023-01-11T21:41:26.4028080Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4028227Z import torch
2023-01-11T21:41:26.4028393Z import random
2023-01-11T21:41:26.4028741Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4029024Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4029042Z 
2023-01-11T21:41:26.4029232Z aten = torch.ops.aten
2023-01-11T21:41:26.4029541Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4029753Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4029766Z 
2023-01-11T21:41:26.4029775Z 
2023-01-11T21:41:26.4030067Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4030414Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4030632Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4030818Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4030997Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4031172Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4031397Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.4031630Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4031834Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.4031972Z {
2023-01-11T21:41:26.4032211Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4032355Z     {
2023-01-11T21:41:26.4032542Z         #pragma omp for 
2023-01-11T21:41:26.4032728Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4032881Z         {
2023-01-11T21:41:26.4033012Z             {
2023-01-11T21:41:26.4033154Z                 {
2023-01-11T21:41:26.4033333Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4033498Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.4033693Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4033856Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.4034045Z                     auto tmp4 = std::floor(tmp3);
2023-01-11T21:41:26.4034216Z                     auto tmp5 = std::trunc(tmp3);
2023-01-11T21:41:26.4034371Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.4034551Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.4034744Z                     out_ptr2[i0] = tmp5;
2023-01-11T21:41:26.4034938Z                     out_ptr3[i0] = tmp3;
2023-01-11T21:41:26.4035142Z                     out_ptr4[i0] = tmp4;
2023-01-11T21:41:26.4035292Z                 }
2023-01-11T21:41:26.4035421Z             }
2023-01-11T21:41:26.4035566Z         }
2023-01-11T21:41:26.4035710Z     }
2023-01-11T21:41:26.4035865Z }
2023-01-11T21:41:26.4036071Z ''')
2023-01-11T21:41:26.4036090Z 
2023-01-11T21:41:26.4036100Z 
2023-01-11T21:41:26.4036287Z async_compile.wait(globals())
2023-01-11T21:41:26.4036431Z del async_compile
2023-01-11T21:41:26.4036442Z 
2023-01-11T21:41:26.4036567Z def call(args):
2023-01-11T21:41:26.4036688Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4036815Z     args.clear()
2023-01-11T21:41:26.4037184Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4037557Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4038108Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4038569Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4039018Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4039536Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4039664Z     del arg0_1
2023-01-11T21:41:26.4039790Z     del arg1_1
2023-01-11T21:41:26.4039964Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4039974Z 
2023-01-11T21:41:26.4039981Z 
2023-01-11T21:41:26.4040116Z if __name__ == "__main__":
2023-01-11T21:41:26.4040383Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4040665Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4041146Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4041595Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4041866Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4041879Z 
2023-01-11T21:41:26.4042039Z ok (1.768s)
2023-01-11T21:41:26.4042956Z   test_div3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4043188Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4043706Z [2023-01-11 21:33:10,517] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 176
2023-01-11T21:41:26.4044316Z [2023-01-11 21:33:12,207] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 176
2023-01-11T21:41:26.4044339Z 
2023-01-11T21:41:26.4044558Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4044725Z import torch
2023-01-11T21:41:26.4044892Z import random
2023-01-11T21:41:26.4045142Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4045417Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4045429Z 
2023-01-11T21:41:26.4045580Z aten = torch.ops.aten
2023-01-11T21:41:26.4045824Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4045992Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4046002Z 
2023-01-11T21:41:26.4046008Z 
2023-01-11T21:41:26.4046263Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4046625Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4046890Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4047099Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4047334Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4047551Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.4047784Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:26.4048015Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4048236Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.4048375Z {
2023-01-11T21:41:26.4048564Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4048678Z     {
2023-01-11T21:41:26.4048817Z         #pragma omp for 
2023-01-11T21:41:26.4048963Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4049224Z         {
2023-01-11T21:41:26.4049340Z             {
2023-01-11T21:41:26.4049459Z                 {
2023-01-11T21:41:26.4049616Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4049777Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.4050132Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4050391Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:26.4050611Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:26.4051218Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:26.4051425Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:26.4051607Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.4051781Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.4051930Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:26.4052075Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:26.4052223Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:26.4052340Z                 }
2023-01-11T21:41:26.4052551Z             }
2023-01-11T21:41:26.4052650Z         }
2023-01-11T21:41:26.4052766Z     }
2023-01-11T21:41:26.4052875Z }
2023-01-11T21:41:26.4053054Z ''')
2023-01-11T21:41:26.4053066Z 
2023-01-11T21:41:26.4053075Z 
2023-01-11T21:41:26.4053290Z async_compile.wait(globals())
2023-01-11T21:41:26.4053472Z del async_compile
2023-01-11T21:41:26.4053484Z 
2023-01-11T21:41:26.4053648Z def call(args):
2023-01-11T21:41:26.4053808Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4053971Z     args.clear()
2023-01-11T21:41:26.4054436Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4054879Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4055226Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4055580Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4055916Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4056469Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4056627Z     del arg0_1
2023-01-11T21:41:26.4056775Z     del arg1_1
2023-01-11T21:41:26.4057004Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4057017Z 
2023-01-11T21:41:26.4057026Z 
2023-01-11T21:41:26.4057207Z if __name__ == "__main__":
2023-01-11T21:41:26.4057484Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4057772Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4058181Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4058537Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4058730Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4058739Z 
2023-01-11T21:41:26.4058865Z ok (1.744s)
2023-01-11T21:41:26.4059837Z   test_div4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4060144Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4060761Z [2023-01-11 21:33:12,261] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 177
2023-01-11T21:41:26.4061317Z [2023-01-11 21:33:12,281] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 177
2023-01-11T21:41:26.4061327Z 
2023-01-11T21:41:26.4061495Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4061625Z import torch
2023-01-11T21:41:26.4061755Z import random
2023-01-11T21:41:26.4061953Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4062175Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4062258Z 
2023-01-11T21:41:26.4062424Z aten = torch.ops.aten
2023-01-11T21:41:26.4062747Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4062962Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4062974Z 
2023-01-11T21:41:26.4062984Z 
2023-01-11T21:41:26.4063394Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4063875Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4064148Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4064343Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4064522Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4064685Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.4064916Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:26.4065096Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4065265Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.4065383Z {
2023-01-11T21:41:26.4065544Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4065684Z     {
2023-01-11T21:41:26.4065867Z         #pragma omp for 
2023-01-11T21:41:26.4066074Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4066226Z         {
2023-01-11T21:41:26.4066379Z             {
2023-01-11T21:41:26.4066535Z                 {
2023-01-11T21:41:26.4066718Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4066930Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.4067182Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4067427Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:26.4067607Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:26.4068100Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:26.4068266Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:26.4068408Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.4068561Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.4068721Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:26.4068925Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:26.4069128Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:26.4069277Z                 }
2023-01-11T21:41:26.4069425Z             }
2023-01-11T21:41:26.4069552Z         }
2023-01-11T21:41:26.4069702Z     }
2023-01-11T21:41:26.4069846Z }
2023-01-11T21:41:26.4070047Z ''')
2023-01-11T21:41:26.4070063Z 
2023-01-11T21:41:26.4070071Z 
2023-01-11T21:41:26.4070288Z async_compile.wait(globals())
2023-01-11T21:41:26.4070463Z del async_compile
2023-01-11T21:41:26.4070479Z 
2023-01-11T21:41:26.4070625Z def call(args):
2023-01-11T21:41:26.4070752Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4070881Z     args.clear()
2023-01-11T21:41:26.4071249Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4071606Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4072021Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4072476Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4072933Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4073540Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4073689Z     del arg0_1
2023-01-11T21:41:26.4073806Z     del arg1_1
2023-01-11T21:41:26.4073982Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4073992Z 
2023-01-11T21:41:26.4074004Z 
2023-01-11T21:41:26.4074140Z if __name__ == "__main__":
2023-01-11T21:41:26.4074348Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4074649Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4075012Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4075453Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4075700Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4075737Z 
2023-01-11T21:41:26.4075870Z ok (0.074s)
2023-01-11T21:41:26.4076939Z   test_div5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4077225Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4077716Z [2023-01-11 21:33:12,328] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 178
2023-01-11T21:41:26.4078209Z [2023-01-11 21:33:13,878] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 178
2023-01-11T21:41:26.4078219Z 
2023-01-11T21:41:26.4078438Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4078609Z import torch
2023-01-11T21:41:26.4078789Z import random
2023-01-11T21:41:26.4079033Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4079309Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4079323Z 
2023-01-11T21:41:26.4079508Z aten = torch.ops.aten
2023-01-11T21:41:26.4079820Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4080041Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4080052Z 
2023-01-11T21:41:26.4080060Z 
2023-01-11T21:41:26.4080330Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4080688Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4080899Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4081074Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4081228Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.4081394Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:26.4081615Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4081834Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.4081965Z {
2023-01-11T21:41:26.4082193Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4082338Z     {
2023-01-11T21:41:26.4082502Z         #pragma omp for 
2023-01-11T21:41:26.4082695Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4082859Z         {
2023-01-11T21:41:26.4083015Z             {
2023-01-11T21:41:26.4083173Z                 {
2023-01-11T21:41:26.4083361Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4083543Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4083730Z                     auto tmp2 = static_cast<float>(16);
2023-01-11T21:41:26.4083895Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.4084081Z                     auto tmp4 = static_cast<long>(16);
2023-01-11T21:41:26.4084562Z                     auto tmp5 = ((tmp0 < 0) != (tmp4 < 0) ? (tmp0 % tmp4 != 0 ? tmp0 / tmp4 - 1 : tmp0 / tmp4) : tmp0 / tmp4);
2023-01-11T21:41:26.4084770Z                     auto tmp6 = tmp0 / tmp4;
2023-01-11T21:41:26.4084962Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.4085162Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.4085347Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:26.4085542Z                     out_ptr3[i0] = tmp3;
2023-01-11T21:41:26.4085740Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:26.4085898Z                 }
2023-01-11T21:41:26.4086050Z             }
2023-01-11T21:41:26.4086195Z         }
2023-01-11T21:41:26.4086419Z     }
2023-01-11T21:41:26.4086526Z }
2023-01-11T21:41:26.4086676Z ''')
2023-01-11T21:41:26.4086686Z 
2023-01-11T21:41:26.4086693Z 
2023-01-11T21:41:26.4086856Z async_compile.wait(globals())
2023-01-11T21:41:26.4086989Z del async_compile
2023-01-11T21:41:26.4086998Z 
2023-01-11T21:41:26.4087122Z def call(args):
2023-01-11T21:41:26.4087247Z     arg0_1, = args
2023-01-11T21:41:26.4087375Z     args.clear()
2023-01-11T21:41:26.4087729Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4088152Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4088612Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4089211Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4089761Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4090216Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4090349Z     del arg0_1
2023-01-11T21:41:26.4090523Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4090534Z 
2023-01-11T21:41:26.4090542Z 
2023-01-11T21:41:26.4090655Z if __name__ == "__main__":
2023-01-11T21:41:26.4090859Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4091084Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4091529Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4091787Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4091803Z 
2023-01-11T21:41:26.4091962Z ok (1.597s)
2023-01-11T21:41:26.4093026Z   test_div6_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4093274Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4093770Z [2023-01-11 21:33:13,928] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 179
2023-01-11T21:41:26.4094248Z [2023-01-11 21:33:15,464] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 179
2023-01-11T21:41:26.4094275Z 
2023-01-11T21:41:26.4094433Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4094595Z import torch
2023-01-11T21:41:26.4094765Z import random
2023-01-11T21:41:26.4095025Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4095320Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4095333Z 
2023-01-11T21:41:26.4095519Z aten = torch.ops.aten
2023-01-11T21:41:26.4095831Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4096038Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4096050Z 
2023-01-11T21:41:26.4096080Z 
2023-01-11T21:41:26.4096363Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4096720Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4096928Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.4097111Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4097283Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4097444Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.4097614Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:26.4097808Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4098034Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.4098173Z {
2023-01-11T21:41:26.4098408Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4098672Z     {
2023-01-11T21:41:26.4098859Z         #pragma omp for 
2023-01-11T21:41:26.4099056Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4099180Z         {
2023-01-11T21:41:26.4099330Z             {
2023-01-11T21:41:26.4099472Z                 {
2023-01-11T21:41:26.4099650Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4099817Z                     auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.4100008Z                     auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:26.4100205Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.4100382Z                     auto tmp4 = static_cast<float>(tmp3);
2023-01-11T21:41:26.4100541Z                     auto tmp5 = tmp2 / tmp4;
2023-01-11T21:41:26.4101146Z                     auto tmp6 = ((tmp1 < 0) != (tmp3 < 0) ? (tmp1 % tmp3 != 0 ? tmp1 / tmp3 - 1 : tmp1 / tmp3) : tmp1 / tmp3);
2023-01-11T21:41:26.4101365Z                     auto tmp7 = tmp1 / tmp3;
2023-01-11T21:41:26.4101626Z                     auto tmp8 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4101840Z                     auto tmp9 = tmp8 / tmp4;
2023-01-11T21:41:26.4102039Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:41:26.4102210Z                     out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.4102408Z                     out_ptr2[i0] = tmp7;
2023-01-11T21:41:26.4102593Z                     out_ptr3[i0] = tmp9;
2023-01-11T21:41:26.4102775Z                     out_ptr4[i0] = tmp6;
2023-01-11T21:41:26.4102907Z                 }
2023-01-11T21:41:26.4103022Z             }
2023-01-11T21:41:26.4103232Z         }
2023-01-11T21:41:26.4103331Z     }
2023-01-11T21:41:26.4103444Z }
2023-01-11T21:41:26.4103600Z ''')
2023-01-11T21:41:26.4103611Z 
2023-01-11T21:41:26.4103619Z 
2023-01-11T21:41:26.4103781Z async_compile.wait(globals())
2023-01-11T21:41:26.4103917Z del async_compile
2023-01-11T21:41:26.4103927Z 
2023-01-11T21:41:26.4104055Z def call(args):
2023-01-11T21:41:26.4104195Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4104336Z     args.clear()
2023-01-11T21:41:26.4104806Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4105250Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4105643Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4105994Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4106343Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4106813Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4106976Z     del arg0_1
2023-01-11T21:41:26.4107123Z     del arg1_1
2023-01-11T21:41:26.4107344Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4107354Z 
2023-01-11T21:41:26.4107365Z 
2023-01-11T21:41:26.4107542Z if __name__ == "__main__":
2023-01-11T21:41:26.4107809Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4108097Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4108557Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.4108954Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4109165Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4109175Z 
2023-01-11T21:41:26.4109278Z ok (1.586s)
2023-01-11T21:41:26.4110102Z   test_div7_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4110540Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4111169Z [2023-01-11 21:33:15,516] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 180
2023-01-11T21:41:26.4111792Z [2023-01-11 21:33:17,052] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 180
2023-01-11T21:41:26.4111805Z 
2023-01-11T21:41:26.4112007Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4112143Z import torch
2023-01-11T21:41:26.4112270Z import random
2023-01-11T21:41:26.4112477Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4112679Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4112707Z 
2023-01-11T21:41:26.4112829Z aten = torch.ops.aten
2023-01-11T21:41:26.4113130Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4113322Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4113337Z 
2023-01-11T21:41:26.4113344Z 
2023-01-11T21:41:26.4113676Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4114146Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4114430Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4114653Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4114889Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4115089Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.4115273Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:26.4115448Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4115618Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.4115729Z {
2023-01-11T21:41:26.4115910Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4116025Z     {
2023-01-11T21:41:26.4116148Z         #pragma omp for 
2023-01-11T21:41:26.4116313Z         for(long i0=0; i0<10000; i0+=1)
2023-01-11T21:41:26.4116469Z         {
2023-01-11T21:41:26.4116624Z             {
2023-01-11T21:41:26.4116787Z                 {
2023-01-11T21:41:26.4117005Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4117183Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.4117429Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4117670Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:26.4117881Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:26.4118399Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:26.4118558Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:26.4118713Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.4118863Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.4119003Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:26.4119152Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:26.4119305Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:26.4119440Z                 }
2023-01-11T21:41:26.4119591Z             }
2023-01-11T21:41:26.4119751Z         }
2023-01-11T21:41:26.4119875Z     }
2023-01-11T21:41:26.4120002Z }
2023-01-11T21:41:26.4120200Z ''')
2023-01-11T21:41:26.4120212Z 
2023-01-11T21:41:26.4120221Z 
2023-01-11T21:41:26.4120434Z async_compile.wait(globals())
2023-01-11T21:41:26.4120603Z del async_compile
2023-01-11T21:41:26.4120617Z 
2023-01-11T21:41:26.4120784Z def call(args):
2023-01-11T21:41:26.4120967Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4121140Z     args.clear()
2023-01-11T21:41:26.4121531Z     buf0 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4121907Z     buf1 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4122288Z     buf2 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4122685Z     buf3 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4123244Z     buf4 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4123843Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4124007Z     del arg0_1
2023-01-11T21:41:26.4124169Z     del arg1_1
2023-01-11T21:41:26.4124361Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4124374Z 
2023-01-11T21:41:26.4124381Z 
2023-01-11T21:41:26.4124537Z if __name__ == "__main__":
2023-01-11T21:41:26.4124746Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4124963Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4125411Z     arg0_1 = rand_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4125817Z     arg1_1 = rand_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4126093Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4126108Z 
2023-01-11T21:41:26.4126270Z ok (1.589s)
2023-01-11T21:41:26.4127001Z   test_div8_cpu (__main__.CpuTests) ... [2023-01-11 21:33:17,101] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 181
2023-01-11T21:41:26.4127617Z [2023-01-11 21:33:18,619] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 181
2023-01-11T21:41:26.4127632Z 
2023-01-11T21:41:26.4127792Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4127919Z import torch
2023-01-11T21:41:26.4128047Z import random
2023-01-11T21:41:26.4128251Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4128467Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4128482Z 
2023-01-11T21:41:26.4128623Z aten = torch.ops.aten
2023-01-11T21:41:26.4128839Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4129266Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4129284Z 
2023-01-11T21:41:26.4129292Z 
2023-01-11T21:41:26.4129624Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4130081Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4130340Z extern "C" void kernel(long* __restrict__ out_ptr0,
2023-01-11T21:41:26.4130565Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.4130779Z                        long* __restrict__ out_ptr2)
2023-01-11T21:41:26.4130922Z {
2023-01-11T21:41:26.4131026Z     {
2023-01-11T21:41:26.4131139Z         {
2023-01-11T21:41:26.4131321Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:41:26.4131497Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:41:26.4131657Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4131804Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.4131918Z         }
2023-01-11T21:41:26.4132018Z     }
2023-01-11T21:41:26.4132127Z     {
2023-01-11T21:41:26.4132238Z         {
2023-01-11T21:41:26.4132465Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:41:26.4132700Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:41:26.4133258Z             auto tmp2 = ((tmp0 < 0) != (tmp1 < 0) ? (tmp0 % tmp1 != 0 ? tmp0 / tmp1 - 1 : tmp0 / tmp1) : tmp0 / tmp1);
2023-01-11T21:41:26.4133448Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.4133577Z         }
2023-01-11T21:41:26.4133726Z     }
2023-01-11T21:41:26.4133863Z     {
2023-01-11T21:41:26.4134014Z         {
2023-01-11T21:41:26.4134232Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:41:26.4134416Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:41:26.4134547Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4134686Z             out_ptr2[0] = tmp2;
2023-01-11T21:41:26.4134805Z         }
2023-01-11T21:41:26.4134918Z     }
2023-01-11T21:41:26.4135027Z }
2023-01-11T21:41:26.4135177Z ''')
2023-01-11T21:41:26.4135298Z 
2023-01-11T21:41:26.4135304Z 
2023-01-11T21:41:26.4135471Z async_compile.wait(globals())
2023-01-11T21:41:26.4135598Z del async_compile
2023-01-11T21:41:26.4135612Z 
2023-01-11T21:41:26.4135776Z def call(args):
2023-01-11T21:41:26.4136211Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4136649Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4137072Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4137431Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4137590Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.4137599Z 
2023-01-11T21:41:26.4137605Z 
2023-01-11T21:41:26.4137741Z if __name__ == "__main__":
2023-01-11T21:41:26.4138027Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4138256Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4138437Z     print_performance(lambda: call([]))
2023-01-11T21:41:26.4138452Z 
2023-01-11T21:41:26.4138569Z ok (1.567s)
2023-01-11T21:41:26.4139625Z   test_div_prim_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4139927Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4140525Z [2023-01-11 21:33:18,675] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 182
2023-01-11T21:41:26.4141017Z [2023-01-11 21:33:20,187] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 182
2023-01-11T21:41:26.4141880Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4142164Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4142775Z [2023-01-11 21:33:20,227] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 183
2023-01-11T21:41:26.4143458Z [2023-01-11 21:33:21,914] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 183
2023-01-11T21:41:26.4143491Z 
2023-01-11T21:41:26.4143651Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4143778Z import torch
2023-01-11T21:41:26.4143904Z import random
2023-01-11T21:41:26.4144118Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4144333Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4144348Z 
2023-01-11T21:41:26.4144487Z aten = torch.ops.aten
2023-01-11T21:41:26.4144734Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4144925Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4144939Z 
2023-01-11T21:41:26.4144947Z 
2023-01-11T21:41:26.4145262Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4145739Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4146016Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4146266Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4146496Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4146619Z {
2023-01-11T21:41:26.4146786Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4146899Z     {
2023-01-11T21:41:26.4147040Z         #pragma omp for 
2023-01-11T21:41:26.4147191Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.4147306Z         {
2023-01-11T21:41:26.4147644Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4147905Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.4148103Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4148296Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4148450Z         }
2023-01-11T21:41:26.4148685Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4148887Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.4149032Z         {
2023-01-11T21:41:26.4149235Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4149403Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4149584Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4149758Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4149880Z         }
2023-01-11T21:41:26.4149992Z     }
2023-01-11T21:41:26.4150159Z }
2023-01-11T21:41:26.4150324Z ''')
2023-01-11T21:41:26.4150334Z 
2023-01-11T21:41:26.4150341Z 
2023-01-11T21:41:26.4150491Z async_compile.wait(globals())
2023-01-11T21:41:26.4150616Z del async_compile
2023-01-11T21:41:26.4150625Z 
2023-01-11T21:41:26.4150744Z def call(args):
2023-01-11T21:41:26.4150877Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4151008Z     args.clear()
2023-01-11T21:41:26.4151460Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4151834Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4151997Z     del arg0_1
2023-01-11T21:41:26.4152139Z     del arg1_1
2023-01-11T21:41:26.4152294Z     return (buf0, )
2023-01-11T21:41:26.4152307Z 
2023-01-11T21:41:26.4152316Z 
2023-01-11T21:41:26.4152497Z if __name__ == "__main__":
2023-01-11T21:41:26.4152773Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4153026Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4153397Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4153763Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4153972Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4153982Z 
2023-01-11T21:41:26.4153989Z 
2023-01-11T21:41:26.4154143Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4154305Z import torch
2023-01-11T21:41:26.4154472Z import random
2023-01-11T21:41:26.4154749Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4155038Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4155047Z 
2023-01-11T21:41:26.4155221Z aten = torch.ops.aten
2023-01-11T21:41:26.4155539Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4155737Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4155773Z 
2023-01-11T21:41:26.4155782Z 
2023-01-11T21:41:26.4156077Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4156440Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4156660Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4156840Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4157015Z                        long* __restrict__ out_ptr0)
2023-01-11T21:41:26.4157125Z {
2023-01-11T21:41:26.4157304Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4157426Z     {
2023-01-11T21:41:26.4157616Z         #pragma omp for 
2023-01-11T21:41:26.4157811Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:26.4157950Z         {
2023-01-11T21:41:26.4158103Z             {
2023-01-11T21:41:26.4158259Z                 {
2023-01-11T21:41:26.4158481Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4158680Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4158893Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4159107Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4159236Z                 }
2023-01-11T21:41:26.4159424Z             }
2023-01-11T21:41:26.4159537Z         }
2023-01-11T21:41:26.4159629Z     }
2023-01-11T21:41:26.4159736Z }
2023-01-11T21:41:26.4159891Z ''')
2023-01-11T21:41:26.4159901Z 
2023-01-11T21:41:26.4159908Z 
2023-01-11T21:41:26.4160069Z async_compile.wait(globals())
2023-01-11T21:41:26.4160198Z del async_compile
2023-01-11T21:41:26.4160207Z 
2023-01-11T21:41:26.4160357Z def call(args):
2023-01-11T21:41:26.4160535Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4160703Z     args.clear()
2023-01-11T21:41:26.4161144Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4161519Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4161681Z     del arg0_1
2023-01-11T21:41:26.4161844Z     del arg1_1
2023-01-11T21:41:26.4161999Z     return (buf0, )
2023-01-11T21:41:26.4162083Z 
2023-01-11T21:41:26.4162098Z 
2023-01-11T21:41:26.4162255Z if __name__ == "__main__":
2023-01-11T21:41:26.4162461Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4162675Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4163045Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4163453Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4163723Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4163737Z 
2023-01-11T21:41:26.4163896Z ok (3.294s)
2023-01-11T21:41:26.4165016Z   test_div_zero_dim_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4165253Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4165747Z [2023-01-11 21:33:21,969] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 184
2023-01-11T21:41:26.4166266Z [2023-01-11 21:33:23,673] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 184
2023-01-11T21:41:26.4167235Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4167530Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4168101Z [2023-01-11 21:33:23,727] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 185
2023-01-11T21:41:26.4168593Z [2023-01-11 21:33:25,364] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 185
2023-01-11T21:41:26.4169602Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4169883Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4170492Z [2023-01-11 21:33:25,411] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 186
2023-01-11T21:41:26.4170505Z 
2023-01-11T21:41:26.4170729Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4170896Z import torch
2023-01-11T21:41:26.4171060Z import random
2023-01-11T21:41:26.4171293Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4171495Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4171505Z 
2023-01-11T21:41:26.4171759Z aten = torch.ops.aten
2023-01-11T21:41:26.4171992Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4172161Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4172173Z 
2023-01-11T21:41:26.4172182Z 
2023-01-11T21:41:26.4172500Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4172954Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4173235Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4173487Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4173684Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4173902Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4174112Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.4174361Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4174535Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.4174652Z {
2023-01-11T21:41:26.4174833Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4174927Z     {
2023-01-11T21:41:26.4175066Z         #pragma omp for 
2023-01-11T21:41:26.4175247Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.4175407Z         {
2023-01-11T21:41:26.4175728Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4176013Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:26.4176209Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4176392Z             auto tmp3 = tmp2.floor();
2023-01-11T21:41:26.4176596Z             auto tmp4 = tmp2.trunc();
2023-01-11T21:41:26.4176810Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4176999Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4177169Z             tmp4.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4177330Z             tmp2.store(out_ptr3 + 8*i0);
2023-01-11T21:41:26.4177489Z             tmp3.store(out_ptr4 + 8*i0);
2023-01-11T21:41:26.4177589Z         }
2023-01-11T21:41:26.4177759Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4177906Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.4178021Z         {
2023-01-11T21:41:26.4178217Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4178415Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.4178605Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4178794Z             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:26.4179012Z             auto tmp4 = std::trunc(tmp2);
2023-01-11T21:41:26.4179203Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4179397Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4179581Z             out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.4179770Z             out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.4179940Z             out_ptr4[i0] = tmp3;
2023-01-11T21:41:26.4180038Z         }
2023-01-11T21:41:26.4180148Z     }
2023-01-11T21:41:26.4180253Z }
2023-01-11T21:41:26.4180423Z ''')
2023-01-11T21:41:26.4180433Z 
2023-01-11T21:41:26.4180441Z 
2023-01-11T21:41:26.4180602Z async_compile.wait(globals())
2023-01-11T21:41:26.4180733Z del async_compile
2023-01-11T21:41:26.4180743Z 
2023-01-11T21:41:26.4180872Z def call(args):
2023-01-11T21:41:26.4181000Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4181162Z     args.clear()
2023-01-11T21:41:26.4181619Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4182076Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4182532Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4182946Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4183379Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4183858Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4184083Z     del arg0_1
2023-01-11T21:41:26.4184214Z     del arg1_1
2023-01-11T21:41:26.4184444Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4184458Z 
2023-01-11T21:41:26.4184467Z 
2023-01-11T21:41:26.4184650Z if __name__ == "__main__":
2023-01-11T21:41:26.4184916Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4185208Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4185669Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4186060Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4186248Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4186277Z 
2023-01-11T21:41:26.4186284Z 
2023-01-11T21:41:26.4186495Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4186629Z import torch
2023-01-11T21:41:26.4186756Z import random
2023-01-11T21:41:26.4186975Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4187246Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4187262Z 
2023-01-11T21:41:26.4187447Z aten = torch.ops.aten
2023-01-11T21:41:26.4187760Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4187954Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4187970Z 
2023-01-11T21:41:26.4188000Z 
2023-01-11T21:41:26.4188284Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4188750Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4189000Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4189180Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4189360Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4189537Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4189708Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.4189866Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4190076Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.4190226Z {
2023-01-11T21:41:26.4190465Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4190603Z     {
2023-01-11T21:41:26.4190789Z         #pragma omp for 
2023-01-11T21:41:26.4190978Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.4191105Z         {
2023-01-11T21:41:26.4191403Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:26.4191708Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.4191882Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4192036Z             auto tmp3 = tmp2.floor();
2023-01-11T21:41:26.4192195Z             auto tmp4 = tmp2.trunc();
2023-01-11T21:41:26.4192357Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4192505Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4192664Z             tmp4.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4192829Z             tmp2.store(out_ptr3 + 8*i0);
2023-01-11T21:41:26.4193028Z             tmp3.store(out_ptr4 + 8*i0);
2023-01-11T21:41:26.4193180Z         }
2023-01-11T21:41:26.4193392Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4193582Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.4193711Z         {
2023-01-11T21:41:26.4193905Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.4194101Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4194295Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4194516Z             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:26.4194717Z             auto tmp4 = std::trunc(tmp2);
2023-01-11T21:41:26.4194874Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4195005Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4195147Z             out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.4195285Z             out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.4195496Z             out_ptr4[i0] = tmp3;
2023-01-11T21:41:26.4195610Z         }
2023-01-11T21:41:26.4195719Z     }
2023-01-11T21:41:26.4195830Z }
2023-01-11T21:41:26.4196005Z ''')
2023-01-11T21:41:26.4196021Z 
2023-01-11T21:41:26.4196031Z 
2023-01-11T21:41:26.4196229Z async_compile.wait(globals())
2023-01-11T21:41:26.4196409Z del async_compile
2023-01-11T21:41:26.4196422Z 
2023-01-11T21:41:26.4196595Z def call(args):
2023-01-11T21:41:26.4196771Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4196941Z     args.clear()
2023-01-11T21:41:26.4197414Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4197824Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4198164Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4198576Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4198923Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4199525Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4199700Z     del arg0_1
2023-01-11T21:41:26.4199862Z     del arg1_1
2023-01-11T21:41:26.4200083Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4200094Z 
2023-01-11T21:41:26.4200101Z 
2023-01-11T21:41:26.4200280Z if __name__ == "__main__":
2023-01-11T21:41:26.4200525Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4200801Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4201155Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4201517Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4201724Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4201738Z 
2023-01-11T21:41:26.4202322Z [2023-01-11 21:33:27,090] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 186
2023-01-11T21:41:26.4203312Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4203607Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4204143Z [2023-01-11 21:33:27,137] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 187
2023-01-11T21:41:26.4204632Z [2023-01-11 21:33:28,781] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 187
2023-01-11T21:41:26.4204661Z 
2023-01-11T21:41:26.4204814Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4204949Z import torch
2023-01-11T21:41:26.4205121Z import random
2023-01-11T21:41:26.4205387Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4205658Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4205671Z 
2023-01-11T21:41:26.4205857Z aten = torch.ops.aten
2023-01-11T21:41:26.4206173Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4206373Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4206388Z 
2023-01-11T21:41:26.4206397Z 
2023-01-11T21:41:26.4206726Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4207106Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4207319Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4207509Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4207687Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4207859Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.4208140Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:26.4208337Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4208565Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.4208715Z {
2023-01-11T21:41:26.4208950Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4209230Z     {
2023-01-11T21:41:26.4209418Z         #pragma omp for 
2023-01-11T21:41:26.4209621Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.4209737Z         {
2023-01-11T21:41:26.4209893Z             {
2023-01-11T21:41:26.4210023Z                 {
2023-01-11T21:41:26.4210200Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4210366Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:41:26.4210644Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4210820Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:26.4210985Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:26.4211545Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:26.4211772Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:26.4211978Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.4212167Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.4212340Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:26.4212538Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:26.4212708Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:26.4212861Z                 }
2023-01-11T21:41:26.4213008Z             }
2023-01-11T21:41:26.4213138Z         }
2023-01-11T21:41:26.4213252Z     }
2023-01-11T21:41:26.4213359Z }
2023-01-11T21:41:26.4213495Z ''')
2023-01-11T21:41:26.4213522Z 
2023-01-11T21:41:26.4213535Z 
2023-01-11T21:41:26.4213682Z async_compile.wait(globals())
2023-01-11T21:41:26.4213814Z del async_compile
2023-01-11T21:41:26.4213828Z 
2023-01-11T21:41:26.4213958Z def call(args):
2023-01-11T21:41:26.4214096Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4214240Z     args.clear()
2023-01-11T21:41:26.4214706Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4215141Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4215568Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4216016Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4216369Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4216837Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4216966Z     del arg0_1
2023-01-11T21:41:26.4217089Z     del arg1_1
2023-01-11T21:41:26.4217291Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4217306Z 
2023-01-11T21:41:26.4217314Z 
2023-01-11T21:41:26.4217499Z if __name__ == "__main__":
2023-01-11T21:41:26.4217732Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4218026Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4218480Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4218922Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4219161Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4219171Z 
2023-01-11T21:41:26.4219178Z 
2023-01-11T21:41:26.4219348Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4219477Z import torch
2023-01-11T21:41:26.4219605Z import random
2023-01-11T21:41:26.4219799Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4220015Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4220025Z 
2023-01-11T21:41:26.4220288Z aten = torch.ops.aten
2023-01-11T21:41:26.4220590Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4220806Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4220820Z 
2023-01-11T21:41:26.4220831Z 
2023-01-11T21:41:26.4221153Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4221624Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4221892Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4222100Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4222287Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4222460Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.4222632Z                        long* __restrict__ out_ptr2,
2023-01-11T21:41:26.4222895Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4223073Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.4223279Z {
2023-01-11T21:41:26.4223481Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4223629Z     {
2023-01-11T21:41:26.4223823Z         #pragma omp for 
2023-01-11T21:41:26.4224016Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.4224173Z         {
2023-01-11T21:41:26.4224327Z             {
2023-01-11T21:41:26.4224480Z                 {
2023-01-11T21:41:26.4224655Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.4224870Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.4225122Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4225331Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:26.4225497Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:41:26.4225993Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:41:26.4226158Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:41:26.4226299Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.4226487Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.4226695Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:41:26.4226895Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:26.4227097Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:26.4227241Z                 }
2023-01-11T21:41:26.4227395Z             }
2023-01-11T21:41:26.4227522Z         }
2023-01-11T21:41:26.4227670Z     }
2023-01-11T21:41:26.4227806Z }
2023-01-11T21:41:26.4228002Z ''')
2023-01-11T21:41:26.4228015Z 
2023-01-11T21:41:26.4228026Z 
2023-01-11T21:41:26.4228234Z async_compile.wait(globals())
2023-01-11T21:41:26.4228380Z del async_compile
2023-01-11T21:41:26.4228389Z 
2023-01-11T21:41:26.4228516Z def call(args):
2023-01-11T21:41:26.4228631Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4228765Z     args.clear()
2023-01-11T21:41:26.4229117Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4229494Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4229929Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4230381Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4230816Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4231380Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4231505Z     del arg0_1
2023-01-11T21:41:26.4231610Z     del arg1_1
2023-01-11T21:41:26.4231782Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4231791Z 
2023-01-11T21:41:26.4231798Z 
2023-01-11T21:41:26.4231939Z if __name__ == "__main__":
2023-01-11T21:41:26.4232144Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4232458Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4232879Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4233325Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4233569Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4233610Z 
2023-01-11T21:41:26.4233744Z ok (6.867s)
2023-01-11T21:41:26.4234474Z   test_dropout_cpu (__main__.CpuTests) ... [2023-01-11 21:33:28,848] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 188
2023-01-11T21:41:26.4234957Z [2023-01-11 21:33:28,848] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.4235461Z [2023-01-11 21:33:30,464] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 188
2023-01-11T21:41:26.4236143Z [2023-01-11 21:33:30,530] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 189
2023-01-11T21:41:26.4236751Z [2023-01-11 21:33:30,530] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.4237378Z [2023-01-11 21:33:30,539] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 189
2023-01-11T21:41:26.4237389Z 
2023-01-11T21:41:26.4237568Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4237674Z import torch
2023-01-11T21:41:26.4237804Z import random
2023-01-11T21:41:26.4238006Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4238226Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4238236Z 
2023-01-11T21:41:26.4238375Z aten = torch.ops.aten
2023-01-11T21:41:26.4238631Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4238849Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4239214Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.4239225Z 
2023-01-11T21:41:26.4239238Z 
2023-01-11T21:41:26.4239550Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4240033Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4240301Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:26.4240533Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4240713Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4240823Z {
2023-01-11T21:41:26.4241002Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4241112Z     {
2023-01-11T21:41:26.4241234Z         #pragma omp for 
2023-01-11T21:41:26.4241383Z         for(long i0=0; i0<1000; i0+=1)
2023-01-11T21:41:26.4241499Z         {
2023-01-11T21:41:26.4241613Z             {
2023-01-11T21:41:26.4241761Z                 {
2023-01-11T21:41:26.4241978Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.4242165Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.4242403Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.4242744Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.4242995Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:26.4243209Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.4243453Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.4243633Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.4243825Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:26.4243972Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.4244123Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.4244240Z                 }
2023-01-11T21:41:26.4244354Z             }
2023-01-11T21:41:26.4244468Z         }
2023-01-11T21:41:26.4244579Z     }
2023-01-11T21:41:26.4244677Z }
2023-01-11T21:41:26.4244863Z ''')
2023-01-11T21:41:26.4244875Z 
2023-01-11T21:41:26.4244886Z 
2023-01-11T21:41:26.4245194Z async_compile.wait(globals())
2023-01-11T21:41:26.4245371Z del async_compile
2023-01-11T21:41:26.4245381Z 
2023-01-11T21:41:26.4245547Z def call(args):
2023-01-11T21:41:26.4245712Z     arg0_1, = args
2023-01-11T21:41:26.4245882Z     args.clear()
2023-01-11T21:41:26.4246179Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.4246632Z     buf0 = empty_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4246938Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4247060Z     del arg0_1
2023-01-11T21:41:26.4247189Z     return (buf0, )
2023-01-11T21:41:26.4247199Z 
2023-01-11T21:41:26.4247206Z 
2023-01-11T21:41:26.4247338Z if __name__ == "__main__":
2023-01-11T21:41:26.4247542Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4247890Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4248372Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4248815Z     arg0_1 = rand_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4249209Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4249224Z 
2023-01-11T21:41:26.4249232Z 
2023-01-11T21:41:26.4249451Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4249601Z import torch
2023-01-11T21:41:26.4249743Z import random
2023-01-11T21:41:26.4249952Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4250169Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4250178Z 
2023-01-11T21:41:26.4250308Z aten = torch.ops.aten
2023-01-11T21:41:26.4250533Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4250701Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4251069Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.4251081Z 
2023-01-11T21:41:26.4251089Z 
2023-01-11T21:41:26.4251416Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4251895Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4252163Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:26.4252407Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4252617Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4252719Z {
2023-01-11T21:41:26.4252897Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4253008Z     {
2023-01-11T21:41:26.4253148Z         #pragma omp for 
2023-01-11T21:41:26.4253298Z         for(long i0=0; i0<1000; i0+=1)
2023-01-11T21:41:26.4253413Z         {
2023-01-11T21:41:26.4253513Z             {
2023-01-11T21:41:26.4253648Z                 {
2023-01-11T21:41:26.4253859Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.4254058Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.4254291Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.4254624Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.4254877Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:26.4255067Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.4255316Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.4255499Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.4255687Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:26.4255851Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.4256010Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.4256127Z                 }
2023-01-11T21:41:26.4256231Z             }
2023-01-11T21:41:26.4256344Z         }
2023-01-11T21:41:26.4256457Z     }
2023-01-11T21:41:26.4256581Z }
2023-01-11T21:41:26.4256790Z ''')
2023-01-11T21:41:26.4256804Z 
2023-01-11T21:41:26.4256812Z 
2023-01-11T21:41:26.4257025Z async_compile.wait(globals())
2023-01-11T21:41:26.4257337Z del async_compile
2023-01-11T21:41:26.4257350Z 
2023-01-11T21:41:26.4257513Z def call(args):
2023-01-11T21:41:26.4257662Z     arg0_1, = args
2023-01-11T21:41:26.4257820Z     args.clear()
2023-01-11T21:41:26.4258127Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.4258549Z     buf0 = empty_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4258864Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4258990Z     del arg0_1
2023-01-11T21:41:26.4259118Z     return (buf0, )
2023-01-11T21:41:26.4259127Z 
2023-01-11T21:41:26.4259133Z 
2023-01-11T21:41:26.4259247Z if __name__ == "__main__":
2023-01-11T21:41:26.4259483Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4259864Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4260339Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4260772Z     arg0_1 = rand_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4260970Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4260981Z 
2023-01-11T21:41:26.4261101Z ok (1.757s)
2023-01-11T21:41:26.4261793Z   test_dropout_deterministic_cpu (__main__.CpuTests) ... [2023-01-11 21:33:30,605] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 190
2023-01-11T21:41:26.4262383Z [2023-01-11 21:33:30,606] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.4263000Z [2023-01-11 21:33:32,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 190
2023-01-11T21:41:26.4263681Z [2023-01-11 21:33:32,210] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 191
2023-01-11T21:41:26.4264180Z [2023-01-11 21:33:32,210] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.4264680Z [2023-01-11 21:33:32,220] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 191
2023-01-11T21:41:26.4264690Z 
2023-01-11T21:41:26.4264857Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4264983Z import torch
2023-01-11T21:41:26.4265113Z import random
2023-01-11T21:41:26.4265321Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4265563Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4265577Z 
2023-01-11T21:41:26.4265760Z aten = torch.ops.aten
2023-01-11T21:41:26.4266070Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4266295Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4266656Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.4266677Z 
2023-01-11T21:41:26.4266688Z 
2023-01-11T21:41:26.4267021Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4267440Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4267655Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:26.4267824Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4268000Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4268107Z {
2023-01-11T21:41:26.4268286Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4268397Z     {
2023-01-11T21:41:26.4268537Z         #pragma omp for 
2023-01-11T21:41:26.4268685Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.4268784Z         {
2023-01-11T21:41:26.4268899Z             {
2023-01-11T21:41:26.4269016Z                 {
2023-01-11T21:41:26.4269172Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.4269366Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.4269607Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.4269937Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.4270245Z                     auto tmp3 = static_cast<float>(0.55);
2023-01-11T21:41:26.4270468Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.4270713Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.4270925Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.4271165Z                     auto tmp8 = static_cast<float>(2.2222222222222223);
2023-01-11T21:41:26.4271333Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.4271479Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.4271582Z                 }
2023-01-11T21:41:26.4271697Z             }
2023-01-11T21:41:26.4271808Z         }
2023-01-11T21:41:26.4271914Z     }
2023-01-11T21:41:26.4272023Z }
2023-01-11T21:41:26.4272182Z ''')
2023-01-11T21:41:26.4272191Z 
2023-01-11T21:41:26.4272256Z 
2023-01-11T21:41:26.4272422Z async_compile.wait(globals())
2023-01-11T21:41:26.4272544Z del async_compile
2023-01-11T21:41:26.4272575Z 
2023-01-11T21:41:26.4272685Z def call(args):
2023-01-11T21:41:26.4272806Z     arg0_1, = args
2023-01-11T21:41:26.4272931Z     args.clear()
2023-01-11T21:41:26.4273175Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.4273653Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4274043Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4274207Z     del arg0_1
2023-01-11T21:41:26.4274356Z     return (buf0, )
2023-01-11T21:41:26.4274369Z 
2023-01-11T21:41:26.4274378Z 
2023-01-11T21:41:26.4274558Z if __name__ == "__main__":
2023-01-11T21:41:26.4274829Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4275095Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4275466Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4275836Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4276034Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4276043Z 
2023-01-11T21:41:26.4276050Z 
2023-01-11T21:41:26.4276217Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4276326Z import torch
2023-01-11T21:41:26.4276456Z import random
2023-01-11T21:41:26.4276662Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4276881Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4276891Z 
2023-01-11T21:41:26.4277044Z aten = torch.ops.aten
2023-01-11T21:41:26.4277346Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4277564Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4277912Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.4277949Z 
2023-01-11T21:41:26.4277958Z 
2023-01-11T21:41:26.4278253Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4278727Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4278952Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:26.4279142Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4279323Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4279435Z {
2023-01-11T21:41:26.4279612Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4279708Z     {
2023-01-11T21:41:26.4279845Z         #pragma omp for 
2023-01-11T21:41:26.4279997Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.4280131Z         {
2023-01-11T21:41:26.4280284Z             {
2023-01-11T21:41:26.4280444Z                 {
2023-01-11T21:41:26.4280654Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.4280848Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.4281091Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.4281405Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.4281729Z                     auto tmp3 = static_cast<float>(0.55);
2023-01-11T21:41:26.4281929Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.4282131Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.4282296Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.4282481Z                     auto tmp8 = static_cast<float>(2.2222222222222223);
2023-01-11T21:41:26.4282644Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.4282796Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.4282915Z                 }
2023-01-11T21:41:26.4283031Z             }
2023-01-11T21:41:26.4283154Z         }
2023-01-11T21:41:26.4283304Z     }
2023-01-11T21:41:26.4283428Z }
2023-01-11T21:41:26.4283636Z ''')
2023-01-11T21:41:26.4283652Z 
2023-01-11T21:41:26.4283733Z 
2023-01-11T21:41:26.4283942Z async_compile.wait(globals())
2023-01-11T21:41:26.4284098Z del async_compile
2023-01-11T21:41:26.4284116Z 
2023-01-11T21:41:26.4284282Z def call(args):
2023-01-11T21:41:26.4284443Z     arg0_1, = args
2023-01-11T21:41:26.4284612Z     args.clear()
2023-01-11T21:41:26.4284897Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.4285281Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4285594Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4285715Z     del arg0_1
2023-01-11T21:41:26.4285841Z     return (buf0, )
2023-01-11T21:41:26.4285851Z 
2023-01-11T21:41:26.4285857Z 
2023-01-11T21:41:26.4285995Z if __name__ == "__main__":
2023-01-11T21:41:26.4286200Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4286490Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4286924Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4287390Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4287649Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4287660Z 
2023-01-11T21:41:26.4287814Z ok (1.681s)
2023-01-11T21:41:26.4288525Z   test_dtype_mismatch_issue_cpu (__main__.CpuTests) ... [2023-01-11 21:33:32,243] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 192
2023-01-11T21:41:26.4289213Z [2023-01-11 21:33:33,824] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 192
2023-01-11T21:41:26.4289224Z 
2023-01-11T21:41:26.4289395Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4289521Z import torch
2023-01-11T21:41:26.4289681Z import random
2023-01-11T21:41:26.4289921Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4290204Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4290228Z 
2023-01-11T21:41:26.4290417Z aten = torch.ops.aten
2023-01-11T21:41:26.4290721Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4290946Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4290960Z 
2023-01-11T21:41:26.4290970Z 
2023-01-11T21:41:26.4291303Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4291694Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4291900Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.4292072Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4292248Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4292421Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4292537Z {
2023-01-11T21:41:26.4292735Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:26.4292972Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4293131Z     {
2023-01-11T21:41:26.4293297Z         #pragma omp for 
2023-01-11T21:41:26.4293494Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:26.4293767Z         {
2023-01-11T21:41:26.4293918Z             {
2023-01-11T21:41:26.4294056Z                 {
2023-01-11T21:41:26.4294579Z                     float tmp5 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.4294749Z                     for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:26.4294855Z                     {
2023-01-11T21:41:26.4294979Z                         {
2023-01-11T21:41:26.4295174Z                             auto tmp0 = static_cast<long>(i1);
2023-01-11T21:41:26.4295367Z                             auto tmp1 = static_cast<long>(63);
2023-01-11T21:41:26.4295538Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.4295720Z                             float tmp3 = 0.0;
2023-01-11T21:41:26.4295902Z                             if(tmp2)
2023-01-11T21:41:26.4296051Z                             {
2023-01-11T21:41:26.4296456Z                                 auto tmp4 = in_ptr0[i1 + (63*i0)];
2023-01-11T21:41:26.4296650Z                                 tmp3 = tmp4;
2023-01-11T21:41:26.4296822Z                             }
2023-01-11T21:41:26.4297068Z                             tmp5 = std::max(tmp5, tmp3);
2023-01-11T21:41:26.4297225Z                         }
2023-01-11T21:41:26.4297386Z                     }
2023-01-11T21:41:26.4297542Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:41:26.4297657Z                 }
2023-01-11T21:41:26.4297768Z             }
2023-01-11T21:41:26.4297885Z         }
2023-01-11T21:41:26.4298026Z         #pragma omp for 
2023-01-11T21:41:26.4298175Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:26.4298271Z         {
2023-01-11T21:41:26.4298384Z             {
2023-01-11T21:41:26.4298497Z                 {
2023-01-11T21:41:26.4298639Z                     float tmp8 = 0;
2023-01-11T21:41:26.4298802Z                     for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:26.4298960Z                     {
2023-01-11T21:41:26.4299131Z                         {
2023-01-11T21:41:26.4299334Z                             auto tmp5 = out_ptr0[i0];
2023-01-11T21:41:26.4299583Z                             auto tmp0 = static_cast<long>(i1);
2023-01-11T21:41:26.4299830Z                             auto tmp1 = static_cast<long>(63);
2023-01-11T21:41:26.4300054Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.4300254Z                             float tmp3 = 0.0;
2023-01-11T21:41:26.4300432Z                             if(tmp2)
2023-01-11T21:41:26.4300593Z                             {
2023-01-11T21:41:26.4300788Z                                 auto tmp4 = in_ptr0[i1 + (63*i0)];
2023-01-11T21:41:26.4300942Z                                 tmp3 = tmp4;
2023-01-11T21:41:26.4301064Z                             }
2023-01-11T21:41:26.4301348Z                             auto tmp6 = tmp3 - tmp5;
2023-01-11T21:41:26.4301540Z                             auto tmp7 = std::exp(tmp6);
2023-01-11T21:41:26.4301719Z                             out_ptr1[i1 + (64*i0)] = tmp7;
2023-01-11T21:41:26.4301872Z                             tmp8 += tmp7;
2023-01-11T21:41:26.4302039Z                         }
2023-01-11T21:41:26.4302174Z                     }
2023-01-11T21:41:26.4302351Z                     out_ptr2[i0] = tmp8;
2023-01-11T21:41:26.4302505Z                 }
2023-01-11T21:41:26.4302659Z             }
2023-01-11T21:41:26.4302809Z         }
2023-01-11T21:41:26.4302989Z         #pragma omp for 
2023-01-11T21:41:26.4303241Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:26.4303400Z         {
2023-01-11T21:41:26.4303597Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.4303746Z             {
2023-01-11T21:41:26.4304031Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (8*i1) + (64*i0));
2023-01-11T21:41:26.4304262Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:26.4304419Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4304616Z                 tmp2.store(in_out_ptr0 + (8*i1) + (64*i0));
2023-01-11T21:41:26.4304717Z             }
2023-01-11T21:41:26.4304965Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.4305137Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:41:26.4305297Z             {
2023-01-11T21:41:26.4305534Z                 auto tmp0 = out_ptr1[i1 + (64*i0)];
2023-01-11T21:41:26.4305747Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:26.4305950Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.4306141Z                 in_out_ptr0[i1 + (64*i0)] = tmp2;
2023-01-11T21:41:26.4306294Z             }
2023-01-11T21:41:26.4306437Z         }
2023-01-11T21:41:26.4306566Z     }
2023-01-11T21:41:26.4306711Z }
2023-01-11T21:41:26.4306909Z ''')
2023-01-11T21:41:26.4306923Z 
2023-01-11T21:41:26.4306931Z 
2023-01-11T21:41:26.4307102Z async_compile.wait(globals())
2023-01-11T21:41:26.4307216Z del async_compile
2023-01-11T21:41:26.4307226Z 
2023-01-11T21:41:26.4307437Z def call(args):
2023-01-11T21:41:26.4307562Z     arg0_1, = args
2023-01-11T21:41:26.4307690Z     args.clear()
2023-01-11T21:41:26.4308088Z     buf0 = empty_strided((128, 32, 1), (32, 1, 4096), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4308534Z     buf1 = empty_strided((128, 32, 64), (2048, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4309021Z     buf2 = empty_strided((128, 32, 1), (32, 1, 4096), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4309196Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:26.4309609Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4309774Z     del arg0_1
2023-01-11T21:41:26.4309939Z     return (buf3, )
2023-01-11T21:41:26.4309951Z 
2023-01-11T21:41:26.4309961Z 
2023-01-11T21:41:26.4310128Z if __name__ == "__main__":
2023-01-11T21:41:26.4310347Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4310576Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4310971Z     arg0_1 = rand_strided((128, 32, 63), (2016, 63, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4311153Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4311179Z 
2023-01-11T21:41:26.4311281Z ok (1.607s)
2023-01-11T21:41:26.4312284Z   test_elu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4312580Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4313208Z [2023-01-11 21:33:33,870] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 193
2023-01-11T21:41:26.4313721Z [2023-01-11 21:33:35,415] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 193
2023-01-11T21:41:26.4313731Z 
2023-01-11T21:41:26.4313905Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4314031Z import torch
2023-01-11T21:41:26.4314157Z import random
2023-01-11T21:41:26.4314351Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4314566Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4314578Z 
2023-01-11T21:41:26.4314757Z aten = torch.ops.aten
2023-01-11T21:41:26.4315046Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4315266Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4315279Z 
2023-01-11T21:41:26.4315288Z 
2023-01-11T21:41:26.4315614Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4316088Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4316368Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4316563Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4316719Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4316899Z {
2023-01-11T21:41:26.4317078Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4317187Z     {
2023-01-11T21:41:26.4317323Z         #pragma omp for 
2023-01-11T21:41:26.4317473Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.4317567Z         {
2023-01-11T21:41:26.4317693Z             {
2023-01-11T21:41:26.4317827Z                 {
2023-01-11T21:41:26.4318048Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4318301Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:26.4318514Z                     auto tmp2 = tmp0 > tmp1;
2023-01-11T21:41:26.4318784Z                     auto tmp3 = static_cast<float>(1.0507009873554805);
2023-01-11T21:41:26.4318972Z                     auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.4319200Z                     auto tmp5 = static_cast<float>(1.0);
2023-01-11T21:41:26.4319490Z                     auto tmp6 = tmp0 * tmp5;
2023-01-11T21:41:26.4319697Z                     auto tmp7 = std::expm1(tmp6);
2023-01-11T21:41:26.4319909Z                     auto tmp8 = static_cast<float>(1.7580993408473766);
2023-01-11T21:41:26.4320071Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.4320248Z                     auto tmp10 = tmp2 ? tmp4 : tmp9;
2023-01-11T21:41:26.4320435Z                     auto tmp11 = static_cast<float>(2);
2023-01-11T21:41:26.4320587Z                     auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.4320788Z                     auto tmp13 = static_cast<float>(1);
2023-01-11T21:41:26.4321011Z                     auto tmp14 = tmp0 + tmp13;
2023-01-11T21:41:26.4321232Z                     auto tmp15 = tmp14 > tmp1;
2023-01-11T21:41:26.4321465Z                     auto tmp16 = static_cast<float>(3);
2023-01-11T21:41:26.4321681Z                     auto tmp17 = tmp14 * tmp16;
2023-01-11T21:41:26.4321911Z                     auto tmp18 = static_cast<float>(4);
2023-01-11T21:41:26.4322109Z                     auto tmp19 = tmp14 * tmp18;
2023-01-11T21:41:26.4322347Z                     auto tmp20 = std::expm1(tmp19);
2023-01-11T21:41:26.4322582Z                     auto tmp21 = static_cast<float>(6);
2023-01-11T21:41:26.4322764Z                     auto tmp22 = tmp20 * tmp21;
2023-01-11T21:41:26.4322941Z                     auto tmp23 = tmp15 ? tmp17 : tmp22;
2023-01-11T21:41:26.4323091Z                     out_ptr0[i0] = tmp12;
2023-01-11T21:41:26.4323244Z                     out_ptr1[i0] = tmp23;
2023-01-11T21:41:26.4323337Z                 }
2023-01-11T21:41:26.4323450Z             }
2023-01-11T21:41:26.4323563Z         }
2023-01-11T21:41:26.4323676Z     }
2023-01-11T21:41:26.4323785Z }
2023-01-11T21:41:26.4323955Z ''')
2023-01-11T21:41:26.4323969Z 
2023-01-11T21:41:26.4323977Z 
2023-01-11T21:41:26.4324188Z async_compile.wait(globals())
2023-01-11T21:41:26.4324343Z del async_compile
2023-01-11T21:41:26.4324356Z 
2023-01-11T21:41:26.4324532Z def call(args):
2023-01-11T21:41:26.4324688Z     arg0_1, = args
2023-01-11T21:41:26.4324861Z     args.clear()
2023-01-11T21:41:26.4325342Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4325810Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4326117Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4326240Z     del arg0_1
2023-01-11T21:41:26.4326362Z     return (buf0, buf1, )
2023-01-11T21:41:26.4326371Z 
2023-01-11T21:41:26.4326379Z 
2023-01-11T21:41:26.4326516Z if __name__ == "__main__":
2023-01-11T21:41:26.4326726Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4326950Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4327396Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4327641Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4327664Z 
2023-01-11T21:41:26.4327825Z ok (1.590s)
2023-01-11T21:41:26.4328929Z   test_embedding_bag_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4329404Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4329888Z [2023-01-11 21:33:35,466] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 194
2023-01-11T21:41:26.4330326Z [2023-01-11 21:33:35,492] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._embedding_bag
2023-01-11T21:41:26.4330940Z [2023-01-11 21:33:35,498] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 194
2023-01-11T21:41:26.4331048Z 
2023-01-11T21:41:26.4331284Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4331453Z import torch
2023-01-11T21:41:26.4331636Z import random
2023-01-11T21:41:26.4331894Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4332174Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4332186Z 
2023-01-11T21:41:26.4332326Z aten = torch.ops.aten
2023-01-11T21:41:26.4332570Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4332739Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4332748Z 
2023-01-11T21:41:26.4332755Z 
2023-01-11T21:41:26.4332913Z async_compile.wait(globals())
2023-01-11T21:41:26.4333044Z del async_compile
2023-01-11T21:41:26.4333052Z 
2023-01-11T21:41:26.4333181Z def call(args):
2023-01-11T21:41:26.4333330Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4333481Z     args.clear()
2023-01-11T21:41:26.4333713Z     buf0 = aten._embedding_bag(arg0_1, arg1_1, arg2_1)
2023-01-11T21:41:26.4333891Z     del arg0_1
2023-01-11T21:41:26.4334058Z     del arg1_1
2023-01-11T21:41:26.4334211Z     del arg2_1
2023-01-11T21:41:26.4334360Z     buf1 = buf0[0]
2023-01-11T21:41:26.4334590Z     assert_size_stride(buf1, (3, 4), (4, 1))
2023-01-11T21:41:26.4334732Z     buf2 = buf0[1]
2023-01-11T21:41:26.4334946Z     assert_size_stride(buf2, (0, ), (1, ))
2023-01-11T21:41:26.4335109Z     buf3 = buf0[2]
2023-01-11T21:41:26.4335316Z     assert_size_stride(buf3, (3, ), (1, ))
2023-01-11T21:41:26.4335452Z     buf4 = buf0[3]
2023-01-11T21:41:26.4335620Z     assert_size_stride(buf4, (3, ), (1, ))
2023-01-11T21:41:26.4335737Z     del buf0
2023-01-11T21:41:26.4335876Z     return (buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.4335902Z 
2023-01-11T21:41:26.4335909Z 
2023-01-11T21:41:26.4336026Z if __name__ == "__main__":
2023-01-11T21:41:26.4336223Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4336443Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4336895Z     arg0_1 = rand_strided((10, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4337345Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4337808Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4338096Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4338107Z 
2023-01-11T21:41:26.4338243Z ok (0.084s)
2023-01-11T21:41:26.4339149Z   test_embedding_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4339379Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4339910Z [2023-01-11 21:33:35,698] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 195
2023-01-11T21:41:26.4340529Z [2023-01-11 21:33:37,904] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 195
2023-01-11T21:41:26.4340652Z 
2023-01-11T21:41:26.4340873Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4341042Z import torch
2023-01-11T21:41:26.4341215Z import random
2023-01-11T21:41:26.4341483Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4341728Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4341757Z 
2023-01-11T21:41:26.4341878Z aten = torch.ops.aten
2023-01-11T21:41:26.4342120Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4342284Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4342293Z 
2023-01-11T21:41:26.4342300Z 
2023-01-11T21:41:26.4342561Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4342998Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4343367Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4343617Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4343849Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4344054Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:41:26.4344267Z                        long* __restrict__ out_ptr2)
2023-01-11T21:41:26.4344413Z {
2023-01-11T21:41:26.4344649Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4344792Z     {
2023-01-11T21:41:26.4344948Z         #pragma omp for 
2023-01-11T21:41:26.4345081Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.4345196Z         {
2023-01-11T21:41:26.4345338Z             #pragma GCC ivdep
2023-01-11T21:41:26.4345489Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.4345602Z             {
2023-01-11T21:41:26.4345719Z                 {
2023-01-11T21:41:26.4345840Z                     {
2023-01-11T21:41:26.4345996Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4346197Z                         auto tmp1 = in_ptr1[i1 + (4*tmp0)];
2023-01-11T21:41:26.4346431Z                         auto tmp2 = tmp1 * (tmp1>0);
2023-01-11T21:41:26.4346661Z                         out_ptr0[i1 + (4*i0)] = tmp2;
2023-01-11T21:41:26.4346823Z                     }
2023-01-11T21:41:26.4346964Z                 }
2023-01-11T21:41:26.4347114Z             }
2023-01-11T21:41:26.4347245Z         }
2023-01-11T21:41:26.4347426Z         #pragma omp for 
2023-01-11T21:41:26.4347622Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4347769Z         {
2023-01-11T21:41:26.4347919Z             {
2023-01-11T21:41:26.4348046Z                 {
2023-01-11T21:41:26.4348221Z                     auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.4348391Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:26.4348554Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:41:26.4348710Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.4348826Z                 }
2023-01-11T21:41:26.4348941Z             }
2023-01-11T21:41:26.4349056Z         }
2023-01-11T21:41:26.4349201Z         #pragma omp for 
2023-01-11T21:41:26.4349330Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.4349446Z         {
2023-01-11T21:41:26.4349575Z             {
2023-01-11T21:41:26.4349687Z                 {
2023-01-11T21:41:26.4349856Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4350101Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4350321Z                     auto tmp2 = static_cast<long>(tmp1);
2023-01-11T21:41:26.4350522Z                     out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.4350676Z                 }
2023-01-11T21:41:26.4350824Z             }
2023-01-11T21:41:26.4350974Z         }
2023-01-11T21:41:26.4351121Z     }
2023-01-11T21:41:26.4351259Z }
2023-01-11T21:41:26.4351450Z ''')
2023-01-11T21:41:26.4351463Z 
2023-01-11T21:41:26.4351471Z 
2023-01-11T21:41:26.4351681Z async_compile.wait(globals())
2023-01-11T21:41:26.4351827Z del async_compile
2023-01-11T21:41:26.4351837Z 
2023-01-11T21:41:26.4351961Z def call(args):
2023-01-11T21:41:26.4352198Z     primals_1, primals_2 = args
2023-01-11T21:41:26.4352328Z     args.clear()
2023-01-11T21:41:26.4352712Z     buf0 = empty_strided((2, 8, 4), (32, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4353077Z     buf1 = empty_strided((2, 8, 4), (32, 4, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.4353412Z     buf2 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4353865Z     kernel_cpp_0(c_void_p(primals_2.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4354054Z     del primals_1
2023-01-11T21:41:26.4354232Z     del primals_2
2023-01-11T21:41:26.4354437Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.4354449Z 
2023-01-11T21:41:26.4354460Z 
2023-01-11T21:41:26.4354724Z if __name__ == "__main__":
2023-01-11T21:41:26.4354999Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4355280Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4355691Z     primals_1 = rand_strided((10, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4356069Z     primals_2 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4356299Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:26.4356309Z 
2023-01-11T21:41:26.4356428Z ok (2.404s)
2023-01-11T21:41:26.4357285Z   test_exp_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4357585Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4358200Z [2023-01-11 21:33:37,923] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 196
2023-01-11T21:41:26.4358816Z [2023-01-11 21:33:39,439] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 196
2023-01-11T21:41:26.4358827Z 
2023-01-11T21:41:26.4359048Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4359179Z import torch
2023-01-11T21:41:26.4359321Z import random
2023-01-11T21:41:26.4359527Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4359744Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4359754Z 
2023-01-11T21:41:26.4359889Z aten = torch.ops.aten
2023-01-11T21:41:26.4360130Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4360294Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4360303Z 
2023-01-11T21:41:26.4360311Z 
2023-01-11T21:41:26.4360565Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4360910Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4361170Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4361414Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4361646Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4361860Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4362005Z {
2023-01-11T21:41:26.4362237Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4362362Z     {
2023-01-11T21:41:26.4362552Z         #pragma omp for 
2023-01-11T21:41:26.4362747Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4362891Z         {
2023-01-11T21:41:26.4363152Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4363393Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.4363553Z             auto tmp1 = tmp0.exp();
2023-01-11T21:41:26.4363698Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.4363831Z             auto tmp4 = tmp3.exp();
2023-01-11T21:41:26.4364065Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4364236Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4364397Z         }
2023-01-11T21:41:26.4364624Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4364808Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.4364939Z         {
2023-01-11T21:41:26.4365141Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4365337Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.4365554Z             auto tmp1 = std::exp(tmp0);
2023-01-11T21:41:26.4365757Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.4365968Z             auto tmp4 = std::exp(tmp3);
2023-01-11T21:41:26.4366134Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.4366264Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.4366379Z         }
2023-01-11T21:41:26.4366548Z     }
2023-01-11T21:41:26.4366664Z }
2023-01-11T21:41:26.4366822Z ''')
2023-01-11T21:41:26.4366833Z 
2023-01-11T21:41:26.4366844Z 
2023-01-11T21:41:26.4367008Z async_compile.wait(globals())
2023-01-11T21:41:26.4367140Z del async_compile
2023-01-11T21:41:26.4367149Z 
2023-01-11T21:41:26.4367277Z def call(args):
2023-01-11T21:41:26.4367397Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4367556Z     args.clear()
2023-01-11T21:41:26.4368028Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4368490Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4368934Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4369229Z     del arg0_1
2023-01-11T21:41:26.4369388Z     del arg1_1
2023-01-11T21:41:26.4369531Z     return (buf0, buf1, )
2023-01-11T21:41:26.4369541Z 
2023-01-11T21:41:26.4369548Z 
2023-01-11T21:41:26.4369694Z if __name__ == "__main__":
2023-01-11T21:41:26.4369904Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4370132Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4370506Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4370880Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4371142Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4371157Z 
2023-01-11T21:41:26.4371323Z ok (1.535s)
2023-01-11T21:41:26.4372424Z   test_expand_as_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4372687Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4373179Z [2023-01-11 21:33:39,477] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 197
2023-01-11T21:41:26.4373680Z [2023-01-11 21:33:41,001] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 197
2023-01-11T21:41:26.4373690Z 
2023-01-11T21:41:26.4373862Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4373992Z import torch
2023-01-11T21:41:26.4374165Z import random
2023-01-11T21:41:26.4374434Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4374704Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4374717Z 
2023-01-11T21:41:26.4374881Z aten = torch.ops.aten
2023-01-11T21:41:26.4375191Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4375425Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4375438Z 
2023-01-11T21:41:26.4375446Z 
2023-01-11T21:41:26.4375779Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4376152Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4376478Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4376656Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4376767Z {
2023-01-11T21:41:26.4376927Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4377036Z     {
2023-01-11T21:41:26.4377238Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.4377443Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.4377573Z         {
2023-01-11T21:41:26.4377777Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.4377925Z             {
2023-01-11T21:41:26.4378109Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:41:26.4378263Z                 {
2023-01-11T21:41:26.4378604Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (100*i0));
2023-01-11T21:41:26.4379003Z                     auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4379179Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4379346Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:26.4379543Z                     tmp3.store(out_ptr0 + (8*i2) + (100*i1) + (12800*i0));
2023-01-11T21:41:26.4379645Z                 }
2023-01-11T21:41:26.4379818Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.4379977Z                 for(long i2=96; i2<100; i2+=1)
2023-01-11T21:41:26.4380098Z                 {
2023-01-11T21:41:26.4380331Z                     auto tmp0 = in_ptr0[i2 + (100*i0)];
2023-01-11T21:41:26.4380570Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4380786Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4380985Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:26.4381219Z                     out_ptr0[i2 + (100*i1) + (12800*i0)] = tmp3;
2023-01-11T21:41:26.4381377Z                 }
2023-01-11T21:41:26.4381535Z             }
2023-01-11T21:41:26.4381678Z         }
2023-01-11T21:41:26.4381822Z     }
2023-01-11T21:41:26.4381957Z }
2023-01-11T21:41:26.4382111Z ''')
2023-01-11T21:41:26.4382121Z 
2023-01-11T21:41:26.4382144Z 
2023-01-11T21:41:26.4382291Z async_compile.wait(globals())
2023-01-11T21:41:26.4382420Z del async_compile
2023-01-11T21:41:26.4382430Z 
2023-01-11T21:41:26.4382557Z def call(args):
2023-01-11T21:41:26.4382693Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4382823Z     args.clear()
2023-01-11T21:41:26.4383327Z     buf0 = empty_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4383635Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4383864Z     return (as_strided(arg0_1, (6, 128, 100), (100, 0, 1)), buf0, )
2023-01-11T21:41:26.4383877Z 
2023-01-11T21:41:26.4383910Z 
2023-01-11T21:41:26.4384067Z if __name__ == "__main__":
2023-01-11T21:41:26.4384337Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4384625Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4385106Z     arg0_1 = rand_strided((6, 1, 100), (100, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4385506Z     arg1_1 = rand_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4385715Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4385727Z 
2023-01-11T21:41:26.4385844Z ok (1.564s)
2023-01-11T21:41:26.4386797Z   test_expand_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4387066Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4387687Z [2023-01-11 21:33:41,031] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 198
2023-01-11T21:41:26.4388357Z [2023-01-11 21:33:42,591] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 198
2023-01-11T21:41:26.4388366Z 
2023-01-11T21:41:26.4388535Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4388661Z import torch
2023-01-11T21:41:26.4388788Z import random
2023-01-11T21:41:26.4388988Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4389206Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4389215Z 
2023-01-11T21:41:26.4389339Z aten = torch.ops.aten
2023-01-11T21:41:26.4389619Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4389830Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4389841Z 
2023-01-11T21:41:26.4389847Z 
2023-01-11T21:41:26.4390175Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4390740Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4391025Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4391250Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4391442Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4391534Z {
2023-01-11T21:41:26.4391710Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4391821Z     {
2023-01-11T21:41:26.4391959Z         #pragma omp for 
2023-01-11T21:41:26.4392106Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.4392217Z         {
2023-01-11T21:41:26.4392359Z             #pragma GCC ivdep
2023-01-11T21:41:26.4392494Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.4392633Z             {
2023-01-11T21:41:26.4392817Z                 #pragma GCC ivdep
2023-01-11T21:41:26.4393026Z                 for(long i2=0; i2<3; i2+=1)
2023-01-11T21:41:26.4393187Z                 {
2023-01-11T21:41:26.4393389Z                     #pragma GCC ivdep
2023-01-11T21:41:26.4393597Z                     for(long i3=0; i3<2; i3+=1)
2023-01-11T21:41:26.4393740Z                     {
2023-01-11T21:41:26.4393908Z                         {
2023-01-11T21:41:26.4394062Z                             {
2023-01-11T21:41:26.4394299Z                                 auto tmp0 = in_ptr0[i3 + (2*i1)];
2023-01-11T21:41:26.4394530Z                                 auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4394706Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4394903Z                                 auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.4395060Z                                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.4395254Z                                 out_ptr0[i3 + (2*i2) + (6*i1) + (12*i0)] = tmp4;
2023-01-11T21:41:26.4395377Z                             }
2023-01-11T21:41:26.4395499Z                         }
2023-01-11T21:41:26.4395616Z                     }
2023-01-11T21:41:26.4395737Z                 }
2023-01-11T21:41:26.4395874Z             }
2023-01-11T21:41:26.4396001Z         }
2023-01-11T21:41:26.4396224Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.4396435Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.4396579Z         {
2023-01-11T21:41:26.4396774Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.4396918Z             {
2023-01-11T21:41:26.4397111Z                 #pragma GCC ivdep
2023-01-11T21:41:26.4397297Z                 for(long i2=0; i2<3; i2+=1)
2023-01-11T21:41:26.4397447Z                 {
2023-01-11T21:41:26.4397636Z                     #pragma GCC ivdep
2023-01-11T21:41:26.4397811Z                     for(long i3=0; i3<2; i3+=1)
2023-01-11T21:41:26.4397935Z                     {
2023-01-11T21:41:26.4398054Z                         {
2023-01-11T21:41:26.4398162Z                             {
2023-01-11T21:41:26.4398347Z                                 auto tmp0 = in_ptr0[i3 + (2*i1)];
2023-01-11T21:41:26.4398548Z                                 auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.4398724Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4399001Z                                 out_ptr1[i3 + (2*i2) + (6*i1) + (12*i0)] = tmp2;
2023-01-11T21:41:26.4399171Z                             }
2023-01-11T21:41:26.4399340Z                         }
2023-01-11T21:41:26.4399502Z                     }
2023-01-11T21:41:26.4399636Z                 }
2023-01-11T21:41:26.4399766Z             }
2023-01-11T21:41:26.4399916Z         }
2023-01-11T21:41:26.4400060Z     }
2023-01-11T21:41:26.4400201Z }
2023-01-11T21:41:26.4400412Z ''')
2023-01-11T21:41:26.4400424Z 
2023-01-11T21:41:26.4400432Z 
2023-01-11T21:41:26.4400638Z async_compile.wait(globals())
2023-01-11T21:41:26.4400789Z del async_compile
2023-01-11T21:41:26.4400801Z 
2023-01-11T21:41:26.4400947Z def call(args):
2023-01-11T21:41:26.4401074Z     arg0_1, = args
2023-01-11T21:41:26.4401202Z     args.clear()
2023-01-11T21:41:26.4401687Z     buf0 = empty_strided((3, 4, 2, 3, 2), (48, 12, 6, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4402095Z     buf1 = empty_strided((2, 1, 2, 3, 2), (12, 12, 6, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4402461Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4402699Z     return (buf0, buf1, as_strided(arg0_1, (2, 2, 5, 2), (0, 2, 0, 1)), )
2023-01-11T21:41:26.4402738Z 
2023-01-11T21:41:26.4402747Z 
2023-01-11T21:41:26.4402906Z if __name__ == "__main__":
2023-01-11T21:41:26.4403175Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4403466Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4403949Z     arg0_1 = rand_strided((2, 1, 2), (2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4404159Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4404169Z 
2023-01-11T21:41:26.4404287Z ok (1.588s)
2023-01-11T21:41:26.4405600Z   test_expanded_reduction_cpu (__main__.CpuTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/87157 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:41:26.4406699Z   test_expm1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4406982Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4407485Z [2023-01-11 21:33:42,612] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 199
2023-01-11T21:41:26.4407978Z [2023-01-11 21:33:44,147] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 199
2023-01-11T21:41:26.4408861Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4409334Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4409947Z [2023-01-11 21:33:44,166] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 200
2023-01-11T21:41:26.4410535Z [2023-01-11 21:33:45,671] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 200
2023-01-11T21:41:26.4411325Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4411654Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4412194Z [2023-01-11 21:33:45,846] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 201
2023-01-11T21:41:26.4412788Z [2023-01-11 21:33:47,387] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 201
2023-01-11T21:41:26.4413852Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4414089Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4414573Z [2023-01-11 21:33:47,404] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 202
2023-01-11T21:41:26.4415043Z [2023-01-11 21:33:49,097] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 202
2023-01-11T21:41:26.4415977Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4416265Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4416888Z [2023-01-11 21:33:49,114] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 203
2023-01-11T21:41:26.4416906Z 
2023-01-11T21:41:26.4417088Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4417219Z import torch
2023-01-11T21:41:26.4417352Z import random
2023-01-11T21:41:26.4417564Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4417784Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4417794Z 
2023-01-11T21:41:26.4417918Z aten = torch.ops.aten
2023-01-11T21:41:26.4418162Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4418351Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4418364Z 
2023-01-11T21:41:26.4418372Z 
2023-01-11T21:41:26.4418685Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4419156Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4419435Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:41:26.4419662Z                        half* __restrict__ out_ptr0,
2023-01-11T21:41:26.4419875Z                        half* __restrict__ out_ptr1)
2023-01-11T21:41:26.4419998Z {
2023-01-11T21:41:26.4420203Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4420322Z     {
2023-01-11T21:41:26.4420464Z         #pragma omp for 
2023-01-11T21:41:26.4420612Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4420725Z         {
2023-01-11T21:41:26.4420839Z             {
2023-01-11T21:41:26.4420943Z                 {
2023-01-11T21:41:26.4421149Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:41:26.4421344Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:26.4421578Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:41:26.4421789Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.4421997Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.4422204Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4422337Z                 }
2023-01-11T21:41:26.4422475Z             }
2023-01-11T21:41:26.4422625Z         }
2023-01-11T21:41:26.4422776Z     }
2023-01-11T21:41:26.4422914Z }
2023-01-11T21:41:26.4423113Z ''')
2023-01-11T21:41:26.4423218Z 
2023-01-11T21:41:26.4423326Z 
2023-01-11T21:41:26.4423513Z async_compile.wait(globals())
2023-01-11T21:41:26.4423628Z del async_compile
2023-01-11T21:41:26.4423638Z 
2023-01-11T21:41:26.4423764Z def call(args):
2023-01-11T21:41:26.4423887Z     arg0_1, = args
2023-01-11T21:41:26.4424013Z     args.clear()
2023-01-11T21:41:26.4424385Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.4424740Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.4425118Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4425261Z     del arg0_1
2023-01-11T21:41:26.4425441Z     return (buf0, buf1, )
2023-01-11T21:41:26.4425454Z 
2023-01-11T21:41:26.4425463Z 
2023-01-11T21:41:26.4425634Z if __name__ == "__main__":
2023-01-11T21:41:26.4425969Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4426198Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4426554Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.4426743Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4426753Z 
2023-01-11T21:41:26.4426759Z 
2023-01-11T21:41:26.4426922Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4427038Z import torch
2023-01-11T21:41:26.4427191Z import random
2023-01-11T21:41:26.4427455Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4427725Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4427739Z 
2023-01-11T21:41:26.4427929Z aten = torch.ops.aten
2023-01-11T21:41:26.4428221Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4428447Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4428462Z 
2023-01-11T21:41:26.4428469Z 
2023-01-11T21:41:26.4428798Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4429213Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4429498Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:41:26.4429724Z                        half* __restrict__ out_ptr0,
2023-01-11T21:41:26.4429943Z                        half* __restrict__ out_ptr1)
2023-01-11T21:41:26.4430083Z {
2023-01-11T21:41:26.4430308Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4430457Z     {
2023-01-11T21:41:26.4430617Z         #pragma omp for 
2023-01-11T21:41:26.4430815Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:26.4430968Z         {
2023-01-11T21:41:26.4431109Z             {
2023-01-11T21:41:26.4431252Z                 {
2023-01-11T21:41:26.4431522Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:41:26.4431762Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:26.4431982Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:41:26.4432215Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.4432412Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.4432625Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4432818Z                 }
2023-01-11T21:41:26.4432969Z             }
2023-01-11T21:41:26.4433116Z         }
2023-01-11T21:41:26.4433228Z     }
2023-01-11T21:41:26.4433363Z }
2023-01-11T21:41:26.4433579Z ''')
2023-01-11T21:41:26.4433590Z 
2023-01-11T21:41:26.4433598Z 
2023-01-11T21:41:26.4433804Z async_compile.wait(globals())
2023-01-11T21:41:26.4433975Z del async_compile
2023-01-11T21:41:26.4433991Z 
2023-01-11T21:41:26.4434151Z def call(args):
2023-01-11T21:41:26.4434305Z     arg0_1, = args
2023-01-11T21:41:26.4434449Z     args.clear()
2023-01-11T21:41:26.4434908Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.4435346Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.4435728Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4435889Z     del arg0_1
2023-01-11T21:41:26.4436185Z     return (buf0, buf1, )
2023-01-11T21:41:26.4436196Z 
2023-01-11T21:41:26.4436202Z 
2023-01-11T21:41:26.4436373Z if __name__ == "__main__":
2023-01-11T21:41:26.4436644Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4436898Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4437348Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.4437602Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4437616Z 
2023-01-11T21:41:26.4437623Z 
2023-01-11T21:41:26.4437834Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4437995Z import torch
2023-01-11T21:41:26.4438162Z import random
2023-01-11T21:41:26.4438416Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4438783Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4438799Z 
2023-01-11T21:41:26.4438960Z aten = torch.ops.aten
2023-01-11T21:41:26.4439253Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4439471Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4439482Z 
2023-01-11T21:41:26.4439491Z 
2023-01-11T21:41:26.4439816Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4440251Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4440535Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4440762Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4440980Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4441102Z {
2023-01-11T21:41:26.4441314Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4441457Z     {
2023-01-11T21:41:26.4441643Z         #pragma omp for 
2023-01-11T21:41:26.4441840Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4441993Z         {
2023-01-11T21:41:26.4442291Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4442475Z             auto tmp1 = tmp0.expm1();
2023-01-11T21:41:26.4442780Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.4442980Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.4443193Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4443386Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4443540Z         }
2023-01-11T21:41:26.4443758Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4443932Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.4444079Z         {
2023-01-11T21:41:26.4444272Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4444486Z             auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:26.4444711Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:26.4444906Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.4445101Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.4445262Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4445391Z         }
2023-01-11T21:41:26.4445545Z     }
2023-01-11T21:41:26.4445685Z }
2023-01-11T21:41:26.4445961Z ''')
2023-01-11T21:41:26.4445971Z 
2023-01-11T21:41:26.4445978Z 
2023-01-11T21:41:26.4446139Z async_compile.wait(globals())
2023-01-11T21:41:26.4446269Z del async_compile
2023-01-11T21:41:26.4446278Z 
2023-01-11T21:41:26.4446369Z def call(args):
2023-01-11T21:41:26.4446492Z     arg0_1, = args
2023-01-11T21:41:26.4446623Z     args.clear()
2023-01-11T21:41:26.4446972Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4447294Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4447573Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4447696Z     del arg0_1
2023-01-11T21:41:26.4447815Z     return (buf0, buf1, )
2023-01-11T21:41:26.4447844Z 
2023-01-11T21:41:26.4447854Z 
2023-01-11T21:41:26.4447975Z if __name__ == "__main__":
2023-01-11T21:41:26.4448165Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4448478Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4448826Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4449136Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4449144Z 
2023-01-11T21:41:26.4449150Z 
2023-01-11T21:41:26.4449323Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4449462Z import torch
2023-01-11T21:41:26.4449588Z import random
2023-01-11T21:41:26.4449763Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4449957Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4449968Z 
2023-01-11T21:41:26.4450107Z aten = torch.ops.aten
2023-01-11T21:41:26.4450338Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4450621Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4450631Z 
2023-01-11T21:41:26.4450637Z 
2023-01-11T21:41:26.4450889Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4451236Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4451445Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4451602Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4451757Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4451865Z {
2023-01-11T21:41:26.4452035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4452152Z     {
2023-01-11T21:41:26.4452292Z         #pragma omp for 
2023-01-11T21:41:26.4452427Z         for(long i0=0; i0<25; i0+=1)
2023-01-11T21:41:26.4452530Z         {
2023-01-11T21:41:26.4452771Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4452928Z             auto tmp1 = tmp0.expm1();
2023-01-11T21:41:26.4453158Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.4453312Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.4453463Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4453630Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4453728Z         }
2023-01-11T21:41:26.4453889Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4454033Z         for(long i0=200; i0<201; i0+=1)
2023-01-11T21:41:26.4454145Z         {
2023-01-11T21:41:26.4454287Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4454460Z             auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:26.4454632Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:26.4454764Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.4454909Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.4455048Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4455158Z         }
2023-01-11T21:41:26.4455277Z     }
2023-01-11T21:41:26.4455389Z }
2023-01-11T21:41:26.4455534Z ''')
2023-01-11T21:41:26.4455570Z 
2023-01-11T21:41:26.4455577Z 
2023-01-11T21:41:26.4455719Z async_compile.wait(globals())
2023-01-11T21:41:26.4455851Z del async_compile
2023-01-11T21:41:26.4455859Z 
2023-01-11T21:41:26.4455972Z def call(args):
2023-01-11T21:41:26.4456098Z     arg0_1, = args
2023-01-11T21:41:26.4456220Z     args.clear()
2023-01-11T21:41:26.4456571Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4456894Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4457157Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4457284Z     del arg0_1
2023-01-11T21:41:26.4457417Z     return (buf0, buf1, )
2023-01-11T21:41:26.4457427Z 
2023-01-11T21:41:26.4457434Z 
2023-01-11T21:41:26.4457569Z if __name__ == "__main__":
2023-01-11T21:41:26.4457760Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4457984Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4458334Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4458631Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4458640Z 
2023-01-11T21:41:26.4459091Z [2023-01-11 21:33:50,642] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 203
2023-01-11T21:41:26.4459790Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4460012Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4460449Z [2023-01-11 21:33:50,660] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 204
2023-01-11T21:41:26.4460998Z [2023-01-11 21:33:52,176] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 204
2023-01-11T21:41:26.4461705Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4461914Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4462369Z [2023-01-11 21:33:52,193] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 205
2023-01-11T21:41:26.4462826Z [2023-01-11 21:33:53,681] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 205
2023-01-11T21:41:26.4463605Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4463823Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4464268Z [2023-01-11 21:33:53,699] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 206
2023-01-11T21:41:26.4464680Z [2023-01-11 21:33:55,241] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 206
2023-01-11T21:41:26.4465370Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4465591Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4466037Z [2023-01-11 21:33:55,267] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 207
2023-01-11T21:41:26.4466056Z 
2023-01-11T21:41:26.4466210Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4466342Z import torch
2023-01-11T21:41:26.4466475Z import random
2023-01-11T21:41:26.4466684Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4466901Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4466911Z 
2023-01-11T21:41:26.4467020Z aten = torch.ops.aten
2023-01-11T21:41:26.4467255Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4467411Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4467420Z 
2023-01-11T21:41:26.4467427Z 
2023-01-11T21:41:26.4467672Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4468002Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4468215Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.4468499Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:26.4468676Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:26.4468751Z {
2023-01-11T21:41:26.4468930Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4469053Z     {
2023-01-11T21:41:26.4469198Z         #pragma omp for 
2023-01-11T21:41:26.4469346Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4469466Z         {
2023-01-11T21:41:26.4469574Z             {
2023-01-11T21:41:26.4469662Z                 {
2023-01-11T21:41:26.4469821Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4470014Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:26.4470196Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:41:26.4470361Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.4470601Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.4470758Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4470864Z                 }
2023-01-11T21:41:26.4470986Z             }
2023-01-11T21:41:26.4471100Z         }
2023-01-11T21:41:26.4471215Z     }
2023-01-11T21:41:26.4471324Z }
2023-01-11T21:41:26.4471474Z ''')
2023-01-11T21:41:26.4471483Z 
2023-01-11T21:41:26.4471489Z 
2023-01-11T21:41:26.4471649Z async_compile.wait(globals())
2023-01-11T21:41:26.4471759Z del async_compile
2023-01-11T21:41:26.4471769Z 
2023-01-11T21:41:26.4471896Z def call(args):
2023-01-11T21:41:26.4472022Z     arg0_1, = args
2023-01-11T21:41:26.4472148Z     args.clear()
2023-01-11T21:41:26.4472479Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.4472819Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.4473081Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4473189Z     del arg0_1
2023-01-11T21:41:26.4473328Z     return (buf0, buf1, )
2023-01-11T21:41:26.4473336Z 
2023-01-11T21:41:26.4473352Z 
2023-01-11T21:41:26.4473486Z if __name__ == "__main__":
2023-01-11T21:41:26.4473682Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4473895Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4474222Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.4474404Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4474417Z 
2023-01-11T21:41:26.4474423Z 
2023-01-11T21:41:26.4474590Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4474715Z import torch
2023-01-11T21:41:26.4474808Z import random
2023-01-11T21:41:26.4475000Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4475205Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4475213Z 
2023-01-11T21:41:26.4475359Z aten = torch.ops.aten
2023-01-11T21:41:26.4475590Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4475736Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4475753Z 
2023-01-11T21:41:26.4475760Z 
2023-01-11T21:41:26.4476007Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4476325Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4476525Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.4476706Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:26.4476882Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:26.4476993Z {
2023-01-11T21:41:26.4477169Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4477283Z     {
2023-01-11T21:41:26.4477387Z         #pragma omp for 
2023-01-11T21:41:26.4477534Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:26.4477652Z         {
2023-01-11T21:41:26.4477771Z             {
2023-01-11T21:41:26.4477882Z                 {
2023-01-11T21:41:26.4478053Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4478222Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:41:26.4478522Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:41:26.4478683Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.4478841Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.4478999Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.4479105Z                 }
2023-01-11T21:41:26.4479226Z             }
2023-01-11T21:41:26.4479342Z         }
2023-01-11T21:41:26.4479438Z     }
2023-01-11T21:41:26.4479549Z }
2023-01-11T21:41:26.4479715Z ''')
2023-01-11T21:41:26.4479724Z 
2023-01-11T21:41:26.4479732Z 
2023-01-11T21:41:26.4479886Z async_compile.wait(globals())
2023-01-11T21:41:26.4479999Z del async_compile
2023-01-11T21:41:26.4480012Z 
2023-01-11T21:41:26.4480142Z def call(args):
2023-01-11T21:41:26.4480265Z     arg0_1, = args
2023-01-11T21:41:26.4480371Z     args.clear()
2023-01-11T21:41:26.4480799Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.4481130Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.4481410Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4481539Z     del arg0_1
2023-01-11T21:41:26.4481681Z     return (buf0, buf1, )
2023-01-11T21:41:26.4481688Z 
2023-01-11T21:41:26.4481697Z 
2023-01-11T21:41:26.4481829Z if __name__ == "__main__":
2023-01-11T21:41:26.4482023Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4482219Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4482553Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.4482735Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4482749Z 
2023-01-11T21:41:26.4482754Z 
2023-01-11T21:41:26.4482927Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4483058Z import torch
2023-01-11T21:41:26.4483189Z import random
2023-01-11T21:41:26.4483382Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4483585Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4483593Z 
2023-01-11T21:41:26.4483713Z aten = torch.ops.aten
2023-01-11T21:41:26.4483949Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4484114Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4484122Z 
2023-01-11T21:41:26.4484128Z 
2023-01-11T21:41:26.4484359Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4484689Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4484890Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.4485064Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4485224Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4485318Z {
2023-01-11T21:41:26.4485497Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4485609Z     {
2023-01-11T21:41:26.4485748Z         #pragma omp for 
2023-01-11T21:41:26.4485904Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4486011Z         {
2023-01-11T21:41:26.4486130Z             {
2023-01-11T21:41:26.4486236Z                 {
2023-01-11T21:41:26.4486399Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4486586Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4486769Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:26.4486939Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.4487109Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.4487260Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4487393Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.4487512Z                 }
2023-01-11T21:41:26.4487629Z             }
2023-01-11T21:41:26.4487733Z         }
2023-01-11T21:41:26.4487850Z     }
2023-01-11T21:41:26.4487966Z }
2023-01-11T21:41:26.4488105Z ''')
2023-01-11T21:41:26.4488138Z 
2023-01-11T21:41:26.4488146Z 
2023-01-11T21:41:26.4488385Z async_compile.wait(globals())
2023-01-11T21:41:26.4488519Z del async_compile
2023-01-11T21:41:26.4488527Z 
2023-01-11T21:41:26.4488639Z def call(args):
2023-01-11T21:41:26.4488768Z     arg0_1, = args
2023-01-11T21:41:26.4488902Z     args.clear()
2023-01-11T21:41:26.4489383Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4489707Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4489959Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4490086Z     del arg0_1
2023-01-11T21:41:26.4490227Z     return (buf0, buf1, )
2023-01-11T21:41:26.4490236Z 
2023-01-11T21:41:26.4490241Z 
2023-01-11T21:41:26.4490378Z if __name__ == "__main__":
2023-01-11T21:41:26.4490677Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4490892Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4491224Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.4491398Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4491408Z 
2023-01-11T21:41:26.4491414Z 
2023-01-11T21:41:26.4491557Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4491683Z import torch
2023-01-11T21:41:26.4491818Z import random
2023-01-11T21:41:26.4492027Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4492224Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4492232Z 
2023-01-11T21:41:26.4492344Z aten = torch.ops.aten
2023-01-11T21:41:26.4492575Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4492735Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4492744Z 
2023-01-11T21:41:26.4492749Z 
2023-01-11T21:41:26.4492973Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4493310Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4493510Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.4493689Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4493858Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4493968Z {
2023-01-11T21:41:26.4494126Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4494223Z     {
2023-01-11T21:41:26.4494364Z         #pragma omp for 
2023-01-11T21:41:26.4494510Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:26.4494624Z         {
2023-01-11T21:41:26.4494742Z             {
2023-01-11T21:41:26.4494858Z                 {
2023-01-11T21:41:26.4495009Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4495183Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4495367Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:26.4495549Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.4495711Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.4495843Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4496000Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.4496119Z                 }
2023-01-11T21:41:26.4496218Z             }
2023-01-11T21:41:26.4496334Z         }
2023-01-11T21:41:26.4496444Z     }
2023-01-11T21:41:26.4496550Z }
2023-01-11T21:41:26.4496696Z ''')
2023-01-11T21:41:26.4496708Z 
2023-01-11T21:41:26.4496713Z 
2023-01-11T21:41:26.4496873Z async_compile.wait(globals())
2023-01-11T21:41:26.4497006Z del async_compile
2023-01-11T21:41:26.4497013Z 
2023-01-11T21:41:26.4497119Z def call(args):
2023-01-11T21:41:26.4497243Z     arg0_1, = args
2023-01-11T21:41:26.4497369Z     args.clear()
2023-01-11T21:41:26.4497697Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4498040Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4498323Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4498560Z     del arg0_1
2023-01-11T21:41:26.4498710Z     return (buf0, buf1, )
2023-01-11T21:41:26.4498725Z 
2023-01-11T21:41:26.4498730Z 
2023-01-11T21:41:26.4498844Z if __name__ == "__main__":
2023-01-11T21:41:26.4499036Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4499254Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4499589Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.4499776Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4499784Z 
2023-01-11T21:41:26.4500240Z [2023-01-11 21:33:56,792] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 207
2023-01-11T21:41:26.4501033Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4501259Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4501714Z [2023-01-11 21:33:56,809] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 208
2023-01-11T21:41:26.4502149Z [2023-01-11 21:33:58,300] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 208
2023-01-11T21:41:26.4502179Z 
2023-01-11T21:41:26.4502325Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4502458Z import torch
2023-01-11T21:41:26.4502592Z import random
2023-01-11T21:41:26.4502800Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4503009Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4503017Z 
2023-01-11T21:41:26.4503231Z aten = torch.ops.aten
2023-01-11T21:41:26.4503458Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4503606Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4503614Z 
2023-01-11T21:41:26.4503636Z 
2023-01-11T21:41:26.4503852Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4504196Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4504401Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4504572Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4504735Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4504852Z {
2023-01-11T21:41:26.4505029Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4505124Z     {
2023-01-11T21:41:26.4505269Z         #pragma omp for 
2023-01-11T21:41:26.4505414Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4505526Z         {
2023-01-11T21:41:26.4505641Z             {
2023-01-11T21:41:26.4505769Z                 {
2023-01-11T21:41:26.4505907Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4506094Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4506285Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:26.4506468Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.4506629Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.4506782Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4506932Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.4507046Z                 }
2023-01-11T21:41:26.4507140Z             }
2023-01-11T21:41:26.4507256Z         }
2023-01-11T21:41:26.4507372Z     }
2023-01-11T21:41:26.4507476Z }
2023-01-11T21:41:26.4507633Z ''')
2023-01-11T21:41:26.4507647Z 
2023-01-11T21:41:26.4507652Z 
2023-01-11T21:41:26.4507806Z async_compile.wait(globals())
2023-01-11T21:41:26.4507932Z del async_compile
2023-01-11T21:41:26.4507944Z 
2023-01-11T21:41:26.4508054Z def call(args):
2023-01-11T21:41:26.4508181Z     arg0_1, = args
2023-01-11T21:41:26.4508308Z     args.clear()
2023-01-11T21:41:26.4508764Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4509086Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4509357Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4509480Z     del arg0_1
2023-01-11T21:41:26.4509594Z     return (buf0, buf1, )
2023-01-11T21:41:26.4509602Z 
2023-01-11T21:41:26.4509623Z 
2023-01-11T21:41:26.4509739Z if __name__ == "__main__":
2023-01-11T21:41:26.4509939Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4510156Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4510487Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4510678Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4510763Z 
2023-01-11T21:41:26.4510772Z 
2023-01-11T21:41:26.4510941Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4511066Z import torch
2023-01-11T21:41:26.4511177Z import random
2023-01-11T21:41:26.4511370Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4511590Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4511598Z 
2023-01-11T21:41:26.4511742Z aten = torch.ops.aten
2023-01-11T21:41:26.4511975Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4512134Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4512142Z 
2023-01-11T21:41:26.4512148Z 
2023-01-11T21:41:26.4512384Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4512718Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4512906Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4513070Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4513251Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4513368Z {
2023-01-11T21:41:26.4513540Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4513656Z     {
2023-01-11T21:41:26.4513792Z         #pragma omp for 
2023-01-11T21:41:26.4513920Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:26.4514035Z         {
2023-01-11T21:41:26.4514153Z             {
2023-01-11T21:41:26.4514268Z                 {
2023-01-11T21:41:26.4514429Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4514619Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.4514801Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:41:26.4514963Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.4515120Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.4515266Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4515416Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.4515539Z                 }
2023-01-11T21:41:26.4515650Z             }
2023-01-11T21:41:26.4515760Z         }
2023-01-11T21:41:26.4515854Z     }
2023-01-11T21:41:26.4515963Z }
2023-01-11T21:41:26.4516123Z ''')
2023-01-11T21:41:26.4516134Z 
2023-01-11T21:41:26.4516139Z 
2023-01-11T21:41:26.4516300Z async_compile.wait(globals())
2023-01-11T21:41:26.4516427Z del async_compile
2023-01-11T21:41:26.4516434Z 
2023-01-11T21:41:26.4516561Z def call(args):
2023-01-11T21:41:26.4516684Z     arg0_1, = args
2023-01-11T21:41:26.4516794Z     args.clear()
2023-01-11T21:41:26.4517138Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4517476Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4517749Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4517877Z     del arg0_1
2023-01-11T21:41:26.4518015Z     return (buf0, buf1, )
2023-01-11T21:41:26.4518023Z 
2023-01-11T21:41:26.4518028Z 
2023-01-11T21:41:26.4518162Z if __name__ == "__main__":
2023-01-11T21:41:26.4518362Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4518650Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4518986Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4519174Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4519182Z 
2023-01-11T21:41:26.4519308Z ok (15.707s)
2023-01-11T21:41:26.4520072Z   test_fill1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4520282Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4520817Z [2023-01-11 21:33:58,343] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 209
2023-01-11T21:41:26.4521278Z [2023-01-11 21:33:59,935] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 209
2023-01-11T21:41:26.4521289Z 
2023-01-11T21:41:26.4521444Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4521553Z import torch
2023-01-11T21:41:26.4521677Z import random
2023-01-11T21:41:26.4521880Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4522093Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4522101Z 
2023-01-11T21:41:26.4522241Z aten = torch.ops.aten
2023-01-11T21:41:26.4522462Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4522621Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4522635Z 
2023-01-11T21:41:26.4522641Z 
2023-01-11T21:41:26.4522887Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4523210Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4523410Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4523589Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4523701Z {
2023-01-11T21:41:26.4523876Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4523977Z     {
2023-01-11T21:41:26.4524120Z         #pragma omp for 
2023-01-11T21:41:26.4524251Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.4524366Z         {
2023-01-11T21:41:26.4524610Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4524780Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4524894Z         }
2023-01-11T21:41:26.4525065Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4525213Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.4525313Z         {
2023-01-11T21:41:26.4525478Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:41:26.4525627Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.4525737Z         }
2023-01-11T21:41:26.4525879Z         #pragma omp for 
2023-01-11T21:41:26.4526034Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.4526146Z         {
2023-01-11T21:41:26.4526374Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.4526532Z             tmp0.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4526646Z         }
2023-01-11T21:41:26.4526820Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4526973Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.4527091Z         {
2023-01-11T21:41:26.4527268Z             auto tmp0 = static_cast<float>(2);
2023-01-11T21:41:26.4527383Z             out_ptr1[i0] = tmp0;
2023-01-11T21:41:26.4527497Z         }
2023-01-11T21:41:26.4527623Z     }
2023-01-11T21:41:26.4527736Z }
2023-01-11T21:41:26.4527898Z ''')
2023-01-11T21:41:26.4527909Z 
2023-01-11T21:41:26.4527914Z 
2023-01-11T21:41:26.4528073Z async_compile.wait(globals())
2023-01-11T21:41:26.4528209Z del async_compile
2023-01-11T21:41:26.4528218Z 
2023-01-11T21:41:26.4528326Z def call(args):
2023-01-11T21:41:26.4528451Z     arg0_1, = args
2023-01-11T21:41:26.4528678Z     args.clear()
2023-01-11T21:41:26.4529157Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4529503Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4529732Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4529869Z     return (buf0, buf1, )
2023-01-11T21:41:26.4529880Z 
2023-01-11T21:41:26.4529889Z 
2023-01-11T21:41:26.4530016Z if __name__ == "__main__":
2023-01-11T21:41:26.4530193Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4530408Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4530763Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4531069Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4531080Z 
2023-01-11T21:41:26.4531208Z ok (1.635s)
2023-01-11T21:41:26.4531963Z   test_fill2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4532195Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4532656Z [2023-01-11 21:33:59,982] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 210
2023-01-11T21:41:26.4533124Z [2023-01-11 21:34:01,500] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 210
2023-01-11T21:41:26.4533138Z 
2023-01-11T21:41:26.4533276Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4533407Z import torch
2023-01-11T21:41:26.4533529Z import random
2023-01-11T21:41:26.4533734Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4533950Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4533958Z 
2023-01-11T21:41:26.4534096Z aten = torch.ops.aten
2023-01-11T21:41:26.4534319Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4534481Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4534495Z 
2023-01-11T21:41:26.4534501Z 
2023-01-11T21:41:26.4534729Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4535060Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4535261Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4535439Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4535546Z {
2023-01-11T21:41:26.4535719Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4535827Z     {
2023-01-11T21:41:26.4535954Z         #pragma omp for 
2023-01-11T21:41:26.4536101Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.4536220Z         {
2023-01-11T21:41:26.4536468Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4536626Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4536742Z         }
2023-01-11T21:41:26.4536914Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4537042Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.4537156Z         {
2023-01-11T21:41:26.4537331Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:41:26.4537469Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.4537583Z         }
2023-01-11T21:41:26.4537724Z         #pragma omp for 
2023-01-11T21:41:26.4537873Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.4537972Z         {
2023-01-11T21:41:26.4538221Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(3.0));
2023-01-11T21:41:26.4538391Z             tmp0.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4538512Z         }
2023-01-11T21:41:26.4538676Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4538954Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.4539066Z         {
2023-01-11T21:41:26.4539222Z             auto tmp0 = static_cast<float>(3.0);
2023-01-11T21:41:26.4539372Z             out_ptr1[i0] = tmp0;
2023-01-11T21:41:26.4539487Z         }
2023-01-11T21:41:26.4539597Z     }
2023-01-11T21:41:26.4539716Z }
2023-01-11T21:41:26.4539882Z ''')
2023-01-11T21:41:26.4539894Z 
2023-01-11T21:41:26.4539901Z 
2023-01-11T21:41:26.4540051Z async_compile.wait(globals())
2023-01-11T21:41:26.4540163Z del async_compile
2023-01-11T21:41:26.4540171Z 
2023-01-11T21:41:26.4540299Z def call(args):
2023-01-11T21:41:26.4540431Z     arg0_1, = args
2023-01-11T21:41:26.4540559Z     args.clear()
2023-01-11T21:41:26.4540903Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4541349Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4541579Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4541707Z     return (buf0, buf1, )
2023-01-11T21:41:26.4541739Z 
2023-01-11T21:41:26.4541745Z 
2023-01-11T21:41:26.4541860Z if __name__ == "__main__":
2023-01-11T21:41:26.4542059Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4542280Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4542622Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4542811Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4542824Z 
2023-01-11T21:41:26.4542947Z ok (1.565s)
2023-01-11T21:41:26.4543805Z   test_flip_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4544036Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4544492Z [2023-01-11 21:34:01,542] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 211
2023-01-11T21:41:26.4544868Z [2023-01-11 21:34:03,108] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 211
2023-01-11T21:41:26.4544890Z 
2023-01-11T21:41:26.4544999Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4545092Z import torch
2023-01-11T21:41:26.4545187Z import random
2023-01-11T21:41:26.4545340Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4545501Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4545508Z 
2023-01-11T21:41:26.4545612Z aten = torch.ops.aten
2023-01-11T21:41:26.4545786Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4545896Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4545903Z 
2023-01-11T21:41:26.4545908Z 
2023-01-11T21:41:26.4546096Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4546357Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4546513Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4546645Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4546777Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4546859Z {
2023-01-11T21:41:26.4546974Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4547055Z     {
2023-01-11T21:41:26.4547157Z         #pragma omp for 
2023-01-11T21:41:26.4547267Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.4547351Z         {
2023-01-11T21:41:26.4547458Z             #pragma GCC ivdep
2023-01-11T21:41:26.4547569Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:26.4547639Z             {
2023-01-11T21:41:26.4547730Z                 {
2023-01-11T21:41:26.4547820Z                     {
2023-01-11T21:41:26.4548053Z                         auto tmp0 = in_ptr0[5 + ((-1)*i1) + (6*i0)];
2023-01-11T21:41:26.4548249Z                         out_ptr0[i1 + (6*i0)] = tmp0;
2023-01-11T21:41:26.4548338Z                     }
2023-01-11T21:41:26.4548426Z                 }
2023-01-11T21:41:26.4548495Z             }
2023-01-11T21:41:26.4548579Z         }
2023-01-11T21:41:26.4548699Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.4548809Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.4548893Z         {
2023-01-11T21:41:26.4549002Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:26.4549089Z             {
2023-01-11T21:41:26.4549185Z                 #pragma GCC ivdep
2023-01-11T21:41:26.4549301Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:41:26.4549388Z                 {
2023-01-11T21:41:26.4549476Z                     {
2023-01-11T21:41:26.4549608Z                         {
2023-01-11T21:41:26.4549864Z                             auto tmp0 = in_ptr0[30 + i2 + ((-6)*i1) + (36*i0)];
2023-01-11T21:41:26.4550012Z                             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.4550199Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.4550333Z                             out_ptr1[i2 + (6*i1) + (36*i0)] = tmp2;
2023-01-11T21:41:26.4550424Z                         }
2023-01-11T21:41:26.4550512Z                     }
2023-01-11T21:41:26.4550598Z                 }
2023-01-11T21:41:26.4550684Z             }
2023-01-11T21:41:26.4550753Z         }
2023-01-11T21:41:26.4550836Z     }
2023-01-11T21:41:26.4550916Z }
2023-01-11T21:41:26.4551023Z ''')
2023-01-11T21:41:26.4551030Z 
2023-01-11T21:41:26.4551035Z 
2023-01-11T21:41:26.4551154Z async_compile.wait(globals())
2023-01-11T21:41:26.4551251Z del async_compile
2023-01-11T21:41:26.4551257Z 
2023-01-11T21:41:26.4551350Z def call(args):
2023-01-11T21:41:26.4551442Z     arg0_1, = args
2023-01-11T21:41:26.4551524Z     args.clear()
2023-01-11T21:41:26.4551809Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4552104Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4552319Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4552409Z     del arg0_1
2023-01-11T21:41:26.4552512Z     return (buf0, buf1, )
2023-01-11T21:41:26.4552518Z 
2023-01-11T21:41:26.4552523Z 
2023-01-11T21:41:26.4552623Z if __name__ == "__main__":
2023-01-11T21:41:26.4552772Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4552921Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4553209Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4553354Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4553361Z 
2023-01-11T21:41:26.4553451Z ok (1.608s)
2023-01-11T21:41:26.4554059Z   test_fmod_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4554230Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4554585Z [2023-01-11 21:34:03,132] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 212
2023-01-11T21:41:26.4554948Z [2023-01-11 21:34:04,818] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 212
2023-01-11T21:41:26.4554955Z 
2023-01-11T21:41:26.4555080Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4555158Z import torch
2023-01-11T21:41:26.4555251Z import random
2023-01-11T21:41:26.4555407Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4555566Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4555611Z 
2023-01-11T21:41:26.4555716Z aten = torch.ops.aten
2023-01-11T21:41:26.4555892Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4556014Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4556021Z 
2023-01-11T21:41:26.4556026Z 
2023-01-11T21:41:26.4556208Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4556454Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4556610Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4556748Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4556880Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4557007Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4557088Z {
2023-01-11T21:41:26.4557251Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4557320Z     {
2023-01-11T21:41:26.4557427Z         #pragma omp for 
2023-01-11T21:41:26.4557542Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:41:26.4557625Z         {
2023-01-11T21:41:26.4557809Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4557984Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.4558105Z             auto tmp2 = tmp0.fmod(tmp1);
2023-01-11T21:41:26.4558284Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(3.0));
2023-01-11T21:41:26.4558382Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.4558502Z             auto tmp5 = tmp4.fmod(tmp1);
2023-01-11T21:41:26.4558683Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(2.0));
2023-01-11T21:41:26.4558856Z             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:41:26.4558975Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4559098Z             tmp7.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4559185Z         }
2023-01-11T21:41:26.4559293Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4559408Z         for(long i0=72; i0<72; i0+=1)
2023-01-11T21:41:26.4559494Z         {
2023-01-11T21:41:26.4559606Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4559719Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4559854Z             auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:41:26.4559989Z             auto tmp3 = static_cast<float>(3.0);
2023-01-11T21:41:26.4560087Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.4560222Z             auto tmp5 = std::fmod(tmp4, tmp1);
2023-01-11T21:41:26.4560356Z             auto tmp6 = static_cast<float>(2.0);
2023-01-11T21:41:26.4560527Z             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:41:26.4560636Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4560742Z             out_ptr1[i0] = tmp7;
2023-01-11T21:41:26.4560827Z         }
2023-01-11T21:41:26.4560896Z     }
2023-01-11T21:41:26.4560976Z }
2023-01-11T21:41:26.4561088Z ''')
2023-01-11T21:41:26.4561095Z 
2023-01-11T21:41:26.4561100Z 
2023-01-11T21:41:26.4561222Z async_compile.wait(globals())
2023-01-11T21:41:26.4561319Z del async_compile
2023-01-11T21:41:26.4561326Z 
2023-01-11T21:41:26.4561418Z def call(args):
2023-01-11T21:41:26.4561520Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4561599Z     args.clear()
2023-01-11T21:41:26.4561890Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4562175Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4562425Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4562516Z     del arg0_1
2023-01-11T21:41:26.4562606Z     del arg1_1
2023-01-11T21:41:26.4562708Z     return (buf0, buf1, )
2023-01-11T21:41:26.4562714Z 
2023-01-11T21:41:26.4562719Z 
2023-01-11T21:41:26.4562824Z if __name__ == "__main__":
2023-01-11T21:41:26.4562959Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4563159Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4563446Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4563733Z     arg1_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4563884Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4563891Z 
2023-01-11T21:41:26.4563977Z ok (1.712s)
2023-01-11T21:41:26.4564632Z   test_fmod_zero_dim_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4564807Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4565163Z [2023-01-11 21:34:04,844] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 213
2023-01-11T21:41:26.4565508Z [2023-01-11 21:34:06,373] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 213
2023-01-11T21:41:26.4566072Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4566235Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4566583Z [2023-01-11 21:34:06,389] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 214
2023-01-11T21:41:26.4566949Z [2023-01-11 21:34:07,946] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 214
2023-01-11T21:41:26.4566959Z 
2023-01-11T21:41:26.4567082Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4567176Z import torch
2023-01-11T21:41:26.4567269Z import random
2023-01-11T21:41:26.4567422Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4567567Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4567590Z 
2023-01-11T21:41:26.4567680Z aten = torch.ops.aten
2023-01-11T21:41:26.4567855Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4567978Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4567985Z 
2023-01-11T21:41:26.4567990Z 
2023-01-11T21:41:26.4568174Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4568436Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4568595Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4568734Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4568869Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4568937Z {
2023-01-11T21:41:26.4569186Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4569271Z     {
2023-01-11T21:41:26.4569376Z         #pragma omp for 
2023-01-11T21:41:26.4569484Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.4569568Z         {
2023-01-11T21:41:26.4569731Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4569895Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:26.4570016Z             auto tmp2 = tmp0.fmod(tmp1);
2023-01-11T21:41:26.4570135Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4570222Z         }
2023-01-11T21:41:26.4570347Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4570457Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.4570529Z         {
2023-01-11T21:41:26.4570644Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4570756Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.4570956Z             auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:41:26.4571063Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4571146Z         }
2023-01-11T21:41:26.4571227Z     }
2023-01-11T21:41:26.4571293Z }
2023-01-11T21:41:26.4571402Z ''')
2023-01-11T21:41:26.4571412Z 
2023-01-11T21:41:26.4571417Z 
2023-01-11T21:41:26.4571535Z async_compile.wait(globals())
2023-01-11T21:41:26.4571632Z del async_compile
2023-01-11T21:41:26.4571638Z 
2023-01-11T21:41:26.4571731Z def call(args):
2023-01-11T21:41:26.4571834Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4571931Z     args.clear()
2023-01-11T21:41:26.4572196Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4572397Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4572532Z     del arg0_1
2023-01-11T21:41:26.4572626Z     del arg1_1
2023-01-11T21:41:26.4572723Z     return (buf0, )
2023-01-11T21:41:26.4572734Z 
2023-01-11T21:41:26.4572739Z 
2023-01-11T21:41:26.4572839Z if __name__ == "__main__":
2023-01-11T21:41:26.4572993Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4573155Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4573404Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4573657Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4573809Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4573816Z 
2023-01-11T21:41:26.4573821Z 
2023-01-11T21:41:26.4573947Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4574041Z import torch
2023-01-11T21:41:26.4574136Z import random
2023-01-11T21:41:26.4574288Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4574452Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4574459Z 
2023-01-11T21:41:26.4574546Z aten = torch.ops.aten
2023-01-11T21:41:26.4574724Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4574845Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4574852Z 
2023-01-11T21:41:26.4574857Z 
2023-01-11T21:41:26.4575043Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4575304Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4575462Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4575600Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4575730Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4575794Z {
2023-01-11T21:41:26.4575922Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4576005Z     {
2023-01-11T21:41:26.4576110Z         #pragma omp for 
2023-01-11T21:41:26.4576222Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.4576309Z         {
2023-01-11T21:41:26.4576470Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:26.4576632Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.4576754Z             auto tmp2 = tmp0.fmod(tmp1);
2023-01-11T21:41:26.4576875Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4576962Z         }
2023-01-11T21:41:26.4577088Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4577197Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.4577281Z         {
2023-01-11T21:41:26.4577376Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.4577485Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4577621Z             auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:41:26.4577730Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4577814Z         }
2023-01-11T21:41:26.4577897Z     }
2023-01-11T21:41:26.4577976Z }
2023-01-11T21:41:26.4578068Z ''')
2023-01-11T21:41:26.4578078Z 
2023-01-11T21:41:26.4578083Z 
2023-01-11T21:41:26.4578201Z async_compile.wait(globals())
2023-01-11T21:41:26.4578301Z del async_compile
2023-01-11T21:41:26.4578343Z 
2023-01-11T21:41:26.4578439Z def call(args):
2023-01-11T21:41:26.4578540Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4578634Z     args.clear()
2023-01-11T21:41:26.4578900Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4579115Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4579194Z     del arg0_1
2023-01-11T21:41:26.4579283Z     del arg1_1
2023-01-11T21:41:26.4579378Z     return (buf0, )
2023-01-11T21:41:26.4579384Z 
2023-01-11T21:41:26.4579389Z 
2023-01-11T21:41:26.4579494Z if __name__ == "__main__":
2023-01-11T21:41:26.4579644Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4579805Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4580096Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4580346Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4580507Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4580514Z 
2023-01-11T21:41:26.4580601Z ok (3.126s)
2023-01-11T21:41:26.4581316Z   test_forced_buffer_realize_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4581632Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4582200Z [2023-01-11 21:34:07,972] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 215
2023-01-11T21:41:26.4582742Z [2023-01-11 21:34:09,805] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 215
2023-01-11T21:41:26.4582758Z 
2023-01-11T21:41:26.4582956Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4583097Z import torch
2023-01-11T21:41:26.4583341Z import random
2023-01-11T21:41:26.4583544Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4583794Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4583805Z 
2023-01-11T21:41:26.4583965Z aten = torch.ops.aten
2023-01-11T21:41:26.4584236Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4584424Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4584433Z 
2023-01-11T21:41:26.4584439Z 
2023-01-11T21:41:26.4584725Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4585123Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4585361Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.4585553Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:41:26.4585681Z {
2023-01-11T21:41:26.4596175Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4596355Z     {
2023-01-11T21:41:26.4596538Z         #pragma omp for 
2023-01-11T21:41:26.4596714Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.4596830Z         {
2023-01-11T21:41:26.4597114Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4597377Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.4597555Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.4597725Z             auto tmp3 = tmp2 * tmp1;
2023-01-11T21:41:26.4597917Z             tmp3.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.4598047Z         }
2023-01-11T21:41:26.4598239Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4598389Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.4598521Z         {
2023-01-11T21:41:26.4598700Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4598904Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.4599207Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.4599382Z             auto tmp3 = tmp2 * tmp1;
2023-01-11T21:41:26.4599557Z             in_out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.4599669Z         }
2023-01-11T21:41:26.4599802Z     }
2023-01-11T21:41:26.4599917Z }
2023-01-11T21:41:26.4600117Z ''')
2023-01-11T21:41:26.4600131Z 
2023-01-11T21:41:26.4600139Z 
2023-01-11T21:41:26.4600324Z async_compile.wait(globals())
2023-01-11T21:41:26.4600476Z del async_compile
2023-01-11T21:41:26.4600487Z 
2023-01-11T21:41:26.4600635Z def call(args):
2023-01-11T21:41:26.4600760Z     arg0_1, = args
2023-01-11T21:41:26.4600898Z     args.clear()
2023-01-11T21:41:26.4601317Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4601499Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.4601847Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:26.4601991Z     del arg0_1
2023-01-11T21:41:26.4602143Z     return (buf1, )
2023-01-11T21:41:26.4602164Z 
2023-01-11T21:41:26.4602171Z 
2023-01-11T21:41:26.4602308Z if __name__ == "__main__":
2023-01-11T21:41:26.4602541Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4602791Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4603205Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4603433Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4603444Z 
2023-01-11T21:41:26.4603577Z ok (1.860s)
2023-01-11T21:41:26.4604523Z   test_full_like_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4604778Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4605321Z [2023-01-11 21:34:09,849] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 216
2023-01-11T21:41:26.4605844Z [2023-01-11 21:34:11,535] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 216
2023-01-11T21:41:26.4605876Z 
2023-01-11T21:41:26.4606048Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4606191Z import torch
2023-01-11T21:41:26.4606329Z import random
2023-01-11T21:41:26.4606568Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4606812Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4606827Z 
2023-01-11T21:41:26.4606983Z aten = torch.ops.aten
2023-01-11T21:41:26.4607247Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4607418Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4607428Z 
2023-01-11T21:41:26.4607457Z 
2023-01-11T21:41:26.4607720Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4608119Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4608357Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4608483Z {
2023-01-11T21:41:26.4608684Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4608819Z     {
2023-01-11T21:41:26.4608982Z         #pragma omp for 
2023-01-11T21:41:26.4609294Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.4609430Z         {
2023-01-11T21:41:26.4609722Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(7.777));
2023-01-11T21:41:26.4610000Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4610284Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.4610473Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4610607Z         }
2023-01-11T21:41:26.4610785Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4610954Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.4611224Z         {
2023-01-11T21:41:26.4611431Z             auto tmp0 = static_cast<float>(7.777);
2023-01-11T21:41:26.4611641Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4611901Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.4612057Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4612167Z         }
2023-01-11T21:41:26.4612297Z     }
2023-01-11T21:41:26.4612423Z }
2023-01-11T21:41:26.4612588Z ''')
2023-01-11T21:41:26.4612602Z 
2023-01-11T21:41:26.4612612Z 
2023-01-11T21:41:26.4612793Z async_compile.wait(globals())
2023-01-11T21:41:26.4612941Z del async_compile
2023-01-11T21:41:26.4612950Z 
2023-01-11T21:41:26.4613099Z def call(args):
2023-01-11T21:41:26.4613224Z     arg0_1, = args
2023-01-11T21:41:26.4613366Z     args.clear()
2023-01-11T21:41:26.4613897Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4614097Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4614253Z     return (buf0, )
2023-01-11T21:41:26.4614270Z 
2023-01-11T21:41:26.4614277Z 
2023-01-11T21:41:26.4614437Z if __name__ == "__main__":
2023-01-11T21:41:26.4614666Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4614908Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4615294Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4615521Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4615529Z 
2023-01-11T21:41:26.4615666Z ok (1.729s)
2023-01-11T21:41:26.4616603Z   test_fuse_tiled_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4616852Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4617399Z [2023-01-11 21:34:11,554] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 217
2023-01-11T21:41:26.4617944Z [2023-01-11 21:34:13,137] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 217
2023-01-11T21:41:26.4617954Z 
2023-01-11T21:41:26.4618145Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4618298Z import torch
2023-01-11T21:41:26.4618429Z import random
2023-01-11T21:41:26.4618655Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4618898Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4618913Z 
2023-01-11T21:41:26.4619072Z aten = torch.ops.aten
2023-01-11T21:41:26.4619350Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4619546Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4619562Z 
2023-01-11T21:41:26.4619572Z 
2023-01-11T21:41:26.4619855Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4620266Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4620492Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4620697Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4620899Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.4621102Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4621307Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4621431Z {
2023-01-11T21:41:26.4621623Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4621736Z     {
2023-01-11T21:41:26.4621899Z         #pragma omp for 
2023-01-11T21:41:26.4622076Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.4622206Z         {
2023-01-11T21:41:26.4622385Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.4622520Z             {
2023-01-11T21:41:26.4622787Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:41:26.4623255Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:26.4623440Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4623642Z                 tmp2.store(out_ptr0 + (8*i1) + (128*i0));
2023-01-11T21:41:26.4623773Z             }
2023-01-11T21:41:26.4623965Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.4624145Z             for(long i1=128; i1<128; i1+=1)
2023-01-11T21:41:26.4624280Z             {
2023-01-11T21:41:26.4624432Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4624595Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.4624778Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4624965Z                 out_ptr0[i1 + (128*i0)] = tmp2;
2023-01-11T21:41:26.4625098Z             }
2023-01-11T21:41:26.4625227Z         }
2023-01-11T21:41:26.4625481Z         #pragma omp for 
2023-01-11T21:41:26.4625623Z         for(long i0=0; i0<2048; i0+=1)
2023-01-11T21:41:26.4625760Z         {
2023-01-11T21:41:26.4626038Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:26.4626308Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4626478Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4626666Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4626801Z         }
2023-01-11T21:41:26.4626980Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4627156Z         for(long i0=16384; i0<16384; i0+=1)
2023-01-11T21:41:26.4627291Z         {
2023-01-11T21:41:26.4627458Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:41:26.4627661Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4627837Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4628000Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.4628116Z         }
2023-01-11T21:41:26.4628243Z     }
2023-01-11T21:41:26.4628376Z }
2023-01-11T21:41:26.4628561Z ''')
2023-01-11T21:41:26.4628572Z 
2023-01-11T21:41:26.4628589Z 
2023-01-11T21:41:26.4628776Z async_compile.wait(globals())
2023-01-11T21:41:26.4628930Z del async_compile
2023-01-11T21:41:26.4628945Z 
2023-01-11T21:41:26.4629096Z def call(args):
2023-01-11T21:41:26.4629242Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4629387Z     args.clear()
2023-01-11T21:41:26.4629830Z     buf0 = empty_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4630245Z     buf1 = empty_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4630678Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4630829Z     del arg0_1
2023-01-11T21:41:26.4630973Z     del arg1_1
2023-01-11T21:41:26.4631117Z     del arg2_1
2023-01-11T21:41:26.4631261Z     return (buf0, buf1, )
2023-01-11T21:41:26.4631273Z 
2023-01-11T21:41:26.4631280Z 
2023-01-11T21:41:26.4631439Z if __name__ == "__main__":
2023-01-11T21:41:26.4631676Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4631936Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4632345Z     arg0_1 = rand_strided((128, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4632755Z     arg1_1 = rand_strided((1, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4633173Z     arg2_1 = rand_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4633427Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4633437Z 
2023-01-11T21:41:26.4633554Z ok (1.603s)
2023-01-11T21:41:26.4634495Z   test_gather1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4634844Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4635392Z [2023-01-11 21:34:13,167] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 218
2023-01-11T21:41:26.4635935Z [2023-01-11 21:34:14,704] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 218
2023-01-11T21:41:26.4635945Z 
2023-01-11T21:41:26.4636141Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4636292Z import torch
2023-01-11T21:41:26.4636436Z import random
2023-01-11T21:41:26.4636672Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4636900Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4636916Z 
2023-01-11T21:41:26.4637077Z aten = torch.ops.aten
2023-01-11T21:41:26.4637448Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4637645Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4637665Z 
2023-01-11T21:41:26.4637673Z 
2023-01-11T21:41:26.4637954Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4638363Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4638610Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4638822Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4638998Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4639196Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4639321Z {
2023-01-11T21:41:26.4639523Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4639650Z     {
2023-01-11T21:41:26.4639805Z         #pragma omp for 
2023-01-11T21:41:26.4639976Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:41:26.4640091Z         {
2023-01-11T21:41:26.4640260Z             #pragma GCC ivdep
2023-01-11T21:41:26.4640436Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.4640580Z             {
2023-01-11T21:41:26.4640711Z                 {
2023-01-11T21:41:26.4640850Z                     {
2023-01-11T21:41:26.4641059Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.4641248Z                         auto tmp1 = static_cast<long>(1);
2023-01-11T21:41:26.4641441Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4641644Z                         auto tmp3 = in_ptr1[tmp2 + (6*i1)];
2023-01-11T21:41:26.4641841Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.4642038Z                         out_ptr1[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.4642184Z                     }
2023-01-11T21:41:26.4642319Z                 }
2023-01-11T21:41:26.4642431Z             }
2023-01-11T21:41:26.4642557Z         }
2023-01-11T21:41:26.4642690Z     }
2023-01-11T21:41:26.4642816Z }
2023-01-11T21:41:26.4643003Z ''')
2023-01-11T21:41:26.4643014Z 
2023-01-11T21:41:26.4643020Z 
2023-01-11T21:41:26.4643214Z async_compile.wait(globals())
2023-01-11T21:41:26.4643376Z del async_compile
2023-01-11T21:41:26.4643388Z 
2023-01-11T21:41:26.4643508Z def call(args):
2023-01-11T21:41:26.4643673Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4643824Z     args.clear()
2023-01-11T21:41:26.4644263Z     buf0 = empty_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4644697Z     buf1 = empty_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4645063Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4645208Z     del arg0_1
2023-01-11T21:41:26.4645348Z     del arg1_1
2023-01-11T21:41:26.4645494Z     return (buf0, buf1, )
2023-01-11T21:41:26.4645508Z 
2023-01-11T21:41:26.4645516Z 
2023-01-11T21:41:26.4645681Z if __name__ == "__main__":
2023-01-11T21:41:26.4645917Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4646162Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4646708Z     arg0_1 = rand_strided((1, 1, 10, 6), (60, 60, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4647131Z     arg1_1 = rand_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4647354Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4647365Z 
2023-01-11T21:41:26.4647503Z ok (1.567s)
2023-01-11T21:41:26.4647712Z   test_gather2_cpu (__main__.CpuTests) ... ok (0.002s)
2023-01-11T21:41:26.4648733Z   test_gather_scatter_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4649141Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4649701Z [2023-01-11 21:34:14,837] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 219
2023-01-11T21:41:26.4650247Z [2023-01-11 21:34:16,409] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 219
2023-01-11T21:41:26.4650259Z 
2023-01-11T21:41:26.4650456Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4650612Z import torch
2023-01-11T21:41:26.4650766Z import random
2023-01-11T21:41:26.4650998Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4651228Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4651237Z 
2023-01-11T21:41:26.4651400Z aten = torch.ops.aten
2023-01-11T21:41:26.4651676Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4651866Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4651887Z 
2023-01-11T21:41:26.4651894Z 
2023-01-11T21:41:26.4652180Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4652594Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4652835Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4653047Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4653231Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4653362Z {
2023-01-11T21:41:26.4653573Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4653704Z     {
2023-01-11T21:41:26.4653861Z         #pragma omp for 
2023-01-11T21:41:26.4654035Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.4654172Z         {
2023-01-11T21:41:26.4654438Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:26.4654622Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4654756Z         }
2023-01-11T21:41:26.4654954Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4655124Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:41:26.4655266Z         {
2023-01-11T21:41:26.4655470Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:26.4655622Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.4655752Z         }
2023-01-11T21:41:26.4655916Z         #pragma omp for 
2023-01-11T21:41:26.4656087Z         for(long i0=0; i0<80; i0+=1)
2023-01-11T21:41:26.4656227Z         {
2023-01-11T21:41:26.4656397Z             #pragma GCC ivdep
2023-01-11T21:41:26.4656551Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:26.4656687Z             {
2023-01-11T21:41:26.4656820Z                 {
2023-01-11T21:41:26.4656957Z                     {
2023-01-11T21:41:26.4657170Z                         auto tmp0 = in_ptr0[80 + i0];
2023-01-11T21:41:26.4657360Z                         auto tmp1 = in_ptr0[i0];
2023-01-11T21:41:26.4657573Z                         auto tmp2 = in_ptr1[i1 + (32*tmp1)];
2023-01-11T21:41:26.4657758Z                         auto tmp3 = in_ptr1[i1 + (32*tmp0)];
2023-01-11T21:41:26.4658084Z                         auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:26.4658451Z                         auto tmp5 = static_cast<float>(1);
2023-01-11T21:41:26.4658655Z                         auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.4658883Z                         atomic_add(&out_ptr0[i1 + (32*tmp0)], tmp6);
2023-01-11T21:41:26.4659026Z                     }
2023-01-11T21:41:26.4659165Z                 }
2023-01-11T21:41:26.4659282Z             }
2023-01-11T21:41:26.4659413Z         }
2023-01-11T21:41:26.4659549Z     }
2023-01-11T21:41:26.4659670Z }
2023-01-11T21:41:26.4659855Z ''')
2023-01-11T21:41:26.4659868Z 
2023-01-11T21:41:26.4659877Z 
2023-01-11T21:41:26.4660066Z async_compile.wait(globals())
2023-01-11T21:41:26.4660216Z del async_compile
2023-01-11T21:41:26.4660226Z 
2023-01-11T21:41:26.4660371Z def call(args):
2023-01-11T21:41:26.4660511Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4660739Z     args.clear()
2023-01-11T21:41:26.4661173Z     buf0 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4661509Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4661648Z     del arg0_1
2023-01-11T21:41:26.4661790Z     del arg1_1
2023-01-11T21:41:26.4661940Z     return (buf0, )
2023-01-11T21:41:26.4661950Z 
2023-01-11T21:41:26.4661958Z 
2023-01-11T21:41:26.4662098Z if __name__ == "__main__":
2023-01-11T21:41:26.4662331Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4662584Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4663011Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4663516Z     arg1_1 = rand_strided((2, 80), (80, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4663747Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4663758Z 
2023-01-11T21:41:26.4663906Z ok (1.708s)
2023-01-11T21:41:26.4664826Z   test_gelu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4665088Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4665614Z [2023-01-11 21:34:16,461] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 220
2023-01-11T21:41:26.4666170Z [2023-01-11 21:34:18,190] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 220
2023-01-11T21:41:26.4666183Z 
2023-01-11T21:41:26.4666375Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4666525Z import torch
2023-01-11T21:41:26.4666688Z import random
2023-01-11T21:41:26.4666930Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4667177Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4667196Z 
2023-01-11T21:41:26.4667352Z aten = torch.ops.aten
2023-01-11T21:41:26.4667591Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4667783Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4667796Z 
2023-01-11T21:41:26.4667803Z 
2023-01-11T21:41:26.4668098Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4668493Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4668746Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4668954Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4669151Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4669269Z {
2023-01-11T21:41:26.4669453Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4669588Z     {
2023-01-11T21:41:26.4669752Z         #pragma omp for 
2023-01-11T21:41:26.4669924Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.4670172Z         {
2023-01-11T21:41:26.4670443Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4670718Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(0.5));
2023-01-11T21:41:26.4670881Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.4671169Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.7071067811865476));
2023-01-11T21:41:26.4671342Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.4671517Z             auto tmp5 = tmp4.erf();
2023-01-11T21:41:26.4671794Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4671974Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.4672145Z             auto tmp8 = tmp2 * tmp7;
2023-01-11T21:41:26.4672487Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.4672663Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.4672841Z             auto tmp11 = tmp0 + tmp6;
2023-01-11T21:41:26.4673030Z             auto tmp12 = tmp11 * tmp1;
2023-01-11T21:41:26.4673196Z             auto tmp13 = tmp11 * tmp3;
2023-01-11T21:41:26.4673374Z             auto tmp14 = tmp13.erf();
2023-01-11T21:41:26.4673554Z             auto tmp15 = tmp14 + tmp6;
2023-01-11T21:41:26.4673732Z             auto tmp16 = tmp12 * tmp15;
2023-01-11T21:41:26.4673903Z             tmp10.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4674077Z             tmp16.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4674212Z         }
2023-01-11T21:41:26.4674414Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4674592Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.4674723Z         {
2023-01-11T21:41:26.4674910Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4675097Z             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:26.4675288Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.4675520Z             auto tmp3 = static_cast<float>(0.7071067811865476);
2023-01-11T21:41:26.4675698Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.4675888Z             auto tmp5 = std::erf(tmp4);
2023-01-11T21:41:26.4676089Z             auto tmp6 = static_cast<float>(1);
2023-01-11T21:41:26.4676268Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.4676423Z             auto tmp8 = tmp2 * tmp7;
2023-01-11T21:41:26.4676627Z             auto tmp9 = static_cast<float>(2);
2023-01-11T21:41:26.4676807Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.4676973Z             auto tmp11 = tmp0 + tmp6;
2023-01-11T21:41:26.4677153Z             auto tmp12 = tmp11 * tmp1;
2023-01-11T21:41:26.4677335Z             auto tmp13 = tmp11 * tmp3;
2023-01-11T21:41:26.4677534Z             auto tmp14 = std::erf(tmp13);
2023-01-11T21:41:26.4677693Z             auto tmp15 = tmp14 + tmp6;
2023-01-11T21:41:26.4677869Z             auto tmp16 = tmp12 * tmp15;
2023-01-11T21:41:26.4678035Z             out_ptr0[i0] = tmp10;
2023-01-11T21:41:26.4678203Z             out_ptr1[i0] = tmp16;
2023-01-11T21:41:26.4678344Z         }
2023-01-11T21:41:26.4678476Z     }
2023-01-11T21:41:26.4678603Z }
2023-01-11T21:41:26.4678781Z ''')
2023-01-11T21:41:26.4678791Z 
2023-01-11T21:41:26.4678799Z 
2023-01-11T21:41:26.4678978Z async_compile.wait(globals())
2023-01-11T21:41:26.4679134Z del async_compile
2023-01-11T21:41:26.4679149Z 
2023-01-11T21:41:26.4679302Z def call(args):
2023-01-11T21:41:26.4679447Z     arg0_1, = args
2023-01-11T21:41:26.4679593Z     args.clear()
2023-01-11T21:41:26.4680013Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4680407Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4680731Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4680869Z     del arg0_1
2023-01-11T21:41:26.4681034Z     return (buf0, buf1, )
2023-01-11T21:41:26.4681046Z 
2023-01-11T21:41:26.4681054Z 
2023-01-11T21:41:26.4681209Z if __name__ == "__main__":
2023-01-11T21:41:26.4681557Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4681799Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4682212Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4682410Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4682420Z 
2023-01-11T21:41:26.4682563Z ok (1.775s)
2023-01-11T21:41:26.4683476Z   test_glu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4683802Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4684342Z [2023-01-11 21:34:18,231] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 221
2023-01-11T21:41:26.4684878Z [2023-01-11 21:34:19,813] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 221
2023-01-11T21:41:26.4684891Z 
2023-01-11T21:41:26.4685092Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4685239Z import torch
2023-01-11T21:41:26.4685393Z import random
2023-01-11T21:41:26.4685606Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4685846Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4685857Z 
2023-01-11T21:41:26.4686017Z aten = torch.ops.aten
2023-01-11T21:41:26.4686299Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4686489Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4686498Z 
2023-01-11T21:41:26.4686505Z 
2023-01-11T21:41:26.4686787Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4687197Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4687444Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4687619Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4687834Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4688032Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4688160Z {
2023-01-11T21:41:26.4688366Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4688493Z     {
2023-01-11T21:41:26.4688655Z         #pragma omp for 
2023-01-11T21:41:26.4688811Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.4688949Z         {
2023-01-11T21:41:26.4689249Z             #pragma GCC ivdep
2023-01-11T21:41:26.4689428Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.4689565Z             {
2023-01-11T21:41:26.4689690Z                 {
2023-01-11T21:41:26.4689838Z                     {
2023-01-11T21:41:26.4690031Z                         auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.4690249Z                         auto tmp1 = in_ptr0[4 + i1 + (8*i0)];
2023-01-11T21:41:26.4690594Z                         auto tmp2 = std::exp(-tmp1);
2023-01-11T21:41:26.4690772Z                         auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:41:26.4690971Z                         auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.4691171Z                         out_ptr0[i1 + (4*i0)] = tmp4;
2023-01-11T21:41:26.4691315Z                     }
2023-01-11T21:41:26.4691437Z                 }
2023-01-11T21:41:26.4691560Z             }
2023-01-11T21:41:26.4691697Z         }
2023-01-11T21:41:26.4691867Z         #pragma omp for 
2023-01-11T21:41:26.4692040Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4692175Z         {
2023-01-11T21:41:26.4692352Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:26.4692468Z             {
2023-01-11T21:41:26.4692764Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (1024*i0));
2023-01-11T21:41:26.4693058Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 512 + (8*i1) + (1024*i0));
2023-01-11T21:41:26.4693470Z                 auto tmp2 = decltype(tmp1)(1)/(decltype(tmp1)(1) + tmp1.neg().exp());
2023-01-11T21:41:26.4693647Z                 auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:26.4693871Z                 tmp3.store(out_ptr1 + (8*i1) + (512*i0));
2023-01-11T21:41:26.4693993Z             }
2023-01-11T21:41:26.4694190Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.4694367Z             for(long i1=512; i1<512; i1+=1)
2023-01-11T21:41:26.4694493Z             {
2023-01-11T21:41:26.4694699Z                 auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:41:26.4694907Z                 auto tmp1 = in_ptr0[512 + i1 + (1024*i0)];
2023-01-11T21:41:26.4695233Z                 auto tmp2 = std::exp(-tmp1);
2023-01-11T21:41:26.4695415Z                 auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:41:26.4695708Z                 auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.4695877Z                 out_ptr1[i1 + (512*i0)] = tmp4;
2023-01-11T21:41:26.4696016Z             }
2023-01-11T21:41:26.4696152Z         }
2023-01-11T21:41:26.4696325Z         #pragma omp for 
2023-01-11T21:41:26.4696489Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.4696626Z         {
2023-01-11T21:41:26.4696777Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.4696909Z             {
2023-01-11T21:41:26.4697198Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (64*i0));
2023-01-11T21:41:26.4697481Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 32 + (8*i1) + (64*i0));
2023-01-11T21:41:26.4697767Z                 auto tmp2 = decltype(tmp1)(1)/(decltype(tmp1)(1) + tmp1.neg().exp());
2023-01-11T21:41:26.4697955Z                 auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:26.4698166Z                 tmp3.store(out_ptr2 + (8*i1) + (32*i0));
2023-01-11T21:41:26.4698302Z             }
2023-01-11T21:41:26.4698474Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.4698650Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:26.4698796Z             {
2023-01-11T21:41:26.4698998Z                 auto tmp0 = in_ptr0[i1 + (64*i0)];
2023-01-11T21:41:26.4699200Z                 auto tmp1 = in_ptr0[32 + i1 + (64*i0)];
2023-01-11T21:41:26.4699512Z                 auto tmp2 = std::exp(-tmp1);
2023-01-11T21:41:26.4699700Z                 auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:41:26.4699863Z                 auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.4700052Z                 out_ptr2[i1 + (32*i0)] = tmp4;
2023-01-11T21:41:26.4700178Z             }
2023-01-11T21:41:26.4700309Z         }
2023-01-11T21:41:26.4700443Z     }
2023-01-11T21:41:26.4700569Z }
2023-01-11T21:41:26.4700741Z ''')
2023-01-11T21:41:26.4700753Z 
2023-01-11T21:41:26.4700764Z 
2023-01-11T21:41:26.4700929Z async_compile.wait(globals())
2023-01-11T21:41:26.4701091Z del async_compile
2023-01-11T21:41:26.4701104Z 
2023-01-11T21:41:26.4701243Z def call(args):
2023-01-11T21:41:26.4701389Z     arg0_1, = args
2023-01-11T21:41:26.4701542Z     args.clear()
2023-01-11T21:41:26.4702005Z     buf0 = empty_strided((8, 16, 8, 4), (512, 32, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4702444Z     buf1 = empty_strided((8, 8, 8, 8), (512, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4702883Z     buf2 = empty_strided((8, 16, 4, 8), (512, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4703303Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4703455Z     del arg0_1
2023-01-11T21:41:26.4703633Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.4703644Z 
2023-01-11T21:41:26.4703653Z 
2023-01-11T21:41:26.4703811Z if __name__ == "__main__":
2023-01-11T21:41:26.4704055Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4704305Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4704753Z     arg0_1 = rand_strided((8, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4705066Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4705077Z 
2023-01-11T21:41:26.4705195Z ok (1.624s)
2023-01-11T21:41:26.4706140Z   test_grid_sampler_2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4706401Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4706931Z [2023-01-11 21:34:21,311] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 222
2023-01-11T21:41:26.4707431Z [2023-01-11 21:34:21,901] torch._inductor.scheduler: [DEBUG] remove_buffer('buf5')
2023-01-11T21:41:26.4707866Z [2023-01-11 21:34:21,901] torch._inductor.scheduler: [DEBUG] remove_buffer('buf3')
2023-01-11T21:41:26.4708261Z [2023-01-11 21:34:21,901] torch._inductor.scheduler: [DEBUG] remove_buffer('buf7')
2023-01-11T21:41:26.4708269Z 
2023-01-11T21:41:26.4708413Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4708520Z import torch
2023-01-11T21:41:26.4708612Z import random
2023-01-11T21:41:26.4708787Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4708969Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4708977Z 
2023-01-11T21:41:26.4709098Z aten = torch.ops.aten
2023-01-11T21:41:26.4709302Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4709442Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4709450Z 
2023-01-11T21:41:26.4709455Z 
2023-01-11T21:41:26.4709671Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4709974Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4710135Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.4710296Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.4710454Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4710612Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4710761Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4710909Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4711054Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.4711195Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.4711326Z                        long* __restrict__ out_ptr4,
2023-01-11T21:41:26.4711466Z                        long* __restrict__ out_ptr5,
2023-01-11T21:41:26.4711613Z                        float* __restrict__ out_ptr6,
2023-01-11T21:41:26.4711759Z                        long* __restrict__ out_ptr7,
2023-01-11T21:41:26.4711901Z                        long* __restrict__ out_ptr8,
2023-01-11T21:41:26.4712052Z                        float* __restrict__ out_ptr9,
2023-01-11T21:41:26.4712196Z                        long* __restrict__ out_ptr10,
2023-01-11T21:41:26.4712325Z                        long* __restrict__ out_ptr11,
2023-01-11T21:41:26.4712477Z                        float* __restrict__ out_ptr12,
2023-01-11T21:41:26.4712620Z                        long* __restrict__ out_ptr13,
2023-01-11T21:41:26.4712763Z                        long* __restrict__ out_ptr14,
2023-01-11T21:41:26.4712911Z                        float* __restrict__ out_ptr15)
2023-01-11T21:41:26.4713005Z {
2023-01-11T21:41:26.4713158Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4713234Z     {
2023-01-11T21:41:26.4713352Z         #pragma omp for 
2023-01-11T21:41:26.4713481Z         for(long i0=0; i0<495616; i0+=1)
2023-01-11T21:41:26.4713577Z         {
2023-01-11T21:41:26.4713678Z             {
2023-01-11T21:41:26.4713777Z                 {
2023-01-11T21:41:26.4713921Z                     auto tmp0 = in_ptr0[2*i0];
2023-01-11T21:41:26.4714102Z                     auto tmp9 = in_ptr0[1 + (2*i0)];
2023-01-11T21:41:26.4714265Z                     auto tmp1 = static_cast<float>(175.5);
2023-01-11T21:41:26.4714404Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.4714541Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:26.4714703Z                     auto tmp4 = std::floor(tmp3);
2023-01-11T21:41:26.4714860Z                     auto tmp5 = static_cast<float>(0);
2023-01-11T21:41:26.4715088Z                     auto tmp6 = tmp4 >= tmp5;
2023-01-11T21:41:26.4715219Z                     auto tmp7 = static_cast<float>(352);
2023-01-11T21:41:26.4715340Z                     auto tmp8 = tmp4 < tmp7;
2023-01-11T21:41:26.4715459Z                     auto tmp10 = tmp9 * tmp1;
2023-01-11T21:41:26.4715582Z                     auto tmp11 = tmp10 + tmp1;
2023-01-11T21:41:26.4715752Z                     auto tmp12 = std::floor(tmp11);
2023-01-11T21:41:26.4715881Z                     auto tmp13 = tmp12 >= tmp5;
2023-01-11T21:41:26.4716008Z                     auto tmp14 = tmp12 < tmp7;
2023-01-11T21:41:26.4716117Z                     auto tmp15 = tmp13 && tmp14;
2023-01-11T21:41:26.4716237Z                     auto tmp16 = tmp8 && tmp15;
2023-01-11T21:41:26.4716359Z                     auto tmp17 = tmp6 && tmp16;
2023-01-11T21:41:26.4716498Z                     auto tmp18 = static_cast<float>(1);
2023-01-11T21:41:26.4716620Z                     auto tmp19 = tmp4 + tmp18;
2023-01-11T21:41:26.4716813Z                     auto tmp20 = tmp19 - tmp3;
2023-01-11T21:41:26.4716934Z                     auto tmp21 = tmp12 + tmp18;
2023-01-11T21:41:26.4717126Z                     auto tmp22 = tmp21 - tmp11;
2023-01-11T21:41:26.4717231Z                     auto tmp23 = tmp20 * tmp22;
2023-01-11T21:41:26.4717362Z                     auto tmp24 = tmp17 ? tmp23 : tmp5;
2023-01-11T21:41:26.4717489Z                     auto tmp25 = tmp19 >= tmp5;
2023-01-11T21:41:26.4717610Z                     auto tmp26 = tmp19 < tmp7;
2023-01-11T21:41:26.4717739Z                     auto tmp27 = tmp26 && tmp15;
2023-01-11T21:41:26.4717861Z                     auto tmp28 = tmp25 && tmp27;
2023-01-11T21:41:26.4718051Z                     auto tmp29 = tmp3 - tmp4;
2023-01-11T21:41:26.4718157Z                     auto tmp30 = tmp29 * tmp22;
2023-01-11T21:41:26.4718287Z                     auto tmp31 = tmp28 ? tmp30 : tmp5;
2023-01-11T21:41:26.4718406Z                     auto tmp32 = tmp21 >= tmp5;
2023-01-11T21:41:26.4718526Z                     auto tmp33 = tmp21 < tmp7;
2023-01-11T21:41:26.4718650Z                     auto tmp34 = tmp32 && tmp33;
2023-01-11T21:41:26.4718771Z                     auto tmp35 = tmp8 && tmp34;
2023-01-11T21:41:26.4718890Z                     auto tmp36 = tmp6 && tmp35;
2023-01-11T21:41:26.4719064Z                     auto tmp37 = tmp11 - tmp12;
2023-01-11T21:41:26.4719187Z                     auto tmp38 = tmp20 * tmp37;
2023-01-11T21:41:26.4719323Z                     auto tmp39 = tmp36 ? tmp38 : tmp5;
2023-01-11T21:41:26.4719448Z                     auto tmp40 = tmp26 && tmp34;
2023-01-11T21:41:26.4719571Z                     auto tmp41 = tmp25 && tmp40;
2023-01-11T21:41:26.4719693Z                     auto tmp42 = tmp29 * tmp37;
2023-01-11T21:41:26.4719824Z                     auto tmp43 = tmp41 ? tmp42 : tmp5;
2023-01-11T21:41:26.4719952Z                     auto tmp44 = static_cast<float>(176.0);
2023-01-11T21:41:26.4720074Z                     auto tmp45 = tmp0 * tmp44;
2023-01-11T21:41:26.4720194Z                     auto tmp46 = tmp45 + tmp1;
2023-01-11T21:41:26.4720336Z                     auto tmp47 = static_cast<float>(0.0);
2023-01-11T21:41:26.4720507Z                     auto tmp48 = (tmp47 != tmp47) ? tmp47 : std::max(tmp46, tmp47);
2023-01-11T21:41:26.4720649Z                     auto tmp49 = static_cast<float>(351.0);
2023-01-11T21:41:26.4720825Z                     auto tmp50 = (tmp49 != tmp49) ? tmp49 : std::min(tmp48, tmp49);
2023-01-11T21:41:26.4720960Z                     auto tmp51 = std::floor(tmp50);
2023-01-11T21:41:26.4721103Z                     auto tmp52 = tmp51 >= tmp5;
2023-01-11T21:41:26.4721224Z                     auto tmp53 = tmp51 < tmp7;
2023-01-11T21:41:26.4721344Z                     auto tmp54 = tmp9 * tmp44;
2023-01-11T21:41:26.4721467Z                     auto tmp55 = tmp54 + tmp1;
2023-01-11T21:41:26.4721640Z                     auto tmp56 = (tmp47 != tmp47) ? tmp47 : std::max(tmp55, tmp47);
2023-01-11T21:41:26.4721808Z                     auto tmp57 = (tmp49 != tmp49) ? tmp49 : std::min(tmp56, tmp49);
2023-01-11T21:41:26.4721942Z                     auto tmp58 = std::floor(tmp57);
2023-01-11T21:41:26.4722063Z                     auto tmp59 = tmp58 >= tmp5;
2023-01-11T21:41:26.4722170Z                     auto tmp60 = tmp58 < tmp7;
2023-01-11T21:41:26.4722292Z                     auto tmp61 = tmp59 && tmp60;
2023-01-11T21:41:26.4722446Z                     auto tmp62 = tmp53 && tmp61;
2023-01-11T21:41:26.4722571Z                     auto tmp63 = tmp52 && tmp62;
2023-01-11T21:41:26.4722717Z                     auto tmp64 = static_cast<long>(tmp51);
2023-01-11T21:41:26.4722853Z                     auto tmp65 = static_cast<long>(0);
2023-01-11T21:41:26.4722987Z                     auto tmp66 = tmp63 ? tmp64 : tmp65;
2023-01-11T21:41:26.4723112Z                     auto tmp67 = static_cast<long>(tmp58);
2023-01-11T21:41:26.4723243Z                     auto tmp68 = tmp63 ? tmp67 : tmp65;
2023-01-11T21:41:26.4723363Z                     auto tmp69 = tmp51 + tmp18;
2023-01-11T21:41:26.4723555Z                     auto tmp70 = tmp69 - tmp50;
2023-01-11T21:41:26.4723677Z                     auto tmp71 = tmp58 + tmp18;
2023-01-11T21:41:26.4723865Z                     auto tmp72 = tmp71 - tmp57;
2023-01-11T21:41:26.4723983Z                     auto tmp73 = tmp70 * tmp72;
2023-01-11T21:41:26.4724120Z                     auto tmp74 = tmp63 ? tmp73 : tmp5;
2023-01-11T21:41:26.4724227Z                     auto tmp75 = tmp69 >= tmp5;
2023-01-11T21:41:26.4724347Z                     auto tmp76 = tmp69 < tmp7;
2023-01-11T21:41:26.4724474Z                     auto tmp77 = tmp76 && tmp61;
2023-01-11T21:41:26.4724598Z                     auto tmp78 = tmp75 && tmp77;
2023-01-11T21:41:26.4724738Z                     auto tmp79 = static_cast<long>(tmp69);
2023-01-11T21:41:26.4724870Z                     auto tmp80 = tmp78 ? tmp79 : tmp65;
2023-01-11T21:41:26.4725000Z                     auto tmp81 = tmp78 ? tmp67 : tmp65;
2023-01-11T21:41:26.4725174Z                     auto tmp82 = tmp50 - tmp51;
2023-01-11T21:41:26.4725294Z                     auto tmp83 = tmp82 * tmp72;
2023-01-11T21:41:26.4725427Z                     auto tmp84 = tmp78 ? tmp83 : tmp5;
2023-01-11T21:41:26.4725548Z                     auto tmp85 = tmp71 >= tmp5;
2023-01-11T21:41:26.4725670Z                     auto tmp86 = tmp71 < tmp7;
2023-01-11T21:41:26.4725796Z                     auto tmp87 = tmp85 && tmp86;
2023-01-11T21:41:26.4725920Z                     auto tmp88 = tmp53 && tmp87;
2023-01-11T21:41:26.4726027Z                     auto tmp89 = tmp52 && tmp88;
2023-01-11T21:41:26.4726160Z                     auto tmp90 = tmp89 ? tmp64 : tmp65;
2023-01-11T21:41:26.4726300Z                     auto tmp91 = static_cast<long>(tmp71);
2023-01-11T21:41:26.4726430Z                     auto tmp92 = tmp89 ? tmp91 : tmp65;
2023-01-11T21:41:26.4726621Z                     auto tmp93 = tmp57 - tmp58;
2023-01-11T21:41:26.4726741Z                     auto tmp94 = tmp70 * tmp93;
2023-01-11T21:41:26.4726873Z                     auto tmp95 = tmp89 ? tmp94 : tmp5;
2023-01-11T21:41:26.4726981Z                     auto tmp96 = tmp76 && tmp87;
2023-01-11T21:41:26.4727102Z                     auto tmp97 = tmp75 && tmp96;
2023-01-11T21:41:26.4727233Z                     auto tmp98 = tmp97 ? tmp79 : tmp65;
2023-01-11T21:41:26.4727365Z                     auto tmp99 = tmp97 ? tmp91 : tmp65;
2023-01-11T21:41:26.4727490Z                     auto tmp100 = tmp82 * tmp93;
2023-01-11T21:41:26.4727626Z                     auto tmp101 = tmp97 ? tmp100 : tmp5;
2023-01-11T21:41:26.4727775Z                     out_ptr0[i0] = tmp24;
2023-01-11T21:41:26.4727887Z                     out_ptr1[i0] = tmp31;
2023-01-11T21:41:26.4727983Z                     out_ptr2[i0] = tmp39;
2023-01-11T21:41:26.4728091Z                     out_ptr3[i0] = tmp43;
2023-01-11T21:41:26.4728199Z                     out_ptr4[i0] = tmp66;
2023-01-11T21:41:26.4728308Z                     out_ptr5[i0] = tmp68;
2023-01-11T21:41:26.4728416Z                     out_ptr6[i0] = tmp74;
2023-01-11T21:41:26.4728526Z                     out_ptr7[i0] = tmp80;
2023-01-11T21:41:26.4728635Z                     out_ptr8[i0] = tmp81;
2023-01-11T21:41:26.4728729Z                     out_ptr9[i0] = tmp84;
2023-01-11T21:41:26.4728840Z                     out_ptr10[i0] = tmp90;
2023-01-11T21:41:26.4728953Z                     out_ptr11[i0] = tmp92;
2023-01-11T21:41:26.4729247Z                     out_ptr12[i0] = tmp95;
2023-01-11T21:41:26.4729364Z                     out_ptr13[i0] = tmp98;
2023-01-11T21:41:26.4729478Z                     out_ptr14[i0] = tmp99;
2023-01-11T21:41:26.4729595Z                     out_ptr15[i0] = tmp101;
2023-01-11T21:41:26.4729668Z                 }
2023-01-11T21:41:26.4729753Z             }
2023-01-11T21:41:26.4729835Z         }
2023-01-11T21:41:26.4729937Z         #pragma omp for 
2023-01-11T21:41:26.4730048Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.4730134Z         {
2023-01-11T21:41:26.4730226Z             #pragma GCC ivdep
2023-01-11T21:41:26.4730337Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.4730420Z             {
2023-01-11T21:41:26.4730528Z                 #pragma GCC ivdep
2023-01-11T21:41:26.4730650Z                 for(long i2=0; i2<123904; i2+=1)
2023-01-11T21:41:26.4730736Z                 {
2023-01-11T21:41:26.4730828Z                     {
2023-01-11T21:41:26.4730906Z                         {
2023-01-11T21:41:26.4731051Z                             auto tmp2 = in_ptr0[(2*i2) + (247808*i0)];
2023-01-11T21:41:26.4731195Z                             auto tmp11 = in_ptr0[1 + (2*i2) + (247808*i0)];
2023-01-11T21:41:26.4731345Z                             auto tmp51 = out_ptr0[i2 + (123904*i0)];
2023-01-11T21:41:26.4731484Z                             auto tmp53 = out_ptr1[i2 + (123904*i0)];
2023-01-11T21:41:26.4731625Z                             auto tmp56 = out_ptr2[i2 + (123904*i0)];
2023-01-11T21:41:26.4731762Z                             auto tmp59 = out_ptr3[i2 + (123904*i0)];
2023-01-11T21:41:26.4731906Z                             auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:26.4732032Z                             auto tmp1 = static_cast<long>(i1);
2023-01-11T21:41:26.4732181Z                             auto tmp3 = static_cast<float>(175.5);
2023-01-11T21:41:26.4732306Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.4732431Z                             auto tmp5 = tmp4 + tmp3;
2023-01-11T21:41:26.4732576Z                             auto tmp6 = std::floor(tmp5);
2023-01-11T21:41:26.4732717Z                             auto tmp7 = static_cast<float>(0);
2023-01-11T21:41:26.4732846Z                             auto tmp8 = tmp6 >= tmp7;
2023-01-11T21:41:26.4732975Z                             auto tmp9 = static_cast<float>(352);
2023-01-11T21:41:26.4733105Z                             auto tmp10 = tmp6 < tmp9;
2023-01-11T21:41:26.4733232Z                             auto tmp12 = tmp11 * tmp3;
2023-01-11T21:41:26.4733361Z                             auto tmp13 = tmp12 + tmp3;
2023-01-11T21:41:26.4733503Z                             auto tmp14 = std::floor(tmp13);
2023-01-11T21:41:26.4733630Z                             auto tmp15 = tmp14 >= tmp7;
2023-01-11T21:41:26.4733759Z                             auto tmp16 = tmp14 < tmp9;
2023-01-11T21:41:26.4733888Z                             auto tmp17 = tmp15 && tmp16;
2023-01-11T21:41:26.4734006Z                             auto tmp18 = tmp10 && tmp17;
2023-01-11T21:41:26.4734129Z                             auto tmp19 = tmp8 && tmp18;
2023-01-11T21:41:26.4734275Z                             auto tmp20 = static_cast<long>(tmp14);
2023-01-11T21:41:26.4734462Z                             auto tmp21 = static_cast<long>(0);
2023-01-11T21:41:26.4734601Z                             auto tmp22 = tmp19 ? tmp20 : tmp21;
2023-01-11T21:41:26.4734745Z                             auto tmp23 = static_cast<long>(tmp6);
2023-01-11T21:41:26.4734883Z                             auto tmp24 = tmp19 ? tmp23 : tmp21;
2023-01-11T21:41:26.4735055Z                             auto tmp25 = in_ptr1[tmp24 + (352*tmp22) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:26.4735183Z                             auto tmp26 = static_cast<float>(1);
2023-01-11T21:41:26.4735308Z                             auto tmp27 = tmp6 + tmp26;
2023-01-11T21:41:26.4735432Z                             auto tmp28 = tmp27 >= tmp7;
2023-01-11T21:41:26.4735590Z                             auto tmp29 = tmp27 < tmp9;
2023-01-11T21:41:26.4735721Z                             auto tmp30 = tmp29 && tmp17;
2023-01-11T21:41:26.4735847Z                             auto tmp31 = tmp28 && tmp30;
2023-01-11T21:41:26.4735990Z                             auto tmp32 = tmp31 ? tmp20 : tmp21;
2023-01-11T21:41:26.4736119Z                             auto tmp33 = static_cast<long>(tmp27);
2023-01-11T21:41:26.4736257Z                             auto tmp34 = tmp31 ? tmp33 : tmp21;
2023-01-11T21:41:26.4736429Z                             auto tmp35 = in_ptr1[tmp34 + (352*tmp32) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:26.4736559Z                             auto tmp36 = tmp14 + tmp26;
2023-01-11T21:41:26.4736686Z                             auto tmp37 = tmp36 >= tmp7;
2023-01-11T21:41:26.4736812Z                             auto tmp38 = tmp36 < tmp9;
2023-01-11T21:41:26.4736943Z                             auto tmp39 = tmp37 && tmp38;
2023-01-11T21:41:26.4737074Z                             auto tmp40 = tmp10 && tmp39;
2023-01-11T21:41:26.4737185Z                             auto tmp41 = tmp8 && tmp40;
2023-01-11T21:41:26.4737330Z                             auto tmp42 = static_cast<long>(tmp36);
2023-01-11T21:41:26.4737469Z                             auto tmp43 = tmp41 ? tmp42 : tmp21;
2023-01-11T21:41:26.4737608Z                             auto tmp44 = tmp41 ? tmp23 : tmp21;
2023-01-11T21:41:26.4737777Z                             auto tmp45 = in_ptr1[tmp44 + (352*tmp43) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:26.4737905Z                             auto tmp46 = tmp29 && tmp39;
2023-01-11T21:41:26.4738033Z                             auto tmp47 = tmp28 && tmp46;
2023-01-11T21:41:26.4738153Z                             auto tmp48 = tmp47 ? tmp42 : tmp21;
2023-01-11T21:41:26.4738288Z                             auto tmp49 = tmp47 ? tmp33 : tmp21;
2023-01-11T21:41:26.4738456Z                             auto tmp50 = in_ptr1[tmp49 + (352*tmp48) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:26.4738585Z                             auto tmp52 = tmp25 * tmp51;
2023-01-11T21:41:26.4738711Z                             auto tmp54 = tmp35 * tmp53;
2023-01-11T21:41:26.4738841Z                             auto tmp55 = tmp52 + tmp54;
2023-01-11T21:41:26.4738968Z                             auto tmp57 = tmp45 * tmp56;
2023-01-11T21:41:26.4739095Z                             auto tmp58 = tmp55 + tmp57;
2023-01-11T21:41:26.4739203Z                             auto tmp60 = tmp50 * tmp59;
2023-01-11T21:41:26.4739329Z                             auto tmp61 = tmp58 + tmp60;
2023-01-11T21:41:26.4739476Z                             in_out_ptr0[i2 + (123904*i1) + (371712*i0)] = tmp61;
2023-01-11T21:41:26.4739569Z                         }
2023-01-11T21:41:26.4739660Z                     }
2023-01-11T21:41:26.4739746Z                 }
2023-01-11T21:41:26.4739830Z             }
2023-01-11T21:41:26.4739899Z         }
2023-01-11T21:41:26.4740001Z         #pragma omp for 
2023-01-11T21:41:26.4740113Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.4740197Z         {
2023-01-11T21:41:26.4740304Z             #pragma GCC ivdep
2023-01-11T21:41:26.4740415Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.4740540Z             {
2023-01-11T21:41:26.4740634Z                 #pragma GCC ivdep
2023-01-11T21:41:26.4740754Z                 for(long i2=0; i2<123904; i2+=1)
2023-01-11T21:41:26.4740840Z                 {
2023-01-11T21:41:26.4740929Z                     {
2023-01-11T21:41:26.4741021Z                         {
2023-01-11T21:41:26.4741164Z                             auto tmp2 = out_ptr5[i2 + (123904*i0)];
2023-01-11T21:41:26.4741300Z                             auto tmp3 = out_ptr4[i2 + (123904*i0)];
2023-01-11T21:41:26.4741420Z                             auto tmp5 = out_ptr6[i2 + (123904*i0)];
2023-01-11T21:41:26.4741559Z                             auto tmp7 = out_ptr8[i2 + (123904*i0)];
2023-01-11T21:41:26.4741694Z                             auto tmp8 = out_ptr7[i2 + (123904*i0)];
2023-01-11T21:41:26.4741869Z                             auto tmp10 = out_ptr9[i2 + (123904*i0)];
2023-01-11T21:41:26.4742016Z                             auto tmp13 = out_ptr11[i2 + (123904*i0)];
2023-01-11T21:41:26.4742157Z                             auto tmp14 = out_ptr10[i2 + (123904*i0)];
2023-01-11T21:41:26.4742293Z                             auto tmp16 = out_ptr12[i2 + (123904*i0)];
2023-01-11T21:41:26.4742414Z                             auto tmp19 = out_ptr14[i2 + (123904*i0)];
2023-01-11T21:41:26.4742554Z                             auto tmp20 = out_ptr13[i2 + (123904*i0)];
2023-01-11T21:41:26.4742693Z                             auto tmp22 = out_ptr15[i2 + (123904*i0)];
2023-01-11T21:41:26.4742835Z                             auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:26.4742976Z                             auto tmp1 = static_cast<long>(i1);
2023-01-11T21:41:26.4743210Z                             auto tmp4 = in_ptr1[tmp3 + (352*tmp2) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:26.4743344Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.4743514Z                             auto tmp9 = in_ptr1[tmp8 + (352*tmp7) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:26.4743632Z                             auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:26.4743759Z                             auto tmp12 = tmp6 + tmp11;
2023-01-11T21:41:26.4743931Z                             auto tmp15 = in_ptr1[tmp14 + (352*tmp13) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:26.4744061Z                             auto tmp17 = tmp15 * tmp16;
2023-01-11T21:41:26.4744189Z                             auto tmp18 = tmp12 + tmp17;
2023-01-11T21:41:26.4744361Z                             auto tmp21 = in_ptr1[tmp20 + (352*tmp19) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:41:26.4744491Z                             auto tmp23 = tmp21 * tmp22;
2023-01-11T21:41:26.4744622Z                             auto tmp24 = tmp18 + tmp23;
2023-01-11T21:41:26.4744763Z                             in_out_ptr1[i2 + (123904*i1) + (371712*i0)] = tmp24;
2023-01-11T21:41:26.4744859Z                         }
2023-01-11T21:41:26.4744953Z                     }
2023-01-11T21:41:26.4745050Z                 }
2023-01-11T21:41:26.4745139Z             }
2023-01-11T21:41:26.4745227Z         }
2023-01-11T21:41:26.4745315Z     }
2023-01-11T21:41:26.4745385Z }
2023-01-11T21:41:26.4745512Z ''')
2023-01-11T21:41:26.4745522Z 
2023-01-11T21:41:26.4745527Z 
2023-01-11T21:41:26.4745649Z async_compile.wait(globals())
2023-01-11T21:41:26.4745753Z del async_compile
2023-01-11T21:41:26.4745760Z 
2023-01-11T21:41:26.4745858Z def call(args):
2023-01-11T21:41:26.4745962Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4746061Z     args.clear()
2023-01-11T21:41:26.4746533Z     buf0 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4746954Z     buf2 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4747545Z     buf4 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4748062Z     buf6 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4748657Z     buf9 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4749168Z     buf10 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4749696Z     buf11 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4750220Z     buf12 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4750737Z     buf13 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4751254Z     buf14 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4751769Z     buf15 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4752360Z     buf16 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4752832Z     buf17 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4753220Z     buf19 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4753607Z     buf20 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4753993Z     buf21 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4754417Z     buf1 = empty_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4754575Z     buf8 = buf1; del buf1  # reuse
2023-01-11T21:41:26.4754991Z     buf18 = empty_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4755147Z     buf22 = buf18; del buf18  # reuse
2023-01-11T21:41:26.4756048Z     kernel_cpp_0(c_void_p(buf8.data_ptr()), c_void_p(buf22.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf13.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf15.data_ptr()), c_void_p(buf16.data_ptr()), c_void_p(buf17.data_ptr()), c_void_p(buf19.data_ptr()), c_void_p(buf20.data_ptr()), c_void_p(buf21.data_ptr()))
2023-01-11T21:41:26.4756152Z     del arg0_1
2023-01-11T21:41:26.4756251Z     del arg1_1
2023-01-11T21:41:26.4756361Z     return (buf8, buf22, )
2023-01-11T21:41:26.4756369Z 
2023-01-11T21:41:26.4756374Z 
2023-01-11T21:41:26.4756476Z if __name__ == "__main__":
2023-01-11T21:41:26.4756635Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4756805Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4757112Z     arg0_1 = rand_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4757432Z     arg1_1 = rand_strided((4, 352, 352, 2), (247808, 704, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4757667Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4758320Z [2023-01-11 21:34:23,534] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 222
2023-01-11T21:41:26.4758332Z 
2023-01-11T21:41:26.4758581Z ok (3.807s)
2023-01-11T21:41:26.4759694Z   test_hardsigmoid_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4760002Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4760650Z [2023-01-11 21:34:23,675] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 223
2023-01-11T21:41:26.4761392Z [2023-01-11 21:34:25,392] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 223
2023-01-11T21:41:26.4761405Z 
2023-01-11T21:41:26.4761630Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4761783Z import torch
2023-01-11T21:41:26.4761949Z import random
2023-01-11T21:41:26.4762222Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4762514Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4762526Z 
2023-01-11T21:41:26.4762711Z aten = torch.ops.aten
2023-01-11T21:41:26.4763042Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4763257Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4763270Z 
2023-01-11T21:41:26.4763279Z 
2023-01-11T21:41:26.4763609Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4763959Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4764176Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4764354Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4764531Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4764701Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4764811Z {
2023-01-11T21:41:26.4764990Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4765085Z     {
2023-01-11T21:41:26.4765225Z         #pragma omp for 
2023-01-11T21:41:26.4765376Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4765495Z         {
2023-01-11T21:41:26.4765745Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4765984Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.4766201Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4766489Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:41:26.4766643Z             auto tmp4 = at::vec::maximum(tmp2, tmp3);
2023-01-11T21:41:26.4766827Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(6.0));
2023-01-11T21:41:26.4766980Z             auto tmp6 = at::vec::minimum(tmp4, tmp5);
2023-01-11T21:41:26.4767164Z             auto tmp7 = at::vec::Vectorized<float>(static_cast<float>(6));
2023-01-11T21:41:26.4767281Z             auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:26.4767399Z             auto tmp9 = tmp2 + tmp1;
2023-01-11T21:41:26.4767550Z             auto tmp10 = at::vec::maximum(tmp9, tmp3);
2023-01-11T21:41:26.4767687Z             auto tmp11 = at::vec::minimum(tmp10, tmp5);
2023-01-11T21:41:26.4767807Z             auto tmp12 = tmp11 / tmp7;
2023-01-11T21:41:26.4767985Z             auto tmp13 = tmp0 - tmp1;
2023-01-11T21:41:26.4768105Z             auto tmp14 = tmp13 + tmp1;
2023-01-11T21:41:26.4768262Z             auto tmp15 = at::vec::maximum(tmp14, tmp3);
2023-01-11T21:41:26.4768411Z             auto tmp16 = at::vec::minimum(tmp15, tmp5);
2023-01-11T21:41:26.4768582Z             auto tmp17 = tmp16 / tmp7;
2023-01-11T21:41:26.4768743Z             tmp8.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4768893Z             tmp12.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4769306Z             tmp17.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4769422Z         }
2023-01-11T21:41:26.4769693Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4769975Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.4770122Z         {
2023-01-11T21:41:26.4770298Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4770538Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.4770731Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4770974Z             auto tmp3 = static_cast<float>(0.0);
2023-01-11T21:41:26.4771272Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::max(tmp2, tmp3);
2023-01-11T21:41:26.4771516Z             auto tmp5 = static_cast<float>(6.0);
2023-01-11T21:41:26.4771802Z             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::min(tmp4, tmp5);
2023-01-11T21:41:26.4772133Z             auto tmp7 = static_cast<float>(6);
2023-01-11T21:41:26.4772318Z             auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:26.4772522Z             auto tmp9 = tmp2 + tmp1;
2023-01-11T21:41:26.4772813Z             auto tmp10 = (tmp3 != tmp3) ? tmp3 : std::max(tmp9, tmp3);
2023-01-11T21:41:26.4773100Z             auto tmp11 = (tmp5 != tmp5) ? tmp5 : std::min(tmp10, tmp5);
2023-01-11T21:41:26.4773301Z             auto tmp12 = tmp11 / tmp7;
2023-01-11T21:41:26.4773619Z             auto tmp13 = tmp0 - tmp1;
2023-01-11T21:41:26.4773824Z             auto tmp14 = tmp13 + tmp1;
2023-01-11T21:41:26.4774110Z             auto tmp15 = (tmp3 != tmp3) ? tmp3 : std::max(tmp14, tmp3);
2023-01-11T21:41:26.4774378Z             auto tmp16 = (tmp5 != tmp5) ? tmp5 : std::min(tmp15, tmp5);
2023-01-11T21:41:26.4774643Z             auto tmp17 = tmp16 / tmp7;
2023-01-11T21:41:26.4774796Z             out_ptr0[i0] = tmp8;
2023-01-11T21:41:26.4774944Z             out_ptr1[i0] = tmp12;
2023-01-11T21:41:26.4775093Z             out_ptr2[i0] = tmp17;
2023-01-11T21:41:26.4775209Z         }
2023-01-11T21:41:26.4775317Z     }
2023-01-11T21:41:26.4775409Z }
2023-01-11T21:41:26.4775559Z ''')
2023-01-11T21:41:26.4775569Z 
2023-01-11T21:41:26.4775576Z 
2023-01-11T21:41:26.4775735Z async_compile.wait(globals())
2023-01-11T21:41:26.4775870Z del async_compile
2023-01-11T21:41:26.4775879Z 
2023-01-11T21:41:26.4776004Z def call(args):
2023-01-11T21:41:26.4776132Z     arg0_1, = args
2023-01-11T21:41:26.4776252Z     args.clear()
2023-01-11T21:41:26.4776604Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4776961Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4777315Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4777728Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4777826Z     del arg0_1
2023-01-11T21:41:26.4777938Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.4777945Z 
2023-01-11T21:41:26.4777950Z 
2023-01-11T21:41:26.4778054Z if __name__ == "__main__":
2023-01-11T21:41:26.4778213Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4778365Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4778636Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4778787Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4778794Z 
2023-01-11T21:41:26.4778884Z ok (1.771s)
2023-01-11T21:41:26.4779541Z   test_hardswish_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4779797Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4780392Z [2023-01-11 21:34:25,453] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 224
2023-01-11T21:41:26.4781209Z [2023-01-11 21:34:27,260] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 224
2023-01-11T21:41:26.4781224Z 
2023-01-11T21:41:26.4781448Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4781616Z import torch
2023-01-11T21:41:26.4781761Z import random
2023-01-11T21:41:26.4782037Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4782323Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4782335Z 
2023-01-11T21:41:26.4782520Z aten = torch.ops.aten
2023-01-11T21:41:26.4782845Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4783073Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4783231Z 
2023-01-11T21:41:26.4783241Z 
2023-01-11T21:41:26.4783575Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4784052Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4784335Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4784573Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4784805Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4785037Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4785185Z {
2023-01-11T21:41:26.4785424Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4785578Z     {
2023-01-11T21:41:26.4785748Z         #pragma omp for 
2023-01-11T21:41:26.4785901Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4786014Z         {
2023-01-11T21:41:26.4786306Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4786551Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.4786709Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4786955Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:41:26.4787128Z             auto tmp4 = at::vec::maximum(tmp2, tmp3);
2023-01-11T21:41:26.4787371Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(6.0));
2023-01-11T21:41:26.4787567Z             auto tmp6 = at::vec::minimum(tmp4, tmp5);
2023-01-11T21:41:26.4787720Z             auto tmp7 = tmp0 * tmp6;
2023-01-11T21:41:26.4787959Z             auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(6));
2023-01-11T21:41:26.4788113Z             auto tmp9 = tmp7 / tmp8;
2023-01-11T21:41:26.4788265Z             auto tmp10 = tmp2 + tmp1;
2023-01-11T21:41:26.4788590Z             auto tmp11 = at::vec::maximum(tmp10, tmp3);
2023-01-11T21:41:26.4788893Z             auto tmp12 = at::vec::minimum(tmp11, tmp5);
2023-01-11T21:41:26.4789073Z             auto tmp13 = tmp2 * tmp12;
2023-01-11T21:41:26.4789238Z             auto tmp14 = tmp13 / tmp8;
2023-01-11T21:41:26.4789646Z             auto tmp15 = tmp0 - tmp1;
2023-01-11T21:41:26.4789843Z             auto tmp16 = tmp15 + tmp1;
2023-01-11T21:41:26.4790098Z             auto tmp17 = at::vec::maximum(tmp16, tmp3);
2023-01-11T21:41:26.4790352Z             auto tmp18 = at::vec::minimum(tmp17, tmp5);
2023-01-11T21:41:26.4790546Z             auto tmp19 = tmp15 * tmp18;
2023-01-11T21:41:26.4790749Z             auto tmp20 = tmp19 / tmp8;
2023-01-11T21:41:26.4790962Z             tmp9.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4791182Z             tmp14.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4791397Z             tmp20.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4791552Z         }
2023-01-11T21:41:26.4791780Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4791962Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.4792112Z         {
2023-01-11T21:41:26.4792317Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4792558Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.4792753Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4792992Z             auto tmp3 = static_cast<float>(0.0);
2023-01-11T21:41:26.4793282Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::max(tmp2, tmp3);
2023-01-11T21:41:26.4793504Z             auto tmp5 = static_cast<float>(6.0);
2023-01-11T21:41:26.4793792Z             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::min(tmp4, tmp5);
2023-01-11T21:41:26.4793992Z             auto tmp7 = tmp0 * tmp6;
2023-01-11T21:41:26.4794221Z             auto tmp8 = static_cast<float>(6);
2023-01-11T21:41:26.4794408Z             auto tmp9 = tmp7 / tmp8;
2023-01-11T21:41:26.4794562Z             auto tmp10 = tmp2 + tmp1;
2023-01-11T21:41:26.4794785Z             auto tmp11 = (tmp3 != tmp3) ? tmp3 : std::max(tmp10, tmp3);
2023-01-11T21:41:26.4795011Z             auto tmp12 = (tmp5 != tmp5) ? tmp5 : std::min(tmp11, tmp5);
2023-01-11T21:41:26.4795153Z             auto tmp13 = tmp2 * tmp12;
2023-01-11T21:41:26.4795397Z             auto tmp14 = tmp13 / tmp8;
2023-01-11T21:41:26.4795665Z             auto tmp15 = tmp0 - tmp1;
2023-01-11T21:41:26.4795865Z             auto tmp16 = tmp15 + tmp1;
2023-01-11T21:41:26.4796142Z             auto tmp17 = (tmp3 != tmp3) ? tmp3 : std::max(tmp16, tmp3);
2023-01-11T21:41:26.4796423Z             auto tmp18 = (tmp5 != tmp5) ? tmp5 : std::min(tmp17, tmp5);
2023-01-11T21:41:26.4796620Z             auto tmp19 = tmp15 * tmp18;
2023-01-11T21:41:26.4796824Z             auto tmp20 = tmp19 / tmp8;
2023-01-11T21:41:26.4796995Z             out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.4797177Z             out_ptr1[i0] = tmp14;
2023-01-11T21:41:26.4797365Z             out_ptr2[i0] = tmp20;
2023-01-11T21:41:26.4797521Z         }
2023-01-11T21:41:26.4797674Z     }
2023-01-11T21:41:26.4797823Z }
2023-01-11T21:41:26.4798003Z ''')
2023-01-11T21:41:26.4798093Z 
2023-01-11T21:41:26.4798108Z 
2023-01-11T21:41:26.4798310Z async_compile.wait(globals())
2023-01-11T21:41:26.4798482Z del async_compile
2023-01-11T21:41:26.4798499Z 
2023-01-11T21:41:26.4798670Z def call(args):
2023-01-11T21:41:26.4798831Z     arg0_1, = args
2023-01-11T21:41:26.4798997Z     args.clear()
2023-01-11T21:41:26.4799473Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4799948Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4800389Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4800828Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4800998Z     del arg0_1
2023-01-11T21:41:26.4801195Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.4801206Z 
2023-01-11T21:41:26.4801216Z 
2023-01-11T21:41:26.4801389Z if __name__ == "__main__":
2023-01-11T21:41:26.4801603Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4801828Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4802196Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4802375Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4802385Z 
2023-01-11T21:41:26.4802502Z ok (1.867s)
2023-01-11T21:41:26.4803566Z   test_hardtanh_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4803859Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4804498Z [2023-01-11 21:34:27,301] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 225
2023-01-11T21:41:26.4805174Z [2023-01-11 21:34:29,001] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 225
2023-01-11T21:41:26.4805191Z 
2023-01-11T21:41:26.4805416Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4805592Z import torch
2023-01-11T21:41:26.4805763Z import random
2023-01-11T21:41:26.4806024Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4806318Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4806330Z 
2023-01-11T21:41:26.4806519Z aten = torch.ops.aten
2023-01-11T21:41:26.4806829Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4807048Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4807059Z 
2023-01-11T21:41:26.4807068Z 
2023-01-11T21:41:26.4807403Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4807902Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4808192Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4808388Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4808633Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4808805Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4808916Z {
2023-01-11T21:41:26.4809234Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4809347Z     {
2023-01-11T21:41:26.4809485Z         #pragma omp for 
2023-01-11T21:41:26.4809613Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4809745Z         {
2023-01-11T21:41:26.4810055Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4810543Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.0));
2023-01-11T21:41:26.4810803Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:41:26.4811126Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:41:26.4811480Z             auto tmp4 = at::vec::minimum(tmp2, tmp3);
2023-01-11T21:41:26.4811806Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4812004Z             auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:26.4812260Z             auto tmp7 = at::vec::maximum(tmp6, tmp1);
2023-01-11T21:41:26.4812509Z             auto tmp8 = at::vec::minimum(tmp7, tmp3);
2023-01-11T21:41:26.4812818Z             auto tmp9 = tmp0 - tmp5;
2023-01-11T21:41:26.4813079Z             auto tmp10 = at::vec::maximum(tmp9, tmp1);
2023-01-11T21:41:26.4813331Z             auto tmp11 = at::vec::minimum(tmp10, tmp3);
2023-01-11T21:41:26.4813555Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4813764Z             tmp8.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4813968Z             tmp11.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4814124Z         }
2023-01-11T21:41:26.4814348Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4814547Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.4814698Z         {
2023-01-11T21:41:26.4814898Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4815266Z             auto tmp1 = static_cast<float>(-1.0);
2023-01-11T21:41:26.4815526Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:26.4815711Z             auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.4815935Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:41:26.4816114Z             auto tmp5 = static_cast<float>(1);
2023-01-11T21:41:26.4816268Z             auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:26.4816486Z             auto tmp7 = (tmp1 != tmp1) ? tmp1 : std::max(tmp6, tmp1);
2023-01-11T21:41:26.4816708Z             auto tmp8 = (tmp3 != tmp3) ? tmp3 : std::min(tmp7, tmp3);
2023-01-11T21:41:26.4817015Z             auto tmp9 = tmp0 - tmp5;
2023-01-11T21:41:26.4817286Z             auto tmp10 = (tmp1 != tmp1) ? tmp1 : std::max(tmp9, tmp1);
2023-01-11T21:41:26.4817580Z             auto tmp11 = (tmp3 != tmp3) ? tmp3 : std::min(tmp10, tmp3);
2023-01-11T21:41:26.4817777Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.4817969Z             out_ptr1[i0] = tmp8;
2023-01-11T21:41:26.4818160Z             out_ptr2[i0] = tmp11;
2023-01-11T21:41:26.4818309Z         }
2023-01-11T21:41:26.4818460Z     }
2023-01-11T21:41:26.4818594Z }
2023-01-11T21:41:26.4818784Z ''')
2023-01-11T21:41:26.4818797Z 
2023-01-11T21:41:26.4818807Z 
2023-01-11T21:41:26.4819021Z async_compile.wait(globals())
2023-01-11T21:41:26.4819200Z del async_compile
2023-01-11T21:41:26.4819212Z 
2023-01-11T21:41:26.4819374Z def call(args):
2023-01-11T21:41:26.4819537Z     arg0_1, = args
2023-01-11T21:41:26.4819694Z     args.clear()
2023-01-11T21:41:26.4820159Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4820631Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4821098Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4821552Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4821812Z     del arg0_1
2023-01-11T21:41:26.4822011Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.4822024Z 
2023-01-11T21:41:26.4822033Z 
2023-01-11T21:41:26.4822215Z if __name__ == "__main__":
2023-01-11T21:41:26.4822485Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4822693Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4823051Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4823341Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.4823352Z 
2023-01-11T21:41:26.4823473Z ok (1.743s)
2023-01-11T21:41:26.4824573Z   test_horizonal_fusion1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4824889Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4825510Z [2023-01-11 21:34:29,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 226
2023-01-11T21:41:26.4826173Z [2023-01-11 21:34:30,698] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 226
2023-01-11T21:41:26.4826185Z 
2023-01-11T21:41:26.4826407Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4826563Z import torch
2023-01-11T21:41:26.4826721Z import random
2023-01-11T21:41:26.4827003Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4827295Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4827307Z 
2023-01-11T21:41:26.4827492Z aten = torch.ops.aten
2023-01-11T21:41:26.4827820Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4828043Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4828060Z 
2023-01-11T21:41:26.4828069Z 
2023-01-11T21:41:26.4828398Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4828887Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4829160Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4829407Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4829625Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.4829810Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4829985Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4830156Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4830271Z {
2023-01-11T21:41:26.4830431Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4830548Z     {
2023-01-11T21:41:26.4830688Z         #pragma omp for 
2023-01-11T21:41:26.4830870Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.4831028Z         {
2023-01-11T21:41:26.4831357Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4831667Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.4831844Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4832057Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4832205Z         }
2023-01-11T21:41:26.4832428Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4832640Z         for(long i0=2048; i0<2048; i0+=1)
2023-01-11T21:41:26.4832793Z         {
2023-01-11T21:41:26.4832996Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4833183Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4833389Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4833577Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4833740Z         }
2023-01-11T21:41:26.4833923Z         #pragma omp for 
2023-01-11T21:41:26.4834114Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4834338Z         {
2023-01-11T21:41:26.4834515Z             #pragma GCC ivdep
2023-01-11T21:41:26.4834719Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.4834869Z             {
2023-01-11T21:41:26.4835078Z                 for(long i2=0; i2<2; i2+=1)
2023-01-11T21:41:26.4835239Z                 {
2023-01-11T21:41:26.4835595Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (16*i1) + (256*i0));
2023-01-11T21:41:26.4835899Z                     auto tmp1 = at::vec::Vectorized<float>(in_ptr2[i1]);
2023-01-11T21:41:26.4836227Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i2) + (16*i1) + (256*i0));
2023-01-11T21:41:26.4836551Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.4836717Z                     auto tmp4 = tmp3 * tmp1;
2023-01-11T21:41:26.4836979Z                     tmp2.store(out_ptr1 + (8*i2) + (16*i1) + (256*i0));
2023-01-11T21:41:26.4837181Z                     tmp4.store(out_ptr2 + (8*i2) + (16*i1) + (256*i0));
2023-01-11T21:41:26.4837303Z                 }
2023-01-11T21:41:26.4837474Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.4837653Z                 for(long i2=16; i2<16; i2+=1)
2023-01-11T21:41:26.4837791Z                 {
2023-01-11T21:41:26.4838036Z                     auto tmp0 = in_ptr0[i2 + (16*i1) + (256*i0)];
2023-01-11T21:41:26.4838248Z                     auto tmp1 = in_ptr2[i1];
2023-01-11T21:41:26.4838494Z                     auto tmp3 = in_ptr1[i2 + (16*i1) + (256*i0)];
2023-01-11T21:41:26.4838815Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.4839027Z                     auto tmp4 = tmp3 * tmp1;
2023-01-11T21:41:26.4839258Z                     out_ptr1[i2 + (16*i1) + (256*i0)] = tmp2;
2023-01-11T21:41:26.4839480Z                     out_ptr2[i2 + (16*i1) + (256*i0)] = tmp4;
2023-01-11T21:41:26.4839648Z                 }
2023-01-11T21:41:26.4839805Z             }
2023-01-11T21:41:26.4839953Z         }
2023-01-11T21:41:26.4840100Z     }
2023-01-11T21:41:26.4840255Z }
2023-01-11T21:41:26.4840453Z ''')
2023-01-11T21:41:26.4840465Z 
2023-01-11T21:41:26.4840475Z 
2023-01-11T21:41:26.4840669Z async_compile.wait(globals())
2023-01-11T21:41:26.4840837Z del async_compile
2023-01-11T21:41:26.4840849Z 
2023-01-11T21:41:26.4841024Z def call(args):
2023-01-11T21:41:26.4841227Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4841392Z     args.clear()
2023-01-11T21:41:26.4841909Z     buf0 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4842410Z     buf1 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4842910Z     buf2 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4843434Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4843559Z     del arg0_1
2023-01-11T21:41:26.4843685Z     del arg1_1
2023-01-11T21:41:26.4843804Z     del arg2_1
2023-01-11T21:41:26.4843952Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.4843961Z 
2023-01-11T21:41:26.4843968Z 
2023-01-11T21:41:26.4844105Z if __name__ == "__main__":
2023-01-11T21:41:26.4844311Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4844512Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4844988Z     arg0_1 = rand_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4845481Z     arg1_1 = rand_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4845963Z     arg2_1 = rand_strided((1, 16, 1), (16, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4846246Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4846267Z 
2023-01-11T21:41:26.4846430Z ok (1.697s)
2023-01-11T21:41:26.4847644Z   test_horizonal_fusion2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4848017Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4848677Z [2023-01-11 21:34:30,729] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 227
2023-01-11T21:41:26.4849484Z [2023-01-11 21:34:32,375] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 227
2023-01-11T21:41:26.4849497Z 
2023-01-11T21:41:26.4849707Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4849864Z import torch
2023-01-11T21:41:26.4850114Z import random
2023-01-11T21:41:26.4850399Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4850629Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4850643Z 
2023-01-11T21:41:26.4850782Z aten = torch.ops.aten
2023-01-11T21:41:26.4851017Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4851183Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4851194Z 
2023-01-11T21:41:26.4851201Z 
2023-01-11T21:41:26.4851437Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4851876Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4852162Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4852407Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4852642Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.4852880Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4853110Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4853335Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4853475Z {
2023-01-11T21:41:26.4853711Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4853861Z     {
2023-01-11T21:41:26.4854051Z         #pragma omp for 
2023-01-11T21:41:26.4854249Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.4854403Z         {
2023-01-11T21:41:26.4854704Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4855025Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4855232Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4855451Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4855608Z         }
2023-01-11T21:41:26.4855836Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4856037Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:41:26.4856193Z         {
2023-01-11T21:41:26.4856385Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4856615Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4856824Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4857015Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.4857171Z         }
2023-01-11T21:41:26.4857336Z         #pragma omp for 
2023-01-11T21:41:26.4857471Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.4857582Z         {
2023-01-11T21:41:26.4857817Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.4858056Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.4858209Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4858375Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4858521Z         }
2023-01-11T21:41:26.4858744Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4858921Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.4859073Z         {
2023-01-11T21:41:26.4859276Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.4859506Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.4859811Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4859996Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.4860141Z         }
2023-01-11T21:41:26.4860311Z         #pragma omp for 
2023-01-11T21:41:26.4860504Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.4860650Z         {
2023-01-11T21:41:26.4860955Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:26.4861271Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.4861474Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4861684Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4861817Z         }
2023-01-11T21:41:26.4862043Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4862235Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.4862388Z         {
2023-01-11T21:41:26.4862645Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:41:26.4862887Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.4863096Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4863348Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.4863506Z         }
2023-01-11T21:41:26.4863655Z     }
2023-01-11T21:41:26.4863801Z }
2023-01-11T21:41:26.4864010Z ''')
2023-01-11T21:41:26.4864023Z 
2023-01-11T21:41:26.4864032Z 
2023-01-11T21:41:26.4864233Z async_compile.wait(globals())
2023-01-11T21:41:26.4864365Z del async_compile
2023-01-11T21:41:26.4864376Z 
2023-01-11T21:41:26.4864482Z def call(args):
2023-01-11T21:41:26.4864626Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4864753Z     args.clear()
2023-01-11T21:41:26.4865146Z     buf0 = empty_strided((8, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4865579Z     buf1 = empty_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4866039Z     buf2 = empty_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4866592Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.4866760Z     del arg0_1
2023-01-11T21:41:26.4866901Z     del arg1_1
2023-01-11T21:41:26.4867058Z     del arg2_1
2023-01-11T21:41:26.4867257Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.4867268Z 
2023-01-11T21:41:26.4867277Z 
2023-01-11T21:41:26.4867450Z if __name__ == "__main__":
2023-01-11T21:41:26.4867724Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4868012Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4868520Z     arg0_1 = rand_strided((8, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4868986Z     arg1_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4869467Z     arg2_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4869757Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4869774Z 
2023-01-11T21:41:26.4869930Z ok (1.677s)
2023-01-11T21:41:26.4871097Z   test_index1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4871328Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4871821Z [2023-01-11 21:34:32,429] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 228
2023-01-11T21:41:26.4872339Z [2023-01-11 21:34:34,014] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 228
2023-01-11T21:41:26.4873343Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4873732Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4874360Z [2023-01-11 21:34:34,056] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 229
2023-01-11T21:41:26.4875006Z [2023-01-11 21:34:35,614] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 229
2023-01-11T21:41:26.4875036Z 
2023-01-11T21:41:26.4875238Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4875406Z import torch
2023-01-11T21:41:26.4875572Z import random
2023-01-11T21:41:26.4875913Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4876207Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4876219Z 
2023-01-11T21:41:26.4876416Z aten = torch.ops.aten
2023-01-11T21:41:26.4876739Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4876940Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4876952Z 
2023-01-11T21:41:26.4876961Z 
2023-01-11T21:41:26.4877293Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4877787Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4878040Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4878226Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4878412Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.4878590Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4878700Z {
2023-01-11T21:41:26.4878866Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4878983Z     {
2023-01-11T21:41:26.4879149Z         #pragma omp for 
2023-01-11T21:41:26.4879340Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.4879500Z         {
2023-01-11T21:41:26.4879699Z             #pragma GCC ivdep
2023-01-11T21:41:26.4879874Z             for(long i1=0; i1<12; i1+=1)
2023-01-11T21:41:26.4880029Z             {
2023-01-11T21:41:26.4880175Z                 {
2023-01-11T21:41:26.4880331Z                     {
2023-01-11T21:41:26.4880551Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4880766Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4881029Z                         auto tmp2 = in_ptr2[i1 + (12*tmp1) + (96*tmp0)];
2023-01-11T21:41:26.4881234Z                         out_ptr0[i1 + (12*i0)] = tmp2;
2023-01-11T21:41:26.4881393Z                     }
2023-01-11T21:41:26.4881544Z                 }
2023-01-11T21:41:26.4881700Z             }
2023-01-11T21:41:26.4881850Z         }
2023-01-11T21:41:26.4881996Z     }
2023-01-11T21:41:26.4882140Z }
2023-01-11T21:41:26.4882314Z ''')
2023-01-11T21:41:26.4882325Z 
2023-01-11T21:41:26.4882333Z 
2023-01-11T21:41:26.4882548Z async_compile.wait(globals())
2023-01-11T21:41:26.4882722Z del async_compile
2023-01-11T21:41:26.4882734Z 
2023-01-11T21:41:26.4882905Z def call(args):
2023-01-11T21:41:26.4883103Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4883273Z     args.clear()
2023-01-11T21:41:26.4883752Z     buf0 = empty_strided((4, 12), (12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4884179Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4884344Z     del arg0_1
2023-01-11T21:41:26.4884500Z     del arg1_1
2023-01-11T21:41:26.4884654Z     del arg2_1
2023-01-11T21:41:26.4884830Z     return (buf0, )
2023-01-11T21:41:26.4884840Z 
2023-01-11T21:41:26.4884846Z 
2023-01-11T21:41:26.4884977Z if __name__ == "__main__":
2023-01-11T21:41:26.4885187Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4885403Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4885773Z     arg0_1 = rand_strided((8, 8, 12), (96, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4886197Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4886674Z     arg2_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4886953Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4886964Z 
2023-01-11T21:41:26.4886972Z 
2023-01-11T21:41:26.4887198Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4887367Z import torch
2023-01-11T21:41:26.4887533Z import random
2023-01-11T21:41:26.4887795Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4888058Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4888070Z 
2023-01-11T21:41:26.4888249Z aten = torch.ops.aten
2023-01-11T21:41:26.4888629Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4888860Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4888874Z 
2023-01-11T21:41:26.4888889Z 
2023-01-11T21:41:26.4889360Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4889851Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4890136Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4890380Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4890610Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.4890837Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4890984Z {
2023-01-11T21:41:26.4891221Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4891375Z     {
2023-01-11T21:41:26.4891555Z         #pragma omp for 
2023-01-11T21:41:26.4891756Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.4891895Z         {
2023-01-11T21:41:26.4892096Z             #pragma GCC ivdep
2023-01-11T21:41:26.4892294Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.4892427Z             {
2023-01-11T21:41:26.4892579Z                 #pragma GCC ivdep
2023-01-11T21:41:26.4892741Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:41:26.4892860Z                 {
2023-01-11T21:41:26.4892962Z                     {
2023-01-11T21:41:26.4893082Z                         {
2023-01-11T21:41:26.4893255Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.4893423Z                             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.4893621Z                             auto tmp2 = in_ptr2[i2 + (12*tmp1) + (96*tmp0)];
2023-01-11T21:41:26.4893838Z                             out_ptr0[i2 + (12*i1) + (48*i0)] = tmp2;
2023-01-11T21:41:26.4894002Z                         }
2023-01-11T21:41:26.4894139Z                     }
2023-01-11T21:41:26.4894299Z                 }
2023-01-11T21:41:26.4894453Z             }
2023-01-11T21:41:26.4894595Z         }
2023-01-11T21:41:26.4894750Z     }
2023-01-11T21:41:26.4894888Z }
2023-01-11T21:41:26.4895070Z ''')
2023-01-11T21:41:26.4895083Z 
2023-01-11T21:41:26.4895122Z 
2023-01-11T21:41:26.4895312Z async_compile.wait(globals())
2023-01-11T21:41:26.4895476Z del async_compile
2023-01-11T21:41:26.4895488Z 
2023-01-11T21:41:26.4895656Z def call(args):
2023-01-11T21:41:26.4895847Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4896014Z     args.clear()
2023-01-11T21:41:26.4896534Z     buf0 = empty_strided((4, 4, 12), (48, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4896978Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4897119Z     del arg0_1
2023-01-11T21:41:26.4897280Z     del arg1_1
2023-01-11T21:41:26.4897440Z     del arg2_1
2023-01-11T21:41:26.4897615Z     return (buf0, )
2023-01-11T21:41:26.4897627Z 
2023-01-11T21:41:26.4897636Z 
2023-01-11T21:41:26.4897817Z if __name__ == "__main__":
2023-01-11T21:41:26.4898092Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4898392Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4899010Z     arg0_1 = rand_strided((8, 8, 12), (96, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4899469Z     arg1_1 = rand_strided((1, 4), (4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4899832Z     arg2_1 = rand_strided((4, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4900054Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4900063Z 
2023-01-11T21:41:26.4900178Z ok (3.238s)
2023-01-11T21:41:26.4901183Z   test_index2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4901485Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4902123Z [2023-01-11 21:34:35,686] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 230
2023-01-11T21:41:26.4902771Z [2023-01-11 21:34:37,440] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 230
2023-01-11T21:41:26.4902784Z 
2023-01-11T21:41:26.4903008Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4903243Z import torch
2023-01-11T21:41:26.4903419Z import random
2023-01-11T21:41:26.4903697Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4903980Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4903992Z 
2023-01-11T21:41:26.4904179Z aten = torch.ops.aten
2023-01-11T21:41:26.4904497Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4904717Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4904734Z 
2023-01-11T21:41:26.4904744Z 
2023-01-11T21:41:26.4905076Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4905547Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4905826Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4906067Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4906296Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4906527Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4906663Z {
2023-01-11T21:41:26.4906842Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4906939Z     {
2023-01-11T21:41:26.4907073Z         #pragma omp for 
2023-01-11T21:41:26.4907218Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.4907333Z         {
2023-01-11T21:41:26.4907477Z             #pragma GCC ivdep
2023-01-11T21:41:26.4907632Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:26.4907752Z             {
2023-01-11T21:41:26.4907859Z                 {
2023-01-11T21:41:26.4908023Z                     {
2023-01-11T21:41:26.4908243Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4908495Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:41:26.4908720Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:41:26.4908882Z                     }
2023-01-11T21:41:26.4909030Z                 }
2023-01-11T21:41:26.4909154Z             }
2023-01-11T21:41:26.4909305Z         }
2023-01-11T21:41:26.4909486Z         #pragma omp for 
2023-01-11T21:41:26.4909681Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.4909835Z         {
2023-01-11T21:41:26.4910016Z             #pragma GCC ivdep
2023-01-11T21:41:26.4910197Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.4910346Z             {
2023-01-11T21:41:26.4910535Z                 #pragma GCC ivdep
2023-01-11T21:41:26.4910743Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:26.4910910Z                 {
2023-01-11T21:41:26.4911069Z                     {
2023-01-11T21:41:26.4911235Z                         {
2023-01-11T21:41:26.4911523Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.4911783Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:26.4912022Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:41:26.4912189Z                         }
2023-01-11T21:41:26.4912344Z                     }
2023-01-11T21:41:26.4912492Z                 }
2023-01-11T21:41:26.4912643Z             }
2023-01-11T21:41:26.4912782Z         }
2023-01-11T21:41:26.4912917Z     }
2023-01-11T21:41:26.4913065Z }
2023-01-11T21:41:26.4913263Z ''')
2023-01-11T21:41:26.4913275Z 
2023-01-11T21:41:26.4913284Z 
2023-01-11T21:41:26.4913503Z async_compile.wait(globals())
2023-01-11T21:41:26.4913655Z del async_compile
2023-01-11T21:41:26.4913665Z 
2023-01-11T21:41:26.4913790Z def call(args):
2023-01-11T21:41:26.4913951Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.4914087Z     args.clear()
2023-01-11T21:41:26.4914483Z     buf0 = empty_strided((1, 4, 8, 8), (256, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4914903Z     buf1 = empty_strided((8, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4915340Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.4915509Z     del arg0_1
2023-01-11T21:41:26.4915668Z     del arg1_1
2023-01-11T21:41:26.4915849Z     return (buf0, buf1, )
2023-01-11T21:41:26.4915861Z 
2023-01-11T21:41:26.4915871Z 
2023-01-11T21:41:26.4916028Z if __name__ == "__main__":
2023-01-11T21:41:26.4916291Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4916584Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4917086Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4917558Z     arg1_1 = rand_strided((1, 4), (4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4917834Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.4917852Z 
2023-01-11T21:41:26.4918018Z ok (1.826s)
2023-01-11T21:41:26.4919170Z   test_index3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4919465Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4920100Z [2023-01-11 21:34:37,484] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 231
2023-01-11T21:41:26.4920642Z [2023-01-11 21:34:37,507] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.index
2023-01-11T21:41:26.4921145Z [2023-01-11 21:34:37,509] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 231
2023-01-11T21:41:26.4921160Z 
2023-01-11T21:41:26.4921334Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4921463Z import torch
2023-01-11T21:41:26.4921593Z import random
2023-01-11T21:41:26.4921800Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4922036Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4922050Z 
2023-01-11T21:41:26.4922213Z aten = torch.ops.aten
2023-01-11T21:41:26.4922516Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4922731Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4922747Z 
2023-01-11T21:41:26.4922755Z 
2023-01-11T21:41:26.4922957Z async_compile.wait(globals())
2023-01-11T21:41:26.4923126Z del async_compile
2023-01-11T21:41:26.4923142Z 
2023-01-11T21:41:26.4923302Z def call(args):
2023-01-11T21:41:26.4923495Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4923675Z     args.clear()
2023-01-11T21:41:26.4924003Z     buf0 = aten.index(as_strided(arg0_1, (3, 4, 1, 4, 3), (192, 48, 0, 12, 1)), [None, arg1_1, None, arg2_1])
2023-01-11T21:41:26.4924243Z     del arg0_1
2023-01-11T21:41:26.4924408Z     del arg1_1
2023-01-11T21:41:26.4924564Z     del arg2_1
2023-01-11T21:41:26.4924727Z     buf1 = buf0
2023-01-11T21:41:26.4924972Z     assert_size_stride(buf1, (3, 3, 1, 3), (9, 3, 3, 1))
2023-01-11T21:41:26.4925124Z     del buf0
2023-01-11T21:41:26.4925268Z     return (buf1, )
2023-01-11T21:41:26.4925281Z 
2023-01-11T21:41:26.4925290Z 
2023-01-11T21:41:26.4925474Z if __name__ == "__main__":
2023-01-11T21:41:26.4925739Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4926033Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4926594Z     arg0_1 = rand_strided((3, 4, 4, 4, 3), (192, 48, 12, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4927135Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4927598Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4927867Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4927877Z 
2023-01-11T21:41:26.4927972Z ok (0.068s)
2023-01-11T21:41:26.4928826Z   test_index_put1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4929200Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4929812Z [2023-01-11 21:34:37,763] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 232
2023-01-11T21:41:26.4930452Z [2023-01-11 21:34:39,650] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 232
2023-01-11T21:41:26.4931479Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4931783Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4932433Z [2023-01-11 21:34:40,546] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 233
2023-01-11T21:41:26.4933108Z [2023-01-11 21:34:42,142] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 233
2023-01-11T21:41:26.4933121Z 
2023-01-11T21:41:26.4933346Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4933511Z import torch
2023-01-11T21:41:26.4933671Z import random
2023-01-11T21:41:26.4933951Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4934239Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4934257Z 
2023-01-11T21:41:26.4934443Z aten = torch.ops.aten
2023-01-11T21:41:26.4934762Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4934992Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4935003Z 
2023-01-11T21:41:26.4935012Z 
2023-01-11T21:41:26.4935269Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4935629Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4935828Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4936014Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4936203Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.4936432Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4936656Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4936878Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4937137Z {
2023-01-11T21:41:26.4937358Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4937501Z     {
2023-01-11T21:41:26.4937685Z         #pragma omp for 
2023-01-11T21:41:26.4937887Z         for(long i0=0; i0<1254400; i0+=1)
2023-01-11T21:41:26.4938030Z         {
2023-01-11T21:41:26.4938362Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4938682Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4938873Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4939091Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4939303Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4939458Z         }
2023-01-11T21:41:26.4939684Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4939973Z         for(long i0=10035200; i0<10035200; i0+=1)
2023-01-11T21:41:26.4940132Z         {
2023-01-11T21:41:26.4940322Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4940569Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4940773Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4940968Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.4941153Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.4941307Z         }
2023-01-11T21:41:26.4941493Z         #pragma omp for 
2023-01-11T21:41:26.4941677Z         for(long i0=0; i0<601; i0+=1)
2023-01-11T21:41:26.4941834Z         {
2023-01-11T21:41:26.4942026Z             #pragma GCC ivdep
2023-01-11T21:41:26.4942194Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.4942313Z             {
2023-01-11T21:41:26.4942431Z                 {
2023-01-11T21:41:26.4942542Z                     {
2023-01-11T21:41:26.4942695Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.4942883Z                         auto tmp1 = in_ptr2[i1 + (12544*i0)];
2023-01-11T21:41:26.4943076Z                         auto tmp2 = static_cast<long>(1);
2023-01-11T21:41:26.4943330Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.4943585Z                         auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.4943808Z                         auto tmp5 = tmp1 + tmp4;
2023-01-11T21:41:26.4944046Z                         out_ptr0[i1 + (12544*tmp0)] = tmp1;
2023-01-11T21:41:26.4944259Z                         out_ptr1[i1 + (12544*tmp3)] = tmp5;
2023-01-11T21:41:26.4944413Z                     }
2023-01-11T21:41:26.4944561Z                 }
2023-01-11T21:41:26.4944714Z             }
2023-01-11T21:41:26.4944866Z         }
2023-01-11T21:41:26.4945050Z         #pragma omp for 
2023-01-11T21:41:26.4945256Z         for(long i0=0; i0<1254400; i0+=1)
2023-01-11T21:41:26.4945395Z         {
2023-01-11T21:41:26.4945711Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4946029Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4946226Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4946444Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4946603Z         }
2023-01-11T21:41:26.4946834Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4947026Z         for(long i0=10035200; i0<10035200; i0+=1)
2023-01-11T21:41:26.4947174Z         {
2023-01-11T21:41:26.4947380Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.4947615Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4947822Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4948005Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.4948160Z         }
2023-01-11T21:41:26.4948297Z     }
2023-01-11T21:41:26.4948440Z }
2023-01-11T21:41:26.4948640Z ''')
2023-01-11T21:41:26.4948653Z 
2023-01-11T21:41:26.4948662Z 
2023-01-11T21:41:26.4948872Z async_compile.wait(globals())
2023-01-11T21:41:26.4949043Z del async_compile
2023-01-11T21:41:26.4949055Z 
2023-01-11T21:41:26.4949185Z def call(args):
2023-01-11T21:41:26.4949338Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4949450Z     args.clear()
2023-01-11T21:41:26.4949944Z     buf0 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4950359Z     buf2 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4950891Z     buf4 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4951414Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4951580Z     del arg0_1
2023-01-11T21:41:26.4951733Z     del arg1_1
2023-01-11T21:41:26.4951896Z     del arg2_1
2023-01-11T21:41:26.4952060Z     return (buf0, buf4, )
2023-01-11T21:41:26.4952071Z 
2023-01-11T21:41:26.4952104Z 
2023-01-11T21:41:26.4952338Z if __name__ == "__main__":
2023-01-11T21:41:26.4952618Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4952914Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4953467Z     arg0_1 = rand_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4953938Z     arg1_1 = rand_strided((601, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4954474Z     arg2_1 = rand_strided((601, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4954773Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4954785Z 
2023-01-11T21:41:26.4954794Z 
2023-01-11T21:41:26.4955020Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4955175Z import torch
2023-01-11T21:41:26.4955350Z import random
2023-01-11T21:41:26.4955626Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4955918Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4955929Z 
2023-01-11T21:41:26.4956125Z aten = torch.ops.aten
2023-01-11T21:41:26.4956360Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4956528Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4956537Z 
2023-01-11T21:41:26.4956545Z 
2023-01-11T21:41:26.4956776Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4957128Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4957364Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4957600Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4957839Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.4958065Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4958295Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.4958531Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.4958656Z {
2023-01-11T21:41:26.4958894Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4959040Z     {
2023-01-11T21:41:26.4959230Z         #pragma omp for 
2023-01-11T21:41:26.4959425Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.4959568Z         {
2023-01-11T21:41:26.4959884Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4960191Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4960397Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4960617Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4960829Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4960982Z         }
2023-01-11T21:41:26.4961207Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4961407Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:26.4961547Z         {
2023-01-11T21:41:26.4961748Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4961976Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4962184Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4962380Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.4962638Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.4962790Z         }
2023-01-11T21:41:26.4962960Z         #pragma omp for 
2023-01-11T21:41:26.4963123Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.4963245Z         {
2023-01-11T21:41:26.4963390Z             #pragma GCC ivdep
2023-01-11T21:41:26.4963542Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.4963660Z             {
2023-01-11T21:41:26.4963762Z                 {
2023-01-11T21:41:26.4963882Z                     {
2023-01-11T21:41:26.4964049Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.4964219Z                         auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:26.4964455Z                         auto tmp2 = static_cast<long>(1);
2023-01-11T21:41:26.4964658Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.4964969Z                         auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.4965187Z                         auto tmp5 = tmp1 + tmp4;
2023-01-11T21:41:26.4965395Z                         out_ptr0[i1 + (8*tmp0)] = tmp1;
2023-01-11T21:41:26.4965625Z                         out_ptr1[i1 + (8*tmp3)] = tmp5;
2023-01-11T21:41:26.4965777Z                     }
2023-01-11T21:41:26.4965926Z                 }
2023-01-11T21:41:26.4966081Z             }
2023-01-11T21:41:26.4966236Z         }
2023-01-11T21:41:26.4966400Z         #pragma omp for 
2023-01-11T21:41:26.4966601Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.4966752Z         {
2023-01-11T21:41:26.4967066Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.4967393Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.4967597Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4967816Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.4967967Z         }
2023-01-11T21:41:26.4968180Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4968386Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:26.4968540Z         {
2023-01-11T21:41:26.4968744Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.4968975Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.4969342Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.4969521Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.4969670Z         }
2023-01-11T21:41:26.4969813Z     }
2023-01-11T21:41:26.4969959Z }
2023-01-11T21:41:26.4970166Z ''')
2023-01-11T21:41:26.4970176Z 
2023-01-11T21:41:26.4970183Z 
2023-01-11T21:41:26.4970348Z async_compile.wait(globals())
2023-01-11T21:41:26.4970480Z del async_compile
2023-01-11T21:41:26.4970489Z 
2023-01-11T21:41:26.4970615Z def call(args):
2023-01-11T21:41:26.4970748Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4970876Z     args.clear()
2023-01-11T21:41:26.4971267Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4971719Z     buf2 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4972214Z     buf4 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4972747Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.4972915Z     del arg0_1
2023-01-11T21:41:26.4973058Z     del arg1_1
2023-01-11T21:41:26.4973216Z     del arg2_1
2023-01-11T21:41:26.4973400Z     return (buf0, buf4, )
2023-01-11T21:41:26.4973413Z 
2023-01-11T21:41:26.4973422Z 
2023-01-11T21:41:26.4973591Z if __name__ == "__main__":
2023-01-11T21:41:26.4973862Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4974161Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4974666Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4975126Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4975594Z     arg2_1 = rand_strided((4, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4976001Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4976013Z 
2023-01-11T21:41:26.4976169Z ok (4.634s)
2023-01-11T21:41:26.4977308Z   test_index_put2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4977537Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4978079Z [2023-01-11 21:34:42,261] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 234
2023-01-11T21:41:26.4978587Z [2023-01-11 21:34:43,901] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 234
2023-01-11T21:41:26.4978602Z 
2023-01-11T21:41:26.4978771Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4978948Z import torch
2023-01-11T21:41:26.4979075Z import random
2023-01-11T21:41:26.4979271Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4979486Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4979496Z 
2023-01-11T21:41:26.4979635Z aten = torch.ops.aten
2023-01-11T21:41:26.4979897Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4980177Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4980190Z 
2023-01-11T21:41:26.4980204Z 
2023-01-11T21:41:26.4980596Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4981201Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4981488Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.4981716Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.4981961Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.4982199Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.4982343Z {
2023-01-11T21:41:26.4982578Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4982731Z     {
2023-01-11T21:41:26.4982908Z         #pragma omp for 
2023-01-11T21:41:26.4983096Z         for(long i0=0; i0<156800; i0+=1)
2023-01-11T21:41:26.4983323Z         {
2023-01-11T21:41:26.4983647Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.4983865Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.4984017Z         }
2023-01-11T21:41:26.4984238Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.4984453Z         for(long i0=1254400; i0<1254400; i0+=1)
2023-01-11T21:41:26.4984591Z         {
2023-01-11T21:41:26.4984798Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.4984983Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.4985137Z         }
2023-01-11T21:41:26.4985322Z         #pragma omp for 
2023-01-11T21:41:26.4985521Z         for(long i0=0; i0<600; i0+=1)
2023-01-11T21:41:26.4985656Z         {
2023-01-11T21:41:26.4985844Z             #pragma GCC ivdep
2023-01-11T21:41:26.4986015Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:41:26.4986132Z             {
2023-01-11T21:41:26.4986249Z                 {
2023-01-11T21:41:26.4986371Z                     {
2023-01-11T21:41:26.4986540Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.4986708Z                         auto tmp1 = in_ptr2[i1 + (12544*i0)];
2023-01-11T21:41:26.4986915Z                         atomic_add(&out_ptr0[i1 + (12544*tmp0)], tmp1);
2023-01-11T21:41:26.4987034Z                     }
2023-01-11T21:41:26.4987153Z                 }
2023-01-11T21:41:26.4987268Z             }
2023-01-11T21:41:26.4987379Z         }
2023-01-11T21:41:26.4987490Z     }
2023-01-11T21:41:26.4987574Z }
2023-01-11T21:41:26.4987723Z ''')
2023-01-11T21:41:26.4987807Z 
2023-01-11T21:41:26.4987814Z 
2023-01-11T21:41:26.4987977Z async_compile.wait(globals())
2023-01-11T21:41:26.4988109Z del async_compile
2023-01-11T21:41:26.4988118Z 
2023-01-11T21:41:26.4988248Z def call(args):
2023-01-11T21:41:26.4988397Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.4988523Z     args.clear()
2023-01-11T21:41:26.4989035Z     buf0 = empty_strided((100, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4989505Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.4989683Z     del arg0_1
2023-01-11T21:41:26.4989929Z     del arg1_1
2023-01-11T21:41:26.4990089Z     del arg2_1
2023-01-11T21:41:26.4990257Z     return (buf0, )
2023-01-11T21:41:26.4990268Z 
2023-01-11T21:41:26.4990277Z 
2023-01-11T21:41:26.4990566Z if __name__ == "__main__":
2023-01-11T21:41:26.4990843Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.4991121Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.4991677Z     arg0_1 = rand_strided((100, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4992154Z     arg1_1 = rand_strided((600, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.4992693Z     arg2_1 = rand_strided((600, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.4992985Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.4992996Z 
2023-01-11T21:41:26.4993145Z ok (1.970s)
2023-01-11T21:41:26.4994326Z   test_index_put3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.4994621Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.4995178Z [2023-01-11 21:34:44,202] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 235
2023-01-11T21:41:26.4995675Z [2023-01-11 21:34:45,746] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 235
2023-01-11T21:41:26.4995687Z 
2023-01-11T21:41:26.4995839Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.4995963Z import torch
2023-01-11T21:41:26.4996089Z import random
2023-01-11T21:41:26.4996295Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.4996510Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.4996519Z 
2023-01-11T21:41:26.4996659Z aten = torch.ops.aten
2023-01-11T21:41:26.4996900Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.4997053Z async_compile = AsyncCompile()
2023-01-11T21:41:26.4997079Z 
2023-01-11T21:41:26.4997087Z 
2023-01-11T21:41:26.4997320Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.4997749Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.4998020Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.4998320Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.4998529Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.4998754Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.4998896Z {
2023-01-11T21:41:26.4999112Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.4999258Z     {
2023-01-11T21:41:26.4999429Z         #pragma omp for 
2023-01-11T21:41:26.4999631Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.4999777Z         {
2023-01-11T21:41:26.4999966Z             #pragma GCC ivdep
2023-01-11T21:41:26.5000172Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.5000308Z             {
2023-01-11T21:41:26.5000502Z                 #pragma GCC ivdep
2023-01-11T21:41:26.5000781Z                 for(long i2=0; i2<2; i2+=1)
2023-01-11T21:41:26.5000935Z                 {
2023-01-11T21:41:26.5001093Z                     {
2023-01-11T21:41:26.5001257Z                         {
2023-01-11T21:41:26.5001489Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.5001716Z                             auto tmp1 = in_ptr1[i2 + (2*i0)];
2023-01-11T21:41:26.5001957Z                             out_ptr0[i2 + (2*tmp0) + (8*i0)] = tmp1;
2023-01-11T21:41:26.5002125Z                         }
2023-01-11T21:41:26.5002287Z                     }
2023-01-11T21:41:26.5002443Z                 }
2023-01-11T21:41:26.5002593Z             }
2023-01-11T21:41:26.5002713Z         }
2023-01-11T21:41:26.5002893Z         #pragma omp for 
2023-01-11T21:41:26.5003094Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.5003291Z         {
2023-01-11T21:41:26.5003552Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5003802Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.5003955Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5004122Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.5004221Z         }
2023-01-11T21:41:26.5004391Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5004546Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:26.5004662Z         {
2023-01-11T21:41:26.5004814Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5004992Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.5005145Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5005267Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.5005377Z         }
2023-01-11T21:41:26.5005517Z         #pragma omp for 
2023-01-11T21:41:26.5005711Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.5005900Z         {
2023-01-11T21:41:26.5006070Z             #pragma GCC ivdep
2023-01-11T21:41:26.5006246Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.5006399Z             {
2023-01-11T21:41:26.5006622Z                 #pragma GCC ivdep
2023-01-11T21:41:26.5006817Z                 for(long i2=0; i2<2; i2+=1)
2023-01-11T21:41:26.5006969Z                 {
2023-01-11T21:41:26.5007114Z                     {
2023-01-11T21:41:26.5007273Z                         {
2023-01-11T21:41:26.5007477Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.5007716Z                             auto tmp3 = in_ptr1[i2 + (2*i0)];
2023-01-11T21:41:26.5007970Z                             auto tmp1 = static_cast<long>(1);
2023-01-11T21:41:26.5008193Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5008442Z                             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.5008660Z                             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.5008912Z                             out_ptr1[i2 + (2*tmp2) + (8*i0)] = tmp5;
2023-01-11T21:41:26.5009194Z                         }
2023-01-11T21:41:26.5009364Z                     }
2023-01-11T21:41:26.5009506Z                 }
2023-01-11T21:41:26.5009660Z             }
2023-01-11T21:41:26.5009807Z         }
2023-01-11T21:41:26.5009954Z     }
2023-01-11T21:41:26.5010101Z }
2023-01-11T21:41:26.5010290Z ''')
2023-01-11T21:41:26.5010301Z 
2023-01-11T21:41:26.5010310Z 
2023-01-11T21:41:26.5010529Z async_compile.wait(globals())
2023-01-11T21:41:26.5010699Z del async_compile
2023-01-11T21:41:26.5010712Z 
2023-01-11T21:41:26.5010870Z def call(args):
2023-01-11T21:41:26.5011062Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5011226Z     args.clear()
2023-01-11T21:41:26.5011737Z     buf1 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5012086Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.5012198Z     del arg0_1
2023-01-11T21:41:26.5012318Z     del arg1_1
2023-01-11T21:41:26.5012442Z     del arg2_1
2023-01-11T21:41:26.5012671Z     return (buf1, )
2023-01-11T21:41:26.5012681Z 
2023-01-11T21:41:26.5012687Z 
2023-01-11T21:41:26.5012823Z if __name__ == "__main__":
2023-01-11T21:41:26.5013031Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5013256Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5013622Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5013974Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5014493Z     arg2_1 = rand_strided((1024, 1, 2), (2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5014840Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5014856Z 
2023-01-11T21:41:26.5014999Z ok (1.634s)
2023-01-11T21:41:26.5016372Z   test_index_put_as_masked_fill_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5016684Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5017340Z [2023-01-11 21:34:45,797] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 236
2023-01-11T21:41:26.5018005Z [2023-01-11 21:34:47,421] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 236
2023-01-11T21:41:26.5019071Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5019376Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5019994Z [2023-01-11 21:34:47,471] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 237
2023-01-11T21:41:26.5020580Z [2023-01-11 21:34:49,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 237
2023-01-11T21:41:26.5020589Z 
2023-01-11T21:41:26.5020760Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5020887Z import torch
2023-01-11T21:41:26.5021020Z import random
2023-01-11T21:41:26.5021230Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5021440Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5021450Z 
2023-01-11T21:41:26.5021593Z aten = torch.ops.aten
2023-01-11T21:41:26.5021816Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5021979Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5021988Z 
2023-01-11T21:41:26.5021995Z 
2023-01-11T21:41:26.5022252Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5022611Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5022819Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.5023009Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.5023377Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.5023585Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5023777Z {
2023-01-11T21:41:26.5023984Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5024184Z     {
2023-01-11T21:41:26.5024460Z         #pragma omp for 
2023-01-11T21:41:26.5024658Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.5024809Z         {
2023-01-11T21:41:26.5025048Z             float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:26.5025318Z             flag_to_float(in_ptr0 + 8*i0, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:26.5025656Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:26.5026026Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:26.5026333Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:26.5026631Z             auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:41:26.5026853Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5027010Z         }
2023-01-11T21:41:26.5027222Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5027426Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:26.5027570Z         {
2023-01-11T21:41:26.5027771Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5027969Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.5028170Z             auto tmp2 = in_ptr2[i0];
2023-01-11T21:41:26.5028445Z             auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:41:26.5028622Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.5028768Z         }
2023-01-11T21:41:26.5028918Z     }
2023-01-11T21:41:26.5029061Z }
2023-01-11T21:41:26.5029237Z ''')
2023-01-11T21:41:26.5029246Z 
2023-01-11T21:41:26.5029253Z 
2023-01-11T21:41:26.5029411Z async_compile.wait(globals())
2023-01-11T21:41:26.5029543Z del async_compile
2023-01-11T21:41:26.5029552Z 
2023-01-11T21:41:26.5029675Z def call(args):
2023-01-11T21:41:26.5029799Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5029925Z     args.clear()
2023-01-11T21:41:26.5030307Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5030641Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5030766Z     del arg0_1
2023-01-11T21:41:26.5030888Z     del arg1_1
2023-01-11T21:41:26.5031043Z     del arg2_1
2023-01-11T21:41:26.5031159Z     return (buf0, )
2023-01-11T21:41:26.5031168Z 
2023-01-11T21:41:26.5031175Z 
2023-01-11T21:41:26.5031314Z if __name__ == "__main__":
2023-01-11T21:41:26.5031581Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5031807Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5032270Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5032752Z     arg1_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5033224Z     arg2_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5033593Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5033606Z 
2023-01-11T21:41:26.5033614Z 
2023-01-11T21:41:26.5033819Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5033969Z import torch
2023-01-11T21:41:26.5034137Z import random
2023-01-11T21:41:26.5034406Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5034701Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5034712Z 
2023-01-11T21:41:26.5034903Z aten = torch.ops.aten
2023-01-11T21:41:26.5035221Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5035436Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5035448Z 
2023-01-11T21:41:26.5035457Z 
2023-01-11T21:41:26.5035775Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5036264Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5036540Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.5036788Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.5037032Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.5037265Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5037418Z {
2023-01-11T21:41:26.5037633Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5037781Z     {
2023-01-11T21:41:26.5037960Z         #pragma omp for 
2023-01-11T21:41:26.5038159Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.5038347Z         {
2023-01-11T21:41:26.5038525Z             float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:26.5038744Z             flag_to_float(in_ptr0 + 8*i0, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:26.5038986Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:26.5039227Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.5039445Z             auto tmp2 = at::vec::Vectorized<float>(in_ptr2[0]);
2023-01-11T21:41:26.5039597Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.5039815Z             auto tmp4 = decltype(tmp3)::blendv(tmp1, tmp3, tmp0);
2023-01-11T21:41:26.5039978Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5040090Z         }
2023-01-11T21:41:26.5040301Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5040570Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:41:26.5040755Z         {
2023-01-11T21:41:26.5040929Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5041197Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.5041376Z             auto tmp2 = in_ptr2[0];
2023-01-11T21:41:26.5041543Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.5041769Z             auto tmp4 = tmp0 ? tmp3 : tmp1;
2023-01-11T21:41:26.5041945Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.5042097Z         }
2023-01-11T21:41:26.5042240Z     }
2023-01-11T21:41:26.5042382Z }
2023-01-11T21:41:26.5042585Z ''')
2023-01-11T21:41:26.5042596Z 
2023-01-11T21:41:26.5042605Z 
2023-01-11T21:41:26.5042813Z async_compile.wait(globals())
2023-01-11T21:41:26.5042985Z del async_compile
2023-01-11T21:41:26.5042997Z 
2023-01-11T21:41:26.5043137Z def call(args):
2023-01-11T21:41:26.5043329Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5043500Z     args.clear()
2023-01-11T21:41:26.5044019Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5044466Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5044637Z     del arg0_1
2023-01-11T21:41:26.5044798Z     del arg1_1
2023-01-11T21:41:26.5044945Z     del arg2_1
2023-01-11T21:41:26.5045119Z     return (buf0, )
2023-01-11T21:41:26.5045130Z 
2023-01-11T21:41:26.5045140Z 
2023-01-11T21:41:26.5045316Z if __name__ == "__main__":
2023-01-11T21:41:26.5045587Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5045884Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5046362Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5046738Z     arg1_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5047077Z     arg2_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5047285Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5047313Z 
2023-01-11T21:41:26.5047427Z ok (3.288s)
2023-01-11T21:41:26.5048529Z   test_index_put_fallback1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5048822Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5049712Z [2023-01-11 21:34:49,097] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 238
2023-01-11T21:41:26.5050390Z [2023-01-11 21:34:50,771] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 238
2023-01-11T21:41:26.5051458Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5051869Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5052514Z [2023-01-11 21:34:50,820] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 239
2023-01-11T21:41:26.5053179Z [2023-01-11 21:34:50,828] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 239
2023-01-11T21:41:26.5053192Z 
2023-01-11T21:41:26.5053421Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5053543Z import torch
2023-01-11T21:41:26.5053664Z import random
2023-01-11T21:41:26.5053867Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5054139Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5054150Z 
2023-01-11T21:41:26.5054295Z aten = torch.ops.aten
2023-01-11T21:41:26.5054541Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5054727Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5054743Z 
2023-01-11T21:41:26.5054751Z 
2023-01-11T21:41:26.5055114Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5055580Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5055923Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5056151Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5056298Z {
2023-01-11T21:41:26.5056484Z     #pragma GCC ivdep
2023-01-11T21:41:26.5056667Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.5056809Z     {
2023-01-11T21:41:26.5056946Z         {
2023-01-11T21:41:26.5057101Z             {
2023-01-11T21:41:26.5057311Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5057511Z                 out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.5057662Z             }
2023-01-11T21:41:26.5057815Z         }
2023-01-11T21:41:26.5057964Z     }
2023-01-11T21:41:26.5058086Z }
2023-01-11T21:41:26.5058280Z ''')
2023-01-11T21:41:26.5058292Z 
2023-01-11T21:41:26.5058302Z 
2023-01-11T21:41:26.5058518Z async_compile.wait(globals())
2023-01-11T21:41:26.5058691Z del async_compile
2023-01-11T21:41:26.5058703Z 
2023-01-11T21:41:26.5058877Z def call(args):
2023-01-11T21:41:26.5059067Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5059236Z     args.clear()
2023-01-11T21:41:26.5059699Z     buf0 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5060012Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5060174Z     del arg0_1
2023-01-11T21:41:26.5060417Z     aten.index_put_(buf0, [arg1_1], arg2_1, False)
2023-01-11T21:41:26.5060571Z     del arg1_1
2023-01-11T21:41:26.5060691Z     del arg2_1
2023-01-11T21:41:26.5060823Z     return (buf0, )
2023-01-11T21:41:26.5060832Z 
2023-01-11T21:41:26.5060838Z 
2023-01-11T21:41:26.5060956Z if __name__ == "__main__":
2023-01-11T21:41:26.5061162Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5061378Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5061726Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5062163Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5062621Z     arg2_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5062906Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5062924Z 
2023-01-11T21:41:26.5062933Z 
2023-01-11T21:41:26.5063209Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5063373Z import torch
2023-01-11T21:41:26.5063520Z import random
2023-01-11T21:41:26.5063799Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5064099Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5064111Z 
2023-01-11T21:41:26.5064290Z aten = torch.ops.aten
2023-01-11T21:41:26.5064696Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5064916Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5064928Z 
2023-01-11T21:41:26.5064937Z 
2023-01-11T21:41:26.5065262Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5065733Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5066006Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5066236Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5066376Z {
2023-01-11T21:41:26.5066547Z     #pragma GCC ivdep
2023-01-11T21:41:26.5066732Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.5066870Z     {
2023-01-11T21:41:26.5066997Z         {
2023-01-11T21:41:26.5067155Z             {
2023-01-11T21:41:26.5067414Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5067614Z                 out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.5067737Z             }
2023-01-11T21:41:26.5067856Z         }
2023-01-11T21:41:26.5067966Z     }
2023-01-11T21:41:26.5068056Z }
2023-01-11T21:41:26.5068206Z ''')
2023-01-11T21:41:26.5068215Z 
2023-01-11T21:41:26.5068222Z 
2023-01-11T21:41:26.5068386Z async_compile.wait(globals())
2023-01-11T21:41:26.5068517Z del async_compile
2023-01-11T21:41:26.5068526Z 
2023-01-11T21:41:26.5068652Z def call(args):
2023-01-11T21:41:26.5068813Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5068973Z     args.clear()
2023-01-11T21:41:26.5069414Z     buf0 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5069727Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5069877Z     del arg0_1
2023-01-11T21:41:26.5070119Z     aten.index_put_(buf0, [arg1_1], arg2_1, True)
2023-01-11T21:41:26.5070286Z     del arg1_1
2023-01-11T21:41:26.5070452Z     del arg2_1
2023-01-11T21:41:26.5070619Z     return (buf0, )
2023-01-11T21:41:26.5070631Z 
2023-01-11T21:41:26.5070639Z 
2023-01-11T21:41:26.5070825Z if __name__ == "__main__":
2023-01-11T21:41:26.5071084Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5071376Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5071844Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5072300Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5072765Z     arg2_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5073054Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5073067Z 
2023-01-11T21:41:26.5073227Z ok (1.793s)
2023-01-11T21:41:26.5074397Z   test_index_put_fallback2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5074689Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5075158Z [2023-01-11 21:34:50,878] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 240
2023-01-11T21:41:26.5075647Z [2023-01-11 21:34:52,942] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 240
2023-01-11T21:41:26.5076556Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5076859Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5077469Z [2023-01-11 21:34:53,003] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 241
2023-01-11T21:41:26.5078225Z [2023-01-11 21:34:53,012] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 241
2023-01-11T21:41:26.5078238Z 
2023-01-11T21:41:26.5078463Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5078628Z import torch
2023-01-11T21:41:26.5078797Z import random
2023-01-11T21:41:26.5079057Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5079349Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5079361Z 
2023-01-11T21:41:26.5079547Z aten = torch.ops.aten
2023-01-11T21:41:26.5079872Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5080096Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5080108Z 
2023-01-11T21:41:26.5080118Z 
2023-01-11T21:41:26.5080503Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5080997Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5081283Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5081506Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5081660Z {
2023-01-11T21:41:26.5081847Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5081958Z     {
2023-01-11T21:41:26.5082099Z         #pragma omp for 
2023-01-11T21:41:26.5082248Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.5082363Z         {
2023-01-11T21:41:26.5082460Z             {
2023-01-11T21:41:26.5082577Z                 {
2023-01-11T21:41:26.5082743Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5082896Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.5083028Z                 }
2023-01-11T21:41:26.5083183Z             }
2023-01-11T21:41:26.5083331Z         }
2023-01-11T21:41:26.5083447Z     }
2023-01-11T21:41:26.5083598Z }
2023-01-11T21:41:26.5083792Z ''')
2023-01-11T21:41:26.5083803Z 
2023-01-11T21:41:26.5083814Z 
2023-01-11T21:41:26.5084030Z async_compile.wait(globals())
2023-01-11T21:41:26.5084202Z del async_compile
2023-01-11T21:41:26.5084214Z 
2023-01-11T21:41:26.5084377Z def call(args):
2023-01-11T21:41:26.5084586Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5084733Z     args.clear()
2023-01-11T21:41:26.5085234Z     buf0 = empty_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5085554Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5085722Z     del arg0_1
2023-01-11T21:41:26.5086003Z     aten.index_put_(buf0, [None,arg1_1,arg2_1], arg3_1, False)
2023-01-11T21:41:26.5086169Z     del arg1_1
2023-01-11T21:41:26.5086333Z     del arg2_1
2023-01-11T21:41:26.5086482Z     del arg3_1
2023-01-11T21:41:26.5086662Z     return (buf0, )
2023-01-11T21:41:26.5086674Z 
2023-01-11T21:41:26.5086684Z 
2023-01-11T21:41:26.5086868Z if __name__ == "__main__":
2023-01-11T21:41:26.5087140Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5087441Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5087937Z     arg0_1 = rand_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5088400Z     arg1_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5088850Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5089320Z     arg3_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5089542Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5089552Z 
2023-01-11T21:41:26.5089558Z 
2023-01-11T21:41:26.5089732Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5089857Z import torch
2023-01-11T21:41:26.5089981Z import random
2023-01-11T21:41:26.5090193Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5090465Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5090480Z 
2023-01-11T21:41:26.5090659Z aten = torch.ops.aten
2023-01-11T21:41:26.5091083Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5091298Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5091316Z 
2023-01-11T21:41:26.5091326Z 
2023-01-11T21:41:26.5091657Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5092127Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5092421Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5092657Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5092801Z {
2023-01-11T21:41:26.5093021Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5093171Z     {
2023-01-11T21:41:26.5093358Z         #pragma omp for 
2023-01-11T21:41:26.5093550Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.5093695Z         {
2023-01-11T21:41:26.5093929Z             {
2023-01-11T21:41:26.5094091Z                 {
2023-01-11T21:41:26.5094293Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5094497Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.5094649Z                 }
2023-01-11T21:41:26.5094807Z             }
2023-01-11T21:41:26.5094953Z         }
2023-01-11T21:41:26.5095102Z     }
2023-01-11T21:41:26.5095240Z }
2023-01-11T21:41:26.5095413Z ''')
2023-01-11T21:41:26.5095424Z 
2023-01-11T21:41:26.5095433Z 
2023-01-11T21:41:26.5095652Z async_compile.wait(globals())
2023-01-11T21:41:26.5095823Z del async_compile
2023-01-11T21:41:26.5095834Z 
2023-01-11T21:41:26.5095991Z def call(args):
2023-01-11T21:41:26.5096156Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5096286Z     args.clear()
2023-01-11T21:41:26.5096669Z     buf0 = empty_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5096893Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5097024Z     del arg0_1
2023-01-11T21:41:26.5097255Z     aten.index_put_(buf0, [None,arg1_1,arg2_1], arg3_1, True)
2023-01-11T21:41:26.5097421Z     del arg1_1
2023-01-11T21:41:26.5097582Z     del arg2_1
2023-01-11T21:41:26.5097754Z     del arg3_1
2023-01-11T21:41:26.5097921Z     return (buf0, )
2023-01-11T21:41:26.5097932Z 
2023-01-11T21:41:26.5097940Z 
2023-01-11T21:41:26.5098127Z if __name__ == "__main__":
2023-01-11T21:41:26.5098369Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5098658Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5099149Z     arg0_1 = rand_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5099612Z     arg1_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5100060Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5100507Z     arg3_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5100814Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5100826Z 
2023-01-11T21:41:26.5100971Z ok (2.185s)
2023-01-11T21:41:26.5102148Z   test_index_select_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5102445Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5103101Z [2023-01-11 21:34:53,058] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 242
2023-01-11T21:41:26.5103677Z [2023-01-11 21:34:54,727] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 242
2023-01-11T21:41:26.5104486Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5104866Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5105474Z [2023-01-11 21:34:54,783] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 243
2023-01-11T21:41:26.5106104Z [2023-01-11 21:34:56,425] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 243
2023-01-11T21:41:26.5106116Z 
2023-01-11T21:41:26.5106344Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5106517Z import torch
2023-01-11T21:41:26.5106664Z import random
2023-01-11T21:41:26.5106941Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5107315Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5107328Z 
2023-01-11T21:41:26.5107525Z aten = torch.ops.aten
2023-01-11T21:41:26.5107852Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5108084Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5108096Z 
2023-01-11T21:41:26.5108106Z 
2023-01-11T21:41:26.5108440Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5108932Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5109193Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.5109439Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.5109667Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.5109895Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.5110118Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.5110246Z {
2023-01-11T21:41:26.5110434Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5110532Z     {
2023-01-11T21:41:26.5110669Z         #pragma omp for 
2023-01-11T21:41:26.5110817Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.5110937Z         {
2023-01-11T21:41:26.5111083Z             #pragma GCC ivdep
2023-01-11T21:41:26.5111239Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:26.5111350Z             {
2023-01-11T21:41:26.5111477Z                 {
2023-01-11T21:41:26.5111634Z                     {
2023-01-11T21:41:26.5111835Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5112083Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:41:26.5112307Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:41:26.5112467Z                     }
2023-01-11T21:41:26.5112620Z                 }
2023-01-11T21:41:26.5112751Z             }
2023-01-11T21:41:26.5112900Z         }
2023-01-11T21:41:26.5113086Z         #pragma omp for 
2023-01-11T21:41:26.5113287Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5113441Z         {
2023-01-11T21:41:26.5113629Z             #pragma GCC ivdep
2023-01-11T21:41:26.5113808Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.5113961Z             {
2023-01-11T21:41:26.5114151Z                 #pragma GCC ivdep
2023-01-11T21:41:26.5114356Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:26.5114507Z                 {
2023-01-11T21:41:26.5114666Z                     {
2023-01-11T21:41:26.5114830Z                         {
2023-01-11T21:41:26.5115034Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.5115295Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:26.5115534Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:41:26.5115692Z                         }
2023-01-11T21:41:26.5115856Z                     }
2023-01-11T21:41:26.5116014Z                 }
2023-01-11T21:41:26.5116165Z             }
2023-01-11T21:41:26.5116304Z         }
2023-01-11T21:41:26.5116498Z         #pragma omp for 
2023-01-11T21:41:26.5116692Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5116847Z         {
2023-01-11T21:41:26.5117114Z             #pragma GCC ivdep
2023-01-11T21:41:26.5117279Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.5117395Z             {
2023-01-11T21:41:26.5117526Z                 #pragma GCC ivdep
2023-01-11T21:41:26.5117685Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:26.5117799Z                 {
2023-01-11T21:41:26.5117920Z                     {
2023-01-11T21:41:26.5118045Z                         {
2023-01-11T21:41:26.5118217Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.5118387Z                             auto tmp1 = in_ptr0[i2];
2023-01-11T21:41:26.5118573Z                             auto tmp2 = in_ptr1[tmp1 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:26.5118779Z                             out_ptr2[i2 + (4*i1) + (16*i0)] = tmp2;
2023-01-11T21:41:26.5118926Z                         }
2023-01-11T21:41:26.5119144Z                     }
2023-01-11T21:41:26.5119298Z                 }
2023-01-11T21:41:26.5119455Z             }
2023-01-11T21:41:26.5119650Z         }
2023-01-11T21:41:26.5119770Z     }
2023-01-11T21:41:26.5119911Z }
2023-01-11T21:41:26.5120128Z ''')
2023-01-11T21:41:26.5120142Z 
2023-01-11T21:41:26.5120150Z 
2023-01-11T21:41:26.5120353Z async_compile.wait(globals())
2023-01-11T21:41:26.5120529Z del async_compile
2023-01-11T21:41:26.5120540Z 
2023-01-11T21:41:26.5120701Z def call(args):
2023-01-11T21:41:26.5120884Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5121032Z     args.clear()
2023-01-11T21:41:26.5121530Z     buf0 = empty_strided((4, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5122026Z     buf1 = empty_strided((8, 4, 8), (32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5122520Z     buf2 = empty_strided((8, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5123036Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.5123203Z     del arg0_1
2023-01-11T21:41:26.5123359Z     del arg1_1
2023-01-11T21:41:26.5123530Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.5123560Z 
2023-01-11T21:41:26.5123569Z 
2023-01-11T21:41:26.5123733Z if __name__ == "__main__":
2023-01-11T21:41:26.5124010Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5124305Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5124759Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5125118Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.5125325Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5125334Z 
2023-01-11T21:41:26.5125341Z 
2023-01-11T21:41:26.5125510Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5125639Z import torch
2023-01-11T21:41:26.5125753Z import random
2023-01-11T21:41:26.5125962Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5126184Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5126193Z 
2023-01-11T21:41:26.5126335Z aten = torch.ops.aten
2023-01-11T21:41:26.5126578Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5126782Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5126795Z 
2023-01-11T21:41:26.5126803Z 
2023-01-11T21:41:26.5127109Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5127552Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5127806Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.5128065Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.5128284Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.5128509Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.5128740Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.5128888Z {
2023-01-11T21:41:26.5129247Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5129489Z     {
2023-01-11T21:41:26.5129666Z         #pragma omp for 
2023-01-11T21:41:26.5129859Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.5130002Z         {
2023-01-11T21:41:26.5130191Z             #pragma GCC ivdep
2023-01-11T21:41:26.5130394Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:41:26.5130548Z             {
2023-01-11T21:41:26.5130681Z                 {
2023-01-11T21:41:26.5130836Z                     {
2023-01-11T21:41:26.5131060Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5131305Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:41:26.5131532Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:41:26.5131687Z                     }
2023-01-11T21:41:26.5131843Z                 }
2023-01-11T21:41:26.5132037Z             }
2023-01-11T21:41:26.5132191Z         }
2023-01-11T21:41:26.5132344Z         #pragma omp for 
2023-01-11T21:41:26.5132492Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5132610Z         {
2023-01-11T21:41:26.5132744Z             #pragma GCC ivdep
2023-01-11T21:41:26.5132878Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.5132992Z             {
2023-01-11T21:41:26.5133136Z                 #pragma GCC ivdep
2023-01-11T21:41:26.5133296Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:26.5133413Z                 {
2023-01-11T21:41:26.5133533Z                     {
2023-01-11T21:41:26.5133685Z                         {
2023-01-11T21:41:26.5133873Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.5134126Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:26.5134361Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:41:26.5134530Z                         }
2023-01-11T21:41:26.5134696Z                     }
2023-01-11T21:41:26.5134844Z                 }
2023-01-11T21:41:26.5134992Z             }
2023-01-11T21:41:26.5135122Z         }
2023-01-11T21:41:26.5135315Z         #pragma omp for 
2023-01-11T21:41:26.5135484Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5135632Z         {
2023-01-11T21:41:26.5135827Z             #pragma GCC ivdep
2023-01-11T21:41:26.5136019Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.5136173Z             {
2023-01-11T21:41:26.5136352Z                 #pragma GCC ivdep
2023-01-11T21:41:26.5136558Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:26.5136708Z                 {
2023-01-11T21:41:26.5136866Z                     {
2023-01-11T21:41:26.5137028Z                         {
2023-01-11T21:41:26.5137251Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.5137453Z                             auto tmp1 = in_ptr0[i2];
2023-01-11T21:41:26.5137724Z                             auto tmp2 = in_ptr1[tmp1 + (8*tmp0) + (64*i0)];
2023-01-11T21:41:26.5137959Z                             out_ptr2[i2 + (4*i1) + (16*i0)] = tmp2;
2023-01-11T21:41:26.5138131Z                         }
2023-01-11T21:41:26.5138289Z                     }
2023-01-11T21:41:26.5138440Z                 }
2023-01-11T21:41:26.5138593Z             }
2023-01-11T21:41:26.5138724Z         }
2023-01-11T21:41:26.5138873Z     }
2023-01-11T21:41:26.5139012Z }
2023-01-11T21:41:26.5139225Z ''')
2023-01-11T21:41:26.5139238Z 
2023-01-11T21:41:26.5139247Z 
2023-01-11T21:41:26.5139457Z async_compile.wait(globals())
2023-01-11T21:41:26.5139632Z del async_compile
2023-01-11T21:41:26.5139642Z 
2023-01-11T21:41:26.5139761Z def call(args):
2023-01-11T21:41:26.5139894Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5140002Z     args.clear()
2023-01-11T21:41:26.5140391Z     buf0 = empty_strided((4, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5140773Z     buf1 = empty_strided((8, 4, 8), (32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5141195Z     buf2 = empty_strided((8, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5141689Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.5141949Z     del arg0_1
2023-01-11T21:41:26.5142105Z     del arg1_1
2023-01-11T21:41:26.5142267Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.5142298Z 
2023-01-11T21:41:26.5142308Z 
2023-01-11T21:41:26.5142463Z if __name__ == "__main__":
2023-01-11T21:41:26.5142736Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5143036Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5143643Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5144106Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5144381Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5144461Z 
2023-01-11T21:41:26.5144616Z ok (3.415s)
2023-01-11T21:41:26.5145849Z   test_indirect_load_broadcast_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5146151Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5146789Z [2023-01-11 21:34:56,489] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 244
2023-01-11T21:41:26.5147320Z [2023-01-11 21:34:58,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 244
2023-01-11T21:41:26.5147331Z 
2023-01-11T21:41:26.5147505Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5147641Z import torch
2023-01-11T21:41:26.5147770Z import random
2023-01-11T21:41:26.5147980Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5148212Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5148228Z 
2023-01-11T21:41:26.5148409Z aten = torch.ops.aten
2023-01-11T21:41:26.5148698Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5148899Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5148911Z 
2023-01-11T21:41:26.5148921Z 
2023-01-11T21:41:26.5149247Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5149721Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5149992Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.5150242Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.5150483Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.5150718Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5150851Z {
2023-01-11T21:41:26.5151086Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5151246Z     {
2023-01-11T21:41:26.5151436Z         #pragma omp for 
2023-01-11T21:41:26.5151638Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.5151788Z         {
2023-01-11T21:41:26.5151963Z             #pragma GCC ivdep
2023-01-11T21:41:26.5152161Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:41:26.5152319Z             {
2023-01-11T21:41:26.5152474Z                 {
2023-01-11T21:41:26.5152631Z                     {
2023-01-11T21:41:26.5152866Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.5153083Z                         auto tmp2 = in_ptr2[i0];
2023-01-11T21:41:26.5153309Z                         auto tmp1 = in_ptr1[i1 + (512*tmp0)];
2023-01-11T21:41:26.5153532Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.5153753Z                         out_ptr0[i1 + (21*i0)] = tmp3;
2023-01-11T21:41:26.5153923Z                     }
2023-01-11T21:41:26.5154049Z                 }
2023-01-11T21:41:26.5154167Z             }
2023-01-11T21:41:26.5154350Z         }
2023-01-11T21:41:26.5154441Z     }
2023-01-11T21:41:26.5154550Z }
2023-01-11T21:41:26.5154707Z ''')
2023-01-11T21:41:26.5154717Z 
2023-01-11T21:41:26.5154724Z 
2023-01-11T21:41:26.5154882Z async_compile.wait(globals())
2023-01-11T21:41:26.5155016Z del async_compile
2023-01-11T21:41:26.5155029Z 
2023-01-11T21:41:26.5155183Z def call(args):
2023-01-11T21:41:26.5155380Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5155515Z     args.clear()
2023-01-11T21:41:26.5155995Z     buf0 = empty_strided((32, 21), (21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5156428Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5156595Z     del arg0_1
2023-01-11T21:41:26.5156752Z     del arg1_1
2023-01-11T21:41:26.5156910Z     del arg2_1
2023-01-11T21:41:26.5157139Z     return (buf0, )
2023-01-11T21:41:26.5157154Z 
2023-01-11T21:41:26.5157163Z 
2023-01-11T21:41:26.5157355Z if __name__ == "__main__":
2023-01-11T21:41:26.5157622Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5157918Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5158411Z     arg0_1 = rand_strided((32, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5158913Z     arg1_1 = rand_strided((9521, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5159385Z     arg2_1 = rand_strided((32, 21), (1, 32), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5159675Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5159687Z 
2023-01-11T21:41:26.5159846Z ok (1.703s)
2023-01-11T21:41:26.5160982Z   test_inf_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5161217Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5161687Z [2023-01-11 21:34:58,149] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 245
2023-01-11T21:41:26.5162236Z [2023-01-11 21:34:59,788] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 245
2023-01-11T21:41:26.5162252Z 
2023-01-11T21:41:26.5162478Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5162653Z import torch
2023-01-11T21:41:26.5162823Z import random
2023-01-11T21:41:26.5163095Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5163378Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5163393Z 
2023-01-11T21:41:26.5163567Z aten = torch.ops.aten
2023-01-11T21:41:26.5163868Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5164091Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5164109Z 
2023-01-11T21:41:26.5164118Z 
2023-01-11T21:41:26.5164452Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5164946Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5165229Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5165453Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.5165685Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.5165910Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.5166048Z {
2023-01-11T21:41:26.5166285Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5166433Z     {
2023-01-11T21:41:26.5166619Z         #pragma omp for 
2023-01-11T21:41:26.5166818Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.5166966Z         {
2023-01-11T21:41:26.5167293Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5167649Z             auto tmp1 = at::vec::Vectorized<float>(std::numeric_limits<float>::infinity());
2023-01-11T21:41:26.5167877Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5168308Z             auto tmp3 = at::vec::Vectorized<float>(-std::numeric_limits<float>::infinity());
2023-01-11T21:41:26.5168462Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.5168613Z             auto tmp5 = tmp0 * tmp3;
2023-01-11T21:41:26.5168779Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5168986Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.5169312Z             tmp5.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.5169470Z         }
2023-01-11T21:41:26.5169701Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5169890Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.5170035Z         {
2023-01-11T21:41:26.5170221Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5170615Z             auto tmp1 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.5170800Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5171344Z             auto tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.5171549Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.5171739Z             auto tmp5 = tmp0 * tmp3;
2023-01-11T21:41:26.5171927Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5172116Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.5172301Z             out_ptr2[i0] = tmp5;
2023-01-11T21:41:26.5172432Z         }
2023-01-11T21:41:26.5172582Z     }
2023-01-11T21:41:26.5172726Z }
2023-01-11T21:41:26.5172914Z ''')
2023-01-11T21:41:26.5172926Z 
2023-01-11T21:41:26.5172935Z 
2023-01-11T21:41:26.5173149Z async_compile.wait(globals())
2023-01-11T21:41:26.5173323Z del async_compile
2023-01-11T21:41:26.5173334Z 
2023-01-11T21:41:26.5173506Z def call(args):
2023-01-11T21:41:26.5173669Z     arg0_1, = args
2023-01-11T21:41:26.5173817Z     args.clear()
2023-01-11T21:41:26.5174283Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5174762Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5175104Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5175446Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.5175563Z     del arg0_1
2023-01-11T21:41:26.5175712Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.5175721Z 
2023-01-11T21:41:26.5175728Z 
2023-01-11T21:41:26.5175863Z if __name__ == "__main__":
2023-01-11T21:41:26.5176071Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5176349Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5176788Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5177045Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5177058Z 
2023-01-11T21:41:26.5177220Z ok (1.659s)
2023-01-11T21:41:26.5178400Z   test_inplace_activations_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5178704Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5179339Z [2023-01-11 21:34:59,947] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 246
2023-01-11T21:41:26.5179992Z [2023-01-11 21:35:01,813] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 246
2023-01-11T21:41:26.5180005Z 
2023-01-11T21:41:26.5180214Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5180385Z import torch
2023-01-11T21:41:26.5180554Z import random
2023-01-11T21:41:26.5180832Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5181224Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5181236Z 
2023-01-11T21:41:26.5181424Z aten = torch.ops.aten
2023-01-11T21:41:26.5181739Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5181898Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5181924Z 
2023-01-11T21:41:26.5181931Z 
2023-01-11T21:41:26.5182160Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5182521Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5182730Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5182909Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.5183085Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.5183404Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.5183608Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.5183803Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.5184008Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:26.5184269Z                        float* __restrict__ out_ptr6)
2023-01-11T21:41:26.5184415Z {
2023-01-11T21:41:26.5184644Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5184792Z     {
2023-01-11T21:41:26.5184974Z         #pragma omp for 
2023-01-11T21:41:26.5185144Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.5185292Z         {
2023-01-11T21:41:26.5185445Z             {
2023-01-11T21:41:26.5185604Z                 {
2023-01-11T21:41:26.5185827Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5186079Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.5186290Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5186520Z                     auto tmp3 = static_cast<float>(3);
2023-01-11T21:41:26.5186732Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.5186984Z                     auto tmp5 = static_cast<float>(0.0);
2023-01-11T21:41:26.5187301Z                     auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp4, tmp5);
2023-01-11T21:41:26.5187548Z                     auto tmp7 = static_cast<float>(6.0);
2023-01-11T21:41:26.5187839Z                     auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp6, tmp7);
2023-01-11T21:41:26.5188053Z                     auto tmp9 = tmp2 * tmp8;
2023-01-11T21:41:26.5188301Z                     auto tmp10 = static_cast<float>(6);
2023-01-11T21:41:26.5188504Z                     auto tmp11 = tmp9 / tmp10;
2023-01-11T21:41:26.5188913Z                     auto tmp12 = static_cast<float>(-1.0);
2023-01-11T21:41:26.5189220Z                     auto tmp13 = (tmp12 != tmp12) ? tmp12 : std::max(tmp2, tmp12);
2023-01-11T21:41:26.5189447Z                     auto tmp14 = static_cast<float>(1.0);
2023-01-11T21:41:26.5189687Z                     auto tmp15 = (tmp14 != tmp14) ? tmp14 : std::min(tmp13, tmp14);
2023-01-11T21:41:26.5189871Z                     auto tmp16 = static_cast<float>(0);
2023-01-11T21:41:26.5190042Z                     auto tmp17 = tmp2 > tmp16;
2023-01-11T21:41:26.5190234Z                     auto tmp18 = static_cast<float>(0.01);
2023-01-11T21:41:26.5190379Z                     auto tmp19 = tmp2 * tmp18;
2023-01-11T21:41:26.5190553Z                     auto tmp20 = tmp17 ? tmp2 : tmp19;
2023-01-11T21:41:26.5190887Z                     auto tmp21 = std::exp(-tmp2);
2023-01-11T21:41:26.5191114Z                     auto tmp22 = 1 / (1 + tmp21);
2023-01-11T21:41:26.5191321Z                     auto tmp23 = tmp2 * tmp22;
2023-01-11T21:41:26.5191556Z                     auto tmp24 = std::log1p(tmp2);
2023-01-11T21:41:26.5191787Z                     auto tmp25 = static_cast<bool>(0);
2023-01-11T21:41:26.5192019Z                     auto tmp26 = static_cast<float>(99.0);
2023-01-11T21:41:26.5192239Z                     auto tmp27 = tmp25 ? tmp26 : tmp2;
2023-01-11T21:41:26.5192473Z                     auto tmp28 = static_cast<bool>(1);
2023-01-11T21:41:26.5192833Z                     auto tmp29 = tmp28 ? tmp26 : tmp2;
2023-01-11T21:41:26.5193028Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:26.5193230Z                     out_ptr1[i0] = tmp15;
2023-01-11T21:41:26.5193429Z                     out_ptr2[i0] = tmp20;
2023-01-11T21:41:26.5193608Z                     out_ptr3[i0] = tmp23;
2023-01-11T21:41:26.5193806Z                     out_ptr4[i0] = tmp24;
2023-01-11T21:41:26.5193998Z                     out_ptr5[i0] = tmp27;
2023-01-11T21:41:26.5194199Z                     out_ptr6[i0] = tmp29;
2023-01-11T21:41:26.5194352Z                 }
2023-01-11T21:41:26.5194503Z             }
2023-01-11T21:41:26.5194658Z         }
2023-01-11T21:41:26.5194790Z     }
2023-01-11T21:41:26.5194937Z }
2023-01-11T21:41:26.5195132Z ''')
2023-01-11T21:41:26.5195146Z 
2023-01-11T21:41:26.5195155Z 
2023-01-11T21:41:26.5195436Z async_compile.wait(globals())
2023-01-11T21:41:26.5195617Z del async_compile
2023-01-11T21:41:26.5195628Z 
2023-01-11T21:41:26.5195797Z def call(args):
2023-01-11T21:41:26.5195963Z     arg0_1, = args
2023-01-11T21:41:26.5196112Z     args.clear()
2023-01-11T21:41:26.5196536Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5196897Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5197251Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5197598Z     buf3 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5198030Z     buf4 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5198486Z     buf5 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5198929Z     buf6 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5199595Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:41:26.5199754Z     del arg0_1
2023-01-11T21:41:26.5200008Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, )
2023-01-11T21:41:26.5200020Z 
2023-01-11T21:41:26.5200030Z 
2023-01-11T21:41:26.5200209Z if __name__ == "__main__":
2023-01-11T21:41:26.5200480Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5200766Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5201248Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5201507Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5201520Z 
2023-01-11T21:41:26.5201684Z ok (2.025s)
2023-01-11T21:41:26.5202503Z   test_inplace_add_cpu (__main__.CpuTests) ... [2023-01-11 21:35:01,828] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 247
2023-01-11T21:41:26.5203175Z [2023-01-11 21:35:03,421] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 247
2023-01-11T21:41:26.5203194Z 
2023-01-11T21:41:26.5203416Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5203544Z import torch
2023-01-11T21:41:26.5203676Z import random
2023-01-11T21:41:26.5203884Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5204105Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5204115Z 
2023-01-11T21:41:26.5204252Z aten = torch.ops.aten
2023-01-11T21:41:26.5204476Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5204642Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5204652Z 
2023-01-11T21:41:26.5204659Z 
2023-01-11T21:41:26.5204970Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5205427Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5205695Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5205937Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.5206251Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5206400Z {
2023-01-11T21:41:26.5206611Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5206764Z     {
2023-01-11T21:41:26.5206951Z         #pragma omp for 
2023-01-11T21:41:26.5217329Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.5217525Z         {
2023-01-11T21:41:26.5217818Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5218063Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.5218203Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5218370Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5218486Z         }
2023-01-11T21:41:26.5218657Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5218969Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.5219113Z         {
2023-01-11T21:41:26.5219329Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5219514Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.5219717Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5219906Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5220057Z         }
2023-01-11T21:41:26.5220209Z     }
2023-01-11T21:41:26.5220349Z }
2023-01-11T21:41:26.5220548Z ''')
2023-01-11T21:41:26.5220592Z 
2023-01-11T21:41:26.5220601Z 
2023-01-11T21:41:26.5220805Z async_compile.wait(globals())
2023-01-11T21:41:26.5220968Z del async_compile
2023-01-11T21:41:26.5220980Z 
2023-01-11T21:41:26.5221150Z def call(args):
2023-01-11T21:41:26.5221331Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5221504Z     args.clear()
2023-01-11T21:41:26.5221887Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:26.5222044Z     del arg1_1
2023-01-11T21:41:26.5222212Z     return (arg0_1, )
2023-01-11T21:41:26.5222224Z 
2023-01-11T21:41:26.5222250Z 
2023-01-11T21:41:26.5222405Z if __name__ == "__main__":
2023-01-11T21:41:26.5222688Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5222989Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5223594Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5224077Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5224351Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5224363Z 
2023-01-11T21:41:26.5224525Z ok (1.607s)
2023-01-11T21:41:26.5225235Z   test_inplace_mixed_dtype_ops_cpu (__main__.CpuTests) ... [2023-01-11 21:35:03,471] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 248
2023-01-11T21:41:26.5225742Z [2023-01-11 21:35:05,166] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 248
2023-01-11T21:41:26.5225752Z 
2023-01-11T21:41:26.5225920Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5226049Z import torch
2023-01-11T21:41:26.5226214Z import random
2023-01-11T21:41:26.5226532Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5226796Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5226814Z 
2023-01-11T21:41:26.5226997Z aten = torch.ops.aten
2023-01-11T21:41:26.5227307Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5227521Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5227534Z 
2023-01-11T21:41:26.5227543Z 
2023-01-11T21:41:26.5227876Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5228367Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5228643Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.5228892Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5229150Z                        const double* __restrict__ in_ptr1)
2023-01-11T21:41:26.5229296Z {
2023-01-11T21:41:26.5229502Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5229764Z     {
2023-01-11T21:41:26.5229957Z         #pragma omp for 
2023-01-11T21:41:26.5230155Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.5230301Z         {
2023-01-11T21:41:26.5230458Z             {
2023-01-11T21:41:26.5230615Z                 {
2023-01-11T21:41:26.5230812Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5231019Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.5231276Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.5231491Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.5231744Z                     auto tmp4 = static_cast<double>(tmp3);
2023-01-11T21:41:26.5231953Z                     auto tmp5 = tmp4 + tmp1;
2023-01-11T21:41:26.5232153Z                     auto tmp6 = static_cast<float>(tmp5);
2023-01-11T21:41:26.5232377Z                     auto tmp7 = static_cast<double>(tmp6);
2023-01-11T21:41:26.5232545Z                     auto tmp8 = tmp7 * tmp1;
2023-01-11T21:41:26.5232738Z                     auto tmp9 = static_cast<float>(tmp8);
2023-01-11T21:41:26.5232901Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.5233015Z                 }
2023-01-11T21:41:26.5233130Z             }
2023-01-11T21:41:26.5233240Z         }
2023-01-11T21:41:26.5233357Z     }
2023-01-11T21:41:26.5233499Z }
2023-01-11T21:41:26.5233700Z ''')
2023-01-11T21:41:26.5233715Z 
2023-01-11T21:41:26.5233723Z 
2023-01-11T21:41:26.5233925Z async_compile.wait(globals())
2023-01-11T21:41:26.5234103Z del async_compile
2023-01-11T21:41:26.5234114Z 
2023-01-11T21:41:26.5234283Z def call(args):
2023-01-11T21:41:26.5234467Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5234613Z     args.clear()
2023-01-11T21:41:26.5235088Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5235302Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.5235685Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()))
2023-01-11T21:41:26.5235851Z     del arg0_1
2023-01-11T21:41:26.5236015Z     del arg1_1
2023-01-11T21:41:26.5236183Z     return (buf1, )
2023-01-11T21:41:26.5236196Z 
2023-01-11T21:41:26.5236205Z 
2023-01-11T21:41:26.5236383Z if __name__ == "__main__":
2023-01-11T21:41:26.5236637Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5236922Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5237401Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5237874Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.5238147Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5238159Z 
2023-01-11T21:41:26.5238323Z ok (1.745s)
2023-01-11T21:41:26.5239162Z   test_input_mutation1_cpu (__main__.CpuTests) ... [2023-01-11 21:35:05,196] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 249
2023-01-11T21:41:26.5239554Z [2023-01-11 21:35:05,210] torch._inductor.scheduler: [DEBUG] remove_buffer('buf0')
2023-01-11T21:41:26.5239919Z [2023-01-11 21:35:05,211] torch._inductor.scheduler: [DEBUG] remove_buffer('buf0')
2023-01-11T21:41:26.5240408Z [2023-01-11 21:35:06,832] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 249
2023-01-11T21:41:26.5240419Z 
2023-01-11T21:41:26.5240640Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5240791Z import torch
2023-01-11T21:41:26.5240962Z import random
2023-01-11T21:41:26.5241236Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5241526Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5241538Z 
2023-01-11T21:41:26.5241726Z aten = torch.ops.aten
2023-01-11T21:41:26.5242016Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5242230Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5242243Z 
2023-01-11T21:41:26.5242252Z 
2023-01-11T21:41:26.5242577Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5243138Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5243426Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5243659Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.5243885Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.5244040Z {
2023-01-11T21:41:26.5244265Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5244406Z     {
2023-01-11T21:41:26.5244593Z         #pragma omp for 
2023-01-11T21:41:26.5244788Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5244943Z         {
2023-01-11T21:41:26.5245262Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5245634Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.5245822Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5246020Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:41:26.5246310Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.5246465Z             auto tmp5 = tmp2 + tmp4;
2023-01-11T21:41:26.5246614Z             auto tmp6 = tmp3 / tmp5;
2023-01-11T21:41:26.5246779Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.5246945Z             tmp6.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.5247044Z         }
2023-01-11T21:41:26.5247216Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5247366Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5247493Z         {
2023-01-11T21:41:26.5247689Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.5247921Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.5248122Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5248298Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:41:26.5248538Z             auto tmp4 = static_cast<float>(2);
2023-01-11T21:41:26.5248743Z             auto tmp5 = tmp2 + tmp4;
2023-01-11T21:41:26.5248927Z             auto tmp6 = tmp3 / tmp5;
2023-01-11T21:41:26.5249280Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.5249472Z             out_ptr2[i0] = tmp6;
2023-01-11T21:41:26.5249629Z         }
2023-01-11T21:41:26.5249762Z     }
2023-01-11T21:41:26.5249906Z }
2023-01-11T21:41:26.5250114Z ''')
2023-01-11T21:41:26.5250127Z 
2023-01-11T21:41:26.5250136Z 
2023-01-11T21:41:26.5250353Z async_compile.wait(globals())
2023-01-11T21:41:26.5250531Z del async_compile
2023-01-11T21:41:26.5250542Z 
2023-01-11T21:41:26.5250701Z def call(args):
2023-01-11T21:41:26.5250867Z     arg0_1, = args
2023-01-11T21:41:26.5251022Z     args.clear()
2023-01-11T21:41:26.5251504Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5251894Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.5252059Z     del arg0_1
2023-01-11T21:41:26.5252228Z     return (buf2, )
2023-01-11T21:41:26.5252239Z 
2023-01-11T21:41:26.5252253Z 
2023-01-11T21:41:26.5252436Z if __name__ == "__main__":
2023-01-11T21:41:26.5252710Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5253000Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5253444Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5253637Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5253646Z 
2023-01-11T21:41:26.5253766Z ok (1.665s)
2023-01-11T21:41:26.5254379Z   test_input_mutation2_cpu (__main__.CpuTests) ... [2023-01-11 21:35:06,901] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 250
2023-01-11T21:41:26.5254792Z [2023-01-11 21:35:06,905] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:41:26.5254966Z   target: aten.expand_copy.default
2023-01-11T21:41:26.5255178Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:41:26.5255348Z     Pointwise(
2023-01-11T21:41:26.5255522Z       'cpu',
2023-01-11T21:41:26.5255824Z       torch.float32,
2023-01-11T21:41:26.5256117Z       tmp0 = constant(66.0, torch.float32)
2023-01-11T21:41:26.5256289Z       return tmp0
2023-01-11T21:41:26.5256435Z       ,
2023-01-11T21:41:26.5256627Z       ranges=[1],
2023-01-11T21:41:26.5256868Z       origins={_tensor_constant0, lift_fresh_copy}
2023-01-11T21:41:26.5257018Z     )
2023-01-11T21:41:26.5257162Z   ))
2023-01-11T21:41:26.5257325Z   args[1]: [64]
2023-01-11T21:41:26.5257999Z [2023-01-11 21:35:06,913] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten.expand_copy.default
2023-01-11T21:41:26.5258667Z [2023-01-11 21:35:08,949] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 250
2023-01-11T21:41:26.5258680Z 
2023-01-11T21:41:26.5258899Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5259060Z import torch
2023-01-11T21:41:26.5259290Z import random
2023-01-11T21:41:26.5259575Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5259872Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5259891Z 
2023-01-11T21:41:26.5260075Z aten = torch.ops.aten
2023-01-11T21:41:26.5260397Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5260621Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5260633Z 
2023-01-11T21:41:26.5260643Z 
2023-01-11T21:41:26.5260980Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5261336Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5261549Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5261722Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.5261896Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.5262006Z {
2023-01-11T21:41:26.5262185Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5262299Z     {
2023-01-11T21:41:26.5262416Z         #pragma omp for 
2023-01-11T21:41:26.5262559Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5262684Z         {
2023-01-11T21:41:26.5263003Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5263395Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.5263600Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5263817Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5263972Z         }
2023-01-11T21:41:26.5264179Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5264366Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5264508Z         {
2023-01-11T21:41:26.5264707Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5264942Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.5265136Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5265328Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5265470Z         }
2023-01-11T21:41:26.5265656Z         #pragma omp single
2023-01-11T21:41:26.5265806Z         {
2023-01-11T21:41:26.5265966Z             {
2023-01-11T21:41:26.5266109Z                 {
2023-01-11T21:41:26.5266358Z                     auto tmp0 = static_cast<float>(66.0);
2023-01-11T21:41:26.5266536Z                     out_ptr1[0] = tmp0;
2023-01-11T21:41:26.5266695Z                 }
2023-01-11T21:41:26.5266851Z             }
2023-01-11T21:41:26.5266996Z         }
2023-01-11T21:41:26.5267138Z     }
2023-01-11T21:41:26.5267279Z }
2023-01-11T21:41:26.5267482Z ''')
2023-01-11T21:41:26.5267494Z 
2023-01-11T21:41:26.5267504Z 
2023-01-11T21:41:26.5267817Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.5268315Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5268555Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5268735Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5268851Z {
2023-01-11T21:41:26.5269028Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5269141Z     {
2023-01-11T21:41:26.5269335Z         #pragma omp for 
2023-01-11T21:41:26.5269482Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5269593Z         {
2023-01-11T21:41:26.5269838Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5270084Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.5270270Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5270470Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5270608Z         }
2023-01-11T21:41:26.5270826Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5271089Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5271235Z         {
2023-01-11T21:41:26.5271443Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5271673Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.5271936Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5272111Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5272265Z         }
2023-01-11T21:41:26.5272420Z     }
2023-01-11T21:41:26.5272565Z }
2023-01-11T21:41:26.5272759Z ''')
2023-01-11T21:41:26.5272771Z 
2023-01-11T21:41:26.5272780Z 
2023-01-11T21:41:26.5272995Z async_compile.wait(globals())
2023-01-11T21:41:26.5273163Z del async_compile
2023-01-11T21:41:26.5273174Z 
2023-01-11T21:41:26.5273347Z def call(args):
2023-01-11T21:41:26.5273519Z     primals_1, = args
2023-01-11T21:41:26.5273684Z     args.clear()
2023-01-11T21:41:26.5274181Z     buf0 = empty_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5274651Z     buf1 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5275054Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.5275231Z     del primals_1
2023-01-11T21:41:26.5275543Z     buf2 = torch.ops.aten.expand_copy.default(buf1, [64])
2023-01-11T21:41:26.5275690Z     del buf1
2023-01-11T21:41:26.5275856Z     buf3 = buf2
2023-01-11T21:41:26.5276083Z     assert_size_stride(buf3, (64, ), (1, ))
2023-01-11T21:41:26.5276209Z     del buf2
2023-01-11T21:41:26.5276565Z     buf4 = empty_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5276803Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.5276996Z     return (as_strided(buf3, (1, 64), (64, 1)), buf0, buf4, )
2023-01-11T21:41:26.5277006Z 
2023-01-11T21:41:26.5277012Z 
2023-01-11T21:41:26.5277145Z if __name__ == "__main__":
2023-01-11T21:41:26.5277334Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5277545Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5277993Z     primals_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5278249Z     print_performance(lambda: call([primals_1]))
2023-01-11T21:41:26.5278263Z 
2023-01-11T21:41:26.5278430Z ok (2.118s)
2023-01-11T21:41:26.5279224Z   test_input_mutation3_cpu (__main__.CpuTests) ... [2023-01-11 21:35:08,988] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 251
2023-01-11T21:41:26.5279884Z [2023-01-11 21:35:10,869] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 251
2023-01-11T21:41:26.5279897Z 
2023-01-11T21:41:26.5280128Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5280273Z import torch
2023-01-11T21:41:26.5280445Z import random
2023-01-11T21:41:26.5280726Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5281011Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5281023Z 
2023-01-11T21:41:26.5281211Z aten = torch.ops.aten
2023-01-11T21:41:26.5281529Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5281750Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5281762Z 
2023-01-11T21:41:26.5281771Z 
2023-01-11T21:41:26.5282105Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5282582Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5282938Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5283170Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5283320Z {
2023-01-11T21:41:26.5283552Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5283664Z     {
2023-01-11T21:41:26.5283803Z         #pragma omp for 
2023-01-11T21:41:26.5283935Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5284042Z         {
2023-01-11T21:41:26.5284290Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5284532Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.5284686Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5284853Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5284968Z         }
2023-01-11T21:41:26.5285211Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5285481Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5285638Z         {
2023-01-11T21:41:26.5285859Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5286078Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.5286281Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5286473Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5286602Z         }
2023-01-11T21:41:26.5286787Z         #pragma omp for 
2023-01-11T21:41:26.5286980Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5287128Z         {
2023-01-11T21:41:26.5287447Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5287761Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.5287969Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.5288160Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5288317Z         }
2023-01-11T21:41:26.5288544Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5288741Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5288897Z         {
2023-01-11T21:41:26.5289243Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5289475Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.5289659Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.5289855Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5290006Z         }
2023-01-11T21:41:26.5290200Z         #pragma omp for 
2023-01-11T21:41:26.5290389Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5290536Z         {
2023-01-11T21:41:26.5290846Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5291114Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:26.5291279Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5291394Z         }
2023-01-11T21:41:26.5291566Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5291715Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5291829Z         {
2023-01-11T21:41:26.5291985Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5292240Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:26.5292386Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:26.5292530Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5292642Z         }
2023-01-11T21:41:26.5292813Z         #pragma omp for 
2023-01-11T21:41:26.5292998Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5293156Z         {
2023-01-11T21:41:26.5293418Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5293734Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.5293938Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5294149Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5294298Z         }
2023-01-11T21:41:26.5294526Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5294719Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5294839Z         {
2023-01-11T21:41:26.5295150Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5295386Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.5295582Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.5295770Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5295924Z         }
2023-01-11T21:41:26.5296113Z         #pragma omp for 
2023-01-11T21:41:26.5296291Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5296430Z         {
2023-01-11T21:41:26.5296745Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5297058Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:41:26.5297261Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.5297476Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5297617Z         }
2023-01-11T21:41:26.5297905Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5298102Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5298242Z         {
2023-01-11T21:41:26.5298440Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5298617Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:41:26.5298766Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.5298909Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5299006Z         }
2023-01-11T21:41:26.5299142Z         #pragma omp for 
2023-01-11T21:41:26.5299285Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5299399Z         {
2023-01-11T21:41:26.5299636Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5299869Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.5300032Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5300146Z         }
2023-01-11T21:41:26.5300346Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5300577Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5300741Z         {
2023-01-11T21:41:26.5300923Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5301135Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.5301320Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5301444Z         }
2023-01-11T21:41:26.5301587Z     }
2023-01-11T21:41:26.5301726Z }
2023-01-11T21:41:26.5301933Z ''')
2023-01-11T21:41:26.5301947Z 
2023-01-11T21:41:26.5301955Z 
2023-01-11T21:41:26.5302160Z async_compile.wait(globals())
2023-01-11T21:41:26.5302329Z del async_compile
2023-01-11T21:41:26.5302341Z 
2023-01-11T21:41:26.5302511Z def call(args):
2023-01-11T21:41:26.5302666Z     arg0_1, = args
2023-01-11T21:41:26.5302837Z     args.clear()
2023-01-11T21:41:26.5303231Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:26.5303462Z     return (as_strided(arg0_1, (64, ), (1, )), )
2023-01-11T21:41:26.5303475Z 
2023-01-11T21:41:26.5303485Z 
2023-01-11T21:41:26.5303657Z if __name__ == "__main__":
2023-01-11T21:41:26.5303927Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5304225Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5304725Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5304963Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5304974Z 
2023-01-11T21:41:26.5305142Z ok (1.920s)
2023-01-11T21:41:26.5305982Z   test_input_mutation4_cpu (__main__.CpuTests) ... [2023-01-11 21:35:10,886] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 252
2023-01-11T21:41:26.5306530Z [2023-01-11 21:35:12,539] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 252
2023-01-11T21:41:26.5306541Z 
2023-01-11T21:41:26.5306709Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5306833Z import torch
2023-01-11T21:41:26.5306962Z import random
2023-01-11T21:41:26.5307169Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5307374Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5307384Z 
2023-01-11T21:41:26.5307525Z aten = torch.ops.aten
2023-01-11T21:41:26.5307876Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5308078Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5308091Z 
2023-01-11T21:41:26.5308099Z 
2023-01-11T21:41:26.5308404Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5308944Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5309218Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5309440Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5309563Z {
2023-01-11T21:41:26.5309792Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5309944Z     {
2023-01-11T21:41:26.5310125Z         #pragma omp for 
2023-01-11T21:41:26.5310312Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5310466Z         {
2023-01-11T21:41:26.5310843Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5311136Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.5311353Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5311503Z         }
2023-01-11T21:41:26.5311731Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5311929Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.5312079Z         {
2023-01-11T21:41:26.5312276Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.5312469Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.5312657Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5312798Z         }
2023-01-11T21:41:26.5312951Z     }
2023-01-11T21:41:26.5313091Z }
2023-01-11T21:41:26.5313289Z ''')
2023-01-11T21:41:26.5313301Z 
2023-01-11T21:41:26.5313310Z 
2023-01-11T21:41:26.5313529Z async_compile.wait(globals())
2023-01-11T21:41:26.5313684Z del async_compile
2023-01-11T21:41:26.5313694Z 
2023-01-11T21:41:26.5313828Z def call(args):
2023-01-11T21:41:26.5313953Z     arg0_1, = args
2023-01-11T21:41:26.5314080Z     args.clear()
2023-01-11T21:41:26.5314322Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:41:26.5314456Z     return (arg0_1, )
2023-01-11T21:41:26.5314466Z 
2023-01-11T21:41:26.5314473Z 
2023-01-11T21:41:26.5314608Z if __name__ == "__main__":
2023-01-11T21:41:26.5314814Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5315020Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5315443Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5315696Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5315708Z 
2023-01-11T21:41:26.5315874Z ok (1.669s)
2023-01-11T21:41:26.5317005Z   test_invalid_operand_issue1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5317317Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5317960Z [2023-01-11 21:35:12,953] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 253
2023-01-11T21:41:26.5318625Z [2023-01-11 21:35:14,710] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 253
2023-01-11T21:41:26.5318638Z 
2023-01-11T21:41:26.5318866Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5319020Z import torch
2023-01-11T21:41:26.5319196Z import random
2023-01-11T21:41:26.5319472Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5319755Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5319767Z 
2023-01-11T21:41:26.5319955Z aten = torch.ops.aten
2023-01-11T21:41:26.5320273Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5320563Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5320575Z 
2023-01-11T21:41:26.5320584Z 
2023-01-11T21:41:26.5320919Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5321291Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5321502Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.5321688Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.5321869Z                        const long* __restrict__ in_ptr2,
2023-01-11T21:41:26.5322058Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:26.5322237Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5322347Z {
2023-01-11T21:41:26.5322506Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5322623Z     {
2023-01-11T21:41:26.5322857Z         #pragma omp for 
2023-01-11T21:41:26.5323125Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.5323263Z         {
2023-01-11T21:41:26.5323465Z             #pragma GCC ivdep
2023-01-11T21:41:26.5323726Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.5323854Z             {
2023-01-11T21:41:26.5324045Z                 #pragma GCC ivdep
2023-01-11T21:41:26.5324256Z                 for(long i2=0; i2<768; i2+=1)
2023-01-11T21:41:26.5324418Z                 {
2023-01-11T21:41:26.5324579Z                     {
2023-01-11T21:41:26.5324744Z                         {
2023-01-11T21:41:26.5324970Z                             auto tmp3 = in_ptr0[i0];
2023-01-11T21:41:26.5325193Z                             auto tmp8 = in_ptr2[i1 + (128*i0)];
2023-01-11T21:41:26.5325438Z                             auto tmp0 = static_cast<int>(i1);
2023-01-11T21:41:26.5325685Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:41:26.5325916Z                             auto tmp2 = tmp0 == tmp1;
2023-01-11T21:41:26.5326160Z                             auto tmp4 = static_cast<int>(1);
2023-01-11T21:41:26.5326384Z                             auto tmp5 = tmp0 >= tmp4;
2023-01-11T21:41:26.5326589Z                             auto tmp6 = 0;
2023-01-11T21:41:26.5326755Z                             if(tmp5)
2023-01-11T21:41:26.5326923Z                             {
2023-01-11T21:41:26.5327360Z                                 auto tmp7 = in_ptr1[(-1) + i1 + (127*i0)];
2023-01-11T21:41:26.5327565Z                                 tmp6 = tmp7;
2023-01-11T21:41:26.5327734Z                             }
2023-01-11T21:41:26.5327977Z                             auto tmp9 = tmp5 ? tmp6 : tmp8;
2023-01-11T21:41:26.5328217Z                             auto tmp10 = tmp2 ? tmp3 : tmp9;
2023-01-11T21:41:26.5328464Z                             auto tmp11 = in_ptr3[i2 + (768*tmp10)];
2023-01-11T21:41:26.5328653Z                             out_ptr0[i2 + (768*i1) + (98304*i0)] = tmp11;
2023-01-11T21:41:26.5328781Z                         }
2023-01-11T21:41:26.5328901Z                     }
2023-01-11T21:41:26.5329163Z                 }
2023-01-11T21:41:26.5329289Z             }
2023-01-11T21:41:26.5329397Z         }
2023-01-11T21:41:26.5329492Z     }
2023-01-11T21:41:26.5329591Z }
2023-01-11T21:41:26.5329734Z ''')
2023-01-11T21:41:26.5329744Z 
2023-01-11T21:41:26.5329751Z 
2023-01-11T21:41:26.5329909Z async_compile.wait(globals())
2023-01-11T21:41:26.5330042Z del async_compile
2023-01-11T21:41:26.5330051Z 
2023-01-11T21:41:26.5330171Z def call(args):
2023-01-11T21:41:26.5330373Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
2023-01-11T21:41:26.5330540Z     args.clear()
2023-01-11T21:41:26.5331013Z     buf0 = empty_strided((8, 128, 768), (98304, 768, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5331513Z     kernel_cpp_0(c_void_p(arg3_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5331676Z     del arg0_1
2023-01-11T21:41:26.5331842Z     del arg2_1
2023-01-11T21:41:26.5332001Z     del arg3_1
2023-01-11T21:41:26.5332147Z     del arg4_1
2023-01-11T21:41:26.5332442Z     return (buf0, )
2023-01-11T21:41:26.5332455Z 
2023-01-11T21:41:26.5332464Z 
2023-01-11T21:41:26.5332632Z if __name__ == "__main__":
2023-01-11T21:41:26.5332902Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5333204Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5333726Z     arg0_1 = rand_strided((50005, 768), (768, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5334211Z     arg1_1 = rand_strided((8, 128), (128, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5334696Z     arg2_1 = rand_strided((8, 127), (127, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5335158Z     arg3_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5335638Z     arg4_1 = rand_strided((8, 128), (128, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.5336006Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1]))
2023-01-11T21:41:26.5336039Z 
2023-01-11T21:41:26.5336165Z ok (3.032s)
2023-01-11T21:41:26.5337022Z   test_isinf2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5337252Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5337763Z [2023-01-11 21:35:15,588] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 254
2023-01-11T21:41:26.5338367Z [2023-01-11 21:35:17,182] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 254
2023-01-11T21:41:26.5338387Z 
2023-01-11T21:41:26.5338649Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5338814Z import torch
2023-01-11T21:41:26.5338980Z import random
2023-01-11T21:41:26.5339238Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5339506Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5339521Z 
2023-01-11T21:41:26.5339707Z aten = torch.ops.aten
2023-01-11T21:41:26.5340018Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5340238Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5340250Z 
2023-01-11T21:41:26.5340260Z 
2023-01-11T21:41:26.5340591Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5341068Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5341347Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5341572Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:26.5341700Z {
2023-01-11T21:41:26.5341941Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5342094Z     {
2023-01-11T21:41:26.5342278Z         #pragma omp for 
2023-01-11T21:41:26.5342472Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:26.5342633Z         {
2023-01-11T21:41:26.5342769Z             {
2023-01-11T21:41:26.5342924Z                 {
2023-01-11T21:41:26.5343213Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5343460Z                     auto tmp1 = static_cast<long>(i0);
2023-01-11T21:41:26.5343696Z                     auto tmp2 = static_cast<long>(2);
2023-01-11T21:41:26.5343860Z                     auto tmp3 = tmp1 < tmp2;
2023-01-11T21:41:26.5344050Z                     auto tmp4 = static_cast<long>(1);
2023-01-11T21:41:26.5344195Z                     auto tmp5 = tmp1 < tmp4;
2023-01-11T21:41:26.5344381Z                     auto tmp6 = static_cast<float>(1.0);
2023-01-11T21:41:26.5344616Z                     auto tmp7 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.5344798Z                     auto tmp8 = tmp5 ? tmp6 : tmp7;
2023-01-11T21:41:26.5344982Z                     auto tmp9 = static_cast<long>(3);
2023-01-11T21:41:26.5345143Z                     auto tmp10 = tmp1 < tmp9;
2023-01-11T21:41:26.5345458Z                     auto tmp11 = static_cast<float>(2.0);
2023-01-11T21:41:26.5345705Z                     auto tmp12 = static_cast<long>(4);
2023-01-11T21:41:26.5345896Z                     auto tmp13 = tmp1 < tmp12;
2023-01-11T21:41:26.5346491Z                     auto tmp14 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.5346788Z                     auto tmp15 = std::numeric_limits<float>::quiet_NaN();
2023-01-11T21:41:26.5347020Z                     auto tmp16 = tmp13 ? tmp14 : tmp15;
2023-01-11T21:41:26.5347249Z                     auto tmp17 = tmp10 ? tmp11 : tmp16;
2023-01-11T21:41:26.5347485Z                     auto tmp18 = tmp3 ? tmp8 : tmp17;
2023-01-11T21:41:26.5347704Z                     auto tmp19 = tmp0 == tmp18;
2023-01-11T21:41:26.5347894Z                     out_ptr0[i0] = tmp19;
2023-01-11T21:41:26.5348106Z                 }
2023-01-11T21:41:26.5348258Z             }
2023-01-11T21:41:26.5348411Z         }
2023-01-11T21:41:26.5348566Z     }
2023-01-11T21:41:26.5348708Z }
2023-01-11T21:41:26.5348900Z ''')
2023-01-11T21:41:26.5348914Z 
2023-01-11T21:41:26.5348924Z 
2023-01-11T21:41:26.5349143Z async_compile.wait(globals())
2023-01-11T21:41:26.5349299Z del async_compile
2023-01-11T21:41:26.5349311Z 
2023-01-11T21:41:26.5349484Z def call(args):
2023-01-11T21:41:26.5349646Z     arg0_1, = args
2023-01-11T21:41:26.5349811Z     args.clear()
2023-01-11T21:41:26.5350266Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5350579Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5350738Z     del arg0_1
2023-01-11T21:41:26.5350889Z     return (buf0, )
2023-01-11T21:41:26.5350900Z 
2023-01-11T21:41:26.5350910Z 
2023-01-11T21:41:26.5351089Z if __name__ == "__main__":
2023-01-11T21:41:26.5351330Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5351554Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5351920Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5352114Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5352124Z 
2023-01-11T21:41:26.5352245Z ok (1.612s)
2023-01-11T21:41:26.5353180Z   test_isinf_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5353473Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5354085Z [2023-01-11 21:35:17,199] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 255
2023-01-11T21:41:26.5354737Z [2023-01-11 21:35:18,833] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 255
2023-01-11T21:41:26.5355826Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5356120Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5356766Z [2023-01-11 21:35:18,850] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 256
2023-01-11T21:41:26.5357433Z [2023-01-11 21:35:20,510] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 256
2023-01-11T21:41:26.5357445Z 
2023-01-11T21:41:26.5357671Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5357844Z import torch
2023-01-11T21:41:26.5358019Z import random
2023-01-11T21:41:26.5358283Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5358635Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5358645Z 
2023-01-11T21:41:26.5358784Z aten = torch.ops.aten
2023-01-11T21:41:26.5359023Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5359184Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5359194Z 
2023-01-11T21:41:26.5359203Z 
2023-01-11T21:41:26.5359450Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5359815Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5360027Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5360204Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:26.5360459Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:26.5360673Z {
2023-01-11T21:41:26.5360970Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5361126Z     {
2023-01-11T21:41:26.5361316Z         #pragma omp for 
2023-01-11T21:41:26.5361512Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:26.5361640Z         {
2023-01-11T21:41:26.5361791Z             {
2023-01-11T21:41:26.5361931Z                 {
2023-01-11T21:41:26.5362155Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5362394Z                     auto tmp1 = std::isinf(tmp0);
2023-01-11T21:41:26.5362631Z                     auto tmp2 = std::isnan(tmp0);
2023-01-11T21:41:26.5362831Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5363018Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.5363174Z                 }
2023-01-11T21:41:26.5363329Z             }
2023-01-11T21:41:26.5363478Z         }
2023-01-11T21:41:26.5363627Z     }
2023-01-11T21:41:26.5363772Z }
2023-01-11T21:41:26.5363977Z ''')
2023-01-11T21:41:26.5363990Z 
2023-01-11T21:41:26.5363999Z 
2023-01-11T21:41:26.5364201Z async_compile.wait(globals())
2023-01-11T21:41:26.5364381Z del async_compile
2023-01-11T21:41:26.5364393Z 
2023-01-11T21:41:26.5364560Z def call(args):
2023-01-11T21:41:26.5364724Z     arg0_1, = args
2023-01-11T21:41:26.5364881Z     args.clear()
2023-01-11T21:41:26.5365338Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5365792Z     buf1 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5366104Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.5366230Z     del arg0_1
2023-01-11T21:41:26.5366371Z     return (buf0, buf1, )
2023-01-11T21:41:26.5366381Z 
2023-01-11T21:41:26.5366388Z 
2023-01-11T21:41:26.5366525Z if __name__ == "__main__":
2023-01-11T21:41:26.5366728Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5366949Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5367299Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5367493Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5367502Z 
2023-01-11T21:41:26.5367514Z 
2023-01-11T21:41:26.5367722Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5367857Z import torch
2023-01-11T21:41:26.5368018Z import random
2023-01-11T21:41:26.5368259Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5368573Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5368585Z 
2023-01-11T21:41:26.5368834Z aten = torch.ops.aten
2023-01-11T21:41:26.5369276Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5369492Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5369504Z 
2023-01-11T21:41:26.5369513Z 
2023-01-11T21:41:26.5369829Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5370308Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5370602Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.5370824Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:26.5371044Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:26.5371301Z {
2023-01-11T21:41:26.5371542Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5371687Z     {
2023-01-11T21:41:26.5371855Z         #pragma omp for 
2023-01-11T21:41:26.5372043Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:26.5372200Z         {
2023-01-11T21:41:26.5372351Z             {
2023-01-11T21:41:26.5372507Z                 {
2023-01-11T21:41:26.5372723Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5372947Z                     auto tmp1 = std::isinf(tmp0);
2023-01-11T21:41:26.5373180Z                     auto tmp2 = std::isnan(tmp0);
2023-01-11T21:41:26.5373379Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5373570Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.5373723Z                 }
2023-01-11T21:41:26.5373897Z             }
2023-01-11T21:41:26.5374018Z         }
2023-01-11T21:41:26.5374115Z     }
2023-01-11T21:41:26.5374221Z }
2023-01-11T21:41:26.5374375Z ''')
2023-01-11T21:41:26.5374385Z 
2023-01-11T21:41:26.5374392Z 
2023-01-11T21:41:26.5374554Z async_compile.wait(globals())
2023-01-11T21:41:26.5374676Z del async_compile
2023-01-11T21:41:26.5374685Z 
2023-01-11T21:41:26.5374810Z def call(args):
2023-01-11T21:41:26.5374938Z     arg0_1, = args
2023-01-11T21:41:26.5375068Z     args.clear()
2023-01-11T21:41:26.5375399Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5375806Z     buf1 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5376151Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.5376312Z     del arg0_1
2023-01-11T21:41:26.5376535Z     return (buf0, buf1, )
2023-01-11T21:41:26.5376547Z 
2023-01-11T21:41:26.5376555Z 
2023-01-11T21:41:26.5376783Z if __name__ == "__main__":
2023-01-11T21:41:26.5377058Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5377327Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5377809Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.5378064Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5378076Z 
2023-01-11T21:41:26.5378237Z ok (3.328s)
2023-01-11T21:41:26.5379085Z   test_kernel_names_cpu (__main__.CpuTests) ... [2023-01-11 21:35:20,523] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 257
2023-01-11T21:41:26.5379743Z [2023-01-11 21:35:22,062] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 257
2023-01-11T21:41:26.5379755Z 
2023-01-11T21:41:26.5379978Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5380134Z import torch
2023-01-11T21:41:26.5380304Z import random
2023-01-11T21:41:26.5380557Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5380846Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5380858Z 
2023-01-11T21:41:26.5381044Z aten = torch.ops.aten
2023-01-11T21:41:26.5381363Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5381579Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5381589Z 
2023-01-11T21:41:26.5381596Z 
2023-01-11T21:41:26.5381843Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5382202Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5382418Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5382582Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.5382692Z {
2023-01-11T21:41:26.5382868Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5382979Z     {
2023-01-11T21:41:26.5383229Z         #pragma omp for 
2023-01-11T21:41:26.5383435Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.5383570Z         {
2023-01-11T21:41:26.5383875Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5384194Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.5384487Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.5384709Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5384861Z         }
2023-01-11T21:41:26.5385088Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5385288Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.5385425Z         {
2023-01-11T21:41:26.5385619Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5385851Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.5386048Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.5386233Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5386377Z         }
2023-01-11T21:41:26.5386511Z     }
2023-01-11T21:41:26.5386653Z }
2023-01-11T21:41:26.5386856Z ''')
2023-01-11T21:41:26.5386869Z 
2023-01-11T21:41:26.5386878Z 
2023-01-11T21:41:26.5387170Z async_compile.wait(globals())
2023-01-11T21:41:26.5387343Z del async_compile
2023-01-11T21:41:26.5387355Z 
2023-01-11T21:41:26.5387532Z def call(args):
2023-01-11T21:41:26.5387691Z     arg0_1, = args
2023-01-11T21:41:26.5387867Z     args.clear()
2023-01-11T21:41:26.5388324Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5388632Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.5388796Z     del arg0_1
2023-01-11T21:41:26.5388949Z     return (buf0, )
2023-01-11T21:41:26.5388959Z 
2023-01-11T21:41:26.5388966Z 
2023-01-11T21:41:26.5389103Z if __name__ == "__main__":
2023-01-11T21:41:26.5389304Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5389527Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5389862Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5390054Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5390067Z 
2023-01-11T21:41:26.5390185Z ok (1.551s)
2023-01-11T21:41:26.5391153Z   test_kwargs_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5391483Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5392103Z [2023-01-11 21:35:22,101] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 258
2023-01-11T21:41:26.5392652Z [2023-01-11 21:35:22,103] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:41:26.5392914Z   target: aten._histogramdd_bin_edges.default
2023-01-11T21:41:26.5393133Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:41:26.5393739Z     InputBuffer(name='arg0_1', layout=FixedLayout('cpu', torch.float32, size=[4, 2], stride=[2, 1]))
2023-01-11T21:41:26.5393878Z   ))
2023-01-11T21:41:26.5394044Z   args[1]: [3, 3]
2023-01-11T21:41:26.5394393Z   kwargs: {'weight': TensorBox(StorageBox(
2023-01-11T21:41:26.5394962Z     InputBuffer(name='arg1_1', layout=FixedLayout('cpu', torch.float32, size=[4], stride=[1]))
2023-01-11T21:41:26.5395110Z   ))}
2023-01-11T21:41:26.5395807Z [2023-01-11 21:35:22,120] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten._histogramdd_bin_edges.default
2023-01-11T21:41:26.5396351Z [2023-01-11 21:35:22,122] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:41:26.5396537Z   target: aten._histogramdd_from_bin_cts.default
2023-01-11T21:41:26.5396695Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:41:26.5397146Z     InputBuffer(name='arg0_1', layout=FixedLayout('cpu', torch.float32, size=[4, 2], stride=[2, 1]))
2023-01-11T21:41:26.5397256Z   ))
2023-01-11T21:41:26.5397388Z   args[1]: [3, 3]
2023-01-11T21:41:26.5397659Z   kwargs: {'weight': TensorBox(StorageBox(
2023-01-11T21:41:26.5398134Z     InputBuffer(name='arg1_1', layout=FixedLayout('cpu', torch.float32, size=[4], stride=[1]))
2023-01-11T21:41:26.5398343Z   ))}
2023-01-11T21:41:26.5399014Z [2023-01-11 21:35:22,139] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten._histogramdd_from_bin_cts.default
2023-01-11T21:41:26.5399636Z [2023-01-11 21:35:22,142] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 258
2023-01-11T21:41:26.5399648Z 
2023-01-11T21:41:26.5399870Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5400030Z import torch
2023-01-11T21:41:26.5400201Z import random
2023-01-11T21:41:26.5400476Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5400766Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5400778Z 
2023-01-11T21:41:26.5400947Z aten = torch.ops.aten
2023-01-11T21:41:26.5401362Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5401592Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5401604Z 
2023-01-11T21:41:26.5401619Z 
2023-01-11T21:41:26.5401833Z async_compile.wait(globals())
2023-01-11T21:41:26.5402007Z del async_compile
2023-01-11T21:41:26.5402020Z 
2023-01-11T21:41:26.5402188Z def call(args):
2023-01-11T21:41:26.5402373Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5402540Z     args.clear()
2023-01-11T21:41:26.5402883Z     buf0 = torch.ops.aten._histogramdd_bin_edges.default(arg0_1, [3, 3], weight=arg1_1)
2023-01-11T21:41:26.5403052Z     buf1 = buf0[0]
2023-01-11T21:41:26.5403277Z     assert_size_stride(buf1, (4, ), (1, ))
2023-01-11T21:41:26.5403443Z     buf2 = buf0[1]
2023-01-11T21:41:26.5403665Z     assert_size_stride(buf2, (4, ), (1, ))
2023-01-11T21:41:26.5403801Z     del buf0
2023-01-11T21:41:26.5404077Z     buf3 = torch.ops.aten._histogramdd_from_bin_cts.default(arg0_1, [3, 3], weight=arg1_1)
2023-01-11T21:41:26.5404188Z     del arg0_1
2023-01-11T21:41:26.5404310Z     del arg1_1
2023-01-11T21:41:26.5404436Z     buf4 = buf3
2023-01-11T21:41:26.5404612Z     assert_size_stride(buf4, (3, 3), (3, 1))
2023-01-11T21:41:26.5404739Z     del buf3
2023-01-11T21:41:26.5404890Z     return (buf4, buf1, buf2, )
2023-01-11T21:41:26.5404899Z 
2023-01-11T21:41:26.5404907Z 
2023-01-11T21:41:26.5405044Z if __name__ == "__main__":
2023-01-11T21:41:26.5405230Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5405475Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5405944Z     arg0_1 = rand_strided((4, 2), (2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5406438Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5406701Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5406719Z 
2023-01-11T21:41:26.5406873Z ok (0.081s)
2023-01-11T21:41:26.5408019Z   test_l1_loss_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5408322Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5408991Z [2023-01-11 21:35:22,169] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 259
2023-01-11T21:41:26.5409841Z [2023-01-11 21:35:24,048] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 259
2023-01-11T21:41:26.5409874Z 
2023-01-11T21:41:26.5410086Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5410258Z import torch
2023-01-11T21:41:26.5410427Z import random
2023-01-11T21:41:26.5410703Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5410988Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5411008Z 
2023-01-11T21:41:26.5411197Z aten = torch.ops.aten
2023-01-11T21:41:26.5411513Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5411782Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5411792Z 
2023-01-11T21:41:26.5411818Z 
2023-01-11T21:41:26.5412051Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5412408Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5412631Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.5412859Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.5413107Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5413346Z                        const float* __restrict__ in_ptr1)
2023-01-11T21:41:26.5413492Z {
2023-01-11T21:41:26.5413674Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.5413867Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:41:26.5414012Z     {
2023-01-11T21:41:26.5414533Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.5414723Z         float tmp4 = 0;
2023-01-11T21:41:26.5415002Z         auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:26.5415182Z         float tmp6 = 0;
2023-01-11T21:41:26.5415435Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.5415681Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5415830Z         {
2023-01-11T21:41:26.5416152Z             #pragma omp for reduction(+:tmp4_vec) reduction(+:tmp6_vec) 
2023-01-11T21:41:26.5416356Z             for(long i0=0; i0<192; i0+=1)
2023-01-11T21:41:26.5416510Z             {
2023-01-11T21:41:26.5416832Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5417146Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.5417460Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.5417672Z                 auto tmp3 = tmp2.abs();
2023-01-11T21:41:26.5417877Z                 auto tmp5 = tmp2 * tmp2;
2023-01-11T21:41:26.5418070Z                 tmp4_vec += tmp3;
2023-01-11T21:41:26.5418249Z                 tmp6_vec += tmp5;
2023-01-11T21:41:26.5418400Z             }
2023-01-11T21:41:26.5418763Z             tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:41:26.5419117Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.5419348Z             #pragma omp for simd simdlen(4) reduction(+:tmp4) reduction(+:tmp6) 
2023-01-11T21:41:26.5419508Z             for(long i0=1536; i0<1536; i0+=1)
2023-01-11T21:41:26.5419638Z             {
2023-01-11T21:41:26.5419845Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5420034Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.5420356Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.5420581Z                 auto tmp3 = std::abs(tmp2);
2023-01-11T21:41:26.5420779Z                 auto tmp5 = tmp2 * tmp2;
2023-01-11T21:41:26.5420953Z                 tmp4 += tmp3;
2023-01-11T21:41:26.5421129Z                 tmp6 += tmp5;
2023-01-11T21:41:26.5421287Z             }
2023-01-11T21:41:26.5421433Z         }
2023-01-11T21:41:26.5421618Z         out_ptr0[0] = tmp4;
2023-01-11T21:41:26.5421800Z         out_ptr1[0] = tmp6;
2023-01-11T21:41:26.5421934Z     }
2023-01-11T21:41:26.5422084Z     {
2023-01-11T21:41:26.5422238Z         {
2023-01-11T21:41:26.5422439Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.5422684Z             auto tmp1 = static_cast<float>(1536);
2023-01-11T21:41:26.5422888Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.5423084Z             in_out_ptr0[0] = tmp2;
2023-01-11T21:41:26.5423288Z         }
2023-01-11T21:41:26.5423433Z     }
2023-01-11T21:41:26.5423577Z     {
2023-01-11T21:41:26.5423732Z         {
2023-01-11T21:41:26.5423929Z             auto tmp0 = out_ptr1[0];
2023-01-11T21:41:26.5424165Z             auto tmp1 = static_cast<float>(1536);
2023-01-11T21:41:26.5424424Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.5424617Z             in_out_ptr1[0] = tmp2;
2023-01-11T21:41:26.5424777Z         }
2023-01-11T21:41:26.5424928Z     }
2023-01-11T21:41:26.5425071Z }
2023-01-11T21:41:26.5425271Z ''')
2023-01-11T21:41:26.5425285Z 
2023-01-11T21:41:26.5425295Z 
2023-01-11T21:41:26.5425483Z async_compile.wait(globals())
2023-01-11T21:41:26.5425596Z del async_compile
2023-01-11T21:41:26.5425605Z 
2023-01-11T21:41:26.5425731Z def call(args):
2023-01-11T21:41:26.5425864Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5425995Z     args.clear()
2023-01-11T21:41:26.5426344Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5426687Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5426892Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:26.5427056Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:26.5427447Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()))
2023-01-11T21:41:26.5427619Z     del arg0_1
2023-01-11T21:41:26.5427815Z     del arg1_1
2023-01-11T21:41:26.5427998Z     return (buf2, buf3, )
2023-01-11T21:41:26.5428010Z 
2023-01-11T21:41:26.5428019Z 
2023-01-11T21:41:26.5428198Z if __name__ == "__main__":
2023-01-11T21:41:26.5428461Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5428742Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5429262Z     arg0_1 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5429793Z     arg1_1 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5430071Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5430087Z 
2023-01-11T21:41:26.5430246Z ok (1.907s)
2023-01-11T21:41:26.5431429Z   test_layer_norm_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5431733Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5432382Z [2023-01-11 21:35:24,161] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 260
2023-01-11T21:41:26.5433046Z [2023-01-11 21:35:25,987] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 260
2023-01-11T21:41:26.5433057Z 
2023-01-11T21:41:26.5433228Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5433354Z import torch
2023-01-11T21:41:26.5433470Z import random
2023-01-11T21:41:26.5433678Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5433894Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5433910Z 
2023-01-11T21:41:26.5434055Z aten = torch.ops.aten
2023-01-11T21:41:26.5434294Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5434459Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5434469Z 
2023-01-11T21:41:26.5434475Z 
2023-01-11T21:41:26.5434728Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5435175Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5435401Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.5435652Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.5435891Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5436156Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.5436444Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.5436674Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.5436995Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:26.5437119Z {
2023-01-11T21:41:26.5437323Z     auto out_ptr2 = in_out_ptr0;
2023-01-11T21:41:26.5437522Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:41:26.5437761Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5437902Z     {
2023-01-11T21:41:26.5438078Z         #pragma omp for 
2023-01-11T21:41:26.5438276Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.5438403Z         {
2023-01-11T21:41:26.5438544Z             {
2023-01-11T21:41:26.5438993Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.5439178Z                 float tmp1 = 0;
2023-01-11T21:41:26.5439468Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.5439756Z                 for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.5439921Z                 {
2023-01-11T21:41:26.5440265Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:26.5440454Z                     tmp1_vec += tmp0;
2023-01-11T21:41:26.5440617Z                 }
2023-01-11T21:41:26.5441001Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.5441218Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.5441381Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:26.5441503Z                 {
2023-01-11T21:41:26.5441683Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.5441805Z                     tmp1 += tmp0;
2023-01-11T21:41:26.5441924Z                 }
2023-01-11T21:41:26.5442071Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5442190Z             }
2023-01-11T21:41:26.5442311Z         }
2023-01-11T21:41:26.5442451Z         #pragma omp for 
2023-01-11T21:41:26.5442601Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.5442698Z         {
2023-01-11T21:41:26.5442816Z             {
2023-01-11T21:41:26.5443306Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.5443498Z                 float tmp6 = 0;
2023-01-11T21:41:26.5443875Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.5444060Z                 float tmp7 = 0;
2023-01-11T21:41:26.5444329Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.5444534Z                 for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.5444667Z                 {
2023-01-11T21:41:26.5445009Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:26.5445317Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.5445647Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:41:26.5445865Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.5446214Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.5446433Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.5446609Z                     tmp6_vec += tmp5;
2023-01-11T21:41:26.5446805Z                     tmp7_vec += tmp0;
2023-01-11T21:41:26.5446950Z                 }
2023-01-11T21:41:26.5447410Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.5447878Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:26.5448209Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:26.5448425Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:26.5448584Z                 {
2023-01-11T21:41:26.5448806Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.5449176Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:26.5449365Z                     auto tmp2 = static_cast<float>(32);
2023-01-11T21:41:26.5449531Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.5449794Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.5449958Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.5450099Z                     tmp6 += tmp5;
2023-01-11T21:41:26.5450237Z                     tmp7 += tmp0;
2023-01-11T21:41:26.5450338Z                 }
2023-01-11T21:41:26.5450483Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.5450634Z                 out_ptr2[i0] = tmp7;
2023-01-11T21:41:26.5450777Z             }
2023-01-11T21:41:26.5450914Z         }
2023-01-11T21:41:26.5451084Z         #pragma omp for 
2023-01-11T21:41:26.5451375Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.5451598Z         {
2023-01-11T21:41:26.5451931Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i0);
2023-01-11T21:41:26.5452254Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:41:26.5452456Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.5452672Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5452828Z         }
2023-01-11T21:41:26.5453056Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5453235Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.5453381Z         {
2023-01-11T21:41:26.5453584Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:26.5453821Z             auto tmp1 = static_cast<float>(32);
2023-01-11T21:41:26.5454021Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.5454216Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5454372Z         }
2023-01-11T21:41:26.5454542Z         #pragma omp for 
2023-01-11T21:41:26.5454737Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.5454891Z         {
2023-01-11T21:41:26.5455202Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.5455527Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:41:26.5455729Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.5456239Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:41:26.5456422Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.5456630Z             auto tmp5 = tmp4.rsqrt();
2023-01-11T21:41:26.5456812Z             tmp5.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:26.5456922Z         }
2023-01-11T21:41:26.5457093Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5457240Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.5457353Z         {
2023-01-11T21:41:26.5457489Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.5457671Z             auto tmp1 = static_cast<float>(32);
2023-01-11T21:41:26.5457822Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.5458147Z             auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:41:26.5458356Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.5458583Z             auto tmp5 = 1 / std::sqrt(tmp4);
2023-01-11T21:41:26.5458786Z             in_out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.5458915Z         }
2023-01-11T21:41:26.5459093Z         #pragma omp for 
2023-01-11T21:41:26.5459280Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.5459424Z         {
2023-01-11T21:41:26.5459621Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.5459765Z             {
2023-01-11T21:41:26.5460099Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:26.5460390Z                 auto tmp1 = at::vec::Vectorized<float>(in_out_ptr0[i0]);
2023-01-11T21:41:26.5460686Z                 auto tmp3 = at::vec::Vectorized<float>(in_out_ptr1[i0]);
2023-01-11T21:41:26.5461011Z                 auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:26.5461324Z                 auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i1);
2023-01-11T21:41:26.5461743Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.5461946Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.5462153Z                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.5462362Z                 auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.5462656Z                 auto tmp9 = at::vec::clamp_min(tmp8, decltype(tmp8)(0));
2023-01-11T21:41:26.5462895Z                 tmp9.store(out_ptr3 + (8*i1) + (32*i0));
2023-01-11T21:41:26.5463047Z             }
2023-01-11T21:41:26.5463349Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.5463553Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:26.5463712Z             {
2023-01-11T21:41:26.5463899Z                 auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.5464102Z                 auto tmp1 = in_out_ptr0[i0];
2023-01-11T21:41:26.5464274Z                 auto tmp3 = in_out_ptr1[i0];
2023-01-11T21:41:26.5464429Z                 auto tmp5 = in_ptr1[i1];
2023-01-11T21:41:26.5464591Z                 auto tmp7 = in_ptr2[i1];
2023-01-11T21:41:26.5464830Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.5464983Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.5465163Z                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.5465353Z                 auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.5465569Z                 auto tmp9 = tmp8 * (tmp8>0);
2023-01-11T21:41:26.5465771Z                 out_ptr3[i1 + (32*i0)] = tmp9;
2023-01-11T21:41:26.5465922Z             }
2023-01-11T21:41:26.5466070Z         }
2023-01-11T21:41:26.5466217Z     }
2023-01-11T21:41:26.5466359Z }
2023-01-11T21:41:26.5466539Z ''')
2023-01-11T21:41:26.5466553Z 
2023-01-11T21:41:26.5466561Z 
2023-01-11T21:41:26.5466783Z async_compile.wait(globals())
2023-01-11T21:41:26.5466953Z del async_compile
2023-01-11T21:41:26.5466971Z 
2023-01-11T21:41:26.5467135Z def call(args):
2023-01-11T21:41:26.5467378Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.5467557Z     args.clear()
2023-01-11T21:41:26.5468041Z     buf0 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5468498Z     buf1 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5468974Z     buf2 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5469231Z     buf3 = as_strided(buf2, (16, 1), (1, 1)); del buf2  # reuse
2023-01-11T21:41:26.5469495Z     buf4 = as_strided(buf1, (16, 1), (1, 1)); del buf1  # reuse
2023-01-11T21:41:26.5469969Z     buf5 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5470630Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:26.5470945Z     return (buf5, primals_1, primals_2, primals_3, buf3, buf4, )
2023-01-11T21:41:26.5470962Z 
2023-01-11T21:41:26.5470970Z 
2023-01-11T21:41:26.5471109Z if __name__ == "__main__":
2023-01-11T21:41:26.5471318Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5471526Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5471898Z     primals_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5472272Z     primals_2 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5472743Z     primals_3 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5473064Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.5473075Z 
2023-01-11T21:41:26.5473233Z ok (1.937s)
2023-01-11T21:41:26.5474368Z   test_leaky_relu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5474743Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5475398Z [2023-01-11 21:35:26,024] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 261
2023-01-11T21:41:26.5476074Z [2023-01-11 21:35:27,633] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 261
2023-01-11T21:41:26.5476087Z 
2023-01-11T21:41:26.5476294Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5476461Z import torch
2023-01-11T21:41:26.5476623Z import random
2023-01-11T21:41:26.5476896Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5477260Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5477274Z 
2023-01-11T21:41:26.5477469Z aten = torch.ops.aten
2023-01-11T21:41:26.5477791Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5478003Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5478030Z 
2023-01-11T21:41:26.5478037Z 
2023-01-11T21:41:26.5478272Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5478640Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5478855Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5479034Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.5479206Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.5479342Z {
2023-01-11T21:41:26.5479564Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5479693Z     {
2023-01-11T21:41:26.5479880Z         #pragma omp for 
2023-01-11T21:41:26.5480074Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.5480227Z         {
2023-01-11T21:41:26.5480380Z             {
2023-01-11T21:41:26.5480532Z                 {
2023-01-11T21:41:26.5480753Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5480958Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:26.5481173Z                     auto tmp2 = tmp0 > tmp1;
2023-01-11T21:41:26.5481424Z                     auto tmp3 = static_cast<float>(0.2);
2023-01-11T21:41:26.5481644Z                     auto tmp4 = tmp0 * tmp3;
2023-01-11T21:41:26.5481873Z                     auto tmp5 = tmp2 ? tmp0 : tmp4;
2023-01-11T21:41:26.5482115Z                     auto tmp6 = static_cast<float>(2);
2023-01-11T21:41:26.5482326Z                     auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.5482548Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:41:26.5482756Z                     auto tmp9 = tmp0 + tmp8;
2023-01-11T21:41:26.5482975Z                     auto tmp10 = tmp9 > tmp1;
2023-01-11T21:41:26.5483231Z                     auto tmp11 = static_cast<float>(0.01);
2023-01-11T21:41:26.5483452Z                     auto tmp12 = tmp9 * tmp11;
2023-01-11T21:41:26.5483692Z                     auto tmp13 = tmp10 ? tmp9 : tmp12;
2023-01-11T21:41:26.5483891Z                     out_ptr0[i0] = tmp7;
2023-01-11T21:41:26.5484073Z                     out_ptr1[i0] = tmp13;
2023-01-11T21:41:26.5484236Z                 }
2023-01-11T21:41:26.5484390Z             }
2023-01-11T21:41:26.5484535Z         }
2023-01-11T21:41:26.5484680Z     }
2023-01-11T21:41:26.5484824Z }
2023-01-11T21:41:26.5485023Z ''')
2023-01-11T21:41:26.5485037Z 
2023-01-11T21:41:26.5485044Z 
2023-01-11T21:41:26.5485188Z async_compile.wait(globals())
2023-01-11T21:41:26.5485319Z del async_compile
2023-01-11T21:41:26.5485328Z 
2023-01-11T21:41:26.5485452Z def call(args):
2023-01-11T21:41:26.5485577Z     arg0_1, = args
2023-01-11T21:41:26.5485709Z     args.clear()
2023-01-11T21:41:26.5486074Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5486495Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5486869Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.5487096Z     del arg0_1
2023-01-11T21:41:26.5487278Z     return (buf0, buf1, )
2023-01-11T21:41:26.5487289Z 
2023-01-11T21:41:26.5487300Z 
2023-01-11T21:41:26.5487464Z if __name__ == "__main__":
2023-01-11T21:41:26.5487738Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5488024Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5488514Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5488772Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5488783Z 
2023-01-11T21:41:26.5488947Z ok (1.647s)
2023-01-11T21:41:26.5490314Z   test_lgamma_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5490620Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5491276Z [2023-01-11 21:35:27,676] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 262
2023-01-11T21:41:26.5491939Z [2023-01-11 21:35:29,482] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 262
2023-01-11T21:41:26.5491951Z 
2023-01-11T21:41:26.5492166Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5492310Z import torch
2023-01-11T21:41:26.5492438Z import random
2023-01-11T21:41:26.5492647Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5492865Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5492876Z 
2023-01-11T21:41:26.5493005Z aten = torch.ops.aten
2023-01-11T21:41:26.5493237Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5493406Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5493415Z 
2023-01-11T21:41:26.5493423Z 
2023-01-11T21:41:26.5493734Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5494193Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5494451Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.5494685Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.5494918Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.5495043Z {
2023-01-11T21:41:26.5495275Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5495419Z     {
2023-01-11T21:41:26.5495607Z         #pragma omp for 
2023-01-11T21:41:26.5495805Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.5495958Z         {
2023-01-11T21:41:26.5496285Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.5496480Z             auto tmp1 = tmp0.lgamma();
2023-01-11T21:41:26.5496797Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.5497000Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.5497309Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.5497503Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.5497701Z             auto tmp6 = tmp5.cos();
2023-01-11T21:41:26.5497918Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.5498115Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.5498272Z         }
2023-01-11T21:41:26.5498502Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5498696Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.5498850Z         {
2023-01-11T21:41:26.5499049Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.5499285Z             auto tmp1 = std::lgamma(tmp0);
2023-01-11T21:41:26.5499449Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:26.5499600Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.5499866Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.5500019Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.5500182Z             auto tmp6 = std::cos(tmp5);
2023-01-11T21:41:26.5500328Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.5500460Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.5500566Z         }
2023-01-11T21:41:26.5500713Z     }
2023-01-11T21:41:26.5500855Z }
2023-01-11T21:41:26.5501052Z ''')
2023-01-11T21:41:26.5501070Z 
2023-01-11T21:41:26.5501081Z 
2023-01-11T21:41:26.5501284Z async_compile.wait(globals())
2023-01-11T21:41:26.5501449Z del async_compile
2023-01-11T21:41:26.5501462Z 
2023-01-11T21:41:26.5501623Z def call(args):
2023-01-11T21:41:26.5501763Z     arg0_1, = args
2023-01-11T21:41:26.5501921Z     args.clear()
2023-01-11T21:41:26.5502498Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5502989Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5503468Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.5503636Z     del arg0_1
2023-01-11T21:41:26.5503812Z     return (buf0, buf1, )
2023-01-11T21:41:26.5503825Z 
2023-01-11T21:41:26.5503834Z 
2023-01-11T21:41:26.5504017Z if __name__ == "__main__":
2023-01-11T21:41:26.5504267Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5504563Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5505048Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5505308Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.5505321Z 
2023-01-11T21:41:26.5505482Z ok (1.849s)
2023-01-11T21:41:26.5506419Z   test_linear1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5506652Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5507135Z [2023-01-11 21:35:29,547] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 263
2023-01-11T21:41:26.5507699Z [2023-01-11 21:35:29,556] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 263
2023-01-11T21:41:26.5507712Z 
2023-01-11T21:41:26.5507934Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5508073Z import torch
2023-01-11T21:41:26.5508240Z import random
2023-01-11T21:41:26.5508511Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5508792Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5508805Z 
2023-01-11T21:41:26.5508987Z aten = torch.ops.aten
2023-01-11T21:41:26.5509310Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5509531Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5509544Z 
2023-01-11T21:41:26.5509554Z 
2023-01-11T21:41:26.5509870Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5510360Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5510635Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.5510783Z {
2023-01-11T21:41:26.5511017Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5511169Z     {
2023-01-11T21:41:26.5511342Z         #pragma omp for 
2023-01-11T21:41:26.5511521Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.5511673Z         {
2023-01-11T21:41:26.5512010Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5512331Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:26.5512562Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5512788Z         }
2023-01-11T21:41:26.5513018Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5513198Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.5513335Z         {
2023-01-11T21:41:26.5513494Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.5513744Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:26.5513894Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:26.5514043Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.5514152Z         }
2023-01-11T21:41:26.5514246Z     }
2023-01-11T21:41:26.5514353Z }
2023-01-11T21:41:26.5514500Z ''')
2023-01-11T21:41:26.5514510Z 
2023-01-11T21:41:26.5514517Z 
2023-01-11T21:41:26.5514671Z async_compile.wait(globals())
2023-01-11T21:41:26.5514806Z del async_compile
2023-01-11T21:41:26.5514815Z 
2023-01-11T21:41:26.5514985Z def call(args):
2023-01-11T21:41:26.5515183Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.5515354Z     args.clear()
2023-01-11T21:41:26.5515763Z     buf0 = empty_strided((2, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5516137Z     aten.addmm.out(primals_2, primals_3, as_strided(primals_1, (8, 16), (1, 8)), beta=1, alpha=1, out=buf0)
2023-01-11T21:41:26.5516345Z     del primals_1
2023-01-11T21:41:26.5516513Z     del primals_2
2023-01-11T21:41:26.5516715Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.5516955Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.5517162Z     return (buf1, primals_3, buf1, )
2023-01-11T21:41:26.5517176Z 
2023-01-11T21:41:26.5517185Z 
2023-01-11T21:41:26.5517337Z if __name__ == "__main__":
2023-01-11T21:41:26.5517605Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5517901Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5518411Z     primals_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5518918Z     primals_2 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5519414Z     primals_3 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5519748Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.5519760Z 
2023-01-11T21:41:26.5519918Z ok (0.073s)
2023-01-11T21:41:26.5521065Z   test_linear2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5521289Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5521774Z [2023-01-11 21:35:29,751] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 264
2023-01-11T21:41:26.5522256Z [2023-01-11 21:35:29,781] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 264
2023-01-11T21:41:26.5522270Z 
2023-01-11T21:41:26.5522443Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5522571Z import torch
2023-01-11T21:41:26.5522694Z import random
2023-01-11T21:41:26.5522902Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5523175Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5523249Z 
2023-01-11T21:41:26.5523413Z aten = torch.ops.aten
2023-01-11T21:41:26.5523704Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5523893Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5523911Z 
2023-01-11T21:41:26.5523921Z 
2023-01-11T21:41:26.5524246Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.5524716Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5524996Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.5525142Z {
2023-01-11T21:41:26.5525459Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5525595Z     {
2023-01-11T21:41:26.5525775Z         #pragma omp for 
2023-01-11T21:41:26.5525967Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.5526110Z         {
2023-01-11T21:41:26.5526439Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5526739Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.5526962Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5527102Z         }
2023-01-11T21:41:26.5527336Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5527528Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.5527679Z         {
2023-01-11T21:41:26.5527920Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.5528124Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.5528372Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5528506Z         }
2023-01-11T21:41:26.5528656Z     }
2023-01-11T21:41:26.5528808Z }
2023-01-11T21:41:26.5528967Z ''')
2023-01-11T21:41:26.5528976Z 
2023-01-11T21:41:26.5528983Z 
2023-01-11T21:41:26.5529377Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.5529743Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5529954Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.5530074Z {
2023-01-11T21:41:26.5530235Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5530377Z     {
2023-01-11T21:41:26.5530550Z         #pragma omp for 
2023-01-11T21:41:26.5530744Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.5530893Z         {
2023-01-11T21:41:26.5531217Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5531520Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.5531725Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5531877Z         }
2023-01-11T21:41:26.5532091Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5532284Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.5532436Z         {
2023-01-11T21:41:26.5532641Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.5532835Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.5533033Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5533185Z         }
2023-01-11T21:41:26.5533326Z     }
2023-01-11T21:41:26.5533469Z }
2023-01-11T21:41:26.5533665Z ''')
2023-01-11T21:41:26.5533677Z 
2023-01-11T21:41:26.5533686Z 
2023-01-11T21:41:26.5534013Z kernel_cpp_2 = async_compile.cpp('''
2023-01-11T21:41:26.5534494Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5534756Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.5534903Z {
2023-01-11T21:41:26.5535141Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5535290Z     {
2023-01-11T21:41:26.5535475Z         #pragma omp for 
2023-01-11T21:41:26.5535677Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.5535809Z         {
2023-01-11T21:41:26.5536114Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5536344Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.5536514Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.5536626Z         }
2023-01-11T21:41:26.5536799Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.5536944Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.5537056Z         {
2023-01-11T21:41:26.5537200Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.5537396Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.5537597Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5537749Z         }
2023-01-11T21:41:26.5537901Z     }
2023-01-11T21:41:26.5538045Z }
2023-01-11T21:41:26.5538223Z ''')
2023-01-11T21:41:26.5538255Z 
2023-01-11T21:41:26.5538266Z 
2023-01-11T21:41:26.5538681Z kernel_cpp_3 = async_compile.cpp('''
2023-01-11T21:41:26.5539159Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.5539436Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.5539663Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:41:26.5539814Z {
2023-01-11T21:41:26.5540048Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.5540204Z     {
2023-01-11T21:41:26.5540368Z         #pragma omp for 
2023-01-11T21:41:26.5540562Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.5540714Z         {
2023-01-11T21:41:26.5540866Z             {
2023-01-11T21:41:26.5541020Z                 {
2023-01-11T21:41:26.5541241Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.5541459Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.5541759Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:41:26.5541983Z                     auto tmp3 = tmp1 <= tmp2;
2023-01-11T21:41:26.5542199Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.5542400Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.5542553Z                 }
2023-01-11T21:41:26.5542711Z             }
2023-01-11T21:41:26.5542836Z         }
2023-01-11T21:41:26.5542984Z     }
2023-01-11T21:41:26.5543107Z }
2023-01-11T21:41:26.5543338Z ''')
2023-01-11T21:41:26.5543348Z 
2023-01-11T21:41:26.5543356Z 
2023-01-11T21:41:26.5543520Z async_compile.wait(globals())
2023-01-11T21:41:26.5543654Z del async_compile
2023-01-11T21:41:26.5543663Z 
2023-01-11T21:41:26.5543789Z def call(args):
2023-01-11T21:41:26.5544100Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
2023-01-11T21:41:26.5544212Z     args.clear()
2023-01-11T21:41:26.5544642Z     buf0 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5545020Z     aten.addmm.out(primals_2, primals_9, as_strided(primals_1, (8, 8), (1, 8)), beta=1, alpha=1, out=buf0)
2023-01-11T21:41:26.5545182Z     del primals_1
2023-01-11T21:41:26.5545348Z     del primals_2
2023-01-11T21:41:26.5545538Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.5545775Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.5546222Z     buf2 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5546596Z     aten.addmm.out(primals_4, buf1, as_strided(primals_3, (8, 8), (1, 8)), beta=1, alpha=1, out=buf2)
2023-01-11T21:41:26.5546757Z     del primals_4
2023-01-11T21:41:26.5546956Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:26.5547192Z     kernel_cpp_1(c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.5547663Z     buf4 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5548028Z     aten.addmm.out(primals_6, buf3, as_strided(primals_5, (8, 8), (1, 8)), beta=1, alpha=1, out=buf4)
2023-01-11T21:41:26.5548202Z     del primals_6
2023-01-11T21:41:26.5548379Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:41:26.5548622Z     kernel_cpp_2(c_void_p(buf5.data_ptr()))
2023-01-11T21:41:26.5549093Z     buf6 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5549461Z     aten.addmm.out(primals_8, buf5, as_strided(primals_7, (8, 8), (1, 8)), beta=1, alpha=1, out=buf6)
2023-01-11T21:41:26.5549638Z     del primals_8
2023-01-11T21:41:26.5549838Z     buf7 = buf6; del buf6  # reuse
2023-01-11T21:41:26.5550274Z     buf8 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.5550495Z     kernel_cpp_3(c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:41:26.5550855Z     return (buf7, primals_9, buf1, buf3, buf5, buf8, as_strided(primals_7, (8, 8), (8, 1)), as_strided(primals_5, (8, 8), (8, 1)), as_strided(primals_3, (8, 8), (8, 1)), )
2023-01-11T21:41:26.5550865Z 
2023-01-11T21:41:26.5550876Z 
2023-01-11T21:41:26.5551015Z if __name__ == "__main__":
2023-01-11T21:41:26.5551225Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5551541Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5552016Z     primals_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5552485Z     primals_2 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5552968Z     primals_3 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5553451Z     primals_4 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5553923Z     primals_5 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5554402Z     primals_6 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5554888Z     primals_7 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5555427Z     primals_8 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5555919Z     primals_9 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5556426Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]))
2023-01-11T21:41:26.5556439Z 
2023-01-11T21:41:26.5556602Z ok (0.225s)
2023-01-11T21:41:26.5557645Z   test_linear_binary_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5557878Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5558344Z [2023-01-11 21:35:29,877] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 265
2023-01-11T21:41:26.5558901Z [2023-01-11 21:35:29,880] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 265
2023-01-11T21:41:26.5559923Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5560207Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5560851Z [2023-01-11 21:35:29,912] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 266
2023-01-11T21:41:26.5561514Z [2023-01-11 21:35:29,914] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 266
2023-01-11T21:41:26.5562574Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5562878Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5563526Z [2023-01-11 21:35:29,999] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 267
2023-01-11T21:41:26.5564202Z [2023-01-11 21:35:30,002] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 267
2023-01-11T21:41:26.5565047Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5565270Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5565856Z [2023-01-11 21:35:30,032] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 268
2023-01-11T21:41:26.5566456Z [2023-01-11 21:35:30,035] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 268
2023-01-11T21:41:26.5567447Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5567745Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5568475Z [2023-01-11 21:35:30,122] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 269
2023-01-11T21:41:26.5569279Z [2023-01-11 21:35:30,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 269
2023-01-11T21:41:26.5570348Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5570642Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5571290Z [2023-01-11 21:35:30,156] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 270
2023-01-11T21:41:26.5571878Z [2023-01-11 21:35:30,159] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 270
2023-01-11T21:41:26.5572662Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5572888Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5573510Z [2023-01-11 21:35:30,243] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 271
2023-01-11T21:41:26.5574132Z [2023-01-11 21:35:30,245] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 271
2023-01-11T21:41:26.5575167Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5575461Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5576091Z [2023-01-11 21:35:30,275] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 272
2023-01-11T21:41:26.5576104Z 
2023-01-11T21:41:26.5576327Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5576497Z import torch
2023-01-11T21:41:26.5576673Z import random
2023-01-11T21:41:26.5576948Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5577238Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5577251Z 
2023-01-11T21:41:26.5577412Z aten = torch.ops.aten
2023-01-11T21:41:26.5577730Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5577946Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5577960Z 
2023-01-11T21:41:26.5577970Z 
2023-01-11T21:41:26.5578186Z async_compile.wait(globals())
2023-01-11T21:41:26.5578373Z del async_compile
2023-01-11T21:41:26.5578384Z 
2023-01-11T21:41:26.5578550Z def call(args):
2023-01-11T21:41:26.5578863Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5578993Z     args.clear()
2023-01-11T21:41:26.5579398Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:26.5579519Z     del arg0_1
2023-01-11T21:41:26.5579641Z     del arg1_1
2023-01-11T21:41:26.5579761Z     del arg2_1
2023-01-11T21:41:26.5579882Z     del arg3_1
2023-01-11T21:41:26.5580010Z     return (buf0, )
2023-01-11T21:41:26.5580020Z 
2023-01-11T21:41:26.5580026Z 
2023-01-11T21:41:26.5580160Z if __name__ == "__main__":
2023-01-11T21:41:26.5580348Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5580572Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5581025Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5581593Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5582091Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5582584Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5582888Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5582902Z 
2023-01-11T21:41:26.5582912Z 
2023-01-11T21:41:26.5583209Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5583363Z import torch
2023-01-11T21:41:26.5583537Z import random
2023-01-11T21:41:26.5583815Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5584102Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5584114Z 
2023-01-11T21:41:26.5584305Z aten = torch.ops.aten
2023-01-11T21:41:26.5584625Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5584848Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5584860Z 
2023-01-11T21:41:26.5584875Z 
2023-01-11T21:41:26.5585093Z async_compile.wait(globals())
2023-01-11T21:41:26.5585250Z del async_compile
2023-01-11T21:41:26.5585268Z 
2023-01-11T21:41:26.5585445Z def call(args):
2023-01-11T21:41:26.5585642Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5585807Z     args.clear()
2023-01-11T21:41:26.5586372Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:26.5586538Z     del arg0_1
2023-01-11T21:41:26.5586653Z     del arg1_1
2023-01-11T21:41:26.5586759Z     del arg2_1
2023-01-11T21:41:26.5586886Z     return (buf0, )
2023-01-11T21:41:26.5586897Z 
2023-01-11T21:41:26.5586904Z 
2023-01-11T21:41:26.5587045Z if __name__ == "__main__":
2023-01-11T21:41:26.5587254Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5587475Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5587855Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5588246Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5588693Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5589014Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5589031Z 
2023-01-11T21:41:26.5589064Z 
2023-01-11T21:41:26.5589315Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5589509Z import torch
2023-01-11T21:41:26.5589675Z import random
2023-01-11T21:41:26.5589944Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5590216Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5590229Z 
2023-01-11T21:41:26.5590414Z aten = torch.ops.aten
2023-01-11T21:41:26.5590733Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5590935Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5590946Z 
2023-01-11T21:41:26.5590956Z 
2023-01-11T21:41:26.5591178Z async_compile.wait(globals())
2023-01-11T21:41:26.5591348Z del async_compile
2023-01-11T21:41:26.5591360Z 
2023-01-11T21:41:26.5591518Z def call(args):
2023-01-11T21:41:26.5591823Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5591991Z     args.clear()
2023-01-11T21:41:26.5592563Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:26.5592724Z     del arg0_1
2023-01-11T21:41:26.5592866Z     del arg1_1
2023-01-11T21:41:26.5593031Z     del arg2_1
2023-01-11T21:41:26.5593189Z     del arg3_1
2023-01-11T21:41:26.5593354Z     return (buf0, )
2023-01-11T21:41:26.5593365Z 
2023-01-11T21:41:26.5593373Z 
2023-01-11T21:41:26.5593550Z if __name__ == "__main__":
2023-01-11T21:41:26.5593820Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5594115Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5594510Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5594916Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5595294Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5595670Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5595903Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5595913Z 
2023-01-11T21:41:26.5595920Z 
2023-01-11T21:41:26.5596122Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5596291Z import torch
2023-01-11T21:41:26.5596508Z import random
2023-01-11T21:41:26.5596755Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5597094Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5597106Z 
2023-01-11T21:41:26.5597284Z aten = torch.ops.aten
2023-01-11T21:41:26.5597588Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5597801Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5597813Z 
2023-01-11T21:41:26.5597832Z 
2023-01-11T21:41:26.5598038Z async_compile.wait(globals())
2023-01-11T21:41:26.5598212Z del async_compile
2023-01-11T21:41:26.5598230Z 
2023-01-11T21:41:26.5598395Z def call(args):
2023-01-11T21:41:26.5598573Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5598742Z     args.clear()
2023-01-11T21:41:26.5599301Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:26.5599472Z     del arg0_1
2023-01-11T21:41:26.5599636Z     del arg1_1
2023-01-11T21:41:26.5599797Z     del arg2_1
2023-01-11T21:41:26.5599960Z     return (buf0, )
2023-01-11T21:41:26.5599971Z 
2023-01-11T21:41:26.5599980Z 
2023-01-11T21:41:26.5600142Z if __name__ == "__main__":
2023-01-11T21:41:26.5600411Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5600708Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5601205Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5601695Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5602127Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5602357Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5602366Z 
2023-01-11T21:41:26.5602373Z 
2023-01-11T21:41:26.5602540Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5602651Z import torch
2023-01-11T21:41:26.5602778Z import random
2023-01-11T21:41:26.5602990Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5603204Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5603214Z 
2023-01-11T21:41:26.5603349Z aten = torch.ops.aten
2023-01-11T21:41:26.5603589Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5603799Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5603811Z 
2023-01-11T21:41:26.5603818Z 
2023-01-11T21:41:26.5604033Z async_compile.wait(globals())
2023-01-11T21:41:26.5604192Z del async_compile
2023-01-11T21:41:26.5604203Z 
2023-01-11T21:41:26.5604372Z def call(args):
2023-01-11T21:41:26.5604573Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5604866Z     args.clear()
2023-01-11T21:41:26.5605411Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:26.5605577Z     del arg0_1
2023-01-11T21:41:26.5605735Z     del arg1_1
2023-01-11T21:41:26.5605884Z     del arg2_1
2023-01-11T21:41:26.5606041Z     del arg3_1
2023-01-11T21:41:26.5606208Z     return (buf0, )
2023-01-11T21:41:26.5606219Z 
2023-01-11T21:41:26.5606229Z 
2023-01-11T21:41:26.5606410Z if __name__ == "__main__":
2023-01-11T21:41:26.5606681Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5606973Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5607471Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5608003Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5608498Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5609152Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5609459Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5609473Z 
2023-01-11T21:41:26.5609482Z 
2023-01-11T21:41:26.5609680Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5609809Z import torch
2023-01-11T21:41:26.5609937Z import random
2023-01-11T21:41:26.5610146Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5610359Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5610368Z 
2023-01-11T21:41:26.5610492Z aten = torch.ops.aten
2023-01-11T21:41:26.5610736Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5610901Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5610911Z 
2023-01-11T21:41:26.5610922Z 
2023-01-11T21:41:26.5611082Z async_compile.wait(globals())
2023-01-11T21:41:26.5611213Z del async_compile
2023-01-11T21:41:26.5611227Z 
2023-01-11T21:41:26.5611380Z def call(args):
2023-01-11T21:41:26.5611544Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5611709Z     args.clear()
2023-01-11T21:41:26.5612374Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:26.5612537Z     del arg0_1
2023-01-11T21:41:26.5612746Z     del arg1_1
2023-01-11T21:41:26.5612901Z     del arg2_1
2023-01-11T21:41:26.5613068Z     return (buf0, )
2023-01-11T21:41:26.5613086Z 
2023-01-11T21:41:26.5613095Z 
2023-01-11T21:41:26.5613275Z if __name__ == "__main__":
2023-01-11T21:41:26.5613545Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5613816Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5614308Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5614805Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5615319Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5615617Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5615629Z 
2023-01-11T21:41:26.5615639Z 
2023-01-11T21:41:26.5615862Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5616026Z import torch
2023-01-11T21:41:26.5616194Z import random
2023-01-11T21:41:26.5616445Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5616740Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5616751Z 
2023-01-11T21:41:26.5616935Z aten = torch.ops.aten
2023-01-11T21:41:26.5617247Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5617460Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5617469Z 
2023-01-11T21:41:26.5617476Z 
2023-01-11T21:41:26.5617634Z async_compile.wait(globals())
2023-01-11T21:41:26.5617774Z del async_compile
2023-01-11T21:41:26.5617783Z 
2023-01-11T21:41:26.5617910Z def call(args):
2023-01-11T21:41:26.5618154Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5618282Z     args.clear()
2023-01-11T21:41:26.5618707Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:26.5618831Z     del arg0_1
2023-01-11T21:41:26.5618951Z     del arg1_1
2023-01-11T21:41:26.5619075Z     del arg2_1
2023-01-11T21:41:26.5619233Z     del arg3_1
2023-01-11T21:41:26.5619370Z     return (buf0, )
2023-01-11T21:41:26.5619384Z 
2023-01-11T21:41:26.5619415Z 
2023-01-11T21:41:26.5619604Z if __name__ == "__main__":
2023-01-11T21:41:26.5619858Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5620172Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5620652Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5621185Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5621678Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5622172Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5622458Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5622487Z 
2023-01-11T21:41:26.5623219Z [2023-01-11 21:35:30,277] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 272
2023-01-11T21:41:26.5624312Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5624617Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5625264Z [2023-01-11 21:35:30,404] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 273
2023-01-11T21:41:26.5625769Z [2023-01-11 21:35:30,407] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 273
2023-01-11T21:41:26.5626551Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5626781Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5627336Z [2023-01-11 21:35:30,452] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 274
2023-01-11T21:41:26.5627976Z [2023-01-11 21:35:30,454] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 274
2023-01-11T21:41:26.5628976Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5629272Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5629895Z [2023-01-11 21:35:30,539] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 275
2023-01-11T21:41:26.5630565Z [2023-01-11 21:35:30,541] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 275
2023-01-11T21:41:26.5631616Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5631993Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5632641Z [2023-01-11 21:35:30,571] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 276
2023-01-11T21:41:26.5633225Z [2023-01-11 21:35:30,573] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 276
2023-01-11T21:41:26.5634005Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5634274Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5634779Z [2023-01-11 21:35:30,659] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 277
2023-01-11T21:41:26.5635391Z [2023-01-11 21:35:30,661] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 277
2023-01-11T21:41:26.5636456Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5636752Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5637366Z [2023-01-11 21:35:30,692] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 278
2023-01-11T21:41:26.5638044Z [2023-01-11 21:35:30,694] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 278
2023-01-11T21:41:26.5639090Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5639388Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5640023Z [2023-01-11 21:35:30,778] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 279
2023-01-11T21:41:26.5640036Z 
2023-01-11T21:41:26.5640252Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5640426Z import torch
2023-01-11T21:41:26.5640595Z import random
2023-01-11T21:41:26.5640805Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5640994Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5641024Z 
2023-01-11T21:41:26.5641145Z aten = torch.ops.aten
2023-01-11T21:41:26.5641386Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5641557Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5641567Z 
2023-01-11T21:41:26.5641574Z 
2023-01-11T21:41:26.5641738Z async_compile.wait(globals())
2023-01-11T21:41:26.5641868Z del async_compile
2023-01-11T21:41:26.5641877Z 
2023-01-11T21:41:26.5642003Z def call(args):
2023-01-11T21:41:26.5642150Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5642268Z     args.clear()
2023-01-11T21:41:26.5642788Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:26.5642958Z     del arg0_1
2023-01-11T21:41:26.5643163Z     del arg1_1
2023-01-11T21:41:26.5643321Z     del arg2_1
2023-01-11T21:41:26.5643488Z     return (buf0, )
2023-01-11T21:41:26.5643506Z 
2023-01-11T21:41:26.5643516Z 
2023-01-11T21:41:26.5643693Z if __name__ == "__main__":
2023-01-11T21:41:26.5643968Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5644245Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5644805Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5645294Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5645786Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5646076Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5646089Z 
2023-01-11T21:41:26.5646098Z 
2023-01-11T21:41:26.5646323Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5646487Z import torch
2023-01-11T21:41:26.5646653Z import random
2023-01-11T21:41:26.5646913Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5647201Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5647213Z 
2023-01-11T21:41:26.5647390Z aten = torch.ops.aten
2023-01-11T21:41:26.5647760Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5647980Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5647998Z 
2023-01-11T21:41:26.5648006Z 
2023-01-11T21:41:26.5648193Z async_compile.wait(globals())
2023-01-11T21:41:26.5648325Z del async_compile
2023-01-11T21:41:26.5648334Z 
2023-01-11T21:41:26.5648463Z def call(args):
2023-01-11T21:41:26.5648606Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5648735Z     args.clear()
2023-01-11T21:41:26.5649297Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:26.5649421Z     del arg0_1
2023-01-11T21:41:26.5649537Z     del arg1_1
2023-01-11T21:41:26.5649657Z     del arg2_1
2023-01-11T21:41:26.5649774Z     del arg3_1
2023-01-11T21:41:26.5649880Z     return (buf0, )
2023-01-11T21:41:26.5649889Z 
2023-01-11T21:41:26.5649895Z 
2023-01-11T21:41:26.5650082Z if __name__ == "__main__":
2023-01-11T21:41:26.5650337Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5650641Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5651180Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5651651Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5652135Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5652644Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5652937Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5652950Z 
2023-01-11T21:41:26.5652975Z 
2023-01-11T21:41:26.5653176Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5653347Z import torch
2023-01-11T21:41:26.5653515Z import random
2023-01-11T21:41:26.5653790Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5654075Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5654092Z 
2023-01-11T21:41:26.5654284Z aten = torch.ops.aten
2023-01-11T21:41:26.5654599Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5654808Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5654819Z 
2023-01-11T21:41:26.5654828Z 
2023-01-11T21:41:26.5655041Z async_compile.wait(globals())
2023-01-11T21:41:26.5655222Z del async_compile
2023-01-11T21:41:26.5655233Z 
2023-01-11T21:41:26.5655401Z def call(args):
2023-01-11T21:41:26.5655588Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5655763Z     args.clear()
2023-01-11T21:41:26.5656251Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:26.5656358Z     del arg0_1
2023-01-11T21:41:26.5656479Z     del arg1_1
2023-01-11T21:41:26.5656600Z     del arg2_1
2023-01-11T21:41:26.5656731Z     return (buf0, )
2023-01-11T21:41:26.5656740Z 
2023-01-11T21:41:26.5656747Z 
2023-01-11T21:41:26.5656885Z if __name__ == "__main__":
2023-01-11T21:41:26.5657095Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5657317Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5657803Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5658273Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5658802Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5659088Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5659103Z 
2023-01-11T21:41:26.5659114Z 
2023-01-11T21:41:26.5659328Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5659493Z import torch
2023-01-11T21:41:26.5659667Z import random
2023-01-11T21:41:26.5659939Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5660227Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5660239Z 
2023-01-11T21:41:26.5660411Z aten = torch.ops.aten
2023-01-11T21:41:26.5660831Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5661058Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5661078Z 
2023-01-11T21:41:26.5661087Z 
2023-01-11T21:41:26.5661305Z async_compile.wait(globals())
2023-01-11T21:41:26.5661482Z del async_compile
2023-01-11T21:41:26.5661494Z 
2023-01-11T21:41:26.5661661Z def call(args):
2023-01-11T21:41:26.5661871Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5662038Z     args.clear()
2023-01-11T21:41:26.5662583Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:26.5662743Z     del arg0_1
2023-01-11T21:41:26.5662901Z     del arg1_1
2023-01-11T21:41:26.5663063Z     del arg2_1
2023-01-11T21:41:26.5663287Z     del arg3_1
2023-01-11T21:41:26.5663466Z     return (buf0, )
2023-01-11T21:41:26.5663478Z 
2023-01-11T21:41:26.5663487Z 
2023-01-11T21:41:26.5663667Z if __name__ == "__main__":
2023-01-11T21:41:26.5663869Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5664090Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5664470Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5664846Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5665215Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5665622Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5665896Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5665924Z 
2023-01-11T21:41:26.5665932Z 
2023-01-11T21:41:26.5666140Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5666333Z import torch
2023-01-11T21:41:26.5666570Z import random
2023-01-11T21:41:26.5666851Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5667138Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5667150Z 
2023-01-11T21:41:26.5667340Z aten = torch.ops.aten
2023-01-11T21:41:26.5667648Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5667875Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5667887Z 
2023-01-11T21:41:26.5667897Z 
2023-01-11T21:41:26.5668113Z async_compile.wait(globals())
2023-01-11T21:41:26.5668275Z del async_compile
2023-01-11T21:41:26.5668286Z 
2023-01-11T21:41:26.5668459Z def call(args):
2023-01-11T21:41:26.5668656Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5668819Z     args.clear()
2023-01-11T21:41:26.5669374Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:26.5669529Z     del arg0_1
2023-01-11T21:41:26.5669687Z     del arg1_1
2023-01-11T21:41:26.5669819Z     del arg2_1
2023-01-11T21:41:26.5669995Z     return (buf0, )
2023-01-11T21:41:26.5670008Z 
2023-01-11T21:41:26.5670016Z 
2023-01-11T21:41:26.5670201Z if __name__ == "__main__":
2023-01-11T21:41:26.5670475Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5670769Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5671353Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5671760Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5672127Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5672330Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5672340Z 
2023-01-11T21:41:26.5672364Z 
2023-01-11T21:41:26.5672511Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5672632Z import torch
2023-01-11T21:41:26.5672760Z import random
2023-01-11T21:41:26.5672970Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5673185Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5673195Z 
2023-01-11T21:41:26.5673363Z aten = torch.ops.aten
2023-01-11T21:41:26.5673732Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5673934Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5673957Z 
2023-01-11T21:41:26.5673987Z 
2023-01-11T21:41:26.5674233Z async_compile.wait(globals())
2023-01-11T21:41:26.5674400Z del async_compile
2023-01-11T21:41:26.5674413Z 
2023-01-11T21:41:26.5674579Z def call(args):
2023-01-11T21:41:26.5674793Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5674956Z     args.clear()
2023-01-11T21:41:26.5675507Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:26.5675672Z     del arg0_1
2023-01-11T21:41:26.5675819Z     del arg1_1
2023-01-11T21:41:26.5675978Z     del arg2_1
2023-01-11T21:41:26.5676143Z     del arg3_1
2023-01-11T21:41:26.5676314Z     return (buf0, )
2023-01-11T21:41:26.5676326Z 
2023-01-11T21:41:26.5676335Z 
2023-01-11T21:41:26.5676511Z if __name__ == "__main__":
2023-01-11T21:41:26.5676779Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5677078Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5677551Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5678044Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5678557Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5679069Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5679324Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5679334Z 
2023-01-11T21:41:26.5679341Z 
2023-01-11T21:41:26.5679511Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5679638Z import torch
2023-01-11T21:41:26.5679767Z import random
2023-01-11T21:41:26.5679959Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5680168Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5680177Z 
2023-01-11T21:41:26.5680319Z aten = torch.ops.aten
2023-01-11T21:41:26.5680549Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5680719Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5680728Z 
2023-01-11T21:41:26.5680735Z 
2023-01-11T21:41:26.5680908Z async_compile.wait(globals())
2023-01-11T21:41:26.5681096Z del async_compile
2023-01-11T21:41:26.5681106Z 
2023-01-11T21:41:26.5681274Z def call(args):
2023-01-11T21:41:26.5681427Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5681632Z     args.clear()
2023-01-11T21:41:26.5682205Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:26.5682353Z     del arg0_1
2023-01-11T21:41:26.5682514Z     del arg1_1
2023-01-11T21:41:26.5682672Z     del arg2_1
2023-01-11T21:41:26.5682847Z     return (buf0, )
2023-01-11T21:41:26.5682860Z 
2023-01-11T21:41:26.5682869Z 
2023-01-11T21:41:26.5683036Z if __name__ == "__main__":
2023-01-11T21:41:26.5683309Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5683606Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5684181Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5684682Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5685189Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5685482Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5685494Z 
2023-01-11T21:41:26.5686162Z [2023-01-11 21:35:30,780] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 279
2023-01-11T21:41:26.5687202Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5687428Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5687903Z [2023-01-11 21:35:30,809] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 280
2023-01-11T21:41:26.5688393Z [2023-01-11 21:35:30,811] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 280
2023-01-11T21:41:26.5689469Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5689804Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5690426Z [2023-01-11 21:35:30,898] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 281
2023-01-11T21:41:26.5691068Z [2023-01-11 21:35:30,901] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 281
2023-01-11T21:41:26.5692136Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5692429Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5693068Z [2023-01-11 21:35:30,933] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 282
2023-01-11T21:41:26.5693745Z [2023-01-11 21:35:30,935] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 282
2023-01-11T21:41:26.5694813Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5695046Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5695514Z [2023-01-11 21:35:31,021] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 283
2023-01-11T21:41:26.5696003Z [2023-01-11 21:35:31,024] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 283
2023-01-11T21:41:26.5696846Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5697112Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5697854Z [2023-01-11 21:35:31,054] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 284
2023-01-11T21:41:26.5698482Z [2023-01-11 21:35:31,056] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 284
2023-01-11T21:41:26.5699562Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5699856Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5700568Z [2023-01-11 21:35:31,142] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 285
2023-01-11T21:41:26.5701236Z [2023-01-11 21:35:31,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 285
2023-01-11T21:41:26.5702330Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5702608Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5703054Z [2023-01-11 21:35:31,177] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 286
2023-01-11T21:41:26.5703064Z 
2023-01-11T21:41:26.5703294Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5703422Z import torch
2023-01-11T21:41:26.5703546Z import random
2023-01-11T21:41:26.5703762Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5703980Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5703996Z 
2023-01-11T21:41:26.5704139Z aten = torch.ops.aten
2023-01-11T21:41:26.5704420Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5704606Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5704622Z 
2023-01-11T21:41:26.5704631Z 
2023-01-11T21:41:26.5704847Z async_compile.wait(globals())
2023-01-11T21:41:26.5705048Z del async_compile
2023-01-11T21:41:26.5705064Z 
2023-01-11T21:41:26.5705230Z def call(args):
2023-01-11T21:41:26.5705510Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5705678Z     args.clear()
2023-01-11T21:41:26.5706219Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'add')
2023-01-11T21:41:26.5706372Z     del arg0_1
2023-01-11T21:41:26.5706524Z     del arg1_1
2023-01-11T21:41:26.5706685Z     del arg2_1
2023-01-11T21:41:26.5706859Z     del arg3_1
2023-01-11T21:41:26.5707037Z     return (buf0, )
2023-01-11T21:41:26.5707049Z 
2023-01-11T21:41:26.5707058Z 
2023-01-11T21:41:26.5707239Z if __name__ == "__main__":
2023-01-11T21:41:26.5707512Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5707798Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5708287Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5708784Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5709271Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5709756Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5710061Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5710073Z 
2023-01-11T21:41:26.5710083Z 
2023-01-11T21:41:26.5710261Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5710393Z import torch
2023-01-11T21:41:26.5710524Z import random
2023-01-11T21:41:26.5710716Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5710991Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5711000Z 
2023-01-11T21:41:26.5711139Z aten = torch.ops.aten
2023-01-11T21:41:26.5711376Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5711541Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5711551Z 
2023-01-11T21:41:26.5711558Z 
2023-01-11T21:41:26.5711715Z async_compile.wait(globals())
2023-01-11T21:41:26.5711854Z del async_compile
2023-01-11T21:41:26.5711865Z 
2023-01-11T21:41:26.5712031Z def call(args):
2023-01-11T21:41:26.5712212Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5712365Z     args.clear()
2023-01-11T21:41:26.5712905Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'add')
2023-01-11T21:41:26.5713061Z     del arg0_1
2023-01-11T21:41:26.5713188Z     del arg1_1
2023-01-11T21:41:26.5713411Z     del arg2_1
2023-01-11T21:41:26.5713583Z     return (buf0, )
2023-01-11T21:41:26.5713597Z 
2023-01-11T21:41:26.5713615Z 
2023-01-11T21:41:26.5713779Z if __name__ == "__main__":
2023-01-11T21:41:26.5714057Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5714355Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5714861Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5715339Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5715826Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5716119Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5716131Z 
2023-01-11T21:41:26.5716140Z 
2023-01-11T21:41:26.5716366Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5716510Z import torch
2023-01-11T21:41:26.5716676Z import random
2023-01-11T21:41:26.5716953Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5717242Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5717259Z 
2023-01-11T21:41:26.5717447Z aten = torch.ops.aten
2023-01-11T21:41:26.5717757Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5717943Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5717952Z 
2023-01-11T21:41:26.5717959Z 
2023-01-11T21:41:26.5718119Z async_compile.wait(globals())
2023-01-11T21:41:26.5718237Z del async_compile
2023-01-11T21:41:26.5718245Z 
2023-01-11T21:41:26.5718371Z def call(args):
2023-01-11T21:41:26.5718532Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5718660Z     args.clear()
2023-01-11T21:41:26.5719082Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:26.5719209Z     del arg0_1
2023-01-11T21:41:26.5719330Z     del arg1_1
2023-01-11T21:41:26.5719433Z     del arg2_1
2023-01-11T21:41:26.5719574Z     del arg3_1
2023-01-11T21:41:26.5719744Z     return (buf0, )
2023-01-11T21:41:26.5719753Z 
2023-01-11T21:41:26.5719760Z 
2023-01-11T21:41:26.5719945Z if __name__ == "__main__":
2023-01-11T21:41:26.5720210Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5720472Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5720950Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5721411Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5721910Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5722408Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5722713Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5722725Z 
2023-01-11T21:41:26.5722734Z 
2023-01-11T21:41:26.5722954Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5723121Z import torch
2023-01-11T21:41:26.5723291Z import random
2023-01-11T21:41:26.5723567Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5723931Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5723943Z 
2023-01-11T21:41:26.5724108Z aten = torch.ops.aten
2023-01-11T21:41:26.5724425Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5724647Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5724659Z 
2023-01-11T21:41:26.5724668Z 
2023-01-11T21:41:26.5724880Z async_compile.wait(globals())
2023-01-11T21:41:26.5725054Z del async_compile
2023-01-11T21:41:26.5725065Z 
2023-01-11T21:41:26.5725235Z def call(args):
2023-01-11T21:41:26.5725412Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5725537Z     args.clear()
2023-01-11T21:41:26.5725940Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:26.5726060Z     del arg0_1
2023-01-11T21:41:26.5726181Z     del arg1_1
2023-01-11T21:41:26.5726344Z     del arg2_1
2023-01-11T21:41:26.5726479Z     return (buf0, )
2023-01-11T21:41:26.5726488Z 
2023-01-11T21:41:26.5726500Z 
2023-01-11T21:41:26.5726634Z if __name__ == "__main__":
2023-01-11T21:41:26.5726844Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5727043Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5727517Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5727998Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5728482Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5728767Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5728779Z 
2023-01-11T21:41:26.5728789Z 
2023-01-11T21:41:26.5729156Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5729329Z import torch
2023-01-11T21:41:26.5729503Z import random
2023-01-11T21:41:26.5729770Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5730062Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5730081Z 
2023-01-11T21:41:26.5730270Z aten = torch.ops.aten
2023-01-11T21:41:26.5730585Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5730803Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5730814Z 
2023-01-11T21:41:26.5730823Z 
2023-01-11T21:41:26.5731033Z async_compile.wait(globals())
2023-01-11T21:41:26.5731195Z del async_compile
2023-01-11T21:41:26.5731206Z 
2023-01-11T21:41:26.5731375Z def call(args):
2023-01-11T21:41:26.5731563Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5731733Z     args.clear()
2023-01-11T21:41:26.5732301Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:26.5732465Z     del arg0_1
2023-01-11T21:41:26.5732624Z     del arg1_1
2023-01-11T21:41:26.5732777Z     del arg2_1
2023-01-11T21:41:26.5732943Z     del arg3_1
2023-01-11T21:41:26.5733057Z     return (buf0, )
2023-01-11T21:41:26.5733066Z 
2023-01-11T21:41:26.5733073Z 
2023-01-11T21:41:26.5733214Z if __name__ == "__main__":
2023-01-11T21:41:26.5733413Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5733631Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5734001Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5734369Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5734784Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5735215Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5735487Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5735497Z 
2023-01-11T21:41:26.5735568Z 
2023-01-11T21:41:26.5735832Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5735995Z import torch
2023-01-11T21:41:26.5736148Z import random
2023-01-11T21:41:26.5736416Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5736815Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5736828Z 
2023-01-11T21:41:26.5737012Z aten = torch.ops.aten
2023-01-11T21:41:26.5737327Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5737531Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5737542Z 
2023-01-11T21:41:26.5737552Z 
2023-01-11T21:41:26.5737767Z async_compile.wait(globals())
2023-01-11T21:41:26.5737946Z del async_compile
2023-01-11T21:41:26.5737957Z 
2023-01-11T21:41:26.5738129Z def call(args):
2023-01-11T21:41:26.5738321Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5738492Z     args.clear()
2023-01-11T21:41:26.5739065Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:26.5739229Z     del arg0_1
2023-01-11T21:41:26.5739380Z     del arg1_1
2023-01-11T21:41:26.5739608Z     del arg2_1
2023-01-11T21:41:26.5739783Z     return (buf0, )
2023-01-11T21:41:26.5739795Z 
2023-01-11T21:41:26.5739809Z 
2023-01-11T21:41:26.5739991Z if __name__ == "__main__":
2023-01-11T21:41:26.5740265Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5740565Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5740957Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5741304Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5741668Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5741887Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5741897Z 
2023-01-11T21:41:26.5741903Z 
2023-01-11T21:41:26.5742066Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5742194Z import torch
2023-01-11T21:41:26.5742330Z import random
2023-01-11T21:41:26.5742589Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5742871Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5742891Z 
2023-01-11T21:41:26.5743056Z aten = torch.ops.aten
2023-01-11T21:41:26.5743438Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5743655Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5743667Z 
2023-01-11T21:41:26.5743675Z 
2023-01-11T21:41:26.5743883Z async_compile.wait(globals())
2023-01-11T21:41:26.5744058Z del async_compile
2023-01-11T21:41:26.5744070Z 
2023-01-11T21:41:26.5744239Z def call(args):
2023-01-11T21:41:26.5744450Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5744621Z     args.clear()
2023-01-11T21:41:26.5745176Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:26.5745337Z     del arg0_1
2023-01-11T21:41:26.5745500Z     del arg1_1
2023-01-11T21:41:26.5745661Z     del arg2_1
2023-01-11T21:41:26.5745815Z     del arg3_1
2023-01-11T21:41:26.5745996Z     return (buf0, )
2023-01-11T21:41:26.5746007Z 
2023-01-11T21:41:26.5746015Z 
2023-01-11T21:41:26.5746193Z if __name__ == "__main__":
2023-01-11T21:41:26.5746452Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5746737Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5747229Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5747713Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5748216Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5748601Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5748834Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5748844Z 
2023-01-11T21:41:26.5749354Z [2023-01-11 21:35:31,179] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 286
2023-01-11T21:41:26.5750205Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5750594Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5751182Z [2023-01-11 21:35:31,264] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 287
2023-01-11T21:41:26.5751821Z [2023-01-11 21:35:31,266] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 287
2023-01-11T21:41:26.5752968Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5753283Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5753923Z [2023-01-11 21:35:31,297] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 288
2023-01-11T21:41:26.5754570Z [2023-01-11 21:35:31,299] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 288
2023-01-11T21:41:26.5755627Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5755900Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5756386Z [2023-01-11 21:35:31,386] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 289
2023-01-11T21:41:26.5756884Z [2023-01-11 21:35:31,388] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 289
2023-01-11T21:41:26.5757719Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5758059Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5758682Z [2023-01-11 21:35:31,418] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 290
2023-01-11T21:41:26.5759357Z [2023-01-11 21:35:31,420] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 290
2023-01-11T21:41:26.5760424Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5760726Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5761371Z [2023-01-11 21:35:31,504] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 291
2023-01-11T21:41:26.5762027Z [2023-01-11 21:35:31,507] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 291
2023-01-11T21:41:26.5763092Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5763449Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5763965Z [2023-01-11 21:35:31,536] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 292
2023-01-11T21:41:26.5764454Z [2023-01-11 21:35:31,538] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 292
2023-01-11T21:41:26.5764465Z 
2023-01-11T21:41:26.5764629Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5764742Z import torch
2023-01-11T21:41:26.5764869Z import random
2023-01-11T21:41:26.5765080Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5765318Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5765335Z 
2023-01-11T21:41:26.5765494Z aten = torch.ops.aten
2023-01-11T21:41:26.5765873Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5766150Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5766162Z 
2023-01-11T21:41:26.5766178Z 
2023-01-11T21:41:26.5766355Z async_compile.wait(globals())
2023-01-11T21:41:26.5766511Z del async_compile
2023-01-11T21:41:26.5766524Z 
2023-01-11T21:41:26.5766696Z def call(args):
2023-01-11T21:41:26.5766889Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5767047Z     args.clear()
2023-01-11T21:41:26.5767618Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:26.5767780Z     del arg0_1
2023-01-11T21:41:26.5767942Z     del arg1_1
2023-01-11T21:41:26.5768087Z     del arg2_1
2023-01-11T21:41:26.5768258Z     return (buf0, )
2023-01-11T21:41:26.5768269Z 
2023-01-11T21:41:26.5768278Z 
2023-01-11T21:41:26.5768453Z if __name__ == "__main__":
2023-01-11T21:41:26.5768720Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5769149Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5769660Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5770177Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5770682Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5770975Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5770988Z 
2023-01-11T21:41:26.5770997Z 
2023-01-11T21:41:26.5771225Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5771374Z import torch
2023-01-11T21:41:26.5771501Z import random
2023-01-11T21:41:26.5771710Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5771932Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5771941Z 
2023-01-11T21:41:26.5772085Z aten = torch.ops.aten
2023-01-11T21:41:26.5772310Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5772483Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5772492Z 
2023-01-11T21:41:26.5772499Z 
2023-01-11T21:41:26.5772658Z async_compile.wait(globals())
2023-01-11T21:41:26.5772795Z del async_compile
2023-01-11T21:41:26.5772804Z 
2023-01-11T21:41:26.5772931Z def call(args):
2023-01-11T21:41:26.5773117Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5773291Z     args.clear()
2023-01-11T21:41:26.5773793Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:26.5773969Z     del arg0_1
2023-01-11T21:41:26.5774186Z     del arg1_1
2023-01-11T21:41:26.5774330Z     del arg2_1
2023-01-11T21:41:26.5774486Z     del arg3_1
2023-01-11T21:41:26.5774655Z     return (buf0, )
2023-01-11T21:41:26.5774667Z 
2023-01-11T21:41:26.5774676Z 
2023-01-11T21:41:26.5774850Z if __name__ == "__main__":
2023-01-11T21:41:26.5775122Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5775393Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5775891Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5776369Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5776980Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5777461Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5777767Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5777779Z 
2023-01-11T21:41:26.5777788Z 
2023-01-11T21:41:26.5778014Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5778186Z import torch
2023-01-11T21:41:26.5778358Z import random
2023-01-11T21:41:26.5778620Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5778912Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5778924Z 
2023-01-11T21:41:26.5779078Z aten = torch.ops.aten
2023-01-11T21:41:26.5779373Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5779537Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5779550Z 
2023-01-11T21:41:26.5779560Z 
2023-01-11T21:41:26.5779719Z async_compile.wait(globals())
2023-01-11T21:41:26.5779850Z del async_compile
2023-01-11T21:41:26.5779859Z 
2023-01-11T21:41:26.5779982Z def call(args):
2023-01-11T21:41:26.5780114Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5780239Z     args.clear()
2023-01-11T21:41:26.5780671Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:26.5780828Z     del arg0_1
2023-01-11T21:41:26.5780974Z     del arg1_1
2023-01-11T21:41:26.5781132Z     del arg2_1
2023-01-11T21:41:26.5781263Z     return (buf0, )
2023-01-11T21:41:26.5781301Z 
2023-01-11T21:41:26.5781309Z 
2023-01-11T21:41:26.5781482Z if __name__ == "__main__":
2023-01-11T21:41:26.5781795Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5782084Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5782568Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5783056Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5783665Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5783948Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5783960Z 
2023-01-11T21:41:26.5783969Z 
2023-01-11T21:41:26.5784192Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5784340Z import torch
2023-01-11T21:41:26.5784500Z import random
2023-01-11T21:41:26.5784778Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5785070Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5785082Z 
2023-01-11T21:41:26.5785269Z aten = torch.ops.aten
2023-01-11T21:41:26.5785586Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5785810Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5785827Z 
2023-01-11T21:41:26.5785836Z 
2023-01-11T21:41:26.5786053Z async_compile.wait(globals())
2023-01-11T21:41:26.5786216Z del async_compile
2023-01-11T21:41:26.5786227Z 
2023-01-11T21:41:26.5786395Z def call(args):
2023-01-11T21:41:26.5786606Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5786745Z     args.clear()
2023-01-11T21:41:26.5787171Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:26.5787294Z     del arg0_1
2023-01-11T21:41:26.5787409Z     del arg1_1
2023-01-11T21:41:26.5787515Z     del arg2_1
2023-01-11T21:41:26.5787635Z     del arg3_1
2023-01-11T21:41:26.5787770Z     return (buf0, )
2023-01-11T21:41:26.5787778Z 
2023-01-11T21:41:26.5787785Z 
2023-01-11T21:41:26.5787921Z if __name__ == "__main__":
2023-01-11T21:41:26.5788126Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5788356Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5788814Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5789261Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5789856Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5790351Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5790658Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5790670Z 
2023-01-11T21:41:26.5790680Z 
2023-01-11T21:41:26.5790904Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5791077Z import torch
2023-01-11T21:41:26.5791253Z import random
2023-01-11T21:41:26.5791527Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5791797Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5791827Z 
2023-01-11T21:41:26.5791994Z aten = torch.ops.aten
2023-01-11T21:41:26.5792362Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5792580Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5792591Z 
2023-01-11T21:41:26.5792605Z 
2023-01-11T21:41:26.5792819Z async_compile.wait(globals())
2023-01-11T21:41:26.5792999Z del async_compile
2023-01-11T21:41:26.5793011Z 
2023-01-11T21:41:26.5793181Z def call(args):
2023-01-11T21:41:26.5793375Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5793530Z     args.clear()
2023-01-11T21:41:26.5794091Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:26.5794259Z     del arg0_1
2023-01-11T21:41:26.5794396Z     del arg1_1
2023-01-11T21:41:26.5794514Z     del arg2_1
2023-01-11T21:41:26.5794647Z     return (buf0, )
2023-01-11T21:41:26.5794656Z 
2023-01-11T21:41:26.5794663Z 
2023-01-11T21:41:26.5794797Z if __name__ == "__main__":
2023-01-11T21:41:26.5795006Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5795211Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5795591Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5795977Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5796438Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5796716Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5796728Z 
2023-01-11T21:41:26.5796737Z 
2023-01-11T21:41:26.5796963Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5797128Z import torch
2023-01-11T21:41:26.5797302Z import random
2023-01-11T21:41:26.5797553Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5797826Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5797838Z 
2023-01-11T21:41:26.5798025Z aten = torch.ops.aten
2023-01-11T21:41:26.5798340Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5798555Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5798571Z 
2023-01-11T21:41:26.5798580Z 
2023-01-11T21:41:26.5798790Z async_compile.wait(globals())
2023-01-11T21:41:26.5798979Z del async_compile
2023-01-11T21:41:26.5798991Z 
2023-01-11T21:41:26.5799162Z def call(args):
2023-01-11T21:41:26.5799354Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5799528Z     args.clear()
2023-01-11T21:41:26.5800087Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg2_1, arg3_1, arg0_1, arg1_1, 'sub')
2023-01-11T21:41:26.5800250Z     del arg0_1
2023-01-11T21:41:26.5800413Z     del arg1_1
2023-01-11T21:41:26.5800575Z     del arg2_1
2023-01-11T21:41:26.5800738Z     del arg3_1
2023-01-11T21:41:26.5800890Z     return (buf0, )
2023-01-11T21:41:26.5800902Z 
2023-01-11T21:41:26.5800911Z 
2023-01-11T21:41:26.5801094Z if __name__ == "__main__":
2023-01-11T21:41:26.5801369Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5801655Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5802079Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5802436Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5802868Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5803237Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5803451Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5803461Z 
2023-01-11T21:41:26.5803467Z 
2023-01-11T21:41:26.5803666Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5803811Z import torch
2023-01-11T21:41:26.5803980Z import random
2023-01-11T21:41:26.5804226Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5804541Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5804553Z 
2023-01-11T21:41:26.5804736Z aten = torch.ops.aten
2023-01-11T21:41:26.5805147Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5805356Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5805368Z 
2023-01-11T21:41:26.5805376Z 
2023-01-11T21:41:26.5805591Z async_compile.wait(globals())
2023-01-11T21:41:26.5805763Z del async_compile
2023-01-11T21:41:26.5805775Z 
2023-01-11T21:41:26.5805932Z def call(args):
2023-01-11T21:41:26.5806123Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5806289Z     args.clear()
2023-01-11T21:41:26.5806864Z     buf0 = torch.ops.mkldnn._linear_pointwise.binary(arg1_1, arg2_1, arg0_1, None, 'sub')
2023-01-11T21:41:26.5807011Z     del arg0_1
2023-01-11T21:41:26.5807176Z     del arg1_1
2023-01-11T21:41:26.5807335Z     del arg2_1
2023-01-11T21:41:26.5807508Z     return (buf0, )
2023-01-11T21:41:26.5807519Z 
2023-01-11T21:41:26.5807529Z 
2023-01-11T21:41:26.5807704Z if __name__ == "__main__":
2023-01-11T21:41:26.5807976Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5808264Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5808772Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5809375Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5809819Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5810033Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5810042Z 
2023-01-11T21:41:26.5810164Z ok (1.757s)
2023-01-11T21:41:26.5811038Z   test_linear_packed_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5811269Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5811832Z [2023-01-11 21:35:31,575] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 293
2023-01-11T21:41:26.5812452Z [2023-01-11 21:35:31,594] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 293
2023-01-11T21:41:26.5813472Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5813768Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5814413Z [2023-01-11 21:35:31,623] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 294
2023-01-11T21:41:26.5815068Z [2023-01-11 21:35:31,635] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 294
2023-01-11T21:41:26.5816136Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5816535Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5817180Z [2023-01-11 21:35:31,669] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 295
2023-01-11T21:41:26.5817748Z [2023-01-11 21:35:31,687] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 295
2023-01-11T21:41:26.5818573Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5818803Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5819334Z [2023-01-11 21:35:31,715] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 296
2023-01-11T21:41:26.5819935Z [2023-01-11 21:35:31,727] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 296
2023-01-11T21:41:26.5819954Z 
2023-01-11T21:41:26.5820240Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5820408Z import torch
2023-01-11T21:41:26.5820558Z import random
2023-01-11T21:41:26.5820833Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5821116Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5821129Z 
2023-01-11T21:41:26.5821317Z aten = torch.ops.aten
2023-01-11T21:41:26.5821640Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5821864Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5821877Z 
2023-01-11T21:41:26.5821891Z 
2023-01-11T21:41:26.5822104Z async_compile.wait(globals())
2023-01-11T21:41:26.5822267Z del async_compile
2023-01-11T21:41:26.5822296Z 
2023-01-11T21:41:26.5822451Z def call(args):
2023-01-11T21:41:26.5822659Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5822833Z     args.clear()
2023-01-11T21:41:26.5823218Z     buf0 = torch.ops.mkl._mkl_linear(arg3_1, arg2_1, arg0_1, arg1_1, 6)
2023-01-11T21:41:26.5823384Z     del arg0_1
2023-01-11T21:41:26.5823533Z     del arg1_1
2023-01-11T21:41:26.5823671Z     del arg2_1
2023-01-11T21:41:26.5823825Z     del arg3_1
2023-01-11T21:41:26.5824000Z     return (buf0, )
2023-01-11T21:41:26.5824012Z 
2023-01-11T21:41:26.5824021Z 
2023-01-11T21:41:26.5824195Z if __name__ == "__main__":
2023-01-11T21:41:26.5824464Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5824760Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5825243Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5825609Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5825970Z     arg2_1 = rand_strided((1982689, 1), (1, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5826354Z     arg3_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5826587Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5826597Z 
2023-01-11T21:41:26.5826604Z 
2023-01-11T21:41:26.5826775Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5826926Z import torch
2023-01-11T21:41:26.5827086Z import random
2023-01-11T21:41:26.5827355Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5827640Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5827652Z 
2023-01-11T21:41:26.5827817Z aten = torch.ops.aten
2023-01-11T21:41:26.5828139Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5828352Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5828452Z 
2023-01-11T21:41:26.5828462Z 
2023-01-11T21:41:26.5828669Z async_compile.wait(globals())
2023-01-11T21:41:26.5828843Z del async_compile
2023-01-11T21:41:26.5828855Z 
2023-01-11T21:41:26.5829022Z def call(args):
2023-01-11T21:41:26.5829215Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5829377Z     args.clear()
2023-01-11T21:41:26.5829656Z     buf0 = torch.ops.mkl._mkl_linear(arg2_1, arg1_1, arg0_1, None, 6)
2023-01-11T21:41:26.5829819Z     del arg0_1
2023-01-11T21:41:26.5829980Z     del arg1_1
2023-01-11T21:41:26.5830140Z     del arg2_1
2023-01-11T21:41:26.5830309Z     return (buf0, )
2023-01-11T21:41:26.5830321Z 
2023-01-11T21:41:26.5830330Z 
2023-01-11T21:41:26.5830510Z if __name__ == "__main__":
2023-01-11T21:41:26.5830775Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5831102Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5831596Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5832099Z     arg1_1 = rand_strided((1982689, 1), (1, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5832603Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5832828Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5832838Z 
2023-01-11T21:41:26.5832845Z 
2023-01-11T21:41:26.5833016Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5833141Z import torch
2023-01-11T21:41:26.5833267Z import random
2023-01-11T21:41:26.5833460Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5833674Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5833683Z 
2023-01-11T21:41:26.5833822Z aten = torch.ops.aten
2023-01-11T21:41:26.5834056Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5834262Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5834271Z 
2023-01-11T21:41:26.5834278Z 
2023-01-11T21:41:26.5834481Z async_compile.wait(globals())
2023-01-11T21:41:26.5834698Z del async_compile
2023-01-11T21:41:26.5834711Z 
2023-01-11T21:41:26.5834860Z def call(args):
2023-01-11T21:41:26.5835044Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:41:26.5835263Z     args.clear()
2023-01-11T21:41:26.5835558Z     buf0 = torch.ops.mkl._mkl_linear(arg3_1, arg2_1, arg0_1, arg1_1, 2)
2023-01-11T21:41:26.5835718Z     del arg0_1
2023-01-11T21:41:26.5835875Z     del arg1_1
2023-01-11T21:41:26.5836037Z     del arg2_1
2023-01-11T21:41:26.5836197Z     del arg3_1
2023-01-11T21:41:26.5836354Z     return (buf0, )
2023-01-11T21:41:26.5836365Z 
2023-01-11T21:41:26.5836391Z 
2023-01-11T21:41:26.5836556Z if __name__ == "__main__":
2023-01-11T21:41:26.5836821Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5837124Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5837629Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5838101Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5838597Z     arg2_1 = rand_strided((1982689, 1), (1, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5839080Z     arg3_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5839364Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:41:26.5839394Z 
2023-01-11T21:41:26.5839404Z 
2023-01-11T21:41:26.5839614Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5839782Z import torch
2023-01-11T21:41:26.5839957Z import random
2023-01-11T21:41:26.5840228Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5840443Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5840453Z 
2023-01-11T21:41:26.5840591Z aten = torch.ops.aten
2023-01-11T21:41:26.5840829Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5840975Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5840984Z 
2023-01-11T21:41:26.5841077Z 
2023-01-11T21:41:26.5841220Z async_compile.wait(globals())
2023-01-11T21:41:26.5841348Z del async_compile
2023-01-11T21:41:26.5841357Z 
2023-01-11T21:41:26.5841483Z def call(args):
2023-01-11T21:41:26.5841632Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5841795Z     args.clear()
2023-01-11T21:41:26.5842084Z     buf0 = torch.ops.mkl._mkl_linear(arg2_1, arg1_1, arg0_1, None, 2)
2023-01-11T21:41:26.5842247Z     del arg0_1
2023-01-11T21:41:26.5842394Z     del arg1_1
2023-01-11T21:41:26.5842551Z     del arg2_1
2023-01-11T21:41:26.5842724Z     return (buf0, )
2023-01-11T21:41:26.5842741Z 
2023-01-11T21:41:26.5842751Z 
2023-01-11T21:41:26.5842933Z if __name__ == "__main__":
2023-01-11T21:41:26.5843196Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5843491Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5844067Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5844554Z     arg1_1 = rand_strided((1982689, 1), (1, 0), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5845037Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.5845326Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5845338Z 
2023-01-11T21:41:26.5845495Z ok (0.189s)
2023-01-11T21:41:26.5846663Z   test_linear_unary_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5846955Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5847592Z [2023-01-11 21:35:31,893] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 297
2023-01-11T21:41:26.5848085Z [2023-01-11 21:35:31,896] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 297
2023-01-11T21:41:26.5848873Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5849244Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5849848Z [2023-01-11 21:35:31,925] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 298
2023-01-11T21:41:26.5850452Z [2023-01-11 21:35:31,927] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 298
2023-01-11T21:41:26.5851478Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5851788Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5852442Z [2023-01-11 21:35:32,010] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 299
2023-01-11T21:41:26.5853096Z [2023-01-11 21:35:32,013] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 299
2023-01-11T21:41:26.5854152Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5854546Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5855171Z [2023-01-11 21:35:32,041] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 300
2023-01-11T21:41:26.5855671Z [2023-01-11 21:35:32,043] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 300
2023-01-11T21:41:26.5856446Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5856694Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5857380Z [2023-01-11 21:35:32,126] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 301
2023-01-11T21:41:26.5857981Z [2023-01-11 21:35:32,129] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 301
2023-01-11T21:41:26.5859045Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5859333Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5859978Z [2023-01-11 21:35:32,158] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 302
2023-01-11T21:41:26.5860644Z [2023-01-11 21:35:32,160] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 302
2023-01-11T21:41:26.5861708Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5862008Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5862590Z [2023-01-11 21:35:32,242] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 303
2023-01-11T21:41:26.5863082Z [2023-01-11 21:35:32,245] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 303
2023-01-11T21:41:26.5863937Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5864223Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5864793Z [2023-01-11 21:35:32,274] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 304
2023-01-11T21:41:26.5865399Z [2023-01-11 21:35:32,276] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 304
2023-01-11T21:41:26.5866429Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5866722Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5867374Z [2023-01-11 21:35:32,362] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 305
2023-01-11T21:41:26.5867458Z 
2023-01-11T21:41:26.5867691Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5867860Z import torch
2023-01-11T21:41:26.5868031Z import random
2023-01-11T21:41:26.5868310Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5868599Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5868611Z 
2023-01-11T21:41:26.5868784Z aten = torch.ops.aten
2023-01-11T21:41:26.5869098Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5869319Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5869331Z 
2023-01-11T21:41:26.5869340Z 
2023-01-11T21:41:26.5869548Z async_compile.wait(globals())
2023-01-11T21:41:26.5869732Z del async_compile
2023-01-11T21:41:26.5869744Z 
2023-01-11T21:41:26.5869907Z def call(args):
2023-01-11T21:41:26.5870056Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5870231Z     args.clear()
2023-01-11T21:41:26.5870621Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'relu', [], '')
2023-01-11T21:41:26.5870743Z     del arg0_1
2023-01-11T21:41:26.5870864Z     del arg1_1
2023-01-11T21:41:26.5870983Z     del arg2_1
2023-01-11T21:41:26.5871111Z     return (buf0, )
2023-01-11T21:41:26.5871120Z 
2023-01-11T21:41:26.5871127Z 
2023-01-11T21:41:26.5871265Z if __name__ == "__main__":
2023-01-11T21:41:26.5871522Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5871782Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5872257Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5872728Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5873223Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5873511Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5873528Z 
2023-01-11T21:41:26.5873538Z 
2023-01-11T21:41:26.5873760Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5873940Z import torch
2023-01-11T21:41:26.5874113Z import random
2023-01-11T21:41:26.5874365Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5874655Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5874667Z 
2023-01-11T21:41:26.5874848Z aten = torch.ops.aten
2023-01-11T21:41:26.5875165Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5875387Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5875398Z 
2023-01-11T21:41:26.5875408Z 
2023-01-11T21:41:26.5875622Z async_compile.wait(globals())
2023-01-11T21:41:26.5875799Z del async_compile
2023-01-11T21:41:26.5875810Z 
2023-01-11T21:41:26.5875968Z def call(args):
2023-01-11T21:41:26.5876120Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5876289Z     args.clear()
2023-01-11T21:41:26.5876826Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'relu', [], '')
2023-01-11T21:41:26.5876995Z     del arg0_1
2023-01-11T21:41:26.5877139Z     del arg1_1
2023-01-11T21:41:26.5877277Z     return (buf0, )
2023-01-11T21:41:26.5877285Z 
2023-01-11T21:41:26.5877292Z 
2023-01-11T21:41:26.5877427Z if __name__ == "__main__":
2023-01-11T21:41:26.5877630Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5877832Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5878212Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5878627Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5878896Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5878910Z 
2023-01-11T21:41:26.5878921Z 
2023-01-11T21:41:26.5879126Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5879286Z import torch
2023-01-11T21:41:26.5879507Z import random
2023-01-11T21:41:26.5879737Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5880025Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5880112Z 
2023-01-11T21:41:26.5880298Z aten = torch.ops.aten
2023-01-11T21:41:26.5880609Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5880834Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5880847Z 
2023-01-11T21:41:26.5880857Z 
2023-01-11T21:41:26.5881067Z async_compile.wait(globals())
2023-01-11T21:41:26.5881240Z del async_compile
2023-01-11T21:41:26.5881251Z 
2023-01-11T21:41:26.5881410Z def call(args):
2023-01-11T21:41:26.5881603Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5881753Z     args.clear()
2023-01-11T21:41:26.5882287Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'relu', [], '')
2023-01-11T21:41:26.5882455Z     del arg0_1
2023-01-11T21:41:26.5882614Z     del arg1_1
2023-01-11T21:41:26.5882772Z     del arg2_1
2023-01-11T21:41:26.5882944Z     return (buf0, )
2023-01-11T21:41:26.5882956Z 
2023-01-11T21:41:26.5883022Z 
2023-01-11T21:41:26.5883210Z if __name__ == "__main__":
2023-01-11T21:41:26.5883457Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5883747Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5884245Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5884670Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5885040Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5885260Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5885269Z 
2023-01-11T21:41:26.5885276Z 
2023-01-11T21:41:26.5885446Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5885570Z import torch
2023-01-11T21:41:26.5885680Z import random
2023-01-11T21:41:26.5885884Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5886139Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5886152Z 
2023-01-11T21:41:26.5886330Z aten = torch.ops.aten
2023-01-11T21:41:26.5886631Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5886845Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5886858Z 
2023-01-11T21:41:26.5886869Z 
2023-01-11T21:41:26.5887072Z async_compile.wait(globals())
2023-01-11T21:41:26.5887229Z del async_compile
2023-01-11T21:41:26.5887241Z 
2023-01-11T21:41:26.5887392Z def call(args):
2023-01-11T21:41:26.5887562Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5887729Z     args.clear()
2023-01-11T21:41:26.5888254Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'relu', [], '')
2023-01-11T21:41:26.5888417Z     del arg0_1
2023-01-11T21:41:26.5888575Z     del arg1_1
2023-01-11T21:41:26.5888746Z     return (buf0, )
2023-01-11T21:41:26.5888758Z 
2023-01-11T21:41:26.5888767Z 
2023-01-11T21:41:26.5888923Z if __name__ == "__main__":
2023-01-11T21:41:26.5889334Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5889628Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5890128Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5890621Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5890899Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5890911Z 
2023-01-11T21:41:26.5890919Z 
2023-01-11T21:41:26.5891138Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5891309Z import torch
2023-01-11T21:41:26.5891421Z import random
2023-01-11T21:41:26.5891627Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5891840Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5891849Z 
2023-01-11T21:41:26.5891986Z aten = torch.ops.aten
2023-01-11T21:41:26.5892220Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5892382Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5892396Z 
2023-01-11T21:41:26.5892403Z 
2023-01-11T21:41:26.5892561Z async_compile.wait(globals())
2023-01-11T21:41:26.5892692Z del async_compile
2023-01-11T21:41:26.5892796Z 
2023-01-11T21:41:26.5892915Z def call(args):
2023-01-11T21:41:26.5893106Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5893273Z     args.clear()
2023-01-11T21:41:26.5893787Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'sigmoid', [], '')
2023-01-11T21:41:26.5893946Z     del arg0_1
2023-01-11T21:41:26.5894106Z     del arg1_1
2023-01-11T21:41:26.5894266Z     del arg2_1
2023-01-11T21:41:26.5894421Z     return (buf0, )
2023-01-11T21:41:26.5894435Z 
2023-01-11T21:41:26.5894445Z 
2023-01-11T21:41:26.5894620Z if __name__ == "__main__":
2023-01-11T21:41:26.5894892Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5895178Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5895673Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5896233Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5896742Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5897035Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5897047Z 
2023-01-11T21:41:26.5897056Z 
2023-01-11T21:41:26.5897281Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5897433Z import torch
2023-01-11T21:41:26.5897606Z import random
2023-01-11T21:41:26.5897879Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5898170Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5898182Z 
2023-01-11T21:41:26.5898364Z aten = torch.ops.aten
2023-01-11T21:41:26.5898680Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5898898Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5898908Z 
2023-01-11T21:41:26.5898915Z 
2023-01-11T21:41:26.5899060Z async_compile.wait(globals())
2023-01-11T21:41:26.5899190Z del async_compile
2023-01-11T21:41:26.5899200Z 
2023-01-11T21:41:26.5899324Z def call(args):
2023-01-11T21:41:26.5899462Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5899590Z     args.clear()
2023-01-11T21:41:26.5899996Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'sigmoid', [], '')
2023-01-11T21:41:26.5900121Z     del arg0_1
2023-01-11T21:41:26.5900225Z     del arg1_1
2023-01-11T21:41:26.5900352Z     return (buf0, )
2023-01-11T21:41:26.5900361Z 
2023-01-11T21:41:26.5900368Z 
2023-01-11T21:41:26.5900527Z if __name__ == "__main__":
2023-01-11T21:41:26.5900792Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5901091Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5901561Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5902038Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5902316Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5902328Z 
2023-01-11T21:41:26.5902337Z 
2023-01-11T21:41:26.5902555Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5902700Z import torch
2023-01-11T21:41:26.5902873Z import random
2023-01-11T21:41:26.5903231Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5903518Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5903529Z 
2023-01-11T21:41:26.5903712Z aten = torch.ops.aten
2023-01-11T21:41:26.5904024Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5904249Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5904260Z 
2023-01-11T21:41:26.5904269Z 
2023-01-11T21:41:26.5904481Z async_compile.wait(globals())
2023-01-11T21:41:26.5904626Z del async_compile
2023-01-11T21:41:26.5904637Z 
2023-01-11T21:41:26.5904806Z def call(args):
2023-01-11T21:41:26.5905285Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5905724Z     args.clear()
2023-01-11T21:41:26.5906432Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'sigmoid', [], '')
2023-01-11T21:41:26.5906863Z     del arg0_1
2023-01-11T21:41:26.5907232Z     del arg1_1
2023-01-11T21:41:26.5907512Z     del arg2_1
2023-01-11T21:41:26.5907810Z     return (buf0, )
2023-01-11T21:41:26.5908081Z 
2023-01-11T21:41:26.5908090Z 
2023-01-11T21:41:26.5908254Z if __name__ == "__main__":
2023-01-11T21:41:26.5908761Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5909367Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5910201Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5911242Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5912093Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5912750Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5913102Z 
2023-01-11T21:41:26.5913194Z 
2023-01-11T21:41:26.5913426Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5913887Z import torch
2023-01-11T21:41:26.5914296Z import random
2023-01-11T21:41:26.5926671Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5927369Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5927741Z 
2023-01-11T21:41:26.5927940Z aten = torch.ops.aten
2023-01-11T21:41:26.5928509Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5929304Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5929606Z 
2023-01-11T21:41:26.5929617Z 
2023-01-11T21:41:26.5929834Z async_compile.wait(globals())
2023-01-11T21:41:26.5930287Z del async_compile
2023-01-11T21:41:26.5930494Z 
2023-01-11T21:41:26.5930625Z def call(args):
2023-01-11T21:41:26.5930937Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5931243Z     args.clear()
2023-01-11T21:41:26.5931883Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'sigmoid', [], '')
2023-01-11T21:41:26.5932448Z     del arg0_1
2023-01-11T21:41:26.5932818Z     del arg1_1
2023-01-11T21:41:26.5933217Z     return (buf0, )
2023-01-11T21:41:26.5933477Z 
2023-01-11T21:41:26.5933488Z 
2023-01-11T21:41:26.5933664Z if __name__ == "__main__":
2023-01-11T21:41:26.5934180Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5934784Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5935659Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5936530Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5937161Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5937512Z 
2023-01-11T21:41:26.5938127Z [2023-01-11 21:35:32,365] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 305
2023-01-11T21:41:26.5939271Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5940530Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5941508Z [2023-01-11 21:35:32,394] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 306
2023-01-11T21:41:26.5942614Z [2023-01-11 21:35:32,397] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 306
2023-01-11T21:41:26.5944226Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5945556Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5946312Z [2023-01-11 21:35:32,478] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 307
2023-01-11T21:41:26.5947324Z [2023-01-11 21:35:32,481] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 307
2023-01-11T21:41:26.5948738Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5950055Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5951046Z [2023-01-11 21:35:32,506] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 308
2023-01-11T21:41:26.5952250Z [2023-01-11 21:35:32,508] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 308
2023-01-11T21:41:26.5953633Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5954642Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5955572Z [2023-01-11 21:35:32,648] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 309
2023-01-11T21:41:26.5956666Z [2023-01-11 21:35:32,651] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 309
2023-01-11T21:41:26.5958173Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5959526Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5960526Z [2023-01-11 21:35:32,766] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 310
2023-01-11T21:41:26.5961452Z [2023-01-11 21:35:32,768] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 310
2023-01-11T21:41:26.5962626Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5963912Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5964927Z [2023-01-11 21:35:32,913] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 311
2023-01-11T21:41:26.5966054Z [2023-01-11 21:35:32,916] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 311
2023-01-11T21:41:26.5967573Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5968738Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5969645Z [2023-01-11 21:35:32,974] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 312
2023-01-11T21:41:26.5970582Z [2023-01-11 21:35:32,977] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 312
2023-01-11T21:41:26.5972286Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.5973644Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.5974632Z [2023-01-11 21:35:33,061] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 313
2023-01-11T21:41:26.5975101Z 
2023-01-11T21:41:26.5975325Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5975794Z import torch
2023-01-11T21:41:26.5976168Z import random
2023-01-11T21:41:26.5976560Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5977132Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5977414Z 
2023-01-11T21:41:26.5977542Z aten = torch.ops.aten
2023-01-11T21:41:26.5978050Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5978611Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5978933Z 
2023-01-11T21:41:26.5978943Z 
2023-01-11T21:41:26.5979155Z async_compile.wait(globals())
2023-01-11T21:41:26.5979594Z del async_compile
2023-01-11T21:41:26.5979852Z 
2023-01-11T21:41:26.5980024Z def call(args):
2023-01-11T21:41:26.5980455Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.5980875Z     args.clear()
2023-01-11T21:41:26.5981655Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'tanh', [], '')
2023-01-11T21:41:26.5982212Z     del arg0_1
2023-01-11T21:41:26.5982595Z     del arg1_1
2023-01-11T21:41:26.5982983Z     del arg2_1
2023-01-11T21:41:26.5983466Z     return (buf0, )
2023-01-11T21:41:26.5983729Z 
2023-01-11T21:41:26.5983739Z 
2023-01-11T21:41:26.5983874Z if __name__ == "__main__":
2023-01-11T21:41:26.5984272Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5984755Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5985506Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5986317Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5987191Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5987871Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.5988226Z 
2023-01-11T21:41:26.5988236Z 
2023-01-11T21:41:26.5988452Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.5988913Z import torch
2023-01-11T21:41:26.5989327Z import random
2023-01-11T21:41:26.5989827Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.5990450Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.5990819Z 
2023-01-11T21:41:26.5990971Z aten = torch.ops.aten
2023-01-11T21:41:26.5991412Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.5991854Z async_compile = AsyncCompile()
2023-01-11T21:41:26.5992079Z 
2023-01-11T21:41:26.5992087Z 
2023-01-11T21:41:26.5992253Z async_compile.wait(globals())
2023-01-11T21:41:26.5992693Z del async_compile
2023-01-11T21:41:26.5992949Z 
2023-01-11T21:41:26.5993098Z def call(args):
2023-01-11T21:41:26.5993511Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.5993912Z     args.clear()
2023-01-11T21:41:26.5994669Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'tanh', [], '')
2023-01-11T21:41:26.5995241Z     del arg0_1
2023-01-11T21:41:26.5995636Z     del arg1_1
2023-01-11T21:41:26.5996031Z     return (buf0, )
2023-01-11T21:41:26.5996293Z 
2023-01-11T21:41:26.5996303Z 
2023-01-11T21:41:26.5996488Z if __name__ == "__main__":
2023-01-11T21:41:26.5997004Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.5997638Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.5998384Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5999128Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.5999610Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.5999918Z 
2023-01-11T21:41:26.5999928Z 
2023-01-11T21:41:26.6000195Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6000650Z import torch
2023-01-11T21:41:26.6001094Z import random
2023-01-11T21:41:26.6001586Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6002219Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6002592Z 
2023-01-11T21:41:26.6002779Z aten = torch.ops.aten
2023-01-11T21:41:26.6003364Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6003938Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6004236Z 
2023-01-11T21:41:26.6004312Z 
2023-01-11T21:41:26.6004540Z async_compile.wait(globals())
2023-01-11T21:41:26.6004990Z del async_compile
2023-01-11T21:41:26.6005260Z 
2023-01-11T21:41:26.6005409Z def call(args):
2023-01-11T21:41:26.6005801Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6006136Z     args.clear()
2023-01-11T21:41:26.6006703Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'tanh', [], '')
2023-01-11T21:41:26.6007128Z     del arg0_1
2023-01-11T21:41:26.6007498Z     del arg1_1
2023-01-11T21:41:26.6007885Z     del arg2_1
2023-01-11T21:41:26.6008283Z     return (buf0, )
2023-01-11T21:41:26.6008535Z 
2023-01-11T21:41:26.6008547Z 
2023-01-11T21:41:26.6008726Z if __name__ == "__main__":
2023-01-11T21:41:26.6009405Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6010010Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6010877Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6011728Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6012571Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6013211Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6013477Z 
2023-01-11T21:41:26.6013485Z 
2023-01-11T21:41:26.6013654Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6014005Z import torch
2023-01-11T21:41:26.6014291Z import random
2023-01-11T21:41:26.6014678Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6015234Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6015526Z 
2023-01-11T21:41:26.6015720Z aten = torch.ops.aten
2023-01-11T21:41:26.6016310Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6016893Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6017197Z 
2023-01-11T21:41:26.6017206Z 
2023-01-11T21:41:26.6017410Z async_compile.wait(globals())
2023-01-11T21:41:26.6017868Z del async_compile
2023-01-11T21:41:26.6018122Z 
2023-01-11T21:41:26.6018293Z def call(args):
2023-01-11T21:41:26.6018698Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6019109Z     args.clear()
2023-01-11T21:41:26.6019886Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'tanh', [], '')
2023-01-11T21:41:26.6020443Z     del arg0_1
2023-01-11T21:41:26.6020823Z     del arg1_1
2023-01-11T21:41:26.6021128Z     return (buf0, )
2023-01-11T21:41:26.6021323Z 
2023-01-11T21:41:26.6021330Z 
2023-01-11T21:41:26.6021470Z if __name__ == "__main__":
2023-01-11T21:41:26.6021849Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6022311Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6023089Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6024020Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6024651Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6025008Z 
2023-01-11T21:41:26.6025017Z 
2023-01-11T21:41:26.6025247Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6025830Z import torch
2023-01-11T21:41:26.6026229Z import random
2023-01-11T21:41:26.6026736Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6027359Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6027711Z 
2023-01-11T21:41:26.6027898Z aten = torch.ops.aten
2023-01-11T21:41:26.6028453Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6028902Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6029132Z 
2023-01-11T21:41:26.6029140Z 
2023-01-11T21:41:26.6029285Z async_compile.wait(globals())
2023-01-11T21:41:26.6029632Z del async_compile
2023-01-11T21:41:26.6029829Z 
2023-01-11T21:41:26.6029969Z def call(args):
2023-01-11T21:41:26.6030405Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6030816Z     args.clear()
2023-01-11T21:41:26.6031695Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardswish', [], '')
2023-01-11T21:41:26.6032273Z     del arg0_1
2023-01-11T21:41:26.6032632Z     del arg1_1
2023-01-11T21:41:26.6033023Z     del arg2_1
2023-01-11T21:41:26.6033424Z     return (buf0, )
2023-01-11T21:41:26.6033663Z 
2023-01-11T21:41:26.6033691Z 
2023-01-11T21:41:26.6033847Z if __name__ == "__main__":
2023-01-11T21:41:26.6034372Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6035001Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6035813Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6036458Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6037117Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6037713Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6038022Z 
2023-01-11T21:41:26.6038031Z 
2023-01-11T21:41:26.6038267Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6038718Z import torch
2023-01-11T21:41:26.6039127Z import random
2023-01-11T21:41:26.6039632Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6040248Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6040611Z 
2023-01-11T21:41:26.6040797Z aten = torch.ops.aten
2023-01-11T21:41:26.6041355Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6041949Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6042255Z 
2023-01-11T21:41:26.6042265Z 
2023-01-11T21:41:26.6042479Z async_compile.wait(globals())
2023-01-11T21:41:26.6042938Z del async_compile
2023-01-11T21:41:26.6043177Z 
2023-01-11T21:41:26.6043303Z def call(args):
2023-01-11T21:41:26.6043619Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6043934Z     args.clear()
2023-01-11T21:41:26.6044510Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardswish', [], '')
2023-01-11T21:41:26.6045066Z     del arg0_1
2023-01-11T21:41:26.6045454Z     del arg1_1
2023-01-11T21:41:26.6045857Z     return (buf0, )
2023-01-11T21:41:26.6046112Z 
2023-01-11T21:41:26.6046130Z 
2023-01-11T21:41:26.6046314Z if __name__ == "__main__":
2023-01-11T21:41:26.6046831Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6047443Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6048313Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6049326Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6049980Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6050308Z 
2023-01-11T21:41:26.6050339Z 
2023-01-11T21:41:26.6050546Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6050950Z import torch
2023-01-11T21:41:26.6051252Z import random
2023-01-11T21:41:26.6051620Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6052081Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6052379Z 
2023-01-11T21:41:26.6052561Z aten = torch.ops.aten
2023-01-11T21:41:26.6053163Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6053905Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6054204Z 
2023-01-11T21:41:26.6054214Z 
2023-01-11T21:41:26.6054435Z async_compile.wait(globals())
2023-01-11T21:41:26.6054896Z del async_compile
2023-01-11T21:41:26.6055132Z 
2023-01-11T21:41:26.6055308Z def call(args):
2023-01-11T21:41:26.6055729Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6056162Z     args.clear()
2023-01-11T21:41:26.6056964Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardswish', [], '')
2023-01-11T21:41:26.6057529Z     del arg0_1
2023-01-11T21:41:26.6057920Z     del arg1_1
2023-01-11T21:41:26.6058297Z     del arg2_1
2023-01-11T21:41:26.6058608Z     return (buf0, )
2023-01-11T21:41:26.6058804Z 
2023-01-11T21:41:26.6058811Z 
2023-01-11T21:41:26.6058949Z if __name__ == "__main__":
2023-01-11T21:41:26.6059394Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6059980Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6060825Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6061676Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6062519Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6063235Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6063600Z 
2023-01-11T21:41:26.6063609Z 
2023-01-11T21:41:26.6063841Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6064269Z import torch
2023-01-11T21:41:26.6064666Z import random
2023-01-11T21:41:26.6065171Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6065720Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6065979Z 
2023-01-11T21:41:26.6066126Z aten = torch.ops.aten
2023-01-11T21:41:26.6066562Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6067108Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6067377Z 
2023-01-11T21:41:26.6067413Z 
2023-01-11T21:41:26.6067611Z async_compile.wait(globals())
2023-01-11T21:41:26.6068066Z del async_compile
2023-01-11T21:41:26.6068328Z 
2023-01-11T21:41:26.6068500Z def call(args):
2023-01-11T21:41:26.6068907Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6069329Z     args.clear()
2023-01-11T21:41:26.6070130Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardswish', [], '')
2023-01-11T21:41:26.6070696Z     del arg0_1
2023-01-11T21:41:26.6071090Z     del arg1_1
2023-01-11T21:41:26.6071486Z     return (buf0, )
2023-01-11T21:41:26.6071742Z 
2023-01-11T21:41:26.6071752Z 
2023-01-11T21:41:26.6071941Z if __name__ == "__main__":
2023-01-11T21:41:26.6072460Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6072948Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6073598Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6074367Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6074988Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6075328Z 
2023-01-11T21:41:26.6075992Z [2023-01-11 21:35:33,063] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 313
2023-01-11T21:41:26.6077507Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6078881Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6079832Z [2023-01-11 21:35:33,093] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 314
2023-01-11T21:41:26.6080680Z [2023-01-11 21:35:33,096] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 314
2023-01-11T21:41:26.6082203Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6083553Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6084526Z [2023-01-11 21:35:33,180] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 315
2023-01-11T21:41:26.6085639Z [2023-01-11 21:35:33,183] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 315
2023-01-11T21:41:26.6087116Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6088192Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6089251Z [2023-01-11 21:35:33,212] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 316
2023-01-11T21:41:26.6090366Z [2023-01-11 21:35:33,214] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 316
2023-01-11T21:41:26.6091907Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6093293Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6094173Z [2023-01-11 21:35:33,300] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 317
2023-01-11T21:41:26.6094995Z [2023-01-11 21:35:33,303] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 317
2023-01-11T21:41:26.6096248Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6097500Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6098513Z [2023-01-11 21:35:33,333] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 318
2023-01-11T21:41:26.6099629Z [2023-01-11 21:35:33,336] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 318
2023-01-11T21:41:26.6101155Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6102280Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6103078Z [2023-01-11 21:35:33,422] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 319
2023-01-11T21:41:26.6104201Z [2023-01-11 21:35:33,425] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 319
2023-01-11T21:41:26.6105729Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6107203Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6108208Z [2023-01-11 21:35:33,455] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 320
2023-01-11T21:41:26.6109171Z [2023-01-11 21:35:33,457] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 320
2023-01-11T21:41:26.6110448Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6111742Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6112747Z [2023-01-11 21:35:33,543] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 321
2023-01-11T21:41:26.6113220Z 
2023-01-11T21:41:26.6113449Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6113894Z import torch
2023-01-11T21:41:26.6114291Z import random
2023-01-11T21:41:26.6114806Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6115422Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6115796Z 
2023-01-11T21:41:26.6115967Z aten = torch.ops.aten
2023-01-11T21:41:26.6116405Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6116843Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6117072Z 
2023-01-11T21:41:26.6117079Z 
2023-01-11T21:41:26.6117286Z async_compile.wait(globals())
2023-01-11T21:41:26.6117738Z del async_compile
2023-01-11T21:41:26.6117999Z 
2023-01-11T21:41:26.6118169Z def call(args):
2023-01-11T21:41:26.6118590Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6119030Z     args.clear()
2023-01-11T21:41:26.6119849Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'leaky_relu', [0.1], '')
2023-01-11T21:41:26.6120394Z     del arg0_1
2023-01-11T21:41:26.6120783Z     del arg1_1
2023-01-11T21:41:26.6121170Z     del arg2_1
2023-01-11T21:41:26.6121561Z     return (buf0, )
2023-01-11T21:41:26.6121817Z 
2023-01-11T21:41:26.6121827Z 
2023-01-11T21:41:26.6122005Z if __name__ == "__main__":
2023-01-11T21:41:26.6122521Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6123068Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6123722Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6124441Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6125284Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6125922Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6126285Z 
2023-01-11T21:41:26.6126294Z 
2023-01-11T21:41:26.6126528Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6126990Z import torch
2023-01-11T21:41:26.6127384Z import random
2023-01-11T21:41:26.6127901Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6128523Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6128893Z 
2023-01-11T21:41:26.6129191Z aten = torch.ops.aten
2023-01-11T21:41:26.6129775Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6130331Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6130565Z 
2023-01-11T21:41:26.6130573Z 
2023-01-11T21:41:26.6130736Z async_compile.wait(globals())
2023-01-11T21:41:26.6131064Z del async_compile
2023-01-11T21:41:26.6131260Z 
2023-01-11T21:41:26.6131387Z def call(args):
2023-01-11T21:41:26.6131741Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6132353Z     args.clear()
2023-01-11T21:41:26.6133214Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'leaky_relu', [0.1], '')
2023-01-11T21:41:26.6133785Z     del arg0_1
2023-01-11T21:41:26.6134163Z     del arg1_1
2023-01-11T21:41:26.6134566Z     return (buf0, )
2023-01-11T21:41:26.6134832Z 
2023-01-11T21:41:26.6134841Z 
2023-01-11T21:41:26.6135026Z if __name__ == "__main__":
2023-01-11T21:41:26.6135528Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6136149Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6136999Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6137778Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6138254Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6138583Z 
2023-01-11T21:41:26.6138594Z 
2023-01-11T21:41:26.6138770Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6139220Z import torch
2023-01-11T21:41:26.6139596Z import random
2023-01-11T21:41:26.6140092Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6140710Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6141078Z 
2023-01-11T21:41:26.6141268Z aten = torch.ops.aten
2023-01-11T21:41:26.6141826Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6142424Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6142724Z 
2023-01-11T21:41:26.6142733Z 
2023-01-11T21:41:26.6142949Z async_compile.wait(globals())
2023-01-11T21:41:26.6143473Z del async_compile
2023-01-11T21:41:26.6143732Z 
2023-01-11T21:41:26.6143904Z def call(args):
2023-01-11T21:41:26.6144326Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6144745Z     args.clear()
2023-01-11T21:41:26.6145392Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'leaky_relu', [0.1], '')
2023-01-11T21:41:26.6145824Z     del arg0_1
2023-01-11T21:41:26.6146105Z     del arg1_1
2023-01-11T21:41:26.6146480Z     del arg2_1
2023-01-11T21:41:26.6146915Z     return (buf0, )
2023-01-11T21:41:26.6147239Z 
2023-01-11T21:41:26.6147250Z 
2023-01-11T21:41:26.6147425Z if __name__ == "__main__":
2023-01-11T21:41:26.6147923Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6148542Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6149406Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6150236Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6151098Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6151753Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6152117Z 
2023-01-11T21:41:26.6152126Z 
2023-01-11T21:41:26.6152307Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6152641Z import torch
2023-01-11T21:41:26.6152944Z import random
2023-01-11T21:41:26.6153330Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6153855Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6154213Z 
2023-01-11T21:41:26.6154399Z aten = torch.ops.aten
2023-01-11T21:41:26.6154976Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6155540Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6155844Z 
2023-01-11T21:41:26.6155853Z 
2023-01-11T21:41:26.6156073Z async_compile.wait(globals())
2023-01-11T21:41:26.6156536Z del async_compile
2023-01-11T21:41:26.6156800Z 
2023-01-11T21:41:26.6156948Z def call(args):
2023-01-11T21:41:26.6157360Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6157783Z     args.clear()
2023-01-11T21:41:26.6158579Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'leaky_relu', [0.1], '')
2023-01-11T21:41:26.6159146Z     del arg0_1
2023-01-11T21:41:26.6159494Z     del arg1_1
2023-01-11T21:41:26.6159797Z     return (buf0, )
2023-01-11T21:41:26.6159975Z 
2023-01-11T21:41:26.6160063Z 
2023-01-11T21:41:26.6160206Z if __name__ == "__main__":
2023-01-11T21:41:26.6160601Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6161194Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6162014Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6162881Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6163517Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6163859Z 
2023-01-11T21:41:26.6163868Z 
2023-01-11T21:41:26.6164099Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6164541Z import torch
2023-01-11T21:41:26.6164939Z import random
2023-01-11T21:41:26.6165444Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6166136Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6166502Z 
2023-01-11T21:41:26.6166649Z aten = torch.ops.aten
2023-01-11T21:41:26.6167076Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6167515Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6167741Z 
2023-01-11T21:41:26.6167748Z 
2023-01-11T21:41:26.6167936Z async_compile.wait(globals())
2023-01-11T21:41:26.6168401Z del async_compile
2023-01-11T21:41:26.6168666Z 
2023-01-11T21:41:26.6168804Z def call(args):
2023-01-11T21:41:26.6169394Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6169820Z     args.clear()
2023-01-11T21:41:26.6170653Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardtanh', [-0.5, 4], '')
2023-01-11T21:41:26.6171202Z     del arg0_1
2023-01-11T21:41:26.6171589Z     del arg1_1
2023-01-11T21:41:26.6171979Z     del arg2_1
2023-01-11T21:41:26.6172358Z     return (buf0, )
2023-01-11T21:41:26.6172611Z 
2023-01-11T21:41:26.6172621Z 
2023-01-11T21:41:26.6172802Z if __name__ == "__main__":
2023-01-11T21:41:26.6173341Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6173906Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6174549Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6175204Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6176063Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6176783Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6177138Z 
2023-01-11T21:41:26.6177148Z 
2023-01-11T21:41:26.6177375Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6177839Z import torch
2023-01-11T21:41:26.6178229Z import random
2023-01-11T21:41:26.6178741Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6179359Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6179725Z 
2023-01-11T21:41:26.6179904Z aten = torch.ops.aten
2023-01-11T21:41:26.6180475Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6181067Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6181319Z 
2023-01-11T21:41:26.6181327Z 
2023-01-11T21:41:26.6181487Z async_compile.wait(globals())
2023-01-11T21:41:26.6181818Z del async_compile
2023-01-11T21:41:26.6182012Z 
2023-01-11T21:41:26.6182143Z def call(args):
2023-01-11T21:41:26.6182458Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6182826Z     args.clear()
2023-01-11T21:41:26.6183694Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardtanh', [-0.5, 4], '')
2023-01-11T21:41:26.6184259Z     del arg0_1
2023-01-11T21:41:26.6184639Z     del arg1_1
2023-01-11T21:41:26.6185040Z     return (buf0, )
2023-01-11T21:41:26.6185297Z 
2023-01-11T21:41:26.6185307Z 
2023-01-11T21:41:26.6185487Z if __name__ == "__main__":
2023-01-11T21:41:26.6185997Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6186623Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6187484Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6188464Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6188931Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6189194Z 
2023-01-11T21:41:26.6189201Z 
2023-01-11T21:41:26.6189374Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6189716Z import torch
2023-01-11T21:41:26.6190078Z import random
2023-01-11T21:41:26.6190597Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6191222Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6191584Z 
2023-01-11T21:41:26.6191756Z aten = torch.ops.aten
2023-01-11T21:41:26.6192334Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6192920Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6193226Z 
2023-01-11T21:41:26.6193235Z 
2023-01-11T21:41:26.6193537Z async_compile.wait(globals())
2023-01-11T21:41:26.6193982Z del async_compile
2023-01-11T21:41:26.6194242Z 
2023-01-11T21:41:26.6194411Z def call(args):
2023-01-11T21:41:26.6194842Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6195262Z     args.clear()
2023-01-11T21:41:26.6195947Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardtanh', [-0.5, 4], '')
2023-01-11T21:41:26.6196383Z     del arg0_1
2023-01-11T21:41:26.6196660Z     del arg1_1
2023-01-11T21:41:26.6196961Z     del arg2_1
2023-01-11T21:41:26.6197361Z     return (buf0, )
2023-01-11T21:41:26.6197618Z 
2023-01-11T21:41:26.6197626Z 
2023-01-11T21:41:26.6197780Z if __name__ == "__main__":
2023-01-11T21:41:26.6198295Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6198920Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6199775Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6200612Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6201458Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6202118Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6202476Z 
2023-01-11T21:41:26.6202486Z 
2023-01-11T21:41:26.6202696Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6203056Z import torch
2023-01-11T21:41:26.6203359Z import random
2023-01-11T21:41:26.6203723Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6204209Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6204551Z 
2023-01-11T21:41:26.6204742Z aten = torch.ops.aten
2023-01-11T21:41:26.6205261Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6205869Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6206170Z 
2023-01-11T21:41:26.6206179Z 
2023-01-11T21:41:26.6206399Z async_compile.wait(globals())
2023-01-11T21:41:26.6206870Z del async_compile
2023-01-11T21:41:26.6207130Z 
2023-01-11T21:41:26.6207275Z def call(args):
2023-01-11T21:41:26.6207691Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6208123Z     args.clear()
2023-01-11T21:41:26.6208923Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardtanh', [-0.5, 4], '')
2023-01-11T21:41:26.6209652Z     del arg0_1
2023-01-11T21:41:26.6210050Z     del arg1_1
2023-01-11T21:41:26.6210402Z     return (buf0, )
2023-01-11T21:41:26.6210603Z 
2023-01-11T21:41:26.6210609Z 
2023-01-11T21:41:26.6210741Z if __name__ == "__main__":
2023-01-11T21:41:26.6211137Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6211591Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6212386Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6213214Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6213851Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6214210Z 
2023-01-11T21:41:26.6214867Z [2023-01-11 21:35:33,546] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 321
2023-01-11T21:41:26.6216508Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6217809Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6218553Z [2023-01-11 21:35:33,577] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 322
2023-01-11T21:41:26.6219568Z [2023-01-11 21:35:33,579] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 322
2023-01-11T21:41:26.6221126Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6222498Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6223590Z [2023-01-11 21:35:33,664] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 323
2023-01-11T21:41:26.6224721Z [2023-01-11 21:35:33,666] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 323
2023-01-11T21:41:26.6225927Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6227093Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6228056Z [2023-01-11 21:35:33,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 324
2023-01-11T21:41:26.6229194Z [2023-01-11 21:35:33,697] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 324
2023-01-11T21:41:26.6230717Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6232079Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6232891Z [2023-01-11 21:35:33,781] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 325
2023-01-11T21:41:26.6233390Z [2023-01-11 21:35:33,784] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 325
2023-01-11T21:41:26.6234335Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6234633Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6235239Z [2023-01-11 21:35:33,814] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 326
2023-01-11T21:41:26.6235893Z [2023-01-11 21:35:33,816] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 326
2023-01-11T21:41:26.6236977Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6237355Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6237979Z [2023-01-11 21:35:33,899] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 327
2023-01-11T21:41:26.6238642Z [2023-01-11 21:35:33,901] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 327
2023-01-11T21:41:26.6239674Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6239907Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6240395Z [2023-01-11 21:35:33,930] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 328
2023-01-11T21:41:26.6240922Z [2023-01-11 21:35:33,933] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 328
2023-01-11T21:41:26.6241971Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6242268Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6242891Z [2023-01-11 21:35:34,141] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 329
2023-01-11T21:41:26.6242906Z 
2023-01-11T21:41:26.6243132Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6243307Z import torch
2023-01-11T21:41:26.6243461Z import random
2023-01-11T21:41:26.6243743Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6244033Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6244045Z 
2023-01-11T21:41:26.6244239Z aten = torch.ops.aten
2023-01-11T21:41:26.6244557Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6244774Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6244787Z 
2023-01-11T21:41:26.6244795Z 
2023-01-11T21:41:26.6245013Z async_compile.wait(globals())
2023-01-11T21:41:26.6245194Z del async_compile
2023-01-11T21:41:26.6245206Z 
2023-01-11T21:41:26.6245358Z def call(args):
2023-01-11T21:41:26.6245556Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6245730Z     args.clear()
2023-01-11T21:41:26.6246285Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'gelu', [], 'none')
2023-01-11T21:41:26.6246445Z     del arg0_1
2023-01-11T21:41:26.6246612Z     del arg1_1
2023-01-11T21:41:26.6246748Z     del arg2_1
2023-01-11T21:41:26.6246863Z     return (buf0, )
2023-01-11T21:41:26.6246872Z 
2023-01-11T21:41:26.6246879Z 
2023-01-11T21:41:26.6247015Z if __name__ == "__main__":
2023-01-11T21:41:26.6247222Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6247446Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6247823Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6248226Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6248721Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6249141Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6249156Z 
2023-01-11T21:41:26.6249166Z 
2023-01-11T21:41:26.6249372Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6249541Z import torch
2023-01-11T21:41:26.6249699Z import random
2023-01-11T21:41:26.6250100Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6250392Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6250404Z 
2023-01-11T21:41:26.6250587Z aten = torch.ops.aten
2023-01-11T21:41:26.6250902Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6251123Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6251135Z 
2023-01-11T21:41:26.6251145Z 
2023-01-11T21:41:26.6251346Z async_compile.wait(globals())
2023-01-11T21:41:26.6251517Z del async_compile
2023-01-11T21:41:26.6251528Z 
2023-01-11T21:41:26.6251699Z def call(args):
2023-01-11T21:41:26.6251879Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6252051Z     args.clear()
2023-01-11T21:41:26.6252592Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'gelu', [], 'none')
2023-01-11T21:41:26.6252755Z     del arg0_1
2023-01-11T21:41:26.6252965Z     del arg1_1
2023-01-11T21:41:26.6253148Z     return (buf0, )
2023-01-11T21:41:26.6253159Z 
2023-01-11T21:41:26.6253173Z 
2023-01-11T21:41:26.6253352Z if __name__ == "__main__":
2023-01-11T21:41:26.6253617Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6253911Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6254312Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6254696Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6254908Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6254917Z 
2023-01-11T21:41:26.6254924Z 
2023-01-11T21:41:26.6255088Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6255198Z import torch
2023-01-11T21:41:26.6255336Z import random
2023-01-11T21:41:26.6255624Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6255987Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6256000Z 
2023-01-11T21:41:26.6256280Z aten = torch.ops.aten
2023-01-11T21:41:26.6256585Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6256806Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6256817Z 
2023-01-11T21:41:26.6256826Z 
2023-01-11T21:41:26.6257017Z async_compile.wait(globals())
2023-01-11T21:41:26.6257193Z del async_compile
2023-01-11T21:41:26.6257205Z 
2023-01-11T21:41:26.6257366Z def call(args):
2023-01-11T21:41:26.6257561Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6257733Z     args.clear()
2023-01-11T21:41:26.6258279Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'gelu', [], 'none')
2023-01-11T21:41:26.6258441Z     del arg0_1
2023-01-11T21:41:26.6258586Z     del arg1_1
2023-01-11T21:41:26.6258751Z     del arg2_1
2023-01-11T21:41:26.6258919Z     return (buf0, )
2023-01-11T21:41:26.6258931Z 
2023-01-11T21:41:26.6258941Z 
2023-01-11T21:41:26.6259110Z if __name__ == "__main__":
2023-01-11T21:41:26.6259387Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6259682Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6260180Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6260664Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6261132Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6261401Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6261410Z 
2023-01-11T21:41:26.6261417Z 
2023-01-11T21:41:26.6261586Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6261707Z import torch
2023-01-11T21:41:26.6261840Z import random
2023-01-11T21:41:26.6262051Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6262267Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6262277Z 
2023-01-11T21:41:26.6262420Z aten = torch.ops.aten
2023-01-11T21:41:26.6262645Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6262845Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6262944Z 
2023-01-11T21:41:26.6262952Z 
2023-01-11T21:41:26.6263239Z async_compile.wait(globals())
2023-01-11T21:41:26.6263413Z del async_compile
2023-01-11T21:41:26.6263425Z 
2023-01-11T21:41:26.6263590Z def call(args):
2023-01-11T21:41:26.6263760Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6263920Z     args.clear()
2023-01-11T21:41:26.6264486Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'gelu', [], 'none')
2023-01-11T21:41:26.6264638Z     del arg0_1
2023-01-11T21:41:26.6264795Z     del arg1_1
2023-01-11T21:41:26.6264965Z     return (buf0, )
2023-01-11T21:41:26.6264976Z 
2023-01-11T21:41:26.6264985Z 
2023-01-11T21:41:26.6265164Z if __name__ == "__main__":
2023-01-11T21:41:26.6265439Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6265810Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6266336Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6266817Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6267100Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6267112Z 
2023-01-11T21:41:26.6267122Z 
2023-01-11T21:41:26.6267353Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6267525Z import torch
2023-01-11T21:41:26.6267690Z import random
2023-01-11T21:41:26.6267964Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6268175Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6268184Z 
2023-01-11T21:41:26.6268317Z aten = torch.ops.aten
2023-01-11T21:41:26.6268532Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6268688Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6268697Z 
2023-01-11T21:41:26.6268704Z 
2023-01-11T21:41:26.6268859Z async_compile.wait(globals())
2023-01-11T21:41:26.6269017Z del async_compile
2023-01-11T21:41:26.6269029Z 
2023-01-11T21:41:26.6269186Z def call(args):
2023-01-11T21:41:26.6269390Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6269557Z     args.clear()
2023-01-11T21:41:26.6270066Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'gelu', [], 'tanh')
2023-01-11T21:41:26.6270213Z     del arg0_1
2023-01-11T21:41:26.6270371Z     del arg1_1
2023-01-11T21:41:26.6270509Z     del arg2_1
2023-01-11T21:41:26.6270677Z     return (buf0, )
2023-01-11T21:41:26.6270688Z 
2023-01-11T21:41:26.6270757Z 
2023-01-11T21:41:26.6271030Z if __name__ == "__main__":
2023-01-11T21:41:26.6271231Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6271447Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6271802Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6272136Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6272497Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6272719Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6272730Z 
2023-01-11T21:41:26.6272736Z 
2023-01-11T21:41:26.6272898Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6273019Z import torch
2023-01-11T21:41:26.6273150Z import random
2023-01-11T21:41:26.6273349Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6273533Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6273562Z 
2023-01-11T21:41:26.6273674Z aten = torch.ops.aten
2023-01-11T21:41:26.6273908Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6274073Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6274081Z 
2023-01-11T21:41:26.6274088Z 
2023-01-11T21:41:26.6274250Z async_compile.wait(globals())
2023-01-11T21:41:26.6274378Z del async_compile
2023-01-11T21:41:26.6274386Z 
2023-01-11T21:41:26.6274512Z def call(args):
2023-01-11T21:41:26.6274652Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6274761Z     args.clear()
2023-01-11T21:41:26.6275255Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'gelu', [], 'tanh')
2023-01-11T21:41:26.6275378Z     del arg0_1
2023-01-11T21:41:26.6275505Z     del arg1_1
2023-01-11T21:41:26.6275641Z     return (buf0, )
2023-01-11T21:41:26.6275651Z 
2023-01-11T21:41:26.6275657Z 
2023-01-11T21:41:26.6275802Z if __name__ == "__main__":
2023-01-11T21:41:26.6275994Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6276196Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6276544Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6276903Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6277095Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6277104Z 
2023-01-11T21:41:26.6277182Z 
2023-01-11T21:41:26.6277356Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6277494Z import torch
2023-01-11T21:41:26.6277629Z import random
2023-01-11T21:41:26.6277828Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6278020Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6278037Z 
2023-01-11T21:41:26.6278158Z aten = torch.ops.aten
2023-01-11T21:41:26.6278379Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6278544Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6278552Z 
2023-01-11T21:41:26.6278558Z 
2023-01-11T21:41:26.6278711Z async_compile.wait(globals())
2023-01-11T21:41:26.6278839Z del async_compile
2023-01-11T21:41:26.6278849Z 
2023-01-11T21:41:26.6278981Z def call(args):
2023-01-11T21:41:26.6279138Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6279271Z     args.clear()
2023-01-11T21:41:26.6279655Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'gelu', [], 'tanh')
2023-01-11T21:41:26.6279779Z     del arg0_1
2023-01-11T21:41:26.6279904Z     del arg1_1
2023-01-11T21:41:26.6280032Z     del arg2_1
2023-01-11T21:41:26.6280171Z     return (buf0, )
2023-01-11T21:41:26.6280178Z 
2023-01-11T21:41:26.6280183Z 
2023-01-11T21:41:26.6280322Z if __name__ == "__main__":
2023-01-11T21:41:26.6280521Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6280718Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6281073Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6281418Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6281765Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6281981Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6281991Z 
2023-01-11T21:41:26.6281999Z 
2023-01-11T21:41:26.6282164Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6282303Z import torch
2023-01-11T21:41:26.6282440Z import random
2023-01-11T21:41:26.6282619Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6282832Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6282842Z 
2023-01-11T21:41:26.6282974Z aten = torch.ops.aten
2023-01-11T21:41:26.6283204Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6283370Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6283379Z 
2023-01-11T21:41:26.6283385Z 
2023-01-11T21:41:26.6283551Z async_compile.wait(globals())
2023-01-11T21:41:26.6283687Z del async_compile
2023-01-11T21:41:26.6283695Z 
2023-01-11T21:41:26.6283816Z def call(args):
2023-01-11T21:41:26.6283927Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6284053Z     args.clear()
2023-01-11T21:41:26.6284445Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'gelu', [], 'tanh')
2023-01-11T21:41:26.6284578Z     del arg0_1
2023-01-11T21:41:26.6284694Z     del arg1_1
2023-01-11T21:41:26.6284835Z     return (buf0, )
2023-01-11T21:41:26.6284843Z 
2023-01-11T21:41:26.6284848Z 
2023-01-11T21:41:26.6284985Z if __name__ == "__main__":
2023-01-11T21:41:26.6285255Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6285473Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6285838Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6286191Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6286388Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6286395Z 
2023-01-11T21:41:26.6286865Z [2023-01-11 21:35:34,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 329
2023-01-11T21:41:26.6287671Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6287904Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6288349Z [2023-01-11 21:35:34,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 330
2023-01-11T21:41:26.6288804Z [2023-01-11 21:35:34,176] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 330
2023-01-11T21:41:26.6289638Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6289857Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6290312Z [2023-01-11 21:35:34,258] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 331
2023-01-11T21:41:26.6290773Z [2023-01-11 21:35:34,260] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 331
2023-01-11T21:41:26.6291465Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6291687Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6292129Z [2023-01-11 21:35:34,299] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 332
2023-01-11T21:41:26.6292579Z [2023-01-11 21:35:34,302] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 332
2023-01-11T21:41:26.6293286Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6293515Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6293950Z [2023-01-11 21:35:34,432] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 333
2023-01-11T21:41:26.6294402Z [2023-01-11 21:35:34,434] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 333
2023-01-11T21:41:26.6295118Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6295446Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6295906Z [2023-01-11 21:35:34,487] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 334
2023-01-11T21:41:26.6296365Z [2023-01-11 21:35:34,489] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 334
2023-01-11T21:41:26.6297070Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6297388Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6297836Z [2023-01-11 21:35:34,594] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 335
2023-01-11T21:41:26.6298298Z [2023-01-11 21:35:34,596] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 335
2023-01-11T21:41:26.6298996Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6299213Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6299661Z [2023-01-11 21:35:34,647] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 336
2023-01-11T21:41:26.6300119Z [2023-01-11 21:35:34,650] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 336
2023-01-11T21:41:26.6300132Z 
2023-01-11T21:41:26.6300275Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6300411Z import torch
2023-01-11T21:41:26.6300534Z import random
2023-01-11T21:41:26.6300739Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6300960Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6300970Z 
2023-01-11T21:41:26.6301109Z aten = torch.ops.aten
2023-01-11T21:41:26.6301331Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6301494Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6301508Z 
2023-01-11T21:41:26.6301514Z 
2023-01-11T21:41:26.6301656Z async_compile.wait(globals())
2023-01-11T21:41:26.6301794Z del async_compile
2023-01-11T21:41:26.6301804Z 
2023-01-11T21:41:26.6301932Z def call(args):
2023-01-11T21:41:26.6302088Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6302215Z     args.clear()
2023-01-11T21:41:26.6302635Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardtanh', [0, 6], '')
2023-01-11T21:41:26.6302770Z     del arg0_1
2023-01-11T21:41:26.6302877Z     del arg1_1
2023-01-11T21:41:26.6302998Z     del arg2_1
2023-01-11T21:41:26.6303193Z     return (buf0, )
2023-01-11T21:41:26.6303202Z 
2023-01-11T21:41:26.6303208Z 
2023-01-11T21:41:26.6303350Z if __name__ == "__main__":
2023-01-11T21:41:26.6303552Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6303770Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6304117Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6304476Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6304812Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6305031Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6305040Z 
2023-01-11T21:41:26.6305046Z 
2023-01-11T21:41:26.6305212Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6305345Z import torch
2023-01-11T21:41:26.6305497Z import random
2023-01-11T21:41:26.6305801Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6306012Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6306021Z 
2023-01-11T21:41:26.6306163Z aten = torch.ops.aten
2023-01-11T21:41:26.6306374Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6306539Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6306546Z 
2023-01-11T21:41:26.6306553Z 
2023-01-11T21:41:26.6306704Z async_compile.wait(globals())
2023-01-11T21:41:26.6306841Z del async_compile
2023-01-11T21:41:26.6306854Z 
2023-01-11T21:41:26.6306983Z def call(args):
2023-01-11T21:41:26.6307133Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6307266Z     args.clear()
2023-01-11T21:41:26.6307662Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardtanh', [0, 6], '')
2023-01-11T21:41:26.6307838Z     del arg0_1
2023-01-11T21:41:26.6307969Z     del arg1_1
2023-01-11T21:41:26.6308097Z     return (buf0, )
2023-01-11T21:41:26.6308112Z 
2023-01-11T21:41:26.6308119Z 
2023-01-11T21:41:26.6308255Z if __name__ == "__main__":
2023-01-11T21:41:26.6308456Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6308670Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6309032Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6309366Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6309570Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6309581Z 
2023-01-11T21:41:26.6309588Z 
2023-01-11T21:41:26.6309757Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6309886Z import torch
2023-01-11T21:41:26.6310018Z import random
2023-01-11T21:41:26.6310218Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6310430Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6310439Z 
2023-01-11T21:41:26.6310578Z aten = torch.ops.aten
2023-01-11T21:41:26.6310785Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6310944Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6310955Z 
2023-01-11T21:41:26.6310963Z 
2023-01-11T21:41:26.6311120Z async_compile.wait(globals())
2023-01-11T21:41:26.6311249Z del async_compile
2023-01-11T21:41:26.6311259Z 
2023-01-11T21:41:26.6311392Z def call(args):
2023-01-11T21:41:26.6311535Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6311665Z     args.clear()
2023-01-11T21:41:26.6312076Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'hardtanh', [0, 6], '')
2023-01-11T21:41:26.6312183Z     del arg0_1
2023-01-11T21:41:26.6312304Z     del arg1_1
2023-01-11T21:41:26.6312423Z     del arg2_1
2023-01-11T21:41:26.6312544Z     return (buf0, )
2023-01-11T21:41:26.6312554Z 
2023-01-11T21:41:26.6312560Z 
2023-01-11T21:41:26.6312709Z if __name__ == "__main__":
2023-01-11T21:41:26.6312904Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6313117Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6313476Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6313804Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6314146Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6314363Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6314371Z 
2023-01-11T21:41:26.6314376Z 
2023-01-11T21:41:26.6314545Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6314678Z import torch
2023-01-11T21:41:26.6314808Z import random
2023-01-11T21:41:26.6315009Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6315207Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6315240Z 
2023-01-11T21:41:26.6315368Z aten = torch.ops.aten
2023-01-11T21:41:26.6315594Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6315848Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6315857Z 
2023-01-11T21:41:26.6315864Z 
2023-01-11T21:41:26.6316023Z async_compile.wait(globals())
2023-01-11T21:41:26.6316167Z del async_compile
2023-01-11T21:41:26.6316178Z 
2023-01-11T21:41:26.6316312Z def call(args):
2023-01-11T21:41:26.6316454Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6316561Z     args.clear()
2023-01-11T21:41:26.6316967Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'hardtanh', [0, 6], '')
2023-01-11T21:41:26.6317097Z     del arg0_1
2023-01-11T21:41:26.6317223Z     del arg1_1
2023-01-11T21:41:26.6317350Z     return (buf0, )
2023-01-11T21:41:26.6317360Z 
2023-01-11T21:41:26.6317367Z 
2023-01-11T21:41:26.6317502Z if __name__ == "__main__":
2023-01-11T21:41:26.6317684Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6317976Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6318319Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6318673Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6318873Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6318886Z 
2023-01-11T21:41:26.6318894Z 
2023-01-11T21:41:26.6319064Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6319185Z import torch
2023-01-11T21:41:26.6319319Z import random
2023-01-11T21:41:26.6319519Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6319729Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6319740Z 
2023-01-11T21:41:26.6319866Z aten = torch.ops.aten
2023-01-11T21:41:26.6320093Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6320252Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6320263Z 
2023-01-11T21:41:26.6320270Z 
2023-01-11T21:41:26.6320439Z async_compile.wait(globals())
2023-01-11T21:41:26.6320576Z del async_compile
2023-01-11T21:41:26.6320586Z 
2023-01-11T21:41:26.6320713Z def call(args):
2023-01-11T21:41:26.6320863Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6320984Z     args.clear()
2023-01-11T21:41:26.6321353Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'swish', [], '')
2023-01-11T21:41:26.6321478Z     del arg0_1
2023-01-11T21:41:26.6321601Z     del arg1_1
2023-01-11T21:41:26.6321718Z     del arg2_1
2023-01-11T21:41:26.6321848Z     return (buf0, )
2023-01-11T21:41:26.6321860Z 
2023-01-11T21:41:26.6321864Z 
2023-01-11T21:41:26.6321997Z if __name__ == "__main__":
2023-01-11T21:41:26.6322204Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6322395Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6322749Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6323095Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6323453Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6323674Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6323687Z 
2023-01-11T21:41:26.6323692Z 
2023-01-11T21:41:26.6323863Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6323994Z import torch
2023-01-11T21:41:26.6324127Z import random
2023-01-11T21:41:26.6324306Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6324508Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6324521Z 
2023-01-11T21:41:26.6324656Z aten = torch.ops.aten
2023-01-11T21:41:26.6324881Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6325044Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6325055Z 
2023-01-11T21:41:26.6325061Z 
2023-01-11T21:41:26.6325204Z async_compile.wait(globals())
2023-01-11T21:41:26.6325348Z del async_compile
2023-01-11T21:41:26.6325357Z 
2023-01-11T21:41:26.6325492Z def call(args):
2023-01-11T21:41:26.6325611Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6325838Z     args.clear()
2023-01-11T21:41:26.6326224Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'swish', [], '')
2023-01-11T21:41:26.6326352Z     del arg0_1
2023-01-11T21:41:26.6326477Z     del arg1_1
2023-01-11T21:41:26.6326607Z     return (buf0, )
2023-01-11T21:41:26.6326618Z 
2023-01-11T21:41:26.6326623Z 
2023-01-11T21:41:26.6326752Z if __name__ == "__main__":
2023-01-11T21:41:26.6326925Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6327147Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6327499Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6327864Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6328073Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6328151Z 
2023-01-11T21:41:26.6328163Z 
2023-01-11T21:41:26.6328336Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6328470Z import torch
2023-01-11T21:41:26.6328590Z import random
2023-01-11T21:41:26.6328780Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6328983Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6328991Z 
2023-01-11T21:41:26.6329501Z aten = torch.ops.aten
2023-01-11T21:41:26.6329726Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6329886Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6329899Z 
2023-01-11T21:41:26.6329905Z 
2023-01-11T21:41:26.6330068Z async_compile.wait(globals())
2023-01-11T21:41:26.6330198Z del async_compile
2023-01-11T21:41:26.6330208Z 
2023-01-11T21:41:26.6330344Z def call(args):
2023-01-11T21:41:26.6330459Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6330579Z     args.clear()
2023-01-11T21:41:26.6330991Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg2_1, arg0_1, arg1_1, 'swish', [], '')
2023-01-11T21:41:26.6331120Z     del arg0_1
2023-01-11T21:41:26.6331237Z     del arg1_1
2023-01-11T21:41:26.6331364Z     del arg2_1
2023-01-11T21:41:26.6331496Z     return (buf0, )
2023-01-11T21:41:26.6331504Z 
2023-01-11T21:41:26.6331510Z 
2023-01-11T21:41:26.6331648Z if __name__ == "__main__":
2023-01-11T21:41:26.6331827Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6332043Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6332394Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6332741Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6333083Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6333288Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6333300Z 
2023-01-11T21:41:26.6333306Z 
2023-01-11T21:41:26.6333480Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6333605Z import torch
2023-01-11T21:41:26.6333711Z import random
2023-01-11T21:41:26.6333907Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6334122Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6334131Z 
2023-01-11T21:41:26.6334274Z aten = torch.ops.aten
2023-01-11T21:41:26.6334496Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6334658Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6334666Z 
2023-01-11T21:41:26.6334672Z 
2023-01-11T21:41:26.6334825Z async_compile.wait(globals())
2023-01-11T21:41:26.6334943Z del async_compile
2023-01-11T21:41:26.6334976Z 
2023-01-11T21:41:26.6335080Z def call(args):
2023-01-11T21:41:26.6335219Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6335350Z     args.clear()
2023-01-11T21:41:26.6335723Z     buf0 = torch.ops.mkldnn._linear_pointwise(arg1_1, arg0_1, None, 'swish', [], '')
2023-01-11T21:41:26.6335850Z     del arg0_1
2023-01-11T21:41:26.6335976Z     del arg1_1
2023-01-11T21:41:26.6336090Z     return (buf0, )
2023-01-11T21:41:26.6336122Z 
2023-01-11T21:41:26.6336128Z 
2023-01-11T21:41:26.6336377Z if __name__ == "__main__":
2023-01-11T21:41:26.6336581Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6336794Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6337168Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6337510Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:41:26.6337711Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6337721Z 
2023-01-11T21:41:26.6337843Z ok (2.922s)
2023-01-11T21:41:26.6338715Z   test_linspace1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6338950Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6339384Z [2023-01-11 21:35:34,683] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 337
2023-01-11T21:41:26.6339844Z [2023-01-11 21:35:36,207] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 337
2023-01-11T21:41:26.6339857Z 
2023-01-11T21:41:26.6340027Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6340163Z import torch
2023-01-11T21:41:26.6340293Z import random
2023-01-11T21:41:26.6340497Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6340701Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6340710Z 
2023-01-11T21:41:26.6340850Z aten = torch.ops.aten
2023-01-11T21:41:26.6341069Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6341234Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6341244Z 
2023-01-11T21:41:26.6341250Z 
2023-01-11T21:41:26.6341493Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6341831Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6342039Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6342209Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6342314Z {
2023-01-11T21:41:26.6342492Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6342593Z     {
2023-01-11T21:41:26.6342742Z         #pragma omp for 
2023-01-11T21:41:26.6342896Z         for(long i0=0; i0<7; i0+=1)
2023-01-11T21:41:26.6343013Z         {
2023-01-11T21:41:26.6343210Z             {
2023-01-11T21:41:26.6343336Z                 {
2023-01-11T21:41:26.6343479Z                     auto tmp4 = in_ptr0[i0];
2023-01-11T21:41:26.6343676Z                     auto tmp0 = static_cast<float>(0.125);
2023-01-11T21:41:26.6343861Z                     auto tmp1 = static_cast<float>(i0);
2023-01-11T21:41:26.6344030Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.6344192Z                     auto tmp3 = tmp2 + tmp0;
2023-01-11T21:41:26.6344357Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.6344511Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:41:26.6344603Z                 }
2023-01-11T21:41:26.6344724Z             }
2023-01-11T21:41:26.6344841Z         }
2023-01-11T21:41:26.6344945Z     }
2023-01-11T21:41:26.6345058Z }
2023-01-11T21:41:26.6345216Z ''')
2023-01-11T21:41:26.6345224Z 
2023-01-11T21:41:26.6345232Z 
2023-01-11T21:41:26.6345388Z async_compile.wait(globals())
2023-01-11T21:41:26.6345501Z del async_compile
2023-01-11T21:41:26.6345534Z 
2023-01-11T21:41:26.6345638Z def call(args):
2023-01-11T21:41:26.6345758Z     arg0_1, = args
2023-01-11T21:41:26.6345885Z     args.clear()
2023-01-11T21:41:26.6346241Z     buf0 = empty_strided((1, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6346470Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6346691Z     del arg0_1
2023-01-11T21:41:26.6346797Z     return (buf0, )
2023-01-11T21:41:26.6346832Z 
2023-01-11T21:41:26.6346838Z 
2023-01-11T21:41:26.6346959Z if __name__ == "__main__":
2023-01-11T21:41:26.6347163Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6347374Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6347716Z     arg0_1 = rand_strided((1, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6347907Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6347916Z 
2023-01-11T21:41:26.6348038Z ok (1.558s)
2023-01-11T21:41:26.6348884Z   test_linspace2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6349107Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6349574Z [2023-01-11 21:35:36,239] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 338
2023-01-11T21:41:26.6350021Z [2023-01-11 21:35:37,720] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 338
2023-01-11T21:41:26.6350053Z 
2023-01-11T21:41:26.6350202Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6350341Z import torch
2023-01-11T21:41:26.6350473Z import random
2023-01-11T21:41:26.6350666Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6350867Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6350875Z 
2023-01-11T21:41:26.6351022Z aten = torch.ops.aten
2023-01-11T21:41:26.6351260Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6351407Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6351417Z 
2023-01-11T21:41:26.6351428Z 
2023-01-11T21:41:26.6351661Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6351993Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6352198Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6352375Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6352477Z {
2023-01-11T21:41:26.6352587Z     {
2023-01-11T21:41:26.6352680Z         {
2023-01-11T21:41:26.6352826Z             auto tmp4 = in_ptr0[0];
2023-01-11T21:41:26.6353004Z             auto tmp0 = static_cast<float>(0.0);
2023-01-11T21:41:26.6353180Z             auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:26.6353320Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.6353473Z             auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:26.6353626Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.6353747Z             out_ptr0[0] = tmp5;
2023-01-11T21:41:26.6353857Z         }
2023-01-11T21:41:26.6353979Z     }
2023-01-11T21:41:26.6354088Z }
2023-01-11T21:41:26.6354224Z ''')
2023-01-11T21:41:26.6354232Z 
2023-01-11T21:41:26.6354238Z 
2023-01-11T21:41:26.6354399Z async_compile.wait(globals())
2023-01-11T21:41:26.6354531Z del async_compile
2023-01-11T21:41:26.6354543Z 
2023-01-11T21:41:26.6354650Z def call(args):
2023-01-11T21:41:26.6354783Z     arg0_1, = args
2023-01-11T21:41:26.6354909Z     args.clear()
2023-01-11T21:41:26.6355251Z     buf0 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6355495Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6355624Z     del arg0_1
2023-01-11T21:41:26.6355755Z     return (buf0, )
2023-01-11T21:41:26.6355763Z 
2023-01-11T21:41:26.6355771Z 
2023-01-11T21:41:26.6355897Z if __name__ == "__main__":
2023-01-11T21:41:26.6356076Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6356286Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6356724Z     arg0_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6356914Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6356922Z 
2023-01-11T21:41:26.6357046Z ok (1.513s)
2023-01-11T21:41:26.6357814Z   test_linspace3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6358040Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6358611Z [2023-01-11 21:35:37,750] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 339
2023-01-11T21:41:26.6359080Z [2023-01-11 21:35:37,752] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 339
2023-01-11T21:41:26.6359096Z 
2023-01-11T21:41:26.6359238Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6359369Z import torch
2023-01-11T21:41:26.6359506Z import random
2023-01-11T21:41:26.6359712Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6359922Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6359933Z 
2023-01-11T21:41:26.6360063Z aten = torch.ops.aten
2023-01-11T21:41:26.6360284Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6360448Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6360457Z 
2023-01-11T21:41:26.6360463Z 
2023-01-11T21:41:26.6360596Z async_compile.wait(globals())
2023-01-11T21:41:26.6360728Z del async_compile
2023-01-11T21:41:26.6360736Z 
2023-01-11T21:41:26.6360859Z def call(args):
2023-01-11T21:41:26.6361197Z     buf0 = empty_strided((0, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6361332Z     return (buf0, )
2023-01-11T21:41:26.6361342Z 
2023-01-11T21:41:26.6361354Z 
2023-01-11T21:41:26.6361483Z if __name__ == "__main__":
2023-01-11T21:41:26.6361684Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6361894Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6362045Z     print_performance(lambda: call([]))
2023-01-11T21:41:26.6362053Z 
2023-01-11T21:41:26.6362176Z ok (0.031s)
2023-01-11T21:41:26.6362761Z   test_list_clearing_cpu (__main__.CpuTests) ... [2023-01-11 21:35:37,780] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:41:26.6363224Z [2023-01-11 21:35:39,329] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:41:26.6363236Z 
2023-01-11T21:41:26.6363404Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6363530Z import torch
2023-01-11T21:41:26.6363667Z import random
2023-01-11T21:41:26.6363867Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6364060Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6364099Z 
2023-01-11T21:41:26.6364223Z aten = torch.ops.aten
2023-01-11T21:41:26.6364440Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6364613Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6364622Z 
2023-01-11T21:41:26.6364627Z 
2023-01-11T21:41:26.6364869Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6365201Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6365413Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6365591Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6365749Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6365868Z {
2023-01-11T21:41:26.6366045Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6366171Z     {
2023-01-11T21:41:26.6366312Z         #pragma omp for 
2023-01-11T21:41:26.6366453Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.6366664Z         {
2023-01-11T21:41:26.6366913Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.6367146Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.6367306Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.6367470Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.6367592Z         }
2023-01-11T21:41:26.6367758Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.6367903Z         for(long i0=24; i0<25; i0+=1)
2023-01-11T21:41:26.6368001Z         {
2023-01-11T21:41:26.6368157Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6368304Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.6368453Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.6368595Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6368781Z         }
2023-01-11T21:41:26.6368902Z     }
2023-01-11T21:41:26.6368994Z }
2023-01-11T21:41:26.6369386Z ''')
2023-01-11T21:41:26.6369403Z 
2023-01-11T21:41:26.6369411Z 
2023-01-11T21:41:26.6369567Z async_compile.wait(globals())
2023-01-11T21:41:26.6369702Z del async_compile
2023-01-11T21:41:26.6369711Z 
2023-01-11T21:41:26.6369904Z def call(args):
2023-01-11T21:41:26.6370032Z     x_1, y_1 = args
2023-01-11T21:41:26.6370162Z     args.clear()
2023-01-11T21:41:26.6370485Z     buf0 = empty_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6370750Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(y_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6370873Z     del x_1
2023-01-11T21:41:26.6370992Z     del y_1
2023-01-11T21:41:26.6371332Z     buf1 = empty_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6371501Z     aten.mm.out(buf0, buf0, out=buf1)
2023-01-11T21:41:26.6371629Z     return (buf1, )
2023-01-11T21:41:26.6371639Z 
2023-01-11T21:41:26.6371651Z 
2023-01-11T21:41:26.6371786Z if __name__ == "__main__":
2023-01-11T21:41:26.6371965Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6372177Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6372499Z     x_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6372831Z     y_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6373008Z     print_performance(lambda: call([x_1, y_1]))
2023-01-11T21:41:26.6373017Z 
2023-01-11T21:41:26.6373135Z ok (1.576s)
2023-01-11T21:41:26.6373889Z   test_log1p_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6374115Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6374562Z [2023-01-11 21:35:39,346] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 340
2023-01-11T21:41:26.6375015Z [2023-01-11 21:35:40,936] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 340
2023-01-11T21:41:26.6375726Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6375948Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6376394Z [2023-01-11 21:35:40,954] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 341
2023-01-11T21:41:26.6376852Z [2023-01-11 21:35:42,530] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 341
2023-01-11T21:41:26.6377687Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6377907Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6378353Z [2023-01-11 21:35:42,547] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 342
2023-01-11T21:41:26.6378802Z [2023-01-11 21:35:44,202] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 342
2023-01-11T21:41:26.6379578Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6379813Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6380258Z [2023-01-11 21:35:44,220] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 343
2023-01-11T21:41:26.6380699Z [2023-01-11 21:35:45,892] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 343
2023-01-11T21:41:26.6381400Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6381620Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6382061Z [2023-01-11 21:35:45,910] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 344
2023-01-11T21:41:26.6382080Z 
2023-01-11T21:41:26.6382250Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6382377Z import torch
2023-01-11T21:41:26.6382475Z import random
2023-01-11T21:41:26.6382628Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6382788Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6382796Z 
2023-01-11T21:41:26.6382884Z aten = torch.ops.aten
2023-01-11T21:41:26.6383060Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6383308Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6383315Z 
2023-01-11T21:41:26.6383321Z 
2023-01-11T21:41:26.6383511Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6383779Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6383941Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:41:26.6384075Z                        half* __restrict__ out_ptr0,
2023-01-11T21:41:26.6384202Z                        half* __restrict__ out_ptr1)
2023-01-11T21:41:26.6384269Z {
2023-01-11T21:41:26.6384401Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6384484Z     {
2023-01-11T21:41:26.6384590Z         #pragma omp for 
2023-01-11T21:41:26.6384702Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.6384789Z         {
2023-01-11T21:41:26.6384876Z             {
2023-01-11T21:41:26.6384947Z                 {
2023-01-11T21:41:26.6385100Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:41:26.6385244Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:26.6385382Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:41:26.6385505Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6385627Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.6385742Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6385899Z                 }
2023-01-11T21:41:26.6385984Z             }
2023-01-11T21:41:26.6386068Z         }
2023-01-11T21:41:26.6386153Z     }
2023-01-11T21:41:26.6386233Z }
2023-01-11T21:41:26.6386345Z ''')
2023-01-11T21:41:26.6386352Z 
2023-01-11T21:41:26.6386357Z 
2023-01-11T21:41:26.6386475Z async_compile.wait(globals())
2023-01-11T21:41:26.6386558Z del async_compile
2023-01-11T21:41:26.6386565Z 
2023-01-11T21:41:26.6386658Z def call(args):
2023-01-11T21:41:26.6386750Z     arg0_1, = args
2023-01-11T21:41:26.6386846Z     args.clear()
2023-01-11T21:41:26.6387113Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.6387379Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.6387599Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6387710Z     del arg0_1
2023-01-11T21:41:26.6387819Z     return (buf0, buf1, )
2023-01-11T21:41:26.6387825Z 
2023-01-11T21:41:26.6387834Z 
2023-01-11T21:41:26.6387936Z if __name__ == "__main__":
2023-01-11T21:41:26.6388086Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6388252Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6388516Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.6388659Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6388666Z 
2023-01-11T21:41:26.6388671Z 
2023-01-11T21:41:26.6388794Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6388886Z import torch
2023-01-11T21:41:26.6388966Z import random
2023-01-11T21:41:26.6389119Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6389277Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6389283Z 
2023-01-11T21:41:26.6389386Z aten = torch.ops.aten
2023-01-11T21:41:26.6389565Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6389688Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6389697Z 
2023-01-11T21:41:26.6389702Z 
2023-01-11T21:41:26.6389886Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6390136Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6390295Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:41:26.6390423Z                        half* __restrict__ out_ptr0,
2023-01-11T21:41:26.6390549Z                        half* __restrict__ out_ptr1)
2023-01-11T21:41:26.6390630Z {
2023-01-11T21:41:26.6390758Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6390843Z     {
2023-01-11T21:41:26.6390930Z         #pragma omp for 
2023-01-11T21:41:26.6391040Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:26.6391123Z         {
2023-01-11T21:41:26.6391208Z             {
2023-01-11T21:41:26.6391293Z                 {
2023-01-11T21:41:26.6391451Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:41:26.6391590Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:26.6391715Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:41:26.6391838Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6391950Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.6392062Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6392147Z                 }
2023-01-11T21:41:26.6392232Z             }
2023-01-11T21:41:26.6392313Z         }
2023-01-11T21:41:26.6392380Z     }
2023-01-11T21:41:26.6392458Z }
2023-01-11T21:41:26.6392564Z ''')
2023-01-11T21:41:26.6392571Z 
2023-01-11T21:41:26.6392575Z 
2023-01-11T21:41:26.6392695Z async_compile.wait(globals())
2023-01-11T21:41:26.6392794Z del async_compile
2023-01-11T21:41:26.6392800Z 
2023-01-11T21:41:26.6392897Z def call(args):
2023-01-11T21:41:26.6392991Z     arg0_1, = args
2023-01-11T21:41:26.6393074Z     args.clear()
2023-01-11T21:41:26.6393342Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.6393644Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.6393857Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6393949Z     del arg0_1
2023-01-11T21:41:26.6394054Z     return (buf0, buf1, )
2023-01-11T21:41:26.6394061Z 
2023-01-11T21:41:26.6394066Z 
2023-01-11T21:41:26.6394170Z if __name__ == "__main__":
2023-01-11T21:41:26.6394325Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6394473Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6394738Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.6394881Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6394888Z 
2023-01-11T21:41:26.6394893Z 
2023-01-11T21:41:26.6395051Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6395147Z import torch
2023-01-11T21:41:26.6395240Z import random
2023-01-11T21:41:26.6395393Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6395554Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6395561Z 
2023-01-11T21:41:26.6395648Z aten = torch.ops.aten
2023-01-11T21:41:26.6395826Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6395948Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6395955Z 
2023-01-11T21:41:26.6395959Z 
2023-01-11T21:41:26.6396145Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6396406Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6396564Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6396697Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6396824Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6396890Z {
2023-01-11T21:41:26.6397024Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6397108Z     {
2023-01-11T21:41:26.6397211Z         #pragma omp for 
2023-01-11T21:41:26.6397323Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6397408Z         {
2023-01-11T21:41:26.6397589Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.6397688Z             auto tmp1 = tmp0.log1p();
2023-01-11T21:41:26.6397864Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.6397978Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6398100Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.6398220Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.6398305Z         }
2023-01-11T21:41:26.6398430Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.6398526Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.6398608Z         {
2023-01-11T21:41:26.6398718Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6398848Z             auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:26.6398981Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:26.6399096Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6399204Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.6399295Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6399377Z         }
2023-01-11T21:41:26.6399458Z     }
2023-01-11T21:41:26.6399539Z }
2023-01-11T21:41:26.6399648Z ''')
2023-01-11T21:41:26.6399655Z 
2023-01-11T21:41:26.6399659Z 
2023-01-11T21:41:26.6399781Z async_compile.wait(globals())
2023-01-11T21:41:26.6399877Z del async_compile
2023-01-11T21:41:26.6399883Z 
2023-01-11T21:41:26.6399962Z def call(args):
2023-01-11T21:41:26.6400056Z     arg0_1, = args
2023-01-11T21:41:26.6400149Z     args.clear()
2023-01-11T21:41:26.6400413Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6400676Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6400897Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6401028Z     del arg0_1
2023-01-11T21:41:26.6401118Z     return (buf0, buf1, )
2023-01-11T21:41:26.6401142Z 
2023-01-11T21:41:26.6401146Z 
2023-01-11T21:41:26.6401232Z if __name__ == "__main__":
2023-01-11T21:41:26.6401382Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6401545Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6401813Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6401962Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6401969Z 
2023-01-11T21:41:26.6401974Z 
2023-01-11T21:41:26.6402098Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6402191Z import torch
2023-01-11T21:41:26.6402284Z import random
2023-01-11T21:41:26.6402420Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6402608Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6402615Z 
2023-01-11T21:41:26.6402720Z aten = torch.ops.aten
2023-01-11T21:41:26.6402901Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6403021Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6403028Z 
2023-01-11T21:41:26.6403033Z 
2023-01-11T21:41:26.6403216Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6403475Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6403633Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6403750Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6403876Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6403957Z {
2023-01-11T21:41:26.6404086Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6404167Z     {
2023-01-11T21:41:26.6404270Z         #pragma omp for 
2023-01-11T21:41:26.6404379Z         for(long i0=0; i0<25; i0+=1)
2023-01-11T21:41:26.6404451Z         {
2023-01-11T21:41:26.6404626Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.6404745Z             auto tmp1 = tmp0.log1p();
2023-01-11T21:41:26.6404922Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.6405035Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6405155Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.6405276Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.6405346Z         }
2023-01-11T21:41:26.6405469Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.6405579Z         for(long i0=200; i0<201; i0+=1)
2023-01-11T21:41:26.6405661Z         {
2023-01-11T21:41:26.6405774Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6405900Z             auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:26.6406031Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:26.6406128Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6406235Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.6406340Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6406427Z         }
2023-01-11T21:41:26.6406509Z     }
2023-01-11T21:41:26.6406588Z }
2023-01-11T21:41:26.6406680Z ''')
2023-01-11T21:41:26.6406702Z 
2023-01-11T21:41:26.6406707Z 
2023-01-11T21:41:26.6406811Z async_compile.wait(globals())
2023-01-11T21:41:26.6406907Z del async_compile
2023-01-11T21:41:26.6406914Z 
2023-01-11T21:41:26.6407007Z def call(args):
2023-01-11T21:41:26.6407100Z     arg0_1, = args
2023-01-11T21:41:26.6407196Z     args.clear()
2023-01-11T21:41:26.6407465Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6407726Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6407923Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6408016Z     del arg0_1
2023-01-11T21:41:26.6408122Z     return (buf0, buf1, )
2023-01-11T21:41:26.6408132Z 
2023-01-11T21:41:26.6408137Z 
2023-01-11T21:41:26.6408239Z if __name__ == "__main__":
2023-01-11T21:41:26.6408425Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6408589Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6408857Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6408999Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6409126Z 
2023-01-11T21:41:26.6409479Z [2023-01-11 21:35:47,571] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 344
2023-01-11T21:41:26.6410045Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6410275Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6410638Z [2023-01-11 21:35:47,604] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 345
2023-01-11T21:41:26.6411004Z [2023-01-11 21:35:49,150] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 345
2023-01-11T21:41:26.6411568Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6411737Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6412092Z [2023-01-11 21:35:49,175] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 346
2023-01-11T21:41:26.6412454Z [2023-01-11 21:35:50,672] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 346
2023-01-11T21:41:26.6413015Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6413181Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6413530Z [2023-01-11 21:35:50,690] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 347
2023-01-11T21:41:26.6413889Z [2023-01-11 21:35:52,298] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 347
2023-01-11T21:41:26.6414437Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6414605Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6414956Z [2023-01-11 21:35:52,316] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 348
2023-01-11T21:41:26.6414962Z 
2023-01-11T21:41:26.6415086Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6415181Z import torch
2023-01-11T21:41:26.6415276Z import random
2023-01-11T21:41:26.6415433Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6415591Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6415598Z 
2023-01-11T21:41:26.6415699Z aten = torch.ops.aten
2023-01-11T21:41:26.6415861Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6415985Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6415992Z 
2023-01-11T21:41:26.6415997Z 
2023-01-11T21:41:26.6416186Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6416500Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6416658Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.6416792Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:26.6416922Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:26.6416991Z {
2023-01-11T21:41:26.6417121Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6417204Z     {
2023-01-11T21:41:26.6417309Z         #pragma omp for 
2023-01-11T21:41:26.6417422Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.6417507Z         {
2023-01-11T21:41:26.6417593Z             {
2023-01-11T21:41:26.6417666Z                 {
2023-01-11T21:41:26.6417790Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6417981Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:26.6418124Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:41:26.6418250Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6418365Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.6418479Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6418551Z                 }
2023-01-11T21:41:26.6418638Z             }
2023-01-11T21:41:26.6418722Z         }
2023-01-11T21:41:26.6418805Z     }
2023-01-11T21:41:26.6418885Z }
2023-01-11T21:41:26.6418994Z ''')
2023-01-11T21:41:26.6419001Z 
2023-01-11T21:41:26.6419006Z 
2023-01-11T21:41:26.6419127Z async_compile.wait(globals())
2023-01-11T21:41:26.6419209Z del async_compile
2023-01-11T21:41:26.6419215Z 
2023-01-11T21:41:26.6419315Z def call(args):
2023-01-11T21:41:26.6419410Z     arg0_1, = args
2023-01-11T21:41:26.6419504Z     args.clear()
2023-01-11T21:41:26.6419775Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6420044Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6420321Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6420414Z     del arg0_1
2023-01-11T21:41:26.6420550Z     return (buf0, buf1, )
2023-01-11T21:41:26.6420560Z 
2023-01-11T21:41:26.6420568Z 
2023-01-11T21:41:26.6420700Z if __name__ == "__main__":
2023-01-11T21:41:26.6420900Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6421111Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6421436Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6421619Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6421629Z 
2023-01-11T21:41:26.6421636Z 
2023-01-11T21:41:26.6421798Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6421923Z import torch
2023-01-11T21:41:26.6422033Z import random
2023-01-11T21:41:26.6422224Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6422428Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6422445Z 
2023-01-11T21:41:26.6422583Z aten = torch.ops.aten
2023-01-11T21:41:26.6422815Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6422979Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6422986Z 
2023-01-11T21:41:26.6422993Z 
2023-01-11T21:41:26.6423285Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6423632Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6423829Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.6424003Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:26.6424174Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:26.6424293Z {
2023-01-11T21:41:26.6424466Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6424578Z     {
2023-01-11T21:41:26.6424724Z         #pragma omp for 
2023-01-11T21:41:26.6424850Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:26.6425036Z         {
2023-01-11T21:41:26.6425156Z             {
2023-01-11T21:41:26.6425271Z                 {
2023-01-11T21:41:26.6425435Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6425611Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:41:26.6425879Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:41:26.6426065Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6426231Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.6426397Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6426533Z                 }
2023-01-11T21:41:26.6426666Z             }
2023-01-11T21:41:26.6426794Z         }
2023-01-11T21:41:26.6426893Z     }
2023-01-11T21:41:26.6427015Z }
2023-01-11T21:41:26.6427183Z ''')
2023-01-11T21:41:26.6427198Z 
2023-01-11T21:41:26.6427203Z 
2023-01-11T21:41:26.6427447Z async_compile.wait(globals())
2023-01-11T21:41:26.6427596Z del async_compile
2023-01-11T21:41:26.6427609Z 
2023-01-11T21:41:26.6427756Z def call(args):
2023-01-11T21:41:26.6427909Z     arg0_1, = args
2023-01-11T21:41:26.6428030Z     args.clear()
2023-01-11T21:41:26.6428434Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6428824Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6429127Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6429271Z     del arg0_1
2023-01-11T21:41:26.6429432Z     return (buf0, buf1, )
2023-01-11T21:41:26.6429444Z 
2023-01-11T21:41:26.6429450Z 
2023-01-11T21:41:26.6429604Z if __name__ == "__main__":
2023-01-11T21:41:26.6429832Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6430052Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6430430Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6430641Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6430657Z 
2023-01-11T21:41:26.6430666Z 
2023-01-11T21:41:26.6430858Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6430998Z import torch
2023-01-11T21:41:26.6431140Z import random
2023-01-11T21:41:26.6431361Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6431600Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6431610Z 
2023-01-11T21:41:26.6431748Z aten = torch.ops.aten
2023-01-11T21:41:26.6431995Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6432177Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6432189Z 
2023-01-11T21:41:26.6432198Z 
2023-01-11T21:41:26.6432537Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6432875Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6433076Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.6433258Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6433431Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6433523Z {
2023-01-11T21:41:26.6433699Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6433815Z     {
2023-01-11T21:41:26.6433954Z         #pragma omp for 
2023-01-11T21:41:26.6434099Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.6434210Z         {
2023-01-11T21:41:26.6434323Z             {
2023-01-11T21:41:26.6434422Z                 {
2023-01-11T21:41:26.6434584Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6434775Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.6434954Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:41:26.6435130Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.6435290Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.6435439Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6435567Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.6435693Z                 }
2023-01-11T21:41:26.6435903Z             }
2023-01-11T21:41:26.6436015Z         }
2023-01-11T21:41:26.6436126Z     }
2023-01-11T21:41:26.6436236Z }
2023-01-11T21:41:26.6436373Z ''')
2023-01-11T21:41:26.6436406Z 
2023-01-11T21:41:26.6436411Z 
2023-01-11T21:41:26.6436550Z async_compile.wait(globals())
2023-01-11T21:41:26.6436674Z del async_compile
2023-01-11T21:41:26.6436682Z 
2023-01-11T21:41:26.6436805Z def call(args):
2023-01-11T21:41:26.6436935Z     arg0_1, = args
2023-01-11T21:41:26.6437065Z     args.clear()
2023-01-11T21:41:26.6437545Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6437914Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6438204Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6438417Z     del arg0_1
2023-01-11T21:41:26.6438579Z     return (buf0, buf1, )
2023-01-11T21:41:26.6438588Z 
2023-01-11T21:41:26.6438594Z 
2023-01-11T21:41:26.6438748Z if __name__ == "__main__":
2023-01-11T21:41:26.6438978Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6439221Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6439607Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.6439806Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6439820Z 
2023-01-11T21:41:26.6439827Z 
2023-01-11T21:41:26.6440015Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6440140Z import torch
2023-01-11T21:41:26.6440327Z import random
2023-01-11T21:41:26.6440559Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6440798Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6440807Z 
2023-01-11T21:41:26.6440959Z aten = torch.ops.aten
2023-01-11T21:41:26.6441230Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6441414Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6441431Z 
2023-01-11T21:41:26.6441439Z 
2023-01-11T21:41:26.6441684Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6442067Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6442299Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.6442498Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6442698Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6442826Z {
2023-01-11T21:41:26.6443013Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6443118Z     {
2023-01-11T21:41:26.6443284Z         #pragma omp for 
2023-01-11T21:41:26.6443460Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:26.6443590Z         {
2023-01-11T21:41:26.6443720Z             {
2023-01-11T21:41:26.6443938Z                 {
2023-01-11T21:41:26.6444102Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6444272Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.6444470Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:41:26.6444653Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.6444811Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.6444954Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6445107Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.6445226Z                 }
2023-01-11T21:41:26.6445321Z             }
2023-01-11T21:41:26.6445439Z         }
2023-01-11T21:41:26.6445548Z     }
2023-01-11T21:41:26.6445660Z }
2023-01-11T21:41:26.6445922Z ''')
2023-01-11T21:41:26.6445932Z 
2023-01-11T21:41:26.6445939Z 
2023-01-11T21:41:26.6446116Z async_compile.wait(globals())
2023-01-11T21:41:26.6446274Z del async_compile
2023-01-11T21:41:26.6446285Z 
2023-01-11T21:41:26.6446408Z def call(args):
2023-01-11T21:41:26.6446555Z     arg0_1, = args
2023-01-11T21:41:26.6446703Z     args.clear()
2023-01-11T21:41:26.6447092Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6447580Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6447899Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6448036Z     del arg0_1
2023-01-11T21:41:26.6448190Z     return (buf0, buf1, )
2023-01-11T21:41:26.6448204Z 
2023-01-11T21:41:26.6448211Z 
2023-01-11T21:41:26.6448343Z if __name__ == "__main__":
2023-01-11T21:41:26.6448578Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6448812Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6449320Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.6449550Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6449560Z 
2023-01-11T21:41:26.6450208Z [2023-01-11 21:35:53,985] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 348
2023-01-11T21:41:26.6451053Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6451308Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6451845Z [2023-01-11 21:35:54,002] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 349
2023-01-11T21:41:26.6452470Z [2023-01-11 21:35:55,491] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 349
2023-01-11T21:41:26.6452486Z 
2023-01-11T21:41:26.6452633Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6452756Z import torch
2023-01-11T21:41:26.6452896Z import random
2023-01-11T21:41:26.6453095Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6453299Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6453316Z 
2023-01-11T21:41:26.6453456Z aten = torch.ops.aten
2023-01-11T21:41:26.6453681Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6453823Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6453832Z 
2023-01-11T21:41:26.6453859Z 
2023-01-11T21:41:26.6454077Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6454403Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6454611Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.6454790Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6454967Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6455067Z {
2023-01-11T21:41:26.6455241Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6455337Z     {
2023-01-11T21:41:26.6455469Z         #pragma omp for 
2023-01-11T21:41:26.6455614Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.6455740Z         {
2023-01-11T21:41:26.6455862Z             {
2023-01-11T21:41:26.6455970Z                 {
2023-01-11T21:41:26.6456114Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6456300Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.6456486Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:41:26.6456664Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.6456816Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.6456967Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6457115Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.6457234Z                 }
2023-01-11T21:41:26.6457330Z             }
2023-01-11T21:41:26.6457551Z         }
2023-01-11T21:41:26.6457675Z     }
2023-01-11T21:41:26.6457796Z }
2023-01-11T21:41:26.6457976Z ''')
2023-01-11T21:41:26.6457986Z 
2023-01-11T21:41:26.6457993Z 
2023-01-11T21:41:26.6458182Z async_compile.wait(globals())
2023-01-11T21:41:26.6458452Z del async_compile
2023-01-11T21:41:26.6458464Z 
2023-01-11T21:41:26.6458592Z def call(args):
2023-01-11T21:41:26.6458745Z     arg0_1, = args
2023-01-11T21:41:26.6458879Z     args.clear()
2023-01-11T21:41:26.6459285Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6459675Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6459988Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6460132Z     del arg0_1
2023-01-11T21:41:26.6460275Z     return (buf0, buf1, )
2023-01-11T21:41:26.6460285Z 
2023-01-11T21:41:26.6460318Z 
2023-01-11T21:41:26.6460454Z if __name__ == "__main__":
2023-01-11T21:41:26.6460685Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6461000Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6461402Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6461632Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6461643Z 
2023-01-11T21:41:26.6461651Z 
2023-01-11T21:41:26.6461845Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6461983Z import torch
2023-01-11T21:41:26.6462106Z import random
2023-01-11T21:41:26.6462336Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6462576Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6462585Z 
2023-01-11T21:41:26.6462746Z aten = torch.ops.aten
2023-01-11T21:41:26.6463002Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6463277Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6463288Z 
2023-01-11T21:41:26.6463296Z 
2023-01-11T21:41:26.6463589Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6464080Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6464284Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.6464467Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6464639Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6464748Z {
2023-01-11T21:41:26.6464922Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6465014Z     {
2023-01-11T21:41:26.6465150Z         #pragma omp for 
2023-01-11T21:41:26.6465280Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:41:26.6465396Z         {
2023-01-11T21:41:26.6465514Z             {
2023-01-11T21:41:26.6465628Z                 {
2023-01-11T21:41:26.6465788Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6465956Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.6466127Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:41:26.6466288Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.6466459Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.6466608Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6466761Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.6466881Z                 }
2023-01-11T21:41:26.6466981Z             }
2023-01-11T21:41:26.6467090Z         }
2023-01-11T21:41:26.6467178Z     }
2023-01-11T21:41:26.6467281Z }
2023-01-11T21:41:26.6467433Z ''')
2023-01-11T21:41:26.6467443Z 
2023-01-11T21:41:26.6467448Z 
2023-01-11T21:41:26.6467606Z async_compile.wait(globals())
2023-01-11T21:41:26.6467743Z del async_compile
2023-01-11T21:41:26.6467753Z 
2023-01-11T21:41:26.6467873Z def call(args):
2023-01-11T21:41:26.6467991Z     arg0_1, = args
2023-01-11T21:41:26.6468101Z     args.clear()
2023-01-11T21:41:26.6468452Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6468778Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6469055Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6469185Z     del arg0_1
2023-01-11T21:41:26.6469414Z     return (buf0, buf1, )
2023-01-11T21:41:26.6469426Z 
2023-01-11T21:41:26.6469432Z 
2023-01-11T21:41:26.6469569Z if __name__ == "__main__":
2023-01-11T21:41:26.6469763Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6469954Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6470259Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6470432Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6470442Z 
2023-01-11T21:41:26.6470557Z ok (16.164s)
2023-01-11T21:41:26.6471365Z   test_log2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6471590Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6472011Z [2023-01-11 21:35:55,514] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 350
2023-01-11T21:41:26.6472467Z [2023-01-11 21:35:57,009] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 350
2023-01-11T21:41:26.6472480Z 
2023-01-11T21:41:26.6472647Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6472750Z import torch
2023-01-11T21:41:26.6472866Z import random
2023-01-11T21:41:26.6473059Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6473361Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6473373Z 
2023-01-11T21:41:26.6473534Z aten = torch.ops.aten
2023-01-11T21:41:26.6473799Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6473969Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6473984Z 
2023-01-11T21:41:26.6473991Z 
2023-01-11T21:41:26.6474271Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6474639Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6474865Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6475050Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6475249Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6475376Z {
2023-01-11T21:41:26.6475574Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6475704Z     {
2023-01-11T21:41:26.6475816Z         #pragma omp for 
2023-01-11T21:41:26.6475944Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6476075Z         {
2023-01-11T21:41:26.6476336Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.6476510Z             auto tmp1 = tmp0.log();
2023-01-11T21:41:26.6476802Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1.4426950408889634));
2023-01-11T21:41:26.6476979Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6477228Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.6477382Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.6477555Z             auto tmp6 = tmp5.log();
2023-01-11T21:41:26.6477731Z             auto tmp7 = tmp6 * tmp2;
2023-01-11T21:41:26.6478001Z             auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.6478262Z             auto tmp9 = tmp7 - tmp8;
2023-01-11T21:41:26.6478409Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.6478577Z             tmp9.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.6478685Z         }
2023-01-11T21:41:26.6478879Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.6479048Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.6479184Z         {
2023-01-11T21:41:26.6479356Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6479531Z             auto tmp1 = std::log(tmp0);
2023-01-11T21:41:26.6479945Z             auto tmp2 = static_cast<float>(1.4426950408889634);
2023-01-11T21:41:26.6480076Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6480251Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.6480406Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.6480557Z             auto tmp6 = std::log(tmp5);
2023-01-11T21:41:26.6480707Z             auto tmp7 = tmp6 * tmp2;
2023-01-11T21:41:26.6480859Z             auto tmp8 = static_cast<float>(2);
2023-01-11T21:41:26.6481095Z             auto tmp9 = tmp7 - tmp8;
2023-01-11T21:41:26.6481213Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.6481356Z             out_ptr1[i0] = tmp9;
2023-01-11T21:41:26.6481468Z         }
2023-01-11T21:41:26.6481581Z     }
2023-01-11T21:41:26.6481693Z }
2023-01-11T21:41:26.6481838Z ''')
2023-01-11T21:41:26.6481848Z 
2023-01-11T21:41:26.6481854Z 
2023-01-11T21:41:26.6482078Z async_compile.wait(globals())
2023-01-11T21:41:26.6482182Z del async_compile
2023-01-11T21:41:26.6482216Z 
2023-01-11T21:41:26.6482331Z def call(args):
2023-01-11T21:41:26.6482443Z     arg0_1, = args
2023-01-11T21:41:26.6482572Z     args.clear()
2023-01-11T21:41:26.6482913Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6483252Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6483533Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6483658Z     del arg0_1
2023-01-11T21:41:26.6483777Z     return (buf0, buf1, )
2023-01-11T21:41:26.6483788Z 
2023-01-11T21:41:26.6483795Z 
2023-01-11T21:41:26.6483930Z if __name__ == "__main__":
2023-01-11T21:41:26.6484131Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6484350Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6484691Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6484883Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6484900Z 
2023-01-11T21:41:26.6485017Z ok (1.519s)
2023-01-11T21:41:26.6485769Z   test_log_fp64_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6485987Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6486413Z [2023-01-11 21:35:57,027] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 351
2023-01-11T21:41:26.6486882Z [2023-01-11 21:35:58,543] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 351
2023-01-11T21:41:26.6486891Z 
2023-01-11T21:41:26.6487050Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6487177Z import torch
2023-01-11T21:41:26.6487311Z import random
2023-01-11T21:41:26.6487515Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6487730Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6487740Z 
2023-01-11T21:41:26.6487867Z aten = torch.ops.aten
2023-01-11T21:41:26.6488079Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6488246Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6488254Z 
2023-01-11T21:41:26.6488261Z 
2023-01-11T21:41:26.6488505Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6488837Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6489172Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.6489358Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:26.6489539Z                        double* __restrict__ out_ptr1)
2023-01-11T21:41:26.6489650Z {
2023-01-11T21:41:26.6489802Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6490031Z     {
2023-01-11T21:41:26.6490170Z         #pragma omp for 
2023-01-11T21:41:26.6490327Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.6490446Z         {
2023-01-11T21:41:26.6490563Z             {
2023-01-11T21:41:26.6490659Z                 {
2023-01-11T21:41:26.6490820Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6490999Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:41:26.6491197Z                     auto tmp2 = static_cast<double>(1.4426950408889634);
2023-01-11T21:41:26.6491359Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.6491615Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.6491787Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6491897Z                 }
2023-01-11T21:41:26.6492026Z             }
2023-01-11T21:41:26.6492253Z         }
2023-01-11T21:41:26.6492389Z     }
2023-01-11T21:41:26.6492515Z }
2023-01-11T21:41:26.6492694Z ''')
2023-01-11T21:41:26.6492712Z 
2023-01-11T21:41:26.6492720Z 
2023-01-11T21:41:26.6492899Z async_compile.wait(globals())
2023-01-11T21:41:26.6493024Z del async_compile
2023-01-11T21:41:26.6493058Z 
2023-01-11T21:41:26.6493178Z def call(args):
2023-01-11T21:41:26.6493313Z     arg0_1, = args
2023-01-11T21:41:26.6493455Z     args.clear()
2023-01-11T21:41:26.6493856Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6494245Z     buf1 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6494565Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6494710Z     del arg0_1
2023-01-11T21:41:26.6494846Z     return (buf0, buf1, )
2023-01-11T21:41:26.6494861Z 
2023-01-11T21:41:26.6494868Z 
2023-01-11T21:41:26.6495020Z if __name__ == "__main__":
2023-01-11T21:41:26.6495249Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6495487Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6495888Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.6496107Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6496121Z 
2023-01-11T21:41:26.6496255Z ok (1.534s)
2023-01-11T21:41:26.6497166Z   test_log_softmax_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6497419Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6497907Z [2023-01-11 21:35:58,594] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 352
2023-01-11T21:41:26.6497943Z 
2023-01-11T21:41:26.6498197Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6498336Z import torch
2023-01-11T21:41:26.6498435Z import random
2023-01-11T21:41:26.6498620Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6498829Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6498839Z 
2023-01-11T21:41:26.6498975Z aten = torch.ops.aten
2023-01-11T21:41:26.6499190Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6499331Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6499343Z 
2023-01-11T21:41:26.6499349Z 
2023-01-11T21:41:26.6499589Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6499926Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6500112Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6500297Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6500467Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6500639Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.6500895Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.6501028Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.6501200Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.6501368Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:26.6501545Z                        float* __restrict__ out_ptr6,
2023-01-11T21:41:26.6501710Z                        float* __restrict__ out_ptr7,
2023-01-11T21:41:26.6501875Z                        float* __restrict__ out_ptr8)
2023-01-11T21:41:26.6501964Z {
2023-01-11T21:41:26.6502117Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6502229Z     {
2023-01-11T21:41:26.6502367Z         #pragma omp for 
2023-01-11T21:41:26.6502520Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6502702Z         {
2023-01-11T21:41:26.6502821Z             {
2023-01-11T21:41:26.6503551Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:26.6503928Z                 float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6504091Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.6504246Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.6504367Z                 {
2023-01-11T21:41:26.6504613Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6504867Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6505028Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.6505235Z                     tmp3_vec = at::vec::maximum(tmp3_vec, tmp2);
2023-01-11T21:41:26.6505360Z                 }
2023-01-11T21:41:26.6505700Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp3_vec);
2023-01-11T21:41:26.6505909Z                 #pragma omp simd simdlen(4)  reduction(max:tmp3)
2023-01-11T21:41:26.6506050Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.6506167Z                 {
2023-01-11T21:41:26.6506342Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.6506502Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.6506656Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.6506815Z                     tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:41:26.6506928Z                 }
2023-01-11T21:41:26.6507056Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.6507175Z             }
2023-01-11T21:41:26.6507289Z         }
2023-01-11T21:41:26.6507428Z         #pragma omp for 
2023-01-11T21:41:26.6507579Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6507673Z         {
2023-01-11T21:41:26.6507791Z             {
2023-01-11T21:41:26.6508242Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.6508409Z                 float tmp6 = 0;
2023-01-11T21:41:26.6508657Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.6509416Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:26.6509858Z                 float tmp7 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6510093Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.6510265Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.6510379Z                 {
2023-01-11T21:41:26.6510658Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6510934Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6511310Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.6511501Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.6511786Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:26.6511970Z                     auto tmp5 = tmp4.exp();
2023-01-11T21:41:26.6512139Z                     tmp6_vec += tmp5;
2023-01-11T21:41:26.6512325Z                     tmp7_vec = at::vec::maximum(tmp7_vec, tmp1);
2023-01-11T21:41:26.6512456Z                 }
2023-01-11T21:41:26.6512824Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.6513290Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp7_vec);
2023-01-11T21:41:26.6513577Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(max:tmp7)
2023-01-11T21:41:26.6513744Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.6513882Z                 {
2023-01-11T21:41:26.6514077Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.6514248Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.6514509Z                     auto tmp3 = out_ptr0[i0];
2023-01-11T21:41:26.6514673Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.6514923Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:26.6515095Z                     auto tmp5 = std::exp(tmp4);
2023-01-11T21:41:26.6515213Z                     tmp6 += tmp5;
2023-01-11T21:41:26.6515387Z                     tmp7 = std::max(tmp7, tmp1);
2023-01-11T21:41:26.6515492Z                 }
2023-01-11T21:41:26.6515618Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.6515761Z                 out_ptr2[i0] = tmp7;
2023-01-11T21:41:26.6515878Z             }
2023-01-11T21:41:26.6516003Z         }
2023-01-11T21:41:26.6516147Z         #pragma omp for 
2023-01-11T21:41:26.6516288Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6516382Z         {
2023-01-11T21:41:26.6516472Z             {
2023-01-11T21:41:26.6516585Z                 {
2023-01-11T21:41:26.6516976Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6517144Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.6517275Z                     {
2023-01-11T21:41:26.6517395Z                         {
2023-01-11T21:41:26.6517546Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:26.6517740Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:26.6517868Z                         }
2023-01-11T21:41:26.6517987Z                     }
2023-01-11T21:41:26.6518147Z                     out_ptr3[i0] = tmp1;
2023-01-11T21:41:26.6518265Z                 }
2023-01-11T21:41:26.6518374Z             }
2023-01-11T21:41:26.6518458Z         }
2023-01-11T21:41:26.6518603Z         #pragma omp for 
2023-01-11T21:41:26.6518739Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6518852Z         {
2023-01-11T21:41:26.6518973Z             {
2023-01-11T21:41:26.6519088Z                 {
2023-01-11T21:41:26.6519234Z                     float tmp4 = 0;
2023-01-11T21:41:26.6519354Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.6519479Z                     {
2023-01-11T21:41:26.6519590Z                         {
2023-01-11T21:41:26.6519774Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:26.6519948Z                             auto tmp1 = out_ptr3[i0];
2023-01-11T21:41:26.6520215Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.6520383Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:26.6520514Z                             tmp4 += tmp3;
2023-01-11T21:41:26.6520639Z                         }
2023-01-11T21:41:26.6520765Z                     }
2023-01-11T21:41:26.6520973Z                     out_ptr4[i0] = tmp4;
2023-01-11T21:41:26.6521098Z                 }
2023-01-11T21:41:26.6521215Z             }
2023-01-11T21:41:26.6521332Z         }
2023-01-11T21:41:26.6521432Z         #pragma omp for 
2023-01-11T21:41:26.6521575Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6521690Z         {
2023-01-11T21:41:26.6521812Z             {
2023-01-11T21:41:26.6522131Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.6522274Z                 float tmp4 = 0;
2023-01-11T21:41:26.6522477Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:26.6522606Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.6522719Z                 {
2023-01-11T21:41:26.6523017Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6523231Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:26.6523494Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.6523658Z                     auto tmp3 = tmp2.exp();
2023-01-11T21:41:26.6523810Z                     tmp4_vec += tmp3;
2023-01-11T21:41:26.6523908Z                 }
2023-01-11T21:41:26.6524238Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:41:26.6524444Z                 #pragma omp simd simdlen(4)  reduction(+:tmp4)
2023-01-11T21:41:26.6524607Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.6524722Z                 {
2023-01-11T21:41:26.6524898Z                     auto tmp0 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.6525057Z                     auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:26.6525306Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.6525464Z                     auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:26.6525597Z                     tmp4 += tmp3;
2023-01-11T21:41:26.6525723Z                 }
2023-01-11T21:41:26.6525866Z                 out_ptr5[i0] = tmp4;
2023-01-11T21:41:26.6525970Z             }
2023-01-11T21:41:26.6526084Z         }
2023-01-11T21:41:26.6526227Z         #pragma omp for 
2023-01-11T21:41:26.6526355Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6526471Z         {
2023-01-11T21:41:26.6526623Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.6526742Z             {
2023-01-11T21:41:26.6526979Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6527206Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6527422Z                 auto tmp3 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.6527651Z                 auto tmp5 = at::vec::Vectorized<float>(out_ptr1[i0]);
2023-01-11T21:41:26.6527862Z                 auto tmp8 = at::vec::Vectorized<float>::loadu(out_ptr3 + 8*i1);
2023-01-11T21:41:26.6528105Z                 auto tmp10 = at::vec::Vectorized<float>::loadu(out_ptr4 + 8*i1);
2023-01-11T21:41:26.6528322Z                 auto tmp13 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:26.6528536Z                 auto tmp15 = at::vec::Vectorized<float>(out_ptr5[i0]);
2023-01-11T21:41:26.6528693Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.6528933Z                 auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:26.6529217Z                 auto tmp6 = tmp5.log();
2023-01-11T21:41:26.6529450Z                 auto tmp7 = tmp4 - tmp6;
2023-01-11T21:41:26.6529656Z                 auto tmp9 = tmp0 - tmp8;
2023-01-11T21:41:26.6529812Z                 auto tmp11 = tmp10.log();
2023-01-11T21:41:26.6530049Z                 auto tmp12 = tmp9 - tmp11;
2023-01-11T21:41:26.6530281Z                 auto tmp14 = tmp1 - tmp13;
2023-01-11T21:41:26.6530451Z                 auto tmp16 = tmp15.log();
2023-01-11T21:41:26.6530688Z                 auto tmp17 = tmp14 - tmp16;
2023-01-11T21:41:26.6530980Z                 tmp7.store(out_ptr6 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6531142Z                 tmp12.store(out_ptr7 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6531317Z                 tmp17.store(out_ptr8 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6531438Z             }
2023-01-11T21:41:26.6531598Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.6531749Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.6531865Z             {
2023-01-11T21:41:26.6532033Z                 auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.6532173Z                 auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.6532310Z                 auto tmp3 = out_ptr0[i0];
2023-01-11T21:41:26.6532463Z                 auto tmp5 = out_ptr1[i0];
2023-01-11T21:41:26.6532604Z                 auto tmp8 = out_ptr3[i1];
2023-01-11T21:41:26.6532842Z                 auto tmp10 = out_ptr4[i1];
2023-01-11T21:41:26.6533012Z                 auto tmp13 = out_ptr2[i0];
2023-01-11T21:41:26.6533162Z                 auto tmp15 = out_ptr5[i0];
2023-01-11T21:41:26.6533263Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.6533501Z                 auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:26.6533668Z                 auto tmp6 = std::log(tmp5);
2023-01-11T21:41:26.6533899Z                 auto tmp7 = tmp4 - tmp6;
2023-01-11T21:41:26.6534132Z                 auto tmp9 = tmp0 - tmp8;
2023-01-11T21:41:26.6534310Z                 auto tmp11 = std::log(tmp10);
2023-01-11T21:41:26.6534533Z                 auto tmp12 = tmp9 - tmp11;
2023-01-11T21:41:26.6534748Z                 auto tmp14 = tmp1 - tmp13;
2023-01-11T21:41:26.6534922Z                 auto tmp16 = std::log(tmp15);
2023-01-11T21:41:26.6535170Z                 auto tmp17 = tmp14 - tmp16;
2023-01-11T21:41:26.6535329Z                 out_ptr6[i1 + (8*i0)] = tmp7;
2023-01-11T21:41:26.6535465Z                 out_ptr7[i1 + (8*i0)] = tmp12;
2023-01-11T21:41:26.6535610Z                 out_ptr8[i1 + (8*i0)] = tmp17;
2023-01-11T21:41:26.6535731Z             }
2023-01-11T21:41:26.6535835Z         }
2023-01-11T21:41:26.6535949Z     }
2023-01-11T21:41:26.6536060Z }
2023-01-11T21:41:26.6536204Z ''')
2023-01-11T21:41:26.6536219Z 
2023-01-11T21:41:26.6536226Z 
2023-01-11T21:41:26.6536352Z async_compile.wait(globals())
2023-01-11T21:41:26.6536476Z del async_compile
2023-01-11T21:41:26.6536484Z 
2023-01-11T21:41:26.6536594Z def call(args):
2023-01-11T21:41:26.6536730Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6536838Z     args.clear()
2023-01-11T21:41:26.6537180Z     buf0 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6537508Z     buf1 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6537842Z     buf6 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6538179Z     buf3 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6538490Z     buf4 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6538817Z     buf7 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6539120Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6539443Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6539776Z     buf8 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6540332Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:41:26.6540458Z     del arg0_1
2023-01-11T21:41:26.6540688Z     del arg1_1
2023-01-11T21:41:26.6540865Z     return (buf2, buf5, buf8, )
2023-01-11T21:41:26.6540878Z 
2023-01-11T21:41:26.6540884Z 
2023-01-11T21:41:26.6541037Z if __name__ == "__main__":
2023-01-11T21:41:26.6541368Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6541581Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6541949Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6542350Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6542565Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6543094Z [2023-01-11 21:36:00,328] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 352
2023-01-11T21:41:26.6543105Z 
2023-01-11T21:41:26.6543318Z ok (1.785s)
2023-01-11T21:41:26.6544288Z   test_logsumexp_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6544553Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6545075Z [2023-01-11 21:36:00,379] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 353
2023-01-11T21:41:26.6545580Z [2023-01-11 21:36:02,009] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 353
2023-01-11T21:41:26.6545616Z 
2023-01-11T21:41:26.6545775Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6545924Z import torch
2023-01-11T21:41:26.6546067Z import random
2023-01-11T21:41:26.6546299Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6546538Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6546548Z 
2023-01-11T21:41:26.6546701Z aten = torch.ops.aten
2023-01-11T21:41:26.6546963Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6547214Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6547233Z 
2023-01-11T21:41:26.6547266Z 
2023-01-11T21:41:26.6547486Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6547812Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6548008Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.6548182Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.6548365Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6548541Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.6548707Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:26.6548778Z {
2023-01-11T21:41:26.6548916Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.6549068Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:41:26.6549254Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6549365Z     {
2023-01-11T21:41:26.6549506Z         #pragma omp for 
2023-01-11T21:41:26.6549660Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6549752Z         {
2023-01-11T21:41:26.6549861Z             {
2023-01-11T21:41:26.6550498Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:26.6550842Z                 float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6551158Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.6551341Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.6551475Z                 {
2023-01-11T21:41:26.6551750Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6551955Z                     tmp1_vec = at::vec::maximum(tmp1_vec, tmp0);
2023-01-11T21:41:26.6552098Z                 }
2023-01-11T21:41:26.6552508Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp1_vec);
2023-01-11T21:41:26.6552833Z                 #pragma omp simd simdlen(4)  reduction(max:tmp1)
2023-01-11T21:41:26.6553010Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.6553153Z                 {
2023-01-11T21:41:26.6553343Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.6553548Z                     tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:26.6553656Z                 }
2023-01-11T21:41:26.6553829Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.6553954Z             }
2023-01-11T21:41:26.6554083Z         }
2023-01-11T21:41:26.6554250Z         #pragma omp for 
2023-01-11T21:41:26.6554418Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6554550Z         {
2023-01-11T21:41:26.6554659Z             {
2023-01-11T21:41:26.6555094Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.6555267Z                 float tmp4 = 0;
2023-01-11T21:41:26.6555509Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:26.6555686Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.6555823Z                 {
2023-01-11T21:41:26.6556097Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.6556351Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.6556630Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.6556819Z                     auto tmp3 = tmp2.exp();
2023-01-11T21:41:26.6556987Z                     tmp4_vec += tmp3;
2023-01-11T21:41:26.6557126Z                 }
2023-01-11T21:41:26.6557505Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:41:26.6557856Z                 #pragma omp simd simdlen(4)  reduction(+:tmp4)
2023-01-11T21:41:26.6558028Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.6558119Z                 {
2023-01-11T21:41:26.6558287Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.6558449Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:26.6558706Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.6558879Z                     auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:26.6559016Z                     tmp4 += tmp3;
2023-01-11T21:41:26.6559133Z                 }
2023-01-11T21:41:26.6559262Z                 out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.6559373Z             }
2023-01-11T21:41:26.6559491Z         }
2023-01-11T21:41:26.6559637Z         #pragma omp for 
2023-01-11T21:41:26.6559779Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6559893Z         {
2023-01-11T21:41:26.6560011Z             {
2023-01-11T21:41:26.6560112Z                 {
2023-01-11T21:41:26.6560271Z                     auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.6560435Z                     auto tmp2 = out_ptr0[i0];
2023-01-11T21:41:26.6560610Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:41:26.6560778Z                     auto tmp3 = std::abs(tmp2);
2023-01-11T21:41:26.6561004Z                     auto tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6561166Z                     auto tmp5 = tmp3 == tmp4;
2023-01-11T21:41:26.6561330Z                     auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:26.6561473Z                     auto tmp7 = tmp5 ? tmp6 : tmp2;
2023-01-11T21:41:26.6561627Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:41:26.6561775Z                     in_out_ptr0[i0] = tmp8;
2023-01-11T21:41:26.6561891Z                 }
2023-01-11T21:41:26.6562015Z             }
2023-01-11T21:41:26.6562130Z         }
2023-01-11T21:41:26.6562253Z         #pragma omp for 
2023-01-11T21:41:26.6562407Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6562518Z         {
2023-01-11T21:41:26.6562637Z             {
2023-01-11T21:41:26.6562840Z                 {
2023-01-11T21:41:26.6563241Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6563398Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.6563501Z                     {
2023-01-11T21:41:26.6563626Z                         {
2023-01-11T21:41:26.6563791Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:26.6563974Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:26.6564097Z                         }
2023-01-11T21:41:26.6564218Z                     }
2023-01-11T21:41:26.6564369Z                     out_ptr2[i0] = tmp1;
2023-01-11T21:41:26.6564463Z                 }
2023-01-11T21:41:26.6564579Z             }
2023-01-11T21:41:26.6564685Z         }
2023-01-11T21:41:26.6564832Z         #pragma omp for 
2023-01-11T21:41:26.6565061Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6565184Z         {
2023-01-11T21:41:26.6565297Z             {
2023-01-11T21:41:26.6565399Z                 {
2023-01-11T21:41:26.6565534Z                     float tmp4 = 0;
2023-01-11T21:41:26.6565683Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.6565805Z                     {
2023-01-11T21:41:26.6565931Z                         {
2023-01-11T21:41:26.6566111Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:26.6566286Z                             auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:26.6566518Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.6566702Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:26.6566851Z                             tmp4 += tmp3;
2023-01-11T21:41:26.6566972Z                         }
2023-01-11T21:41:26.6567069Z                     }
2023-01-11T21:41:26.6567212Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:41:26.6567338Z                 }
2023-01-11T21:41:26.6567432Z             }
2023-01-11T21:41:26.6567532Z         }
2023-01-11T21:41:26.6567669Z         #pragma omp for 
2023-01-11T21:41:26.6567810Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6567929Z         {
2023-01-11T21:41:26.6568034Z             {
2023-01-11T21:41:26.6568131Z                 {
2023-01-11T21:41:26.6568294Z                     auto tmp0 = out_ptr3[i0];
2023-01-11T21:41:26.6568456Z                     auto tmp2 = out_ptr2[i0];
2023-01-11T21:41:26.6568610Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:41:26.6568785Z                     auto tmp3 = std::abs(tmp2);
2023-01-11T21:41:26.6569122Z                     auto tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6569292Z                     auto tmp5 = tmp3 == tmp4;
2023-01-11T21:41:26.6569473Z                     auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:26.6569611Z                     auto tmp7 = tmp5 ? tmp6 : tmp2;
2023-01-11T21:41:26.6569777Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:41:26.6569968Z                     auto tmp9 = static_cast<float>(2);
2023-01-11T21:41:26.6570244Z                     auto tmp10 = tmp8 - tmp9;
2023-01-11T21:41:26.6570389Z                     in_out_ptr1[i0] = tmp10;
2023-01-11T21:41:26.6570515Z                 }
2023-01-11T21:41:26.6570620Z             }
2023-01-11T21:41:26.6570720Z         }
2023-01-11T21:41:26.6570828Z     }
2023-01-11T21:41:26.6570941Z }
2023-01-11T21:41:26.6571086Z ''')
2023-01-11T21:41:26.6571099Z 
2023-01-11T21:41:26.6571106Z 
2023-01-11T21:41:26.6571263Z async_compile.wait(globals())
2023-01-11T21:41:26.6571398Z del async_compile
2023-01-11T21:41:26.6571412Z 
2023-01-11T21:41:26.6571530Z def call(args):
2023-01-11T21:41:26.6571618Z     arg0_1, = args
2023-01-11T21:41:26.6571741Z     args.clear()
2023-01-11T21:41:26.6572093Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6572432Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6572616Z     buf2 = as_strided(buf0, (8, ), (1, )); del buf0  # reuse
2023-01-11T21:41:26.6573064Z     buf3 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6573514Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6573700Z     buf5 = as_strided(buf3, (8, ), (1, )); del buf3  # reuse
2023-01-11T21:41:26.6574120Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.6574263Z     del arg0_1
2023-01-11T21:41:26.6574422Z     return (buf2, buf5, )
2023-01-11T21:41:26.6574431Z 
2023-01-11T21:41:26.6574440Z 
2023-01-11T21:41:26.6574644Z if __name__ == "__main__":
2023-01-11T21:41:26.6574904Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6575202Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6575714Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6575918Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6576014Z 
2023-01-11T21:41:26.6576129Z ok (1.681s)
2023-01-11T21:41:26.6590943Z   test_long_tensor_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6591298Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6591885Z [2023-01-11 21:36:02,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 354
2023-01-11T21:41:26.6592437Z [2023-01-11 21:36:03,583] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 354
2023-01-11T21:41:26.6592452Z 
2023-01-11T21:41:26.6592639Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6592791Z import torch
2023-01-11T21:41:26.6592944Z import random
2023-01-11T21:41:26.6593178Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6593399Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6593409Z 
2023-01-11T21:41:26.6593564Z aten = torch.ops.aten
2023-01-11T21:41:26.6593815Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6594010Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6594019Z 
2023-01-11T21:41:26.6594027Z 
2023-01-11T21:41:26.6594307Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6594692Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6594931Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.6595135Z                        long* __restrict__ out_ptr0,
2023-01-11T21:41:26.6595319Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.6595451Z {
2023-01-11T21:41:26.6595649Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6595784Z     {
2023-01-11T21:41:26.6595945Z         #pragma omp for 
2023-01-11T21:41:26.6596112Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.6596223Z         {
2023-01-11T21:41:26.6596354Z             {
2023-01-11T21:41:26.6596489Z                 {
2023-01-11T21:41:26.6596672Z                     auto tmp1 = in_ptr0[i0];
2023-01-11T21:41:26.6596888Z                     auto tmp0 = static_cast<long>(294);
2023-01-11T21:41:26.6597177Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.6597382Z                     auto tmp3 = static_cast<long>(295);
2023-01-11T21:41:26.6597543Z                     auto tmp4 = tmp3 + tmp1;
2023-01-11T21:41:26.6597720Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6597893Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.6598041Z                 }
2023-01-11T21:41:26.6598179Z             }
2023-01-11T21:41:26.6598310Z         }
2023-01-11T21:41:26.6598443Z     }
2023-01-11T21:41:26.6598675Z }
2023-01-11T21:41:26.6598843Z ''')
2023-01-11T21:41:26.6598853Z 
2023-01-11T21:41:26.6598862Z 
2023-01-11T21:41:26.6599050Z async_compile.wait(globals())
2023-01-11T21:41:26.6599203Z del async_compile
2023-01-11T21:41:26.6599213Z 
2023-01-11T21:41:26.6599353Z def call(args):
2023-01-11T21:41:26.6599498Z     arg0_1, = args
2023-01-11T21:41:26.6599632Z     args.clear()
2023-01-11T21:41:26.6600004Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6600380Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6600688Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6600836Z     del arg0_1
2023-01-11T21:41:26.6600997Z     return (buf0, buf1, )
2023-01-11T21:41:26.6601010Z 
2023-01-11T21:41:26.6601017Z 
2023-01-11T21:41:26.6601267Z if __name__ == "__main__":
2023-01-11T21:41:26.6601506Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6601750Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6602105Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6602328Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6602340Z 
2023-01-11T21:41:26.6602482Z ok (1.573s)
2023-01-11T21:41:26.6603152Z   test_lowmem_dropout1_cpu (__main__.CpuTests) ... [2023-01-11 21:36:03,605] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 355
2023-01-11T21:41:26.6603679Z [2023-01-11 21:36:03,612] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 355
2023-01-11T21:41:26.6604204Z [2023-01-11 21:36:03,614] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 355
2023-01-11T21:41:26.6604741Z [2023-01-11 21:36:03,620] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 355
2023-01-11T21:41:26.6605253Z [2023-01-11 21:36:03,738] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 356
2023-01-11T21:41:26.6605763Z [2023-01-11 21:36:03,739] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.6606411Z [2023-01-11 21:36:03,749] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 356
2023-01-11T21:41:26.6606851Z [2023-01-11 21:36:03,751] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 356
2023-01-11T21:41:26.6606862Z 
2023-01-11T21:41:26.6607019Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6607149Z import torch
2023-01-11T21:41:26.6607279Z import random
2023-01-11T21:41:26.6607581Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6607816Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6607825Z 
2023-01-11T21:41:26.6607991Z aten = torch.ops.aten
2023-01-11T21:41:26.6608232Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6608432Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6608444Z 
2023-01-11T21:41:26.6608452Z 
2023-01-11T21:41:26.6608729Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6609273Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6609521Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6609725Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6609913Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6610041Z {
2023-01-11T21:41:26.6610220Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6610343Z     {
2023-01-11T21:41:26.6610505Z         #pragma omp for 
2023-01-11T21:41:26.6610675Z         for(long i0=0; i0<12500; i0+=1)
2023-01-11T21:41:26.6610808Z         {
2023-01-11T21:41:26.6611081Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.6611340Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.6611632Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.6611814Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.6611945Z         }
2023-01-11T21:41:26.6612138Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.6612322Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:41:26.6612449Z         {
2023-01-11T21:41:26.6612621Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6612778Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.6612941Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.6613099Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6613230Z         }
2023-01-11T21:41:26.6613355Z     }
2023-01-11T21:41:26.6613477Z }
2023-01-11T21:41:26.6613659Z ''')
2023-01-11T21:41:26.6613669Z 
2023-01-11T21:41:26.6613676Z 
2023-01-11T21:41:26.6613912Z async_compile.wait(globals())
2023-01-11T21:41:26.6614171Z del async_compile
2023-01-11T21:41:26.6614179Z 
2023-01-11T21:41:26.6614320Z def call(args):
2023-01-11T21:41:26.6614485Z     primals_1, primals_2 = args
2023-01-11T21:41:26.6614618Z     args.clear()
2023-01-11T21:41:26.6614967Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6615263Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6615396Z     del primals_2
2023-01-11T21:41:26.6615527Z     return (buf0, primals_1, )
2023-01-11T21:41:26.6615536Z 
2023-01-11T21:41:26.6615543Z 
2023-01-11T21:41:26.6615678Z if __name__ == "__main__":
2023-01-11T21:41:26.6615857Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6616056Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6616545Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6616950Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6617204Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:26.6617222Z 
2023-01-11T21:41:26.6617229Z 
2023-01-11T21:41:26.6617419Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6617540Z import torch
2023-01-11T21:41:26.6617687Z import random
2023-01-11T21:41:26.6617918Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6618144Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6618155Z 
2023-01-11T21:41:26.6618312Z aten = torch.ops.aten
2023-01-11T21:41:26.6618574Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6618756Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6618766Z 
2023-01-11T21:41:26.6618772Z 
2023-01-11T21:41:26.6619035Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6619412Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6619642Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6619856Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6620054Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6620184Z {
2023-01-11T21:41:26.6620383Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6620514Z     {
2023-01-11T21:41:26.6620650Z         #pragma omp for 
2023-01-11T21:41:26.6620816Z         for(long i0=0; i0<12500; i0+=1)
2023-01-11T21:41:26.6620947Z         {
2023-01-11T21:41:26.6621208Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.6621471Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.6621648Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.6621829Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.6621937Z         }
2023-01-11T21:41:26.6622131Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.6622324Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:41:26.6622456Z         {
2023-01-11T21:41:26.6622722Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6622981Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.6623197Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.6623330Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6623447Z         }
2023-01-11T21:41:26.6623560Z     }
2023-01-11T21:41:26.6623671Z }
2023-01-11T21:41:26.6623832Z ''')
2023-01-11T21:41:26.6623841Z 
2023-01-11T21:41:26.6623847Z 
2023-01-11T21:41:26.6624002Z async_compile.wait(globals())
2023-01-11T21:41:26.6624137Z del async_compile
2023-01-11T21:41:26.6624146Z 
2023-01-11T21:41:26.6624252Z def call(args):
2023-01-11T21:41:26.6624415Z     primals_1, tangents_1 = args
2023-01-11T21:41:26.6624550Z     args.clear()
2023-01-11T21:41:26.6624896Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6625270Z     kernel_cpp_0(c_void_p(tangents_1.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6625410Z     del primals_1
2023-01-11T21:41:26.6625550Z     del tangents_1
2023-01-11T21:41:26.6625673Z     return (None, buf0, )
2023-01-11T21:41:26.6625701Z 
2023-01-11T21:41:26.6625709Z 
2023-01-11T21:41:26.6625821Z if __name__ == "__main__":
2023-01-11T21:41:26.6626015Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6626228Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6626587Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6627074Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6627334Z     print_performance(lambda: call([primals_1, tangents_1]))
2023-01-11T21:41:26.6627346Z 
2023-01-11T21:41:26.6627353Z 
2023-01-11T21:41:26.6627541Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6627689Z import torch
2023-01-11T21:41:26.6627815Z import random
2023-01-11T21:41:26.6628049Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6628287Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6628305Z 
2023-01-11T21:41:26.6628468Z aten = torch.ops.aten
2023-01-11T21:41:26.6628733Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6628921Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6629232Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.6629248Z 
2023-01-11T21:41:26.6629254Z 
2023-01-11T21:41:26.6629531Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6629889Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6630117Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:26.6630329Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6630544Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.6630730Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6630856Z {
2023-01-11T21:41:26.6631058Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6631167Z     {
2023-01-11T21:41:26.6631324Z         #pragma omp for 
2023-01-11T21:41:26.6631499Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:41:26.6631635Z         {
2023-01-11T21:41:26.6631745Z             {
2023-01-11T21:41:26.6631885Z                 {
2023-01-11T21:41:26.6632065Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.6632231Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.6632421Z                     auto tmp7 = in_ptr2[i0];
2023-01-11T21:41:26.6632631Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.6632895Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.6633114Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:41:26.6633305Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.6633595Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.6633805Z                     auto tmp8 = tmp6 * tmp7;
2023-01-11T21:41:26.6633966Z                     auto tmp9 = tmp5 * tmp8;
2023-01-11T21:41:26.6634164Z                     auto tmp10 = static_cast<float>(1.492537313432836);
2023-01-11T21:41:26.6634331Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:26.6634486Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:26.6634605Z                 }
2023-01-11T21:41:26.6634713Z             }
2023-01-11T21:41:26.6634810Z         }
2023-01-11T21:41:26.6634921Z     }
2023-01-11T21:41:26.6635030Z }
2023-01-11T21:41:26.6635189Z ''')
2023-01-11T21:41:26.6635200Z 
2023-01-11T21:41:26.6635206Z 
2023-01-11T21:41:26.6635368Z async_compile.wait(globals())
2023-01-11T21:41:26.6635505Z del async_compile
2023-01-11T21:41:26.6635514Z 
2023-01-11T21:41:26.6635630Z def call(args):
2023-01-11T21:41:26.6635881Z     primals_1, primals_2 = args
2023-01-11T21:41:26.6636020Z     args.clear()
2023-01-11T21:41:26.6636257Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.6636600Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6636957Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6637096Z     del primals_2
2023-01-11T21:41:26.6637303Z     return (buf0, primals_1, seed_cpu_None.clone(), )
2023-01-11T21:41:26.6637314Z 
2023-01-11T21:41:26.6637320Z 
2023-01-11T21:41:26.6637449Z if __name__ == "__main__":
2023-01-11T21:41:26.6637606Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6637778Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6638119Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6638477Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6638817Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6639038Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:41:26.6639052Z 
2023-01-11T21:41:26.6639497Z [2023-01-11 21:36:03,760] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 356
2023-01-11T21:41:26.6639510Z 
2023-01-11T21:41:26.6639667Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6639797Z import torch
2023-01-11T21:41:26.6639909Z import random
2023-01-11T21:41:26.6640111Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6640315Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6640323Z 
2023-01-11T21:41:26.6640466Z aten = torch.ops.aten
2023-01-11T21:41:26.6640678Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6640846Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6640861Z 
2023-01-11T21:41:26.6640870Z 
2023-01-11T21:41:26.6641103Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6641432Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6641583Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.6641793Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6641975Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.6642155Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6642265Z {
2023-01-11T21:41:26.6642440Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6642538Z     {
2023-01-11T21:41:26.6642658Z         #pragma omp for 
2023-01-11T21:41:26.6642810Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:41:26.6642927Z         {
2023-01-11T21:41:26.6643042Z             {
2023-01-11T21:41:26.6643168Z                 {
2023-01-11T21:41:26.6643342Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.6643495Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.6643639Z                     auto tmp10 = in_ptr2[i0];
2023-01-11T21:41:26.6643902Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.6644143Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.6644326Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:41:26.6644483Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.6644683Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.6644845Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.6645023Z                     auto tmp8 = static_cast<float>(1.492537313432836);
2023-01-11T21:41:26.6645188Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.6645334Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:41:26.6645493Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:41:26.6645685Z                 }
2023-01-11T21:41:26.6645804Z             }
2023-01-11T21:41:26.6645919Z         }
2023-01-11T21:41:26.6646015Z     }
2023-01-11T21:41:26.6646123Z }
2023-01-11T21:41:26.6646288Z ''')
2023-01-11T21:41:26.6646296Z 
2023-01-11T21:41:26.6646303Z 
2023-01-11T21:41:26.6646457Z async_compile.wait(globals())
2023-01-11T21:41:26.6646589Z del async_compile
2023-01-11T21:41:26.6646599Z 
2023-01-11T21:41:26.6646728Z def call(args):
2023-01-11T21:41:26.6646927Z     primals_1, philox_seed_like, tangents_1 = args
2023-01-11T21:41:26.6647035Z     args.clear()
2023-01-11T21:41:26.6647381Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6647738Z     kernel_cpp_0(c_void_p(philox_seed_like.data_ptr()), c_void_p(tangents_1.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6647878Z     del philox_seed_like
2023-01-11T21:41:26.6647999Z     del primals_1
2023-01-11T21:41:26.6648120Z     del tangents_1
2023-01-11T21:41:26.6648264Z     return (None, buf0, )
2023-01-11T21:41:26.6648274Z 
2023-01-11T21:41:26.6648280Z 
2023-01-11T21:41:26.6648414Z if __name__ == "__main__":
2023-01-11T21:41:26.6648591Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6648800Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6649288Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6649636Z     philox_seed_like = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6649988Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6650241Z     print_performance(lambda: call([primals_1, philox_seed_like, tangents_1]))
2023-01-11T21:41:26.6650249Z 
2023-01-11T21:41:26.6650374Z ok (0.183s)
2023-01-11T21:41:26.6650955Z   test_lowmem_dropout2_cpu (__main__.CpuTests) ... [2023-01-11 21:36:04,000] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 357
2023-01-11T21:41:26.6651389Z [2023-01-11 21:36:04,001] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.6651853Z [2023-01-11 21:36:05,969] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 357
2023-01-11T21:41:26.6652299Z [2023-01-11 21:36:05,972] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 357
2023-01-11T21:41:26.6652756Z [2023-01-11 21:36:08,682] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 357
2023-01-11T21:41:26.6652765Z 
2023-01-11T21:41:26.6652936Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6653069Z import torch
2023-01-11T21:41:26.6653197Z import random
2023-01-11T21:41:26.6653397Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6653582Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6653613Z 
2023-01-11T21:41:26.6653733Z aten = torch.ops.aten
2023-01-11T21:41:26.6653972Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6654123Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6654520Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.6654530Z 
2023-01-11T21:41:26.6654537Z 
2023-01-11T21:41:26.6654788Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6655126Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6655328Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.6655510Z                        const long* __restrict__ seed0)
2023-01-11T21:41:26.6655602Z {
2023-01-11T21:41:26.6655787Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6655905Z     {
2023-01-11T21:41:26.6656050Z         #pragma omp for 
2023-01-11T21:41:26.6656203Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.6656323Z         {
2023-01-11T21:41:26.6656425Z             {
2023-01-11T21:41:26.6656627Z                 {
2023-01-11T21:41:26.6656783Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.6656960Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:41:26.6657146Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.6657395Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.6657583Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:26.6657751Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.6657916Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.6658082Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.6658268Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:26.6658433Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.6658599Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.6658714Z                 }
2023-01-11T21:41:26.6658825Z             }
2023-01-11T21:41:26.6658924Z         }
2023-01-11T21:41:26.6659040Z     }
2023-01-11T21:41:26.6659151Z }
2023-01-11T21:41:26.6659314Z ''')
2023-01-11T21:41:26.6659327Z 
2023-01-11T21:41:26.6659332Z 
2023-01-11T21:41:26.6659568Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.6659909Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6660116Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.6660272Z                        const long* __restrict__ seed0)
2023-01-11T21:41:26.6660389Z {
2023-01-11T21:41:26.6660559Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6660676Z     {
2023-01-11T21:41:26.6660932Z         #pragma omp for 
2023-01-11T21:41:26.6661106Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.6661232Z         {
2023-01-11T21:41:26.6661348Z             {
2023-01-11T21:41:26.6661475Z                 {
2023-01-11T21:41:26.6661651Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.6661854Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:41:26.6662072Z                     auto tmp1 = static_cast<int>(256 + i0);
2023-01-11T21:41:26.6662351Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.6662566Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:26.6662732Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.6662946Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.6663217Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.6663435Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:26.6663619Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.6663799Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.6663935Z                 }
2023-01-11T21:41:26.6664049Z             }
2023-01-11T21:41:26.6664178Z         }
2023-01-11T21:41:26.6664308Z     }
2023-01-11T21:41:26.6664430Z }
2023-01-11T21:41:26.6664610Z ''')
2023-01-11T21:41:26.6664621Z 
2023-01-11T21:41:26.6664630Z 
2023-01-11T21:41:26.6664815Z async_compile.wait(globals())
2023-01-11T21:41:26.6665066Z del async_compile
2023-01-11T21:41:26.6665076Z 
2023-01-11T21:41:26.6665201Z def call(args):
2023-01-11T21:41:26.6665406Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.6665551Z     args.clear()
2023-01-11T21:41:26.6665821Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.6666235Z     buf0 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6666494Z     aten.mm.out(primals_3, as_strided(primals_1, (32, 32), (1, 32)), out=buf0)
2023-01-11T21:41:26.6666644Z     del primals_1
2023-01-11T21:41:26.6666799Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.6667091Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(seed_cpu_None.data_ptr()))
2023-01-11T21:41:26.6667575Z     buf2 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6667880Z     aten.mm.out(buf1, as_strided(primals_2, (32, 32), (1, 32)), out=buf2)
2023-01-11T21:41:26.6668040Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:26.6668296Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(seed_cpu_None.data_ptr()))
2023-01-11T21:41:26.6668557Z     return (buf3, primals_3, seed_cpu_None.clone(), buf1, as_strided(primals_2, (32, 32), (32, 1)), )
2023-01-11T21:41:26.6668567Z 
2023-01-11T21:41:26.6668574Z 
2023-01-11T21:41:26.6668710Z if __name__ == "__main__":
2023-01-11T21:41:26.6668914Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6669108Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6669454Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6669816Z     primals_1 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6670161Z     primals_2 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6670527Z     primals_3 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6670760Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.6670775Z 
2023-01-11T21:41:26.6670782Z 
2023-01-11T21:41:26.6670950Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6671073Z import torch
2023-01-11T21:41:26.6671181Z import random
2023-01-11T21:41:26.6671377Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6671597Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6671610Z 
2023-01-11T21:41:26.6671750Z aten = torch.ops.aten
2023-01-11T21:41:26.6671980Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6672143Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6672153Z 
2023-01-11T21:41:26.6672159Z 
2023-01-11T21:41:26.6672403Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6672745Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6672927Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.6673116Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6673291Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6673404Z {
2023-01-11T21:41:26.6673573Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6673681Z     {
2023-01-11T21:41:26.6673825Z         #pragma omp for 
2023-01-11T21:41:26.6673954Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.6674072Z         {
2023-01-11T21:41:26.6674190Z             {
2023-01-11T21:41:26.6674309Z                 {
2023-01-11T21:41:26.6674466Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.6674632Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:41:26.6674819Z                     auto tmp1 = static_cast<int>(256 + i0);
2023-01-11T21:41:26.6675042Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.6675232Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:26.6675385Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.6675665Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.6675829Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.6676017Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:26.6676180Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.6676307Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.6676430Z                 }
2023-01-11T21:41:26.6676550Z             }
2023-01-11T21:41:26.6676668Z         }
2023-01-11T21:41:26.6676783Z     }
2023-01-11T21:41:26.6676893Z }
2023-01-11T21:41:26.6677033Z ''')
2023-01-11T21:41:26.6677058Z 
2023-01-11T21:41:26.6677063Z 
2023-01-11T21:41:26.6677285Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:41:26.6677717Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6677927Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.6678105Z                        const long* __restrict__ in_ptr0)
2023-01-11T21:41:26.6678228Z {
2023-01-11T21:41:26.6678405Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6678516Z     {
2023-01-11T21:41:26.6678639Z         #pragma omp for 
2023-01-11T21:41:26.6678793Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:41:26.6678898Z         {
2023-01-11T21:41:26.6679022Z             {
2023-01-11T21:41:26.6679148Z                 {
2023-01-11T21:41:26.6679313Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.6679481Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:41:26.6679642Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.6679874Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.6680060Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:26.6680231Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:41:26.6680417Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:41:26.6680581Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.6680762Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:26.6680904Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:41:26.6681065Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:41:26.6681187Z                 }
2023-01-11T21:41:26.6681307Z             }
2023-01-11T21:41:26.6681422Z         }
2023-01-11T21:41:26.6681529Z     }
2023-01-11T21:41:26.6681641Z }
2023-01-11T21:41:26.6681785Z ''')
2023-01-11T21:41:26.6681793Z 
2023-01-11T21:41:26.6681799Z 
2023-01-11T21:41:26.6681959Z async_compile.wait(globals())
2023-01-11T21:41:26.6682098Z del async_compile
2023-01-11T21:41:26.6682107Z 
2023-01-11T21:41:26.6682237Z def call(args):
2023-01-11T21:41:26.6682451Z     primals_3, philox_seed_like, mul_1, permute_4, tangents_1 = args
2023-01-11T21:41:26.6682566Z     args.clear()
2023-01-11T21:41:26.6682844Z     buf0 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6683091Z     kernel_cpp_0(c_void_p(philox_seed_like.data_ptr()), c_void_p(tangents_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6683176Z     del tangents_1
2023-01-11T21:41:26.6683479Z     buf1 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6683689Z     aten.mm.out(as_strided(buf0, (32, 8), (1, 32)), mul_1, out=buf1)
2023-01-11T21:41:26.6683818Z     del mul_1
2023-01-11T21:41:26.6684156Z     buf2 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6684321Z     aten.mm.out(buf0, permute_4, out=buf2)
2023-01-11T21:41:26.6684444Z     del buf0
2023-01-11T21:41:26.6684554Z     del permute_4
2023-01-11T21:41:26.6684705Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:26.6684976Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(philox_seed_like.data_ptr()))
2023-01-11T21:41:26.6685108Z     del philox_seed_like
2023-01-11T21:41:26.6685439Z     buf4 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6685732Z     aten.mm.out(as_strided(buf3, (32, 8), (1, 32)), primals_3, out=buf4)
2023-01-11T21:41:26.6685854Z     del buf3
2023-01-11T21:41:26.6685961Z     del primals_3
2023-01-11T21:41:26.6686175Z     return (as_strided(buf4, (32, 32), (32, 1)), as_strided(buf1, (32, 32), (32, 1)), None, )
2023-01-11T21:41:26.6686184Z 
2023-01-11T21:41:26.6686191Z 
2023-01-11T21:41:26.6686323Z if __name__ == "__main__":
2023-01-11T21:41:26.6686520Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6686738Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6687084Z     primals_3 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6687425Z     philox_seed_like = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6687827Z     mul_1 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6688163Z     permute_4 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6688529Z     tangents_1 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6688800Z     print_performance(lambda: call([primals_3, philox_seed_like, mul_1, permute_4, tangents_1]))
2023-01-11T21:41:26.6688809Z 
2023-01-11T21:41:26.6688929Z ok (4.927s)
2023-01-11T21:41:26.6690045Z   test_masked_fill_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6690290Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6690820Z [2023-01-11 21:36:08,750] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 358
2023-01-11T21:41:26.6691344Z [2023-01-11 21:36:11,066] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 358
2023-01-11T21:41:26.6701110Z 
2023-01-11T21:41:26.6701456Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6701602Z import torch
2023-01-11T21:41:26.6701717Z import random
2023-01-11T21:41:26.6701948Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6702181Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6702190Z 
2023-01-11T21:41:26.6702352Z aten = torch.ops.aten
2023-01-11T21:41:26.6702614Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6702803Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6702813Z 
2023-01-11T21:41:26.6702823Z 
2023-01-11T21:41:26.6703111Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6703595Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6703810Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.6704022Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6704217Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6704403Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6704529Z {
2023-01-11T21:41:26.6704727Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6704857Z     {
2023-01-11T21:41:26.6704998Z         #pragma omp for 
2023-01-11T21:41:26.6705155Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.6705287Z         {
2023-01-11T21:41:26.6705450Z             #pragma GCC ivdep
2023-01-11T21:41:26.6705626Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.6705760Z             {
2023-01-11T21:41:26.6705898Z                 {
2023-01-11T21:41:26.6706012Z                     {
2023-01-11T21:41:26.6706205Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.6706404Z                         auto tmp2 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:41:26.6706907Z                         auto tmp1 = static_cast<float>(-10000.0);
2023-01-11T21:41:26.6707112Z                         auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:41:26.6707329Z                         auto tmp4 = static_cast<float>(2);
2023-01-11T21:41:26.6707524Z                         auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.6707688Z                         auto tmp6 = tmp0 == 0;
2023-01-11T21:41:26.6707907Z                         auto tmp7 = static_cast<float>(667.0);
2023-01-11T21:41:26.6708114Z                         auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:41:26.6708299Z                         auto tmp9 = tmp2 / tmp8;
2023-01-11T21:41:26.6708503Z                         auto tmp10 = tmp6 ? tmp7 : tmp9;
2023-01-11T21:41:26.6708688Z                         out_ptr0[i1 + (16*i0)] = tmp5;
2023-01-11T21:41:26.6709054Z                         out_ptr1[i1 + (16*i0)] = tmp10;
2023-01-11T21:41:26.6709180Z                     }
2023-01-11T21:41:26.6709279Z                 }
2023-01-11T21:41:26.6709403Z             }
2023-01-11T21:41:26.6709525Z         }
2023-01-11T21:41:26.6709639Z     }
2023-01-11T21:41:26.6709750Z }
2023-01-11T21:41:26.6709905Z ''')
2023-01-11T21:41:26.6709915Z 
2023-01-11T21:41:26.6709921Z 
2023-01-11T21:41:26.6710058Z async_compile.wait(globals())
2023-01-11T21:41:26.6710191Z del async_compile
2023-01-11T21:41:26.6710200Z 
2023-01-11T21:41:26.6710323Z def call(args):
2023-01-11T21:41:26.6710460Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6710586Z     args.clear()
2023-01-11T21:41:26.6710937Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6711275Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6711602Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6711709Z     del arg0_1
2023-01-11T21:41:26.6711826Z     del arg1_1
2023-01-11T21:41:26.6711966Z     return (buf0, buf1, )
2023-01-11T21:41:26.6711979Z 
2023-01-11T21:41:26.6711987Z 
2023-01-11T21:41:26.6712129Z if __name__ == "__main__":
2023-01-11T21:41:26.6712322Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6712536Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6712871Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.6713221Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6713407Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6713416Z 
2023-01-11T21:41:26.6713536Z ok (2.373s)
2023-01-11T21:41:26.6714133Z   test_masked_fill_promotion_cpu (__main__.CpuTests) ... [2023-01-11 21:36:11,092] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 359
2023-01-11T21:41:26.6714597Z [2023-01-11 21:36:12,762] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 359
2023-01-11T21:41:26.6715051Z [2023-01-11 21:36:12,785] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 360
2023-01-11T21:41:26.6715498Z [2023-01-11 21:36:14,388] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 360
2023-01-11T21:41:26.6715508Z 
2023-01-11T21:41:26.6715676Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6715802Z import torch
2023-01-11T21:41:26.6715908Z import random
2023-01-11T21:41:26.6716111Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6716316Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6716324Z 
2023-01-11T21:41:26.6716459Z aten = torch.ops.aten
2023-01-11T21:41:26.6716695Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6716856Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6716869Z 
2023-01-11T21:41:26.6716879Z 
2023-01-11T21:41:26.6717108Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6717448Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6717728Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.6717917Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6718088Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6718202Z {
2023-01-11T21:41:26.6718383Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6718492Z     {
2023-01-11T21:41:26.6718631Z         #pragma omp for 
2023-01-11T21:41:26.6718756Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.6718870Z         {
2023-01-11T21:41:26.6719010Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.6719125Z             {
2023-01-11T21:41:26.6719308Z                 float g_tmp_buffer_in_ptr0[8] = {0};
2023-01-11T21:41:26.6719590Z                 flag_to_float(in_ptr0 + 8*i1, g_tmp_buffer_in_ptr0, 8);
2023-01-11T21:41:26.6719849Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:41:26.6720105Z                 auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (16*i0));
2023-01-11T21:41:26.6720324Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3.5));
2023-01-11T21:41:26.6720550Z                 auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:41:26.6720725Z                 tmp3.store(out_ptr0 + (8*i1) + (16*i0));
2023-01-11T21:41:26.6720838Z             }
2023-01-11T21:41:26.6721008Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.6721162Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:41:26.6721279Z             {
2023-01-11T21:41:26.6721412Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.6721579Z                 auto tmp2 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:41:26.6721750Z                 auto tmp1 = static_cast<float>(3.5);
2023-01-11T21:41:26.6721927Z                 auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:41:26.6722094Z                 out_ptr0[i1 + (16*i0)] = tmp3;
2023-01-11T21:41:26.6722214Z             }
2023-01-11T21:41:26.6722337Z         }
2023-01-11T21:41:26.6722431Z     }
2023-01-11T21:41:26.6722537Z }
2023-01-11T21:41:26.6722700Z ''')
2023-01-11T21:41:26.6722712Z 
2023-01-11T21:41:26.6722718Z 
2023-01-11T21:41:26.6722875Z async_compile.wait(globals())
2023-01-11T21:41:26.6723017Z del async_compile
2023-01-11T21:41:26.6723027Z 
2023-01-11T21:41:26.6723155Z def call(args):
2023-01-11T21:41:26.6723289Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6723394Z     args.clear()
2023-01-11T21:41:26.6723743Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6724015Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6724137Z     del arg0_1
2023-01-11T21:41:26.6724260Z     del arg1_1
2023-01-11T21:41:26.6724385Z     return (buf0, )
2023-01-11T21:41:26.6724395Z 
2023-01-11T21:41:26.6724403Z 
2023-01-11T21:41:26.6724538Z if __name__ == "__main__":
2023-01-11T21:41:26.6724749Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6724949Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6725277Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.6725619Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6725822Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6725831Z 
2023-01-11T21:41:26.6725837Z 
2023-01-11T21:41:26.6726005Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6726121Z import torch
2023-01-11T21:41:26.6726248Z import random
2023-01-11T21:41:26.6726449Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6726636Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6726645Z 
2023-01-11T21:41:26.6726787Z aten = torch.ops.aten
2023-01-11T21:41:26.6727018Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6727269Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6727278Z 
2023-01-11T21:41:26.6727284Z 
2023-01-11T21:41:26.6727521Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6727860Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6728056Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.6728239Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.6728394Z                        long* __restrict__ out_ptr0)
2023-01-11T21:41:26.6728507Z {
2023-01-11T21:41:26.6728687Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6728800Z     {
2023-01-11T21:41:26.6728927Z         #pragma omp for 
2023-01-11T21:41:26.6729192Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.6729314Z         {
2023-01-11T21:41:26.6729542Z             #pragma GCC ivdep
2023-01-11T21:41:26.6729696Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.6729813Z             {
2023-01-11T21:41:26.6729930Z                 {
2023-01-11T21:41:26.6730047Z                     {
2023-01-11T21:41:26.6730212Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.6730393Z                         auto tmp3 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:41:26.6730562Z                         auto tmp1 = static_cast<float>(3.5);
2023-01-11T21:41:26.6730752Z                         auto tmp2 = static_cast<long>(tmp1);
2023-01-11T21:41:26.6730928Z                         auto tmp4 = tmp0 ? tmp2 : tmp3;
2023-01-11T21:41:26.6731096Z                         out_ptr0[i1 + (16*i0)] = tmp4;
2023-01-11T21:41:26.6731219Z                     }
2023-01-11T21:41:26.6731339Z                 }
2023-01-11T21:41:26.6731456Z             }
2023-01-11T21:41:26.6731552Z         }
2023-01-11T21:41:26.6731666Z     }
2023-01-11T21:41:26.6731774Z }
2023-01-11T21:41:26.6731942Z ''')
2023-01-11T21:41:26.6731958Z 
2023-01-11T21:41:26.6731964Z 
2023-01-11T21:41:26.6732129Z async_compile.wait(globals())
2023-01-11T21:41:26.6732267Z del async_compile
2023-01-11T21:41:26.6732274Z 
2023-01-11T21:41:26.6732398Z def call(args):
2023-01-11T21:41:26.6732516Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6732649Z     args.clear()
2023-01-11T21:41:26.6732991Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6733263Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6733385Z     del arg0_1
2023-01-11T21:41:26.6733510Z     del arg1_1
2023-01-11T21:41:26.6733638Z     return (buf0, )
2023-01-11T21:41:26.6733645Z 
2023-01-11T21:41:26.6733651Z 
2023-01-11T21:41:26.6733766Z if __name__ == "__main__":
2023-01-11T21:41:26.6733962Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6734170Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6734508Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.6734835Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6735047Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6735055Z 
2023-01-11T21:41:26.6735175Z ok (3.321s)
2023-01-11T21:41:26.6735937Z   test_max_min_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6736151Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6736578Z [2023-01-11 21:36:14,405] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 361
2023-01-11T21:41:26.6737050Z [2023-01-11 21:36:16,106] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 361
2023-01-11T21:41:26.6737874Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6738092Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6738539Z [2023-01-11 21:36:16,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 362
2023-01-11T21:41:26.6739030Z [2023-01-11 21:36:16,135] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 362
2023-01-11T21:41:26.6739041Z 
2023-01-11T21:41:26.6739198Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6739331Z import torch
2023-01-11T21:41:26.6739528Z import random
2023-01-11T21:41:26.6739733Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6739935Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6739944Z 
2023-01-11T21:41:26.6740075Z aten = torch.ops.aten
2023-01-11T21:41:26.6740303Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6740472Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6740481Z 
2023-01-11T21:41:26.6740487Z 
2023-01-11T21:41:26.6740733Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6741064Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6741275Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6741454Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6741607Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6741782Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6741890Z {
2023-01-11T21:41:26.6742060Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6742176Z     {
2023-01-11T21:41:26.6742319Z         #pragma omp for 
2023-01-11T21:41:26.6742461Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.6742557Z         {
2023-01-11T21:41:26.6742784Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.6743021Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.6743282Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:41:26.6743475Z             auto tmp3 = at::vec::minimum(tmp0, tmp1);
2023-01-11T21:41:26.6743639Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.6743792Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.6743892Z         }
2023-01-11T21:41:26.6744055Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.6744209Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.6744323Z         {
2023-01-11T21:41:26.6744585Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6744753Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.6745003Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:26.6745245Z             auto tmp3 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:41:26.6745385Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6745550Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6745683Z         }
2023-01-11T21:41:26.6745806Z     }
2023-01-11T21:41:26.6745931Z }
2023-01-11T21:41:26.6746116Z ''')
2023-01-11T21:41:26.6746125Z 
2023-01-11T21:41:26.6746131Z 
2023-01-11T21:41:26.6746307Z async_compile.wait(globals())
2023-01-11T21:41:26.6746431Z del async_compile
2023-01-11T21:41:26.6746441Z 
2023-01-11T21:41:26.6746588Z def call(args):
2023-01-11T21:41:26.6746736Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6746877Z     args.clear()
2023-01-11T21:41:26.6747267Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6747644Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6748101Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6748223Z     del arg0_1
2023-01-11T21:41:26.6748364Z     del arg1_1
2023-01-11T21:41:26.6748523Z     return (buf0, buf1, )
2023-01-11T21:41:26.6748537Z 
2023-01-11T21:41:26.6748544Z 
2023-01-11T21:41:26.6748699Z if __name__ == "__main__":
2023-01-11T21:41:26.6748916Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6749158Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6749547Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6749920Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6750123Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6750133Z 
2023-01-11T21:41:26.6750238Z 
2023-01-11T21:41:26.6750412Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6750548Z import torch
2023-01-11T21:41:26.6750698Z import random
2023-01-11T21:41:26.6750928Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6751260Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6751269Z 
2023-01-11T21:41:26.6751408Z aten = torch.ops.aten
2023-01-11T21:41:26.6751637Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6751776Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6751784Z 
2023-01-11T21:41:26.6751792Z 
2023-01-11T21:41:26.6752038Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6752368Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6752569Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6752747Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6752937Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6753110Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.6753225Z {
2023-01-11T21:41:26.6753376Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6753488Z     {
2023-01-11T21:41:26.6753621Z         #pragma omp for 
2023-01-11T21:41:26.6753767Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.6753891Z         {
2023-01-11T21:41:26.6754132Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.6754364Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.6754527Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:41:26.6754711Z             auto tmp3 = at::vec::minimum(tmp0, tmp1);
2023-01-11T21:41:26.6754882Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.6755044Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.6755154Z         }
2023-01-11T21:41:26.6755322Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.6755457Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.6755551Z         {
2023-01-11T21:41:26.6755712Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.6755860Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.6756079Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:26.6756285Z             auto tmp3 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:41:26.6756418Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.6756566Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.6756660Z         }
2023-01-11T21:41:26.6756773Z     }
2023-01-11T21:41:26.6756875Z }
2023-01-11T21:41:26.6757036Z ''')
2023-01-11T21:41:26.6757049Z 
2023-01-11T21:41:26.6757057Z 
2023-01-11T21:41:26.6757213Z async_compile.wait(globals())
2023-01-11T21:41:26.6757339Z del async_compile
2023-01-11T21:41:26.6757349Z 
2023-01-11T21:41:26.6757477Z def call(args):
2023-01-11T21:41:26.6757595Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.6757721Z     args.clear()
2023-01-11T21:41:26.6758063Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6758481Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6758920Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6759061Z     del arg0_1
2023-01-11T21:41:26.6759192Z     del arg1_1
2023-01-11T21:41:26.6759351Z     return (buf0, buf1, )
2023-01-11T21:41:26.6759362Z 
2023-01-11T21:41:26.6759373Z 
2023-01-11T21:41:26.6759506Z if __name__ == "__main__":
2023-01-11T21:41:26.6759735Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6759974Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6760352Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6760844Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6761082Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.6761093Z 
2023-01-11T21:41:26.6761233Z ok (1.747s)
2023-01-11T21:41:26.6762142Z   test_max_pool2d1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6762390Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6762889Z [2023-01-11 21:36:16,156] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 363
2023-01-11T21:41:26.6763405Z [2023-01-11 21:36:18,245] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 363
2023-01-11T21:41:26.6763415Z 
2023-01-11T21:41:26.6763611Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6763760Z import torch
2023-01-11T21:41:26.6763907Z import random
2023-01-11T21:41:26.6764143Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6764377Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6764386Z 
2023-01-11T21:41:26.6764539Z aten = torch.ops.aten
2023-01-11T21:41:26.6764778Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6764958Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6764970Z 
2023-01-11T21:41:26.6764977Z 
2023-01-11T21:41:26.6765348Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6765680Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6765890Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6766066Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6766229Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.6766344Z {
2023-01-11T21:41:26.6766495Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6766611Z     {
2023-01-11T21:41:26.6766754Z         #pragma omp for 
2023-01-11T21:41:26.6766902Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6767022Z         {
2023-01-11T21:41:26.6767159Z             #pragma GCC ivdep
2023-01-11T21:41:26.6767304Z             for(long i1=0; i1<7; i1+=1)
2023-01-11T21:41:26.6767404Z             {
2023-01-11T21:41:26.6767557Z                 #pragma GCC ivdep
2023-01-11T21:41:26.6767708Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:41:26.6767828Z                 {
2023-01-11T21:41:26.6767951Z                     {
2023-01-11T21:41:26.6768073Z                         {
2023-01-11T21:41:26.6768246Z                             auto tmp0 = in_ptr0[(2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6768446Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6768638Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6768825Z                             auto tmp5 = in_ptr0[16 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6769224Z                             auto tmp7 = in_ptr0[17 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6769422Z                             auto tmp9 = in_ptr0[18 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6769623Z                             auto tmp11 = in_ptr0[32 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6769829Z                             auto tmp13 = in_ptr0[33 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6770028Z                             auto tmp15 = in_ptr0[34 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:41:26.6770242Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:41:26.6770460Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:41:26.6770774Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:41:26.6770988Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:41:26.6771223Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:41:26.6771463Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:41:26.6771687Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:41:26.6771897Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:41:26.6772094Z                             auto tmp17 = static_cast<long>((2*i2) + (32*i1));
2023-01-11T21:41:26.6772279Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (32*i1));
2023-01-11T21:41:26.6772448Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:41:26.6772630Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:41:26.6772829Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (32*i1));
2023-01-11T21:41:26.6773004Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:41:26.6773182Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:41:26.6773392Z                             auto tmp24 = static_cast<long>(16 + (2*i2) + (32*i1));
2023-01-11T21:41:26.6773556Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:41:26.6773713Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:41:26.6773919Z                             auto tmp27 = static_cast<long>(17 + (2*i2) + (32*i1));
2023-01-11T21:41:26.6774096Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:41:26.6774281Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:41:26.6774483Z                             auto tmp30 = static_cast<long>(18 + (2*i2) + (32*i1));
2023-01-11T21:41:26.6774663Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:41:26.6774853Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:41:26.6775064Z                             auto tmp33 = static_cast<long>(32 + (2*i2) + (32*i1));
2023-01-11T21:41:26.6775214Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:41:26.6775394Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:41:26.6775592Z                             auto tmp36 = static_cast<long>(33 + (2*i2) + (32*i1));
2023-01-11T21:41:26.6775758Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:41:26.6775951Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:41:26.6776156Z                             auto tmp39 = static_cast<long>(34 + (2*i2) + (32*i1));
2023-01-11T21:41:26.6776315Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:41:26.6776479Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:41:26.6776657Z                             out_ptr0[i2 + (7*i1) + (49*i0)] = tmp16;
2023-01-11T21:41:26.6776835Z                             out_ptr1[i2 + (7*i1) + (49*i0)] = tmp41;
2023-01-11T21:41:26.6777065Z                         }
2023-01-11T21:41:26.6777179Z                     }
2023-01-11T21:41:26.6777301Z                 }
2023-01-11T21:41:26.6777517Z             }
2023-01-11T21:41:26.6777625Z         }
2023-01-11T21:41:26.6777755Z     }
2023-01-11T21:41:26.6777880Z }
2023-01-11T21:41:26.6778064Z ''')
2023-01-11T21:41:26.6778078Z 
2023-01-11T21:41:26.6778085Z 
2023-01-11T21:41:26.6778263Z async_compile.wait(globals())
2023-01-11T21:41:26.6778420Z del async_compile
2023-01-11T21:41:26.6778429Z 
2023-01-11T21:41:26.6778577Z def call(args):
2023-01-11T21:41:26.6778722Z     arg0_1, = args
2023-01-11T21:41:26.6778847Z     args.clear()
2023-01-11T21:41:26.6779270Z     buf0 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6779768Z     buf1 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6780096Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6780244Z     del arg0_1
2023-01-11T21:41:26.6780400Z     return (buf0, buf1, )
2023-01-11T21:41:26.6780410Z 
2023-01-11T21:41:26.6780420Z 
2023-01-11T21:41:26.6780575Z if __name__ == "__main__":
2023-01-11T21:41:26.6780780Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6781028Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6781520Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6781737Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6781752Z 
2023-01-11T21:41:26.6781889Z ok (2.112s)
2023-01-11T21:41:26.6782799Z   test_max_pool2d2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6783054Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6783664Z [2023-01-11 21:36:18,307] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 364
2023-01-11T21:41:26.6784321Z [2023-01-11 21:36:21,030] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 364
2023-01-11T21:41:26.6784331Z 
2023-01-11T21:41:26.6784493Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6784602Z import torch
2023-01-11T21:41:26.6784735Z import random
2023-01-11T21:41:26.6784932Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6785145Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6785160Z 
2023-01-11T21:41:26.6785309Z aten = torch.ops.aten
2023-01-11T21:41:26.6785535Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6785712Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6785720Z 
2023-01-11T21:41:26.6785726Z 
2023-01-11T21:41:26.6785973Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6786283Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6786489Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6786666Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6786836Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.6786953Z {
2023-01-11T21:41:26.6787131Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6787231Z     {
2023-01-11T21:41:26.6787351Z         #pragma omp for 
2023-01-11T21:41:26.6787502Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.6787617Z         {
2023-01-11T21:41:26.6787765Z             #pragma GCC ivdep
2023-01-11T21:41:26.6788033Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:41:26.6788251Z             {
2023-01-11T21:41:26.6788425Z                 #pragma GCC ivdep
2023-01-11T21:41:26.6788583Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:26.6788716Z                 {
2023-01-11T21:41:26.6788853Z                     {
2023-01-11T21:41:26.6788998Z                         {
2023-01-11T21:41:26.6789219Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6789432Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6789662Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6789876Z                             auto tmp5 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6790104Z                             auto tmp7 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6790396Z                             auto tmp9 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6790626Z                             auto tmp11 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6790853Z                             auto tmp13 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6791082Z                             auto tmp15 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:41:26.6791342Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:41:26.6791592Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:41:26.6791829Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:41:26.6792051Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:41:26.6792301Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:41:26.6792562Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:41:26.6792832Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:41:26.6793099Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:41:26.6793324Z                             auto tmp17 = static_cast<long>((2*i2) + (110*i1));
2023-01-11T21:41:26.6793565Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (110*i1));
2023-01-11T21:41:26.6793758Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:41:26.6793968Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:41:26.6794184Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (110*i1));
2023-01-11T21:41:26.6794364Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:41:26.6794692Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:41:26.6794909Z                             auto tmp24 = static_cast<long>(55 + (2*i2) + (110*i1));
2023-01-11T21:41:26.6795079Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:41:26.6795268Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:41:26.6795469Z                             auto tmp27 = static_cast<long>(56 + (2*i2) + (110*i1));
2023-01-11T21:41:26.6795617Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:41:26.6795806Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:41:26.6796008Z                             auto tmp30 = static_cast<long>(57 + (2*i2) + (110*i1));
2023-01-11T21:41:26.6796177Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:41:26.6796353Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:41:26.6796687Z                             auto tmp33 = static_cast<long>(110 + (2*i2) + (110*i1));
2023-01-11T21:41:26.6796887Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:41:26.6797092Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:41:26.6797388Z                             auto tmp36 = static_cast<long>(111 + (2*i2) + (110*i1));
2023-01-11T21:41:26.6797584Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:41:26.6797785Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:41:26.6798024Z                             auto tmp39 = static_cast<long>(112 + (2*i2) + (110*i1));
2023-01-11T21:41:26.6798213Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:41:26.6798408Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:41:26.6798615Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp16;
2023-01-11T21:41:26.6798819Z                             out_ptr1[i2 + (27*i1) + (729*i0)] = tmp41;
2023-01-11T21:41:26.6798987Z                         }
2023-01-11T21:41:26.6799198Z                     }
2023-01-11T21:41:26.6799338Z                 }
2023-01-11T21:41:26.6799461Z             }
2023-01-11T21:41:26.6799601Z         }
2023-01-11T21:41:26.6799724Z     }
2023-01-11T21:41:26.6799825Z }
2023-01-11T21:41:26.6800009Z ''')
2023-01-11T21:41:26.6800021Z 
2023-01-11T21:41:26.6800028Z 
2023-01-11T21:41:26.6800214Z async_compile.wait(globals())
2023-01-11T21:41:26.6800359Z del async_compile
2023-01-11T21:41:26.6800368Z 
2023-01-11T21:41:26.6800510Z def call(args):
2023-01-11T21:41:26.6800650Z     arg0_1, = args
2023-01-11T21:41:26.6800800Z     args.clear()
2023-01-11T21:41:26.6801261Z     buf0 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6801664Z     buf1 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6801979Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6802125Z     del arg0_1
2023-01-11T21:41:26.6802293Z     return (buf0, buf1, )
2023-01-11T21:41:26.6802303Z 
2023-01-11T21:41:26.6802310Z 
2023-01-11T21:41:26.6802450Z if __name__ == "__main__":
2023-01-11T21:41:26.6802685Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6802941Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6803427Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6803593Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6803603Z 
2023-01-11T21:41:26.6803719Z ok (2.856s)
2023-01-11T21:41:26.6804501Z   test_max_pool2d3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6804724Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6805163Z [2023-01-11 21:36:21,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 365
2023-01-11T21:41:26.6805179Z 
2023-01-11T21:41:26.6805333Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6805463Z import torch
2023-01-11T21:41:26.6805593Z import random
2023-01-11T21:41:26.6805794Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6805985Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6805995Z 
2023-01-11T21:41:26.6806124Z aten = torch.ops.aten
2023-01-11T21:41:26.6806352Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6806519Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6806527Z 
2023-01-11T21:41:26.6806533Z 
2023-01-11T21:41:26.6806768Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6807100Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6807312Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6807610Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6807763Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.6807882Z {
2023-01-11T21:41:26.6808050Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6808166Z     {
2023-01-11T21:41:26.6808303Z         #pragma omp for 
2023-01-11T21:41:26.6808450Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.6808567Z         {
2023-01-11T21:41:26.6808689Z             #pragma GCC ivdep
2023-01-11T21:41:26.6808836Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.6808953Z             {
2023-01-11T21:41:26.6809193Z                 {
2023-01-11T21:41:26.6809322Z                     {
2023-01-11T21:41:26.6809638Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:41:26.6809915Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:41:26.6810080Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.6810264Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:41:26.6810442Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.6810614Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:26.6810913Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:41:26.6811080Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:26.6811243Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:41:26.6811387Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:41:26.6811562Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:41:26.6811984Z                         float tmp11 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6812120Z                         if(tmp10)
2023-01-11T21:41:26.6812251Z                         {
2023-01-11T21:41:26.6812557Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6812702Z                             tmp11 = tmp12;
2023-01-11T21:41:26.6812815Z                         }
2023-01-11T21:41:26.6813002Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:41:26.6813175Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:41:26.6813343Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:41:26.6813510Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:41:26.6813669Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:41:26.6814053Z                         float tmp18 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6814192Z                         if(tmp17)
2023-01-11T21:41:26.6814300Z                         {
2023-01-11T21:41:26.6814604Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6814757Z                             tmp18 = tmp19;
2023-01-11T21:41:26.6814886Z                         }
2023-01-11T21:41:26.6815114Z                         auto tmp20 = (tmp11 != tmp11) ? tmp11 : std::max(tmp18, tmp11);
2023-01-11T21:41:26.6815315Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:41:26.6815469Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:41:26.6815613Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:41:26.6815782Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:41:26.6815949Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:41:26.6816331Z                         float tmp26 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6816472Z                         if(tmp25)
2023-01-11T21:41:26.6816600Z                         {
2023-01-11T21:41:26.6816907Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6817061Z                             tmp26 = tmp27;
2023-01-11T21:41:26.6817173Z                         }
2023-01-11T21:41:26.6817389Z                         auto tmp28 = (tmp20 != tmp20) ? tmp20 : std::max(tmp26, tmp20);
2023-01-11T21:41:26.6817694Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:41:26.6817862Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:41:26.6818032Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:41:26.6818191Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:41:26.6818354Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:41:26.6818800Z                         float tmp34 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6818917Z                         if(tmp33)
2023-01-11T21:41:26.6819040Z                         {
2023-01-11T21:41:26.6819335Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6819481Z                             tmp34 = tmp35;
2023-01-11T21:41:26.6819603Z                         }
2023-01-11T21:41:26.6819902Z                         auto tmp36 = (tmp28 != tmp28) ? tmp28 : std::max(tmp34, tmp28);
2023-01-11T21:41:26.6820067Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:41:26.6820440Z                         float tmp38 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6820577Z                         if(tmp37)
2023-01-11T21:41:26.6820704Z                         {
2023-01-11T21:41:26.6820915Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:26.6821047Z                             tmp38 = tmp39;
2023-01-11T21:41:26.6821171Z                         }
2023-01-11T21:41:26.6821394Z                         auto tmp40 = (tmp36 != tmp36) ? tmp36 : std::max(tmp38, tmp36);
2023-01-11T21:41:26.6821556Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:41:26.6822057Z                         float tmp42 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6822215Z                         if(tmp41)
2023-01-11T21:41:26.6822372Z                         {
2023-01-11T21:41:26.6822594Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6822769Z                             tmp42 = tmp43;
2023-01-11T21:41:26.6822915Z                         }
2023-01-11T21:41:26.6823249Z                         auto tmp44 = (tmp40 != tmp40) ? tmp40 : std::max(tmp42, tmp40);
2023-01-11T21:41:26.6823452Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:41:26.6823647Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:41:26.6823844Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:41:26.6824037Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:41:26.6824215Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:41:26.6824673Z                         float tmp50 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6824834Z                         if(tmp49)
2023-01-11T21:41:26.6824971Z                         {
2023-01-11T21:41:26.6825164Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6825329Z                             tmp50 = tmp51;
2023-01-11T21:41:26.6825480Z                         }
2023-01-11T21:41:26.6825741Z                         auto tmp52 = (tmp44 != tmp44) ? tmp44 : std::max(tmp50, tmp44);
2023-01-11T21:41:26.6825924Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:41:26.6826363Z                         float tmp54 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6826522Z                         if(tmp53)
2023-01-11T21:41:26.6826646Z                         {
2023-01-11T21:41:26.6826861Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6827035Z                             tmp54 = tmp55;
2023-01-11T21:41:26.6827164Z                         }
2023-01-11T21:41:26.6827426Z                         auto tmp56 = (tmp52 != tmp52) ? tmp52 : std::max(tmp54, tmp52);
2023-01-11T21:41:26.6827621Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:41:26.6828053Z                         float tmp58 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6828299Z                         if(tmp57)
2023-01-11T21:41:26.6828421Z                         {
2023-01-11T21:41:26.6828638Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6828817Z                             tmp58 = tmp59;
2023-01-11T21:41:26.6828966Z                         }
2023-01-11T21:41:26.6829215Z                         auto tmp60 = (tmp56 != tmp56) ? tmp56 : std::max(tmp58, tmp56);
2023-01-11T21:41:26.6829657Z                         float tmp61 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6829817Z                         if(tmp10)
2023-01-11T21:41:26.6829937Z                         {
2023-01-11T21:41:26.6830283Z                             auto tmp62 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6830449Z                             tmp61 = tmp62;
2023-01-11T21:41:26.6830680Z                         }
2023-01-11T21:41:26.6831057Z                         auto tmp63 = static_cast<long>((-9) + (2*i1) + (16*i0));
2023-01-11T21:41:26.6831491Z                         float tmp64 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6831645Z                         if(tmp17)
2023-01-11T21:41:26.6831782Z                         {
2023-01-11T21:41:26.6832099Z                             auto tmp65 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6832265Z                             tmp64 = tmp65;
2023-01-11T21:41:26.6832406Z                         }
2023-01-11T21:41:26.6832785Z                         auto tmp66 = static_cast<long>((-8) + (2*i1) + (16*i0));
2023-01-11T21:41:26.6832975Z                         auto tmp67 = tmp64 > tmp61;
2023-01-11T21:41:26.6833180Z                         auto tmp68 = tmp67 ? tmp66 : tmp63;
2023-01-11T21:41:26.6833441Z                         auto tmp69 = (tmp61 != tmp61) ? tmp61 : std::max(tmp64, tmp61);
2023-01-11T21:41:26.6833885Z                         float tmp70 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6834021Z                         if(tmp25)
2023-01-11T21:41:26.6834159Z                         {
2023-01-11T21:41:26.6834512Z                             auto tmp71 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6834683Z                             tmp70 = tmp71;
2023-01-11T21:41:26.6834827Z                         }
2023-01-11T21:41:26.6835195Z                         auto tmp72 = static_cast<long>((-7) + (2*i1) + (16*i0));
2023-01-11T21:41:26.6835387Z                         auto tmp73 = tmp70 > tmp69;
2023-01-11T21:41:26.6835578Z                         auto tmp74 = tmp73 ? tmp72 : tmp68;
2023-01-11T21:41:26.6835835Z                         auto tmp75 = (tmp69 != tmp69) ? tmp69 : std::max(tmp70, tmp69);
2023-01-11T21:41:26.6836274Z                         float tmp76 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6836427Z                         if(tmp33)
2023-01-11T21:41:26.6836576Z                         {
2023-01-11T21:41:26.6836921Z                             auto tmp77 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6837091Z                             tmp76 = tmp77;
2023-01-11T21:41:26.6837228Z                         }
2023-01-11T21:41:26.6837581Z                         auto tmp78 = static_cast<long>((-1) + (2*i1) + (16*i0));
2023-01-11T21:41:26.6837770Z                         auto tmp79 = tmp76 > tmp75;
2023-01-11T21:41:26.6837973Z                         auto tmp80 = tmp79 ? tmp78 : tmp74;
2023-01-11T21:41:26.6838228Z                         auto tmp81 = (tmp75 != tmp75) ? tmp75 : std::max(tmp76, tmp75);
2023-01-11T21:41:26.6838670Z                         float tmp82 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6838828Z                         if(tmp37)
2023-01-11T21:41:26.6838965Z                         {
2023-01-11T21:41:26.6839143Z                             auto tmp83 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:41:26.6839311Z                             tmp82 = tmp83;
2023-01-11T21:41:26.6839504Z                         }
2023-01-11T21:41:26.6839732Z                         auto tmp84 = static_cast<long>((2*i1) + (16*i0));
2023-01-11T21:41:26.6840026Z                         auto tmp85 = tmp82 > tmp81;
2023-01-11T21:41:26.6840225Z                         auto tmp86 = tmp85 ? tmp84 : tmp80;
2023-01-11T21:41:26.6840483Z                         auto tmp87 = (tmp81 != tmp81) ? tmp81 : std::max(tmp82, tmp81);
2023-01-11T21:41:26.6840932Z                         float tmp88 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6841060Z                         if(tmp41)
2023-01-11T21:41:26.6841202Z                         {
2023-01-11T21:41:26.6841410Z                             auto tmp89 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6841580Z                             tmp88 = tmp89;
2023-01-11T21:41:26.6841715Z                         }
2023-01-11T21:41:26.6842018Z                         auto tmp90 = static_cast<long>(1 + (2*i1) + (16*i0));
2023-01-11T21:41:26.6842209Z                         auto tmp91 = tmp88 > tmp87;
2023-01-11T21:41:26.6842391Z                         auto tmp92 = tmp91 ? tmp90 : tmp86;
2023-01-11T21:41:26.6842664Z                         auto tmp93 = (tmp87 != tmp87) ? tmp87 : std::max(tmp88, tmp87);
2023-01-11T21:41:26.6843112Z                         float tmp94 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6843265Z                         if(tmp49)
2023-01-11T21:41:26.6843410Z                         {
2023-01-11T21:41:26.6843625Z                             auto tmp95 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6843798Z                             tmp94 = tmp95;
2023-01-11T21:41:26.6843941Z                         }
2023-01-11T21:41:26.6844149Z                         auto tmp96 = static_cast<long>(7 + (2*i1) + (16*i0));
2023-01-11T21:41:26.6844339Z                         auto tmp97 = tmp94 > tmp93;
2023-01-11T21:41:26.6844544Z                         auto tmp98 = tmp97 ? tmp96 : tmp92;
2023-01-11T21:41:26.6844802Z                         auto tmp99 = (tmp93 != tmp93) ? tmp93 : std::max(tmp94, tmp93);
2023-01-11T21:41:26.6845251Z                         float tmp100 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6845411Z                         if(tmp53)
2023-01-11T21:41:26.6845554Z                         {
2023-01-11T21:41:26.6845778Z                             auto tmp101 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6845934Z                             tmp100 = tmp101;
2023-01-11T21:41:26.6846059Z                         }
2023-01-11T21:41:26.6846290Z                         auto tmp102 = static_cast<long>(8 + (2*i1) + (16*i0));
2023-01-11T21:41:26.6846482Z                         auto tmp103 = tmp100 > tmp99;
2023-01-11T21:41:26.6846694Z                         auto tmp104 = tmp103 ? tmp102 : tmp98;
2023-01-11T21:41:26.6846950Z                         auto tmp105 = (tmp99 != tmp99) ? tmp99 : std::max(tmp100, tmp99);
2023-01-11T21:41:26.6847394Z                         float tmp106 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.6847549Z                         if(tmp57)
2023-01-11T21:41:26.6847673Z                         {
2023-01-11T21:41:26.6847889Z                             auto tmp107 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:41:26.6848059Z                             tmp106 = tmp107;
2023-01-11T21:41:26.6848194Z                         }
2023-01-11T21:41:26.6848428Z                         auto tmp108 = static_cast<long>(9 + (2*i1) + (16*i0));
2023-01-11T21:41:26.6848626Z                         auto tmp109 = tmp106 > tmp105;
2023-01-11T21:41:26.6848834Z                         auto tmp110 = tmp109 ? tmp108 : tmp104;
2023-01-11T21:41:26.6849196Z                         auto tmp111 = (tmp105 != tmp105) ? tmp105 : std::max(tmp106, tmp105);
2023-01-11T21:41:26.6849391Z                         out_ptr0[i1 + (4*i0)] = tmp60;
2023-01-11T21:41:26.6849580Z                         out_ptr1[i1 + (4*i0)] = tmp110;
2023-01-11T21:41:26.6849727Z                     }
2023-01-11T21:41:26.6849863Z                 }
2023-01-11T21:41:26.6849997Z             }
2023-01-11T21:41:26.6850243Z         }
2023-01-11T21:41:26.6850349Z     }
2023-01-11T21:41:26.6850464Z }
2023-01-11T21:41:26.6850641Z ''')
2023-01-11T21:41:26.6850656Z 
2023-01-11T21:41:26.6850663Z 
2023-01-11T21:41:26.6850849Z async_compile.wait(globals())
2023-01-11T21:41:26.6851003Z del async_compile
2023-01-11T21:41:26.6851012Z 
2023-01-11T21:41:26.6851162Z def call(args):
2023-01-11T21:41:26.6851299Z     arg0_1, = args
2023-01-11T21:41:26.6851427Z     args.clear()
2023-01-11T21:41:26.6851855Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6852265Z     buf1 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6852586Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6852734Z     del arg0_1
2023-01-11T21:41:26.6852981Z     return (buf0, buf1, )
2023-01-11T21:41:26.6852992Z 
2023-01-11T21:41:26.6852999Z 
2023-01-11T21:41:26.6853154Z if __name__ == "__main__":
2023-01-11T21:41:26.6853381Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6853601Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6854023Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6854229Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6854853Z [2023-01-11 21:36:24,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 365
2023-01-11T21:41:26.6854863Z 
2023-01-11T21:41:26.6854988Z ok (3.645s)
2023-01-11T21:41:26.6855765Z   test_max_pool2d4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6855989Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6856437Z [2023-01-11 21:36:24,845] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 366
2023-01-11T21:41:26.6856891Z [2023-01-11 21:36:26,534] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 366
2023-01-11T21:41:26.6856901Z 
2023-01-11T21:41:26.6857067Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6857168Z import torch
2023-01-11T21:41:26.6857298Z import random
2023-01-11T21:41:26.6857509Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6857729Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6857738Z 
2023-01-11T21:41:26.6857882Z aten = torch.ops.aten
2023-01-11T21:41:26.6858108Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6858269Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6858279Z 
2023-01-11T21:41:26.6858285Z 
2023-01-11T21:41:26.6858532Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6858853Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6859046Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6859219Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6859395Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.6859508Z {
2023-01-11T21:41:26.6859680Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6859791Z     {
2023-01-11T21:41:26.6859903Z         #pragma omp for 
2023-01-11T21:41:26.6860042Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.6860163Z         {
2023-01-11T21:41:26.6860310Z             #pragma GCC ivdep
2023-01-11T21:41:26.6860459Z             for(long i1=0; i1<55; i1+=1)
2023-01-11T21:41:26.6860584Z             {
2023-01-11T21:41:26.6860732Z                 #pragma GCC ivdep
2023-01-11T21:41:26.6860865Z                 for(long i2=0; i2<55; i2+=1)
2023-01-11T21:41:26.6861071Z                 {
2023-01-11T21:41:26.6861196Z                     {
2023-01-11T21:41:26.6861321Z                         {
2023-01-11T21:41:26.6861520Z                             auto tmp0 = in_ptr0[(2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6861704Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6861897Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6862074Z                             auto tmp5 = in_ptr0[111 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6862270Z                             auto tmp7 = in_ptr0[112 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6862469Z                             auto tmp9 = in_ptr0[113 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6862732Z                             auto tmp11 = in_ptr0[222 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6862926Z                             auto tmp13 = in_ptr0[223 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6863200Z                             auto tmp15 = in_ptr0[224 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:41:26.6863430Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:41:26.6863653Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:41:26.6863867Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:41:26.6864063Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:41:26.6864285Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:41:26.6864513Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:41:26.6864739Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:41:26.6864962Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:41:26.6865170Z                             auto tmp17 = static_cast<long>((2*i2) + (222*i1));
2023-01-11T21:41:26.6865369Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (222*i1));
2023-01-11T21:41:26.6865533Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:41:26.6865715Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:41:26.6865911Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (222*i1));
2023-01-11T21:41:26.6866079Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:41:26.6866255Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:41:26.6866458Z                             auto tmp24 = static_cast<long>(111 + (2*i2) + (222*i1));
2023-01-11T21:41:26.6866625Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:41:26.6866806Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:41:26.6867019Z                             auto tmp27 = static_cast<long>(112 + (2*i2) + (222*i1));
2023-01-11T21:41:26.6867172Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:41:26.6867345Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:41:26.6867552Z                             auto tmp30 = static_cast<long>(113 + (2*i2) + (222*i1));
2023-01-11T21:41:26.6867721Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:41:26.6867894Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:41:26.6868096Z                             auto tmp33 = static_cast<long>(222 + (2*i2) + (222*i1));
2023-01-11T21:41:26.6868261Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:41:26.6868448Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:41:26.6868626Z                             auto tmp36 = static_cast<long>(223 + (2*i2) + (222*i1));
2023-01-11T21:41:26.6868893Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:41:26.6869063Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:41:26.6869260Z                             auto tmp39 = static_cast<long>(224 + (2*i2) + (222*i1));
2023-01-11T21:41:26.6869433Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:41:26.6869617Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:41:26.6869798Z                             out_ptr0[i2 + (55*i1) + (3025*i0)] = tmp16;
2023-01-11T21:41:26.6869974Z                             out_ptr1[i2 + (55*i1) + (3025*i0)] = tmp41;
2023-01-11T21:41:26.6870076Z                         }
2023-01-11T21:41:26.6870201Z                     }
2023-01-11T21:41:26.6870315Z                 }
2023-01-11T21:41:26.6870434Z             }
2023-01-11T21:41:26.6870610Z         }
2023-01-11T21:41:26.6870726Z     }
2023-01-11T21:41:26.6870809Z }
2023-01-11T21:41:26.6870986Z ''')
2023-01-11T21:41:26.6871002Z 
2023-01-11T21:41:26.6871008Z 
2023-01-11T21:41:26.6871169Z async_compile.wait(globals())
2023-01-11T21:41:26.6871296Z del async_compile
2023-01-11T21:41:26.6871305Z 
2023-01-11T21:41:26.6871431Z def call(args):
2023-01-11T21:41:26.6871560Z     arg0_1, = args
2023-01-11T21:41:26.6871687Z     args.clear()
2023-01-11T21:41:26.6872075Z     buf0 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6872423Z     buf1 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6872692Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6872811Z     del arg0_1
2023-01-11T21:41:26.6872957Z     return (buf0, buf1, )
2023-01-11T21:41:26.6872966Z 
2023-01-11T21:41:26.6872972Z 
2023-01-11T21:41:26.6873109Z if __name__ == "__main__":
2023-01-11T21:41:26.6873313Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6873531Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6873930Z     arg0_1 = rand_strided((2, 8, 111, 111), (98568, 12321, 111, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6874099Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6874113Z 
2023-01-11T21:41:26.6874233Z ok (1.798s)
2023-01-11T21:41:26.6875010Z   test_max_pool2d5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6875226Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6875674Z [2023-01-11 21:36:26,593] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 367
2023-01-11T21:41:26.6876135Z [2023-01-11 21:36:28,247] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 367
2023-01-11T21:41:26.6876145Z 
2023-01-11T21:41:26.6876303Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6876436Z import torch
2023-01-11T21:41:26.6876572Z import random
2023-01-11T21:41:26.6876754Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6876960Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6876971Z 
2023-01-11T21:41:26.6877116Z aten = torch.ops.aten
2023-01-11T21:41:26.6877332Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6877495Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6877505Z 
2023-01-11T21:41:26.6877513Z 
2023-01-11T21:41:26.6877753Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6878085Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6878298Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.6878538Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.6878718Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.6878832Z {
2023-01-11T21:41:26.6878997Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6879110Z     {
2023-01-11T21:41:26.6879255Z         #pragma omp for 
2023-01-11T21:41:26.6879404Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.6879498Z         {
2023-01-11T21:41:26.6879643Z             #pragma GCC ivdep
2023-01-11T21:41:26.6879795Z             for(long i1=0; i1<18; i1+=1)
2023-01-11T21:41:26.6879907Z             {
2023-01-11T21:41:26.6880056Z                 #pragma GCC ivdep
2023-01-11T21:41:26.6880217Z                 for(long i2=0; i2<18; i2+=1)
2023-01-11T21:41:26.6880339Z                 {
2023-01-11T21:41:26.6880440Z                     {
2023-01-11T21:41:26.6880634Z                         {
2023-01-11T21:41:26.6880832Z                             auto tmp0 = in_ptr0[(3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6881031Z                             auto tmp1 = in_ptr0[1 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6881227Z                             auto tmp3 = in_ptr0[2 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6881421Z                             auto tmp5 = in_ptr0[55 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6881622Z                             auto tmp7 = in_ptr0[56 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6881808Z                             auto tmp9 = in_ptr0[57 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6881996Z                             auto tmp11 = in_ptr0[110 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6882196Z                             auto tmp13 = in_ptr0[111 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6882391Z                             auto tmp15 = in_ptr0[112 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:41:26.6882608Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:41:26.6882831Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:41:26.6883047Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:41:26.6883266Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:41:26.6883483Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:41:26.6883717Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:41:26.6883929Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:41:26.6884156Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:41:26.6884360Z                             auto tmp17 = static_cast<long>((3*i2) + (165*i1));
2023-01-11T21:41:26.6884563Z                             auto tmp18 = static_cast<long>(1 + (3*i2) + (165*i1));
2023-01-11T21:41:26.6884745Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:41:26.6884933Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:41:26.6885144Z                             auto tmp21 = static_cast<long>(2 + (3*i2) + (165*i1));
2023-01-11T21:41:26.6885310Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:41:26.6885473Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:41:26.6885677Z                             auto tmp24 = static_cast<long>(55 + (3*i2) + (165*i1));
2023-01-11T21:41:26.6885848Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:41:26.6886031Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:41:26.6886230Z                             auto tmp27 = static_cast<long>(56 + (3*i2) + (165*i1));
2023-01-11T21:41:26.6886398Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:41:26.6886661Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:41:26.6886864Z                             auto tmp30 = static_cast<long>(57 + (3*i2) + (165*i1));
2023-01-11T21:41:26.6887016Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:41:26.6887187Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:41:26.6887395Z                             auto tmp33 = static_cast<long>(110 + (3*i2) + (165*i1));
2023-01-11T21:41:26.6887565Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:41:26.6887755Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:41:26.6887952Z                             auto tmp36 = static_cast<long>(111 + (3*i2) + (165*i1));
2023-01-11T21:41:26.6888117Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:41:26.6888364Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:41:26.6888548Z                             auto tmp39 = static_cast<long>(112 + (3*i2) + (165*i1));
2023-01-11T21:41:26.6888723Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:41:26.6888896Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:41:26.6889190Z                             out_ptr0[i2 + (18*i1) + (324*i0)] = tmp16;
2023-01-11T21:41:26.6889372Z                             out_ptr1[i2 + (18*i1) + (324*i0)] = tmp41;
2023-01-11T21:41:26.6889496Z                         }
2023-01-11T21:41:26.6889626Z                     }
2023-01-11T21:41:26.6889727Z                 }
2023-01-11T21:41:26.6889836Z             }
2023-01-11T21:41:26.6889949Z         }
2023-01-11T21:41:26.6890060Z     }
2023-01-11T21:41:26.6890176Z }
2023-01-11T21:41:26.6890345Z ''')
2023-01-11T21:41:26.6890355Z 
2023-01-11T21:41:26.6890361Z 
2023-01-11T21:41:26.6890526Z async_compile.wait(globals())
2023-01-11T21:41:26.6890643Z del async_compile
2023-01-11T21:41:26.6890656Z 
2023-01-11T21:41:26.6890782Z def call(args):
2023-01-11T21:41:26.6890906Z     arg0_1, = args
2023-01-11T21:41:26.6891048Z     args.clear()
2023-01-11T21:41:26.6891442Z     buf0 = empty_strided((16, 64, 18, 18), (20736, 324, 18, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6891809Z     buf1 = empty_strided((16, 64, 18, 18), (20736, 324, 18, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6892088Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.6892217Z     del arg0_1
2023-01-11T21:41:26.6892334Z     return (buf0, buf1, )
2023-01-11T21:41:26.6892345Z 
2023-01-11T21:41:26.6892351Z 
2023-01-11T21:41:26.6892483Z if __name__ == "__main__":
2023-01-11T21:41:26.6892670Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6892881Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6893282Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6893463Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6893475Z 
2023-01-11T21:41:26.6893593Z ok (1.763s)
2023-01-11T21:41:26.6894360Z   test_max_pool2d6_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6894577Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6895011Z [2023-01-11 21:36:28,352] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 368
2023-01-11T21:41:26.6895432Z [2023-01-11 21:36:28,358] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.max_pool2d_with_indices
2023-01-11T21:41:26.6895898Z [2023-01-11 21:36:28,360] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 368
2023-01-11T21:41:26.6896028Z 
2023-01-11T21:41:26.6896191Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6896325Z import torch
2023-01-11T21:41:26.6896461Z import random
2023-01-11T21:41:26.6896662Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6896869Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6896878Z 
2023-01-11T21:41:26.6896987Z aten = torch.ops.aten
2023-01-11T21:41:26.6897222Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6897386Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6897399Z 
2023-01-11T21:41:26.6897406Z 
2023-01-11T21:41:26.6897560Z async_compile.wait(globals())
2023-01-11T21:41:26.6897696Z del async_compile
2023-01-11T21:41:26.6897707Z 
2023-01-11T21:41:26.6897836Z def call(args):
2023-01-11T21:41:26.6897957Z     arg0_1, = args
2023-01-11T21:41:26.6898184Z     args.clear()
2023-01-11T21:41:26.6898385Z     buf0 = aten.max_pool2d_with_indices(arg0_1, [13, 13], [13, 13], [0, 0], 1, False)
2023-01-11T21:41:26.6898519Z     del arg0_1
2023-01-11T21:41:26.6898647Z     buf1 = buf0[0]
2023-01-11T21:41:26.6898828Z     assert_size_stride(buf1, (16, 64, 4, 4), (1024, 16, 4, 1))
2023-01-11T21:41:26.6898957Z     buf2 = buf0[1]
2023-01-11T21:41:26.6899143Z     assert_size_stride(buf2, (16, 64, 4, 4), (1024, 16, 4, 1))
2023-01-11T21:41:26.6899259Z     del buf0
2023-01-11T21:41:26.6899382Z     return (buf1, buf2, )
2023-01-11T21:41:26.6899392Z 
2023-01-11T21:41:26.6899400Z 
2023-01-11T21:41:26.6899532Z if __name__ == "__main__":
2023-01-11T21:41:26.6899725Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6899938Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6900351Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6900542Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.6900558Z 
2023-01-11T21:41:26.6900682Z ok (0.088s)
2023-01-11T21:41:26.6901482Z   test_max_pool2d_with_indices_backward2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6901717Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6902158Z [2023-01-11 21:36:28,423] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 369
2023-01-11T21:41:26.6902609Z [2023-01-11 21:36:30,025] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 369
2023-01-11T21:41:26.6902618Z 
2023-01-11T21:41:26.6902785Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6902923Z import torch
2023-01-11T21:41:26.6903054Z import random
2023-01-11T21:41:26.6903338Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6903553Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6903563Z 
2023-01-11T21:41:26.6903703Z aten = torch.ops.aten
2023-01-11T21:41:26.6903914Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6904083Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6904091Z 
2023-01-11T21:41:26.6904097Z 
2023-01-11T21:41:26.6904336Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6904686Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6904886Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.6905062Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6905235Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6905349Z {
2023-01-11T21:41:26.6905506Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6905620Z     {
2023-01-11T21:41:26.6905762Z         #pragma omp for 
2023-01-11T21:41:26.6905996Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.6906113Z         {
2023-01-11T21:41:26.6906255Z             #pragma GCC ivdep
2023-01-11T21:41:26.6906409Z             for(long i1=0; i1<40; i1+=1)
2023-01-11T21:41:26.6906509Z             {
2023-01-11T21:41:26.6906650Z                 #pragma GCC ivdep
2023-01-11T21:41:26.6906809Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:41:26.6906923Z                 {
2023-01-11T21:41:26.6907045Z                     {
2023-01-11T21:41:26.6907169Z                         {
2023-01-11T21:41:26.6907344Z                             auto tmp0 = static_cast<int>(i2 + (56*i1));
2023-01-11T21:41:26.6907531Z                             auto tmp1 = static_cast<int>((i1 / 2));
2023-01-11T21:41:26.6907718Z                             auto tmp2 = static_cast<int>((i2 / 2));
2023-01-11T21:41:26.6907979Z                             auto tmp3 = static_cast<int>(1 + (((1 + i1) / 2)));
2023-01-11T21:41:26.6908184Z                             auto tmp4 = static_cast<int>(1 + (((1 + i2) / 2)));
2023-01-11T21:41:26.6908376Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:41:26.6908610Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:41:26.6908827Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:41:26.6909015Z                             auto tmp8 = static_cast<int>(21);
2023-01-11T21:41:26.6909220Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:41:26.6909404Z                             auto tmp10 = static_cast<int>(29);
2023-01-11T21:41:26.6909623Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:41:26.6909805Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:41:26.6909972Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:41:26.6910155Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:41:26.6910442Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:41:26.6910670Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:41:26.6910923Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:41:26.6911143Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:41:26.6911337Z                             auto tmp19 = in_ptr0[tmp18 + (29*tmp16) + (609*i0)];
2023-01-11T21:41:26.6911540Z                             auto tmp20 = in_ptr1[tmp18 + (29*tmp16) + (609*i0)];
2023-01-11T21:41:26.6911715Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:41:26.6911907Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:26.6912095Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:41:26.6912264Z                             auto tmp24 = tmp7 + tmp14;
2023-01-11T21:41:26.6912479Z                             auto tmp25 = (tmp17 != tmp17) ? tmp17 : std::min(tmp24, tmp17);
2023-01-11T21:41:26.6912680Z                             auto tmp26 = in_ptr0[tmp25 + (29*tmp16) + (609*i0)];
2023-01-11T21:41:26.6912879Z                             auto tmp27 = in_ptr1[tmp25 + (29*tmp16) + (609*i0)];
2023-01-11T21:41:26.6913052Z                             auto tmp28 = tmp26 == tmp0;
2023-01-11T21:41:26.6913216Z                             auto tmp29 = tmp12 < tmp9;
2023-01-11T21:41:26.6913386Z                             auto tmp30 = tmp24 < tmp11;
2023-01-11T21:41:26.6913559Z                             auto tmp31 = tmp29 & tmp30;
2023-01-11T21:41:26.6913727Z                             auto tmp32 = tmp31 & tmp28;
2023-01-11T21:41:26.6913872Z                             auto tmp33 = tmp23 + tmp27;
2023-01-11T21:41:26.6914057Z                             auto tmp34 = tmp32 ? tmp33 : tmp23;
2023-01-11T21:41:26.6914329Z                             auto tmp35 = tmp6 + tmp14;
2023-01-11T21:41:26.6914682Z                             auto tmp36 = (tmp15 != tmp15) ? tmp15 : std::min(tmp35, tmp15);
2023-01-11T21:41:26.6914913Z                             auto tmp37 = in_ptr0[tmp18 + (29*tmp36) + (609*i0)];
2023-01-11T21:41:26.6915147Z                             auto tmp38 = in_ptr1[tmp18 + (29*tmp36) + (609*i0)];
2023-01-11T21:41:26.6915344Z                             auto tmp39 = tmp37 == tmp0;
2023-01-11T21:41:26.6915534Z                             auto tmp40 = tmp35 < tmp9;
2023-01-11T21:41:26.6915706Z                             auto tmp41 = tmp13 < tmp11;
2023-01-11T21:41:26.6915893Z                             auto tmp42 = tmp40 & tmp41;
2023-01-11T21:41:26.6916079Z                             auto tmp43 = tmp42 & tmp39;
2023-01-11T21:41:26.6916277Z                             auto tmp44 = tmp34 + tmp38;
2023-01-11T21:41:26.6916566Z                             auto tmp45 = tmp43 ? tmp44 : tmp34;
2023-01-11T21:41:26.6916804Z                             auto tmp46 = in_ptr0[tmp25 + (29*tmp36) + (609*i0)];
2023-01-11T21:41:26.6917036Z                             auto tmp47 = in_ptr1[tmp25 + (29*tmp36) + (609*i0)];
2023-01-11T21:41:26.6917207Z                             auto tmp48 = tmp46 == tmp0;
2023-01-11T21:41:26.6917401Z                             auto tmp49 = tmp40 & tmp30;
2023-01-11T21:41:26.6917591Z                             auto tmp50 = tmp49 & tmp48;
2023-01-11T21:41:26.6917774Z                             auto tmp51 = tmp45 + tmp47;
2023-01-11T21:41:26.6917980Z                             auto tmp52 = tmp50 ? tmp51 : tmp45;
2023-01-11T21:41:26.6918189Z                             out_ptr0[i2 + (56*i1) + (2240*i0)] = tmp52;
2023-01-11T21:41:26.6918339Z                         }
2023-01-11T21:41:26.6918460Z                     }
2023-01-11T21:41:26.6918592Z                 }
2023-01-11T21:41:26.6918727Z             }
2023-01-11T21:41:26.6918870Z         }
2023-01-11T21:41:26.6918993Z     }
2023-01-11T21:41:26.6919120Z }
2023-01-11T21:41:26.6919308Z ''')
2023-01-11T21:41:26.6919329Z 
2023-01-11T21:41:26.6919336Z 
2023-01-11T21:41:26.6919501Z async_compile.wait(globals())
2023-01-11T21:41:26.6919650Z del async_compile
2023-01-11T21:41:26.6919663Z 
2023-01-11T21:41:26.6919813Z def call(args):
2023-01-11T21:41:26.6919974Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6920127Z     args.clear()
2023-01-11T21:41:26.6920581Z     buf0 = empty_strided((2, 4, 40, 56), (8960, 2240, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6920904Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6921044Z     del arg0_1
2023-01-11T21:41:26.6921166Z     del arg2_1
2023-01-11T21:41:26.6921307Z     return (buf0, )
2023-01-11T21:41:26.6921322Z 
2023-01-11T21:41:26.6921329Z 
2023-01-11T21:41:26.6921484Z if __name__ == "__main__":
2023-01-11T21:41:26.6921717Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6921960Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6922402Z     arg0_1 = rand_strided((2, 4, 21, 29), (2436, 609, 29, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6922838Z     arg1_1 = rand_strided((2, 4, 40, 56), (8960, 2240, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6923273Z     arg2_1 = rand_strided((2, 4, 21, 29), (2436, 609, 29, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6923494Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6923504Z 
2023-01-11T21:41:26.6923642Z ok (1.629s)
2023-01-11T21:41:26.6924580Z   test_max_pool2d_with_indices_backward3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6924855Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6925484Z [2023-01-11 21:36:30,150] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 370
2023-01-11T21:41:26.6926016Z [2023-01-11 21:36:31,706] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 370
2023-01-11T21:41:26.6926032Z 
2023-01-11T21:41:26.6926216Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6926360Z import torch
2023-01-11T21:41:26.6926510Z import random
2023-01-11T21:41:26.6926724Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6926965Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6926979Z 
2023-01-11T21:41:26.6927131Z aten = torch.ops.aten
2023-01-11T21:41:26.6927402Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6927664Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6927677Z 
2023-01-11T21:41:26.6927684Z 
2023-01-11T21:41:26.6927959Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6928359Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6928598Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.6928787Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6928977Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6929235Z {
2023-01-11T21:41:26.6929444Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6929575Z     {
2023-01-11T21:41:26.6929734Z         #pragma omp for 
2023-01-11T21:41:26.6929900Z         for(long i0=0; i0<8192; i0+=1)
2023-01-11T21:41:26.6930004Z         {
2023-01-11T21:41:26.6930172Z             #pragma GCC ivdep
2023-01-11T21:41:26.6930351Z             for(long i1=0; i1<37; i1+=1)
2023-01-11T21:41:26.6930484Z             {
2023-01-11T21:41:26.6930659Z                 #pragma GCC ivdep
2023-01-11T21:41:26.6930839Z                 for(long i2=0; i2<38; i2+=1)
2023-01-11T21:41:26.6930973Z                 {
2023-01-11T21:41:26.6931087Z                     {
2023-01-11T21:41:26.6931226Z                         {
2023-01-11T21:41:26.6931508Z                             auto tmp0 = static_cast<int>(i2 + (38*i1));
2023-01-11T21:41:26.6931745Z                             auto tmp1 = static_cast<int>(((1 + i1) / 2));
2023-01-11T21:41:26.6931973Z                             auto tmp2 = static_cast<int>(((1 + i2) / 2));
2023-01-11T21:41:26.6932194Z                             auto tmp3 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:41:26.6932414Z                             auto tmp4 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:41:26.6932599Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:41:26.6932866Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:41:26.6933113Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:41:26.6933334Z                             auto tmp8 = static_cast<int>(19);
2023-01-11T21:41:26.6933596Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:41:26.6933848Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::min(tmp4, tmp8);
2023-01-11T21:41:26.6934123Z                             auto tmp11 = tmp6 + tmp5;
2023-01-11T21:41:26.6934302Z                             auto tmp12 = tmp7 + tmp5;
2023-01-11T21:41:26.6934490Z                             auto tmp13 = static_cast<int>(1);
2023-01-11T21:41:26.6934756Z                             auto tmp14 = tmp9 - tmp13;
2023-01-11T21:41:26.6934992Z                             auto tmp15 = (tmp14 != tmp14) ? tmp14 : std::min(tmp11, tmp14);
2023-01-11T21:41:26.6935262Z                             auto tmp16 = tmp10 - tmp13;
2023-01-11T21:41:26.6935499Z                             auto tmp17 = (tmp16 != tmp16) ? tmp16 : std::min(tmp12, tmp16);
2023-01-11T21:41:26.6935714Z                             auto tmp18 = in_ptr0[tmp17 + (19*tmp15) + (361*i0)];
2023-01-11T21:41:26.6936030Z                             auto tmp19 = in_ptr1[tmp17 + (19*tmp15) + (361*i0)];
2023-01-11T21:41:26.6936208Z                             auto tmp20 = tmp18 == tmp0;
2023-01-11T21:41:26.6936403Z                             auto tmp21 = static_cast<float>(0.0);
2023-01-11T21:41:26.6936566Z                             auto tmp22 = tmp20 ? tmp19 : tmp21;
2023-01-11T21:41:26.6936747Z                             out_ptr0[i2 + (38*i1) + (1406*i0)] = tmp22;
2023-01-11T21:41:26.6936873Z                         }
2023-01-11T21:41:26.6936994Z                     }
2023-01-11T21:41:26.6937118Z                 }
2023-01-11T21:41:26.6937338Z             }
2023-01-11T21:41:26.6937467Z         }
2023-01-11T21:41:26.6937573Z     }
2023-01-11T21:41:26.6937700Z }
2023-01-11T21:41:26.6937871Z ''')
2023-01-11T21:41:26.6937887Z 
2023-01-11T21:41:26.6937979Z 
2023-01-11T21:41:26.6938162Z async_compile.wait(globals())
2023-01-11T21:41:26.6938293Z del async_compile
2023-01-11T21:41:26.6938306Z 
2023-01-11T21:41:26.6938415Z def call(args):
2023-01-11T21:41:26.6938545Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6938640Z     args.clear()
2023-01-11T21:41:26.6939014Z     buf0 = empty_strided((32, 256, 37, 38), (359936, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6939300Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6939428Z     del arg0_1
2023-01-11T21:41:26.6939552Z     del arg2_1
2023-01-11T21:41:26.6939684Z     return (buf0, )
2023-01-11T21:41:26.6939694Z 
2023-01-11T21:41:26.6939700Z 
2023-01-11T21:41:26.6939838Z if __name__ == "__main__":
2023-01-11T21:41:26.6940048Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6940258Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6940708Z     arg0_1 = rand_strided((32, 256, 19, 19), (92416, 361, 19, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6941149Z     arg1_1 = rand_strided((32, 256, 37, 38), (359936, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6941581Z     arg2_1 = rand_strided((32, 256, 19, 19), (92416, 361, 19, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6941802Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6941815Z 
2023-01-11T21:41:26.6941936Z ok (2.128s)
2023-01-11T21:41:26.6942863Z   test_max_pool2d_with_indices_backward4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6943113Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6943722Z [2023-01-11 21:36:32,178] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 371
2023-01-11T21:41:26.6943744Z 
2023-01-11T21:41:26.6943995Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6944095Z import torch
2023-01-11T21:41:26.6944207Z import random
2023-01-11T21:41:26.6944392Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6944587Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6944595Z 
2023-01-11T21:41:26.6944718Z aten = torch.ops.aten
2023-01-11T21:41:26.6944933Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6945078Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6945087Z 
2023-01-11T21:41:26.6945093Z 
2023-01-11T21:41:26.6945326Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.6945652Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.6945844Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.6946013Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.6946298Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.6946400Z {
2023-01-11T21:41:26.6946557Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.6946660Z     {
2023-01-11T21:41:26.6946767Z         #pragma omp for 
2023-01-11T21:41:26.6946903Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.6947004Z         {
2023-01-11T21:41:26.6947133Z             #pragma GCC ivdep
2023-01-11T21:41:26.6947266Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.6947373Z             {
2023-01-11T21:41:26.6947486Z                 #pragma GCC ivdep
2023-01-11T21:41:26.6947630Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:26.6947732Z                 {
2023-01-11T21:41:26.6947843Z                     {
2023-01-11T21:41:26.6947952Z                         {
2023-01-11T21:41:26.6948213Z                             auto tmp0 = static_cast<int>(i2 + (4*i1));
2023-01-11T21:41:26.6948540Z                             auto tmp1 = static_cast<int>((-2) + i1);
2023-01-11T21:41:26.6948813Z                             auto tmp2 = static_cast<int>((-2) + i2);
2023-01-11T21:41:26.6948987Z                             auto tmp3 = static_cast<int>(3 + i1);
2023-01-11T21:41:26.6949155Z                             auto tmp4 = static_cast<int>(3 + i2);
2023-01-11T21:41:26.6949322Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:41:26.6949542Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:41:26.6949741Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:41:26.6949913Z                             auto tmp8 = static_cast<int>(3);
2023-01-11T21:41:26.6950126Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:41:26.6950305Z                             auto tmp10 = static_cast<int>(4);
2023-01-11T21:41:26.6950507Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:41:26.6950666Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:41:26.6950813Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:41:26.6950981Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:41:26.6951256Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:41:26.6951470Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:41:26.6951728Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:41:26.6951935Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:41:26.6952103Z                             auto tmp19 = in_ptr0[tmp18 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6952293Z                             auto tmp20 = in_ptr1[tmp18 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6952446Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:41:26.6952617Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:26.6952791Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:41:26.6952944Z                             auto tmp24 = tmp7 + tmp14;
2023-01-11T21:41:26.6953158Z                             auto tmp25 = (tmp17 != tmp17) ? tmp17 : std::min(tmp24, tmp17);
2023-01-11T21:41:26.6953346Z                             auto tmp26 = in_ptr0[tmp25 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6953513Z                             auto tmp27 = in_ptr1[tmp25 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6953666Z                             auto tmp28 = tmp26 == tmp0;
2023-01-11T21:41:26.6953819Z                             auto tmp29 = tmp12 < tmp9;
2023-01-11T21:41:26.6953972Z                             auto tmp30 = tmp24 < tmp11;
2023-01-11T21:41:26.6954133Z                             auto tmp31 = tmp29 & tmp30;
2023-01-11T21:41:26.6954289Z                             auto tmp32 = tmp31 & tmp28;
2023-01-11T21:41:26.6954557Z                             auto tmp33 = tmp23 + tmp27;
2023-01-11T21:41:26.6954704Z                             auto tmp34 = tmp32 ? tmp33 : tmp23;
2023-01-11T21:41:26.6954877Z                             auto tmp35 = static_cast<int>(2);
2023-01-11T21:41:26.6955028Z                             auto tmp36 = tmp7 + tmp35;
2023-01-11T21:41:26.6955248Z                             auto tmp37 = (tmp17 != tmp17) ? tmp17 : std::min(tmp36, tmp17);
2023-01-11T21:41:26.6955431Z                             auto tmp38 = in_ptr0[tmp37 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6955613Z                             auto tmp39 = in_ptr1[tmp37 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6955769Z                             auto tmp40 = tmp38 == tmp0;
2023-01-11T21:41:26.6955919Z                             auto tmp41 = tmp36 < tmp11;
2023-01-11T21:41:26.6956161Z                             auto tmp42 = tmp29 & tmp41;
2023-01-11T21:41:26.6956340Z                             auto tmp43 = tmp42 & tmp40;
2023-01-11T21:41:26.6956502Z                             auto tmp44 = tmp34 + tmp39;
2023-01-11T21:41:26.6956668Z                             auto tmp45 = tmp43 ? tmp44 : tmp34;
2023-01-11T21:41:26.6956815Z                             auto tmp46 = tmp7 + tmp8;
2023-01-11T21:41:26.6957038Z                             auto tmp47 = (tmp17 != tmp17) ? tmp17 : std::min(tmp46, tmp17);
2023-01-11T21:41:26.6957221Z                             auto tmp48 = in_ptr0[tmp47 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6957401Z                             auto tmp49 = in_ptr1[tmp47 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6957539Z                             auto tmp50 = tmp48 == tmp0;
2023-01-11T21:41:26.6957692Z                             auto tmp51 = tmp46 < tmp11;
2023-01-11T21:41:26.6957845Z                             auto tmp52 = tmp29 & tmp51;
2023-01-11T21:41:26.6958003Z                             auto tmp53 = tmp52 & tmp50;
2023-01-11T21:41:26.6958153Z                             auto tmp54 = tmp45 + tmp49;
2023-01-11T21:41:26.6958326Z                             auto tmp55 = tmp53 ? tmp54 : tmp45;
2023-01-11T21:41:26.6958479Z                             auto tmp56 = tmp7 + tmp10;
2023-01-11T21:41:26.6958695Z                             auto tmp57 = (tmp17 != tmp17) ? tmp17 : std::min(tmp56, tmp17);
2023-01-11T21:41:26.6958864Z                             auto tmp58 = in_ptr0[tmp57 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6959048Z                             auto tmp59 = in_ptr1[tmp57 + (4*tmp16) + (12*i0)];
2023-01-11T21:41:26.6959199Z                             auto tmp60 = tmp58 == tmp0;
2023-01-11T21:41:26.6959352Z                             auto tmp61 = tmp56 < tmp11;
2023-01-11T21:41:26.6959495Z                             auto tmp62 = tmp29 & tmp61;
2023-01-11T21:41:26.6959647Z                             auto tmp63 = tmp62 & tmp60;
2023-01-11T21:41:26.6959801Z                             auto tmp64 = tmp55 + tmp59;
2023-01-11T21:41:26.6959955Z                             auto tmp65 = tmp63 ? tmp64 : tmp55;
2023-01-11T21:41:26.6960117Z                             auto tmp66 = tmp6 + tmp14;
2023-01-11T21:41:26.6960328Z                             auto tmp67 = (tmp15 != tmp15) ? tmp15 : std::min(tmp66, tmp15);
2023-01-11T21:41:26.6960511Z                             auto tmp68 = in_ptr0[tmp18 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6960693Z                             auto tmp69 = in_ptr1[tmp18 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6960843Z                             auto tmp70 = tmp68 == tmp0;
2023-01-11T21:41:26.6960995Z                             auto tmp71 = tmp66 < tmp9;
2023-01-11T21:41:26.6961144Z                             auto tmp72 = tmp13 < tmp11;
2023-01-11T21:41:26.6961280Z                             auto tmp73 = tmp71 & tmp72;
2023-01-11T21:41:26.6961432Z                             auto tmp74 = tmp73 & tmp70;
2023-01-11T21:41:26.6961588Z                             auto tmp75 = tmp65 + tmp69;
2023-01-11T21:41:26.6961756Z                             auto tmp76 = tmp74 ? tmp75 : tmp65;
2023-01-11T21:41:26.6962048Z                             auto tmp77 = in_ptr0[tmp25 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6962231Z                             auto tmp78 = in_ptr1[tmp25 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6962385Z                             auto tmp79 = tmp77 == tmp0;
2023-01-11T21:41:26.6962535Z                             auto tmp80 = tmp71 & tmp30;
2023-01-11T21:41:26.6962666Z                             auto tmp81 = tmp80 & tmp79;
2023-01-11T21:41:26.6962815Z                             auto tmp82 = tmp76 + tmp78;
2023-01-11T21:41:26.6962975Z                             auto tmp83 = tmp81 ? tmp82 : tmp76;
2023-01-11T21:41:26.6963159Z                             auto tmp84 = in_ptr0[tmp37 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6963340Z                             auto tmp85 = in_ptr1[tmp37 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6963597Z                             auto tmp86 = tmp84 == tmp0;
2023-01-11T21:41:26.6963773Z                             auto tmp87 = tmp71 & tmp41;
2023-01-11T21:41:26.6963935Z                             auto tmp88 = tmp87 & tmp86;
2023-01-11T21:41:26.6964071Z                             auto tmp89 = tmp83 + tmp85;
2023-01-11T21:41:26.6964230Z                             auto tmp90 = tmp88 ? tmp89 : tmp83;
2023-01-11T21:41:26.6964408Z                             auto tmp91 = in_ptr0[tmp47 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6964589Z                             auto tmp92 = in_ptr1[tmp47 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6964738Z                             auto tmp93 = tmp91 == tmp0;
2023-01-11T21:41:26.6964891Z                             auto tmp94 = tmp71 & tmp51;
2023-01-11T21:41:26.6965036Z                             auto tmp95 = tmp94 & tmp93;
2023-01-11T21:41:26.6965171Z                             auto tmp96 = tmp90 + tmp92;
2023-01-11T21:41:26.6965337Z                             auto tmp97 = tmp95 ? tmp96 : tmp90;
2023-01-11T21:41:26.6965518Z                             auto tmp98 = in_ptr0[tmp57 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6965708Z                             auto tmp99 = in_ptr1[tmp57 + (4*tmp67) + (12*i0)];
2023-01-11T21:41:26.6965864Z                             auto tmp100 = tmp98 == tmp0;
2023-01-11T21:41:26.6966021Z                             auto tmp101 = tmp71 & tmp61;
2023-01-11T21:41:26.6966178Z                             auto tmp102 = tmp101 & tmp100;
2023-01-11T21:41:26.6966326Z                             auto tmp103 = tmp97 + tmp99;
2023-01-11T21:41:26.6966479Z                             auto tmp104 = tmp102 ? tmp103 : tmp97;
2023-01-11T21:41:26.6966636Z                             auto tmp105 = tmp6 + tmp35;
2023-01-11T21:41:26.6966861Z                             auto tmp106 = (tmp15 != tmp15) ? tmp15 : std::min(tmp105, tmp15);
2023-01-11T21:41:26.6967055Z                             auto tmp107 = in_ptr0[tmp18 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6967247Z                             auto tmp108 = in_ptr1[tmp18 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6967406Z                             auto tmp109 = tmp107 == tmp0;
2023-01-11T21:41:26.6967569Z                             auto tmp110 = tmp105 < tmp9;
2023-01-11T21:41:26.6967720Z                             auto tmp111 = tmp110 & tmp72;
2023-01-11T21:41:26.6967865Z                             auto tmp112 = tmp111 & tmp109;
2023-01-11T21:41:26.6968024Z                             auto tmp113 = tmp104 + tmp108;
2023-01-11T21:41:26.6968193Z                             auto tmp114 = tmp112 ? tmp113 : tmp104;
2023-01-11T21:41:26.6968376Z                             auto tmp115 = in_ptr0[tmp25 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6968560Z                             auto tmp116 = in_ptr1[tmp25 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6968717Z                             auto tmp117 = tmp115 == tmp0;
2023-01-11T21:41:26.6968874Z                             auto tmp118 = tmp110 & tmp30;
2023-01-11T21:41:26.6969252Z                             auto tmp119 = tmp118 & tmp117;
2023-01-11T21:41:26.6969429Z                             auto tmp120 = tmp114 + tmp116;
2023-01-11T21:41:26.6969732Z                             auto tmp121 = tmp119 ? tmp120 : tmp114;
2023-01-11T21:41:26.6969920Z                             auto tmp122 = in_ptr0[tmp37 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6970103Z                             auto tmp123 = in_ptr1[tmp37 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6970260Z                             auto tmp124 = tmp122 == tmp0;
2023-01-11T21:41:26.6970418Z                             auto tmp125 = tmp110 & tmp41;
2023-01-11T21:41:26.6970575Z                             auto tmp126 = tmp125 & tmp124;
2023-01-11T21:41:26.6970716Z                             auto tmp127 = tmp121 + tmp123;
2023-01-11T21:41:26.6970884Z                             auto tmp128 = tmp126 ? tmp127 : tmp121;
2023-01-11T21:41:26.6971164Z                             auto tmp129 = in_ptr0[tmp47 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6971375Z                             auto tmp130 = in_ptr1[tmp47 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6971538Z                             auto tmp131 = tmp129 == tmp0;
2023-01-11T21:41:26.6971693Z                             auto tmp132 = tmp110 & tmp51;
2023-01-11T21:41:26.6971853Z                             auto tmp133 = tmp132 & tmp131;
2023-01-11T21:41:26.6972005Z                             auto tmp134 = tmp128 + tmp130;
2023-01-11T21:41:26.6972158Z                             auto tmp135 = tmp133 ? tmp134 : tmp128;
2023-01-11T21:41:26.6972349Z                             auto tmp136 = in_ptr0[tmp57 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6972534Z                             auto tmp137 = in_ptr1[tmp57 + (4*tmp106) + (12*i0)];
2023-01-11T21:41:26.6972688Z                             auto tmp138 = tmp136 == tmp0;
2023-01-11T21:41:26.6972840Z                             auto tmp139 = tmp110 & tmp61;
2023-01-11T21:41:26.6972999Z                             auto tmp140 = tmp139 & tmp138;
2023-01-11T21:41:26.6973156Z                             auto tmp141 = tmp135 + tmp137;
2023-01-11T21:41:26.6973317Z                             auto tmp142 = tmp140 ? tmp141 : tmp135;
2023-01-11T21:41:26.6973474Z                             auto tmp143 = tmp6 + tmp8;
2023-01-11T21:41:26.6973695Z                             auto tmp144 = (tmp15 != tmp15) ? tmp15 : std::min(tmp143, tmp15);
2023-01-11T21:41:26.6973880Z                             auto tmp145 = in_ptr0[tmp18 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6974063Z                             auto tmp146 = in_ptr1[tmp18 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6974217Z                             auto tmp147 = tmp145 == tmp0;
2023-01-11T21:41:26.6974367Z                             auto tmp148 = tmp143 < tmp9;
2023-01-11T21:41:26.6974519Z                             auto tmp149 = tmp148 & tmp72;
2023-01-11T21:41:26.6974657Z                             auto tmp150 = tmp149 & tmp147;
2023-01-11T21:41:26.6974817Z                             auto tmp151 = tmp142 + tmp146;
2023-01-11T21:41:26.6974984Z                             auto tmp152 = tmp150 ? tmp151 : tmp142;
2023-01-11T21:41:26.6975177Z                             auto tmp153 = in_ptr0[tmp25 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6975354Z                             auto tmp154 = in_ptr1[tmp25 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6975507Z                             auto tmp155 = tmp153 == tmp0;
2023-01-11T21:41:26.6975660Z                             auto tmp156 = tmp148 & tmp30;
2023-01-11T21:41:26.6975819Z                             auto tmp157 = tmp156 & tmp155;
2023-01-11T21:41:26.6975957Z                             auto tmp158 = tmp152 + tmp154;
2023-01-11T21:41:26.6976126Z                             auto tmp159 = tmp157 ? tmp158 : tmp152;
2023-01-11T21:41:26.6976311Z                             auto tmp160 = in_ptr0[tmp37 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6976501Z                             auto tmp161 = in_ptr1[tmp37 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6976657Z                             auto tmp162 = tmp160 == tmp0;
2023-01-11T21:41:26.6976809Z                             auto tmp163 = tmp148 & tmp41;
2023-01-11T21:41:26.6977081Z                             auto tmp164 = tmp163 & tmp162;
2023-01-11T21:41:26.6977232Z                             auto tmp165 = tmp159 + tmp161;
2023-01-11T21:41:26.6977386Z                             auto tmp166 = tmp164 ? tmp165 : tmp159;
2023-01-11T21:41:26.6977566Z                             auto tmp167 = in_ptr0[tmp47 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6977749Z                             auto tmp168 = in_ptr1[tmp47 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6977906Z                             auto tmp169 = tmp167 == tmp0;
2023-01-11T21:41:26.6978057Z                             auto tmp170 = tmp148 & tmp51;
2023-01-11T21:41:26.6978211Z                             auto tmp171 = tmp170 & tmp169;
2023-01-11T21:41:26.6978445Z                             auto tmp172 = tmp166 + tmp168;
2023-01-11T21:41:26.6978613Z                             auto tmp173 = tmp171 ? tmp172 : tmp166;
2023-01-11T21:41:26.6978805Z                             auto tmp174 = in_ptr0[tmp57 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6978995Z                             auto tmp175 = in_ptr1[tmp57 + (4*tmp144) + (12*i0)];
2023-01-11T21:41:26.6979152Z                             auto tmp176 = tmp174 == tmp0;
2023-01-11T21:41:26.6979304Z                             auto tmp177 = tmp148 & tmp61;
2023-01-11T21:41:26.6979459Z                             auto tmp178 = tmp177 & tmp176;
2023-01-11T21:41:26.6979613Z                             auto tmp179 = tmp173 + tmp175;
2023-01-11T21:41:26.6979782Z                             auto tmp180 = tmp178 ? tmp179 : tmp173;
2023-01-11T21:41:26.6979919Z                             auto tmp181 = tmp6 + tmp10;
2023-01-11T21:41:26.6980144Z                             auto tmp182 = (tmp15 != tmp15) ? tmp15 : std::min(tmp181, tmp15);
2023-01-11T21:41:26.6980333Z                             auto tmp183 = in_ptr0[tmp18 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6980518Z                             auto tmp184 = in_ptr1[tmp18 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6980682Z                             auto tmp185 = tmp183 == tmp0;
2023-01-11T21:41:26.6980837Z                             auto tmp186 = tmp181 < tmp9;
2023-01-11T21:41:26.6980990Z                             auto tmp187 = tmp186 & tmp72;
2023-01-11T21:41:26.6981145Z                             auto tmp188 = tmp187 & tmp185;
2023-01-11T21:41:26.6981281Z                             auto tmp189 = tmp180 + tmp184;
2023-01-11T21:41:26.6981446Z                             auto tmp190 = tmp188 ? tmp189 : tmp180;
2023-01-11T21:41:26.6981625Z                             auto tmp191 = in_ptr0[tmp25 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6981805Z                             auto tmp192 = in_ptr1[tmp25 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6981955Z                             auto tmp193 = tmp191 == tmp0;
2023-01-11T21:41:26.6982113Z                             auto tmp194 = tmp186 & tmp30;
2023-01-11T21:41:26.6982268Z                             auto tmp195 = tmp194 & tmp193;
2023-01-11T21:41:26.6982431Z                             auto tmp196 = tmp190 + tmp192;
2023-01-11T21:41:26.6982581Z                             auto tmp197 = tmp195 ? tmp196 : tmp190;
2023-01-11T21:41:26.6982764Z                             auto tmp198 = in_ptr0[tmp37 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6982946Z                             auto tmp199 = in_ptr1[tmp37 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6983100Z                             auto tmp200 = tmp198 == tmp0;
2023-01-11T21:41:26.6983377Z                             auto tmp201 = tmp186 & tmp41;
2023-01-11T21:41:26.6983540Z                             auto tmp202 = tmp201 & tmp200;
2023-01-11T21:41:26.6983696Z                             auto tmp203 = tmp197 + tmp199;
2023-01-11T21:41:26.6983845Z                             auto tmp204 = tmp202 ? tmp203 : tmp197;
2023-01-11T21:41:26.6984036Z                             auto tmp205 = in_ptr0[tmp47 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6984218Z                             auto tmp206 = in_ptr1[tmp47 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6984485Z                             auto tmp207 = tmp205 == tmp0;
2023-01-11T21:41:26.6984635Z                             auto tmp208 = tmp186 & tmp51;
2023-01-11T21:41:26.6984792Z                             auto tmp209 = tmp208 & tmp207;
2023-01-11T21:41:26.6984945Z                             auto tmp210 = tmp204 + tmp206;
2023-01-11T21:41:26.6985111Z                             auto tmp211 = tmp209 ? tmp210 : tmp204;
2023-01-11T21:41:26.6985278Z                             auto tmp212 = in_ptr0[tmp57 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6985460Z                             auto tmp213 = in_ptr1[tmp57 + (4*tmp182) + (12*i0)];
2023-01-11T21:41:26.6985615Z                             auto tmp214 = tmp212 == tmp0;
2023-01-11T21:41:26.6985842Z                             auto tmp215 = tmp186 & tmp61;
2023-01-11T21:41:26.6986020Z                             auto tmp216 = tmp215 & tmp214;
2023-01-11T21:41:26.6986181Z                             auto tmp217 = tmp211 + tmp213;
2023-01-11T21:41:26.6986347Z                             auto tmp218 = tmp216 ? tmp217 : tmp211;
2023-01-11T21:41:26.6986514Z                             out_ptr0[i2 + (4*i1) + (12*i0)] = tmp218;
2023-01-11T21:41:26.6986609Z                         }
2023-01-11T21:41:26.6986713Z                     }
2023-01-11T21:41:26.6986816Z                 }
2023-01-11T21:41:26.6986922Z             }
2023-01-11T21:41:26.6987024Z         }
2023-01-11T21:41:26.6987124Z     }
2023-01-11T21:41:26.6987201Z }
2023-01-11T21:41:26.6987374Z ''')
2023-01-11T21:41:26.6987385Z 
2023-01-11T21:41:26.6987391Z 
2023-01-11T21:41:26.6987545Z async_compile.wait(globals())
2023-01-11T21:41:26.6987660Z del async_compile
2023-01-11T21:41:26.6987669Z 
2023-01-11T21:41:26.6987783Z def call(args):
2023-01-11T21:41:26.6987919Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6988034Z     args.clear()
2023-01-11T21:41:26.6988415Z     buf0 = empty_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6988660Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.6988777Z     del arg0_1
2023-01-11T21:41:26.6988887Z     del arg2_1
2023-01-11T21:41:26.6989005Z     return (buf0, )
2023-01-11T21:41:26.6989014Z 
2023-01-11T21:41:26.6989020Z 
2023-01-11T21:41:26.6989142Z if __name__ == "__main__":
2023-01-11T21:41:26.6989328Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6989524Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6989897Z     arg0_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6990243Z     arg1_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6990605Z     arg2_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6990802Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6991268Z [2023-01-11 21:36:34,015] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 371
2023-01-11T21:41:26.6991279Z 
2023-01-11T21:41:26.6991388Z ok (1.880s)
2023-01-11T21:41:26.6992183Z   test_max_pool2d_with_indices_backward5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6992396Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.6992857Z [2023-01-11 21:36:34,061] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 372
2023-01-11T21:41:26.6993292Z [2023-01-11 21:36:34,076] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.max_pool2d_with_indices_backward
2023-01-11T21:41:26.6993843Z [2023-01-11 21:36:34,079] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 372
2023-01-11T21:41:26.6993857Z 
2023-01-11T21:41:26.6993990Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.6994102Z import torch
2023-01-11T21:41:26.6994214Z import random
2023-01-11T21:41:26.6994403Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.6994593Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.6994601Z 
2023-01-11T21:41:26.6994723Z aten = torch.ops.aten
2023-01-11T21:41:26.6994931Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.6995060Z async_compile = AsyncCompile()
2023-01-11T21:41:26.6995084Z 
2023-01-11T21:41:26.6995092Z 
2023-01-11T21:41:26.6995307Z async_compile.wait(globals())
2023-01-11T21:41:26.6995450Z del async_compile
2023-01-11T21:41:26.6995459Z 
2023-01-11T21:41:26.6995573Z def call(args):
2023-01-11T21:41:26.6995712Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.6995830Z     args.clear()
2023-01-11T21:41:26.6996084Z     buf0 = aten.max_pool2d_with_indices_backward(arg0_1, arg1_1, [13, 13], [1, 1], [2, 2], [1, 1], False, arg2_1)
2023-01-11T21:41:26.6996197Z     del arg0_1
2023-01-11T21:41:26.6996289Z     del arg1_1
2023-01-11T21:41:26.6996398Z     del arg2_1
2023-01-11T21:41:26.6996507Z     buf1 = buf0
2023-01-11T21:41:26.6996679Z     assert_size_stride(buf1, (2, 64, 20, 20), (25600, 400, 20, 1))
2023-01-11T21:41:26.6996790Z     del buf0
2023-01-11T21:41:26.6996906Z     return (buf1, )
2023-01-11T21:41:26.6996915Z 
2023-01-11T21:41:26.6996920Z 
2023-01-11T21:41:26.6997042Z if __name__ == "__main__":
2023-01-11T21:41:26.6997207Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.6997404Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.6997798Z     arg0_1 = rand_strided((2, 64, 12, 12), (9216, 144, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6998188Z     arg1_1 = rand_strided((2, 64, 20, 20), (25600, 400, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.6998546Z     arg2_1 = rand_strided((2, 64, 12, 12), (9216, 144, 12, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.6998738Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.6998747Z 
2023-01-11T21:41:26.6998854Z ok (0.046s)
2023-01-11T21:41:26.6999646Z   test_max_pool2d_with_indices_backward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.6999868Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7000331Z [2023-01-11 21:36:34,104] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 373
2023-01-11T21:41:26.7000786Z [2023-01-11 21:36:35,830] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 373
2023-01-11T21:41:26.7000816Z 
2023-01-11T21:41:26.7000952Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7001065Z import torch
2023-01-11T21:41:26.7001180Z import random
2023-01-11T21:41:26.7001362Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7001552Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7001562Z 
2023-01-11T21:41:26.7001690Z aten = torch.ops.aten
2023-01-11T21:41:26.7001904Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7002035Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7002042Z 
2023-01-11T21:41:26.7002048Z 
2023-01-11T21:41:26.7002302Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7002636Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7002943Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.7003109Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7003265Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7003368Z {
2023-01-11T21:41:26.7003508Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7003612Z     {
2023-01-11T21:41:26.7003734Z         #pragma omp for 
2023-01-11T21:41:26.7003867Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7003968Z         {
2023-01-11T21:41:26.7004099Z             #pragma GCC ivdep
2023-01-11T21:41:26.7004233Z             for(long i1=0; i1<18; i1+=1)
2023-01-11T21:41:26.7004319Z             {
2023-01-11T21:41:26.7004455Z                 #pragma GCC ivdep
2023-01-11T21:41:26.7004594Z                 for(long i2=0; i2<14; i2+=1)
2023-01-11T21:41:26.7004781Z                 {
2023-01-11T21:41:26.7004908Z                     {
2023-01-11T21:41:26.7005017Z                         {
2023-01-11T21:41:26.7005206Z                             auto tmp0 = static_cast<int>(i2 + (14*i1));
2023-01-11T21:41:26.7005367Z                             auto tmp1 = static_cast<int>((i1 / 2));
2023-01-11T21:41:26.7005542Z                             auto tmp2 = static_cast<int>((i2 / 2));
2023-01-11T21:41:26.7005716Z                             auto tmp3 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:41:26.7005887Z                             auto tmp4 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:41:26.7006051Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:41:26.7006271Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:41:26.7006482Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:41:26.7006656Z                             auto tmp8 = static_cast<int>(9);
2023-01-11T21:41:26.7006844Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:41:26.7007023Z                             auto tmp10 = static_cast<int>(7);
2023-01-11T21:41:26.7007231Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:41:26.7007385Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:41:26.7007531Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:41:26.7007695Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:41:26.7007978Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:41:26.7008193Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:41:26.7008441Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:41:26.7008663Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:41:26.7008845Z                             auto tmp19 = in_ptr0[tmp18 + (7*tmp16) + (63*i0)];
2023-01-11T21:41:26.7009168Z                             auto tmp20 = in_ptr1[tmp18 + (7*tmp16) + (63*i0)];
2023-01-11T21:41:26.7009325Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:41:26.7009497Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:41:26.7009665Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:41:26.7009827Z                             out_ptr0[i2 + (14*i1) + (252*i0)] = tmp23;
2023-01-11T21:41:26.7009920Z                         }
2023-01-11T21:41:26.7010030Z                     }
2023-01-11T21:41:26.7010131Z                 }
2023-01-11T21:41:26.7010233Z             }
2023-01-11T21:41:26.7010332Z         }
2023-01-11T21:41:26.7010434Z     }
2023-01-11T21:41:26.7010533Z }
2023-01-11T21:41:26.7010669Z ''')
2023-01-11T21:41:26.7010680Z 
2023-01-11T21:41:26.7010686Z 
2023-01-11T21:41:26.7010850Z async_compile.wait(globals())
2023-01-11T21:41:26.7010971Z del async_compile
2023-01-11T21:41:26.7010979Z 
2023-01-11T21:41:26.7011094Z def call(args):
2023-01-11T21:41:26.7011365Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7011480Z     args.clear()
2023-01-11T21:41:26.7011870Z     buf0 = empty_strided((2, 4, 18, 14), (1008, 252, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7012107Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7012222Z     del arg0_1
2023-01-11T21:41:26.7012330Z     del arg2_1
2023-01-11T21:41:26.7012446Z     return (buf0, )
2023-01-11T21:41:26.7012455Z 
2023-01-11T21:41:26.7012460Z 
2023-01-11T21:41:26.7012581Z if __name__ == "__main__":
2023-01-11T21:41:26.7012756Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7012951Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7013426Z     arg0_1 = rand_strided((2, 4, 9, 7), (252, 63, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7013795Z     arg1_1 = rand_strided((2, 4, 18, 14), (1008, 252, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7014161Z     arg2_1 = rand_strided((2, 4, 9, 7), (252, 63, 7, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7014357Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7014368Z 
2023-01-11T21:41:26.7014476Z ok (1.752s)
2023-01-11T21:41:26.7015219Z   test_mean_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7015428Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7015899Z [2023-01-11 21:36:35,864] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 374
2023-01-11T21:41:26.7016361Z [2023-01-11 21:36:37,554] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 374
2023-01-11T21:41:26.7016378Z 
2023-01-11T21:41:26.7016534Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7016652Z import torch
2023-01-11T21:41:26.7016751Z import random
2023-01-11T21:41:26.7016935Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7017125Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7017134Z 
2023-01-11T21:41:26.7017257Z aten = torch.ops.aten
2023-01-11T21:41:26.7017468Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7017615Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7017623Z 
2023-01-11T21:41:26.7017629Z 
2023-01-11T21:41:26.7017879Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7018207Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7018375Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.7018532Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.7018704Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7018860Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.7019015Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:26.7019111Z {
2023-01-11T21:41:26.7019251Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:26.7019370Z     auto out_ptr0 = in_out_ptr1;
2023-01-11T21:41:26.7019471Z     {
2023-01-11T21:41:26.7019791Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.7019918Z         float tmp1 = 0;
2023-01-11T21:41:26.7020115Z         auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.7020279Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7020384Z         {
2023-01-11T21:41:26.7020546Z             #pragma omp for reduction(+:tmp1_vec) 
2023-01-11T21:41:26.7020685Z             for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7020899Z             {
2023-01-11T21:41:26.7021118Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7021245Z                 tmp1_vec += tmp0;
2023-01-11T21:41:26.7021335Z             }
2023-01-11T21:41:26.7021606Z             tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.7021789Z             #pragma omp for simd simdlen(4) reduction(+:tmp1) 
2023-01-11T21:41:26.7021904Z             for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7022013Z             {
2023-01-11T21:41:26.7022145Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7022264Z                 tmp1 += tmp0;
2023-01-11T21:41:26.7022362Z             }
2023-01-11T21:41:26.7022463Z         }
2023-01-11T21:41:26.7022671Z         out_ptr0[0] = tmp1;
2023-01-11T21:41:26.7022763Z     }
2023-01-11T21:41:26.7022916Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7023018Z     {
2023-01-11T21:41:26.7023210Z         #pragma omp for 
2023-01-11T21:41:26.7023338Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7023434Z         {
2023-01-11T21:41:26.7023516Z             {
2023-01-11T21:41:26.7023821Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.7023942Z                 float tmp1 = 0;
2023-01-11T21:41:26.7024135Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.7024276Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.7024380Z                 {
2023-01-11T21:41:26.7024600Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7024728Z                     tmp1_vec += tmp0;
2023-01-11T21:41:26.7024821Z                 }
2023-01-11T21:41:26.7025134Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.7025327Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.7025462Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.7025564Z                 {
2023-01-11T21:41:26.7025720Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.7025845Z                     tmp1 += tmp0;
2023-01-11T21:41:26.7025946Z                 }
2023-01-11T21:41:26.7026057Z                 out_ptr1[i0] = tmp1;
2023-01-11T21:41:26.7026157Z             }
2023-01-11T21:41:26.7026256Z         }
2023-01-11T21:41:26.7026377Z         #pragma omp for 
2023-01-11T21:41:26.7026504Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.7026605Z         {
2023-01-11T21:41:26.7026818Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7027022Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:26.7027159Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.7027313Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7027412Z         }
2023-01-11T21:41:26.7027564Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7027693Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.7027792Z         {
2023-01-11T21:41:26.7027909Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.7028064Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:26.7028197Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.7028326Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7028425Z         }
2023-01-11T21:41:26.7028548Z         #pragma omp for 
2023-01-11T21:41:26.7028674Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7028758Z         {
2023-01-11T21:41:26.7028890Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.7028989Z             {
2023-01-11T21:41:26.7029221Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:26.7029550Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8 + (8*i1) + (32*i0));
2023-01-11T21:41:26.7029772Z                 auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16 + (8*i1) + (32*i0));
2023-01-11T21:41:26.7029994Z                 auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr0 + 24 + (8*i1) + (32*i0));
2023-01-11T21:41:26.7030133Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7030253Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7030389Z                 auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7030605Z                 auto tmp7 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:41:26.7030742Z                 auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:26.7030903Z                 tmp8.store(out_ptr2 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7031004Z             }
2023-01-11T21:41:26.7031219Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7031349Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.7031458Z             {
2023-01-11T21:41:26.7031610Z                 auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.7031766Z                 auto tmp1 = in_ptr0[8 + i1 + (32*i0)];
2023-01-11T21:41:26.7031920Z                 auto tmp3 = in_ptr0[16 + i1 + (32*i0)];
2023-01-11T21:41:26.7032078Z                 auto tmp5 = in_ptr0[24 + i1 + (32*i0)];
2023-01-11T21:41:26.7032216Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7032337Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7032473Z                 auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7032631Z                 auto tmp7 = static_cast<float>(4);
2023-01-11T21:41:26.7032767Z                 auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:26.7032910Z                 out_ptr2[i1 + (8*i0)] = tmp8;
2023-01-11T21:41:26.7033011Z             }
2023-01-11T21:41:26.7033112Z         }
2023-01-11T21:41:26.7033224Z         #pragma omp for 
2023-01-11T21:41:26.7033350Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7033450Z         {
2023-01-11T21:41:26.7033671Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7033888Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 32 + (8*i0));
2023-01-11T21:41:26.7034020Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7034229Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.7034362Z             auto tmp4 = tmp2 / tmp3;
2023-01-11T21:41:26.7034495Z             tmp4.store(out_ptr3 + 8*i0);
2023-01-11T21:41:26.7034594Z         }
2023-01-11T21:41:26.7034744Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7034874Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.7034973Z         {
2023-01-11T21:41:26.7035107Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7035230Z             auto tmp1 = in_ptr0[32 + i0];
2023-01-11T21:41:26.7035372Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7035525Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.7035666Z             auto tmp4 = tmp2 / tmp3;
2023-01-11T21:41:26.7035794Z             out_ptr3[i0] = tmp4;
2023-01-11T21:41:26.7035895Z         }
2023-01-11T21:41:26.7036023Z         #pragma omp single
2023-01-11T21:41:26.7036107Z         {
2023-01-11T21:41:26.7036209Z             {
2023-01-11T21:41:26.7036313Z                 {
2023-01-11T21:41:26.7036461Z                     auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.7036625Z                     auto tmp1 = static_cast<float>(64);
2023-01-11T21:41:26.7036766Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.7036906Z                     in_out_ptr1[0] = tmp2;
2023-01-11T21:41:26.7036994Z                 }
2023-01-11T21:41:26.7037094Z             }
2023-01-11T21:41:26.7037193Z         }
2023-01-11T21:41:26.7037290Z     }
2023-01-11T21:41:26.7037384Z }
2023-01-11T21:41:26.7037544Z ''')
2023-01-11T21:41:26.7037561Z 
2023-01-11T21:41:26.7037568Z 
2023-01-11T21:41:26.7037717Z async_compile.wait(globals())
2023-01-11T21:41:26.7037919Z del async_compile
2023-01-11T21:41:26.7037927Z 
2023-01-11T21:41:26.7038038Z def call(args):
2023-01-11T21:41:26.7038147Z     arg0_1, = args
2023-01-11T21:41:26.7038258Z     args.clear()
2023-01-11T21:41:26.7038581Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7038915Z     buf1 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7039047Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:41:26.7039369Z     buf3 = empty_strided((1, 2, 1, 8), (16, 8, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7039687Z     buf4 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7039819Z     buf5 = buf0; del buf0  # reuse
2023-01-11T21:41:26.7040222Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.7040342Z     del arg0_1
2023-01-11T21:41:26.7040484Z     return (buf5, buf2, buf3, buf4, )
2023-01-11T21:41:26.7040499Z 
2023-01-11T21:41:26.7040505Z 
2023-01-11T21:41:26.7040622Z if __name__ == "__main__":
2023-01-11T21:41:26.7040804Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7040983Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7041336Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7041503Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7041512Z 
2023-01-11T21:41:26.7041617Z ok (1.733s)
2023-01-11T21:41:26.7042388Z   test_min_max_reduction_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7042590Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7043030Z [2023-01-11 21:36:37,594] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 375
2023-01-11T21:41:26.7043464Z [2023-01-11 21:36:39,115] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 375
2023-01-11T21:41:26.7043473Z 
2023-01-11T21:41:26.7043624Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7043737Z import torch
2023-01-11T21:41:26.7043833Z import random
2023-01-11T21:41:26.7044018Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7044208Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7044216Z 
2023-01-11T21:41:26.7044342Z aten = torch.ops.aten
2023-01-11T21:41:26.7044555Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7044708Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7044716Z 
2023-01-11T21:41:26.7044722Z 
2023-01-11T21:41:26.7044960Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7045283Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7045457Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7045615Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7045771Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7045927Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7046079Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.7046176Z {
2023-01-11T21:41:26.7046271Z     {
2023-01-11T21:41:26.7046918Z         #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:26.7047271Z         float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7047457Z         auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.7047937Z         #pragma omp declare reduction(min:at::vec::Vectorized<float>:omp_out = at::vec::minimum(omp_out, omp_in)) initializer(omp_priv={{std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:26.7048133Z         float tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7048317Z         auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:26.7048661Z         float tmp7 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7048849Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.7049140Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7049244Z         {
2023-01-11T21:41:26.7049486Z             #pragma omp for reduction(max:tmp3_vec) reduction(min:tmp4_vec) reduction(max:tmp7_vec) 
2023-01-11T21:41:26.7049715Z             for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7049821Z             {
2023-01-11T21:41:26.7050034Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7050252Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.7050392Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7050608Z                 auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7050731Z                 auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:26.7050913Z                 tmp3_vec = at::vec::maximum(tmp3_vec, tmp2);
2023-01-11T21:41:26.7051091Z                 tmp4_vec = at::vec::minimum(tmp4_vec, tmp2);
2023-01-11T21:41:26.7051268Z                 tmp7_vec = at::vec::maximum(tmp7_vec, tmp6);
2023-01-11T21:41:26.7051367Z             }
2023-01-11T21:41:26.7051700Z             tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp3_vec);
2023-01-11T21:41:26.7052031Z             tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::minimum(x, y);}, tmp4_vec);
2023-01-11T21:41:26.7052368Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp7_vec);
2023-01-11T21:41:26.7052634Z             #pragma omp for simd simdlen(4) reduction(max:tmp3) reduction(min:tmp4) reduction(max:tmp7) 
2023-01-11T21:41:26.7052774Z             for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7052858Z             {
2023-01-11T21:41:26.7052995Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7053131Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.7053269Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7053430Z                 auto tmp5 = static_cast<float>(1);
2023-01-11T21:41:26.7053565Z                 auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:26.7053725Z                 tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:41:26.7053859Z                 tmp4 = std::min(tmp4, tmp2);
2023-01-11T21:41:26.7054013Z                 tmp7 = std::max(tmp7, tmp6);
2023-01-11T21:41:26.7054118Z             }
2023-01-11T21:41:26.7054219Z         }
2023-01-11T21:41:26.7054343Z         out_ptr0[0] = tmp3;
2023-01-11T21:41:26.7054464Z         out_ptr1[0] = tmp4;
2023-01-11T21:41:26.7054585Z         out_ptr2[0] = tmp7;
2023-01-11T21:41:26.7054669Z     }
2023-01-11T21:41:26.7054767Z }
2023-01-11T21:41:26.7054921Z ''')
2023-01-11T21:41:26.7054931Z 
2023-01-11T21:41:26.7054937Z 
2023-01-11T21:41:26.7055082Z async_compile.wait(globals())
2023-01-11T21:41:26.7055199Z del async_compile
2023-01-11T21:41:26.7055206Z 
2023-01-11T21:41:26.7055319Z def call(args):
2023-01-11T21:41:26.7055438Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7055535Z     args.clear()
2023-01-11T21:41:26.7055855Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7056162Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7056481Z     buf2 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7056932Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.7057044Z     del arg0_1
2023-01-11T21:41:26.7057152Z     del arg1_1
2023-01-11T21:41:26.7057285Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.7057295Z 
2023-01-11T21:41:26.7057301Z 
2023-01-11T21:41:26.7057403Z if __name__ == "__main__":
2023-01-11T21:41:26.7057586Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7057778Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7058114Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7058434Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7058691Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7058702Z 
2023-01-11T21:41:26.7058815Z ok (1.549s)
2023-01-11T21:41:26.7059595Z   test_misaligned_address_issue1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7059796Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7060221Z [2023-01-11 21:36:39,138] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 376
2023-01-11T21:41:26.7060667Z [2023-01-11 21:36:40,604] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 376
2023-01-11T21:41:26.7060680Z 
2023-01-11T21:41:26.7060831Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7060944Z import torch
2023-01-11T21:41:26.7061055Z import random
2023-01-11T21:41:26.7061240Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7061432Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7061441Z 
2023-01-11T21:41:26.7061563Z aten = torch.ops.aten
2023-01-11T21:41:26.7061757Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7061900Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7061908Z 
2023-01-11T21:41:26.7061914Z 
2023-01-11T21:41:26.7062141Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7062463Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7062651Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.7062818Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7062974Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7063071Z {
2023-01-11T21:41:26.7063235Z     {
2023-01-11T21:41:26.7063343Z         {
2023-01-11T21:41:26.7063477Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.7063618Z             auto tmp1 = in_ptr1[tmp0];
2023-01-11T21:41:26.7063744Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:26.7063843Z         }
2023-01-11T21:41:26.7063938Z     }
2023-01-11T21:41:26.7064018Z }
2023-01-11T21:41:26.7064158Z ''')
2023-01-11T21:41:26.7064167Z 
2023-01-11T21:41:26.7064173Z 
2023-01-11T21:41:26.7064316Z async_compile.wait(globals())
2023-01-11T21:41:26.7064430Z del async_compile
2023-01-11T21:41:26.7064438Z 
2023-01-11T21:41:26.7064548Z def call(args):
2023-01-11T21:41:26.7064667Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7064778Z     args.clear()
2023-01-11T21:41:26.7065091Z     buf0 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7065348Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7065458Z     del arg0_1
2023-01-11T21:41:26.7065571Z     del arg1_1
2023-01-11T21:41:26.7065685Z     return (buf0, )
2023-01-11T21:41:26.7065695Z 
2023-01-11T21:41:26.7065785Z 
2023-01-11T21:41:26.7065915Z if __name__ == "__main__":
2023-01-11T21:41:26.7066092Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7066290Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7066617Z     arg0_1 = rand_strided((1, 1000), (1000, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7066934Z     arg1_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7067112Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7067122Z 
2023-01-11T21:41:26.7067229Z ok (1.489s)
2023-01-11T21:41:26.7068060Z   test_mm_views_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7068285Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7068729Z [2023-01-11 21:36:40,625] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 377
2023-01-11T21:41:26.7069173Z [2023-01-11 21:36:40,628] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 377
2023-01-11T21:41:26.7069183Z 
2023-01-11T21:41:26.7069332Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7069427Z import torch
2023-01-11T21:41:26.7069538Z import random
2023-01-11T21:41:26.7069723Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7069915Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7069922Z 
2023-01-11T21:41:26.7070046Z aten = torch.ops.aten
2023-01-11T21:41:26.7070261Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7070409Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7070418Z 
2023-01-11T21:41:26.7070429Z 
2023-01-11T21:41:26.7070568Z async_compile.wait(globals())
2023-01-11T21:41:26.7070665Z del async_compile
2023-01-11T21:41:26.7070674Z 
2023-01-11T21:41:26.7070788Z def call(args):
2023-01-11T21:41:26.7070906Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7071017Z     args.clear()
2023-01-11T21:41:26.7071367Z     buf0 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7071556Z     aten.mm.out(arg0_1, as_strided(arg1_1, (32, 32), (32, 1)), out=buf0)
2023-01-11T21:41:26.7071667Z     del arg0_1
2023-01-11T21:41:26.7071758Z     del arg1_1
2023-01-11T21:41:26.7071871Z     return (buf0, )
2023-01-11T21:41:26.7071880Z 
2023-01-11T21:41:26.7071886Z 
2023-01-11T21:41:26.7072002Z if __name__ == "__main__":
2023-01-11T21:41:26.7072181Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7072379Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7072726Z     arg0_1 = rand_strided((32, 32), (1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7073083Z     arg1_1 = rand_strided((32, 1, 32), (32, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7073269Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7073277Z 
2023-01-11T21:41:26.7073363Z ok (0.023s)
2023-01-11T21:41:26.7074122Z   test_move_arange_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7074323Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7074774Z [2023-01-11 21:36:40,659] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 378
2023-01-11T21:41:26.7075217Z [2023-01-11 21:36:42,276] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 378
2023-01-11T21:41:26.7075310Z 
2023-01-11T21:41:26.7075465Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7075576Z import torch
2023-01-11T21:41:26.7075689Z import random
2023-01-11T21:41:26.7075876Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7076049Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7076075Z 
2023-01-11T21:41:26.7076181Z aten = torch.ops.aten
2023-01-11T21:41:26.7076394Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7076540Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7076548Z 
2023-01-11T21:41:26.7076554Z 
2023-01-11T21:41:26.7076785Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7077173Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7077380Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7077537Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7077625Z {
2023-01-11T21:41:26.7077779Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7077873Z     {
2023-01-11T21:41:26.7077996Z         #pragma omp for 
2023-01-11T21:41:26.7078127Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.7078230Z         {
2023-01-11T21:41:26.7078332Z             {
2023-01-11T21:41:26.7078417Z                 {
2023-01-11T21:41:26.7078563Z                     auto tmp2 = in_ptr0[i0];
2023-01-11T21:41:26.7078723Z                     auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:26.7078893Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.7079038Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.7079176Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.7079278Z                 }
2023-01-11T21:41:26.7079366Z             }
2023-01-11T21:41:26.7079464Z         }
2023-01-11T21:41:26.7079563Z     }
2023-01-11T21:41:26.7079658Z }
2023-01-11T21:41:26.7079809Z ''')
2023-01-11T21:41:26.7079819Z 
2023-01-11T21:41:26.7079825Z 
2023-01-11T21:41:26.7079970Z async_compile.wait(globals())
2023-01-11T21:41:26.7080088Z del async_compile
2023-01-11T21:41:26.7080097Z 
2023-01-11T21:41:26.7080190Z def call(args):
2023-01-11T21:41:26.7080302Z     arg0_1, = args
2023-01-11T21:41:26.7080414Z     args.clear()
2023-01-11T21:41:26.7080744Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7080956Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7081065Z     del arg0_1
2023-01-11T21:41:26.7081179Z     return (buf0, )
2023-01-11T21:41:26.7081186Z 
2023-01-11T21:41:26.7081192Z 
2023-01-11T21:41:26.7081311Z if __name__ == "__main__":
2023-01-11T21:41:26.7081478Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7081678Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7082012Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7082188Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7082197Z 
2023-01-11T21:41:26.7082304Z ok (1.649s)
2023-01-11T21:41:26.7082525Z   test_multi_device_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:26.7082785Z   test_multi_gpu_device_cpu (__main__.CpuTests) ... skip: requires multiple cuda devices (0.000s)
2023-01-11T21:41:26.7083018Z   test_multilayer_low_prec_cpu (__main__.CpuTests) ... skip: requires CUDA (0.001s)
2023-01-11T21:41:26.7083235Z   test_nan_to_num_cpu (__main__.CpuTests) ... skip: Skipping due to op bugs (0.001s)
2023-01-11T21:41:26.7083994Z   test_narrow_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7084292Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7084737Z [2023-01-11 21:36:42,312] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 379
2023-01-11T21:41:26.7085174Z [2023-01-11 21:36:43,792] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 379
2023-01-11T21:41:26.7085184Z 
2023-01-11T21:41:26.7085335Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7085446Z import torch
2023-01-11T21:41:26.7085560Z import random
2023-01-11T21:41:26.7085742Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7085919Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7085927Z 
2023-01-11T21:41:26.7086048Z aten = torch.ops.aten
2023-01-11T21:41:26.7086345Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7086507Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7086515Z 
2023-01-11T21:41:26.7086527Z 
2023-01-11T21:41:26.7086761Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7087085Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7087274Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7087431Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7087509Z {
2023-01-11T21:41:26.7087664Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7087762Z     {
2023-01-11T21:41:26.7087887Z         #pragma omp for 
2023-01-11T21:41:26.7088017Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.7088118Z         {
2023-01-11T21:41:26.7088345Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 640 + (8*i0));
2023-01-11T21:41:26.7088541Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.7088675Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7088891Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7089182Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7089329Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7089423Z         }
2023-01-11T21:41:26.7089574Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7089691Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:41:26.7089791Z         {
2023-01-11T21:41:26.7089933Z             auto tmp0 = in_ptr0[640 + i0];
2023-01-11T21:41:26.7090089Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.7090223Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7090365Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:26.7090485Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7090588Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.7090681Z         }
2023-01-11T21:41:26.7090774Z     }
2023-01-11T21:41:26.7090874Z }
2023-01-11T21:41:26.7091029Z ''')
2023-01-11T21:41:26.7091038Z 
2023-01-11T21:41:26.7091044Z 
2023-01-11T21:41:26.7091191Z async_compile.wait(globals())
2023-01-11T21:41:26.7091310Z del async_compile
2023-01-11T21:41:26.7091318Z 
2023-01-11T21:41:26.7091412Z def call(args):
2023-01-11T21:41:26.7091523Z     arg0_1, = args
2023-01-11T21:41:26.7091634Z     args.clear()
2023-01-11T21:41:26.7091974Z     buf0 = empty_strided((16, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7092185Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7092355Z     return (as_strided(arg0_1, (64, 16), (64, 1), 10), buf0, )
2023-01-11T21:41:26.7092365Z 
2023-01-11T21:41:26.7092371Z 
2023-01-11T21:41:26.7092488Z if __name__ == "__main__":
2023-01-11T21:41:26.7092669Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7092848Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7093192Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7093361Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7093528Z 
2023-01-11T21:41:26.7093642Z ok (1.514s)
2023-01-11T21:41:26.7094414Z   test_new_empty_strided_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7094615Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7095056Z [2023-01-11 21:36:43,834] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 380
2023-01-11T21:41:26.7095503Z [2023-01-11 21:36:45,343] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 380
2023-01-11T21:41:26.7095600Z 
2023-01-11T21:41:26.7095756Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7095866Z import torch
2023-01-11T21:41:26.7095970Z import random
2023-01-11T21:41:26.7096155Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7096348Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7096356Z 
2023-01-11T21:41:26.7096482Z aten = torch.ops.aten
2023-01-11T21:41:26.7096692Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7096839Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7096847Z 
2023-01-11T21:41:26.7096853Z 
2023-01-11T21:41:26.7097088Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7097415Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7097575Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7097667Z {
2023-01-11T21:41:26.7097827Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7097930Z     {
2023-01-11T21:41:26.7098053Z         #pragma omp for 
2023-01-11T21:41:26.7098185Z         for(long i0=0; i0<2048; i0+=1)
2023-01-11T21:41:26.7098271Z         {
2023-01-11T21:41:26.7098503Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(123));
2023-01-11T21:41:26.7098647Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7098748Z         }
2023-01-11T21:41:26.7098900Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7099040Z         for(long i0=16384; i0<16384; i0+=1)
2023-01-11T21:41:26.7099138Z         {
2023-01-11T21:41:26.7099279Z             auto tmp0 = static_cast<float>(123);
2023-01-11T21:41:26.7099408Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7099505Z         }
2023-01-11T21:41:26.7099602Z     }
2023-01-11T21:41:26.7099696Z }
2023-01-11T21:41:26.7099839Z ''')
2023-01-11T21:41:26.7099848Z 
2023-01-11T21:41:26.7099854Z 
2023-01-11T21:41:26.7099998Z async_compile.wait(globals())
2023-01-11T21:41:26.7100097Z del async_compile
2023-01-11T21:41:26.7100128Z 
2023-01-11T21:41:26.7100224Z def call(args):
2023-01-11T21:41:26.7100334Z     arg0_1, = args
2023-01-11T21:41:26.7100449Z     args.clear()
2023-01-11T21:41:26.7100811Z     buf0 = empty_strided((1, 128, 128), (16384, 128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7100972Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7101089Z     return (buf0, )
2023-01-11T21:41:26.7101097Z 
2023-01-11T21:41:26.7101103Z 
2023-01-11T21:41:26.7101224Z if __name__ == "__main__":
2023-01-11T21:41:26.7101385Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7101580Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7101908Z     arg0_1 = rand_strided((55, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7102078Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7102088Z 
2023-01-11T21:41:26.7102196Z ok (1.551s)
2023-01-11T21:41:26.7102954Z   test_new_ones_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7103314Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7103755Z [2023-01-11 21:36:45,411] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 381
2023-01-11T21:41:26.7104188Z [2023-01-11 21:36:46,945] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 381
2023-01-11T21:41:26.7104197Z 
2023-01-11T21:41:26.7104345Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7104442Z import torch
2023-01-11T21:41:26.7104554Z import random
2023-01-11T21:41:26.7104738Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7105008Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7105020Z 
2023-01-11T21:41:26.7105148Z aten = torch.ops.aten
2023-01-11T21:41:26.7105364Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7105508Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7105518Z 
2023-01-11T21:41:26.7105524Z 
2023-01-11T21:41:26.7105739Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7106063Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7106239Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7106392Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7106491Z {
2023-01-11T21:41:26.7106586Z     {
2023-01-11T21:41:26.7106689Z         {
2023-01-11T21:41:26.7106829Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:41:26.7106953Z             out_ptr0[0] = tmp0;
2023-01-11T21:41:26.7107059Z         }
2023-01-11T21:41:26.7107153Z     }
2023-01-11T21:41:26.7107256Z     {
2023-01-11T21:41:26.7107353Z         {
2023-01-11T21:41:26.7107507Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:26.7107620Z             out_ptr1[0] = tmp0;
2023-01-11T21:41:26.7107718Z         }
2023-01-11T21:41:26.7107817Z     }
2023-01-11T21:41:26.7107909Z }
2023-01-11T21:41:26.7108056Z ''')
2023-01-11T21:41:26.7108065Z 
2023-01-11T21:41:26.7108071Z 
2023-01-11T21:41:26.7108214Z async_compile.wait(globals())
2023-01-11T21:41:26.7108336Z del async_compile
2023-01-11T21:41:26.7108344Z 
2023-01-11T21:41:26.7108436Z def call(args):
2023-01-11T21:41:26.7108548Z     arg0_1, = args
2023-01-11T21:41:26.7108660Z     args.clear()
2023-01-11T21:41:26.7108975Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7109276Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7109484Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7109605Z     return (buf0, buf1, )
2023-01-11T21:41:26.7109619Z 
2023-01-11T21:41:26.7109626Z 
2023-01-11T21:41:26.7109747Z if __name__ == "__main__":
2023-01-11T21:41:26.7109914Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7110107Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7110422Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7110592Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7110600Z 
2023-01-11T21:41:26.7110707Z ok (1.601s)
2023-01-11T21:41:26.7111465Z   test_nll_loss_forward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7111667Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7112108Z [2023-01-11 21:36:46,999] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 382
2023-01-11T21:41:26.7112645Z [2023-01-11 21:36:48,497] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 382
2023-01-11T21:41:26.7112654Z 
2023-01-11T21:41:26.7112781Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7112897Z import torch
2023-01-11T21:41:26.7113007Z import random
2023-01-11T21:41:26.7113188Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7113380Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7113388Z 
2023-01-11T21:41:26.7113510Z aten = torch.ops.aten
2023-01-11T21:41:26.7113720Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7113869Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7113878Z 
2023-01-11T21:41:26.7113884Z 
2023-01-11T21:41:26.7114170Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7114506Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7114695Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.7114860Z                        const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.7115019Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7115172Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7115265Z {
2023-01-11T21:41:26.7115383Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.7115478Z     {
2023-01-11T21:41:26.7115577Z         {
2023-01-11T21:41:26.7115699Z             float tmp3 = 0;
2023-01-11T21:41:26.7115865Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7115964Z             {
2023-01-11T21:41:26.7116133Z                 #pragma omp for reduction(+:tmp3) 
2023-01-11T21:41:26.7116256Z                 for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:26.7116357Z                 {
2023-01-11T21:41:26.7116471Z                     {
2023-01-11T21:41:26.7116619Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7116786Z                         auto tmp1 = in_ptr1[tmp0 + (5*i0)];
2023-01-11T21:41:26.7117024Z                         auto tmp2 = -tmp1;
2023-01-11T21:41:26.7117150Z                         tmp3 += tmp2;
2023-01-11T21:41:26.7117237Z                     }
2023-01-11T21:41:26.7117340Z                 }
2023-01-11T21:41:26.7117438Z             }
2023-01-11T21:41:26.7117568Z             out_ptr0[0] = tmp3;
2023-01-11T21:41:26.7117665Z         }
2023-01-11T21:41:26.7117764Z     }
2023-01-11T21:41:26.7117859Z     {
2023-01-11T21:41:26.7117939Z         {
2023-01-11T21:41:26.7118068Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.7118225Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:41:26.7118357Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.7118487Z             in_out_ptr0[0] = tmp2;
2023-01-11T21:41:26.7118636Z         }
2023-01-11T21:41:26.7118726Z     }
2023-01-11T21:41:26.7119022Z     {
2023-01-11T21:41:26.7119165Z         {
2023-01-11T21:41:26.7119370Z             auto tmp0 = static_cast<float>(5.0);
2023-01-11T21:41:26.7129819Z             out_ptr1[0] = tmp0;
2023-01-11T21:41:26.7130004Z         }
2023-01-11T21:41:26.7130102Z     }
2023-01-11T21:41:26.7130178Z }
2023-01-11T21:41:26.7130344Z ''')
2023-01-11T21:41:26.7130354Z 
2023-01-11T21:41:26.7130360Z 
2023-01-11T21:41:26.7130525Z async_compile.wait(globals())
2023-01-11T21:41:26.7130643Z del async_compile
2023-01-11T21:41:26.7130651Z 
2023-01-11T21:41:26.7130762Z def call(args):
2023-01-11T21:41:26.7130885Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7131000Z     args.clear()
2023-01-11T21:41:26.7131305Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7131443Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.7131747Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7132055Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.7132163Z     del arg0_1
2023-01-11T21:41:26.7132452Z     del arg1_1
2023-01-11T21:41:26.7132574Z     return (buf1, buf2, )
2023-01-11T21:41:26.7132583Z 
2023-01-11T21:41:26.7132589Z 
2023-01-11T21:41:26.7132705Z if __name__ == "__main__":
2023-01-11T21:41:26.7132873Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7133072Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7133411Z     arg0_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7133723Z     arg1_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7133907Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7133917Z 
2023-01-11T21:41:26.7134019Z ok (1.552s)
2023-01-11T21:41:26.7134897Z   test_no_mega_fusion_during_lowering_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7135121Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7135566Z [2023-01-11 21:36:48,691] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 383
2023-01-11T21:41:26.7135575Z 
2023-01-11T21:41:26.7135722Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7135818Z import torch
2023-01-11T21:41:26.7135926Z import random
2023-01-11T21:41:26.7136108Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7136298Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7136306Z 
2023-01-11T21:41:26.7136432Z aten = torch.ops.aten
2023-01-11T21:41:26.7136653Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7136798Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7136811Z 
2023-01-11T21:41:26.7136818Z 
2023-01-11T21:41:26.7137035Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7137363Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7137549Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.7137715Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7137879Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7138040Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7138201Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:26.7138362Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:41:26.7138504Z                        const float* __restrict__ in_ptr5,
2023-01-11T21:41:26.7138668Z                        const float* __restrict__ in_ptr6,
2023-01-11T21:41:26.7138828Z                        const float* __restrict__ in_ptr7,
2023-01-11T21:41:26.7138993Z                        const float* __restrict__ in_ptr8,
2023-01-11T21:41:26.7139155Z                        const float* __restrict__ in_ptr9,
2023-01-11T21:41:26.7139323Z                        const float* __restrict__ in_ptr10,
2023-01-11T21:41:26.7139486Z                        const float* __restrict__ in_ptr11,
2023-01-11T21:41:26.7139653Z                        const float* __restrict__ in_ptr12,
2023-01-11T21:41:26.7139804Z                        const float* __restrict__ in_ptr13,
2023-01-11T21:41:26.7139966Z                        const float* __restrict__ in_ptr14,
2023-01-11T21:41:26.7140128Z                        const float* __restrict__ in_ptr15,
2023-01-11T21:41:26.7140291Z                        const float* __restrict__ in_ptr16,
2023-01-11T21:41:26.7140452Z                        const float* __restrict__ in_ptr17,
2023-01-11T21:41:26.7140619Z                        const float* __restrict__ in_ptr18,
2023-01-11T21:41:26.7140781Z                        const float* __restrict__ in_ptr19,
2023-01-11T21:41:26.7141023Z                        const float* __restrict__ in_ptr20,
2023-01-11T21:41:26.7141186Z                        const float* __restrict__ in_ptr21,
2023-01-11T21:41:26.7141350Z                        const float* __restrict__ in_ptr22,
2023-01-11T21:41:26.7141511Z                        const float* __restrict__ in_ptr23,
2023-01-11T21:41:26.7141673Z                        const float* __restrict__ in_ptr24,
2023-01-11T21:41:26.7141834Z                        const float* __restrict__ in_ptr25,
2023-01-11T21:41:26.7141995Z                        const float* __restrict__ in_ptr26,
2023-01-11T21:41:26.7142157Z                        const float* __restrict__ in_ptr27,
2023-01-11T21:41:26.7142303Z                        const float* __restrict__ in_ptr28,
2023-01-11T21:41:26.7142463Z                        const float* __restrict__ in_ptr29,
2023-01-11T21:41:26.7142693Z                        const float* __restrict__ in_ptr30,
2023-01-11T21:41:26.7142870Z                        const float* __restrict__ in_ptr31,
2023-01-11T21:41:26.7143040Z                        const float* __restrict__ in_ptr32,
2023-01-11T21:41:26.7143272Z                        const float* __restrict__ in_ptr33,
2023-01-11T21:41:26.7143435Z                        const float* __restrict__ in_ptr34,
2023-01-11T21:41:26.7143582Z                        const float* __restrict__ in_ptr35,
2023-01-11T21:41:26.7143743Z                        const float* __restrict__ in_ptr36,
2023-01-11T21:41:26.7143902Z                        const float* __restrict__ in_ptr37,
2023-01-11T21:41:26.7144060Z                        const float* __restrict__ in_ptr38,
2023-01-11T21:41:26.7144220Z                        const float* __restrict__ in_ptr39,
2023-01-11T21:41:26.7144384Z                        const float* __restrict__ in_ptr40,
2023-01-11T21:41:26.7144550Z                        const float* __restrict__ in_ptr41,
2023-01-11T21:41:26.7144710Z                        const float* __restrict__ in_ptr42,
2023-01-11T21:41:26.7144859Z                        const float* __restrict__ in_ptr43,
2023-01-11T21:41:26.7145017Z                        const float* __restrict__ in_ptr44,
2023-01-11T21:41:26.7145181Z                        const float* __restrict__ in_ptr45,
2023-01-11T21:41:26.7145339Z                        const float* __restrict__ in_ptr46,
2023-01-11T21:41:26.7145502Z                        const float* __restrict__ in_ptr47,
2023-01-11T21:41:26.7145665Z                        const float* __restrict__ in_ptr48,
2023-01-11T21:41:26.7145824Z                        const float* __restrict__ in_ptr49)
2023-01-11T21:41:26.7145903Z {
2023-01-11T21:41:26.7146038Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.7146196Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7146294Z     {
2023-01-11T21:41:26.7146418Z         #pragma omp for 
2023-01-11T21:41:26.7146552Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7146651Z         {
2023-01-11T21:41:26.7146864Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7147085Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.7147293Z             auto tmp4 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:26.7147499Z             auto tmp6 = at::vec::Vectorized<float>::loadu(in_ptr3 + 8*i0);
2023-01-11T21:41:26.7147704Z             auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr4 + 8*i0);
2023-01-11T21:41:26.7147912Z             auto tmp10 = at::vec::Vectorized<float>::loadu(in_ptr5 + 8*i0);
2023-01-11T21:41:26.7148117Z             auto tmp12 = at::vec::Vectorized<float>::loadu(in_ptr6 + 8*i0);
2023-01-11T21:41:26.7148324Z             auto tmp14 = at::vec::Vectorized<float>::loadu(in_ptr7 + 8*i0);
2023-01-11T21:41:26.7148528Z             auto tmp16 = at::vec::Vectorized<float>::loadu(in_ptr8 + 8*i0);
2023-01-11T21:41:26.7148653Z             auto tmp1 = tmp0 + tmp0;
2023-01-11T21:41:26.7148790Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.7149019Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.7149152Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.7149280Z             auto tmp9 = tmp7 + tmp8;
2023-01-11T21:41:26.7149414Z             auto tmp11 = tmp9 + tmp10;
2023-01-11T21:41:26.7149551Z             auto tmp13 = tmp11 + tmp12;
2023-01-11T21:41:26.7149667Z             auto tmp15 = tmp13 + tmp14;
2023-01-11T21:41:26.7149800Z             auto tmp17 = tmp15 + tmp16;
2023-01-11T21:41:26.7149946Z             tmp17.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7150048Z         }
2023-01-11T21:41:26.7150203Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7150334Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7150436Z         {
2023-01-11T21:41:26.7150554Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7150683Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.7150883Z             auto tmp4 = in_ptr2[i0];
2023-01-11T21:41:26.7151033Z             auto tmp6 = in_ptr3[i0];
2023-01-11T21:41:26.7151169Z             auto tmp8 = in_ptr4[i0];
2023-01-11T21:41:26.7151302Z             auto tmp10 = in_ptr5[i0];
2023-01-11T21:41:26.7151436Z             auto tmp12 = in_ptr6[i0];
2023-01-11T21:41:26.7151551Z             auto tmp14 = in_ptr7[i0];
2023-01-11T21:41:26.7151680Z             auto tmp16 = in_ptr8[i0];
2023-01-11T21:41:26.7151817Z             auto tmp1 = tmp0 + tmp0;
2023-01-11T21:41:26.7151949Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.7152080Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.7152212Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.7152340Z             auto tmp9 = tmp7 + tmp8;
2023-01-11T21:41:26.7152457Z             auto tmp11 = tmp9 + tmp10;
2023-01-11T21:41:26.7152591Z             auto tmp13 = tmp11 + tmp12;
2023-01-11T21:41:26.7152726Z             auto tmp15 = tmp13 + tmp14;
2023-01-11T21:41:26.7152866Z             auto tmp17 = tmp15 + tmp16;
2023-01-11T21:41:26.7152996Z             out_ptr0[i0] = tmp17;
2023-01-11T21:41:26.7153097Z         }
2023-01-11T21:41:26.7153208Z         #pragma omp for 
2023-01-11T21:41:26.7153336Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7153436Z         {
2023-01-11T21:41:26.7153652Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7153860Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr9 + 8*i0);
2023-01-11T21:41:26.7154069Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr10 + 8*i0);
2023-01-11T21:41:26.7154276Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr11 + 8*i0);
2023-01-11T21:41:26.7154477Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr12 + 8*i0);
2023-01-11T21:41:26.7154679Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr13 + 8*i0);
2023-01-11T21:41:26.7154876Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr14 + 8*i0);
2023-01-11T21:41:26.7155091Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr15 + 8*i0);
2023-01-11T21:41:26.7155309Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr16 + 8*i0);
2023-01-11T21:41:26.7155445Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7155577Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7155705Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7155836Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7155954Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7156091Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7156223Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7156358Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7156509Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7156610Z         }
2023-01-11T21:41:26.7156761Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7156873Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7156977Z         {
2023-01-11T21:41:26.7157112Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.7157250Z             auto tmp1 = in_ptr9[i0];
2023-01-11T21:41:26.7157483Z             auto tmp3 = in_ptr10[i0];
2023-01-11T21:41:26.7157615Z             auto tmp5 = in_ptr11[i0];
2023-01-11T21:41:26.7157751Z             auto tmp7 = in_ptr12[i0];
2023-01-11T21:41:26.7157866Z             auto tmp9 = in_ptr13[i0];
2023-01-11T21:41:26.7158004Z             auto tmp11 = in_ptr14[i0];
2023-01-11T21:41:26.7158129Z             auto tmp13 = in_ptr15[i0];
2023-01-11T21:41:26.7158250Z             auto tmp15 = in_ptr16[i0];
2023-01-11T21:41:26.7158371Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7158489Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7158605Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7158705Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7158822Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7158943Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7159117Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7159243Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7159359Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:26.7159451Z         }
2023-01-11T21:41:26.7159561Z         #pragma omp for 
2023-01-11T21:41:26.7159691Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7159794Z         {
2023-01-11T21:41:26.7160019Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7160230Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr17 + 8*i0);
2023-01-11T21:41:26.7160438Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr18 + 8*i0);
2023-01-11T21:41:26.7160645Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr19 + 8*i0);
2023-01-11T21:41:26.7160854Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr20 + 8*i0);
2023-01-11T21:41:26.7161052Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr21 + 8*i0);
2023-01-11T21:41:26.7161268Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr22 + 8*i0);
2023-01-11T21:41:26.7161490Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr23 + 8*i0);
2023-01-11T21:41:26.7161701Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr24 + 8*i0);
2023-01-11T21:41:26.7161837Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7161973Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7162108Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7162245Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7162364Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7162501Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7162637Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7162771Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7162927Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7163031Z         }
2023-01-11T21:41:26.7163191Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7163307Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7163409Z         {
2023-01-11T21:41:26.7163555Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.7163694Z             auto tmp1 = in_ptr17[i0];
2023-01-11T21:41:26.7163831Z             auto tmp3 = in_ptr18[i0];
2023-01-11T21:41:26.7163964Z             auto tmp5 = in_ptr19[i0];
2023-01-11T21:41:26.7164103Z             auto tmp7 = in_ptr20[i0];
2023-01-11T21:41:26.7164223Z             auto tmp9 = in_ptr21[i0];
2023-01-11T21:41:26.7164362Z             auto tmp11 = in_ptr22[i0];
2023-01-11T21:41:26.7164500Z             auto tmp13 = in_ptr23[i0];
2023-01-11T21:41:26.7164638Z             auto tmp15 = in_ptr24[i0];
2023-01-11T21:41:26.7164774Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7164905Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7165039Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7165156Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7165293Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7165433Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7165646Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7165784Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7165914Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:26.7166020Z         }
2023-01-11T21:41:26.7166127Z         #pragma omp for 
2023-01-11T21:41:26.7166260Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7166363Z         {
2023-01-11T21:41:26.7166584Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7166796Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr25 + 8*i0);
2023-01-11T21:41:26.7167006Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr26 + 8*i0);
2023-01-11T21:41:26.7167213Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr27 + 8*i0);
2023-01-11T21:41:26.7167466Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr28 + 8*i0);
2023-01-11T21:41:26.7167664Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr29 + 8*i0);
2023-01-11T21:41:26.7167881Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr30 + 8*i0);
2023-01-11T21:41:26.7168094Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr31 + 8*i0);
2023-01-11T21:41:26.7168303Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr32 + 8*i0);
2023-01-11T21:41:26.7168437Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7168569Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7168700Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7168834Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7168953Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7169220Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7169358Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7169495Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7169656Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7169759Z         }
2023-01-11T21:41:26.7169910Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7170028Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7170127Z         {
2023-01-11T21:41:26.7170272Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.7170409Z             auto tmp1 = in_ptr25[i0];
2023-01-11T21:41:26.7170544Z             auto tmp3 = in_ptr26[i0];
2023-01-11T21:41:26.7170680Z             auto tmp5 = in_ptr27[i0];
2023-01-11T21:41:26.7170815Z             auto tmp7 = in_ptr28[i0];
2023-01-11T21:41:26.7170934Z             auto tmp9 = in_ptr29[i0];
2023-01-11T21:41:26.7171071Z             auto tmp11 = in_ptr30[i0];
2023-01-11T21:41:26.7171208Z             auto tmp13 = in_ptr31[i0];
2023-01-11T21:41:26.7171345Z             auto tmp15 = in_ptr32[i0];
2023-01-11T21:41:26.7171482Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7171614Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7171751Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7171869Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7172002Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7172146Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7172280Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7172416Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7172550Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:26.7172654Z         }
2023-01-11T21:41:26.7172763Z         #pragma omp for 
2023-01-11T21:41:26.7172890Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7172992Z         {
2023-01-11T21:41:26.7173217Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7173425Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr33 + 8*i0);
2023-01-11T21:41:26.7173637Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr34 + 8*i0);
2023-01-11T21:41:26.7173848Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr35 + 8*i0);
2023-01-11T21:41:26.7174058Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr36 + 8*i0);
2023-01-11T21:41:26.7174346Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr37 + 8*i0);
2023-01-11T21:41:26.7174556Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr38 + 8*i0);
2023-01-11T21:41:26.7174768Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr39 + 8*i0);
2023-01-11T21:41:26.7174976Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr40 + 8*i0);
2023-01-11T21:41:26.7175109Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7175244Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7175378Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7175510Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7175629Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7175764Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7175962Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7176101Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7176257Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7176361Z         }
2023-01-11T21:41:26.7176499Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7176631Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7176731Z         {
2023-01-11T21:41:26.7176874Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.7177013Z             auto tmp1 = in_ptr33[i0];
2023-01-11T21:41:26.7177149Z             auto tmp3 = in_ptr34[i0];
2023-01-11T21:41:26.7177286Z             auto tmp5 = in_ptr35[i0];
2023-01-11T21:41:26.7177402Z             auto tmp7 = in_ptr36[i0];
2023-01-11T21:41:26.7177537Z             auto tmp9 = in_ptr37[i0];
2023-01-11T21:41:26.7177679Z             auto tmp11 = in_ptr38[i0];
2023-01-11T21:41:26.7177814Z             auto tmp13 = in_ptr39[i0];
2023-01-11T21:41:26.7177949Z             auto tmp15 = in_ptr40[i0];
2023-01-11T21:41:26.7178089Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7178222Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7178341Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7178481Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7178616Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7178756Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7178894Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7179033Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7179168Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:26.7179250Z         }
2023-01-11T21:41:26.7179370Z         #pragma omp for 
2023-01-11T21:41:26.7179499Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7179603Z         {
2023-01-11T21:41:26.7179825Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7180036Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr41 + 8*i0);
2023-01-11T21:41:26.7180250Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr42 + 8*i0);
2023-01-11T21:41:26.7180457Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr43 + 8*i0);
2023-01-11T21:41:26.7180651Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr44 + 8*i0);
2023-01-11T21:41:26.7180859Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr45 + 8*i0);
2023-01-11T21:41:26.7181070Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr46 + 8*i0);
2023-01-11T21:41:26.7181283Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr47 + 8*i0);
2023-01-11T21:41:26.7181491Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr48 + 8*i0);
2023-01-11T21:41:26.7181631Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7181766Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7181900Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7182018Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7182155Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7182299Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7182435Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7182650Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7182805Z             tmp16.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7182907Z         }
2023-01-11T21:41:26.7183048Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7183248Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7183352Z         {
2023-01-11T21:41:26.7183498Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.7183637Z             auto tmp1 = in_ptr41[i0];
2023-01-11T21:41:26.7183775Z             auto tmp3 = in_ptr42[i0];
2023-01-11T21:41:26.7183911Z             auto tmp5 = in_ptr43[i0];
2023-01-11T21:41:26.7184030Z             auto tmp7 = in_ptr44[i0];
2023-01-11T21:41:26.7184166Z             auto tmp9 = in_ptr45[i0];
2023-01-11T21:41:26.7184303Z             auto tmp11 = in_ptr46[i0];
2023-01-11T21:41:26.7184442Z             auto tmp13 = in_ptr47[i0];
2023-01-11T21:41:26.7184638Z             auto tmp15 = in_ptr48[i0];
2023-01-11T21:41:26.7184780Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7184919Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7185037Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7185171Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.7185308Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:41:26.7185446Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:41:26.7185582Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:41:26.7185717Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:41:26.7185848Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:41:26.7185934Z         }
2023-01-11T21:41:26.7186060Z         #pragma omp for 
2023-01-11T21:41:26.7186191Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7186292Z         {
2023-01-11T21:41:26.7186518Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7186734Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr49 + 8*i0);
2023-01-11T21:41:26.7186870Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7187012Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7187114Z         }
2023-01-11T21:41:26.7187270Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7187404Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7187510Z         {
2023-01-11T21:41:26.7187655Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.7187794Z             auto tmp1 = in_ptr49[i0];
2023-01-11T21:41:26.7187913Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7188043Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7188146Z         }
2023-01-11T21:41:26.7188244Z     }
2023-01-11T21:41:26.7188340Z }
2023-01-11T21:41:26.7188495Z ''')
2023-01-11T21:41:26.7188506Z 
2023-01-11T21:41:26.7188512Z 
2023-01-11T21:41:26.7188658Z async_compile.wait(globals())
2023-01-11T21:41:26.7188759Z del async_compile
2023-01-11T21:41:26.7188768Z 
2023-01-11T21:41:26.7188888Z def call(args):
2023-01-11T21:41:26.7189419Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1 = args
2023-01-11T21:41:26.7189539Z     args.clear()
2023-01-11T21:41:26.7189870Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7190010Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.7190144Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:41:26.7190277Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:41:26.7190409Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:41:26.7190523Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:41:26.7190660Z     buf6 = buf5; del buf5  # reuse
2023-01-11T21:41:26.7192791Z     kernel_cpp_0(c_void_p(buf6.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg3_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg5_1.data_ptr()), c_void_p(arg6_1.data_ptr()), c_void_p(arg7_1.data_ptr()), c_void_p(arg8_1.data_ptr()), c_void_p(arg9_1.data_ptr()), c_void_p(arg10_1.data_ptr()), c_void_p(arg11_1.data_ptr()), c_void_p(arg12_1.data_ptr()), c_void_p(arg13_1.data_ptr()), c_void_p(arg14_1.data_ptr()), c_void_p(arg15_1.data_ptr()), c_void_p(arg16_1.data_ptr()), c_void_p(arg17_1.data_ptr()), c_void_p(arg18_1.data_ptr()), c_void_p(arg19_1.data_ptr()), c_void_p(arg20_1.data_ptr()), c_void_p(arg21_1.data_ptr()), c_void_p(arg22_1.data_ptr()), c_void_p(arg23_1.data_ptr()), c_void_p(arg24_1.data_ptr()), c_void_p(arg25_1.data_ptr()), c_void_p(arg26_1.data_ptr()), c_void_p(arg27_1.data_ptr()), c_void_p(arg28_1.data_ptr()), c_void_p(arg29_1.data_ptr()), c_void_p(arg30_1.data_ptr()), c_void_p(arg31_1.data_ptr()), c_void_p(arg32_1.data_ptr()), c_void_p(arg33_1.data_ptr()), c_void_p(arg34_1.data_ptr()), c_void_p(arg35_1.data_ptr()), c_void_p(arg36_1.data_ptr()), c_void_p(arg37_1.data_ptr()), c_void_p(arg38_1.data_ptr()), c_void_p(arg39_1.data_ptr()), c_void_p(arg40_1.data_ptr()), c_void_p(arg41_1.data_ptr()), c_void_p(arg42_1.data_ptr()), c_void_p(arg43_1.data_ptr()), c_void_p(arg44_1.data_ptr()), c_void_p(arg45_1.data_ptr()), c_void_p(arg46_1.data_ptr()), c_void_p(arg47_1.data_ptr()), c_void_p(arg48_1.data_ptr()), c_void_p(arg49_1.data_ptr()))
2023-01-11T21:41:26.7192979Z     del arg0_1
2023-01-11T21:41:26.7193088Z     del arg10_1
2023-01-11T21:41:26.7193181Z     del arg11_1
2023-01-11T21:41:26.7193285Z     del arg12_1
2023-01-11T21:41:26.7193393Z     del arg13_1
2023-01-11T21:41:26.7193500Z     del arg14_1
2023-01-11T21:41:26.7193608Z     del arg15_1
2023-01-11T21:41:26.7193718Z     del arg16_1
2023-01-11T21:41:26.7193810Z     del arg17_1
2023-01-11T21:41:26.7193916Z     del arg18_1
2023-01-11T21:41:26.7194027Z     del arg19_1
2023-01-11T21:41:26.7194140Z     del arg1_1
2023-01-11T21:41:26.7194246Z     del arg20_1
2023-01-11T21:41:26.7194363Z     del arg21_1
2023-01-11T21:41:26.7194469Z     del arg22_1
2023-01-11T21:41:26.7194559Z     del arg23_1
2023-01-11T21:41:26.7194669Z     del arg24_1
2023-01-11T21:41:26.7194779Z     del arg25_1
2023-01-11T21:41:26.7194885Z     del arg26_1
2023-01-11T21:41:26.7194995Z     del arg27_1
2023-01-11T21:41:26.7195100Z     del arg28_1
2023-01-11T21:41:26.7195191Z     del arg29_1
2023-01-11T21:41:26.7195301Z     del arg2_1
2023-01-11T21:41:26.7195407Z     del arg30_1
2023-01-11T21:41:26.7195513Z     del arg31_1
2023-01-11T21:41:26.7195620Z     del arg32_1
2023-01-11T21:41:26.7195727Z     del arg33_1
2023-01-11T21:41:26.7195815Z     del arg34_1
2023-01-11T21:41:26.7195925Z     del arg35_1
2023-01-11T21:41:26.7196037Z     del arg36_1
2023-01-11T21:41:26.7196144Z     del arg37_1
2023-01-11T21:41:26.7196251Z     del arg38_1
2023-01-11T21:41:26.7196357Z     del arg39_1
2023-01-11T21:41:26.7196470Z     del arg3_1
2023-01-11T21:41:26.7196561Z     del arg40_1
2023-01-11T21:41:26.7196667Z     del arg41_1
2023-01-11T21:41:26.7196781Z     del arg42_1
2023-01-11T21:41:26.7196886Z     del arg43_1
2023-01-11T21:41:26.7196995Z     del arg44_1
2023-01-11T21:41:26.7197106Z     del arg45_1
2023-01-11T21:41:26.7197196Z     del arg46_1
2023-01-11T21:41:26.7197302Z     del arg47_1
2023-01-11T21:41:26.7197409Z     del arg48_1
2023-01-11T21:41:26.7197517Z     del arg49_1
2023-01-11T21:41:26.7197623Z     del arg4_1
2023-01-11T21:41:26.7197732Z     del arg5_1
2023-01-11T21:41:26.7197840Z     del arg6_1
2023-01-11T21:41:26.7197933Z     del arg7_1
2023-01-11T21:41:26.7198040Z     del arg8_1
2023-01-11T21:41:26.7198144Z     del arg9_1
2023-01-11T21:41:26.7198261Z     return (buf6, )
2023-01-11T21:41:26.7198271Z 
2023-01-11T21:41:26.7198278Z 
2023-01-11T21:41:26.7198403Z if __name__ == "__main__":
2023-01-11T21:41:26.7198594Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7198797Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7199135Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7199526Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7199838Z     arg2_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7200151Z     arg3_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7200459Z     arg4_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7200776Z     arg5_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7201088Z     arg6_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7201399Z     arg7_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7201694Z     arg8_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7202069Z     arg9_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7202395Z     arg10_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7202721Z     arg11_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7203041Z     arg12_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7203360Z     arg13_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7203678Z     arg14_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7203992Z     arg15_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7204289Z     arg16_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7204608Z     arg17_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7204936Z     arg18_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7205251Z     arg19_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7205573Z     arg20_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7205887Z     arg21_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7206205Z     arg22_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7206521Z     arg23_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7206820Z     arg24_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7207137Z     arg25_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7207451Z     arg26_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7207765Z     arg27_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7208080Z     arg28_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7208398Z     arg29_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7208717Z     arg30_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7209162Z     arg31_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7209467Z     arg32_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7209775Z     arg33_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7210087Z     arg34_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7210402Z     arg35_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7210716Z     arg36_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7211028Z     arg37_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7211344Z     arg38_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7211656Z     arg39_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7212082Z     arg40_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7212391Z     arg41_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7212704Z     arg42_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7213019Z     arg43_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7213332Z     arg44_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7213643Z     arg45_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7213956Z     arg46_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7214329Z     arg47_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7214636Z     arg48_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7214948Z     arg49_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7215532Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1]))
2023-01-11T21:41:26.7215986Z [2023-01-11 21:36:50,438] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 383
2023-01-11T21:41:26.7215997Z 
2023-01-11T21:41:26.7216129Z --> 7
2023-01-11T21:41:26.7216237Z ok (1.949s)
2023-01-11T21:41:26.7217020Z   test_no_op_reduction_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7217230Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7217663Z [2023-01-11 21:36:50,465] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 384
2023-01-11T21:41:26.7218107Z [2023-01-11 21:36:51,995] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 384
2023-01-11T21:41:26.7218116Z 
2023-01-11T21:41:26.7218267Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7218364Z import torch
2023-01-11T21:41:26.7218476Z import random
2023-01-11T21:41:26.7218673Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7218872Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7218881Z 
2023-01-11T21:41:26.7219012Z aten = torch.ops.aten
2023-01-11T21:41:26.7219230Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7219378Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7219386Z 
2023-01-11T21:41:26.7219392Z 
2023-01-11T21:41:26.7219617Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7219934Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7220100Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7220235Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7220368Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7220452Z {
2023-01-11T21:41:26.7220586Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7220673Z     {
2023-01-11T21:41:26.7220764Z         #pragma omp for 
2023-01-11T21:41:26.7220875Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.7220961Z         {
2023-01-11T21:41:26.7221151Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7221397Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7221513Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7221642Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7221747Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7221839Z         }
2023-01-11T21:41:26.7221999Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7222140Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.7222248Z         {
2023-01-11T21:41:26.7222393Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7222564Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.7222694Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7222837Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7223027Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.7223216Z         }
2023-01-11T21:41:26.7223321Z     }
2023-01-11T21:41:26.7223429Z }
2023-01-11T21:41:26.7223574Z ''')
2023-01-11T21:41:26.7223583Z 
2023-01-11T21:41:26.7223590Z 
2023-01-11T21:41:26.7223731Z async_compile.wait(globals())
2023-01-11T21:41:26.7223854Z del async_compile
2023-01-11T21:41:26.7223863Z 
2023-01-11T21:41:26.7223982Z def call(args):
2023-01-11T21:41:26.7224101Z     arg0_1, = args
2023-01-11T21:41:26.7224221Z     args.clear()
2023-01-11T21:41:26.7224574Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7224937Z     buf1 = empty_strided((8, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7225220Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7225323Z     del arg0_1
2023-01-11T21:41:26.7225456Z     return (buf0, buf1, )
2023-01-11T21:41:26.7225465Z 
2023-01-11T21:41:26.7225472Z 
2023-01-11T21:41:26.7225608Z if __name__ == "__main__":
2023-01-11T21:41:26.7225806Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7226028Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7226396Z     arg0_1 = rand_strided((8, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7226588Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7226597Z 
2023-01-11T21:41:26.7226712Z ok (1.550s)
2023-01-11T21:41:26.7227303Z   test_output_strides_cpu (__main__.CpuTests) ... [2023-01-11 21:36:52,017] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 385
2023-01-11T21:41:26.7227785Z [2023-01-11 21:36:53,548] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 385
2023-01-11T21:41:26.7228259Z [2023-01-11 21:36:53,561] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 386
2023-01-11T21:41:26.7228747Z [2023-01-11 21:36:53,562] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 386
2023-01-11T21:41:26.7229203Z [2023-01-11 21:36:53,580] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 387
2023-01-11T21:41:26.7229687Z [2023-01-11 21:36:53,583] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 387
2023-01-11T21:41:26.7230462Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:3148: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7230667Z   self.assertEqual(inp.storage(), out.storage())
2023-01-11T21:41:26.7231913Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7232155Z   device=typed_storage.device,
2023-01-11T21:41:26.7232165Z 
2023-01-11T21:41:26.7232333Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7232442Z import torch
2023-01-11T21:41:26.7232566Z import random
2023-01-11T21:41:26.7232766Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7232979Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7232988Z 
2023-01-11T21:41:26.7233125Z aten = torch.ops.aten
2023-01-11T21:41:26.7233365Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7233530Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7233538Z 
2023-01-11T21:41:26.7233545Z 
2023-01-11T21:41:26.7233794Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7234195Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7234434Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7234616Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7234725Z {
2023-01-11T21:41:26.7234900Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7235008Z     {
2023-01-11T21:41:26.7235146Z         #pragma omp for 
2023-01-11T21:41:26.7235270Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7235379Z         {
2023-01-11T21:41:26.7235520Z             #pragma GCC ivdep
2023-01-11T21:41:26.7235669Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.7235782Z             {
2023-01-11T21:41:26.7235926Z                 #pragma GCC ivdep
2023-01-11T21:41:26.7236063Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:41:26.7236178Z                 {
2023-01-11T21:41:26.7236293Z                     {
2023-01-11T21:41:26.7236414Z                         {
2023-01-11T21:41:26.7236609Z                             auto tmp0 = in_ptr0[i1 + (16*i2) + (64*i0)];
2023-01-11T21:41:26.7236796Z                             out_ptr0[i2 + (4*i1) + (64*i0)] = tmp0;
2023-01-11T21:41:26.7236921Z                         }
2023-01-11T21:41:26.7237021Z                     }
2023-01-11T21:41:26.7237130Z                 }
2023-01-11T21:41:26.7237240Z             }
2023-01-11T21:41:26.7237349Z         }
2023-01-11T21:41:26.7237456Z     }
2023-01-11T21:41:26.7237564Z }
2023-01-11T21:41:26.7237717Z ''')
2023-01-11T21:41:26.7237726Z 
2023-01-11T21:41:26.7237734Z 
2023-01-11T21:41:26.7237876Z async_compile.wait(globals())
2023-01-11T21:41:26.7238007Z del async_compile
2023-01-11T21:41:26.7238015Z 
2023-01-11T21:41:26.7238136Z def call(args):
2023-01-11T21:41:26.7238254Z     arg0_1, = args
2023-01-11T21:41:26.7238375Z     args.clear()
2023-01-11T21:41:26.7238762Z     buf0 = empty_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7238999Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7239118Z     del arg0_1
2023-01-11T21:41:26.7239226Z     return (buf0, )
2023-01-11T21:41:26.7239239Z 
2023-01-11T21:41:26.7239245Z 
2023-01-11T21:41:26.7239375Z if __name__ == "__main__":
2023-01-11T21:41:26.7239572Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7239787Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7240173Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7240361Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7240370Z 
2023-01-11T21:41:26.7240377Z 
2023-01-11T21:41:26.7240547Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7240673Z import torch
2023-01-11T21:41:26.7240781Z import random
2023-01-11T21:41:26.7240980Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7241193Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7241202Z 
2023-01-11T21:41:26.7241347Z aten = torch.ops.aten
2023-01-11T21:41:26.7241585Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7241818Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7241827Z 
2023-01-11T21:41:26.7241833Z 
2023-01-11T21:41:26.7241989Z async_compile.wait(globals())
2023-01-11T21:41:26.7242121Z del async_compile
2023-01-11T21:41:26.7242130Z 
2023-01-11T21:41:26.7242237Z def call(args):
2023-01-11T21:41:26.7242356Z     arg0_1, = args
2023-01-11T21:41:26.7242481Z     args.clear()
2023-01-11T21:41:26.7242653Z     return (as_strided(arg0_1, (64, 4), (4, 1)), )
2023-01-11T21:41:26.7242661Z 
2023-01-11T21:41:26.7242668Z 
2023-01-11T21:41:26.7242800Z if __name__ == "__main__":
2023-01-11T21:41:26.7242997Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7243212Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7243587Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7243828Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7243839Z 
2023-01-11T21:41:26.7243845Z 
2023-01-11T21:41:26.7244012Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7244141Z import torch
2023-01-11T21:41:26.7244268Z import random
2023-01-11T21:41:26.7244470Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7244682Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7244690Z 
2023-01-11T21:41:26.7244827Z aten = torch.ops.aten
2023-01-11T21:41:26.7245045Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7245206Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7245216Z 
2023-01-11T21:41:26.7245222Z 
2023-01-11T21:41:26.7245379Z async_compile.wait(globals())
2023-01-11T21:41:26.7245505Z del async_compile
2023-01-11T21:41:26.7245513Z 
2023-01-11T21:41:26.7245638Z def call(args):
2023-01-11T21:41:26.7245760Z     arg0_1, = args
2023-01-11T21:41:26.7245886Z     args.clear()
2023-01-11T21:41:26.7246074Z     return (as_strided(arg0_1, (4, 4, 1), (4, 16, 0), 3), )
2023-01-11T21:41:26.7246083Z 
2023-01-11T21:41:26.7246089Z 
2023-01-11T21:41:26.7246204Z if __name__ == "__main__":
2023-01-11T21:41:26.7246406Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7246619Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7247008Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7247198Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7247207Z 
2023-01-11T21:41:26.7247326Z ok (1.586s)
2023-01-11T21:41:26.7248169Z   test_permute_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7248398Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7248874Z [2023-01-11 21:36:53,605] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 388
2023-01-11T21:41:26.7249496Z [2023-01-11 21:36:55,111] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 388
2023-01-11T21:41:26.7249507Z 
2023-01-11T21:41:26.7249653Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7249775Z import torch
2023-01-11T21:41:26.7249896Z import random
2023-01-11T21:41:26.7250100Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7250315Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7250324Z 
2023-01-11T21:41:26.7250458Z aten = torch.ops.aten
2023-01-11T21:41:26.7250695Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7250839Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7250865Z 
2023-01-11T21:41:26.7250873Z 
2023-01-11T21:41:26.7251105Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7251463Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7251769Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7251945Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7252115Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7252226Z {
2023-01-11T21:41:26.7252401Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7252492Z     {
2023-01-11T21:41:26.7252627Z         #pragma omp for 
2023-01-11T21:41:26.7252770Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7252882Z         {
2023-01-11T21:41:26.7253122Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7253360Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7253510Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7253792Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.7253946Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7254096Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:26.7254260Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7254419Z             tmp5.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7254532Z         }
2023-01-11T21:41:26.7254700Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7254829Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.7254940Z         {
2023-01-11T21:41:26.7255085Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7255259Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.7255407Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7255580Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.7255728Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7255857Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:26.7255999Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.7256138Z             out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.7256248Z         }
2023-01-11T21:41:26.7256362Z     }
2023-01-11T21:41:26.7256467Z }
2023-01-11T21:41:26.7256617Z ''')
2023-01-11T21:41:26.7256627Z 
2023-01-11T21:41:26.7256634Z 
2023-01-11T21:41:26.7256776Z async_compile.wait(globals())
2023-01-11T21:41:26.7256907Z del async_compile
2023-01-11T21:41:26.7256916Z 
2023-01-11T21:41:26.7257039Z def call(args):
2023-01-11T21:41:26.7257161Z     arg0_1, = args
2023-01-11T21:41:26.7257287Z     args.clear()
2023-01-11T21:41:26.7257682Z     buf0 = empty_strided((2, 2, 2, 2, 2), (4, 8, 1, 16, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7258068Z     buf1 = empty_strided((2, 2, 2, 2, 2), (4, 8, 1, 16, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7258350Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7258456Z     del arg0_1
2023-01-11T21:41:26.7258595Z     return (buf0, buf1, )
2023-01-11T21:41:26.7258604Z 
2023-01-11T21:41:26.7258610Z 
2023-01-11T21:41:26.7258741Z if __name__ == "__main__":
2023-01-11T21:41:26.7258947Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7259167Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7259562Z     arg0_1 = rand_strided((2, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7259752Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7259761Z 
2023-01-11T21:41:26.7259892Z ok (1.529s)
2023-01-11T21:41:26.7260711Z   test_pow1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7260934Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7261404Z [2023-01-11 21:36:55,268] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 389
2023-01-11T21:41:26.7261986Z [2023-01-11 21:36:56,967] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 389
2023-01-11T21:41:26.7261996Z 
2023-01-11T21:41:26.7262164Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7262289Z import torch
2023-01-11T21:41:26.7262414Z import random
2023-01-11T21:41:26.7262623Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7262834Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7262843Z 
2023-01-11T21:41:26.7262964Z aten = torch.ops.aten
2023-01-11T21:41:26.7263270Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7263436Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7263444Z 
2023-01-11T21:41:26.7263451Z 
2023-01-11T21:41:26.7263758Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7264120Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7264338Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7264511Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7264683Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7264837Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.7265007Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.7265179Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.7265345Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:26.7265510Z                        float* __restrict__ out_ptr6,
2023-01-11T21:41:26.7265675Z                        float* __restrict__ out_ptr7,
2023-01-11T21:41:26.7265840Z                        float* __restrict__ out_ptr8,
2023-01-11T21:41:26.7265992Z                        float* __restrict__ out_ptr9,
2023-01-11T21:41:26.7266166Z                        float* __restrict__ out_ptr10,
2023-01-11T21:41:26.7266346Z                        float* __restrict__ out_ptr11,
2023-01-11T21:41:26.7266516Z                        float* __restrict__ out_ptr12,
2023-01-11T21:41:26.7266687Z                        float* __restrict__ out_ptr13,
2023-01-11T21:41:26.7266856Z                        float* __restrict__ out_ptr14,
2023-01-11T21:41:26.7267024Z                        float* __restrict__ out_ptr15)
2023-01-11T21:41:26.7267130Z {
2023-01-11T21:41:26.7267286Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7267395Z     {
2023-01-11T21:41:26.7267532Z         #pragma omp for 
2023-01-11T21:41:26.7267676Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.7267789Z         {
2023-01-11T21:41:26.7268031Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7268186Z             auto tmp1 = tmp0.reciprocal();
2023-01-11T21:41:26.7268338Z             auto tmp2 = tmp1 * tmp1;
2023-01-11T21:41:26.7268486Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:41:26.7268635Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:26.7268784Z             auto tmp5 = tmp2 * tmp1;
2023-01-11T21:41:26.7268928Z             auto tmp6 = tmp5 * tmp5;
2023-01-11T21:41:26.7269070Z             auto tmp7 = tmp6 * tmp1;
2023-01-11T21:41:26.7269196Z             auto tmp8 = tmp3 * tmp1;
2023-01-11T21:41:26.7269432Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7269581Z             auto tmp10 = tmp0 * tmp0;
2023-01-11T21:41:26.7269730Z             auto tmp11 = tmp10 * tmp0;
2023-01-11T21:41:26.7269883Z             auto tmp12 = tmp10 * tmp10;
2023-01-11T21:41:26.7270031Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:41:26.7270180Z             auto tmp14 = tmp11 * tmp11;
2023-01-11T21:41:26.7270329Z             auto tmp15 = tmp14 * tmp0;
2023-01-11T21:41:26.7270460Z             auto tmp16 = tmp12 * tmp12;
2023-01-11T21:41:26.7270625Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7270784Z             tmp7.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7271020Z             tmp6.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.7271174Z             tmp8.store(out_ptr3 + 8*i0);
2023-01-11T21:41:26.7271327Z             tmp3.store(out_ptr4 + 8*i0);
2023-01-11T21:41:26.7271449Z             tmp5.store(out_ptr5 + 8*i0);
2023-01-11T21:41:26.7271554Z             tmp2.store(out_ptr6 + 8*i0);
2023-01-11T21:41:26.7271673Z             tmp1.store(out_ptr7 + 8*i0);
2023-01-11T21:41:26.7271796Z             tmp9.store(out_ptr8 + 8*i0);
2023-01-11T21:41:26.7271921Z             tmp10.store(out_ptr9 + 8*i0);
2023-01-11T21:41:26.7272049Z             tmp11.store(out_ptr10 + 8*i0);
2023-01-11T21:41:26.7272175Z             tmp12.store(out_ptr11 + 8*i0);
2023-01-11T21:41:26.7272303Z             tmp13.store(out_ptr12 + 8*i0);
2023-01-11T21:41:26.7272413Z             tmp14.store(out_ptr13 + 8*i0);
2023-01-11T21:41:26.7272582Z             tmp15.store(out_ptr14 + 8*i0);
2023-01-11T21:41:26.7272712Z             tmp16.store(out_ptr15 + 8*i0);
2023-01-11T21:41:26.7272800Z         }
2023-01-11T21:41:26.7272934Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7273045Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.7273134Z         {
2023-01-11T21:41:26.7273234Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7273375Z             auto tmp1 = 1 / tmp0;
2023-01-11T21:41:26.7273536Z             auto tmp2 = tmp1 * tmp1;
2023-01-11T21:41:26.7273674Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:41:26.7273917Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:41:26.7274095Z             auto tmp5 = tmp2 * tmp1;
2023-01-11T21:41:26.7274301Z             auto tmp6 = tmp5 * tmp5;
2023-01-11T21:41:26.7274474Z             auto tmp7 = tmp6 * tmp1;
2023-01-11T21:41:26.7274643Z             auto tmp8 = tmp3 * tmp1;
2023-01-11T21:41:26.7274828Z             auto tmp9 = static_cast<float>(1);
2023-01-11T21:41:26.7275012Z             auto tmp10 = tmp0 * tmp0;
2023-01-11T21:41:26.7275188Z             auto tmp11 = tmp10 * tmp0;
2023-01-11T21:41:26.7275360Z             auto tmp12 = tmp10 * tmp10;
2023-01-11T21:41:26.7275521Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:41:26.7275688Z             auto tmp14 = tmp11 * tmp11;
2023-01-11T21:41:26.7275852Z             auto tmp15 = tmp14 * tmp0;
2023-01-11T21:41:26.7276037Z             auto tmp16 = tmp12 * tmp12;
2023-01-11T21:41:26.7276210Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.7276381Z             out_ptr1[i0] = tmp7;
2023-01-11T21:41:26.7276539Z             out_ptr2[i0] = tmp6;
2023-01-11T21:41:26.7276684Z             out_ptr3[i0] = tmp8;
2023-01-11T21:41:26.7276843Z             out_ptr4[i0] = tmp3;
2023-01-11T21:41:26.7277008Z             out_ptr5[i0] = tmp5;
2023-01-11T21:41:26.7277168Z             out_ptr6[i0] = tmp2;
2023-01-11T21:41:26.7277332Z             out_ptr7[i0] = tmp1;
2023-01-11T21:41:26.7277481Z             out_ptr8[i0] = tmp9;
2023-01-11T21:41:26.7277651Z             out_ptr9[i0] = tmp10;
2023-01-11T21:41:26.7277803Z             out_ptr10[i0] = tmp11;
2023-01-11T21:41:26.7277965Z             out_ptr11[i0] = tmp12;
2023-01-11T21:41:26.7278132Z             out_ptr12[i0] = tmp13;
2023-01-11T21:41:26.7278279Z             out_ptr13[i0] = tmp14;
2023-01-11T21:41:26.7278443Z             out_ptr14[i0] = tmp15;
2023-01-11T21:41:26.7278606Z             out_ptr15[i0] = tmp16;
2023-01-11T21:41:26.7278737Z         }
2023-01-11T21:41:26.7278846Z     }
2023-01-11T21:41:26.7278976Z }
2023-01-11T21:41:26.7279171Z ''')
2023-01-11T21:41:26.7279182Z 
2023-01-11T21:41:26.7279188Z 
2023-01-11T21:41:26.7279369Z async_compile.wait(globals())
2023-01-11T21:41:26.7279527Z del async_compile
2023-01-11T21:41:26.7279539Z 
2023-01-11T21:41:26.7279690Z def call(args):
2023-01-11T21:41:26.7279834Z     arg0_1, = args
2023-01-11T21:41:26.7279958Z     args.clear()
2023-01-11T21:41:26.7280384Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7280793Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7281192Z     buf2 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7281709Z     buf3 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7282101Z     buf4 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7282504Z     buf5 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7282897Z     buf6 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7283269Z     buf7 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7283650Z     buf8 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7284056Z     buf9 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7284530Z     buf10 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7284939Z     buf11 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7285335Z     buf12 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7285724Z     buf13 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7286110Z     buf14 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7286486Z     buf15 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7287434Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf13.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf15.data_ptr()))
2023-01-11T21:41:26.7287761Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, arg0_1, buf9, buf10, buf11, buf12, buf13, buf14, buf15, )
2023-01-11T21:41:26.7287787Z 
2023-01-11T21:41:26.7287817Z 
2023-01-11T21:41:26.7287945Z if __name__ == "__main__":
2023-01-11T21:41:26.7288174Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7288431Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7288829Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7289183Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7289194Z 
2023-01-11T21:41:26.7289340Z ok (1.893s)
2023-01-11T21:41:26.7290247Z   test_pow2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7290499Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7291032Z [2023-01-11 21:36:57,046] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 390
2023-01-11T21:41:26.7291555Z [2023-01-11 21:36:58,590] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 390
2023-01-11T21:41:26.7291566Z 
2023-01-11T21:41:26.7291750Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7291907Z import torch
2023-01-11T21:41:26.7292073Z import random
2023-01-11T21:41:26.7292308Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7292551Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7292567Z 
2023-01-11T21:41:26.7292732Z aten = torch.ops.aten
2023-01-11T21:41:26.7292984Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7293177Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7293187Z 
2023-01-11T21:41:26.7293330Z 
2023-01-11T21:41:26.7293624Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7294034Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7294271Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7294473Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7294673Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7294813Z {
2023-01-11T21:41:26.7294982Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7295114Z     {
2023-01-11T21:41:26.7295284Z         #pragma omp for 
2023-01-11T21:41:26.7295452Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.7295586Z         {
2023-01-11T21:41:26.7295845Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7296212Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1000));
2023-01-11T21:41:26.7296389Z             auto tmp2 = tmp0.pow(tmp1);
2023-01-11T21:41:26.7296582Z             auto tmp3 = tmp1.pow(tmp0);
2023-01-11T21:41:26.7296755Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7296940Z             tmp3.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7297085Z         }
2023-01-11T21:41:26.7297282Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7297453Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.7297565Z         {
2023-01-11T21:41:26.7297735Z             auto tmp1 = in_ptr0[i0];
2023-01-11T21:41:26.7297945Z             auto tmp0 = static_cast<float>(1000);
2023-01-11T21:41:26.7298161Z             auto tmp2 = std::pow(tmp0, tmp1);
2023-01-11T21:41:26.7298371Z             auto tmp3 = std::pow(tmp1, tmp0);
2023-01-11T21:41:26.7298529Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7298699Z             out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.7298827Z         }
2023-01-11T21:41:26.7298952Z     }
2023-01-11T21:41:26.7299079Z }
2023-01-11T21:41:26.7299268Z ''')
2023-01-11T21:41:26.7299287Z 
2023-01-11T21:41:26.7299296Z 
2023-01-11T21:41:26.7299480Z async_compile.wait(globals())
2023-01-11T21:41:26.7299635Z del async_compile
2023-01-11T21:41:26.7299648Z 
2023-01-11T21:41:26.7299802Z def call(args):
2023-01-11T21:41:26.7299951Z     arg0_1, = args
2023-01-11T21:41:26.7300078Z     args.clear()
2023-01-11T21:41:26.7300491Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7300896Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7301218Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7301369Z     del arg0_1
2023-01-11T21:41:26.7301543Z     return (buf0, buf1, )
2023-01-11T21:41:26.7301556Z 
2023-01-11T21:41:26.7301563Z 
2023-01-11T21:41:26.7301715Z if __name__ == "__main__":
2023-01-11T21:41:26.7301932Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7302176Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7302597Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7302817Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7302827Z 
2023-01-11T21:41:26.7302960Z ok (1.585s)
2023-01-11T21:41:26.7303694Z   test_pow3_cpu (__main__.CpuTests) ... [2023-01-11 21:36:58,609] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 391
2023-01-11T21:41:26.7304231Z [2023-01-11 21:37:00,201] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 391
2023-01-11T21:41:26.7304243Z 
2023-01-11T21:41:26.7304438Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7304592Z import torch
2023-01-11T21:41:26.7304712Z import random
2023-01-11T21:41:26.7304939Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7305193Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7305204Z 
2023-01-11T21:41:26.7305363Z aten = torch.ops.aten
2023-01-11T21:41:26.7305734Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7305929Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7305939Z 
2023-01-11T21:41:26.7305947Z 
2023-01-11T21:41:26.7306232Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7306623Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7306847Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7307043Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7307170Z {
2023-01-11T21:41:26.7307296Z     {
2023-01-11T21:41:26.7307420Z         {
2023-01-11T21:41:26.7307605Z             auto tmp1 = in_ptr0[0];
2023-01-11T21:41:26.7307826Z             auto tmp0 = static_cast<float>(0.12300000339746475);
2023-01-11T21:41:26.7308061Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7308253Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:41:26.7308415Z             out_ptr0[0] = tmp3;
2023-01-11T21:41:26.7308559Z         }
2023-01-11T21:41:26.7308688Z     }
2023-01-11T21:41:26.7308819Z }
2023-01-11T21:41:26.7308985Z ''')
2023-01-11T21:41:26.7308994Z 
2023-01-11T21:41:26.7309026Z 
2023-01-11T21:41:26.7309183Z async_compile.wait(globals())
2023-01-11T21:41:26.7309338Z del async_compile
2023-01-11T21:41:26.7309350Z 
2023-01-11T21:41:26.7309494Z def call(args):
2023-01-11T21:41:26.7309644Z     arg0_1, = args
2023-01-11T21:41:26.7309790Z     args.clear()
2023-01-11T21:41:26.7310161Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7310433Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7310553Z     del arg0_1
2023-01-11T21:41:26.7310707Z     return (buf0, )
2023-01-11T21:41:26.7310717Z 
2023-01-11T21:41:26.7310724Z 
2023-01-11T21:41:26.7310878Z if __name__ == "__main__":
2023-01-11T21:41:26.7311099Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7311357Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7311743Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7311953Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7311963Z 
2023-01-11T21:41:26.7312099Z ok (1.610s)
2023-01-11T21:41:26.7312750Z   test_profiler_mark_wrapper_call_cpu (__main__.CpuTests) ... STAGE:2023-01-11 21:37:00 1448:1448 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:41:26.7313282Z [2023-01-11 21:37:00,214] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 392
2023-01-11T21:41:26.7313800Z [2023-01-11 21:37:00,220] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 392
2023-01-11T21:41:26.7314294Z STAGE:2023-01-11 21:37:00 1448:1448 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:41:26.7314821Z STAGE:2023-01-11 21:37:00 1448:1448 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:41:26.7314839Z 
2023-01-11T21:41:26.7315032Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7315188Z import torch
2023-01-11T21:41:26.7315325Z import random
2023-01-11T21:41:26.7315544Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7315788Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7315801Z 
2023-01-11T21:41:26.7315959Z aten = torch.ops.aten
2023-01-11T21:41:26.7316215Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7316407Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7316417Z 
2023-01-11T21:41:26.7316427Z 
2023-01-11T21:41:26.7316708Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7317103Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7317356Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7317536Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7317743Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7317967Z {
2023-01-11T21:41:26.7318179Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7318310Z     {
2023-01-11T21:41:26.7318468Z         #pragma omp for 
2023-01-11T21:41:26.7318645Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.7318757Z         {
2023-01-11T21:41:26.7319028Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7319292Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.7319472Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7319650Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7319785Z         }
2023-01-11T21:41:26.7319982Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7320126Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.7320340Z         {
2023-01-11T21:41:26.7320520Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7320681Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.7320863Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7321035Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7321172Z         }
2023-01-11T21:41:26.7321279Z     }
2023-01-11T21:41:26.7321409Z }
2023-01-11T21:41:26.7321590Z ''')
2023-01-11T21:41:26.7321604Z 
2023-01-11T21:41:26.7321611Z 
2023-01-11T21:41:26.7321795Z async_compile.wait(globals())
2023-01-11T21:41:26.7321952Z del async_compile
2023-01-11T21:41:26.7321962Z 
2023-01-11T21:41:26.7322116Z def call(args):
2023-01-11T21:41:26.7322283Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7322407Z     args.clear()
2023-01-11T21:41:26.7322629Z     from torch.profiler import record_function
2023-01-11T21:41:26.7322968Z     with record_function('inductor_wrapper_call'):
2023-01-11T21:41:26.7323371Z         buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7323701Z         kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7323854Z         del arg0_1
2023-01-11T21:41:26.7324002Z         del arg1_1
2023-01-11T21:41:26.7324146Z         return (buf0, )
2023-01-11T21:41:26.7324160Z 
2023-01-11T21:41:26.7324169Z 
2023-01-11T21:41:26.7324304Z if __name__ == "__main__":
2023-01-11T21:41:26.7324538Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7324785Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7325183Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7325579Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7325813Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7325826Z 
2023-01-11T21:41:26.7325958Z ok (0.022s)
2023-01-11T21:41:26.7326676Z   test_rand_like_deterministic_cpu (__main__.CpuTests) ... [2023-01-11 21:37:00,298] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 393
2023-01-11T21:41:26.7327188Z [2023-01-11 21:37:00,299] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:41:26.7327744Z [2023-01-11 21:37:01,981] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 393
2023-01-11T21:41:26.7327755Z 
2023-01-11T21:41:26.7327946Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7328101Z import torch
2023-01-11T21:41:26.7328258Z import random
2023-01-11T21:41:26.7328485Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7328733Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7328742Z 
2023-01-11T21:41:26.7328905Z aten = torch.ops.aten
2023-01-11T21:41:26.7329302Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7329489Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7329816Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:41:26.7329828Z 
2023-01-11T21:41:26.7329835Z 
2023-01-11T21:41:26.7330269Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7330657Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7330893Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:41:26.7331103Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7331286Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7331392Z {
2023-01-11T21:41:26.7331593Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7331719Z     {
2023-01-11T21:41:26.7331891Z         #pragma omp for 
2023-01-11T21:41:26.7332057Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.7332193Z         {
2023-01-11T21:41:26.7332326Z             {
2023-01-11T21:41:26.7332444Z                 {
2023-01-11T21:41:26.7332624Z                     auto tmp0 = seed0[0];
2023-01-11T21:41:26.7332927Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.7333203Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:41:26.7333427Z                     auto tmp3 = static_cast<int>(1024 + i0);
2023-01-11T21:41:26.7333706Z                     auto tmp4 = static_cast<float>(normalized_rand_cpu(tmp0, tmp3));;
2023-01-11T21:41:26.7333872Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7334020Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.7334166Z                 }
2023-01-11T21:41:26.7334305Z             }
2023-01-11T21:41:26.7334441Z         }
2023-01-11T21:41:26.7334580Z     }
2023-01-11T21:41:26.7334700Z }
2023-01-11T21:41:26.7334886Z ''')
2023-01-11T21:41:26.7334899Z 
2023-01-11T21:41:26.7334907Z 
2023-01-11T21:41:26.7335077Z async_compile.wait(globals())
2023-01-11T21:41:26.7335225Z del async_compile
2023-01-11T21:41:26.7335236Z 
2023-01-11T21:41:26.7335384Z def call(args):
2023-01-11T21:41:26.7335538Z     arg0_1, = args
2023-01-11T21:41:26.7335669Z     args.clear()
2023-01-11T21:41:26.7335930Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:41:26.7336342Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7336717Z     buf1 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7337062Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7337223Z     return (buf0, buf1, )
2023-01-11T21:41:26.7337236Z 
2023-01-11T21:41:26.7337242Z 
2023-01-11T21:41:26.7337401Z if __name__ == "__main__":
2023-01-11T21:41:26.7337628Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7337884Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7338265Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7338680Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7338908Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7338928Z 
2023-01-11T21:41:26.7339042Z ok (1.760s)
2023-01-11T21:41:26.7339943Z   test_reduction1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7340203Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7340730Z [2023-01-11 21:37:02,020] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 394
2023-01-11T21:41:26.7341273Z [2023-01-11 21:37:03,584] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 394
2023-01-11T21:41:26.7341283Z 
2023-01-11T21:41:26.7341476Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7341628Z import torch
2023-01-11T21:41:26.7341905Z import random
2023-01-11T21:41:26.7342144Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7342379Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7342389Z 
2023-01-11T21:41:26.7342538Z aten = torch.ops.aten
2023-01-11T21:41:26.7342807Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7342995Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7343007Z 
2023-01-11T21:41:26.7343014Z 
2023-01-11T21:41:26.7343376Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7343765Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7344012Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7344213Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7344475Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7344684Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.7344875Z                        long* __restrict__ out_ptr3,
2023-01-11T21:41:26.7345079Z                        long* __restrict__ out_ptr4)
2023-01-11T21:41:26.7345210Z {
2023-01-11T21:41:26.7345328Z     {
2023-01-11T21:41:26.7345470Z         {
2023-01-11T21:41:26.7345609Z             float tmp1 = 0;
2023-01-11T21:41:26.7346086Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7346327Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7346574Z             struct IndexValue_9 {size_t index; float value;};
2023-01-11T21:41:26.7347031Z             IndexValue_9 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.7347302Z             #pragma omp declare reduction(argmax : struct IndexValue_9 :\
2023-01-11T21:41:26.7347602Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.7347871Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.7348334Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.7348572Z             struct IndexValue_10 {size_t index; float value;};
2023-01-11T21:41:26.7348839Z             IndexValue_10 tmp5{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.7349114Z             #pragma omp declare reduction(argmin : struct IndexValue_10 :\
2023-01-11T21:41:26.7349408Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.7349685Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.7349978Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.7350164Z             for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.7350284Z             {
2023-01-11T21:41:26.7350427Z                 {
2023-01-11T21:41:26.7350608Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7350776Z                     tmp1 += tmp0;
2023-01-11T21:41:26.7350977Z                     tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:41:26.7351180Z                     tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:41:26.7351363Z                     if (tmp4.value < tmp0) {
2023-01-11T21:41:26.7351560Z                         tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:41:26.7351705Z                     }
2023-01-11T21:41:26.7351887Z                     if (tmp5.value > tmp0) {
2023-01-11T21:41:26.7352109Z                         tmp5.index = i0; tmp5.value = tmp0;
2023-01-11T21:41:26.7352246Z                     }
2023-01-11T21:41:26.7352382Z                 }
2023-01-11T21:41:26.7352524Z             }
2023-01-11T21:41:26.7352670Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:26.7352838Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.7352997Z             out_ptr2[0] = tmp3;
2023-01-11T21:41:26.7353165Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:41:26.7353334Z             out_ptr4[0] = tmp5.index;
2023-01-11T21:41:26.7353585Z         }
2023-01-11T21:41:26.7353724Z     }
2023-01-11T21:41:26.7353835Z }
2023-01-11T21:41:26.7354017Z ''')
2023-01-11T21:41:26.7354027Z 
2023-01-11T21:41:26.7354034Z 
2023-01-11T21:41:26.7354221Z async_compile.wait(globals())
2023-01-11T21:41:26.7354370Z del async_compile
2023-01-11T21:41:26.7354383Z 
2023-01-11T21:41:26.7354529Z def call(args):
2023-01-11T21:41:26.7354678Z     arg0_1, = args
2023-01-11T21:41:26.7354834Z     args.clear()
2023-01-11T21:41:26.7355202Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7355580Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7355943Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7356317Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7356763Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7357236Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.7357385Z     del arg0_1
2023-01-11T21:41:26.7357584Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.7357595Z 
2023-01-11T21:41:26.7357602Z 
2023-01-11T21:41:26.7357755Z if __name__ == "__main__":
2023-01-11T21:41:26.7357966Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7358211Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7358604Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7358825Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7358837Z 
2023-01-11T21:41:26.7358977Z ok (1.602s)
2023-01-11T21:41:26.7359874Z   test_reduction2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7360117Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7360658Z [2023-01-11 21:37:03,603] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 395
2023-01-11T21:41:26.7361182Z [2023-01-11 21:37:05,122] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 395
2023-01-11T21:41:26.7361195Z 
2023-01-11T21:41:26.7361377Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7361531Z import torch
2023-01-11T21:41:26.7361675Z import random
2023-01-11T21:41:26.7361905Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7362162Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7362177Z 
2023-01-11T21:41:26.7362336Z aten = torch.ops.aten
2023-01-11T21:41:26.7362605Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7362783Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7362796Z 
2023-01-11T21:41:26.7362804Z 
2023-01-11T21:41:26.7363071Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7363470Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7363703Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7363902Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7364098Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7364293Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.7364484Z                        long* __restrict__ out_ptr3)
2023-01-11T21:41:26.7364596Z {
2023-01-11T21:41:26.7364736Z     {
2023-01-11T21:41:26.7364880Z         {
2023-01-11T21:41:26.7365048Z             float tmp1 = 0;
2023-01-11T21:41:26.7365477Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7365837Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7366075Z             struct IndexValue_11 {size_t index; float value;};
2023-01-11T21:41:26.7366334Z             IndexValue_11 tmp4{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.7366586Z             #pragma omp declare reduction(argmin : struct IndexValue_11 :\
2023-01-11T21:41:26.7366882Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.7367167Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.7367456Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.7367669Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7367872Z             {
2023-01-11T21:41:26.7368220Z                 #pragma omp for reduction(+:tmp1) reduction(max:tmp2) reduction(min:tmp3) reduction(argmin:tmp4) 
2023-01-11T21:41:26.7368412Z                 for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7368531Z                 {
2023-01-11T21:41:26.7368671Z                     {
2023-01-11T21:41:26.7368864Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7369198Z                         tmp1 += tmp0;
2023-01-11T21:41:26.7369418Z                         tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:41:26.7369622Z                         tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:41:26.7369804Z                         if (tmp4.value > tmp0) {
2023-01-11T21:41:26.7370007Z                             tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:41:26.7370155Z                         }
2023-01-11T21:41:26.7370303Z                     }
2023-01-11T21:41:26.7370433Z                 }
2023-01-11T21:41:26.7370566Z             }
2023-01-11T21:41:26.7370727Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:26.7370895Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.7371042Z             out_ptr2[0] = tmp3;
2023-01-11T21:41:26.7371222Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:41:26.7371361Z         }
2023-01-11T21:41:26.7371492Z     }
2023-01-11T21:41:26.7371615Z }
2023-01-11T21:41:26.7371809Z ''')
2023-01-11T21:41:26.7371820Z 
2023-01-11T21:41:26.7371829Z 
2023-01-11T21:41:26.7372021Z async_compile.wait(globals())
2023-01-11T21:41:26.7372152Z del async_compile
2023-01-11T21:41:26.7372162Z 
2023-01-11T21:41:26.7372303Z def call(args):
2023-01-11T21:41:26.7372440Z     arg0_1, = args
2023-01-11T21:41:26.7372594Z     args.clear()
2023-01-11T21:41:26.7372992Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7373350Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7373722Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7374079Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7374489Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.7374643Z     del arg0_1
2023-01-11T21:41:26.7374825Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:26.7374838Z 
2023-01-11T21:41:26.7374845Z 
2023-01-11T21:41:26.7374989Z if __name__ == "__main__":
2023-01-11T21:41:26.7375211Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7375468Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7375870Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7376061Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7376095Z 
2023-01-11T21:41:26.7376215Z ok (1.538s)
2023-01-11T21:41:26.7377128Z   test_reduction3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7377530Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7378063Z [2023-01-11 21:37:05,141] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 396
2023-01-11T21:41:26.7378598Z [2023-01-11 21:37:06,975] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 396
2023-01-11T21:41:26.7378609Z 
2023-01-11T21:41:26.7378796Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7378954Z import torch
2023-01-11T21:41:26.7379108Z import random
2023-01-11T21:41:26.7379318Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7379690Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7379707Z 
2023-01-11T21:41:26.7379872Z aten = torch.ops.aten
2023-01-11T21:41:26.7380146Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7380335Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7380344Z 
2023-01-11T21:41:26.7380351Z 
2023-01-11T21:41:26.7380631Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7381029Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7381265Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7381473Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7381650Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7381846Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.7382030Z                        long* __restrict__ out_ptr3)
2023-01-11T21:41:26.7382163Z {
2023-01-11T21:41:26.7382292Z     {
2023-01-11T21:41:26.7382432Z         {
2023-01-11T21:41:26.7382579Z             float tmp1 = 0;
2023-01-11T21:41:26.7383012Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7383364Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7383609Z             struct IndexValue_12 {size_t index; float value;};
2023-01-11T21:41:26.7384070Z             IndexValue_12 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.7384356Z             #pragma omp declare reduction(argmax : struct IndexValue_12 :\
2023-01-11T21:41:26.7384647Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.7384935Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.7385430Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.7385618Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7385752Z             {
2023-01-11T21:41:26.7386106Z                 #pragma omp for reduction(+:tmp1) reduction(max:tmp2) reduction(min:tmp3) reduction(argmax:tmp4) 
2023-01-11T21:41:26.7386290Z                 for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7386433Z                 {
2023-01-11T21:41:26.7386566Z                     {
2023-01-11T21:41:26.7386762Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7386909Z                         tmp1 += tmp0;
2023-01-11T21:41:26.7387123Z                         tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:41:26.7387325Z                         tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:41:26.7387505Z                         if (tmp4.value < tmp0) {
2023-01-11T21:41:26.7387732Z                             tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:41:26.7387882Z                         }
2023-01-11T21:41:26.7388026Z                     }
2023-01-11T21:41:26.7388143Z                 }
2023-01-11T21:41:26.7388270Z             }
2023-01-11T21:41:26.7388440Z             out_ptr0[0] = tmp1;
2023-01-11T21:41:26.7388617Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.7388778Z             out_ptr2[0] = tmp3;
2023-01-11T21:41:26.7389067Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:41:26.7389194Z         }
2023-01-11T21:41:26.7389301Z     }
2023-01-11T21:41:26.7389434Z }
2023-01-11T21:41:26.7389615Z ''')
2023-01-11T21:41:26.7389628Z 
2023-01-11T21:41:26.7389634Z 
2023-01-11T21:41:26.7389824Z async_compile.wait(globals())
2023-01-11T21:41:26.7389986Z del async_compile
2023-01-11T21:41:26.7389996Z 
2023-01-11T21:41:26.7390134Z def call(args):
2023-01-11T21:41:26.7390285Z     arg0_1, = args
2023-01-11T21:41:26.7390415Z     args.clear()
2023-01-11T21:41:26.7390800Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7391166Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7391534Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7392003Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7392430Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.7392590Z     del arg0_1
2023-01-11T21:41:26.7392760Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:26.7392770Z 
2023-01-11T21:41:26.7392777Z 
2023-01-11T21:41:26.7392916Z if __name__ == "__main__":
2023-01-11T21:41:26.7393149Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7393395Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7393785Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7394018Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7394028Z 
2023-01-11T21:41:26.7394170Z ok (1.853s)
2023-01-11T21:41:26.7394641Z   test_reduction4_cpu (__main__.CpuTests) ... skip: Non-deterministic CPU results (0.001s)
2023-01-11T21:41:26.7395583Z   test_reflection_pad2d_backward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7395854Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7396385Z [2023-01-11 21:37:07,004] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 397
2023-01-11T21:41:26.7396910Z [2023-01-11 21:37:08,548] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 397
2023-01-11T21:41:26.7397730Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7397981Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7398498Z [2023-01-11 21:37:08,568] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 398
2023-01-11T21:41:26.7399036Z [2023-01-11 21:37:10,629] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 398
2023-01-11T21:41:26.7399858Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7400110Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7400635Z [2023-01-11 21:37:10,650] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 399
2023-01-11T21:41:26.7400748Z 
2023-01-11T21:41:26.7400941Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7401089Z import torch
2023-01-11T21:41:26.7401215Z import random
2023-01-11T21:41:26.7401437Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7401670Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7401685Z 
2023-01-11T21:41:26.7401850Z aten = torch.ops.aten
2023-01-11T21:41:26.7402116Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7402300Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7402310Z 
2023-01-11T21:41:26.7402317Z 
2023-01-11T21:41:26.7402597Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7402989Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7403281Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7403490Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7403618Z {
2023-01-11T21:41:26.7403827Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7403947Z     {
2023-01-11T21:41:26.7404119Z         #pragma omp for 
2023-01-11T21:41:26.7404299Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7404412Z         {
2023-01-11T21:41:26.7404580Z             #pragma GCC ivdep
2023-01-11T21:41:26.7404752Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.7404874Z             {
2023-01-11T21:41:26.7405019Z                 {
2023-01-11T21:41:26.7405161Z                     {
2023-01-11T21:41:26.7405378Z                         auto tmp0 = static_cast<int>(i0);
2023-01-11T21:41:26.7405571Z                         auto tmp1 = static_cast<int>(i1);
2023-01-11T21:41:26.7405772Z                         auto tmp2 = in_ptr0[tmp1 + (8*tmp0)];
2023-01-11T21:41:26.7405969Z                         out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:26.7406119Z                     }
2023-01-11T21:41:26.7406258Z                 }
2023-01-11T21:41:26.7406394Z             }
2023-01-11T21:41:26.7406531Z         }
2023-01-11T21:41:26.7406632Z     }
2023-01-11T21:41:26.7406756Z }
2023-01-11T21:41:26.7406934Z ''')
2023-01-11T21:41:26.7406945Z 
2023-01-11T21:41:26.7406952Z 
2023-01-11T21:41:26.7407139Z async_compile.wait(globals())
2023-01-11T21:41:26.7407299Z del async_compile
2023-01-11T21:41:26.7407310Z 
2023-01-11T21:41:26.7407450Z def call(args):
2023-01-11T21:41:26.7407611Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7407735Z     args.clear()
2023-01-11T21:41:26.7408170Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7408432Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7408584Z     del arg0_1
2023-01-11T21:41:26.7408735Z     return (buf0, )
2023-01-11T21:41:26.7408747Z 
2023-01-11T21:41:26.7408753Z 
2023-01-11T21:41:26.7408913Z if __name__ == "__main__":
2023-01-11T21:41:26.7409285Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7409528Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7409952Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7410387Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7410613Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7410622Z 
2023-01-11T21:41:26.7410630Z 
2023-01-11T21:41:26.7410832Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7410985Z import torch
2023-01-11T21:41:26.7411124Z import random
2023-01-11T21:41:26.7411365Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7411605Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7411617Z 
2023-01-11T21:41:26.7411755Z aten = torch.ops.aten
2023-01-11T21:41:26.7412024Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7412223Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7412235Z 
2023-01-11T21:41:26.7412372Z 
2023-01-11T21:41:26.7412666Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7413063Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7413294Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7413489Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7413620Z {
2023-01-11T21:41:26.7413795Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7413919Z     {
2023-01-11T21:41:26.7414086Z         #pragma omp for 
2023-01-11T21:41:26.7414257Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7414396Z         {
2023-01-11T21:41:26.7414556Z             #pragma GCC ivdep
2023-01-11T21:41:26.7414711Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.7414855Z             {
2023-01-11T21:41:26.7414988Z                 {
2023-01-11T21:41:26.7415230Z                     {
2023-01-11T21:41:26.7415445Z                         auto tmp0 = static_cast<int>(1 + i0);
2023-01-11T21:41:26.7415668Z                         auto tmp1 = static_cast<int>(1 + i1);
2023-01-11T21:41:26.7415883Z                         auto tmp2 = in_ptr0[tmp1 + (10*tmp0)];
2023-01-11T21:41:26.7416076Z                         auto tmp3 = static_cast<int>(i1);
2023-01-11T21:41:26.7416261Z                         auto tmp4 = tmp3 >= 1;
2023-01-11T21:41:26.7416442Z                         auto tmp5 = tmp3 <= 1;
2023-01-11T21:41:26.7416636Z                         auto tmp6 = tmp4 & tmp5;
2023-01-11T21:41:26.7416811Z                         float tmp7 = 0.0;
2023-01-11T21:41:26.7416975Z                         if(tmp6)
2023-01-11T21:41:26.7417116Z                         {
2023-01-11T21:41:26.7417321Z                             auto tmp8 = static_cast<int>(1 + i0);
2023-01-11T21:41:26.7417690Z                             auto tmp9 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:26.7417914Z                             auto tmp10 = in_ptr0[tmp9 + (10*tmp8)];
2023-01-11T21:41:26.7418073Z                             tmp7 = tmp10;
2023-01-11T21:41:26.7418222Z                         }
2023-01-11T21:41:26.7418419Z                         auto tmp11 = tmp2 + tmp7;
2023-01-11T21:41:26.7418609Z                         auto tmp12 = tmp3 >= 6;
2023-01-11T21:41:26.7418802Z                         auto tmp13 = tmp3 <= 6;
2023-01-11T21:41:26.7418968Z                         auto tmp14 = tmp12 & tmp13;
2023-01-11T21:41:26.7419146Z                         float tmp15 = 0.0;
2023-01-11T21:41:26.7419309Z                         if(tmp14)
2023-01-11T21:41:26.7419445Z                         {
2023-01-11T21:41:26.7419671Z                             auto tmp16 = static_cast<int>(1 + i0);
2023-01-11T21:41:26.7420031Z                             auto tmp17 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:26.7420262Z                             auto tmp18 = in_ptr0[tmp17 + (10*tmp16)];
2023-01-11T21:41:26.7420417Z                             tmp15 = tmp18;
2023-01-11T21:41:26.7420564Z                         }
2023-01-11T21:41:26.7420751Z                         auto tmp19 = tmp11 + tmp15;
2023-01-11T21:41:26.7420959Z                         auto tmp20 = static_cast<int>(i0);
2023-01-11T21:41:26.7421149Z                         auto tmp21 = tmp20 >= 1;
2023-01-11T21:41:26.7421337Z                         auto tmp22 = tmp20 <= 1;
2023-01-11T21:41:26.7421534Z                         auto tmp23 = tmp21 & tmp22;
2023-01-11T21:41:26.7421680Z                         float tmp24 = 0.0;
2023-01-11T21:41:26.7421840Z                         if(tmp23)
2023-01-11T21:41:26.7421985Z                         {
2023-01-11T21:41:26.7422340Z                             auto tmp25 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:41:26.7422563Z                             auto tmp26 = static_cast<int>(1 + i1);
2023-01-11T21:41:26.7422787Z                             auto tmp27 = in_ptr0[tmp26 + (10*tmp25)];
2023-01-11T21:41:26.7422970Z                             tmp24 = tmp27;
2023-01-11T21:41:26.7423115Z                         }
2023-01-11T21:41:26.7423495Z                         auto tmp28 = tmp19 + tmp24;
2023-01-11T21:41:26.7423681Z                         auto tmp29 = tmp20 >= 6;
2023-01-11T21:41:26.7423877Z                         auto tmp30 = tmp20 <= 6;
2023-01-11T21:41:26.7424075Z                         auto tmp31 = tmp29 & tmp30;
2023-01-11T21:41:26.7424253Z                         float tmp32 = 0.0;
2023-01-11T21:41:26.7424403Z                         if(tmp31)
2023-01-11T21:41:26.7424545Z                         {
2023-01-11T21:41:26.7424898Z                             auto tmp33 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:41:26.7425125Z                             auto tmp34 = static_cast<int>(1 + i1);
2023-01-11T21:41:26.7425336Z                             auto tmp35 = in_ptr0[tmp34 + (10*tmp33)];
2023-01-11T21:41:26.7425509Z                             tmp32 = tmp35;
2023-01-11T21:41:26.7425733Z                         }
2023-01-11T21:41:26.7425936Z                         auto tmp36 = tmp28 + tmp32;
2023-01-11T21:41:26.7426115Z                         auto tmp37 = tmp23 & tmp6;
2023-01-11T21:41:26.7426266Z                         float tmp38 = 0.0;
2023-01-11T21:41:26.7426437Z                         if(tmp37)
2023-01-11T21:41:26.7426583Z                         {
2023-01-11T21:41:26.7426939Z                             auto tmp39 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:41:26.7427288Z                             auto tmp40 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:26.7427522Z                             auto tmp41 = in_ptr0[tmp40 + (10*tmp39)];
2023-01-11T21:41:26.7427688Z                             tmp38 = tmp41;
2023-01-11T21:41:26.7427808Z                         }
2023-01-11T21:41:26.7427993Z                         auto tmp42 = tmp36 + tmp38;
2023-01-11T21:41:26.7428179Z                         auto tmp43 = tmp23 & tmp14;
2023-01-11T21:41:26.7428365Z                         float tmp44 = 0.0;
2023-01-11T21:41:26.7428528Z                         if(tmp43)
2023-01-11T21:41:26.7428664Z                         {
2023-01-11T21:41:26.7429029Z                             auto tmp45 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:41:26.7429396Z                             auto tmp46 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:26.7429589Z                             auto tmp47 = in_ptr0[tmp46 + (10*tmp45)];
2023-01-11T21:41:26.7429758Z                             tmp44 = tmp47;
2023-01-11T21:41:26.7429900Z                         }
2023-01-11T21:41:26.7430098Z                         auto tmp48 = tmp42 + tmp44;
2023-01-11T21:41:26.7430299Z                         auto tmp49 = tmp31 & tmp6;
2023-01-11T21:41:26.7430460Z                         float tmp50 = 0.0;
2023-01-11T21:41:26.7430622Z                         if(tmp49)
2023-01-11T21:41:26.7430753Z                         {
2023-01-11T21:41:26.7431126Z                             auto tmp51 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:41:26.7431471Z                             auto tmp52 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:26.7431708Z                             auto tmp53 = in_ptr0[tmp52 + (10*tmp51)];
2023-01-11T21:41:26.7431879Z                             tmp50 = tmp53;
2023-01-11T21:41:26.7432022Z                         }
2023-01-11T21:41:26.7432211Z                         auto tmp54 = tmp48 + tmp50;
2023-01-11T21:41:26.7432375Z                         auto tmp55 = tmp31 & tmp14;
2023-01-11T21:41:26.7432554Z                         float tmp56 = 0.0;
2023-01-11T21:41:26.7432708Z                         if(tmp55)
2023-01-11T21:41:26.7432854Z                         {
2023-01-11T21:41:26.7433213Z                             auto tmp57 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:41:26.7433569Z                             auto tmp58 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:26.7433791Z                             auto tmp59 = in_ptr0[tmp58 + (10*tmp57)];
2023-01-11T21:41:26.7433941Z                             tmp56 = tmp59;
2023-01-11T21:41:26.7434078Z                         }
2023-01-11T21:41:26.7434277Z                         auto tmp60 = tmp54 + tmp56;
2023-01-11T21:41:26.7434571Z                         out_ptr0[i1 + (8*i0)] = tmp60;
2023-01-11T21:41:26.7434723Z                     }
2023-01-11T21:41:26.7434856Z                 }
2023-01-11T21:41:26.7434988Z             }
2023-01-11T21:41:26.7435103Z         }
2023-01-11T21:41:26.7435235Z     }
2023-01-11T21:41:26.7435366Z }
2023-01-11T21:41:26.7435544Z ''')
2023-01-11T21:41:26.7435561Z 
2023-01-11T21:41:26.7435569Z 
2023-01-11T21:41:26.7435742Z async_compile.wait(globals())
2023-01-11T21:41:26.7435898Z del async_compile
2023-01-11T21:41:26.7435914Z 
2023-01-11T21:41:26.7436065Z def call(args):
2023-01-11T21:41:26.7436227Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7436359Z     args.clear()
2023-01-11T21:41:26.7436797Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7437135Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7437285Z     del arg0_1
2023-01-11T21:41:26.7437441Z     return (buf0, )
2023-01-11T21:41:26.7437451Z 
2023-01-11T21:41:26.7437457Z 
2023-01-11T21:41:26.7437616Z if __name__ == "__main__":
2023-01-11T21:41:26.7437848Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7438075Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7438506Z     arg0_1 = rand_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7438930Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7439158Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7439167Z 
2023-01-11T21:41:26.7439704Z [2023-01-11 21:37:12,357] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 399
2023-01-11T21:41:26.7439716Z 
2023-01-11T21:41:26.7439914Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7440059Z import torch
2023-01-11T21:41:26.7440211Z import random
2023-01-11T21:41:26.7440431Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7440679Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7440688Z 
2023-01-11T21:41:26.7440845Z aten = torch.ops.aten
2023-01-11T21:41:26.7441111Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7441307Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7441317Z 
2023-01-11T21:41:26.7441326Z 
2023-01-11T21:41:26.7441605Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7442002Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7442239Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7442426Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7442556Z {
2023-01-11T21:41:26.7442756Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7442895Z     {
2023-01-11T21:41:26.7443062Z         #pragma omp for 
2023-01-11T21:41:26.7443232Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7443376Z         {
2023-01-11T21:41:26.7443518Z             #pragma GCC ivdep
2023-01-11T21:41:26.7443676Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.7443812Z             {
2023-01-11T21:41:26.7443952Z                 {
2023-01-11T21:41:26.7444094Z                     {
2023-01-11T21:41:26.7444317Z                         auto tmp0 = static_cast<int>(3 + i0);
2023-01-11T21:41:26.7444519Z                         auto tmp1 = static_cast<int>(1 + i1);
2023-01-11T21:41:26.7444720Z                         auto tmp2 = in_ptr0[tmp1 + (11*tmp0)];
2023-01-11T21:41:26.7444937Z                         auto tmp3 = static_cast<int>(i1);
2023-01-11T21:41:26.7445124Z                         auto tmp4 = tmp3 >= 1;
2023-01-11T21:41:26.7445310Z                         auto tmp5 = tmp3 <= 1;
2023-01-11T21:41:26.7445504Z                         auto tmp6 = tmp4 & tmp5;
2023-01-11T21:41:26.7445686Z                         float tmp7 = 0.0;
2023-01-11T21:41:26.7445845Z                         if(tmp6)
2023-01-11T21:41:26.7446080Z                         {
2023-01-11T21:41:26.7446292Z                             auto tmp8 = static_cast<int>(3 + i0);
2023-01-11T21:41:26.7446676Z                             auto tmp9 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:26.7446905Z                             auto tmp10 = in_ptr0[tmp9 + (11*tmp8)];
2023-01-11T21:41:26.7447065Z                             tmp7 = tmp10;
2023-01-11T21:41:26.7447211Z                         }
2023-01-11T21:41:26.7447410Z                         auto tmp11 = tmp2 + tmp7;
2023-01-11T21:41:26.7447598Z                         auto tmp12 = tmp3 >= 5;
2023-01-11T21:41:26.7447774Z                         auto tmp13 = tmp3 <= 6;
2023-01-11T21:41:26.7447956Z                         auto tmp14 = tmp12 & tmp13;
2023-01-11T21:41:26.7448201Z                         float tmp15 = 0.0;
2023-01-11T21:41:26.7448369Z                         if(tmp14)
2023-01-11T21:41:26.7448519Z                         {
2023-01-11T21:41:26.7448742Z                             auto tmp16 = static_cast<int>(3 + i0);
2023-01-11T21:41:26.7449242Z                             auto tmp17 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:26.7449447Z                             auto tmp18 = in_ptr0[tmp17 + (11*tmp16)];
2023-01-11T21:41:26.7449616Z                             tmp15 = tmp18;
2023-01-11T21:41:26.7449767Z                         }
2023-01-11T21:41:26.7449953Z                         auto tmp19 = tmp11 + tmp15;
2023-01-11T21:41:26.7450172Z                         auto tmp20 = static_cast<int>(i0);
2023-01-11T21:41:26.7450362Z                         auto tmp21 = tmp20 >= 1;
2023-01-11T21:41:26.7450561Z                         auto tmp22 = tmp20 <= 3;
2023-01-11T21:41:26.7450727Z                         auto tmp23 = tmp21 & tmp22;
2023-01-11T21:41:26.7450913Z                         float tmp24 = 0.0;
2023-01-11T21:41:26.7451070Z                         if(tmp23)
2023-01-11T21:41:26.7451211Z                         {
2023-01-11T21:41:26.7451586Z                             auto tmp25 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:41:26.7451807Z                             auto tmp26 = static_cast<int>(1 + i1);
2023-01-11T21:41:26.7452031Z                             auto tmp27 = in_ptr0[tmp26 + (11*tmp25)];
2023-01-11T21:41:26.7452183Z                             tmp24 = tmp27;
2023-01-11T21:41:26.7452326Z                         }
2023-01-11T21:41:26.7452521Z                         auto tmp28 = tmp19 + tmp24;
2023-01-11T21:41:26.7452708Z                         auto tmp29 = tmp20 >= 3;
2023-01-11T21:41:26.7452908Z                         auto tmp30 = tmp20 <= 6;
2023-01-11T21:41:26.7453101Z                         auto tmp31 = tmp29 & tmp30;
2023-01-11T21:41:26.7453273Z                         float tmp32 = 0.0;
2023-01-11T21:41:26.7453408Z                         if(tmp31)
2023-01-11T21:41:26.7453560Z                         {
2023-01-11T21:41:26.7453938Z                             auto tmp33 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:41:26.7454168Z                             auto tmp34 = static_cast<int>(1 + i1);
2023-01-11T21:41:26.7454390Z                             auto tmp35 = in_ptr0[tmp34 + (11*tmp33)];
2023-01-11T21:41:26.7454567Z                             tmp32 = tmp35;
2023-01-11T21:41:26.7454713Z                         }
2023-01-11T21:41:26.7454902Z                         auto tmp36 = tmp28 + tmp32;
2023-01-11T21:41:26.7455068Z                         auto tmp37 = tmp23 & tmp6;
2023-01-11T21:41:26.7455248Z                         float tmp38 = 0.0;
2023-01-11T21:41:26.7455400Z                         if(tmp37)
2023-01-11T21:41:26.7455545Z                         {
2023-01-11T21:41:26.7455903Z                             auto tmp39 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:41:26.7456259Z                             auto tmp40 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:26.7456487Z                             auto tmp41 = in_ptr0[tmp40 + (11*tmp39)];
2023-01-11T21:41:26.7456641Z                             tmp38 = tmp41;
2023-01-11T21:41:26.7456910Z                         }
2023-01-11T21:41:26.7457106Z                         auto tmp42 = tmp36 + tmp38;
2023-01-11T21:41:26.7457294Z                         auto tmp43 = tmp23 & tmp14;
2023-01-11T21:41:26.7457469Z                         float tmp44 = 0.0;
2023-01-11T21:41:26.7457632Z                         if(tmp43)
2023-01-11T21:41:26.7457763Z                         {
2023-01-11T21:41:26.7458104Z                             auto tmp45 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:41:26.7458466Z                             auto tmp46 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:26.7458676Z                             auto tmp47 = in_ptr0[tmp46 + (11*tmp45)];
2023-01-11T21:41:26.7458849Z                             tmp44 = tmp47;
2023-01-11T21:41:26.7458998Z                         }
2023-01-11T21:41:26.7459291Z                         auto tmp48 = tmp42 + tmp44;
2023-01-11T21:41:26.7459493Z                         auto tmp49 = tmp31 & tmp6;
2023-01-11T21:41:26.7459682Z                         float tmp50 = 0.0;
2023-01-11T21:41:26.7459825Z                         if(tmp49)
2023-01-11T21:41:26.7459967Z                         {
2023-01-11T21:41:26.7460325Z                             auto tmp51 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:41:26.7460682Z                             auto tmp52 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:41:26.7460899Z                             auto tmp53 = in_ptr0[tmp52 + (11*tmp51)];
2023-01-11T21:41:26.7461072Z                             tmp50 = tmp53;
2023-01-11T21:41:26.7461207Z                         }
2023-01-11T21:41:26.7461370Z                         auto tmp54 = tmp48 + tmp50;
2023-01-11T21:41:26.7461555Z                         auto tmp55 = tmp31 & tmp14;
2023-01-11T21:41:26.7461739Z                         float tmp56 = 0.0;
2023-01-11T21:41:26.7461912Z                         if(tmp55)
2023-01-11T21:41:26.7462060Z                         {
2023-01-11T21:41:26.7462418Z                             auto tmp57 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:41:26.7462791Z                             auto tmp58 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:41:26.7462983Z                             auto tmp59 = in_ptr0[tmp58 + (11*tmp57)];
2023-01-11T21:41:26.7463228Z                             tmp56 = tmp59;
2023-01-11T21:41:26.7463383Z                         }
2023-01-11T21:41:26.7463575Z                         auto tmp60 = tmp54 + tmp56;
2023-01-11T21:41:26.7463774Z                         out_ptr0[i1 + (8*i0)] = tmp60;
2023-01-11T21:41:26.7463912Z                     }
2023-01-11T21:41:26.7464037Z                 }
2023-01-11T21:41:26.7464155Z             }
2023-01-11T21:41:26.7464297Z         }
2023-01-11T21:41:26.7464433Z     }
2023-01-11T21:41:26.7464566Z }
2023-01-11T21:41:26.7464739Z ''')
2023-01-11T21:41:26.7464752Z 
2023-01-11T21:41:26.7464765Z 
2023-01-11T21:41:26.7464938Z async_compile.wait(globals())
2023-01-11T21:41:26.7465098Z del async_compile
2023-01-11T21:41:26.7465115Z 
2023-01-11T21:41:26.7465245Z def call(args):
2023-01-11T21:41:26.7465415Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7465566Z     args.clear()
2023-01-11T21:41:26.7465996Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7466259Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7466408Z     del arg0_1
2023-01-11T21:41:26.7466559Z     return (buf0, )
2023-01-11T21:41:26.7466571Z 
2023-01-11T21:41:26.7466579Z 
2023-01-11T21:41:26.7466726Z if __name__ == "__main__":
2023-01-11T21:41:26.7466942Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7467191Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7467629Z     arg0_1 = rand_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7468072Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7468302Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7468413Z 
2023-01-11T21:41:26.7468562Z ok (5.381s)
2023-01-11T21:41:26.7469477Z   test_reflection_pad2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7469739Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7470266Z [2023-01-11 21:37:12,387] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 400
2023-01-11T21:41:26.7470860Z [2023-01-11 21:37:13,932] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 400
2023-01-11T21:41:26.7470893Z 
2023-01-11T21:41:26.7471067Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7471224Z import torch
2023-01-11T21:41:26.7471373Z import random
2023-01-11T21:41:26.7471612Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7471852Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7471863Z 
2023-01-11T21:41:26.7472027Z aten = torch.ops.aten
2023-01-11T21:41:26.7472293Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7472463Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7472472Z 
2023-01-11T21:41:26.7472502Z 
2023-01-11T21:41:26.7472753Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7473156Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7473404Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7473614Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7473818Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7473952Z {
2023-01-11T21:41:26.7474167Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7474282Z     {
2023-01-11T21:41:26.7474439Z         #pragma omp for 
2023-01-11T21:41:26.7474615Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.7474755Z         {
2023-01-11T21:41:26.7474926Z             #pragma GCC ivdep
2023-01-11T21:41:26.7475111Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.7475218Z             {
2023-01-11T21:41:26.7475351Z                 {
2023-01-11T21:41:26.7475495Z                     {
2023-01-11T21:41:26.7475716Z                         auto tmp0 = static_cast<int>(7);
2023-01-11T21:41:26.7475920Z                         auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.7476124Z                         auto tmp2 = static_cast<int>(1);
2023-01-11T21:41:26.7476428Z                         auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:26.7476625Z                         auto tmp4 = std::abs(tmp3);
2023-01-11T21:41:26.7476957Z                         auto tmp5 = tmp0 - tmp4;
2023-01-11T21:41:26.7477168Z                         auto tmp6 = std::abs(tmp5);
2023-01-11T21:41:26.7477458Z                         auto tmp7 = tmp0 - tmp6;
2023-01-11T21:41:26.7477682Z                         auto tmp8 = static_cast<int>(i1);
2023-01-11T21:41:26.7477963Z                         auto tmp9 = tmp8 - tmp2;
2023-01-11T21:41:26.7478173Z                         auto tmp10 = std::abs(tmp9);
2023-01-11T21:41:26.7478468Z                         auto tmp11 = tmp0 - tmp10;
2023-01-11T21:41:26.7478654Z                         auto tmp12 = std::abs(tmp11);
2023-01-11T21:41:26.7478944Z                         auto tmp13 = tmp0 - tmp12;
2023-01-11T21:41:26.7479160Z                         auto tmp14 = in_ptr0[tmp13 + (8*tmp7)];
2023-01-11T21:41:26.7479363Z                         out_ptr0[i1 + (10*i0)] = tmp14;
2023-01-11T21:41:26.7479504Z                     }
2023-01-11T21:41:26.7479640Z                 }
2023-01-11T21:41:26.7479772Z             }
2023-01-11T21:41:26.7479887Z         }
2023-01-11T21:41:26.7480154Z         #pragma omp for 
2023-01-11T21:41:26.7480325Z         for(long i0=0; i0<15; i0+=1)
2023-01-11T21:41:26.7480456Z         {
2023-01-11T21:41:26.7480626Z             #pragma GCC ivdep
2023-01-11T21:41:26.7480801Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:41:26.7480939Z             {
2023-01-11T21:41:26.7481060Z                 {
2023-01-11T21:41:26.7481205Z                     {
2023-01-11T21:41:26.7481410Z                         auto tmp0 = static_cast<int>(7);
2023-01-11T21:41:26.7481622Z                         auto tmp1 = static_cast<int>(i0);
2023-01-11T21:41:26.7481824Z                         auto tmp2 = static_cast<int>(3);
2023-01-11T21:41:26.7482121Z                         auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:26.7482331Z                         auto tmp4 = std::abs(tmp3);
2023-01-11T21:41:26.7482689Z                         auto tmp5 = tmp0 - tmp4;
2023-01-11T21:41:26.7482908Z                         auto tmp6 = std::abs(tmp5);
2023-01-11T21:41:26.7483195Z                         auto tmp7 = tmp0 - tmp6;
2023-01-11T21:41:26.7483420Z                         auto tmp8 = static_cast<int>(i1);
2023-01-11T21:41:26.7483637Z                         auto tmp9 = static_cast<int>(1);
2023-01-11T21:41:26.7483915Z                         auto tmp10 = tmp8 - tmp9;
2023-01-11T21:41:26.7484127Z                         auto tmp11 = std::abs(tmp10);
2023-01-11T21:41:26.7484405Z                         auto tmp12 = tmp0 - tmp11;
2023-01-11T21:41:26.7484615Z                         auto tmp13 = std::abs(tmp12);
2023-01-11T21:41:26.7484902Z                         auto tmp14 = tmp0 - tmp13;
2023-01-11T21:41:26.7485117Z                         auto tmp15 = in_ptr0[tmp14 + (8*tmp7)];
2023-01-11T21:41:26.7485323Z                         out_ptr1[i1 + (11*i0)] = tmp15;
2023-01-11T21:41:26.7485471Z                     }
2023-01-11T21:41:26.7485604Z                 }
2023-01-11T21:41:26.7485716Z             }
2023-01-11T21:41:26.7485859Z         }
2023-01-11T21:41:26.7485994Z     }
2023-01-11T21:41:26.7486129Z }
2023-01-11T21:41:26.7486296Z ''')
2023-01-11T21:41:26.7486310Z 
2023-01-11T21:41:26.7486318Z 
2023-01-11T21:41:26.7486498Z async_compile.wait(globals())
2023-01-11T21:41:26.7486650Z del async_compile
2023-01-11T21:41:26.7486665Z 
2023-01-11T21:41:26.7486810Z def call(args):
2023-01-11T21:41:26.7486934Z     arg0_1, = args
2023-01-11T21:41:26.7487093Z     args.clear()
2023-01-11T21:41:26.7487542Z     buf0 = empty_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7487993Z     buf1 = empty_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7488318Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7488474Z     del arg0_1
2023-01-11T21:41:26.7488640Z     return (buf0, buf1, )
2023-01-11T21:41:26.7488659Z 
2023-01-11T21:41:26.7488667Z 
2023-01-11T21:41:26.7488807Z if __name__ == "__main__":
2023-01-11T21:41:26.7489161Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7489427Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7489861Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7490076Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7490089Z 
2023-01-11T21:41:26.7490232Z ok (1.574s)
2023-01-11T21:41:26.7491146Z   test_relu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7491406Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7491957Z [2023-01-11 21:37:13,955] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 401
2023-01-11T21:41:26.7492636Z [2023-01-11 21:37:13,969] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 401
2023-01-11T21:41:26.7492652Z 
2023-01-11T21:41:26.7492823Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7492974Z import torch
2023-01-11T21:41:26.7493122Z import random
2023-01-11T21:41:26.7493355Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7493603Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7493611Z 
2023-01-11T21:41:26.7493772Z aten = torch.ops.aten
2023-01-11T21:41:26.7494043Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7494204Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7494234Z 
2023-01-11T21:41:26.7494243Z 
2023-01-11T21:41:26.7494504Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7495017Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7495268Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7495481Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7495691Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7495887Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7496007Z {
2023-01-11T21:41:26.7496187Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7496314Z     {
2023-01-11T21:41:26.7496475Z         #pragma omp for 
2023-01-11T21:41:26.7496657Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7496790Z         {
2023-01-11T21:41:26.7497054Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7497328Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.7497574Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.7497752Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.7498009Z             auto tmp4 = at::vec::clamp_min(tmp3, decltype(tmp3)(0));
2023-01-11T21:41:26.7498289Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:26.7498469Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:41:26.7498641Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7498832Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7498970Z         }
2023-01-11T21:41:26.7499145Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7499314Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7499438Z         {
2023-01-11T21:41:26.7499615Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7499786Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.7499974Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.7500150Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.7500314Z             auto tmp4 = tmp3 * (tmp3>0);
2023-01-11T21:41:26.7500514Z             auto tmp5 = static_cast<float>(10);
2023-01-11T21:41:26.7500697Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:41:26.7500868Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.7501029Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.7501152Z         }
2023-01-11T21:41:26.7501282Z     }
2023-01-11T21:41:26.7501390Z }
2023-01-11T21:41:26.7501588Z ''')
2023-01-11T21:41:26.7501600Z 
2023-01-11T21:41:26.7501607Z 
2023-01-11T21:41:26.7501803Z async_compile.wait(globals())
2023-01-11T21:41:26.7501957Z del async_compile
2023-01-11T21:41:26.7501967Z 
2023-01-11T21:41:26.7502110Z def call(args):
2023-01-11T21:41:26.7502272Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7502428Z     args.clear()
2023-01-11T21:41:26.7502803Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7503281Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7503685Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7503956Z     del arg0_1
2023-01-11T21:41:26.7504104Z     del arg1_1
2023-01-11T21:41:26.7504275Z     return (buf0, buf1, )
2023-01-11T21:41:26.7504288Z 
2023-01-11T21:41:26.7504295Z 
2023-01-11T21:41:26.7504456Z if __name__ == "__main__":
2023-01-11T21:41:26.7504684Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7504910Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7505318Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7505710Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7505951Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7505960Z 
2023-01-11T21:41:26.7506104Z ok (0.037s)
2023-01-11T21:41:26.7507096Z   test_remainder_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7507364Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7507903Z [2023-01-11 21:37:13,996] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 402
2023-01-11T21:41:26.7508350Z [2023-01-11 21:37:15,533] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 402
2023-01-11T21:41:26.7508358Z 
2023-01-11T21:41:26.7508490Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7508599Z import torch
2023-01-11T21:41:26.7508708Z import random
2023-01-11T21:41:26.7508887Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7509078Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7509087Z 
2023-01-11T21:41:26.7509257Z aten = torch.ops.aten
2023-01-11T21:41:26.7509524Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7509722Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7509734Z 
2023-01-11T21:41:26.7509744Z 
2023-01-11T21:41:26.7509995Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7510394Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7510622Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7510837Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7511028Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7511233Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7511435Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.7511561Z {
2023-01-11T21:41:26.7511752Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7511883Z     {
2023-01-11T21:41:26.7512048Z         #pragma omp for 
2023-01-11T21:41:26.7512227Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.7512370Z         {
2023-01-11T21:41:26.7512502Z             {
2023-01-11T21:41:26.7512608Z                 {
2023-01-11T21:41:26.7512798Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7512981Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.7513184Z                     auto tmp2 = mod(tmp0, tmp1);
2023-01-11T21:41:26.7513378Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:41:26.7513617Z                     auto tmp4 = ((tmp2 != 0) & ((tmp2 < 0) != (tmp1 < 0))) ? tmp3 : tmp2;
2023-01-11T21:41:26.7513833Z                     auto tmp5 = static_cast<float>(1);
2023-01-11T21:41:26.7513999Z                     auto tmp6 = tmp0 + tmp5;
2023-01-11T21:41:26.7514293Z                     auto tmp7 = tmp1 - tmp5;
2023-01-11T21:41:26.7514484Z                     auto tmp8 = mod(tmp6, tmp7);
2023-01-11T21:41:26.7514680Z                     auto tmp9 = tmp8 + tmp7;
2023-01-11T21:41:26.7515039Z                     auto tmp10 = ((tmp8 != 0) & ((tmp8 < 0) != (tmp7 < 0))) ? tmp9 : tmp8;
2023-01-11T21:41:26.7515439Z                     auto tmp11 = tmp0 - tmp5;
2023-01-11T21:41:26.7515612Z                     auto tmp12 = tmp1 + tmp5;
2023-01-11T21:41:26.7515789Z                     auto tmp13 = mod(tmp11, tmp12);
2023-01-11T21:41:26.7515938Z                     auto tmp14 = tmp13 + tmp12;
2023-01-11T21:41:26.7516156Z                     auto tmp15 = ((tmp13 != 0) & ((tmp13 < 0) != (tmp12 < 0))) ? tmp14 : tmp13;
2023-01-11T21:41:26.7516311Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.7516475Z                     out_ptr1[i0] = tmp10;
2023-01-11T21:41:26.7516632Z                     out_ptr2[i0] = tmp15;
2023-01-11T21:41:26.7516755Z                 }
2023-01-11T21:41:26.7516878Z             }
2023-01-11T21:41:26.7516973Z         }
2023-01-11T21:41:26.7517082Z     }
2023-01-11T21:41:26.7517276Z }
2023-01-11T21:41:26.7517440Z ''')
2023-01-11T21:41:26.7517452Z 
2023-01-11T21:41:26.7517458Z 
2023-01-11T21:41:26.7517625Z async_compile.wait(globals())
2023-01-11T21:41:26.7517763Z del async_compile
2023-01-11T21:41:26.7517770Z 
2023-01-11T21:41:26.7517897Z def call(args):
2023-01-11T21:41:26.7518020Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7518156Z     args.clear()
2023-01-11T21:41:26.7518512Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7518847Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7519185Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7519537Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.7519671Z     del arg0_1
2023-01-11T21:41:26.7519806Z     del arg1_1
2023-01-11T21:41:26.7519937Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.7519953Z 
2023-01-11T21:41:26.7519960Z 
2023-01-11T21:41:26.7520101Z if __name__ == "__main__":
2023-01-11T21:41:26.7520300Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7520530Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7520870Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7521221Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7521431Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7521441Z 
2023-01-11T21:41:26.7521565Z ok (1.565s)
2023-01-11T21:41:26.7522321Z   test_repeat_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7522537Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7522989Z [2023-01-11 21:37:15,553] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 403
2023-01-11T21:41:26.7523452Z [2023-01-11 21:37:17,171] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 403
2023-01-11T21:41:26.7523464Z 
2023-01-11T21:41:26.7523631Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7523769Z import torch
2023-01-11T21:41:26.7523903Z import random
2023-01-11T21:41:26.7524114Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7524325Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7524337Z 
2023-01-11T21:41:26.7524462Z aten = torch.ops.aten
2023-01-11T21:41:26.7524694Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7524862Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7524878Z 
2023-01-11T21:41:26.7524884Z 
2023-01-11T21:41:26.7525133Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7525562Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7525758Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7525923Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7526102Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7526249Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.7526362Z {
2023-01-11T21:41:26.7526542Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7526648Z     {
2023-01-11T21:41:26.7526818Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.7526967Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7527089Z         {
2023-01-11T21:41:26.7527229Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.7527349Z             {
2023-01-11T21:41:26.7527571Z                 #pragma GCC ivdep
2023-01-11T21:41:26.7527738Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:41:26.7527860Z                 {
2023-01-11T21:41:26.7528035Z                     for(long i3=0; i3<1; i3+=1)
2023-01-11T21:41:26.7528139Z                     {
2023-01-11T21:41:26.7528405Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i3) + (8*(i2 % 4)) + (32*(i1 % 2)));
2023-01-11T21:41:26.7528610Z                         tmp0.store(out_ptr0 + (8*i2) + (8*i3) + (96*i1) + (384*i0));
2023-01-11T21:41:26.7528742Z                     }
2023-01-11T21:41:26.7528923Z                     #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7529197Z                     for(long i3=8; i3<8; i3+=1)
2023-01-11T21:41:26.7529327Z                     {
2023-01-11T21:41:26.7529524Z                         auto tmp0 = in_ptr0[i3 + (8*(i2 % 4)) + (32*(i1 % 2))];
2023-01-11T21:41:26.7529696Z                         out_ptr0[i3 + (8*i2) + (96*i1) + (384*i0)] = tmp0;
2023-01-11T21:41:26.7529827Z                     }
2023-01-11T21:41:26.7529953Z                 }
2023-01-11T21:41:26.7530058Z             }
2023-01-11T21:41:26.7530181Z         }
2023-01-11T21:41:26.7530328Z         #pragma omp for 
2023-01-11T21:41:26.7530483Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7530579Z         {
2023-01-11T21:41:26.7530731Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.7530846Z             {
2023-01-11T21:41:26.7531083Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:26.7531264Z                 tmp0.store(out_ptr1 + (8*i1) + (64*i0));
2023-01-11T21:41:26.7531382Z             }
2023-01-11T21:41:26.7531552Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7531682Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:41:26.7531803Z             {
2023-01-11T21:41:26.7531964Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.7532132Z                 out_ptr1[i1 + (64*i0)] = tmp0;
2023-01-11T21:41:26.7532256Z             }
2023-01-11T21:41:26.7532374Z         }
2023-01-11T21:41:26.7532504Z         #pragma omp for 
2023-01-11T21:41:26.7532632Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7532747Z         {
2023-01-11T21:41:26.7532891Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.7533017Z             {
2023-01-11T21:41:26.7533252Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:26.7533428Z                 tmp0.store(out_ptr2 + (8*i1) + (64*i0));
2023-01-11T21:41:26.7533551Z             }
2023-01-11T21:41:26.7533703Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7533866Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:41:26.7533984Z             {
2023-01-11T21:41:26.7534135Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.7534302Z                 out_ptr2[i1 + (64*i0)] = tmp0;
2023-01-11T21:41:26.7534425Z             }
2023-01-11T21:41:26.7534524Z         }
2023-01-11T21:41:26.7534636Z     }
2023-01-11T21:41:26.7534753Z }
2023-01-11T21:41:26.7534932Z ''')
2023-01-11T21:41:26.7534942Z 
2023-01-11T21:41:26.7534947Z 
2023-01-11T21:41:26.7535258Z async_compile.wait(globals())
2023-01-11T21:41:26.7535396Z del async_compile
2023-01-11T21:41:26.7535408Z 
2023-01-11T21:41:26.7535542Z def call(args):
2023-01-11T21:41:26.7535675Z     arg0_1, = args
2023-01-11T21:41:26.7535782Z     args.clear()
2023-01-11T21:41:26.7536183Z     buf0 = empty_strided((2, 4, 12, 8), (384, 96, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7536548Z     buf1 = empty_strided((8, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7536936Z     buf2 = empty_strided((2, 1, 1, 2, 4, 8), (64, 64, 64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7537269Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.7537391Z     del arg0_1
2023-01-11T21:41:26.7537552Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.7537652Z 
2023-01-11T21:41:26.7537660Z 
2023-01-11T21:41:26.7537802Z if __name__ == "__main__":
2023-01-11T21:41:26.7537998Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7538207Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7538585Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7538771Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7538779Z 
2023-01-11T21:41:26.7538908Z ok (1.638s)
2023-01-11T21:41:26.7539677Z   test_roi_align_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7539906Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7540371Z [2023-01-11 21:37:18,988] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 404
2023-01-11T21:41:26.7540822Z [2023-01-11 21:37:19,412] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.torchvision.roi_align
2023-01-11T21:41:26.7541270Z [2023-01-11 21:37:19,415] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 404
2023-01-11T21:41:26.7541298Z 
2023-01-11T21:41:26.7541434Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7541565Z import torch
2023-01-11T21:41:26.7541699Z import random
2023-01-11T21:41:26.7541905Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7542124Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7542131Z 
2023-01-11T21:41:26.7542271Z aten = torch.ops.aten
2023-01-11T21:41:26.7542512Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7542665Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7542673Z 
2023-01-11T21:41:26.7542699Z 
2023-01-11T21:41:26.7542837Z async_compile.wait(globals())
2023-01-11T21:41:26.7542962Z del async_compile
2023-01-11T21:41:26.7542972Z 
2023-01-11T21:41:26.7543100Z def call(args):
2023-01-11T21:41:26.7543318Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7543455Z     args.clear()
2023-01-11T21:41:26.7543693Z     buf0 = torch.ops.torchvision.roi_align(arg0_1, arg1_1, 0.25, 7, 7, 2, False)
2023-01-11T21:41:26.7543819Z     del arg0_1
2023-01-11T21:41:26.7543920Z     del arg1_1
2023-01-11T21:41:26.7544040Z     buf1 = buf0
2023-01-11T21:41:26.7544233Z     assert_size_stride(buf1, (2292, 256, 7, 7), (12544, 49, 7, 1))
2023-01-11T21:41:26.7544363Z     del buf0
2023-01-11T21:41:26.7544492Z     return (buf1, )
2023-01-11T21:41:26.7544503Z 
2023-01-11T21:41:26.7544509Z 
2023-01-11T21:41:26.7544645Z if __name__ == "__main__":
2023-01-11T21:41:26.7544836Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7545046Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7545472Z     arg0_1 = rand_strided((4, 256, 296, 304), (23035904, 89984, 304, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7545936Z     arg1_1 = rand_strided((2292, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7546143Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7546152Z 
2023-01-11T21:41:26.7546274Z ok (5.123s)
2023-01-11T21:41:26.7547034Z   test_roll_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7547252Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7547768Z [2023-01-11 21:37:22,328] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 405
2023-01-11T21:41:26.7548236Z [2023-01-11 21:37:24,211] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 405
2023-01-11T21:41:26.7548252Z 
2023-01-11T21:41:26.7548426Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7548531Z import torch
2023-01-11T21:41:26.7548651Z import random
2023-01-11T21:41:26.7548847Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7549058Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7549067Z 
2023-01-11T21:41:26.7549212Z aten = torch.ops.aten
2023-01-11T21:41:26.7549441Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7549598Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7549609Z 
2023-01-11T21:41:26.7549614Z 
2023-01-11T21:41:26.7549846Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7550192Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7550389Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7550576Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7550752Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7550870Z {
2023-01-11T21:41:26.7551040Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7551144Z     {
2023-01-11T21:41:26.7551286Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.7551437Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7551560Z         {
2023-01-11T21:41:26.7551719Z             for(long i1=0; i1<56; i1+=1)
2023-01-11T21:41:26.7551842Z             {
2023-01-11T21:41:26.7551988Z                 #pragma GCC ivdep
2023-01-11T21:41:26.7552129Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:41:26.7552255Z                 {
2023-01-11T21:41:26.7552417Z                     for(long i3=0; i3<2; i3+=1)
2023-01-11T21:41:26.7552540Z                     {
2023-01-11T21:41:26.7552828Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i3) + (16*((46 + i2) % 56)) + (896*((3 + i1) % 56)) + (50176*i0));
2023-01-11T21:41:26.7553050Z                         tmp0.store(out_ptr0 + (8*i3) + (16*i2) + (896*i1) + (50176*i0));
2023-01-11T21:41:26.7553177Z                     }
2023-01-11T21:41:26.7553361Z                     #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7553499Z                     for(long i3=16; i3<16; i3+=1)
2023-01-11T21:41:26.7553617Z                     {
2023-01-11T21:41:26.7553844Z                         auto tmp0 = in_ptr0[i3 + (16*((46 + i2) % 56)) + (896*((3 + i1) % 56)) + (50176*i0)];
2023-01-11T21:41:26.7554042Z                         out_ptr0[i3 + (16*i2) + (896*i1) + (50176*i0)] = tmp0;
2023-01-11T21:41:26.7554168Z                     }
2023-01-11T21:41:26.7554282Z                 }
2023-01-11T21:41:26.7554395Z             }
2023-01-11T21:41:26.7554495Z         }
2023-01-11T21:41:26.7554658Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.7554805Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7554936Z         {
2023-01-11T21:41:26.7555219Z             for(long i1=0; i1<56; i1+=1)
2023-01-11T21:41:26.7555343Z             {
2023-01-11T21:41:26.7555496Z                 #pragma GCC ivdep
2023-01-11T21:41:26.7555635Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:41:26.7555765Z                 {
2023-01-11T21:41:26.7555917Z                     #pragma GCC ivdep
2023-01-11T21:41:26.7556083Z                     for(long i3=0; i3<16; i3+=1)
2023-01-11T21:41:26.7556210Z                     {
2023-01-11T21:41:26.7556346Z                         {
2023-01-11T21:41:26.7556475Z                             {
2023-01-11T21:41:26.7556675Z                                 auto tmp0 = in_ptr0[(100347 + i3 + (16*i2) + (896*i1) + (50176*i0)) % 100352];
2023-01-11T21:41:26.7556856Z                                 out_ptr1[i3 + (16*i2) + (896*i1) + (50176*i0)] = tmp0;
2023-01-11T21:41:26.7557064Z                             }
2023-01-11T21:41:26.7557192Z                         }
2023-01-11T21:41:26.7557312Z                     }
2023-01-11T21:41:26.7557430Z                 }
2023-01-11T21:41:26.7557545Z             }
2023-01-11T21:41:26.7557640Z         }
2023-01-11T21:41:26.7557755Z     }
2023-01-11T21:41:26.7557871Z }
2023-01-11T21:41:26.7558039Z ''')
2023-01-11T21:41:26.7558053Z 
2023-01-11T21:41:26.7558059Z 
2023-01-11T21:41:26.7558224Z async_compile.wait(globals())
2023-01-11T21:41:26.7558349Z del async_compile
2023-01-11T21:41:26.7558362Z 
2023-01-11T21:41:26.7558490Z def call(args):
2023-01-11T21:41:26.7558598Z     arg0_1, = args
2023-01-11T21:41:26.7558733Z     args.clear()
2023-01-11T21:41:26.7559129Z     buf0 = empty_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7559513Z     buf1 = empty_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7559802Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7559933Z     del arg0_1
2023-01-11T21:41:26.7560071Z     return (buf0, buf1, )
2023-01-11T21:41:26.7560085Z 
2023-01-11T21:41:26.7560091Z 
2023-01-11T21:41:26.7560235Z if __name__ == "__main__":
2023-01-11T21:41:26.7560421Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7560645Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7561043Z     arg0_1 = rand_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7561242Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7561251Z 
2023-01-11T21:41:26.7561377Z ok (1.920s)
2023-01-11T21:41:26.7562177Z   test_round_correctness_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7562407Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7562871Z [2023-01-11 21:37:24,234] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 406
2023-01-11T21:41:26.7563326Z [2023-01-11 21:37:25,760] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 406
2023-01-11T21:41:26.7563339Z 
2023-01-11T21:41:26.7563512Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7563627Z import torch
2023-01-11T21:41:26.7563760Z import random
2023-01-11T21:41:26.7563957Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7564173Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7564181Z 
2023-01-11T21:41:26.7564325Z aten = torch.ops.aten
2023-01-11T21:41:26.7564562Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7564713Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7564727Z 
2023-01-11T21:41:26.7564733Z 
2023-01-11T21:41:26.7565070Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7565415Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7565621Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.7565805Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.7565916Z {
2023-01-11T21:41:26.7566099Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7566222Z     {
2023-01-11T21:41:26.7566348Z         #pragma omp for 
2023-01-11T21:41:26.7566498Z         for(long i0=0; i0<200; i0+=1)
2023-01-11T21:41:26.7566627Z         {
2023-01-11T21:41:26.7566746Z             {
2023-01-11T21:41:26.7566878Z                 {
2023-01-11T21:41:26.7567049Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7567243Z                     auto tmp1 = std::nearbyint(tmp0);
2023-01-11T21:41:26.7567444Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.7567579Z                 }
2023-01-11T21:41:26.7567700Z             }
2023-01-11T21:41:26.7567820Z         }
2023-01-11T21:41:26.7567942Z     }
2023-01-11T21:41:26.7568048Z }
2023-01-11T21:41:26.7568206Z ''')
2023-01-11T21:41:26.7568220Z 
2023-01-11T21:41:26.7568226Z 
2023-01-11T21:41:26.7568370Z async_compile.wait(globals())
2023-01-11T21:41:26.7568509Z del async_compile
2023-01-11T21:41:26.7568520Z 
2023-01-11T21:41:26.7568651Z def call(args):
2023-01-11T21:41:26.7568782Z     arg0_1, = args
2023-01-11T21:41:26.7568900Z     args.clear()
2023-01-11T21:41:26.7569483Z     buf0 = empty_strided((200, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.7569715Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7569815Z     del arg0_1
2023-01-11T21:41:26.7569953Z     return (buf0, )
2023-01-11T21:41:26.7569962Z 
2023-01-11T21:41:26.7569970Z 
2023-01-11T21:41:26.7570124Z if __name__ == "__main__":
2023-01-11T21:41:26.7570331Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7570545Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7570913Z     arg0_1 = rand_strided((200, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.7571106Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7571115Z 
2023-01-11T21:41:26.7571243Z ok (1.546s)
2023-01-11T21:41:26.7571998Z   test_round_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7572212Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7572648Z [2023-01-11 21:37:25,791] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 407
2023-01-11T21:41:26.7573105Z [2023-01-11 21:37:27,309] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 407
2023-01-11T21:41:26.7573121Z 
2023-01-11T21:41:26.7573291Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7573421Z import torch
2023-01-11T21:41:26.7573568Z import random
2023-01-11T21:41:26.7573767Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7573973Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7573984Z 
2023-01-11T21:41:26.7574129Z aten = torch.ops.aten
2023-01-11T21:41:26.7574339Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7574509Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7574519Z 
2023-01-11T21:41:26.7574527Z 
2023-01-11T21:41:26.7574758Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7575105Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7575313Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7575661Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7575835Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7576009Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7576163Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.7576270Z {
2023-01-11T21:41:26.7576454Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7576569Z     {
2023-01-11T21:41:26.7576709Z         #pragma omp for 
2023-01-11T21:41:26.7576859Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7576959Z         {
2023-01-11T21:41:26.7577191Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7577349Z             auto tmp1 = tmp0.round();
2023-01-11T21:41:26.7577595Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0));
2023-01-11T21:41:26.7577836Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:26.7577995Z             auto tmp4 = tmp3.round();
2023-01-11T21:41:26.7578236Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(0.01));
2023-01-11T21:41:26.7578397Z             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.7578542Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7578699Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7578813Z         }
2023-01-11T21:41:26.7578987Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7579146Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7579265Z         {
2023-01-11T21:41:26.7579423Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7579582Z             auto tmp1 = std::nearbyint(tmp0);
2023-01-11T21:41:26.7579763Z             auto tmp2 = static_cast<float>(100.0);
2023-01-11T21:41:26.7579921Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:26.7580111Z             auto tmp4 = std::nearbyint(tmp3);
2023-01-11T21:41:26.7580301Z             auto tmp5 = static_cast<float>(0.01);
2023-01-11T21:41:26.7580458Z             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.7580611Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.7580740Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.7580863Z         }
2023-01-11T21:41:26.7581010Z         #pragma omp for 
2023-01-11T21:41:26.7581151Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7581277Z         {
2023-01-11T21:41:26.7581520Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.7581749Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7581883Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7582029Z             auto tmp3 = tmp2.round();
2023-01-11T21:41:26.7582187Z             tmp3.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.7582308Z         }
2023-01-11T21:41:26.7582485Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7582643Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7582760Z         {
2023-01-11T21:41:26.7582897Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.7583079Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.7583344Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7583536Z             auto tmp3 = std::nearbyint(tmp2);
2023-01-11T21:41:26.7583678Z             out_ptr2[i0] = tmp3;
2023-01-11T21:41:26.7583794Z         }
2023-01-11T21:41:26.7583920Z     }
2023-01-11T21:41:26.7584010Z }
2023-01-11T21:41:26.7584192Z ''')
2023-01-11T21:41:26.7584205Z 
2023-01-11T21:41:26.7584213Z 
2023-01-11T21:41:26.7584382Z async_compile.wait(globals())
2023-01-11T21:41:26.7584504Z del async_compile
2023-01-11T21:41:26.7584515Z 
2023-01-11T21:41:26.7584648Z def call(args):
2023-01-11T21:41:26.7584790Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7584926Z     args.clear()
2023-01-11T21:41:26.7585266Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7585603Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7585934Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7586390Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7586530Z     del arg0_1
2023-01-11T21:41:26.7586658Z     del arg1_1
2023-01-11T21:41:26.7586818Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.7586827Z 
2023-01-11T21:41:26.7586833Z 
2023-01-11T21:41:26.7586964Z if __name__ == "__main__":
2023-01-11T21:41:26.7587146Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7587362Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7587711Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7588048Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7588321Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7588333Z 
2023-01-11T21:41:26.7588464Z ok (1.549s)
2023-01-11T21:41:26.7589233Z   test_rsqrt_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7589451Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7589918Z [2023-01-11 21:37:27,331] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 408
2023-01-11T21:41:26.7590388Z [2023-01-11 21:37:28,869] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 408
2023-01-11T21:41:26.7590403Z 
2023-01-11T21:41:26.7590553Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7590687Z import torch
2023-01-11T21:41:26.7590820Z import random
2023-01-11T21:41:26.7591025Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7591250Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7591260Z 
2023-01-11T21:41:26.7591404Z aten = torch.ops.aten
2023-01-11T21:41:26.7591637Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7591776Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7591804Z 
2023-01-11T21:41:26.7591810Z 
2023-01-11T21:41:26.7592030Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7592373Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7592573Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7592756Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7592948Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7593069Z {
2023-01-11T21:41:26.7593250Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7593340Z     {
2023-01-11T21:41:26.7593485Z         #pragma omp for 
2023-01-11T21:41:26.7593631Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7593751Z         {
2023-01-11T21:41:26.7593990Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7594148Z             auto tmp1 = tmp0.rsqrt();
2023-01-11T21:41:26.7594366Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7594501Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.7594653Z             auto tmp4 = tmp3.rsqrt();
2023-01-11T21:41:26.7594892Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.7595113Z             auto tmp6 = tmp4 - tmp5;
2023-01-11T21:41:26.7595292Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7595456Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7595574Z         }
2023-01-11T21:41:26.7595738Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7595885Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7596112Z         {
2023-01-11T21:41:26.7596271Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7596445Z             auto tmp1 = 1 / std::sqrt(tmp0);
2023-01-11T21:41:26.7596617Z             auto tmp2 = static_cast<float>(1);
2023-01-11T21:41:26.7596763Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.7596923Z             auto tmp4 = 1 / std::sqrt(tmp3);
2023-01-11T21:41:26.7597109Z             auto tmp5 = static_cast<float>(2);
2023-01-11T21:41:26.7597342Z             auto tmp6 = tmp4 - tmp5;
2023-01-11T21:41:26.7597487Z             out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.7597627Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.7597749Z         }
2023-01-11T21:41:26.7597869Z     }
2023-01-11T21:41:26.7597965Z }
2023-01-11T21:41:26.7598117Z ''')
2023-01-11T21:41:26.7598127Z 
2023-01-11T21:41:26.7598134Z 
2023-01-11T21:41:26.7598356Z async_compile.wait(globals())
2023-01-11T21:41:26.7598505Z del async_compile
2023-01-11T21:41:26.7598514Z 
2023-01-11T21:41:26.7598652Z def call(args):
2023-01-11T21:41:26.7598785Z     arg0_1, = args
2023-01-11T21:41:26.7598915Z     args.clear()
2023-01-11T21:41:26.7599257Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7599588Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7599861Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7599985Z     del arg0_1
2023-01-11T21:41:26.7600118Z     return (buf0, buf1, )
2023-01-11T21:41:26.7600126Z 
2023-01-11T21:41:26.7600131Z 
2023-01-11T21:41:26.7600269Z if __name__ == "__main__":
2023-01-11T21:41:26.7600477Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7600694Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7601032Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7601231Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7601247Z 
2023-01-11T21:41:26.7601372Z ok (1.560s)
2023-01-11T21:41:26.7602150Z   test_scatter1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7602369Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7602830Z [2023-01-11 21:37:28,892] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 409
2023-01-11T21:41:26.7603292Z [2023-01-11 21:37:30,515] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 409
2023-01-11T21:41:26.7603306Z 
2023-01-11T21:41:26.7603477Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7603607Z import torch
2023-01-11T21:41:26.7603748Z import random
2023-01-11T21:41:26.7603932Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7604131Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7604144Z 
2023-01-11T21:41:26.7604282Z aten = torch.ops.aten
2023-01-11T21:41:26.7604519Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7604680Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7604689Z 
2023-01-11T21:41:26.7604697Z 
2023-01-11T21:41:26.7604937Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7605281Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7605496Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7605656Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.7605844Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7606025Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7606239Z {
2023-01-11T21:41:26.7606421Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7606527Z     {
2023-01-11T21:41:26.7606675Z         #pragma omp for 
2023-01-11T21:41:26.7606806Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.7606930Z         {
2023-01-11T21:41:26.7607051Z             {
2023-01-11T21:41:26.7607173Z                 {
2023-01-11T21:41:26.7607331Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7607488Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7607597Z                 }
2023-01-11T21:41:26.7607716Z             }
2023-01-11T21:41:26.7607839Z         }
2023-01-11T21:41:26.7607992Z         #pragma omp single
2023-01-11T21:41:26.7608106Z         {
2023-01-11T21:41:26.7608215Z             {
2023-01-11T21:41:26.7608337Z                 {
2023-01-11T21:41:26.7608559Z                     auto tmp0 = in_ptr1[0];
2023-01-11T21:41:26.7608730Z                     auto tmp1 = in_ptr2[0];
2023-01-11T21:41:26.7608892Z                     out_ptr0[tmp0] = tmp1;
2023-01-11T21:41:26.7609129Z                 }
2023-01-11T21:41:26.7609231Z             }
2023-01-11T21:41:26.7609350Z         }
2023-01-11T21:41:26.7609467Z     }
2023-01-11T21:41:26.7609562Z }
2023-01-11T21:41:26.7609735Z ''')
2023-01-11T21:41:26.7609747Z 
2023-01-11T21:41:26.7609752Z 
2023-01-11T21:41:26.7609915Z async_compile.wait(globals())
2023-01-11T21:41:26.7610056Z del async_compile
2023-01-11T21:41:26.7610064Z 
2023-01-11T21:41:26.7610196Z def call(args):
2023-01-11T21:41:26.7610357Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7610490Z     args.clear()
2023-01-11T21:41:26.7610813Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7611138Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7611269Z     del arg0_1
2023-01-11T21:41:26.7611405Z     del arg1_1
2023-01-11T21:41:26.7611537Z     del arg2_1
2023-01-11T21:41:26.7611664Z     return (buf0, )
2023-01-11T21:41:26.7611677Z 
2023-01-11T21:41:26.7611683Z 
2023-01-11T21:41:26.7611824Z if __name__ == "__main__":
2023-01-11T21:41:26.7612025Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7612228Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7612574Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7612918Z     arg1_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7613240Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7613453Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7613467Z 
2023-01-11T21:41:26.7613594Z ok (1.674s)
2023-01-11T21:41:26.7614368Z   test_scatter2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7614595Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7615041Z [2023-01-11 21:37:30,578] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 410
2023-01-11T21:41:26.7615483Z [2023-01-11 21:37:32,133] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 410
2023-01-11T21:41:26.7615513Z 
2023-01-11T21:41:26.7615649Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7615776Z import torch
2023-01-11T21:41:26.7615910Z import random
2023-01-11T21:41:26.7616125Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7616345Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7616354Z 
2023-01-11T21:41:26.7616486Z aten = torch.ops.aten
2023-01-11T21:41:26.7616855Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7617003Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7617012Z 
2023-01-11T21:41:26.7617018Z 
2023-01-11T21:41:26.7617266Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7617606Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7617815Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7618011Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.7618189Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7618363Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7618479Z {
2023-01-11T21:41:26.7618635Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7618759Z     {
2023-01-11T21:41:26.7618980Z         #pragma omp for 
2023-01-11T21:41:26.7619149Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:26.7619277Z         {
2023-01-11T21:41:26.7619525Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7619692Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7619785Z         }
2023-01-11T21:41:26.7619958Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7620117Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:41:26.7620242Z         {
2023-01-11T21:41:26.7620403Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7620551Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7620647Z         }
2023-01-11T21:41:26.7620794Z         #pragma omp for 
2023-01-11T21:41:26.7620947Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.7621070Z         {
2023-01-11T21:41:26.7621225Z             #pragma GCC ivdep
2023-01-11T21:41:26.7621371Z             for(long i1=0; i1<512; i1+=1)
2023-01-11T21:41:26.7621495Z             {
2023-01-11T21:41:26.7621606Z                 {
2023-01-11T21:41:26.7621743Z                     {
2023-01-11T21:41:26.7621929Z                         auto tmp0 = in_ptr1[i1 + (512*i0)];
2023-01-11T21:41:26.7622121Z                         auto tmp1 = in_ptr2[i1 + (512*i0)];
2023-01-11T21:41:26.7622320Z                         atomic_add(&out_ptr0[i1 + (512*tmp0)], tmp1);
2023-01-11T21:41:26.7622452Z                     }
2023-01-11T21:41:26.7622583Z                 }
2023-01-11T21:41:26.7622681Z             }
2023-01-11T21:41:26.7622801Z         }
2023-01-11T21:41:26.7622918Z     }
2023-01-11T21:41:26.7623026Z }
2023-01-11T21:41:26.7623269Z ''')
2023-01-11T21:41:26.7623281Z 
2023-01-11T21:41:26.7623287Z 
2023-01-11T21:41:26.7623457Z async_compile.wait(globals())
2023-01-11T21:41:26.7623596Z del async_compile
2023-01-11T21:41:26.7623609Z 
2023-01-11T21:41:26.7623717Z def call(args):
2023-01-11T21:41:26.7623872Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7624004Z     args.clear()
2023-01-11T21:41:26.7624388Z     buf0 = empty_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7624715Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7624854Z     del arg0_1
2023-01-11T21:41:26.7624982Z     del arg1_1
2023-01-11T21:41:26.7625087Z     del arg2_1
2023-01-11T21:41:26.7625222Z     return (buf0, )
2023-01-11T21:41:26.7625232Z 
2023-01-11T21:41:26.7625239Z 
2023-01-11T21:41:26.7625380Z if __name__ == "__main__":
2023-01-11T21:41:26.7625567Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7625789Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7626143Z     arg0_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7626493Z     arg1_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7626842Z     arg2_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7627040Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7627168Z 
2023-01-11T21:41:26.7627272Z ok (1.592s)
2023-01-11T21:41:26.7628040Z   test_scatter3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7628260Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7628722Z [2023-01-11 21:37:32,159] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 411
2023-01-11T21:41:26.7629187Z [2023-01-11 21:37:33,683] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 411
2023-01-11T21:41:26.7629200Z 
2023-01-11T21:41:26.7629434Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7629568Z import torch
2023-01-11T21:41:26.7629692Z import random
2023-01-11T21:41:26.7629883Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7630097Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7630106Z 
2023-01-11T21:41:26.7630253Z aten = torch.ops.aten
2023-01-11T21:41:26.7630476Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7630651Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7630665Z 
2023-01-11T21:41:26.7630672Z 
2023-01-11T21:41:26.7630910Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7631241Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7631450Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7631641Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.7631801Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7631914Z {
2023-01-11T21:41:26.7632085Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7632207Z     {
2023-01-11T21:41:26.7632352Z         #pragma omp for 
2023-01-11T21:41:26.7632507Z         for(long i0=0; i0<235; i0+=1)
2023-01-11T21:41:26.7632604Z         {
2023-01-11T21:41:26.7632839Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7632983Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7633101Z         }
2023-01-11T21:41:26.7633289Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7633441Z         for(long i0=1880; i0<1885; i0+=1)
2023-01-11T21:41:26.7633557Z         {
2023-01-11T21:41:26.7633688Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7633818Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7633933Z         }
2023-01-11T21:41:26.7634083Z         #pragma omp for 
2023-01-11T21:41:26.7634238Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7634357Z         {
2023-01-11T21:41:26.7634476Z             {
2023-01-11T21:41:26.7634569Z                 {
2023-01-11T21:41:26.7634733Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.7634935Z                     auto tmp1 = static_cast<float>(0.8);
2023-01-11T21:41:26.7635115Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:41:26.7635244Z                 }
2023-01-11T21:41:26.7635356Z             }
2023-01-11T21:41:26.7635459Z         }
2023-01-11T21:41:26.7635553Z     }
2023-01-11T21:41:26.7635669Z }
2023-01-11T21:41:26.7635827Z ''')
2023-01-11T21:41:26.7635840Z 
2023-01-11T21:41:26.7635845Z 
2023-01-11T21:41:26.7636011Z async_compile.wait(globals())
2023-01-11T21:41:26.7636146Z del async_compile
2023-01-11T21:41:26.7636154Z 
2023-01-11T21:41:26.7636273Z def call(args):
2023-01-11T21:41:26.7636421Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7636535Z     args.clear()
2023-01-11T21:41:26.7636924Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7637203Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7637434Z     del arg0_1
2023-01-11T21:41:26.7637557Z     del arg1_1
2023-01-11T21:41:26.7637695Z     return (buf0, )
2023-01-11T21:41:26.7637703Z 
2023-01-11T21:41:26.7637709Z 
2023-01-11T21:41:26.7637851Z if __name__ == "__main__":
2023-01-11T21:41:26.7638048Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7638247Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7638624Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7638972Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7639179Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7639188Z 
2023-01-11T21:41:26.7639317Z ok (1.547s)
2023-01-11T21:41:26.7640155Z   test_scatter4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7640387Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7640856Z [2023-01-11 21:37:33,702] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 412
2023-01-11T21:41:26.7641315Z [2023-01-11 21:37:35,255] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 412
2023-01-11T21:41:26.7641325Z 
2023-01-11T21:41:26.7641503Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7641616Z import torch
2023-01-11T21:41:26.7641757Z import random
2023-01-11T21:41:26.7641951Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7642171Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7642181Z 
2023-01-11T21:41:26.7642320Z aten = torch.ops.aten
2023-01-11T21:41:26.7642556Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7642720Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7642728Z 
2023-01-11T21:41:26.7642734Z 
2023-01-11T21:41:26.7642962Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7643303Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7643515Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7643686Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.7643868Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7644050Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7644159Z {
2023-01-11T21:41:26.7644336Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7644426Z     {
2023-01-11T21:41:26.7644578Z         #pragma omp for 
2023-01-11T21:41:26.7644732Z         for(long i0=0; i0<24304; i0+=1)
2023-01-11T21:41:26.7644859Z         {
2023-01-11T21:41:26.7645095Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7645249Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7645365Z         }
2023-01-11T21:41:26.7645522Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7645686Z         for(long i0=194432; i0<194432; i0+=1)
2023-01-11T21:41:26.7645802Z         {
2023-01-11T21:41:26.7645963Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7646098Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7646218Z         }
2023-01-11T21:41:26.7646340Z         #pragma omp for 
2023-01-11T21:41:26.7646494Z         for(long i0=0; i0<992; i0+=1)
2023-01-11T21:41:26.7646612Z         {
2023-01-11T21:41:26.7646739Z             {
2023-01-11T21:41:26.7646858Z                 {
2023-01-11T21:41:26.7647024Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.7647192Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:26.7647347Z                     out_ptr0[i0 + (992*tmp0)] = tmp1;
2023-01-11T21:41:26.7647598Z                 }
2023-01-11T21:41:26.7647714Z             }
2023-01-11T21:41:26.7647834Z         }
2023-01-11T21:41:26.7647949Z     }
2023-01-11T21:41:26.7648066Z }
2023-01-11T21:41:26.7648218Z ''')
2023-01-11T21:41:26.7648246Z 
2023-01-11T21:41:26.7648253Z 
2023-01-11T21:41:26.7648391Z async_compile.wait(globals())
2023-01-11T21:41:26.7648518Z del async_compile
2023-01-11T21:41:26.7648529Z 
2023-01-11T21:41:26.7648653Z def call(args):
2023-01-11T21:41:26.7648806Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7648933Z     args.clear()
2023-01-11T21:41:26.7649435Z     buf0 = empty_strided((196, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7649763Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7649989Z     del arg0_1
2023-01-11T21:41:26.7650122Z     del arg1_1
2023-01-11T21:41:26.7650244Z     del arg2_1
2023-01-11T21:41:26.7650376Z     return (buf0, )
2023-01-11T21:41:26.7650385Z 
2023-01-11T21:41:26.7650391Z 
2023-01-11T21:41:26.7650535Z if __name__ == "__main__":
2023-01-11T21:41:26.7650749Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7650965Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7651324Z     arg0_1 = rand_strided((196, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7651654Z     arg1_1 = rand_strided((1, 992), (992, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7651999Z     arg2_1 = rand_strided((1, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7652215Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7652224Z 
2023-01-11T21:41:26.7652350Z ok (1.575s)
2023-01-11T21:41:26.7652620Z   test_scatter_add1_cpu (__main__.CpuTests) ... skip: Flaky test, needs debugging (0.001s)
2023-01-11T21:41:26.7653393Z   test_scatter_add2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7653600Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7654053Z [2023-01-11 21:37:35,283] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 413
2023-01-11T21:41:26.7654522Z [2023-01-11 21:37:36,856] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 413
2023-01-11T21:41:26.7654533Z 
2023-01-11T21:41:26.7654711Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7654823Z import torch
2023-01-11T21:41:26.7654952Z import random
2023-01-11T21:41:26.7655140Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7655362Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7655377Z 
2023-01-11T21:41:26.7655528Z aten = torch.ops.aten
2023-01-11T21:41:26.7655759Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7655922Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7655931Z 
2023-01-11T21:41:26.7655936Z 
2023-01-11T21:41:26.7656188Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7656512Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7656715Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7656910Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.7657141Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7657320Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7657443Z {
2023-01-11T21:41:26.7657617Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7657715Z     {
2023-01-11T21:41:26.7657984Z         #pragma omp for 
2023-01-11T21:41:26.7658142Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.7658261Z         {
2023-01-11T21:41:26.7658377Z             {
2023-01-11T21:41:26.7658500Z                 {
2023-01-11T21:41:26.7658694Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7658852Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7658975Z                 }
2023-01-11T21:41:26.7659101Z             }
2023-01-11T21:41:26.7659220Z         }
2023-01-11T21:41:26.7659349Z         #pragma omp for 
2023-01-11T21:41:26.7659503Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7659602Z         {
2023-01-11T21:41:26.7659750Z             #pragma GCC ivdep
2023-01-11T21:41:26.7659901Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.7660029Z             {
2023-01-11T21:41:26.7660145Z                 {
2023-01-11T21:41:26.7660348Z                     {
2023-01-11T21:41:26.7660541Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:41:26.7660704Z                         auto tmp1 = in_ptr2[i1 + (3*i0)];
2023-01-11T21:41:26.7660899Z                         atomic_add(&out_ptr0[i1 + (3*tmp0)], tmp1);
2023-01-11T21:41:26.7661025Z                     }
2023-01-11T21:41:26.7661155Z                 }
2023-01-11T21:41:26.7661279Z             }
2023-01-11T21:41:26.7661399Z         }
2023-01-11T21:41:26.7661516Z     }
2023-01-11T21:41:26.7661611Z }
2023-01-11T21:41:26.7661776Z ''')
2023-01-11T21:41:26.7661787Z 
2023-01-11T21:41:26.7661793Z 
2023-01-11T21:41:26.7661961Z async_compile.wait(globals())
2023-01-11T21:41:26.7662095Z del async_compile
2023-01-11T21:41:26.7662103Z 
2023-01-11T21:41:26.7662237Z def call(args):
2023-01-11T21:41:26.7662391Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7662514Z     args.clear()
2023-01-11T21:41:26.7662844Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7663255Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7663391Z     del arg0_1
2023-01-11T21:41:26.7663512Z     del arg1_1
2023-01-11T21:41:26.7663640Z     del arg2_1
2023-01-11T21:41:26.7663775Z     return (buf0, )
2023-01-11T21:41:26.7663784Z 
2023-01-11T21:41:26.7663791Z 
2023-01-11T21:41:26.7663941Z if __name__ == "__main__":
2023-01-11T21:41:26.7664145Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7664333Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7664680Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7665008Z     arg1_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7665351Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7665570Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7665580Z 
2023-01-11T21:41:26.7665701Z ok (1.597s)
2023-01-11T21:41:26.7666479Z   test_scatter_add3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7666697Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7667160Z [2023-01-11 21:37:36,878] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 414
2023-01-11T21:41:26.7667599Z [2023-01-11 21:37:38,581] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 414
2023-01-11T21:41:26.7667629Z 
2023-01-11T21:41:26.7667775Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7667913Z import torch
2023-01-11T21:41:26.7668050Z import random
2023-01-11T21:41:26.7668245Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7668555Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7668563Z 
2023-01-11T21:41:26.7668704Z aten = torch.ops.aten
2023-01-11T21:41:26.7668943Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7669078Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7669089Z 
2023-01-11T21:41:26.7669096Z 
2023-01-11T21:41:26.7669349Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7669692Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7669899Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7670080Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.7670263Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7670503Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7670617Z {
2023-01-11T21:41:26.7670775Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7670893Z     {
2023-01-11T21:41:26.7671040Z         #pragma omp for 
2023-01-11T21:41:26.7671183Z         for(long i0=0; i0<235; i0+=1)
2023-01-11T21:41:26.7671303Z         {
2023-01-11T21:41:26.7682716Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7682986Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7683087Z         }
2023-01-11T21:41:26.7683256Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7683411Z         for(long i0=1880; i0<1885; i0+=1)
2023-01-11T21:41:26.7683517Z         {
2023-01-11T21:41:26.7683651Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7683791Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7683892Z         }
2023-01-11T21:41:26.7684023Z         #pragma omp for 
2023-01-11T21:41:26.7684164Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7684288Z         {
2023-01-11T21:41:26.7684405Z             {
2023-01-11T21:41:26.7684517Z                 {
2023-01-11T21:41:26.7684693Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.7684835Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:26.7685016Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:41:26.7685135Z                 }
2023-01-11T21:41:26.7685243Z             }
2023-01-11T21:41:26.7685358Z         }
2023-01-11T21:41:26.7685464Z     }
2023-01-11T21:41:26.7685575Z }
2023-01-11T21:41:26.7685749Z ''')
2023-01-11T21:41:26.7685757Z 
2023-01-11T21:41:26.7685766Z 
2023-01-11T21:41:26.7685930Z async_compile.wait(globals())
2023-01-11T21:41:26.7686057Z del async_compile
2023-01-11T21:41:26.7686064Z 
2023-01-11T21:41:26.7686188Z def call(args):
2023-01-11T21:41:26.7686337Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7686472Z     args.clear()
2023-01-11T21:41:26.7686857Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7687153Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7687284Z     del arg0_1
2023-01-11T21:41:26.7687409Z     del arg1_1
2023-01-11T21:41:26.7687535Z     del arg2_1
2023-01-11T21:41:26.7687662Z     return (buf0, )
2023-01-11T21:41:26.7687670Z 
2023-01-11T21:41:26.7687675Z 
2023-01-11T21:41:26.7687812Z if __name__ == "__main__":
2023-01-11T21:41:26.7688020Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7688247Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7688596Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7688947Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7689474Z     arg2_1 = rand_strided((1, 1, 10), (10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7689699Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7689708Z 
2023-01-11T21:41:26.7689830Z ok (1.725s)
2023-01-11T21:41:26.7690775Z   test_scatter_reduce1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7691002Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7691360Z [W TensorAdvancedIndexing.cpp:1739] Warning: scatter_reduce() is in beta and the API may change at any time. (function operator())
2023-01-11T21:41:26.7691822Z [2023-01-11 21:37:38,604] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 415
2023-01-11T21:41:26.7692384Z [2023-01-11 21:37:38,615] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 415
2023-01-11T21:41:26.7692398Z 
2023-01-11T21:41:26.7692549Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7692678Z import torch
2023-01-11T21:41:26.7692814Z import random
2023-01-11T21:41:26.7693019Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7693228Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7693238Z 
2023-01-11T21:41:26.7693378Z aten = torch.ops.aten
2023-01-11T21:41:26.7693602Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7693750Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7693777Z 
2023-01-11T21:41:26.7693784Z 
2023-01-11T21:41:26.7694016Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7694357Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7694568Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7694759Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.7694943Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7695124Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7695236Z {
2023-01-11T21:41:26.7695393Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7695504Z     {
2023-01-11T21:41:26.7695651Z         #pragma omp for 
2023-01-11T21:41:26.7695796Z         for(long i0=0; i0<235; i0+=1)
2023-01-11T21:41:26.7695910Z         {
2023-01-11T21:41:26.7696155Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7696319Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7696417Z         }
2023-01-11T21:41:26.7696577Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7696730Z         for(long i0=1880; i0<1885; i0+=1)
2023-01-11T21:41:26.7696850Z         {
2023-01-11T21:41:26.7697008Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7697158Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7697274Z         }
2023-01-11T21:41:26.7697388Z         #pragma omp for 
2023-01-11T21:41:26.7697531Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7697656Z         {
2023-01-11T21:41:26.7697779Z             {
2023-01-11T21:41:26.7697899Z                 {
2023-01-11T21:41:26.7698057Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.7698195Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:26.7698379Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:41:26.7698496Z                 }
2023-01-11T21:41:26.7698616Z             }
2023-01-11T21:41:26.7698722Z         }
2023-01-11T21:41:26.7698839Z     }
2023-01-11T21:41:26.7698946Z }
2023-01-11T21:41:26.7699088Z ''')
2023-01-11T21:41:26.7699097Z 
2023-01-11T21:41:26.7699123Z 
2023-01-11T21:41:26.7699267Z async_compile.wait(globals())
2023-01-11T21:41:26.7699405Z del async_compile
2023-01-11T21:41:26.7699412Z 
2023-01-11T21:41:26.7699544Z def call(args):
2023-01-11T21:41:26.7699696Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7699827Z     args.clear()
2023-01-11T21:41:26.7700205Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7700619Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7700733Z     del arg0_1
2023-01-11T21:41:26.7700854Z     del arg1_1
2023-01-11T21:41:26.7700980Z     del arg2_1
2023-01-11T21:41:26.7701106Z     return (buf0, )
2023-01-11T21:41:26.7701114Z 
2023-01-11T21:41:26.7701120Z 
2023-01-11T21:41:26.7701250Z if __name__ == "__main__":
2023-01-11T21:41:26.7701443Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7701656Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7702018Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7702446Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7702822Z     arg2_1 = rand_strided((1, 1, 10), (10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7703033Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7703042Z 
2023-01-11T21:41:26.7703258Z ok (0.033s)
2023-01-11T21:41:26.7704058Z   test_scatter_reduce2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7704281Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7704734Z [2023-01-11 21:37:38,636] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 416
2023-01-11T21:41:26.7705219Z [2023-01-11 21:37:40,334] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 416
2023-01-11T21:41:26.7705232Z 
2023-01-11T21:41:26.7705410Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7705513Z import torch
2023-01-11T21:41:26.7705644Z import random
2023-01-11T21:41:26.7705855Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7706071Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7706081Z 
2023-01-11T21:41:26.7706217Z aten = torch.ops.aten
2023-01-11T21:41:26.7706451Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7706622Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7706631Z 
2023-01-11T21:41:26.7706637Z 
2023-01-11T21:41:26.7706882Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7707197Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7707401Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7707591Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:41:26.7707777Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7707957Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7708069Z {
2023-01-11T21:41:26.7708246Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7708343Z     {
2023-01-11T21:41:26.7708488Z         #pragma omp for 
2023-01-11T21:41:26.7708632Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.7708753Z         {
2023-01-11T21:41:26.7708900Z             {
2023-01-11T21:41:26.7709018Z                 {
2023-01-11T21:41:26.7709178Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7709317Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7709437Z                 }
2023-01-11T21:41:26.7709547Z             }
2023-01-11T21:41:26.7709662Z         }
2023-01-11T21:41:26.7709808Z         #pragma omp for 
2023-01-11T21:41:26.7709953Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7710049Z         {
2023-01-11T21:41:26.7710200Z             #pragma GCC ivdep
2023-01-11T21:41:26.7710339Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.7710576Z             {
2023-01-11T21:41:26.7710690Z                 {
2023-01-11T21:41:26.7710814Z                     {
2023-01-11T21:41:26.7710988Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:41:26.7711148Z                         auto tmp1 = static_cast<float>(0);
2023-01-11T21:41:26.7711321Z                         out_ptr0[i1 + (3*tmp0)] = tmp1;
2023-01-11T21:41:26.7711448Z                     }
2023-01-11T21:41:26.7711564Z                 }
2023-01-11T21:41:26.7711682Z             }
2023-01-11T21:41:26.7711797Z         }
2023-01-11T21:41:26.7711928Z         #pragma omp for 
2023-01-11T21:41:26.7712061Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7712174Z         {
2023-01-11T21:41:26.7712323Z             #pragma GCC ivdep
2023-01-11T21:41:26.7712538Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.7712658Z             {
2023-01-11T21:41:26.7712774Z                 {
2023-01-11T21:41:26.7712894Z                     {
2023-01-11T21:41:26.7713060Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:41:26.7713234Z                         auto tmp1 = in_ptr2[i1 + (3*i0)];
2023-01-11T21:41:26.7713434Z                         atomic_add(&out_ptr0[i1 + (3*tmp0)], tmp1);
2023-01-11T21:41:26.7713548Z                     }
2023-01-11T21:41:26.7713663Z                 }
2023-01-11T21:41:26.7713785Z             }
2023-01-11T21:41:26.7713906Z         }
2023-01-11T21:41:26.7713998Z     }
2023-01-11T21:41:26.7714110Z }
2023-01-11T21:41:26.7714283Z ''')
2023-01-11T21:41:26.7714297Z 
2023-01-11T21:41:26.7714302Z 
2023-01-11T21:41:26.7714461Z async_compile.wait(globals())
2023-01-11T21:41:26.7714596Z del async_compile
2023-01-11T21:41:26.7714606Z 
2023-01-11T21:41:26.7714730Z def call(args):
2023-01-11T21:41:26.7714880Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7714994Z     args.clear()
2023-01-11T21:41:26.7715339Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7715675Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7715800Z     del arg0_1
2023-01-11T21:41:26.7715923Z     del arg1_1
2023-01-11T21:41:26.7716041Z     del arg2_1
2023-01-11T21:41:26.7716170Z     return (buf0, )
2023-01-11T21:41:26.7716183Z 
2023-01-11T21:41:26.7716189Z 
2023-01-11T21:41:26.7716315Z if __name__ == "__main__":
2023-01-11T21:41:26.7716513Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7716722Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7717079Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7717425Z     arg1_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7717764Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7717982Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7717998Z 
2023-01-11T21:41:26.7718116Z ok (1.719s)
2023-01-11T21:41:26.7718918Z   test_scheduler_vertical_fusion1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7719127Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7719571Z [2023-01-11 21:37:40,421] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 417
2023-01-11T21:41:26.7720026Z [2023-01-11 21:37:42,014] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 417
2023-01-11T21:41:26.7720036Z 
2023-01-11T21:41:26.7720219Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7720353Z import torch
2023-01-11T21:41:26.7720586Z import random
2023-01-11T21:41:26.7720773Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7720992Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7721002Z 
2023-01-11T21:41:26.7721139Z aten = torch.ops.aten
2023-01-11T21:41:26.7721360Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7721505Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7721513Z 
2023-01-11T21:41:26.7721523Z 
2023-01-11T21:41:26.7721775Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7722132Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7722328Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.7722514Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.7722807Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7723005Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7723174Z                        const float* __restrict__ in_ptr2)
2023-01-11T21:41:26.7723286Z {
2023-01-11T21:41:26.7723441Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:41:26.7723621Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7723728Z     {
2023-01-11T21:41:26.7723867Z         #pragma omp for 
2023-01-11T21:41:26.7724002Z         for(long i0=0; i0<135252; i0+=1)
2023-01-11T21:41:26.7724117Z         {
2023-01-11T21:41:26.7724357Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7724595Z             auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.7725006Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.061519070296458e-11));
2023-01-11T21:41:26.7725170Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.7725573Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(-1.988366587925593e-08));
2023-01-11T21:41:26.7725734Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7725875Z             auto tmp5 = tmp0 * tmp4;
2023-01-11T21:41:26.7726284Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(-3.087032500374211e-07));
2023-01-11T21:41:26.7726435Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.7726832Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1.55093272922008e-10));
2023-01-11T21:41:26.7726982Z             auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:26.7727138Z             auto tmp11 = tmp7 + tmp10;
2023-01-11T21:41:26.7727316Z             auto tmp12 = tmp11.reciprocal();
2023-01-11T21:41:26.7727508Z             auto tmp13 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:41:26.7727609Z             auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:26.7727739Z             tmp11.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7727862Z             tmp14.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7727961Z         }
2023-01-11T21:41:26.7728132Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7728280Z         for(long i0=1082016; i0<1082016; i0+=1)
2023-01-11T21:41:26.7728394Z         {
2023-01-11T21:41:26.7728633Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7728857Z             auto tmp8 = in_ptr1[i0];
2023-01-11T21:41:26.7729348Z             auto tmp1 = static_cast<float>(-1.061519070296458e-11);
2023-01-11T21:41:26.7729520Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.7729871Z             auto tmp3 = static_cast<float>(-1.988366587925593e-08);
2023-01-11T21:41:26.7730040Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.7730215Z             auto tmp5 = tmp0 * tmp4;
2023-01-11T21:41:26.7730549Z             auto tmp6 = static_cast<float>(-3.087032500374211e-07);
2023-01-11T21:41:26.7730726Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.7731087Z             auto tmp9 = static_cast<float>(1.55093272922008e-10);
2023-01-11T21:41:26.7731261Z             auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:26.7731440Z             auto tmp11 = tmp7 + tmp10;
2023-01-11T21:41:26.7731767Z             auto tmp12 = 1 / tmp11;
2023-01-11T21:41:26.7731977Z             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:41:26.7732148Z             auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:26.7732305Z             in_out_ptr0[i0] = tmp11;
2023-01-11T21:41:26.7732459Z             out_ptr1[i0] = tmp14;
2023-01-11T21:41:26.7732594Z         }
2023-01-11T21:41:26.7732761Z         #pragma omp for 
2023-01-11T21:41:26.7732924Z         for(long i0=0; i0<41616; i0+=1)
2023-01-11T21:41:26.7733052Z         {
2023-01-11T21:41:26.7733197Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.7733329Z             {
2023-01-11T21:41:26.7733615Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + (8*i1) + (26*i0));
2023-01-11T21:41:26.7733976Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i1);
2023-01-11T21:41:26.7734159Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.7734370Z                 tmp2.store(in_out_ptr0 + (8*i1) + (26*i0));
2023-01-11T21:41:26.7734513Z             }
2023-01-11T21:41:26.7734697Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7734846Z             for(long i1=24; i1<26; i1+=1)
2023-01-11T21:41:26.7734975Z             {
2023-01-11T21:41:26.7735186Z                 auto tmp0 = in_out_ptr0[i1 + (26*i0)];
2023-01-11T21:41:26.7735363Z                 auto tmp1 = in_ptr2[i1];
2023-01-11T21:41:26.7735537Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.7735725Z                 in_out_ptr0[i1 + (26*i0)] = tmp2;
2023-01-11T21:41:26.7735843Z             }
2023-01-11T21:41:26.7735954Z         }
2023-01-11T21:41:26.7736114Z         #pragma omp for 
2023-01-11T21:41:26.7736295Z         for(long i0=0; i0<135252; i0+=1)
2023-01-11T21:41:26.7736424Z         {
2023-01-11T21:41:26.7736692Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7736966Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.7737144Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7737314Z             tmp2.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:26.7737439Z         }
2023-01-11T21:41:26.7737622Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7737804Z         for(long i0=1082016; i0<1082016; i0+=1)
2023-01-11T21:41:26.7737937Z         {
2023-01-11T21:41:26.7738113Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.7738284Z             auto tmp1 = in_out_ptr0[i0];
2023-01-11T21:41:26.7738429Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7738607Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.7738735Z         }
2023-01-11T21:41:26.7738858Z     }
2023-01-11T21:41:26.7738978Z }
2023-01-11T21:41:26.7739168Z ''')
2023-01-11T21:41:26.7739180Z 
2023-01-11T21:41:26.7739189Z 
2023-01-11T21:41:26.7739374Z async_compile.wait(globals())
2023-01-11T21:41:26.7739509Z del async_compile
2023-01-11T21:41:26.7739521Z 
2023-01-11T21:41:26.7739669Z def call(args):
2023-01-11T21:41:26.7739840Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7739983Z     args.clear()
2023-01-11T21:41:26.7740420Z     buf0 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7740599Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.7741011Z     buf2 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7741158Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:26.7741322Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:41:26.7741734Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:41:26.7741870Z     del arg0_1
2023-01-11T21:41:26.7742006Z     del arg1_1
2023-01-11T21:41:26.7742144Z     del arg2_1
2023-01-11T21:41:26.7742299Z     return (buf4, )
2023-01-11T21:41:26.7742308Z 
2023-01-11T21:41:26.7742314Z 
2023-01-11T21:41:26.7742469Z if __name__ == "__main__":
2023-01-11T21:41:26.7742780Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7743026Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7743541Z     arg0_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7743963Z     arg1_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7744361Z     arg2_1 = rand_strided((26, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7744599Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7744609Z 
2023-01-11T21:41:26.7744753Z ok (1.723s)
2023-01-11T21:41:26.7745759Z   test_select_scatter_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7746019Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7746521Z [2023-01-11 21:37:42,102] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 418
2023-01-11T21:41:26.7747048Z [2023-01-11 21:37:43,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 418
2023-01-11T21:41:26.7747058Z 
2023-01-11T21:41:26.7747246Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7747391Z import torch
2023-01-11T21:41:26.7747534Z import random
2023-01-11T21:41:26.7747765Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7748006Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7748016Z 
2023-01-11T21:41:26.7748171Z aten = torch.ops.aten
2023-01-11T21:41:26.7748422Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7748621Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7748634Z 
2023-01-11T21:41:26.7748642Z 
2023-01-11T21:41:26.7748917Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7749307Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7749537Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7749757Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7749951Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.7750153Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7750328Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7750461Z {
2023-01-11T21:41:26.7750664Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7750788Z     {
2023-01-11T21:41:26.7750962Z         #pragma omp for 
2023-01-11T21:41:26.7751124Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7751255Z         {
2023-01-11T21:41:26.7751404Z             #pragma GCC ivdep
2023-01-11T21:41:26.7751573Z             for(long i1=0; i1<197; i1+=1)
2023-01-11T21:41:26.7751702Z             {
2023-01-11T21:41:26.7751867Z                 #pragma GCC ivdep
2023-01-11T21:41:26.7752044Z                 for(long i2=0; i2<38; i2+=1)
2023-01-11T21:41:26.7752178Z                 {
2023-01-11T21:41:26.7752291Z                     {
2023-01-11T21:41:26.7752435Z                         {
2023-01-11T21:41:26.7752640Z                             auto tmp3 = in_ptr0[i2 + (38*i0)];
2023-01-11T21:41:26.7752864Z                             auto tmp4 = in_ptr1[i2 + (38*i1) + (7486*i0)];
2023-01-11T21:41:26.7753072Z                             auto tmp0 = static_cast<int>(i1);
2023-01-11T21:41:26.7753274Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:41:26.7753467Z                             auto tmp2 = tmp0 == tmp1;
2023-01-11T21:41:26.7753675Z                             auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:41:26.7753870Z                             out_ptr0[i2 + (38*i1) + (7486*i0)] = tmp5;
2023-01-11T21:41:26.7754117Z                         }
2023-01-11T21:41:26.7754248Z                     }
2023-01-11T21:41:26.7754380Z                 }
2023-01-11T21:41:26.7754508Z             }
2023-01-11T21:41:26.7754641Z         }
2023-01-11T21:41:26.7754804Z         #pragma omp for 
2023-01-11T21:41:26.7754957Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7755088Z         {
2023-01-11T21:41:26.7755247Z             #pragma GCC ivdep
2023-01-11T21:41:26.7755426Z             for(long i1=0; i1<7486; i1+=1)
2023-01-11T21:41:26.7755557Z             {
2023-01-11T21:41:26.7755694Z                 {
2023-01-11T21:41:26.7755813Z                     {
2023-01-11T21:41:26.7756005Z                         auto tmp3 = in_ptr2[i1];
2023-01-11T21:41:26.7756273Z                         auto tmp4 = in_ptr1[i1 + (7486*i0)];
2023-01-11T21:41:26.7756493Z                         auto tmp0 = static_cast<int>(i0);
2023-01-11T21:41:26.7756702Z                         auto tmp1 = static_cast<int>(1);
2023-01-11T21:41:26.7756893Z                         auto tmp2 = tmp0 == tmp1;
2023-01-11T21:41:26.7757093Z                         auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:41:26.7757283Z                         out_ptr1[i1 + (7486*i0)] = tmp5;
2023-01-11T21:41:26.7757400Z                     }
2023-01-11T21:41:26.7757534Z                 }
2023-01-11T21:41:26.7757664Z             }
2023-01-11T21:41:26.7757791Z         }
2023-01-11T21:41:26.7757909Z     }
2023-01-11T21:41:26.7758029Z }
2023-01-11T21:41:26.7758203Z ''')
2023-01-11T21:41:26.7758215Z 
2023-01-11T21:41:26.7758242Z 
2023-01-11T21:41:26.7758402Z async_compile.wait(globals())
2023-01-11T21:41:26.7758545Z del async_compile
2023-01-11T21:41:26.7758556Z 
2023-01-11T21:41:26.7758691Z def call(args):
2023-01-11T21:41:26.7758848Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.7758997Z     args.clear()
2023-01-11T21:41:26.7759435Z     buf0 = empty_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7759860Z     buf1 = empty_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7760243Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7760380Z     del arg0_1
2023-01-11T21:41:26.7760513Z     del arg1_1
2023-01-11T21:41:26.7760659Z     del arg2_1
2023-01-11T21:41:26.7760823Z     return (buf0, buf1, )
2023-01-11T21:41:26.7760837Z 
2023-01-11T21:41:26.7760844Z 
2023-01-11T21:41:26.7761000Z if __name__ == "__main__":
2023-01-11T21:41:26.7761231Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7761467Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7761876Z     arg0_1 = rand_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7762276Z     arg1_1 = rand_strided((8, 38), (38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7762679Z     arg2_1 = rand_strided((197, 38), (38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7762925Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.7762937Z 
2023-01-11T21:41:26.7763067Z ok (1.768s)
2023-01-11T21:41:26.7763944Z   test_sgn_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7764190Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7764718Z [2023-01-11 21:37:43,847] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 419
2023-01-11T21:41:26.7765265Z [2023-01-11 21:37:46,148] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 419
2023-01-11T21:41:26.7765377Z 
2023-01-11T21:41:26.7765549Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7765688Z import torch
2023-01-11T21:41:26.7765826Z import random
2023-01-11T21:41:26.7766062Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7766307Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7766320Z 
2023-01-11T21:41:26.7766475Z aten = torch.ops.aten
2023-01-11T21:41:26.7766735Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7766922Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7766936Z 
2023-01-11T21:41:26.7766943Z 
2023-01-11T21:41:26.7767203Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7767594Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7767901Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7768106Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7768302Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7768430Z {
2023-01-11T21:41:26.7768630Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7768739Z     {
2023-01-11T21:41:26.7768893Z         #pragma omp for 
2023-01-11T21:41:26.7769183Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:26.7769314Z         {
2023-01-11T21:41:26.7769588Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7769914Z             auto tmp1 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), decltype(tmp0)(0) < tmp0);
2023-01-11T21:41:26.7770248Z             auto tmp2 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), tmp0 < decltype(tmp0)(0));
2023-01-11T21:41:26.7770524Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:26.7770774Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7770943Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.7771262Z             auto tmp6 = decltype(tmp5)::blendv(decltype(tmp5)(0), decltype(tmp5)(1), decltype(tmp5)(0) < tmp5);
2023-01-11T21:41:26.7771592Z             auto tmp7 = decltype(tmp5)::blendv(decltype(tmp5)(0), decltype(tmp5)(1), tmp5 < decltype(tmp5)(0));
2023-01-11T21:41:26.7771852Z             auto tmp8 = tmp6 - tmp7;
2023-01-11T21:41:26.7772099Z             auto tmp9 = tmp8 - tmp4;
2023-01-11T21:41:26.7772290Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7772471Z             tmp9.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7772580Z         }
2023-01-11T21:41:26.7772772Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7772935Z         for(long i0=40; i0<41; i0+=1)
2023-01-11T21:41:26.7773062Z         {
2023-01-11T21:41:26.7773225Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7773409Z             auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:41:26.7773581Z             auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:41:26.7773813Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:26.7774017Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.7774195Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.7774372Z             auto tmp6 = tmp5 > 0 ? 1 : 0;
2023-01-11T21:41:26.7774542Z             auto tmp7 = tmp5 < 0 ? 1 : 0;
2023-01-11T21:41:26.7774792Z             auto tmp8 = tmp6 - tmp7;
2023-01-11T21:41:26.7775046Z             auto tmp9 = tmp8 - tmp4;
2023-01-11T21:41:26.7775189Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.7775348Z             out_ptr1[i0] = tmp9;
2023-01-11T21:41:26.7775477Z         }
2023-01-11T21:41:26.7775599Z     }
2023-01-11T21:41:26.7775723Z }
2023-01-11T21:41:26.7775884Z ''')
2023-01-11T21:41:26.7775895Z 
2023-01-11T21:41:26.7775901Z 
2023-01-11T21:41:26.7776081Z async_compile.wait(globals())
2023-01-11T21:41:26.7776215Z del async_compile
2023-01-11T21:41:26.7776254Z 
2023-01-11T21:41:26.7776374Z def call(args):
2023-01-11T21:41:26.7776511Z     arg0_1, = args
2023-01-11T21:41:26.7776659Z     args.clear()
2023-01-11T21:41:26.7777190Z     buf0 = empty_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7777571Z     buf1 = empty_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7777894Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7778035Z     del arg0_1
2023-01-11T21:41:26.7778168Z     return (buf0, buf1, )
2023-01-11T21:41:26.7778177Z 
2023-01-11T21:41:26.7778186Z 
2023-01-11T21:41:26.7778341Z if __name__ == "__main__":
2023-01-11T21:41:26.7778564Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7778806Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7779194Z     arg0_1 = rand_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7779509Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7779526Z 
2023-01-11T21:41:26.7779664Z ok (2.323s)
2023-01-11T21:41:26.7780570Z   test_sgn_extremal_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7780822Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7781330Z [2023-01-11 21:37:46,163] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 420
2023-01-11T21:41:26.7781863Z [2023-01-11 21:37:48,894] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 420
2023-01-11T21:41:26.7781873Z 
2023-01-11T21:41:26.7782058Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7782206Z import torch
2023-01-11T21:41:26.7782360Z import random
2023-01-11T21:41:26.7782587Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7782829Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7782840Z 
2023-01-11T21:41:26.7782993Z aten = torch.ops.aten
2023-01-11T21:41:26.7783314Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7783502Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7783512Z 
2023-01-11T21:41:26.7783519Z 
2023-01-11T21:41:26.7783805Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7784201Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7784441Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7784641Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7784765Z {
2023-01-11T21:41:26.7784966Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7785076Z     {
2023-01-11T21:41:26.7785234Z         #pragma omp for 
2023-01-11T21:41:26.7785399Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.7785530Z         {
2023-01-11T21:41:26.7785668Z             {
2023-01-11T21:41:26.7785803Z                 {
2023-01-11T21:41:26.7785966Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7786149Z                     auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:41:26.7786330Z                     auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:41:26.7786618Z                     auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:26.7786790Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.7786924Z                 }
2023-01-11T21:41:26.7787047Z             }
2023-01-11T21:41:26.7787155Z         }
2023-01-11T21:41:26.7787273Z     }
2023-01-11T21:41:26.7787394Z }
2023-01-11T21:41:26.7787555Z ''')
2023-01-11T21:41:26.7787565Z 
2023-01-11T21:41:26.7787573Z 
2023-01-11T21:41:26.7787755Z async_compile.wait(globals())
2023-01-11T21:41:26.7787894Z del async_compile
2023-01-11T21:41:26.7787911Z 
2023-01-11T21:41:26.7788052Z def call(args):
2023-01-11T21:41:26.7788180Z     arg0_1, = args
2023-01-11T21:41:26.7788327Z     args.clear()
2023-01-11T21:41:26.7788828Z     buf0 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7789091Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7789236Z     del arg0_1
2023-01-11T21:41:26.7789383Z     return (buf0, )
2023-01-11T21:41:26.7789392Z 
2023-01-11T21:41:26.7789398Z 
2023-01-11T21:41:26.7789550Z if __name__ == "__main__":
2023-01-11T21:41:26.7789771Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7790002Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7790394Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7790608Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7790616Z 
2023-01-11T21:41:26.7790738Z ok (2.745s)
2023-01-11T21:41:26.7791736Z   test_shape_prop_torch_ones_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7791996Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7792528Z [2023-01-11 21:37:49,281] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 421
2023-01-11T21:41:26.7793060Z [2023-01-11 21:37:51,616] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 421
2023-01-11T21:41:26.7793070Z 
2023-01-11T21:41:26.7793255Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7793376Z import torch
2023-01-11T21:41:26.7793522Z import random
2023-01-11T21:41:26.7793754Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7793998Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7794008Z 
2023-01-11T21:41:26.7794166Z aten = torch.ops.aten
2023-01-11T21:41:26.7794424Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7794610Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7794622Z 
2023-01-11T21:41:26.7794629Z 
2023-01-11T21:41:26.7794905Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7795276Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7795510Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7795709Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7795835Z {
2023-01-11T21:41:26.7796034Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7796162Z     {
2023-01-11T21:41:26.7796323Z         #pragma omp for 
2023-01-11T21:41:26.7796481Z         for(long i0=0; i0<3145728; i0+=1)
2023-01-11T21:41:26.7796612Z         {
2023-01-11T21:41:26.7796884Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7797155Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7797334Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7797508Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7797633Z         }
2023-01-11T21:41:26.7797822Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7797989Z         for(long i0=25165824; i0<25165824; i0+=1)
2023-01-11T21:41:26.7798113Z         {
2023-01-11T21:41:26.7798287Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7798475Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.7798642Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7798807Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7798913Z         }
2023-01-11T21:41:26.7799033Z     }
2023-01-11T21:41:26.7799159Z }
2023-01-11T21:41:26.7799345Z ''')
2023-01-11T21:41:26.7799363Z 
2023-01-11T21:41:26.7799372Z 
2023-01-11T21:41:26.7799557Z async_compile.wait(globals())
2023-01-11T21:41:26.7799700Z del async_compile
2023-01-11T21:41:26.7799809Z 
2023-01-11T21:41:26.7799964Z def call(args):
2023-01-11T21:41:26.7800098Z     arg0_1, = args
2023-01-11T21:41:26.7800229Z     args.clear()
2023-01-11T21:41:26.7800708Z     buf0 = empty_strided((8, 12, 512, 512), (3145728, 262144, 512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7800961Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7801101Z     del arg0_1
2023-01-11T21:41:26.7801251Z     return (buf0, )
2023-01-11T21:41:26.7801260Z 
2023-01-11T21:41:26.7801267Z 
2023-01-11T21:41:26.7801413Z if __name__ == "__main__":
2023-01-11T21:41:26.7801639Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7801858Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7802403Z     arg0_1 = rand_strided((8, 12, 512, 512), (3145728, 262144, 512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7802626Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7802644Z 
2023-01-11T21:41:26.7802777Z ok (3.865s)
2023-01-11T21:41:26.7803668Z   test_sigmoid_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7803924Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7804442Z [2023-01-11 21:37:52,783] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 422
2023-01-11T21:41:26.7804958Z [2023-01-11 21:37:54,332] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 422
2023-01-11T21:41:26.7804978Z 
2023-01-11T21:41:26.7805171Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7805313Z import torch
2023-01-11T21:41:26.7805435Z import random
2023-01-11T21:41:26.7805657Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7805899Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7805912Z 
2023-01-11T21:41:26.7806072Z aten = torch.ops.aten
2023-01-11T21:41:26.7806333Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7806514Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7806529Z 
2023-01-11T21:41:26.7806536Z 
2023-01-11T21:41:26.7806814Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7807194Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7807419Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7807627Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7807824Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7808015Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7808150Z {
2023-01-11T21:41:26.7808350Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7808475Z     {
2023-01-11T21:41:26.7808622Z         #pragma omp for 
2023-01-11T21:41:26.7808792Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7808921Z         {
2023-01-11T21:41:26.7809334Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7809600Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.7809866Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:26.7810029Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.7810273Z             auto tmp4 = decltype(tmp3)(1)/(decltype(tmp3)(1) + tmp3.neg().exp());
2023-01-11T21:41:26.7810462Z             tmp1.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7810652Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7810775Z         }
2023-01-11T21:41:26.7810962Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7811283Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7811413Z         {
2023-01-11T21:41:26.7811571Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7811740Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.7812045Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:26.7812212Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:26.7812386Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.7812659Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:41:26.7812834Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:41:26.7812980Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7813150Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.7813280Z         }
2023-01-11T21:41:26.7813404Z     }
2023-01-11T21:41:26.7813525Z }
2023-01-11T21:41:26.7813697Z ''')
2023-01-11T21:41:26.7813710Z 
2023-01-11T21:41:26.7813815Z 
2023-01-11T21:41:26.7814002Z async_compile.wait(globals())
2023-01-11T21:41:26.7814133Z del async_compile
2023-01-11T21:41:26.7814170Z 
2023-01-11T21:41:26.7814297Z def call(args):
2023-01-11T21:41:26.7814439Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7814585Z     args.clear()
2023-01-11T21:41:26.7814984Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7815368Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7815735Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7815869Z     del arg0_1
2023-01-11T21:41:26.7815991Z     del arg1_1
2023-01-11T21:41:26.7816140Z     return (buf0, buf1, )
2023-01-11T21:41:26.7816153Z 
2023-01-11T21:41:26.7816161Z 
2023-01-11T21:41:26.7816315Z if __name__ == "__main__":
2023-01-11T21:41:26.7816540Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7816792Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7817173Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7817567Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7817801Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7817811Z 
2023-01-11T21:41:26.7817926Z ok (1.583s)
2023-01-11T21:41:26.7818815Z   test_signbit_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7819070Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7819589Z [2023-01-11 21:37:54,366] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 423
2023-01-11T21:41:26.7820119Z [2023-01-11 21:37:55,936] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 423
2023-01-11T21:41:26.7820140Z 
2023-01-11T21:41:26.7820335Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7820481Z import torch
2023-01-11T21:41:26.7820629Z import random
2023-01-11T21:41:26.7820853Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7821078Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7821090Z 
2023-01-11T21:41:26.7821245Z aten = torch.ops.aten
2023-01-11T21:41:26.7821505Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7821689Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7821698Z 
2023-01-11T21:41:26.7821706Z 
2023-01-11T21:41:26.7821992Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7822379Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7822621Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7822812Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:41:26.7823079Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.7823286Z {
2023-01-11T21:41:26.7823493Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7823617Z     {
2023-01-11T21:41:26.7823775Z         #pragma omp for 
2023-01-11T21:41:26.7823944Z         for(long i0=0; i0<72; i0+=1)
2023-01-11T21:41:26.7824068Z         {
2023-01-11T21:41:26.7824179Z             {
2023-01-11T21:41:26.7824306Z                 {
2023-01-11T21:41:26.7824501Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7824712Z                     auto tmp1 = std::signbit(tmp0);
2023-01-11T21:41:26.7824974Z                     auto tmp2 = -tmp0;
2023-01-11T21:41:26.7825176Z                     auto tmp3 = std::signbit(tmp2);
2023-01-11T21:41:26.7825361Z                     auto tmp4 = tmp3 == 0;
2023-01-11T21:41:26.7825636Z                     auto tmp5 = static_cast<long>(tmp4);
2023-01-11T21:41:26.7825847Z                     auto tmp6 = static_cast<long>(1);
2023-01-11T21:41:26.7826033Z                     auto tmp7 = tmp5 & tmp6;
2023-01-11T21:41:26.7826202Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.7826376Z                     out_ptr1[i0] = tmp7;
2023-01-11T21:41:26.7826512Z                 }
2023-01-11T21:41:26.7826636Z             }
2023-01-11T21:41:26.7826740Z         }
2023-01-11T21:41:26.7826867Z     }
2023-01-11T21:41:26.7826989Z }
2023-01-11T21:41:26.7827166Z ''')
2023-01-11T21:41:26.7827176Z 
2023-01-11T21:41:26.7827184Z 
2023-01-11T21:41:26.7827367Z async_compile.wait(globals())
2023-01-11T21:41:26.7827513Z del async_compile
2023-01-11T21:41:26.7827524Z 
2023-01-11T21:41:26.7827665Z def call(args):
2023-01-11T21:41:26.7827784Z     arg0_1, = args
2023-01-11T21:41:26.7827924Z     args.clear()
2023-01-11T21:41:26.7828354Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.7828765Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.7829084Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7829225Z     del arg0_1
2023-01-11T21:41:26.7829380Z     return (buf0, buf1, )
2023-01-11T21:41:26.7829391Z 
2023-01-11T21:41:26.7829398Z 
2023-01-11T21:41:26.7829556Z if __name__ == "__main__":
2023-01-11T21:41:26.7829761Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7830011Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7830423Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7830638Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7830648Z 
2023-01-11T21:41:26.7830786Z ok (1.594s)
2023-01-11T21:41:26.7831664Z   test_silu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7831915Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7832440Z [2023-01-11 21:37:55,954] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 424
2023-01-11T21:41:26.7832952Z [2023-01-11 21:37:55,962] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 424
2023-01-11T21:41:26.7832962Z 
2023-01-11T21:41:26.7833150Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7833273Z import torch
2023-01-11T21:41:26.7833419Z import random
2023-01-11T21:41:26.7833644Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7833895Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7833908Z 
2023-01-11T21:41:26.7834072Z aten = torch.ops.aten
2023-01-11T21:41:26.7834430Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7834610Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7834620Z 
2023-01-11T21:41:26.7834628Z 
2023-01-11T21:41:26.7834885Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7835271Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7835493Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7835695Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7835818Z {
2023-01-11T21:41:26.7836012Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7836139Z     {
2023-01-11T21:41:26.7836275Z         #pragma omp for 
2023-01-11T21:41:26.7836437Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7836563Z         {
2023-01-11T21:41:26.7836918Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7837187Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:41:26.7837365Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.7837563Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7837683Z         }
2023-01-11T21:41:26.7837859Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7838025Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7838147Z         {
2023-01-11T21:41:26.7838319Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7838615Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:41:26.7838784Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:41:26.7838937Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:41:26.7839102Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.7839228Z         }
2023-01-11T21:41:26.7839354Z     }
2023-01-11T21:41:26.7839473Z }
2023-01-11T21:41:26.7839642Z ''')
2023-01-11T21:41:26.7839655Z 
2023-01-11T21:41:26.7839666Z 
2023-01-11T21:41:26.7839847Z async_compile.wait(globals())
2023-01-11T21:41:26.7839976Z del async_compile
2023-01-11T21:41:26.7840013Z 
2023-01-11T21:41:26.7840141Z def call(args):
2023-01-11T21:41:26.7840280Z     arg0_1, = args
2023-01-11T21:41:26.7840421Z     args.clear()
2023-01-11T21:41:26.7840821Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7841089Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7841232Z     del arg0_1
2023-01-11T21:41:26.7841382Z     return (buf0, )
2023-01-11T21:41:26.7841397Z 
2023-01-11T21:41:26.7841404Z 
2023-01-11T21:41:26.7841531Z if __name__ == "__main__":
2023-01-11T21:41:26.7841748Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7841994Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7842387Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7842607Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7842618Z 
2023-01-11T21:41:26.7842751Z ok (0.025s)
2023-01-11T21:41:26.7843659Z   test_simplify_loops_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7843910Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7844432Z [2023-01-11 21:37:55,975] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 425
2023-01-11T21:41:26.7844929Z [2023-01-11 21:37:57,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 425
2023-01-11T21:41:26.7844963Z 
2023-01-11T21:41:26.7845137Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7845280Z import torch
2023-01-11T21:41:26.7845429Z import random
2023-01-11T21:41:26.7845659Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7845990Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7846003Z 
2023-01-11T21:41:26.7846163Z aten = torch.ops.aten
2023-01-11T21:41:26.7846428Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7846595Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7846604Z 
2023-01-11T21:41:26.7846629Z 
2023-01-11T21:41:26.7846886Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7847281Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7847520Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7847722Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7847924Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7848047Z {
2023-01-11T21:41:26.7848317Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7848432Z     {
2023-01-11T21:41:26.7848609Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.7848784Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.7848911Z         {
2023-01-11T21:41:26.7849218Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.7849356Z             {
2023-01-11T21:41:26.7849523Z                 for(long i2=0; i2<3; i2+=1)
2023-01-11T21:41:26.7849649Z                 {
2023-01-11T21:41:26.7849949Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i2) + (30*i1) + (120*i0));
2023-01-11T21:41:26.7850235Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i2) + (30*i0) + (180*i1));
2023-01-11T21:41:26.7850413Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7850627Z                     tmp2.store(out_ptr0 + (8*i2) + (30*i1) + (120*i0));
2023-01-11T21:41:26.7850766Z                 }
2023-01-11T21:41:26.7850966Z                 #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7851132Z                 for(long i2=24; i2<30; i2+=1)
2023-01-11T21:41:26.7851264Z                 {
2023-01-11T21:41:26.7851469Z                     auto tmp0 = in_ptr0[i2 + (30*i1) + (120*i0)];
2023-01-11T21:41:26.7851681Z                     auto tmp1 = in_ptr1[i2 + (30*i0) + (180*i1)];
2023-01-11T21:41:26.7851864Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7852062Z                     out_ptr0[i2 + (30*i1) + (120*i0)] = tmp2;
2023-01-11T21:41:26.7852191Z                 }
2023-01-11T21:41:26.7852305Z             }
2023-01-11T21:41:26.7852429Z         }
2023-01-11T21:41:26.7852556Z     }
2023-01-11T21:41:26.7852679Z }
2023-01-11T21:41:26.7852868Z ''')
2023-01-11T21:41:26.7852880Z 
2023-01-11T21:41:26.7852888Z 
2023-01-11T21:41:26.7853066Z async_compile.wait(globals())
2023-01-11T21:41:26.7853214Z del async_compile
2023-01-11T21:41:26.7853223Z 
2023-01-11T21:41:26.7853371Z def call(args):
2023-01-11T21:41:26.7853514Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7853660Z     args.clear()
2023-01-11T21:41:26.7854114Z     buf0 = empty_strided((2, 3, 4, 5, 6), (360, 120, 30, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7854442Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7854581Z     del arg0_1
2023-01-11T21:41:26.7854723Z     del arg1_1
2023-01-11T21:41:26.7854863Z     return (buf0, )
2023-01-11T21:41:26.7854877Z 
2023-01-11T21:41:26.7854883Z 
2023-01-11T21:41:26.7855018Z if __name__ == "__main__":
2023-01-11T21:41:26.7855248Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7855500Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7855950Z     arg0_1 = rand_strided((2, 3, 4, 5, 6), (360, 120, 30, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7856393Z     arg1_1 = rand_strided((2, 3, 4, 5, 6), (90, 30, 180, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7856625Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7856639Z 
2023-01-11T21:41:26.7856774Z ok (1.772s)
2023-01-11T21:41:26.7857805Z   test_sin_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7858051Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7858551Z [2023-01-11 21:37:57,777] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 426
2023-01-11T21:41:26.7859094Z [2023-01-11 21:37:59,323] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 426
2023-01-11T21:41:26.7859104Z 
2023-01-11T21:41:26.7859382Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7859538Z import torch
2023-01-11T21:41:26.7859692Z import random
2023-01-11T21:41:26.7859916Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7860158Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7860168Z 
2023-01-11T21:41:26.7860330Z aten = torch.ops.aten
2023-01-11T21:41:26.7860571Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7860752Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7860762Z 
2023-01-11T21:41:26.7860769Z 
2023-01-11T21:41:26.7861044Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7861430Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7861666Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7861863Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7862054Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7862182Z {
2023-01-11T21:41:26.7862370Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7862491Z     {
2023-01-11T21:41:26.7862651Z         #pragma omp for 
2023-01-11T21:41:26.7862827Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.7862954Z         {
2023-01-11T21:41:26.7863305Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7863480Z             auto tmp1 = tmp0.sin();
2023-01-11T21:41:26.7863730Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.7863902Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.7864166Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7864333Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.7864500Z             auto tmp6 = tmp5.sin();
2023-01-11T21:41:26.7864682Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7864867Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7864976Z         }
2023-01-11T21:41:26.7865172Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7865343Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.7865474Z         {
2023-01-11T21:41:26.7865646Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7865824Z             auto tmp1 = std::sin(tmp0);
2023-01-11T21:41:26.7866019Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:26.7866169Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.7866360Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.7866529Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.7866708Z             auto tmp6 = std::sin(tmp5);
2023-01-11T21:41:26.7866871Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.7867029Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.7867152Z         }
2023-01-11T21:41:26.7867259Z     }
2023-01-11T21:41:26.7867386Z }
2023-01-11T21:41:26.7867578Z ''')
2023-01-11T21:41:26.7867591Z 
2023-01-11T21:41:26.7867598Z 
2023-01-11T21:41:26.7867794Z async_compile.wait(globals())
2023-01-11T21:41:26.7867947Z del async_compile
2023-01-11T21:41:26.7867959Z 
2023-01-11T21:41:26.7868096Z def call(args):
2023-01-11T21:41:26.7868349Z     arg0_1, = args
2023-01-11T21:41:26.7868473Z     args.clear()
2023-01-11T21:41:26.7868889Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7869290Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7869611Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7869754Z     del arg0_1
2023-01-11T21:41:26.7869911Z     return (buf0, buf1, )
2023-01-11T21:41:26.7869921Z 
2023-01-11T21:41:26.7869931Z 
2023-01-11T21:41:26.7870086Z if __name__ == "__main__":
2023-01-11T21:41:26.7870315Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7870539Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7871018Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7871230Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7871245Z 
2023-01-11T21:41:26.7871388Z ok (1.590s)
2023-01-11T21:41:26.7872301Z   test_sizehint_issue1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7872558Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7873081Z [2023-01-11 21:37:59,484] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 427
2023-01-11T21:41:26.7873616Z [2023-01-11 21:38:01,063] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 427
2023-01-11T21:41:26.7873632Z 
2023-01-11T21:41:26.7873831Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7873973Z import torch
2023-01-11T21:41:26.7874106Z import random
2023-01-11T21:41:26.7874326Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7874566Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7874581Z 
2023-01-11T21:41:26.7874735Z aten = torch.ops.aten
2023-01-11T21:41:26.7874992Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7875178Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7875190Z 
2023-01-11T21:41:26.7875198Z 
2023-01-11T21:41:26.7875471Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7875858Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7876064Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7876264Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7876384Z {
2023-01-11T21:41:26.7876586Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7876713Z     {
2023-01-11T21:41:26.7876896Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.7877059Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7877175Z         {
2023-01-11T21:41:26.7877341Z             for(long i1=0; i1<384; i1+=1)
2023-01-11T21:41:26.7877473Z             {
2023-01-11T21:41:26.7877642Z                 #pragma GCC ivdep
2023-01-11T21:41:26.7877814Z                 for(long i2=0; i2<196; i2+=1)
2023-01-11T21:41:26.7877946Z                 {
2023-01-11T21:41:26.7878063Z                     {
2023-01-11T21:41:26.7878201Z                         {
2023-01-11T21:41:26.7878429Z                             auto tmp0 = static_cast<long>(4*(i2 / 14));
2023-01-11T21:41:26.7878646Z                             auto tmp1 = static_cast<long>((i1 / 4) % 4);
2023-01-11T21:41:26.7878838Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7879064Z                             auto tmp3 = static_cast<long>(4*(i2 % 14));
2023-01-11T21:41:26.7879288Z                             auto tmp4 = static_cast<long>(i1 % 4);
2023-01-11T21:41:26.7879576Z                             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.7879801Z                             auto tmp6 = in_ptr0[tmp5 + (56*tmp2) + (3136*(i1 / 16)) + (75264*i0)];
2023-01-11T21:41:26.7880004Z                             out_ptr0[i2 + (196*i1) + (75264*i0)] = tmp6;
2023-01-11T21:41:26.7880141Z                         }
2023-01-11T21:41:26.7880273Z                     }
2023-01-11T21:41:26.7880447Z                 }
2023-01-11T21:41:26.7880580Z             }
2023-01-11T21:41:26.7880707Z         }
2023-01-11T21:41:26.7880813Z     }
2023-01-11T21:41:26.7880931Z }
2023-01-11T21:41:26.7881124Z ''')
2023-01-11T21:41:26.7881138Z 
2023-01-11T21:41:26.7881145Z 
2023-01-11T21:41:26.7881318Z async_compile.wait(globals())
2023-01-11T21:41:26.7881457Z del async_compile
2023-01-11T21:41:26.7881469Z 
2023-01-11T21:41:26.7881613Z def call(args):
2023-01-11T21:41:26.7881825Z     arg0_1, = args
2023-01-11T21:41:26.7881962Z     args.clear()
2023-01-11T21:41:26.7882403Z     buf0 = empty_strided((2, 384, 196), (75264, 196, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7882717Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7882860Z     del arg0_1
2023-01-11T21:41:26.7883007Z     return (buf0, )
2023-01-11T21:41:26.7883017Z 
2023-01-11T21:41:26.7883027Z 
2023-01-11T21:41:26.7883174Z if __name__ == "__main__":
2023-01-11T21:41:26.7883399Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7883638Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7884065Z     arg0_1 = rand_strided((2, 24, 56, 56), (75264, 3136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7884277Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7884286Z 
2023-01-11T21:41:26.7884421Z ok (1.743s)
2023-01-11T21:41:26.7885322Z   test_slice1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7885579Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7886098Z [2023-01-11 21:38:01,112] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 428
2023-01-11T21:41:26.7886615Z [2023-01-11 21:38:02,621] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 428
2023-01-11T21:41:26.7886625Z 
2023-01-11T21:41:26.7886810Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7886953Z import torch
2023-01-11T21:41:26.7887077Z import random
2023-01-11T21:41:26.7887309Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7887555Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7887571Z 
2023-01-11T21:41:26.7887725Z aten = torch.ops.aten
2023-01-11T21:41:26.7887999Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7888182Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7888192Z 
2023-01-11T21:41:26.7888201Z 
2023-01-11T21:41:26.7888470Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7888856Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7889210Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7889415Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7889607Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7889733Z {
2023-01-11T21:41:26.7889931Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7890063Z     {
2023-01-11T21:41:26.7890243Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.7890395Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7890527Z         {
2023-01-11T21:41:26.7890696Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.7891006Z             {
2023-01-11T21:41:26.7891137Z                 {
2023-01-11T21:41:26.7891284Z                     {
2023-01-11T21:41:26.7891490Z                         auto tmp0 = in_ptr0[(2*i1) + (40*i0)];
2023-01-11T21:41:26.7891679Z                         auto tmp1 = in_ptr0[20 + (2*i1) + (40*i0)];
2023-01-11T21:41:26.7891874Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7892076Z                         auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:26.7892265Z                         auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.7892449Z                         auto tmp5 = tmp1 + tmp3;
2023-01-11T21:41:26.7892627Z                         auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.7892814Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.7893066Z                         out_ptr1[i1 + (10*i0)] = tmp6;
2023-01-11T21:41:26.7893211Z                     }
2023-01-11T21:41:26.7893343Z                 }
2023-01-11T21:41:26.7893483Z             }
2023-01-11T21:41:26.7893614Z         }
2023-01-11T21:41:26.7893731Z     }
2023-01-11T21:41:26.7893855Z }
2023-01-11T21:41:26.7894026Z ''')
2023-01-11T21:41:26.7894039Z 
2023-01-11T21:41:26.7894047Z 
2023-01-11T21:41:26.7894228Z async_compile.wait(globals())
2023-01-11T21:41:26.7894382Z del async_compile
2023-01-11T21:41:26.7894391Z 
2023-01-11T21:41:26.7894532Z def call(args):
2023-01-11T21:41:26.7894662Z     arg0_1, = args
2023-01-11T21:41:26.7894808Z     args.clear()
2023-01-11T21:41:26.7895208Z     buf0 = empty_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7895588Z     buf1 = empty_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7895911Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7896049Z     del arg0_1
2023-01-11T21:41:26.7896209Z     return (buf0, buf1, )
2023-01-11T21:41:26.7896223Z 
2023-01-11T21:41:26.7896230Z 
2023-01-11T21:41:26.7896382Z if __name__ == "__main__":
2023-01-11T21:41:26.7896604Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7896850Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7897268Z     arg0_1 = rand_strided((2, 20, 2), (40, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7897469Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7897498Z 
2023-01-11T21:41:26.7897615Z ok (1.555s)
2023-01-11T21:41:26.7898504Z   test_slice2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7898754Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7899274Z [2023-01-11 21:38:02,665] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 429
2023-01-11T21:41:26.7899812Z [2023-01-11 21:38:04,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 429
2023-01-11T21:41:26.7899821Z 
2023-01-11T21:41:26.7900006Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7900150Z import torch
2023-01-11T21:41:26.7900304Z import random
2023-01-11T21:41:26.7900534Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7900746Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7900755Z 
2023-01-11T21:41:26.7900907Z aten = torch.ops.aten
2023-01-11T21:41:26.7901170Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7901357Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7901367Z 
2023-01-11T21:41:26.7901373Z 
2023-01-11T21:41:26.7901650Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7902042Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7902423Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7902630Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7902800Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7902922Z {
2023-01-11T21:41:26.7903192Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7903325Z     {
2023-01-11T21:41:26.7903475Z         #pragma omp for 
2023-01-11T21:41:26.7903649Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.7903760Z         {
2023-01-11T21:41:26.7903888Z             {
2023-01-11T21:41:26.7904017Z                 {
2023-01-11T21:41:26.7904204Z                     auto tmp0 = in_ptr0[1 + (4*i0)];
2023-01-11T21:41:26.7904396Z                     auto tmp1 = in_ptr0[42 + (4*i0)];
2023-01-11T21:41:26.7904652Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7904869Z                     auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:26.7905037Z                     auto tmp4 = tmp0 + tmp3;
2023-01-11T21:41:26.7905233Z                     auto tmp5 = static_cast<float>(2);
2023-01-11T21:41:26.7905413Z                     auto tmp6 = tmp1 + tmp5;
2023-01-11T21:41:26.7905591Z                     auto tmp7 = tmp4 + tmp6;
2023-01-11T21:41:26.7905759Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7905934Z                     out_ptr1[i0] = tmp7;
2023-01-11T21:41:26.7906054Z                 }
2023-01-11T21:41:26.7906166Z             }
2023-01-11T21:41:26.7906288Z         }
2023-01-11T21:41:26.7906412Z     }
2023-01-11T21:41:26.7906531Z }
2023-01-11T21:41:26.7906716Z ''')
2023-01-11T21:41:26.7906726Z 
2023-01-11T21:41:26.7906733Z 
2023-01-11T21:41:26.7906907Z async_compile.wait(globals())
2023-01-11T21:41:26.7907052Z del async_compile
2023-01-11T21:41:26.7907066Z 
2023-01-11T21:41:26.7907211Z def call(args):
2023-01-11T21:41:26.7907334Z     arg0_1, = args
2023-01-11T21:41:26.7907474Z     args.clear()
2023-01-11T21:41:26.7907883Z     buf0 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7908288Z     buf1 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7908609Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7908750Z     del arg0_1
2023-01-11T21:41:26.7908909Z     return (buf0, buf1, )
2023-01-11T21:41:26.7908921Z 
2023-01-11T21:41:26.7908928Z 
2023-01-11T21:41:26.7909063Z if __name__ == "__main__":
2023-01-11T21:41:26.7909301Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7909535Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7909947Z     arg0_1 = rand_strided((2, 20, 2), (40, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7910173Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7910193Z 
2023-01-11T21:41:26.7910332Z ok (1.552s)
2023-01-11T21:41:26.7911237Z   test_slice_mutation1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7911487Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7912027Z [2023-01-11 21:38:04,224] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 430
2023-01-11T21:41:26.7912551Z [2023-01-11 21:38:05,883] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 430
2023-01-11T21:41:26.7912563Z 
2023-01-11T21:41:26.7912731Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7912876Z import torch
2023-01-11T21:41:26.7913027Z import random
2023-01-11T21:41:26.7913257Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7913604Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7913615Z 
2023-01-11T21:41:26.7913771Z aten = torch.ops.aten
2023-01-11T21:41:26.7914036Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7914202Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7914213Z 
2023-01-11T21:41:26.7914241Z 
2023-01-11T21:41:26.7914502Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7914899Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7915129Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7915315Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7915503Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.7915693Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:26.7915895Z {
2023-01-11T21:41:26.7916078Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7916200Z     {
2023-01-11T21:41:26.7916364Z         #pragma omp for 
2023-01-11T21:41:26.7916526Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7916655Z         {
2023-01-11T21:41:26.7916934Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:26.7917200Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7917352Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7917532Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7917715Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.7917847Z         }
2023-01-11T21:41:26.7918042Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7918211Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7918330Z         {
2023-01-11T21:41:26.7918509Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:26.7918705Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.7918878Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7919044Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7919198Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.7919329Z         }
2023-01-11T21:41:26.7919481Z         #pragma omp for 
2023-01-11T21:41:26.7919630Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7919760Z         {
2023-01-11T21:41:26.7919896Z             {
2023-01-11T21:41:26.7920030Z                 {
2023-01-11T21:41:26.7920245Z                     auto tmp0 = static_cast<float>(3.0);
2023-01-11T21:41:26.7920422Z                     out_ptr0[3 + (8*i0)] = tmp0;
2023-01-11T21:41:26.7920555Z                 }
2023-01-11T21:41:26.7920669Z             }
2023-01-11T21:41:26.7920792Z         }
2023-01-11T21:41:26.7920947Z         #pragma omp for 
2023-01-11T21:41:26.7921117Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7921240Z         {
2023-01-11T21:41:26.7921525Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7921691Z             tmp0.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.7921822Z         }
2023-01-11T21:41:26.7922010Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7922180Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7922312Z         {
2023-01-11T21:41:26.7922485Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.7922648Z             out_ptr2[i0] = tmp0;
2023-01-11T21:41:26.7922751Z         }
2023-01-11T21:41:26.7922901Z         #pragma omp for 
2023-01-11T21:41:26.7923067Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.7923192Z         {
2023-01-11T21:41:26.7923462Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(4.0));
2023-01-11T21:41:26.7923657Z             tmp0.store(out_ptr0 + 32 + (8*i0));
2023-01-11T21:41:26.7923786Z         }
2023-01-11T21:41:26.7923952Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7924114Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.7924250Z         {
2023-01-11T21:41:26.7924449Z             auto tmp0 = static_cast<float>(4.0);
2023-01-11T21:41:26.7924725Z             out_ptr0[32 + i0] = tmp0;
2023-01-11T21:41:26.7924848Z         }
2023-01-11T21:41:26.7925011Z         #pragma omp for 
2023-01-11T21:41:26.7925159Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7925284Z         {
2023-01-11T21:41:26.7925546Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7925817Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7925982Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7926172Z             tmp2.store(out_ptr3 + 8*i0);
2023-01-11T21:41:26.7926292Z         }
2023-01-11T21:41:26.7926459Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7926631Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.7926759Z         {
2023-01-11T21:41:26.7926932Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.7927199Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.7927379Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7927549Z             out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.7927664Z         }
2023-01-11T21:41:26.7927791Z     }
2023-01-11T21:41:26.7927915Z }
2023-01-11T21:41:26.7928098Z ''')
2023-01-11T21:41:26.7928110Z 
2023-01-11T21:41:26.7928119Z 
2023-01-11T21:41:26.7928302Z async_compile.wait(globals())
2023-01-11T21:41:26.7928446Z del async_compile
2023-01-11T21:41:26.7928460Z 
2023-01-11T21:41:26.7928604Z def call(args):
2023-01-11T21:41:26.7928732Z     arg0_1, = args
2023-01-11T21:41:26.7928871Z     args.clear()
2023-01-11T21:41:26.7929418Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7929800Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7930191Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7930581Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7930944Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:26.7931132Z     return (buf0, buf1, buf3, buf5, )
2023-01-11T21:41:26.7931145Z 
2023-01-11T21:41:26.7931152Z 
2023-01-11T21:41:26.7931284Z if __name__ == "__main__":
2023-01-11T21:41:26.7931507Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7931755Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7932146Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7932360Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7932369Z 
2023-01-11T21:41:26.7932504Z ok (1.710s)
2023-01-11T21:41:26.7933173Z   test_slice_mutation2_cpu (__main__.CpuTests) ... [2023-01-11 21:38:05,920] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 431
2023-01-11T21:41:26.7933716Z [2023-01-11 21:38:07,463] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 431
2023-01-11T21:41:26.7933731Z 
2023-01-11T21:41:26.7933916Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7934035Z import torch
2023-01-11T21:41:26.7934178Z import random
2023-01-11T21:41:26.7934415Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7934660Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7934669Z 
2023-01-11T21:41:26.7934829Z aten = torch.ops.aten
2023-01-11T21:41:26.7935088Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7935270Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7935280Z 
2023-01-11T21:41:26.7935287Z 
2023-01-11T21:41:26.7935564Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7935933Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7936179Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7936381Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7936571Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7936896Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.7937023Z {
2023-01-11T21:41:26.7937226Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7937337Z     {
2023-01-11T21:41:26.7937493Z         #pragma omp for 
2023-01-11T21:41:26.7937657Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7937790Z         {
2023-01-11T21:41:26.7938077Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 20 + (8*i0));
2023-01-11T21:41:26.7938338Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.7938512Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7938670Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7938807Z         }
2023-01-11T21:41:26.7939001Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7939261Z         for(long i0=16; i0<20; i0+=1)
2023-01-11T21:41:26.7939403Z         {
2023-01-11T21:41:26.7939585Z             auto tmp0 = in_ptr0[20 + i0];
2023-01-11T21:41:26.7939797Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.7939949Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7940113Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.7940245Z         }
2023-01-11T21:41:26.7940405Z         #pragma omp for 
2023-01-11T21:41:26.7940568Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.7940703Z         {
2023-01-11T21:41:26.7940960Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7941137Z             tmp0.store(out_ptr1 + 20 + (8*i0));
2023-01-11T21:41:26.7941264Z         }
2023-01-11T21:41:26.7941461Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7941628Z         for(long i0=16; i0<20; i0+=1)
2023-01-11T21:41:26.7941754Z         {
2023-01-11T21:41:26.7941924Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.7942087Z             out_ptr1[20 + i0] = tmp0;
2023-01-11T21:41:26.7942200Z         }
2023-01-11T21:41:26.7942352Z         #pragma omp for 
2023-01-11T21:41:26.7942521Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.7942650Z         {
2023-01-11T21:41:26.7942920Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 1 + (8*i0));
2023-01-11T21:41:26.7943278Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.7943463Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7943625Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.7943756Z         }
2023-01-11T21:41:26.7943952Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7944115Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:26.7944246Z         {
2023-01-11T21:41:26.7944423Z             auto tmp0 = out_ptr1[1 + i0];
2023-01-11T21:41:26.7944620Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.7944782Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7944943Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.7945071Z         }
2023-01-11T21:41:26.7945237Z         #pragma omp for 
2023-01-11T21:41:26.7945398Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.7945529Z         {
2023-01-11T21:41:26.7945791Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i0);
2023-01-11T21:41:26.7945964Z             tmp0.store(out_ptr1 + 2 + (8*i0));
2023-01-11T21:41:26.7946092Z         }
2023-01-11T21:41:26.7946275Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7946443Z         for(long i0=8; i0<9; i0+=1)
2023-01-11T21:41:26.7946573Z         {
2023-01-11T21:41:26.7946749Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:26.7946909Z             out_ptr1[2 + i0] = tmp0;
2023-01-11T21:41:26.7947024Z         }
2023-01-11T21:41:26.7947148Z     }
2023-01-11T21:41:26.7947268Z }
2023-01-11T21:41:26.7947469Z ''')
2023-01-11T21:41:26.7947481Z 
2023-01-11T21:41:26.7947488Z 
2023-01-11T21:41:26.7947674Z async_compile.wait(globals())
2023-01-11T21:41:26.7947830Z del async_compile
2023-01-11T21:41:26.7947840Z 
2023-01-11T21:41:26.7948090Z def call(args):
2023-01-11T21:41:26.7948218Z     arg0_1, = args
2023-01-11T21:41:26.7948358Z     args.clear()
2023-01-11T21:41:26.7948764Z     buf0 = empty_strided((1, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7949153Z     buf2 = empty_strided((1, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7949510Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.7949649Z     del arg0_1
2023-01-11T21:41:26.7949778Z     return ()
2023-01-11T21:41:26.7949787Z 
2023-01-11T21:41:26.7949795Z 
2023-01-11T21:41:26.7949943Z if __name__ == "__main__":
2023-01-11T21:41:26.7950151Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7950396Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7950881Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7951108Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.7951126Z 
2023-01-11T21:41:26.7951271Z ok (1.579s)
2023-01-11T21:41:26.7952187Z   test_slice_scatter2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7952429Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7952958Z [2023-01-11 21:38:07,492] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 432
2023-01-11T21:41:26.7953487Z [2023-01-11 21:38:09,028] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 432
2023-01-11T21:41:26.7953497Z 
2023-01-11T21:41:26.7953663Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7953815Z import torch
2023-01-11T21:41:26.7953957Z import random
2023-01-11T21:41:26.7954195Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7954431Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7954442Z 
2023-01-11T21:41:26.7954599Z aten = torch.ops.aten
2023-01-11T21:41:26.7954857Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7955023Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7955055Z 
2023-01-11T21:41:26.7955062Z 
2023-01-11T21:41:26.7955328Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7955708Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7955950Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7956151Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.7956283Z {
2023-01-11T21:41:26.7956483Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7956602Z     {
2023-01-11T21:41:26.7956746Z         #pragma omp for 
2023-01-11T21:41:26.7956914Z         for(long i0=0; i0<75648; i0+=1)
2023-01-11T21:41:26.7957039Z         {
2023-01-11T21:41:26.7957314Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.7957485Z             tmp0.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.7957618Z         }
2023-01-11T21:41:26.7957811Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.7957976Z         for(long i0=605184; i0<605184; i0+=1)
2023-01-11T21:41:26.7958104Z         {
2023-01-11T21:41:26.7958274Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.7958437Z             out_ptr0[i0] = tmp0;
2023-01-11T21:41:26.7958563Z         }
2023-01-11T21:41:26.7958692Z     }
2023-01-11T21:41:26.7958815Z }
2023-01-11T21:41:26.7958972Z ''')
2023-01-11T21:41:26.7958982Z 
2023-01-11T21:41:26.7958988Z 
2023-01-11T21:41:26.7959172Z async_compile.wait(globals())
2023-01-11T21:41:26.7959318Z del async_compile
2023-01-11T21:41:26.7959329Z 
2023-01-11T21:41:26.7959588Z def call(args):
2023-01-11T21:41:26.7959743Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7959892Z     args.clear()
2023-01-11T21:41:26.7960333Z     buf0 = empty_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7960577Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.7960714Z     del arg1_1
2023-01-11T21:41:26.7960856Z     return (buf0, )
2023-01-11T21:41:26.7960868Z 
2023-01-11T21:41:26.7960876Z 
2023-01-11T21:41:26.7961026Z if __name__ == "__main__":
2023-01-11T21:41:26.7961252Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7961495Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7961926Z     arg0_1 = rand_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7962424Z     arg1_1 = rand_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7962635Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7962667Z 
2023-01-11T21:41:26.7962789Z ok (1.582s)
2023-01-11T21:41:26.7963655Z   test_slice_scatter_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7963854Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7964285Z [2023-01-11 21:38:09,081] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 433
2023-01-11T21:41:26.7964717Z [2023-01-11 21:38:10,600] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 433
2023-01-11T21:41:26.7964726Z 
2023-01-11T21:41:26.7964868Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7964979Z import torch
2023-01-11T21:41:26.7965086Z import random
2023-01-11T21:41:26.7965247Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7965427Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7965435Z 
2023-01-11T21:41:26.7965551Z aten = torch.ops.aten
2023-01-11T21:41:26.7965750Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7965888Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7965896Z 
2023-01-11T21:41:26.7965903Z 
2023-01-11T21:41:26.7966113Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7966412Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7966590Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7966749Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7966884Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7967030Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.7967125Z {
2023-01-11T21:41:26.7967272Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7967365Z     {
2023-01-11T21:41:26.7967482Z         #pragma omp for 
2023-01-11T21:41:26.7967591Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.7967685Z         {
2023-01-11T21:41:26.7967805Z             #pragma GCC ivdep
2023-01-11T21:41:26.7967933Z             for(long i1=0; i1<100; i1+=1)
2023-01-11T21:41:26.7968029Z             {
2023-01-11T21:41:26.7968128Z                 {
2023-01-11T21:41:26.7968228Z                     {
2023-01-11T21:41:26.7968373Z                         auto tmp8 = in_ptr1[i1 + (100*i0)];
2023-01-11T21:41:26.7968529Z                         auto tmp0 = static_cast<int>(i1);
2023-01-11T21:41:26.7968684Z                         auto tmp1 = static_cast<int>(10);
2023-01-11T21:41:26.7968828Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.7969128Z                         auto tmp3 = static_cast<int>(90);
2023-01-11T21:41:26.7969388Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.7969529Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:41:26.7969643Z                         float tmp6 = 0.0;
2023-01-11T21:41:26.7969756Z                         if(tmp5)
2023-01-11T21:41:26.7969858Z                         {
2023-01-11T21:41:26.7970132Z                             auto tmp7 = in_ptr0[(-10) + i1 + (80*i0)];
2023-01-11T21:41:26.7970350Z                             tmp6 = tmp7;
2023-01-11T21:41:26.7970441Z                         }
2023-01-11T21:41:26.7970573Z                         auto tmp9 = tmp5 ? tmp6 : tmp8;
2023-01-11T21:41:26.7970722Z                         auto tmp10 = static_cast<int>(i1 % 2);
2023-01-11T21:41:26.7970845Z                         auto tmp11 = static_cast<int>(0);
2023-01-11T21:41:26.7971013Z                         auto tmp12 = tmp10 == tmp11;
2023-01-11T21:41:26.7971139Z                         auto tmp13 = tmp5 & tmp12;
2023-01-11T21:41:26.7971256Z                         float tmp14 = 0.0;
2023-01-11T21:41:26.7971356Z                         if(tmp13)
2023-01-11T21:41:26.7971451Z                         {
2023-01-11T21:41:26.7971695Z                             auto tmp15 = in_ptr0[(-5) + (80*i0) + (i1 / 2)];
2023-01-11T21:41:26.7971792Z                             tmp14 = tmp15;
2023-01-11T21:41:26.7971881Z                         }
2023-01-11T21:41:26.7972016Z                         auto tmp16 = tmp13 ? tmp14 : tmp8;
2023-01-11T21:41:26.7972143Z                         out_ptr0[i1 + (100*i0)] = tmp9;
2023-01-11T21:41:26.7972269Z                         out_ptr1[i1 + (100*i0)] = tmp16;
2023-01-11T21:41:26.7972356Z                     }
2023-01-11T21:41:26.7972442Z                 }
2023-01-11T21:41:26.7972513Z             }
2023-01-11T21:41:26.7972596Z         }
2023-01-11T21:41:26.7972677Z     }
2023-01-11T21:41:26.7972759Z }
2023-01-11T21:41:26.7972865Z ''')
2023-01-11T21:41:26.7972872Z 
2023-01-11T21:41:26.7972877Z 
2023-01-11T21:41:26.7972997Z async_compile.wait(globals())
2023-01-11T21:41:26.7973093Z del async_compile
2023-01-11T21:41:26.7973099Z 
2023-01-11T21:41:26.7973178Z def call(args):
2023-01-11T21:41:26.7973276Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.7973369Z     args.clear()
2023-01-11T21:41:26.7973658Z     buf0 = empty_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7973943Z     buf1 = empty_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7974190Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.7974280Z     del arg0_1
2023-01-11T21:41:26.7974356Z     del arg1_1
2023-01-11T21:41:26.7974458Z     return (buf0, buf1, )
2023-01-11T21:41:26.7974465Z 
2023-01-11T21:41:26.7974470Z 
2023-01-11T21:41:26.7974573Z if __name__ == "__main__":
2023-01-11T21:41:26.7974721Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.7974881Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.7975174Z     arg0_1 = rand_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7975457Z     arg1_1 = rand_strided((4, 8, 80), (640, 80, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.7975608Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.7975615Z 
2023-01-11T21:41:26.7975703Z ok (1.556s)
2023-01-11T21:41:26.7976298Z   test_softmax_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.7976465Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.7976820Z [2023-01-11 21:38:10,645] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 434
2023-01-11T21:41:26.7976864Z 
2023-01-11T21:41:26.7976990Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.7977083Z import torch
2023-01-11T21:41:26.7977175Z import random
2023-01-11T21:41:26.7977326Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.7977484Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.7977491Z 
2023-01-11T21:41:26.7977591Z aten = torch.ops.aten
2023-01-11T21:41:26.7977753Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.7977873Z async_compile = AsyncCompile()
2023-01-11T21:41:26.7977880Z 
2023-01-11T21:41:26.7977885Z 
2023-01-11T21:41:26.7978067Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.7978362Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.7978519Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.7978652Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.7978787Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:41:26.7978922Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.7979045Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.7979173Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.7979301Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.7979424Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.7979547Z                        float* __restrict__ out_ptr6,
2023-01-11T21:41:26.7979670Z                        float* __restrict__ out_ptr7,
2023-01-11T21:41:26.7979793Z                        float* __restrict__ out_ptr8)
2023-01-11T21:41:26.7979859Z {
2023-01-11T21:41:26.7979973Z     auto out_ptr3 = in_out_ptr0;
2023-01-11T21:41:26.7980088Z     auto out_ptr4 = in_out_ptr1;
2023-01-11T21:41:26.7980205Z     auto out_ptr5 = in_out_ptr2;
2023-01-11T21:41:26.7980338Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.7980418Z     {
2023-01-11T21:41:26.7980519Z         #pragma omp for 
2023-01-11T21:41:26.7980616Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7980698Z         {
2023-01-11T21:41:26.7980781Z             {
2023-01-11T21:41:26.7981308Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:26.7981617Z                 float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7981775Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.7982058Z                 float tmp4 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7982223Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:26.7982326Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.7982412Z                 {
2023-01-11T21:41:26.7982604Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7982791Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7982912Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7983066Z                     tmp3_vec = at::vec::maximum(tmp3_vec, tmp2);
2023-01-11T21:41:26.7983287Z                     tmp4_vec = at::vec::maximum(tmp4_vec, tmp1);
2023-01-11T21:41:26.7983374Z                 }
2023-01-11T21:41:26.7983640Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp3_vec);
2023-01-11T21:41:26.7983913Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp4_vec);
2023-01-11T21:41:26.7984106Z                 #pragma omp simd simdlen(4)  reduction(max:tmp3) reduction(max:tmp4)
2023-01-11T21:41:26.7984260Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.7984348Z                 {
2023-01-11T21:41:26.7984478Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.7984605Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.7984726Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7984860Z                     tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:41:26.7984977Z                     tmp4 = std::max(tmp4, tmp1);
2023-01-11T21:41:26.7985061Z                 }
2023-01-11T21:41:26.7985171Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.7985282Z                 out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.7985367Z             }
2023-01-11T21:41:26.7985449Z         }
2023-01-11T21:41:26.7985536Z         #pragma omp for 
2023-01-11T21:41:26.7985676Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7985762Z         {
2023-01-11T21:41:26.7985845Z             {
2023-01-11T21:41:26.7985933Z                 {
2023-01-11T21:41:26.7986229Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.7986347Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.7986421Z                     {
2023-01-11T21:41:26.7986512Z                         {
2023-01-11T21:41:26.7986645Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:26.7986783Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:26.7986872Z                         }
2023-01-11T21:41:26.7986961Z                     }
2023-01-11T21:41:26.7987072Z                     out_ptr2[i0] = tmp1;
2023-01-11T21:41:26.7987144Z                 }
2023-01-11T21:41:26.7987229Z             }
2023-01-11T21:41:26.7987315Z         }
2023-01-11T21:41:26.7987415Z         #pragma omp for 
2023-01-11T21:41:26.7987524Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7987607Z         {
2023-01-11T21:41:26.7987694Z             {
2023-01-11T21:41:26.7987931Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.7988038Z                 float tmp12 = 0;
2023-01-11T21:41:26.7988199Z                 auto tmp12_vec = at::vec::Vectorized<float>(tmp12);
2023-01-11T21:41:26.7988312Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.7988397Z                 {
2023-01-11T21:41:26.7988584Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7988765Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7988931Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.7989106Z                     auto tmp6 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i1);
2023-01-11T21:41:26.7989261Z                     auto tmp9 = at::vec::Vectorized<float>(out_ptr1[i0]);
2023-01-11T21:41:26.7989379Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7989564Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:26.7989689Z                     auto tmp5 = tmp4.exp();
2023-01-11T21:41:26.7989869Z                     auto tmp7 = tmp0 - tmp6;
2023-01-11T21:41:26.7989987Z                     auto tmp8 = tmp7.exp();
2023-01-11T21:41:26.7990173Z                     auto tmp10 = tmp1 - tmp9;
2023-01-11T21:41:26.7990279Z                     auto tmp11 = tmp10.exp();
2023-01-11T21:41:26.7990414Z                     tmp5.store(out_ptr3 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7990550Z                     tmp8.store(out_ptr4 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7990688Z                     tmp11.store(out_ptr5 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7990795Z                     tmp12_vec += tmp5;
2023-01-11T21:41:26.7990883Z                 }
2023-01-11T21:41:26.7991144Z                 tmp12 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp12_vec);
2023-01-11T21:41:26.7991337Z                 #pragma omp simd simdlen(4)  reduction(+:tmp12)
2023-01-11T21:41:26.7991438Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.7991522Z                 {
2023-01-11T21:41:26.7991650Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.7991774Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.7991893Z                     auto tmp3 = out_ptr0[i0];
2023-01-11T21:41:26.7992014Z                     auto tmp6 = out_ptr2[i1];
2023-01-11T21:41:26.7992131Z                     auto tmp9 = out_ptr1[i0];
2023-01-11T21:41:26.7992237Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.7992423Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:41:26.7992554Z                     auto tmp5 = std::exp(tmp4);
2023-01-11T21:41:26.7992771Z                     auto tmp7 = tmp0 - tmp6;
2023-01-11T21:41:26.7992904Z                     auto tmp8 = std::exp(tmp7);
2023-01-11T21:41:26.7993088Z                     auto tmp10 = tmp1 - tmp9;
2023-01-11T21:41:26.7993222Z                     auto tmp11 = std::exp(tmp10);
2023-01-11T21:41:26.7993327Z                     out_ptr3[i1 + (8*i0)] = tmp5;
2023-01-11T21:41:26.7993445Z                     out_ptr4[i1 + (8*i0)] = tmp8;
2023-01-11T21:41:26.7993565Z                     out_ptr5[i1 + (8*i0)] = tmp11;
2023-01-11T21:41:26.7993667Z                     tmp12 += tmp5;
2023-01-11T21:41:26.7993753Z                 }
2023-01-11T21:41:26.7993859Z                 out_ptr6[i0] = tmp12;
2023-01-11T21:41:26.7993944Z             }
2023-01-11T21:41:26.7994012Z         }
2023-01-11T21:41:26.7994113Z         #pragma omp for 
2023-01-11T21:41:26.7994219Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7994303Z         {
2023-01-11T21:41:26.7994414Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.7994498Z             {
2023-01-11T21:41:26.7994686Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr3 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7994841Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr6[i0]);
2023-01-11T21:41:26.7994955Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.7995092Z                 tmp2.store(in_out_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7995176Z             }
2023-01-11T21:41:26.7995296Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7995404Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.7995488Z             {
2023-01-11T21:41:26.7995601Z                 auto tmp0 = out_ptr3[i1 + (8*i0)];
2023-01-11T21:41:26.7995716Z                 auto tmp1 = out_ptr6[i0];
2023-01-11T21:41:26.7995831Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.7995954Z                 in_out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:26.7996036Z             }
2023-01-11T21:41:26.7996120Z         }
2023-01-11T21:41:26.7996221Z         #pragma omp for 
2023-01-11T21:41:26.7996318Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7996399Z         {
2023-01-11T21:41:26.7996482Z             {
2023-01-11T21:41:26.7996570Z                 {
2023-01-11T21:41:26.7996675Z                     float tmp1 = 0;
2023-01-11T21:41:26.7996794Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.7996884Z                     {
2023-01-11T21:41:26.7996962Z                         {
2023-01-11T21:41:26.7997096Z                             auto tmp0 = out_ptr4[i0 + (8*i1)];
2023-01-11T21:41:26.7997203Z                             tmp1 += tmp0;
2023-01-11T21:41:26.7997293Z                         }
2023-01-11T21:41:26.7997381Z                     }
2023-01-11T21:41:26.7997491Z                     out_ptr7[i0] = tmp1;
2023-01-11T21:41:26.7997578Z                 }
2023-01-11T21:41:26.7997649Z             }
2023-01-11T21:41:26.7997731Z         }
2023-01-11T21:41:26.7997834Z         #pragma omp for 
2023-01-11T21:41:26.7997942Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.7998025Z         {
2023-01-11T21:41:26.7998134Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.7998241Z             {
2023-01-11T21:41:26.7998426Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr4 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7998603Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(out_ptr7 + 8*i1);
2023-01-11T21:41:26.7998719Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.7998854Z                 tmp2.store(in_out_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.7998939Z             }
2023-01-11T21:41:26.7999059Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.7999168Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.7999238Z             {
2023-01-11T21:41:26.7999361Z                 auto tmp0 = out_ptr4[i1 + (8*i0)];
2023-01-11T21:41:26.7999476Z                 auto tmp1 = out_ptr7[i1];
2023-01-11T21:41:26.7999588Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.7999740Z                 in_out_ptr1[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:26.7999828Z             }
2023-01-11T21:41:26.7999912Z         }
2023-01-11T21:41:26.8000002Z         #pragma omp for 
2023-01-11T21:41:26.8000108Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8000192Z         {
2023-01-11T21:41:26.8000275Z             {
2023-01-11T21:41:26.8000521Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8000624Z                 float tmp1 = 0;
2023-01-11T21:41:26.8000784Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8000886Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8000972Z                 {
2023-01-11T21:41:26.8001158Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr5 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8001268Z                     tmp1_vec += tmp0;
2023-01-11T21:41:26.8001355Z                 }
2023-01-11T21:41:26.8001615Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.8001775Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.8001889Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8001961Z                 {
2023-01-11T21:41:26.8002090Z                     auto tmp0 = out_ptr5[i1 + (8*i0)];
2023-01-11T21:41:26.8002192Z                     tmp1 += tmp0;
2023-01-11T21:41:26.8002277Z                 }
2023-01-11T21:41:26.8002385Z                 out_ptr8[i0] = tmp1;
2023-01-11T21:41:26.8002469Z             }
2023-01-11T21:41:26.8002550Z         }
2023-01-11T21:41:26.8002637Z         #pragma omp for 
2023-01-11T21:41:26.8002743Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8002827Z         {
2023-01-11T21:41:26.8002934Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8003020Z             {
2023-01-11T21:41:26.8003207Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr5 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8003374Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr8[i0]);
2023-01-11T21:41:26.8003478Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8003617Z                 tmp2.store(in_out_ptr2 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8003699Z             }
2023-01-11T21:41:26.8003821Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.8003928Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8004011Z             {
2023-01-11T21:41:26.8004136Z                 auto tmp0 = out_ptr5[i1 + (8*i0)];
2023-01-11T21:41:26.8004240Z                 auto tmp1 = out_ptr8[i0];
2023-01-11T21:41:26.8004354Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8004474Z                 in_out_ptr2[i1 + (8*i0)] = tmp2;
2023-01-11T21:41:26.8004558Z             }
2023-01-11T21:41:26.8004642Z         }
2023-01-11T21:41:26.8004723Z     }
2023-01-11T21:41:26.8004801Z }
2023-01-11T21:41:26.8004901Z ''')
2023-01-11T21:41:26.8004909Z 
2023-01-11T21:41:26.8004914Z 
2023-01-11T21:41:26.8005030Z async_compile.wait(globals())
2023-01-11T21:41:26.8005163Z del async_compile
2023-01-11T21:41:26.8005170Z 
2023-01-11T21:41:26.8005260Z def call(args):
2023-01-11T21:41:26.8005358Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8005450Z     args.clear()
2023-01-11T21:41:26.8005720Z     buf0 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8005974Z     buf8 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8006233Z     buf4 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8006494Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8006751Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8007006Z     buf9 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8007299Z     buf2 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8007417Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:26.8007676Z     buf6 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8007774Z     buf7 = buf5; del buf5  # reuse
2023-01-11T21:41:26.8008038Z     buf10 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8008150Z     buf11 = buf9; del buf9  # reuse
2023-01-11T21:41:26.8008611Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf10.data_ptr()))
2023-01-11T21:41:26.8008704Z     del arg0_1
2023-01-11T21:41:26.8008791Z     del arg1_1
2023-01-11T21:41:26.8008903Z     return (buf3, buf7, buf11, )
2023-01-11T21:41:26.8008910Z 
2023-01-11T21:41:26.8008915Z 
2023-01-11T21:41:26.8009139Z if __name__ == "__main__":
2023-01-11T21:41:26.8009280Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8009445Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8009712Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8009980Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8010134Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8010481Z [2023-01-11 21:38:12,283] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 434
2023-01-11T21:41:26.8010487Z 
2023-01-11T21:41:26.8010555Z ok (1.683s)
2023-01-11T21:41:26.8011034Z   test_softmax_one_kernel_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8011166Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8011427Z [2023-01-11 21:38:12,308] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 435
2023-01-11T21:41:26.8011681Z [2023-01-11 21:38:13,834] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 435
2023-01-11T21:41:26.8011698Z 
2023-01-11T21:41:26.8011777Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8011845Z import torch
2023-01-11T21:41:26.8011913Z import random
2023-01-11T21:41:26.8012023Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8012143Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8012148Z 
2023-01-11T21:41:26.8012223Z aten = torch.ops.aten
2023-01-11T21:41:26.8012356Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8012434Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8012439Z 
2023-01-11T21:41:26.8012503Z 
2023-01-11T21:41:26.8012640Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8012843Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8012958Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8013063Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8013160Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8013255Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.8013316Z {
2023-01-11T21:41:26.8013388Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:26.8013483Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8013546Z     {
2023-01-11T21:41:26.8013622Z         #pragma omp for 
2023-01-11T21:41:26.8013703Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.8013766Z         {
2023-01-11T21:41:26.8013851Z             {
2023-01-11T21:41:26.8014235Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:41:26.8014455Z                 float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.8014576Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8014664Z                 for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.8014727Z                 {
2023-01-11T21:41:26.8014868Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8014983Z                     tmp1_vec = at::vec::maximum(tmp1_vec, tmp0);
2023-01-11T21:41:26.8015046Z                 }
2023-01-11T21:41:26.8015245Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp1_vec);
2023-01-11T21:41:26.8015365Z                 #pragma omp simd simdlen(4)  reduction(max:tmp1)
2023-01-11T21:41:26.8015451Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:26.8015518Z                 {
2023-01-11T21:41:26.8015615Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.8015713Z                     tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:41:26.8015774Z                 }
2023-01-11T21:41:26.8015853Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.8015902Z             }
2023-01-11T21:41:26.8015962Z         }
2023-01-11T21:41:26.8016036Z         #pragma omp for 
2023-01-11T21:41:26.8016114Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.8016174Z         {
2023-01-11T21:41:26.8016233Z             {
2023-01-11T21:41:26.8016406Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8016482Z                 float tmp4 = 0;
2023-01-11T21:41:26.8016600Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:41:26.8016688Z                 for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.8016775Z                 {
2023-01-11T21:41:26.8016913Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8017035Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.8017123Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8017200Z                     auto tmp3 = tmp2.exp();
2023-01-11T21:41:26.8017301Z                     tmp3.store(out_ptr1 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8017381Z                     tmp4_vec += tmp3;
2023-01-11T21:41:26.8017442Z                 }
2023-01-11T21:41:26.8017634Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:41:26.8017753Z                 #pragma omp simd simdlen(4)  reduction(+:tmp4)
2023-01-11T21:41:26.8017840Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:26.8017901Z                 {
2023-01-11T21:41:26.8018032Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.8018124Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:26.8018213Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8018310Z                     auto tmp3 = std::exp(tmp2);
2023-01-11T21:41:26.8018400Z                     out_ptr1[i1 + (32*i0)] = tmp3;
2023-01-11T21:41:26.8018475Z                     tmp4 += tmp3;
2023-01-11T21:41:26.8018536Z                 }
2023-01-11T21:41:26.8018603Z                 out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.8018665Z             }
2023-01-11T21:41:26.8018725Z         }
2023-01-11T21:41:26.8018798Z         #pragma omp for 
2023-01-11T21:41:26.8018877Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.8018936Z         {
2023-01-11T21:41:26.8019018Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.8019096Z             {
2023-01-11T21:41:26.8019237Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8019364Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:41:26.8019449Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8019553Z                 tmp2.store(in_out_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8019615Z             }
2023-01-11T21:41:26.8019703Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.8019776Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:41:26.8019836Z             {
2023-01-11T21:41:26.8019929Z                 auto tmp0 = out_ptr1[i1 + (32*i0)];
2023-01-11T21:41:26.8020012Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:41:26.8020095Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8020186Z                 in_out_ptr0[i1 + (32*i0)] = tmp2;
2023-01-11T21:41:26.8020247Z             }
2023-01-11T21:41:26.8020296Z         }
2023-01-11T21:41:26.8020357Z     }
2023-01-11T21:41:26.8020414Z }
2023-01-11T21:41:26.8020495Z ''')
2023-01-11T21:41:26.8020500Z 
2023-01-11T21:41:26.8020506Z 
2023-01-11T21:41:26.8020593Z async_compile.wait(globals())
2023-01-11T21:41:26.8020663Z del async_compile
2023-01-11T21:41:26.8020668Z 
2023-01-11T21:41:26.8020736Z def call(args):
2023-01-11T21:41:26.8020792Z     arg0_1, = args
2023-01-11T21:41:26.8020860Z     args.clear()
2023-01-11T21:41:26.8021059Z     buf0 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8021257Z     buf1 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8021450Z     buf2 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8021532Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:26.8021716Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.8021783Z     del arg0_1
2023-01-11T21:41:26.8021843Z     return (buf3, )
2023-01-11T21:41:26.8021848Z 
2023-01-11T21:41:26.8021852Z 
2023-01-11T21:41:26.8021926Z if __name__ == "__main__":
2023-01-11T21:41:26.8022040Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8022160Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8022357Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8022462Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8022467Z 
2023-01-11T21:41:26.8022532Z ok (1.551s)
2023-01-11T21:41:26.8022996Z   test_sort_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8023195Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8023457Z [2023-01-11 21:38:13,850] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 436
2023-01-11T21:41:26.8023713Z [2023-01-11 21:38:13,855] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.sort
2023-01-11T21:41:26.8023975Z [2023-01-11 21:38:13,859] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 436
2023-01-11T21:41:26.8023980Z 
2023-01-11T21:41:26.8024074Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8024143Z import torch
2023-01-11T21:41:26.8024214Z import random
2023-01-11T21:41:26.8024327Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8024446Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8024451Z 
2023-01-11T21:41:26.8024516Z aten = torch.ops.aten
2023-01-11T21:41:26.8024649Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8024768Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8024773Z 
2023-01-11T21:41:26.8024778Z 
2023-01-11T21:41:26.8024866Z async_compile.wait(globals())
2023-01-11T21:41:26.8024938Z del async_compile
2023-01-11T21:41:26.8024943Z 
2023-01-11T21:41:26.8025011Z def call(args):
2023-01-11T21:41:26.8025079Z     arg0_1, = args
2023-01-11T21:41:26.8025147Z     args.clear()
2023-01-11T21:41:26.8025215Z     buf0 = aten.sort(arg0_1)
2023-01-11T21:41:26.8025279Z     del arg0_1
2023-01-11T21:41:26.8025345Z     buf1 = buf0[0]
2023-01-11T21:41:26.8025450Z     assert_size_stride(buf1, (1, 1, 8, 8), (64, 64, 8, 1))
2023-01-11T21:41:26.8025516Z     buf2 = buf0[1]
2023-01-11T21:41:26.8025617Z     assert_size_stride(buf2, (1, 1, 8, 8), (64, 64, 8, 1))
2023-01-11T21:41:26.8025679Z     del buf0
2023-01-11T21:41:26.8025743Z     return (buf1, buf2, )
2023-01-11T21:41:26.8025748Z 
2023-01-11T21:41:26.8025752Z 
2023-01-11T21:41:26.8025825Z if __name__ == "__main__":
2023-01-11T21:41:26.8025934Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8026056Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8026268Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8026375Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8026380Z 
2023-01-11T21:41:26.8026445Z ok (0.023s)
2023-01-11T21:41:26.8026906Z   test_split_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8027031Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8027275Z [2023-01-11 21:38:13,880] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 437
2023-01-11T21:41:26.8027539Z [2023-01-11 21:38:13,884] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 437
2023-01-11T21:41:26.8027965Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8028087Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8028342Z [2023-01-11 21:38:13,908] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 438
2023-01-11T21:41:26.8028604Z [2023-01-11 21:38:15,401] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 438
2023-01-11T21:41:26.8028610Z 
2023-01-11T21:41:26.8028702Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8028771Z import torch
2023-01-11T21:41:26.8028839Z import random
2023-01-11T21:41:26.8028940Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8029093Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8029098Z 
2023-01-11T21:41:26.8029175Z aten = torch.ops.aten
2023-01-11T21:41:26.8029306Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8029395Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8029400Z 
2023-01-11T21:41:26.8029405Z 
2023-01-11T21:41:26.8029490Z async_compile.wait(globals())
2023-01-11T21:41:26.8029561Z del async_compile
2023-01-11T21:41:26.8029566Z 
2023-01-11T21:41:26.8029634Z def call(args):
2023-01-11T21:41:26.8029689Z     arg0_1, = args
2023-01-11T21:41:26.8029758Z     args.clear()
2023-01-11T21:41:26.8029949Z     return (as_strided(arg0_1, (2, 2, 3), (20, 10, 1)), as_strided(arg0_1, (2, 2, 3), (20, 10, 1), 3), as_strided(arg0_1, (2, 2, 3), (20, 10, 1), 6), as_strided(arg0_1, (2, 2, 1), (20, 10, 1), 9), )
2023-01-11T21:41:26.8030008Z 
2023-01-11T21:41:26.8030013Z 
2023-01-11T21:41:26.8030088Z if __name__ == "__main__":
2023-01-11T21:41:26.8030200Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8030320Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8030527Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8030634Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8030639Z 
2023-01-11T21:41:26.8030644Z 
2023-01-11T21:41:26.8030734Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8030789Z import torch
2023-01-11T21:41:26.8030857Z import random
2023-01-11T21:41:26.8030966Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8031082Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8031087Z 
2023-01-11T21:41:26.8031162Z aten = torch.ops.aten
2023-01-11T21:41:26.8031293Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8031384Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8031389Z 
2023-01-11T21:41:26.8031393Z 
2023-01-11T21:41:26.8031527Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8031722Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8031837Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8031936Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8031995Z {
2023-01-11T21:41:26.8032090Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8032152Z     {
2023-01-11T21:41:26.8032227Z         #pragma omp for 
2023-01-11T21:41:26.8032295Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:41:26.8032354Z         {
2023-01-11T21:41:26.8032488Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8032618Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.8032703Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8032792Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8032854Z         }
2023-01-11T21:41:26.8032937Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8033017Z         for(long i0=40; i0<40; i0+=1)
2023-01-11T21:41:26.8033078Z         {
2023-01-11T21:41:26.8033157Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8033253Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.8033334Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8033411Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8033462Z         }
2023-01-11T21:41:26.8033523Z     }
2023-01-11T21:41:26.8033581Z }
2023-01-11T21:41:26.8033660Z ''')
2023-01-11T21:41:26.8033665Z 
2023-01-11T21:41:26.8033670Z 
2023-01-11T21:41:26.8033755Z async_compile.wait(globals())
2023-01-11T21:41:26.8033824Z del async_compile
2023-01-11T21:41:26.8033829Z 
2023-01-11T21:41:26.8033896Z def call(args):
2023-01-11T21:41:26.8033952Z     arg0_1, = args
2023-01-11T21:41:26.8034021Z     args.clear()
2023-01-11T21:41:26.8034227Z     buf0 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8034392Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8034459Z     del arg0_1
2023-01-11T21:41:26.8034646Z     return (as_strided(buf0, (2, 2, 3), (20, 10, 1)), as_strided(buf0, (2, 2, 3), (20, 10, 1), 3), as_strided(buf0, (2, 2, 3), (20, 10, 1), 6), as_strided(buf0, (2, 2, 1), (20, 10, 0), 9), )
2023-01-11T21:41:26.8034652Z 
2023-01-11T21:41:26.8034656Z 
2023-01-11T21:41:26.8034729Z if __name__ == "__main__":
2023-01-11T21:41:26.8034840Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8034949Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8035155Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8035259Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8035264Z 
2023-01-11T21:41:26.8035354Z ok (1.543s)
2023-01-11T21:41:26.8035825Z   test_split_with_sizes_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8035952Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8036210Z [2023-01-11 21:38:15,432] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 439
2023-01-11T21:41:26.8036473Z [2023-01-11 21:38:16,971] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 439
2023-01-11T21:41:26.8036898Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8037023Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8037276Z [2023-01-11 21:38:17,001] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 440
2023-01-11T21:41:26.8037528Z [2023-01-11 21:38:18,534] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 440
2023-01-11T21:41:26.8037949Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8038074Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8038326Z [2023-01-11 21:38:18,568] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 441
2023-01-11T21:41:26.8038333Z 
2023-01-11T21:41:26.8038425Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8038494Z import torch
2023-01-11T21:41:26.8038562Z import random
2023-01-11T21:41:26.8038675Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8038791Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8038796Z 
2023-01-11T21:41:26.8038860Z aten = torch.ops.aten
2023-01-11T21:41:26.8038990Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8039078Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8039083Z 
2023-01-11T21:41:26.8039088Z 
2023-01-11T21:41:26.8039221Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8039422Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8039540Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8039637Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8039761Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8039840Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.8039898Z {
2023-01-11T21:41:26.8039994Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8040053Z     {
2023-01-11T21:41:26.8040128Z         #pragma omp for 
2023-01-11T21:41:26.8040208Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8040269Z         {
2023-01-11T21:41:26.8040335Z             #pragma GCC ivdep
2023-01-11T21:41:26.8040414Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.8040475Z             {
2023-01-11T21:41:26.8040540Z                 {
2023-01-11T21:41:26.8040604Z                     {
2023-01-11T21:41:26.8040705Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.8040839Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8040922Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8041027Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8041119Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8041212Z                         out_ptr0[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:26.8041276Z                     }
2023-01-11T21:41:26.8041338Z                 }
2023-01-11T21:41:26.8041399Z             }
2023-01-11T21:41:26.8041448Z         }
2023-01-11T21:41:26.8041523Z         #pragma omp for 
2023-01-11T21:41:26.8041602Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8041666Z         {
2023-01-11T21:41:26.8041742Z             #pragma GCC ivdep
2023-01-11T21:41:26.8041822Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.8041871Z             {
2023-01-11T21:41:26.8041932Z                 {
2023-01-11T21:41:26.8041995Z                     {
2023-01-11T21:41:26.8042099Z                         auto tmp0 = in_ptr0[3 + i1 + (10*i0)];
2023-01-11T21:41:26.8042203Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8042295Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8042399Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8042477Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8042568Z                         out_ptr1[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:26.8042631Z                     }
2023-01-11T21:41:26.8042692Z                 }
2023-01-11T21:41:26.8042752Z             }
2023-01-11T21:41:26.8042811Z         }
2023-01-11T21:41:26.8042885Z         #pragma omp for 
2023-01-11T21:41:26.8042951Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8043010Z         {
2023-01-11T21:41:26.8043088Z             #pragma GCC ivdep
2023-01-11T21:41:26.8043167Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.8043229Z             {
2023-01-11T21:41:26.8043293Z                 {
2023-01-11T21:41:26.8043361Z                     {
2023-01-11T21:41:26.8043451Z                         auto tmp0 = in_ptr0[6 + i1 + (10*i0)];
2023-01-11T21:41:26.8043554Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8043645Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8043747Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8043836Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8043927Z                         out_ptr2[i1 + (4*i0)] = tmp4;
2023-01-11T21:41:26.8043989Z                     }
2023-01-11T21:41:26.8044040Z                 }
2023-01-11T21:41:26.8044101Z             }
2023-01-11T21:41:26.8044160Z         }
2023-01-11T21:41:26.8044218Z     }
2023-01-11T21:41:26.8044274Z }
2023-01-11T21:41:26.8044356Z ''')
2023-01-11T21:41:26.8044361Z 
2023-01-11T21:41:26.8044365Z 
2023-01-11T21:41:26.8044453Z async_compile.wait(globals())
2023-01-11T21:41:26.8044511Z del async_compile
2023-01-11T21:41:26.8044516Z 
2023-01-11T21:41:26.8044585Z def call(args):
2023-01-11T21:41:26.8044652Z     arg0_1, = args
2023-01-11T21:41:26.8044721Z     args.clear()
2023-01-11T21:41:26.8044956Z     buf0 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8045156Z     buf1 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8045352Z     buf2 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8045540Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.8045596Z     del arg0_1
2023-01-11T21:41:26.8045676Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.8045681Z 
2023-01-11T21:41:26.8045685Z 
2023-01-11T21:41:26.8045759Z if __name__ == "__main__":
2023-01-11T21:41:26.8045871Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8045991Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8046236Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8046345Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8046352Z 
2023-01-11T21:41:26.8046356Z 
2023-01-11T21:41:26.8046447Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8046502Z import torch
2023-01-11T21:41:26.8046569Z import random
2023-01-11T21:41:26.8046682Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8046800Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8046805Z 
2023-01-11T21:41:26.8046879Z aten = torch.ops.aten
2023-01-11T21:41:26.8047010Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8047101Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8047106Z 
2023-01-11T21:41:26.8047110Z 
2023-01-11T21:41:26.8047243Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8047437Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8047553Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8047651Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8047752Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8047846Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.8047907Z {
2023-01-11T21:41:26.8048005Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8048054Z     {
2023-01-11T21:41:26.8048130Z         #pragma omp for 
2023-01-11T21:41:26.8048211Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8048273Z         {
2023-01-11T21:41:26.8048351Z             #pragma GCC ivdep
2023-01-11T21:41:26.8048435Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.8048500Z             {
2023-01-11T21:41:26.8048552Z                 {
2023-01-11T21:41:26.8048616Z                     {
2023-01-11T21:41:26.8048716Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.8048826Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8048917Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8049167Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8049311Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8049440Z                         out_ptr0[i1 + (4*i0)] = tmp4;
2023-01-11T21:41:26.8049528Z                     }
2023-01-11T21:41:26.8049597Z                 }
2023-01-11T21:41:26.8049659Z             }
2023-01-11T21:41:26.8049721Z         }
2023-01-11T21:41:26.8049797Z         #pragma omp for 
2023-01-11T21:41:26.8049866Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8049929Z         {
2023-01-11T21:41:26.8050009Z             #pragma GCC ivdep
2023-01-11T21:41:26.8050092Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.8050157Z             {
2023-01-11T21:41:26.8050221Z                 {
2023-01-11T21:41:26.8050287Z                     {
2023-01-11T21:41:26.8050380Z                         auto tmp0 = in_ptr0[4 + i1 + (10*i0)];
2023-01-11T21:41:26.8050487Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8050636Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8050741Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8050833Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8050927Z                         out_ptr1[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:26.8050993Z                     }
2023-01-11T21:41:26.8051045Z                 }
2023-01-11T21:41:26.8051108Z             }
2023-01-11T21:41:26.8051170Z         }
2023-01-11T21:41:26.8051247Z         #pragma omp for 
2023-01-11T21:41:26.8051331Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8051392Z         {
2023-01-11T21:41:26.8051471Z             #pragma GCC ivdep
2023-01-11T21:41:26.8051540Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.8051602Z             {
2023-01-11T21:41:26.8051701Z                 {
2023-01-11T21:41:26.8051768Z                     {
2023-01-11T21:41:26.8051870Z                         auto tmp0 = in_ptr0[7 + i1 + (10*i0)];
2023-01-11T21:41:26.8051976Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8052066Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8052158Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8052247Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8052339Z                         out_ptr2[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:26.8052404Z                     }
2023-01-11T21:41:26.8052466Z                 }
2023-01-11T21:41:26.8052526Z             }
2023-01-11T21:41:26.8052588Z         }
2023-01-11T21:41:26.8052635Z     }
2023-01-11T21:41:26.8052691Z }
2023-01-11T21:41:26.8052777Z ''')
2023-01-11T21:41:26.8052782Z 
2023-01-11T21:41:26.8052786Z 
2023-01-11T21:41:26.8052875Z async_compile.wait(globals())
2023-01-11T21:41:26.8052946Z del async_compile
2023-01-11T21:41:26.8052953Z 
2023-01-11T21:41:26.8053022Z def call(args):
2023-01-11T21:41:26.8053090Z     arg0_1, = args
2023-01-11T21:41:26.8053149Z     args.clear()
2023-01-11T21:41:26.8053355Z     buf0 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8053553Z     buf1 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8053749Z     buf2 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8053936Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.8054003Z     del arg0_1
2023-01-11T21:41:26.8054083Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.8054088Z 
2023-01-11T21:41:26.8054093Z 
2023-01-11T21:41:26.8054166Z if __name__ == "__main__":
2023-01-11T21:41:26.8054267Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8054389Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8054594Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8054702Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8054707Z 
2023-01-11T21:41:26.8054972Z [2023-01-11 21:38:20,123] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 441
2023-01-11T21:41:26.8054978Z 
2023-01-11T21:41:26.8055069Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8055136Z import torch
2023-01-11T21:41:26.8055203Z import random
2023-01-11T21:41:26.8055304Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8055421Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8055426Z 
2023-01-11T21:41:26.8055502Z aten = torch.ops.aten
2023-01-11T21:41:26.8055632Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8055723Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8055728Z 
2023-01-11T21:41:26.8055732Z 
2023-01-11T21:41:26.8055865Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8056067Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8056284Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8056370Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8056464Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8056556Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8056651Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:26.8056708Z {
2023-01-11T21:41:26.8056805Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8056863Z     {
2023-01-11T21:41:26.8056926Z         #pragma omp for 
2023-01-11T21:41:26.8057005Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8057065Z         {
2023-01-11T21:41:26.8057126Z             {
2023-01-11T21:41:26.8057187Z                 {
2023-01-11T21:41:26.8057309Z                     auto tmp0 = in_ptr0[10*i0];
2023-01-11T21:41:26.8057415Z                     auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8057496Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8057598Z                     auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8057687Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8057771Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.8057833Z                 }
2023-01-11T21:41:26.8057894Z             }
2023-01-11T21:41:26.8057954Z         }
2023-01-11T21:41:26.8058016Z         #pragma omp for 
2023-01-11T21:41:26.8058093Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8058156Z         {
2023-01-11T21:41:26.8058235Z             #pragma GCC ivdep
2023-01-11T21:41:26.8058315Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.8058376Z             {
2023-01-11T21:41:26.8058426Z                 {
2023-01-11T21:41:26.8058490Z                     {
2023-01-11T21:41:26.8058593Z                         auto tmp0 = in_ptr0[1 + i1 + (10*i0)];
2023-01-11T21:41:26.8058699Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8058792Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8058897Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8058986Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8059079Z                         out_ptr1[i1 + (2*i0)] = tmp4;
2023-01-11T21:41:26.8059132Z                     }
2023-01-11T21:41:26.8059194Z                 }
2023-01-11T21:41:26.8059255Z             }
2023-01-11T21:41:26.8059315Z         }
2023-01-11T21:41:26.8059388Z         #pragma omp for 
2023-01-11T21:41:26.8059467Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8059516Z         {
2023-01-11T21:41:26.8059592Z             #pragma GCC ivdep
2023-01-11T21:41:26.8059671Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.8059731Z             {
2023-01-11T21:41:26.8059792Z                 {
2023-01-11T21:41:26.8059857Z                     {
2023-01-11T21:41:26.8059959Z                         auto tmp0 = in_ptr0[3 + i1 + (10*i0)];
2023-01-11T21:41:26.8060053Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8060143Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8060247Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8060335Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8060426Z                         out_ptr2[i1 + (3*i0)] = tmp4;
2023-01-11T21:41:26.8060490Z                     }
2023-01-11T21:41:26.8060550Z                 }
2023-01-11T21:41:26.8060599Z             }
2023-01-11T21:41:26.8060659Z         }
2023-01-11T21:41:26.8060732Z         #pragma omp for 
2023-01-11T21:41:26.8060810Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8060870Z         {
2023-01-11T21:41:26.8060946Z             #pragma GCC ivdep
2023-01-11T21:41:26.8061028Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.8061078Z             {
2023-01-11T21:41:26.8061139Z                 {
2023-01-11T21:41:26.8061203Z                     {
2023-01-11T21:41:26.8061336Z                         auto tmp0 = in_ptr0[6 + i1 + (10*i0)];
2023-01-11T21:41:26.8061440Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:41:26.8061530Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8061633Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:41:26.8061710Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8061802Z                         out_ptr3[i1 + (4*i0)] = tmp4;
2023-01-11T21:41:26.8061864Z                     }
2023-01-11T21:41:26.8061925Z                 }
2023-01-11T21:41:26.8061984Z             }
2023-01-11T21:41:26.8062044Z         }
2023-01-11T21:41:26.8062102Z     }
2023-01-11T21:41:26.8062148Z }
2023-01-11T21:41:26.8062230Z ''')
2023-01-11T21:41:26.8062235Z 
2023-01-11T21:41:26.8062239Z 
2023-01-11T21:41:26.8062356Z async_compile.wait(globals())
2023-01-11T21:41:26.8062430Z del async_compile
2023-01-11T21:41:26.8062434Z 
2023-01-11T21:41:26.8062505Z def call(args):
2023-01-11T21:41:26.8062572Z     arg0_1, = args
2023-01-11T21:41:26.8062641Z     args.clear()
2023-01-11T21:41:26.8062830Z     buf0 = empty_strided((2, 2, 1), (2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8063027Z     buf1 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8063319Z     buf2 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8063525Z     buf3 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8063734Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.8063802Z     del arg0_1
2023-01-11T21:41:26.8063890Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:26.8063895Z 
2023-01-11T21:41:26.8063902Z 
2023-01-11T21:41:26.8063975Z if __name__ == "__main__":
2023-01-11T21:41:26.8064075Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8064198Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8064403Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8064509Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8064514Z 
2023-01-11T21:41:26.8064578Z ok (4.723s)
2023-01-11T21:41:26.8065040Z   test_squeeze1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8065168Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8065428Z [2023-01-11 21:38:20,146] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 442
2023-01-11T21:41:26.8065694Z [2023-01-11 21:38:21,644] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 442
2023-01-11T21:41:26.8065699Z 
2023-01-11T21:41:26.8065790Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8065846Z import torch
2023-01-11T21:41:26.8065913Z import random
2023-01-11T21:41:26.8066024Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8066142Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8066147Z 
2023-01-11T21:41:26.8066222Z aten = torch.ops.aten
2023-01-11T21:41:26.8066352Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8066441Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8066447Z 
2023-01-11T21:41:26.8066451Z 
2023-01-11T21:41:26.8066581Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8066774Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8066889Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8067022Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8067117Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8067175Z {
2023-01-11T21:41:26.8067270Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8067329Z     {
2023-01-11T21:41:26.8067393Z         #pragma omp for 
2023-01-11T21:41:26.8067472Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.8067532Z         {
2023-01-11T21:41:26.8067671Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8067801Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.8067885Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8068011Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.8068109Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8068190Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:26.8068283Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8068371Z             tmp5.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8068430Z         }
2023-01-11T21:41:26.8068522Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8068600Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.8068649Z         {
2023-01-11T21:41:26.8068730Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8068826Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.8068906Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8069002Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.8069082Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8069161Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:26.8069226Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.8069301Z             out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.8069362Z         }
2023-01-11T21:41:26.8069421Z     }
2023-01-11T21:41:26.8069477Z }
2023-01-11T21:41:26.8069557Z ''')
2023-01-11T21:41:26.8069564Z 
2023-01-11T21:41:26.8069568Z 
2023-01-11T21:41:26.8069654Z async_compile.wait(globals())
2023-01-11T21:41:26.8069712Z del async_compile
2023-01-11T21:41:26.8069717Z 
2023-01-11T21:41:26.8069785Z def call(args):
2023-01-11T21:41:26.8069856Z     arg0_1, = args
2023-01-11T21:41:26.8069924Z     args.clear()
2023-01-11T21:41:26.8070125Z     buf0 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8070322Z     buf1 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8070484Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8070550Z     del arg0_1
2023-01-11T21:41:26.8070613Z     return (buf0, buf1, )
2023-01-11T21:41:26.8070618Z 
2023-01-11T21:41:26.8070622Z 
2023-01-11T21:41:26.8070698Z if __name__ == "__main__":
2023-01-11T21:41:26.8070808Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8070928Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8071157Z     arg0_1 = rand_strided((1, 2, 1, 2, 2, 1, 1), (8, 4, 4, 2, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8071262Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8071267Z 
2023-01-11T21:41:26.8071331Z ok (1.520s)
2023-01-11T21:41:26.8071792Z   test_squeeze2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8071916Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8072164Z [2023-01-11 21:38:21,669] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 443
2023-01-11T21:41:26.8072427Z [2023-01-11 21:38:23,156] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 443
2023-01-11T21:41:26.8072461Z 
2023-01-11T21:41:26.8072555Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8072623Z import torch
2023-01-11T21:41:26.8072691Z import random
2023-01-11T21:41:26.8072803Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8072920Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8072925Z 
2023-01-11T21:41:26.8073000Z aten = torch.ops.aten
2023-01-11T21:41:26.8073118Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8073206Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8073211Z 
2023-01-11T21:41:26.8073215Z 
2023-01-11T21:41:26.8073347Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8073581Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8073699Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8073797Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8073894Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8073951Z {
2023-01-11T21:41:26.8074035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8074094Z     {
2023-01-11T21:41:26.8074168Z         #pragma omp for 
2023-01-11T21:41:26.8074248Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8074308Z         {
2023-01-11T21:41:26.8074443Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8074574Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.8074646Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8074773Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.8074857Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8074937Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:26.8075026Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8075115Z             tmp5.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8075176Z         }
2023-01-11T21:41:26.8075256Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8075336Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.8075396Z         {
2023-01-11T21:41:26.8075477Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8075572Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.8075653Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8075749Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.8075818Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8075896Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:26.8075974Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.8076050Z             out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.8076109Z         }
2023-01-11T21:41:26.8076170Z     }
2023-01-11T21:41:26.8076227Z }
2023-01-11T21:41:26.8076295Z ''')
2023-01-11T21:41:26.8076299Z 
2023-01-11T21:41:26.8076306Z 
2023-01-11T21:41:26.8076398Z async_compile.wait(globals())
2023-01-11T21:41:26.8076469Z del async_compile
2023-01-11T21:41:26.8076474Z 
2023-01-11T21:41:26.8076542Z def call(args):
2023-01-11T21:41:26.8076609Z     arg0_1, = args
2023-01-11T21:41:26.8076677Z     args.clear()
2023-01-11T21:41:26.8076893Z     buf0 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8077103Z     buf1 = empty_strided((2, 1, 2, 2, 2, 1), (8, 8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8077261Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8077326Z     del arg0_1
2023-01-11T21:41:26.8077401Z     return (buf0, buf1, )
2023-01-11T21:41:26.8077406Z 
2023-01-11T21:41:26.8077410Z 
2023-01-11T21:41:26.8077484Z if __name__ == "__main__":
2023-01-11T21:41:26.8077598Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8077717Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8077985Z     arg0_1 = rand_strided((1, 2, 1, 2, 2, 2, 1), (16, 8, 8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8078079Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8078095Z 
2023-01-11T21:41:26.8078148Z ok (1.512s)
2023-01-11T21:41:26.8078606Z   test_stack_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8078732Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8079032Z [2023-01-11 21:38:23,177] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 444
2023-01-11T21:41:26.8079297Z [2023-01-11 21:38:24,746] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 444
2023-01-11T21:41:26.8079306Z 
2023-01-11T21:41:26.8079397Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8079464Z import torch
2023-01-11T21:41:26.8079532Z import random
2023-01-11T21:41:26.8079632Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8079749Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8079755Z 
2023-01-11T21:41:26.8079829Z aten = torch.ops.aten
2023-01-11T21:41:26.8079960Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8080048Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8080054Z 
2023-01-11T21:41:26.8080058Z 
2023-01-11T21:41:26.8080189Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8080391Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8080507Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8080610Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8080696Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8080790Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8080848Z {
2023-01-11T21:41:26.8080944Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8081005Z     {
2023-01-11T21:41:26.8081080Z         #pragma omp for 
2023-01-11T21:41:26.8081149Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.8081210Z         {
2023-01-11T21:41:26.8081287Z             #pragma GCC ivdep
2023-01-11T21:41:26.8081371Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.8081432Z             {
2023-01-11T21:41:26.8081494Z                 {
2023-01-11T21:41:26.8081558Z                     {
2023-01-11T21:41:26.8081640Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.8081738Z                         out_ptr0[(2*i1) + (32*i0)] = tmp0;
2023-01-11T21:41:26.8081803Z                     }
2023-01-11T21:41:26.8081866Z                 }
2023-01-11T21:41:26.8081926Z             }
2023-01-11T21:41:26.8081985Z         }
2023-01-11T21:41:26.8082059Z         #pragma omp for 
2023-01-11T21:41:26.8082125Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.8082186Z         {
2023-01-11T21:41:26.8082263Z             #pragma GCC ivdep
2023-01-11T21:41:26.8082344Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.8082406Z             {
2023-01-11T21:41:26.8082468Z                 {
2023-01-11T21:41:26.8082532Z                     {
2023-01-11T21:41:26.8082611Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.8082706Z                         out_ptr1[(2*i1) + (32*i0)] = tmp0;
2023-01-11T21:41:26.8082770Z                     }
2023-01-11T21:41:26.8082833Z                 }
2023-01-11T21:41:26.8082893Z             }
2023-01-11T21:41:26.8082954Z         }
2023-01-11T21:41:26.8083002Z     }
2023-01-11T21:41:26.8083058Z }
2023-01-11T21:41:26.8083136Z ''')
2023-01-11T21:41:26.8083141Z 
2023-01-11T21:41:26.8083173Z 
2023-01-11T21:41:26.8083262Z async_compile.wait(globals())
2023-01-11T21:41:26.8083331Z del async_compile
2023-01-11T21:41:26.8083337Z 
2023-01-11T21:41:26.8083405Z def call(args):
2023-01-11T21:41:26.8083478Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8083546Z     args.clear()
2023-01-11T21:41:26.8083741Z     buf2 = empty_strided((12, 16, 2), (32, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8083844Z     buf0 = as_strided(buf2, (12, 16, 1), (32, 2, 1))  # alias
2023-01-11T21:41:26.8083947Z     buf1 = as_strided(buf2, (12, 16, 1), (32, 2, 1), 1)  # alias
2023-01-11T21:41:26.8084136Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8084201Z     del arg0_1
2023-01-11T21:41:26.8084266Z     del arg1_1
2023-01-11T21:41:26.8084364Z     return (buf2, )
2023-01-11T21:41:26.8084370Z 
2023-01-11T21:41:26.8084374Z 
2023-01-11T21:41:26.8084436Z if __name__ == "__main__":
2023-01-11T21:41:26.8084550Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8084669Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8084868Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8085062Z     arg1_1 = rand_strided((12, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8085175Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8085180Z 
2023-01-11T21:41:26.8085245Z ok (1.590s)
2023-01-11T21:41:26.8085706Z   test_std_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8085833Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8086079Z [2023-01-11 21:38:24,786] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 445
2023-01-11T21:41:26.8086096Z 
2023-01-11T21:41:26.8086176Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8086243Z import torch
2023-01-11T21:41:26.8086311Z import random
2023-01-11T21:41:26.8086422Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8086547Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8086555Z 
2023-01-11T21:41:26.8086678Z aten = torch.ops.aten
2023-01-11T21:41:26.8086934Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8087083Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8087093Z 
2023-01-11T21:41:26.8087122Z 
2023-01-11T21:41:26.8087419Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8087787Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8088001Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8088175Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.8088352Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:41:26.8088527Z                        float* __restrict__ in_out_ptr3,
2023-01-11T21:41:26.8088698Z                        float* __restrict__ in_out_ptr4,
2023-01-11T21:41:26.8088850Z                        float* __restrict__ in_out_ptr5,
2023-01-11T21:41:26.8089131Z                        float* __restrict__ in_out_ptr6,
2023-01-11T21:41:26.8089309Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8089489Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8089660Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8089840Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.8090015Z                        float* __restrict__ out_ptr5,
2023-01-11T21:41:26.8090155Z                        float* __restrict__ out_ptr7,
2023-01-11T21:41:26.8090574Z                        float* __restrict__ out_ptr10,
2023-01-11T21:41:26.8090773Z                        float* __restrict__ out_ptr12,
2023-01-11T21:41:26.8090967Z                        float* __restrict__ out_ptr14)
2023-01-11T21:41:26.8091094Z {
2023-01-11T21:41:26.8091275Z     auto out_ptr6 = in_out_ptr0;
2023-01-11T21:41:26.8091460Z     auto out_ptr8 = in_out_ptr1;
2023-01-11T21:41:26.8091624Z     auto out_ptr11 = in_out_ptr2;
2023-01-11T21:41:26.8091809Z     auto out_ptr13 = in_out_ptr3;
2023-01-11T21:41:26.8091978Z     auto out_ptr1 = in_out_ptr4;
2023-01-11T21:41:26.8092153Z     auto out_ptr3 = in_out_ptr5;
2023-01-11T21:41:26.8092331Z     auto out_ptr9 = in_out_ptr6;
2023-01-11T21:41:26.8092453Z     {
2023-01-11T21:41:26.8092927Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8093107Z         float tmp1 = 0;
2023-01-11T21:41:26.8093326Z         auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8093540Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8093671Z         {
2023-01-11T21:41:26.8093884Z             #pragma omp for reduction(+:tmp1_vec) 
2023-01-11T21:41:26.8094070Z             for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8094207Z             {
2023-01-11T21:41:26.8094475Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8094624Z                 tmp1_vec += tmp0;
2023-01-11T21:41:26.8094760Z             }
2023-01-11T21:41:26.8095140Z             tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.8095377Z             #pragma omp for simd simdlen(4) reduction(+:tmp1) 
2023-01-11T21:41:26.8095565Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.8095703Z             {
2023-01-11T21:41:26.8095880Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8096027Z                 tmp1 += tmp0;
2023-01-11T21:41:26.8096160Z             }
2023-01-11T21:41:26.8096291Z         }
2023-01-11T21:41:26.8096452Z         out_ptr0[0] = tmp1;
2023-01-11T21:41:26.8096583Z     }
2023-01-11T21:41:26.8096710Z     {
2023-01-11T21:41:26.8097078Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8097223Z         float tmp6 = 0;
2023-01-11T21:41:26.8097459Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8097619Z         float tmp7 = 0;
2023-01-11T21:41:26.8097862Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.8098065Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8098201Z         {
2023-01-11T21:41:26.8098476Z             #pragma omp for reduction(+:tmp6_vec) reduction(+:tmp7_vec) 
2023-01-11T21:41:26.8098642Z             for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8098760Z             {
2023-01-11T21:41:26.8099037Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8099283Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr0[0]);
2023-01-11T21:41:26.8099561Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:41:26.8099742Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8100052Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8100235Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8100381Z                 tmp6_vec += tmp5;
2023-01-11T21:41:26.8100542Z                 tmp7_vec += tmp0;
2023-01-11T21:41:26.8100678Z             }
2023-01-11T21:41:26.8101064Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8101445Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:26.8101854Z             #pragma omp for simd simdlen(4) reduction(+:tmp6) reduction(+:tmp7) 
2023-01-11T21:41:26.8102037Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.8102176Z             {
2023-01-11T21:41:26.8102341Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8102522Z                 auto tmp1 = out_ptr0[0];
2023-01-11T21:41:26.8102726Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:41:26.8102909Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8103274Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8103454Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8103612Z                 tmp6 += tmp5;
2023-01-11T21:41:26.8103750Z                 tmp7 += tmp0;
2023-01-11T21:41:26.8103883Z             }
2023-01-11T21:41:26.8104018Z         }
2023-01-11T21:41:26.8104280Z         out_ptr1[0] = tmp6;
2023-01-11T21:41:26.8104445Z         out_ptr2[0] = tmp7;
2023-01-11T21:41:26.8104576Z     }
2023-01-11T21:41:26.8104715Z     {
2023-01-11T21:41:26.8105069Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8105236Z         float tmp6 = 0;
2023-01-11T21:41:26.8105466Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8105629Z         float tmp7 = 0;
2023-01-11T21:41:26.8105860Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.8106070Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8106207Z         {
2023-01-11T21:41:26.8106478Z             #pragma omp for reduction(+:tmp6_vec) reduction(+:tmp7_vec) 
2023-01-11T21:41:26.8106634Z             for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8106774Z             {
2023-01-11T21:41:26.8107049Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8107307Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[0]);
2023-01-11T21:41:26.8107573Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:41:26.8107763Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8108069Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8108232Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8108399Z                 tmp6_vec += tmp5;
2023-01-11T21:41:26.8108560Z                 tmp7_vec += tmp0;
2023-01-11T21:41:26.8108694Z             }
2023-01-11T21:41:26.8109080Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8109454Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:26.8109745Z             #pragma omp for simd simdlen(4) reduction(+:tmp6) reduction(+:tmp7) 
2023-01-11T21:41:26.8109925Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.8110055Z             {
2023-01-11T21:41:26.8110221Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8110397Z                 auto tmp1 = out_ptr2[0];
2023-01-11T21:41:26.8110606Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:41:26.8110784Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8111064Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8111233Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8111401Z                 tmp6 += tmp5;
2023-01-11T21:41:26.8111540Z                 tmp7 += tmp0;
2023-01-11T21:41:26.8111675Z             }
2023-01-11T21:41:26.8111818Z         }
2023-01-11T21:41:26.8111981Z         out_ptr3[0] = tmp6;
2023-01-11T21:41:26.8112141Z         out_ptr4[0] = tmp7;
2023-01-11T21:41:26.8112273Z     }
2023-01-11T21:41:26.8112463Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8112593Z     {
2023-01-11T21:41:26.8112762Z         #pragma omp for 
2023-01-11T21:41:26.8112944Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8113071Z         {
2023-01-11T21:41:26.8113318Z             {
2023-01-11T21:41:26.8113708Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8113855Z                 float tmp1 = 0;
2023-01-11T21:41:26.8114099Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8114281Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8114423Z                 {
2023-01-11T21:41:26.8114705Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8114875Z                     tmp1_vec += tmp0;
2023-01-11T21:41:26.8115004Z                 }
2023-01-11T21:41:26.8115380Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.8115695Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.8115886Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8116035Z                 {
2023-01-11T21:41:26.8116239Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8116399Z                     tmp1 += tmp0;
2023-01-11T21:41:26.8116538Z                 }
2023-01-11T21:41:26.8116710Z                 out_ptr5[i0] = tmp1;
2023-01-11T21:41:26.8116824Z             }
2023-01-11T21:41:26.8116955Z         }
2023-01-11T21:41:26.8117121Z         #pragma omp for 
2023-01-11T21:41:26.8117297Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8117431Z         {
2023-01-11T21:41:26.8117564Z             {
2023-01-11T21:41:26.8117928Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8118073Z                 float tmp6 = 0;
2023-01-11T21:41:26.8118328Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8118494Z                 float tmp7 = 0;
2023-01-11T21:41:26.8118737Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.8118920Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8119056Z                 {
2023-01-11T21:41:26.8119344Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8119606Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr5[i0]);
2023-01-11T21:41:26.8119859Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:26.8120053Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8120369Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8120560Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8120732Z                     tmp6_vec += tmp5;
2023-01-11T21:41:26.8120897Z                     tmp7_vec += tmp0;
2023-01-11T21:41:26.8121037Z                 }
2023-01-11T21:41:26.8121414Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8121780Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:26.8122057Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:26.8122237Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8122370Z                 {
2023-01-11T21:41:26.8122567Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8122751Z                     auto tmp1 = out_ptr5[i0];
2023-01-11T21:41:26.8122961Z                     auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:26.8123137Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8123418Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8123607Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8123774Z                     tmp6 += tmp5;
2023-01-11T21:41:26.8124043Z                     tmp7 += tmp0;
2023-01-11T21:41:26.8124174Z                 }
2023-01-11T21:41:26.8124341Z                 out_ptr6[i0] = tmp6;
2023-01-11T21:41:26.8124516Z                 out_ptr7[i0] = tmp7;
2023-01-11T21:41:26.8124627Z             }
2023-01-11T21:41:26.8124761Z         }
2023-01-11T21:41:26.8124923Z         #pragma omp for 
2023-01-11T21:41:26.8125091Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8125230Z         {
2023-01-11T21:41:26.8125495Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr6 + 8*i0);
2023-01-11T21:41:26.8125762Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(7));
2023-01-11T21:41:26.8125915Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8126104Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.8126240Z         }
2023-01-11T21:41:26.8126521Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8126699Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.8126834Z         {
2023-01-11T21:41:26.8127015Z             auto tmp0 = out_ptr6[i0];
2023-01-11T21:41:26.8127198Z             auto tmp1 = static_cast<float>(7);
2023-01-11T21:41:26.8127372Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8127538Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8127671Z         }
2023-01-11T21:41:26.8127828Z         #pragma omp for 
2023-01-11T21:41:26.8127993Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8128108Z         {
2023-01-11T21:41:26.8128236Z             {
2023-01-11T21:41:26.8128617Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8128779Z                 float tmp6 = 0;
2023-01-11T21:41:26.8129156Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8129356Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8129490Z                 {
2023-01-11T21:41:26.8129780Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8130023Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr7[i0]);
2023-01-11T21:41:26.8130302Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:26.8130492Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8130802Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8130996Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8131173Z                     tmp6_vec += tmp5;
2023-01-11T21:41:26.8131312Z                 }
2023-01-11T21:41:26.8131684Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8131907Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6)
2023-01-11T21:41:26.8132093Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8132235Z                 {
2023-01-11T21:41:26.8132442Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8132637Z                     auto tmp1 = out_ptr7[i0];
2023-01-11T21:41:26.8132858Z                     auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:26.8133043Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8133309Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8133492Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8133653Z                     tmp6 += tmp5;
2023-01-11T21:41:26.8133792Z                 }
2023-01-11T21:41:26.8133963Z                 out_ptr8[i0] = tmp6;
2023-01-11T21:41:26.8134097Z             }
2023-01-11T21:41:26.8134232Z         }
2023-01-11T21:41:26.8134383Z         #pragma omp for 
2023-01-11T21:41:26.8134557Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8134696Z         {
2023-01-11T21:41:26.8134980Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr8 + 8*i0);
2023-01-11T21:41:26.8135253Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:26.8135581Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8135787Z             tmp2.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:26.8135920Z         }
2023-01-11T21:41:26.8136101Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8136262Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.8136392Z         {
2023-01-11T21:41:26.8136571Z             auto tmp0 = out_ptr8[i0];
2023-01-11T21:41:26.8136760Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:26.8136936Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8137085Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.8137216Z         }
2023-01-11T21:41:26.8137346Z     }
2023-01-11T21:41:26.8137478Z     {
2023-01-11T21:41:26.8137960Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8138142Z         float tmp6 = 0;
2023-01-11T21:41:26.8138389Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8138612Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8138729Z         {
2023-01-11T21:41:26.8138955Z             #pragma omp for reduction(+:tmp6_vec) 
2023-01-11T21:41:26.8139123Z             for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8139264Z             {
2023-01-11T21:41:26.8139537Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8150790Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr4[0]);
2023-01-11T21:41:26.8151202Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:41:26.8151378Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8151698Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8151884Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8152072Z                 tmp6_vec += tmp5;
2023-01-11T21:41:26.8152217Z             }
2023-01-11T21:41:26.8152612Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8152860Z             #pragma omp for simd simdlen(4) reduction(+:tmp6) 
2023-01-11T21:41:26.8153050Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.8153174Z             {
2023-01-11T21:41:26.8153354Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8153532Z                 auto tmp1 = out_ptr4[0];
2023-01-11T21:41:26.8153738Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:41:26.8153917Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8154207Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8154390Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8154524Z                 tmp6 += tmp5;
2023-01-11T21:41:26.8154664Z             }
2023-01-11T21:41:26.8154805Z         }
2023-01-11T21:41:26.8154972Z         out_ptr9[0] = tmp6;
2023-01-11T21:41:26.8155106Z     }
2023-01-11T21:41:26.8155314Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8155427Z     {
2023-01-11T21:41:26.8155577Z         #pragma omp for 
2023-01-11T21:41:26.8155748Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8155881Z         {
2023-01-11T21:41:26.8156014Z             {
2023-01-11T21:41:26.8156152Z                 {
2023-01-11T21:41:26.8156319Z                     float tmp1 = 0;
2023-01-11T21:41:26.8156481Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.8156627Z                     {
2023-01-11T21:41:26.8156773Z                         {
2023-01-11T21:41:26.8156986Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.8157160Z                             tmp1 += tmp0;
2023-01-11T21:41:26.8157304Z                         }
2023-01-11T21:41:26.8157441Z                     }
2023-01-11T21:41:26.8157603Z                     out_ptr10[i0] = tmp1;
2023-01-11T21:41:26.8157747Z                 }
2023-01-11T21:41:26.8157886Z             }
2023-01-11T21:41:26.8158021Z         }
2023-01-11T21:41:26.8158372Z         #pragma omp for 
2023-01-11T21:41:26.8158539Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8158654Z         {
2023-01-11T21:41:26.8158791Z             {
2023-01-11T21:41:26.8158924Z                 {
2023-01-11T21:41:26.8159093Z                     float tmp6 = 0;
2023-01-11T21:41:26.8159261Z                     float tmp7 = 0;
2023-01-11T21:41:26.8159439Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.8159581Z                     {
2023-01-11T21:41:26.8159712Z                         {
2023-01-11T21:41:26.8159921Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.8160126Z                             auto tmp1 = out_ptr10[i0];
2023-01-11T21:41:26.8160335Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:26.8160619Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8160952Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8161143Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8161316Z                             tmp6 += tmp5;
2023-01-11T21:41:26.8161466Z                             tmp7 += tmp0;
2023-01-11T21:41:26.8161613Z                         }
2023-01-11T21:41:26.8161753Z                     }
2023-01-11T21:41:26.8161929Z                     out_ptr11[i0] = tmp6;
2023-01-11T21:41:26.8162101Z                     out_ptr12[i0] = tmp7;
2023-01-11T21:41:26.8162238Z                 }
2023-01-11T21:41:26.8162346Z             }
2023-01-11T21:41:26.8162482Z         }
2023-01-11T21:41:26.8162653Z         #pragma omp for 
2023-01-11T21:41:26.8162820Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8162957Z         {
2023-01-11T21:41:26.8163237Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr11 + 8*i0);
2023-01-11T21:41:26.8163512Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(7));
2023-01-11T21:41:26.8163685Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8163842Z             auto tmp3 = tmp2.sqrt();
2023-01-11T21:41:26.8164042Z             tmp3.store(in_out_ptr2 + 8*i0);
2023-01-11T21:41:26.8164172Z         }
2023-01-11T21:41:26.8164363Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8164532Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.8164661Z         {
2023-01-11T21:41:26.8164824Z             auto tmp0 = out_ptr11[i0];
2023-01-11T21:41:26.8165032Z             auto tmp1 = static_cast<float>(7);
2023-01-11T21:41:26.8165196Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8165397Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:41:26.8165568Z             in_out_ptr2[i0] = tmp3;
2023-01-11T21:41:26.8165709Z         }
2023-01-11T21:41:26.8165871Z         #pragma omp for 
2023-01-11T21:41:26.8166016Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8166149Z         {
2023-01-11T21:41:26.8166295Z             {
2023-01-11T21:41:26.8166432Z                 {
2023-01-11T21:41:26.8166596Z                     float tmp6 = 0;
2023-01-11T21:41:26.8166787Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.8166929Z                     {
2023-01-11T21:41:26.8167059Z                         {
2023-01-11T21:41:26.8167260Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.8167462Z                             auto tmp1 = out_ptr12[i0];
2023-01-11T21:41:26.8167673Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:26.8167872Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8168195Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8168390Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8168541Z                             tmp6 += tmp5;
2023-01-11T21:41:26.8168682Z                         }
2023-01-11T21:41:26.8168819Z                     }
2023-01-11T21:41:26.8168998Z                     out_ptr13[i0] = tmp6;
2023-01-11T21:41:26.8169294Z                 }
2023-01-11T21:41:26.8169430Z             }
2023-01-11T21:41:26.8169714Z         }
2023-01-11T21:41:26.8169859Z         #pragma omp for 
2023-01-11T21:41:26.8170028Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8170156Z         {
2023-01-11T21:41:26.8170430Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr13 + 8*i0);
2023-01-11T21:41:26.8170703Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:26.8170881Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8171056Z             auto tmp3 = tmp2.sqrt();
2023-01-11T21:41:26.8171228Z             tmp3.store(in_out_ptr3 + 8*i0);
2023-01-11T21:41:26.8171363Z         }
2023-01-11T21:41:26.8171555Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8171729Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:41:26.8171856Z         {
2023-01-11T21:41:26.8172135Z             auto tmp0 = out_ptr13[i0];
2023-01-11T21:41:26.8172346Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:26.8172507Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8172709Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:41:26.8172882Z             in_out_ptr3[i0] = tmp3;
2023-01-11T21:41:26.8173020Z         }
2023-01-11T21:41:26.8173176Z         #pragma omp for 
2023-01-11T21:41:26.8173343Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8173481Z         {
2023-01-11T21:41:26.8173640Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8173776Z             {
2023-01-11T21:41:26.8174052Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8174335Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8174617Z                 auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8174907Z                 auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr0 + 24 + (8*i1) + (32*i0));
2023-01-11T21:41:26.8175088Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8175276Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8175439Z                 auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.8175715Z                 auto tmp7 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:41:26.8175890Z                 auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:26.8176198Z                 auto tmp9 = tmp0 - tmp8;
2023-01-11T21:41:26.8176384Z                 auto tmp10 = tmp9.pow(2);
2023-01-11T21:41:26.8176652Z                 auto tmp11 = tmp1 - tmp8;
2023-01-11T21:41:26.8176838Z                 auto tmp12 = tmp11.pow(2);
2023-01-11T21:41:26.8176991Z                 auto tmp13 = tmp10 + tmp12;
2023-01-11T21:41:26.8177267Z                 auto tmp14 = tmp3 - tmp8;
2023-01-11T21:41:26.8177450Z                 auto tmp15 = tmp14.pow(2);
2023-01-11T21:41:26.8177642Z                 auto tmp16 = tmp13 + tmp15;
2023-01-11T21:41:26.8177917Z                 auto tmp17 = tmp5 - tmp8;
2023-01-11T21:41:26.8178101Z                 auto tmp18 = tmp17.pow(2);
2023-01-11T21:41:26.8178293Z                 auto tmp19 = tmp16 + tmp18;
2023-01-11T21:41:26.8178576Z                 auto tmp20 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.8178744Z                 auto tmp21 = tmp19 / tmp20;
2023-01-11T21:41:26.8178921Z                 auto tmp22 = tmp21.sqrt();
2023-01-11T21:41:26.8179134Z                 tmp22.store(out_ptr14 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8179272Z             }
2023-01-11T21:41:26.8179460Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.8179635Z             for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8179768Z             {
2023-01-11T21:41:26.8179941Z                 auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.8180138Z                 auto tmp1 = in_ptr0[8 + i1 + (32*i0)];
2023-01-11T21:41:26.8180343Z                 auto tmp3 = in_ptr0[16 + i1 + (32*i0)];
2023-01-11T21:41:26.8180547Z                 auto tmp5 = in_ptr0[24 + i1 + (32*i0)];
2023-01-11T21:41:26.8180722Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8181028Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8181205Z                 auto tmp6 = tmp4 + tmp5;
2023-01-11T21:41:26.8181392Z                 auto tmp7 = static_cast<float>(4);
2023-01-11T21:41:26.8181579Z                 auto tmp8 = tmp6 / tmp7;
2023-01-11T21:41:26.8181864Z                 auto tmp9 = tmp0 - tmp8;
2023-01-11T21:41:26.8182053Z                 auto tmp10 = tmp9 * tmp9;
2023-01-11T21:41:26.8182328Z                 auto tmp11 = tmp1 - tmp8;
2023-01-11T21:41:26.8182513Z                 auto tmp12 = tmp11 * tmp11;
2023-01-11T21:41:26.8182700Z                 auto tmp13 = tmp10 + tmp12;
2023-01-11T21:41:26.8182964Z                 auto tmp14 = tmp3 - tmp8;
2023-01-11T21:41:26.8183257Z                 auto tmp15 = tmp14 * tmp14;
2023-01-11T21:41:26.8183537Z                 auto tmp16 = tmp13 + tmp15;
2023-01-11T21:41:26.8183821Z                 auto tmp17 = tmp5 - tmp8;
2023-01-11T21:41:26.8184003Z                 auto tmp18 = tmp17 * tmp17;
2023-01-11T21:41:26.8184194Z                 auto tmp19 = tmp16 + tmp18;
2023-01-11T21:41:26.8184399Z                 auto tmp20 = static_cast<float>(3);
2023-01-11T21:41:26.8184569Z                 auto tmp21 = tmp19 / tmp20;
2023-01-11T21:41:26.8184783Z                 auto tmp22 = std::sqrt(tmp21);
2023-01-11T21:41:26.8184969Z                 out_ptr14[i1 + (8*i0)] = tmp22;
2023-01-11T21:41:26.8185108Z             }
2023-01-11T21:41:26.8185246Z         }
2023-01-11T21:41:26.8185414Z         #pragma omp single
2023-01-11T21:41:26.8185554Z         {
2023-01-11T21:41:26.8185662Z             {
2023-01-11T21:41:26.8185794Z                 {
2023-01-11T21:41:26.8185982Z                     auto tmp0 = out_ptr1[0];
2023-01-11T21:41:26.8186195Z                     auto tmp1 = static_cast<float>(255);
2023-01-11T21:41:26.8186394Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8186567Z                     in_out_ptr4[0] = tmp2;
2023-01-11T21:41:26.8186695Z                 }
2023-01-11T21:41:26.8186820Z             }
2023-01-11T21:41:26.8186952Z         }
2023-01-11T21:41:26.8187115Z         #pragma omp single
2023-01-11T21:41:26.8187245Z         {
2023-01-11T21:41:26.8187386Z             {
2023-01-11T21:41:26.8187520Z                 {
2023-01-11T21:41:26.8187681Z                     auto tmp0 = out_ptr3[0];
2023-01-11T21:41:26.8187895Z                     auto tmp1 = static_cast<float>(256);
2023-01-11T21:41:26.8188082Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8188267Z                     in_out_ptr5[0] = tmp2;
2023-01-11T21:41:26.8188400Z                 }
2023-01-11T21:41:26.8188533Z             }
2023-01-11T21:41:26.8188661Z         }
2023-01-11T21:41:26.8188800Z         #pragma omp single
2023-01-11T21:41:26.8188930Z         {
2023-01-11T21:41:26.8189062Z             {
2023-01-11T21:41:26.8189199Z                 {
2023-01-11T21:41:26.8189386Z                     auto tmp0 = out_ptr9[0];
2023-01-11T21:41:26.8189603Z                     auto tmp1 = static_cast<float>(256);
2023-01-11T21:41:26.8189792Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8189986Z                     auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:41:26.8190167Z                     in_out_ptr6[0] = tmp3;
2023-01-11T21:41:26.8190304Z                 }
2023-01-11T21:41:26.8190439Z             }
2023-01-11T21:41:26.8190566Z         }
2023-01-11T21:41:26.8190694Z     }
2023-01-11T21:41:26.8190825Z }
2023-01-11T21:41:26.8191005Z ''')
2023-01-11T21:41:26.8191017Z 
2023-01-11T21:41:26.8191024Z 
2023-01-11T21:41:26.8191213Z async_compile.wait(globals())
2023-01-11T21:41:26.8191365Z del async_compile
2023-01-11T21:41:26.8191377Z 
2023-01-11T21:41:26.8191513Z def call(args):
2023-01-11T21:41:26.8191659Z     arg0_1, = args
2023-01-11T21:41:26.8191812Z     args.clear()
2023-01-11T21:41:26.8192265Z     buf0 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8192626Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8193191Z     buf2 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8193557Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8193974Z     buf10 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8194399Z     buf4 = empty_strided((2, 4, 4, 1), (16, 4, 1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8194807Z     buf5 = empty_strided((2, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8195224Z     buf7 = empty_strided((2, 4, 4, 1), (16, 4, 1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8195397Z     buf6 = buf5; del buf5  # reuse
2023-01-11T21:41:26.8195789Z     buf8 = empty_strided((2, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8196059Z     buf9 = buf8; del buf8  # reuse
2023-01-11T21:41:26.8196435Z     buf11 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8196872Z     buf12 = empty_strided((1, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8197262Z     buf13 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8197690Z     buf15 = empty_strided((1, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8197871Z     buf14 = buf13; del buf13  # reuse
2023-01-11T21:41:26.8198264Z     buf16 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8198416Z     buf17 = buf16; del buf16  # reuse
2023-01-11T21:41:26.8198822Z     buf18 = empty_strided((2, 4, 1, 8), (32, 8, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8199001Z     buf19 = buf1; del buf1  # reuse
2023-01-11T21:41:26.8199170Z     buf20 = buf3; del buf3  # reuse
2023-01-11T21:41:26.8199349Z     buf21 = buf11; del buf11  # reuse
2023-01-11T21:41:26.8200223Z     kernel_cpp_0(c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf17.data_ptr()), c_void_p(buf19.data_ptr()), c_void_p(buf20.data_ptr()), c_void_p(buf21.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf15.data_ptr()), c_void_p(buf18.data_ptr()))
2023-01-11T21:41:26.8200385Z     del arg0_1
2023-01-11T21:41:26.8200618Z     return (buf19, buf20, buf6, buf9, buf21, buf14, buf17, buf18, )
2023-01-11T21:41:26.8200631Z 
2023-01-11T21:41:26.8200642Z 
2023-01-11T21:41:26.8200799Z if __name__ == "__main__":
2023-01-11T21:41:26.8201014Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8201257Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8201722Z     arg0_1 = rand_strided((2, 4, 4, 8), (128, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8201943Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8202489Z [2023-01-11 21:38:26,677] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 445
2023-01-11T21:41:26.8202511Z 
2023-01-11T21:41:26.8202654Z ok (1.932s)
2023-01-11T21:41:26.8203310Z   test_strided_inputs_cpu (__main__.CpuTests) ... [2023-01-11 21:38:26,693] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 446
2023-01-11T21:41:26.8203837Z [2023-01-11 21:38:28,166] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 446
2023-01-11T21:41:26.8203847Z 
2023-01-11T21:41:26.8204039Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8204167Z import torch
2023-01-11T21:41:26.8204317Z import random
2023-01-11T21:41:26.8204545Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8204783Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8204794Z 
2023-01-11T21:41:26.8204963Z aten = torch.ops.aten
2023-01-11T21:41:26.8205239Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8205539Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8205555Z 
2023-01-11T21:41:26.8205563Z 
2023-01-11T21:41:26.8205841Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8206212Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8206448Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8206653Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8206854Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8206984Z {
2023-01-11T21:41:26.8207188Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8207304Z     {
2023-01-11T21:41:26.8207455Z         #pragma omp for 
2023-01-11T21:41:26.8207627Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.8207754Z         {
2023-01-11T21:41:26.8207972Z             {
2023-01-11T21:41:26.8208120Z                 {
2023-01-11T21:41:26.8208319Z                     auto tmp0 = in_ptr0[2*i0];
2023-01-11T21:41:26.8208503Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.8208687Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8208857Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8209161Z                 }
2023-01-11T21:41:26.8209310Z             }
2023-01-11T21:41:26.8209447Z         }
2023-01-11T21:41:26.8209577Z     }
2023-01-11T21:41:26.8209687Z }
2023-01-11T21:41:26.8209873Z ''')
2023-01-11T21:41:26.8209887Z 
2023-01-11T21:41:26.8209894Z 
2023-01-11T21:41:26.8210082Z async_compile.wait(globals())
2023-01-11T21:41:26.8210242Z del async_compile
2023-01-11T21:41:26.8210253Z 
2023-01-11T21:41:26.8210398Z def call(args):
2023-01-11T21:41:26.8210559Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8210713Z     args.clear()
2023-01-11T21:41:26.8211123Z     buf0 = empty_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8211429Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8211581Z     del arg0_1
2023-01-11T21:41:26.8211729Z     del arg1_1
2023-01-11T21:41:26.8211873Z     return (buf0, )
2023-01-11T21:41:26.8211883Z 
2023-01-11T21:41:26.8211892Z 
2023-01-11T21:41:26.8212045Z if __name__ == "__main__":
2023-01-11T21:41:26.8212279Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8212521Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8212907Z     arg0_1 = rand_strided((8, 16), (32, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8213313Z     arg1_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8213549Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8213564Z 
2023-01-11T21:41:26.8213699Z ok (1.487s)
2023-01-11T21:41:26.8214586Z   test_sum1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8214849Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8215393Z [2023-01-11 21:38:28,182] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 447
2023-01-11T21:41:26.8215915Z [2023-01-11 21:38:29,816] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 447
2023-01-11T21:41:26.8215929Z 
2023-01-11T21:41:26.8216121Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8216274Z import torch
2023-01-11T21:41:26.8216404Z import random
2023-01-11T21:41:26.8216629Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8216878Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8216891Z 
2023-01-11T21:41:26.8217057Z aten = torch.ops.aten
2023-01-11T21:41:26.8217478Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8217660Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8217672Z 
2023-01-11T21:41:26.8217678Z 
2023-01-11T21:41:26.8217973Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8218359Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8218575Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8218788Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8218991Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8219115Z {
2023-01-11T21:41:26.8219313Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8219435Z     {
2023-01-11T21:41:26.8219600Z         #pragma omp for 
2023-01-11T21:41:26.8219853Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8219995Z         {
2023-01-11T21:41:26.8220133Z             {
2023-01-11T21:41:26.8220507Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8220678Z                 float tmp3 = 0;
2023-01-11T21:41:26.8220925Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.8221115Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8221240Z                 {
2023-01-11T21:41:26.8221521Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8221809Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8221991Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8222168Z                     tmp3_vec += tmp2;
2023-01-11T21:41:26.8222302Z                 }
2023-01-11T21:41:26.8222687Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:26.8222928Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:26.8223093Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8223320Z                 {
2023-01-11T21:41:26.8223515Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8223705Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.8223890Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8224056Z                     tmp3 += tmp2;
2023-01-11T21:41:26.8224188Z                 }
2023-01-11T21:41:26.8224334Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.8224470Z             }
2023-01-11T21:41:26.8224599Z         }
2023-01-11T21:41:26.8224730Z     }
2023-01-11T21:41:26.8224855Z }
2023-01-11T21:41:26.8225056Z ''')
2023-01-11T21:41:26.8225073Z 
2023-01-11T21:41:26.8225081Z 
2023-01-11T21:41:26.8225269Z async_compile.wait(globals())
2023-01-11T21:41:26.8225408Z del async_compile
2023-01-11T21:41:26.8225437Z 
2023-01-11T21:41:26.8225568Z def call(args):
2023-01-11T21:41:26.8225741Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8225894Z     args.clear()
2023-01-11T21:41:26.8226287Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8226608Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8226757Z     del arg0_1
2023-01-11T21:41:26.8226903Z     del arg1_1
2023-01-11T21:41:26.8227036Z     return (buf0, )
2023-01-11T21:41:26.8227048Z 
2023-01-11T21:41:26.8227056Z 
2023-01-11T21:41:26.8227200Z if __name__ == "__main__":
2023-01-11T21:41:26.8227434Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8227681Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8228084Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8228474Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8228710Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8228834Z 
2023-01-11T21:41:26.8228979Z ok (1.651s)
2023-01-11T21:41:26.8229821Z   test_sum2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8230070Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8230588Z [2023-01-11 21:38:29,839] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 448
2023-01-11T21:41:26.8231193Z [2023-01-11 21:38:31,378] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 448
2023-01-11T21:41:26.8231210Z 
2023-01-11T21:41:26.8231404Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8231551Z import torch
2023-01-11T21:41:26.8231697Z import random
2023-01-11T21:41:26.8231930Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8232163Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8232174Z 
2023-01-11T21:41:26.8232316Z aten = torch.ops.aten
2023-01-11T21:41:26.8232586Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8232776Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8232788Z 
2023-01-11T21:41:26.8232795Z 
2023-01-11T21:41:26.8233079Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8233481Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8233719Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8233930Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8234146Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8234321Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8234461Z {
2023-01-11T21:41:26.8234662Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8234795Z     {
2023-01-11T21:41:26.8234950Z         #pragma omp for 
2023-01-11T21:41:26.8235129Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8235265Z         {
2023-01-11T21:41:26.8235416Z             #pragma GCC ivdep
2023-01-11T21:41:26.8235594Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:41:26.8235731Z             {
2023-01-11T21:41:26.8235860Z                 {
2023-01-11T21:41:26.8235998Z                     {
2023-01-11T21:41:26.8236166Z                         float tmp3 = 0;
2023-01-11T21:41:26.8236337Z                         for(long i2=0; i2<27; i2+=1)
2023-01-11T21:41:26.8236487Z                         {
2023-01-11T21:41:26.8236635Z                             {
2023-01-11T21:41:26.8236858Z                                 auto tmp0 = in_ptr0[i1 + (21*i2) + (567*i0)];
2023-01-11T21:41:26.8237083Z                                 auto tmp1 = in_ptr1[i1 + (21*i2) + (567*i0)];
2023-01-11T21:41:26.8237289Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8237468Z                                 tmp3 += tmp2;
2023-01-11T21:41:26.8237617Z                             }
2023-01-11T21:41:26.8237740Z                         }
2023-01-11T21:41:26.8237923Z                         out_ptr0[i1 + (21*i0)] = tmp3;
2023-01-11T21:41:26.8238065Z                     }
2023-01-11T21:41:26.8238204Z                 }
2023-01-11T21:41:26.8238341Z             }
2023-01-11T21:41:26.8238478Z         }
2023-01-11T21:41:26.8238621Z         #pragma omp for 
2023-01-11T21:41:26.8238781Z         for(long i0=0; i0<216; i0+=1)
2023-01-11T21:41:26.8238924Z         {
2023-01-11T21:41:26.8239060Z             {
2023-01-11T21:41:26.8239453Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8239621Z                 float tmp3 = 0;
2023-01-11T21:41:26.8239856Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.8240147Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.8240270Z                 {
2023-01-11T21:41:26.8240551Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (21*i0));
2023-01-11T21:41:26.8240829Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (21*i0));
2023-01-11T21:41:26.8241013Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8241180Z                     tmp3_vec += tmp2;
2023-01-11T21:41:26.8241313Z                 }
2023-01-11T21:41:26.8241688Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:26.8241944Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:26.8242197Z                 for(long i1=16; i1<21; i1+=1)
2023-01-11T21:41:26.8242346Z                 {
2023-01-11T21:41:26.8242551Z                     auto tmp0 = in_ptr0[i1 + (21*i0)];
2023-01-11T21:41:26.8242748Z                     auto tmp1 = in_ptr1[i1 + (21*i0)];
2023-01-11T21:41:26.8242944Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8243103Z                     tmp3 += tmp2;
2023-01-11T21:41:26.8243244Z                 }
2023-01-11T21:41:26.8243394Z                 out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.8243534Z             }
2023-01-11T21:41:26.8243654Z         }
2023-01-11T21:41:26.8243781Z     }
2023-01-11T21:41:26.8243907Z }
2023-01-11T21:41:26.8244107Z ''')
2023-01-11T21:41:26.8244121Z 
2023-01-11T21:41:26.8244129Z 
2023-01-11T21:41:26.8244310Z async_compile.wait(globals())
2023-01-11T21:41:26.8244443Z del async_compile
2023-01-11T21:41:26.8244454Z 
2023-01-11T21:41:26.8244592Z def call(args):
2023-01-11T21:41:26.8244750Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8244906Z     args.clear()
2023-01-11T21:41:26.8245323Z     buf0 = empty_strided((8, 21), (21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8245728Z     buf1 = empty_strided((8, 9, 3), (27, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8246101Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8246251Z     del arg0_1
2023-01-11T21:41:26.8246379Z     del arg1_1
2023-01-11T21:41:26.8246536Z     return (buf0, buf1, )
2023-01-11T21:41:26.8246548Z 
2023-01-11T21:41:26.8246558Z 
2023-01-11T21:41:26.8246713Z if __name__ == "__main__":
2023-01-11T21:41:26.8246949Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8247200Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8247633Z     arg0_1 = rand_strided((8, 9, 3, 21), (567, 63, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8248076Z     arg1_1 = rand_strided((8, 9, 3, 21), (567, 63, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8248288Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8248327Z 
2023-01-11T21:41:26.8248441Z ok (1.562s)
2023-01-11T21:41:26.8249574Z   test_sum3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8249829Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8250370Z [2023-01-11 21:38:31,401] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 449
2023-01-11T21:41:26.8250896Z [2023-01-11 21:38:32,917] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 449
2023-01-11T21:41:26.8250916Z 
2023-01-11T21:41:26.8251103Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8251252Z import torch
2023-01-11T21:41:26.8251534Z import random
2023-01-11T21:41:26.8251776Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8252003Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8252016Z 
2023-01-11T21:41:26.8252176Z aten = torch.ops.aten
2023-01-11T21:41:26.8252435Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8252625Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8252636Z 
2023-01-11T21:41:26.8252643Z 
2023-01-11T21:41:26.8252937Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8253321Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8253541Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8253757Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8254039Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8254240Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8254433Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.8254562Z {
2023-01-11T21:41:26.8254760Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8254888Z     {
2023-01-11T21:41:26.8255050Z         #pragma omp for 
2023-01-11T21:41:26.8255210Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.8255332Z         {
2023-01-11T21:41:26.8255465Z             {
2023-01-11T21:41:26.8255844Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8256011Z                 float tmp3 = 0;
2023-01-11T21:41:26.8256257Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.8256430Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8256551Z                 {
2023-01-11T21:41:26.8256849Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.8257119Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:26.8257306Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8257520Z                     tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.8257695Z                     tmp3_vec += tmp2;
2023-01-11T21:41:26.8257829Z                 }
2023-01-11T21:41:26.8258203Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:26.8258425Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:26.8258617Z                 for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.8258758Z                 {
2023-01-11T21:41:26.8258951Z                     auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.8259136Z                     auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.8259328Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8259519Z                     out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.8259667Z                     tmp3 += tmp2;
2023-01-11T21:41:26.8259807Z                 }
2023-01-11T21:41:26.8259980Z                 out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.8260107Z             }
2023-01-11T21:41:26.8260235Z         }
2023-01-11T21:41:26.8260395Z         #pragma omp for 
2023-01-11T21:41:26.8260559Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.8260671Z         {
2023-01-11T21:41:26.8260940Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.8261208Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:26.8261385Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8261565Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.8261701Z         }
2023-01-11T21:41:26.8261889Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8262044Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.8262169Z         {
2023-01-11T21:41:26.8262338Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.8262645Z             auto tmp1 = static_cast<float>(10);
2023-01-11T21:41:26.8262816Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8262986Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.8263110Z         }
2023-01-11T21:41:26.8263296Z     }
2023-01-11T21:41:26.8263432Z }
2023-01-11T21:41:26.8263633Z ''')
2023-01-11T21:41:26.8263644Z 
2023-01-11T21:41:26.8263655Z 
2023-01-11T21:41:26.8263840Z async_compile.wait(globals())
2023-01-11T21:41:26.8263995Z del async_compile
2023-01-11T21:41:26.8264010Z 
2023-01-11T21:41:26.8264142Z def call(args):
2023-01-11T21:41:26.8264305Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8264434Z     args.clear()
2023-01-11T21:41:26.8264844Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8265350Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8265746Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8266158Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.8266306Z     del arg0_1
2023-01-11T21:41:26.8266450Z     del arg1_1
2023-01-11T21:41:26.8266616Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.8266627Z 
2023-01-11T21:41:26.8266635Z 
2023-01-11T21:41:26.8266796Z if __name__ == "__main__":
2023-01-11T21:41:26.8266997Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8267243Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8267652Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8268043Z     arg1_1 = rand_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8268284Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8268293Z 
2023-01-11T21:41:26.8268432Z ok (1.538s)
2023-01-11T21:41:26.8269305Z   test_sum4_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8269566Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8270100Z [2023-01-11 21:38:32,941] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 450
2023-01-11T21:41:26.8270609Z [2023-01-11 21:38:34,482] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 450
2023-01-11T21:41:26.8270625Z 
2023-01-11T21:41:26.8270822Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8270984Z import torch
2023-01-11T21:41:26.8271133Z import random
2023-01-11T21:41:26.8271358Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8271616Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8271627Z 
2023-01-11T21:41:26.8271781Z aten = torch.ops.aten
2023-01-11T21:41:26.8272026Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8272218Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8272229Z 
2023-01-11T21:41:26.8272235Z 
2023-01-11T21:41:26.8272514Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8272902Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8273136Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8273342Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8273540Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8273735Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8273918Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.8274119Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.8274383Z {
2023-01-11T21:41:26.8274586Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8274710Z     {
2023-01-11T21:41:26.8274874Z         #pragma omp for 
2023-01-11T21:41:26.8275041Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.8275157Z         {
2023-01-11T21:41:26.8275435Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8275692Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.8275878Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8276065Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8276200Z         }
2023-01-11T21:41:26.8276397Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8276544Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:41:26.8276762Z         {
2023-01-11T21:41:26.8276952Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8277147Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.8277331Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8277490Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8277616Z         }
2023-01-11T21:41:26.8277767Z         #pragma omp for 
2023-01-11T21:41:26.8277938Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:41:26.8278072Z         {
2023-01-11T21:41:26.8278210Z             {
2023-01-11T21:41:26.8278575Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8278754Z                 float tmp1 = 0;
2023-01-11T21:41:26.8278997Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8279183Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8279305Z                 {
2023-01-11T21:41:26.8279586Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8279757Z                     tmp1_vec += tmp0;
2023-01-11T21:41:26.8279902Z                 }
2023-01-11T21:41:26.8280285Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.8280517Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.8280700Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8280840Z                 {
2023-01-11T21:41:26.8281022Z                     auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8281185Z                     tmp1 += tmp0;
2023-01-11T21:41:26.8281326Z                 }
2023-01-11T21:41:26.8281489Z                 out_ptr1[i0] = tmp1;
2023-01-11T21:41:26.8281626Z             }
2023-01-11T21:41:26.8281772Z         }
2023-01-11T21:41:26.8281912Z         #pragma omp for 
2023-01-11T21:41:26.8282083Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.8282219Z         {
2023-01-11T21:41:26.8282480Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8282755Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.8282930Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8283122Z             tmp2.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.8283252Z         }
2023-01-11T21:41:26.8283425Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8283604Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:41:26.8283733Z         {
2023-01-11T21:41:26.8283905Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.8284106Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.8284270Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8284402Z             out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.8284537Z         }
2023-01-11T21:41:26.8284701Z         #pragma omp for 
2023-01-11T21:41:26.8284868Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.8285008Z         {
2023-01-11T21:41:26.8285144Z             {
2023-01-11T21:41:26.8285517Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8285767Z                 float tmp1 = 0;
2023-01-11T21:41:26.8286029Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8286216Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8286341Z                 {
2023-01-11T21:41:26.8286632Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8286799Z                     tmp1_vec += tmp0;
2023-01-11T21:41:26.8286945Z                 }
2023-01-11T21:41:26.8287323Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.8287565Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.8287812Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8287962Z                 {
2023-01-11T21:41:26.8288177Z                     auto tmp0 = out_ptr2[i1 + (8*i0)];
2023-01-11T21:41:26.8288333Z                     tmp1 += tmp0;
2023-01-11T21:41:26.8288468Z                 }
2023-01-11T21:41:26.8288634Z                 out_ptr3[i0] = tmp1;
2023-01-11T21:41:26.8288753Z             }
2023-01-11T21:41:26.8288888Z         }
2023-01-11T21:41:26.8289177Z         #pragma omp for 
2023-01-11T21:41:26.8289339Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8289468Z         {
2023-01-11T21:41:26.8289742Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr3 + 8*i0);
2023-01-11T21:41:26.8290002Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(5));
2023-01-11T21:41:26.8290182Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8290343Z             tmp2.store(out_ptr4 + 8*i0);
2023-01-11T21:41:26.8290478Z         }
2023-01-11T21:41:26.8290679Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8290850Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.8290992Z         {
2023-01-11T21:41:26.8291173Z             auto tmp0 = out_ptr3[i0];
2023-01-11T21:41:26.8291352Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:41:26.8291540Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8291700Z             out_ptr4[i0] = tmp2;
2023-01-11T21:41:26.8291837Z         }
2023-01-11T21:41:26.8291968Z     }
2023-01-11T21:41:26.8292097Z }
2023-01-11T21:41:26.8292287Z ''')
2023-01-11T21:41:26.8292300Z 
2023-01-11T21:41:26.8292306Z 
2023-01-11T21:41:26.8292500Z async_compile.wait(globals())
2023-01-11T21:41:26.8292630Z del async_compile
2023-01-11T21:41:26.8292639Z 
2023-01-11T21:41:26.8292791Z def call(args):
2023-01-11T21:41:26.8292935Z     arg0_1, = args
2023-01-11T21:41:26.8293083Z     args.clear()
2023-01-11T21:41:26.8293520Z     buf0 = empty_strided((1, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8293948Z     buf1 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8294349Z     buf2 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8294724Z     buf3 = empty_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8295111Z     buf4 = empty_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8295553Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.8295701Z     del arg0_1
2023-01-11T21:41:26.8295901Z     return (buf4, buf3, buf2, buf1, buf0, )
2023-01-11T21:41:26.8295916Z 
2023-01-11T21:41:26.8295924Z 
2023-01-11T21:41:26.8296083Z if __name__ == "__main__":
2023-01-11T21:41:26.8296306Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8296557Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8296975Z     arg0_1 = rand_strided((1, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8297322Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8297335Z 
2023-01-11T21:41:26.8297481Z ok (1.566s)
2023-01-11T21:41:26.8298352Z   test_sum5_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8298612Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8299144Z [2023-01-11 21:38:34,506] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 451
2023-01-11T21:41:26.8299791Z [2023-01-11 21:38:36,024] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 451
2023-01-11T21:41:26.8299810Z 
2023-01-11T21:41:26.8300005Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8300154Z import torch
2023-01-11T21:41:26.8300302Z import random
2023-01-11T21:41:26.8300518Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8300757Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8300767Z 
2023-01-11T21:41:26.8300926Z aten = torch.ops.aten
2023-01-11T21:41:26.8301189Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8301382Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8301393Z 
2023-01-11T21:41:26.8301399Z 
2023-01-11T21:41:26.8301691Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8302079Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8302318Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8302528Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8302728Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8302868Z {
2023-01-11T21:41:26.8303045Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:26.8303328Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8303460Z     {
2023-01-11T21:41:26.8303623Z         #pragma omp for 
2023-01-11T21:41:26.8303780Z         for(long i0=0; i0<136; i0+=1)
2023-01-11T21:41:26.8303912Z         {
2023-01-11T21:41:26.8304044Z             {
2023-01-11T21:41:26.8304417Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8304583Z                 float tmp3 = 0;
2023-01-11T21:41:26.8304821Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.8305010Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8305136Z                 {
2023-01-11T21:41:26.8305433Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (9*i0));
2023-01-11T21:41:26.8305711Z                     auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.8305899Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8306063Z                     tmp3_vec += tmp2;
2023-01-11T21:41:26.8306205Z                 }
2023-01-11T21:41:26.8306597Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:26.8306829Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:26.8306989Z                 for(long i1=8; i1<9; i1+=1)
2023-01-11T21:41:26.8307140Z                 {
2023-01-11T21:41:26.8307341Z                     auto tmp0 = in_ptr0[i1 + (9*i0)];
2023-01-11T21:41:26.8307547Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.8307734Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8307890Z                     tmp3 += tmp2;
2023-01-11T21:41:26.8308038Z                 }
2023-01-11T21:41:26.8308191Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.8308430Z             }
2023-01-11T21:41:26.8308560Z         }
2023-01-11T21:41:26.8308730Z         #pragma omp for 
2023-01-11T21:41:26.8308889Z         for(long i0=0; i0<17; i0+=1)
2023-01-11T21:41:26.8309024Z         {
2023-01-11T21:41:26.8309161Z             {
2023-01-11T21:41:26.8309517Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8309686Z                 float tmp3 = 0;
2023-01-11T21:41:26.8309924Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.8310107Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8310243Z                 {
2023-01-11T21:41:26.8310522Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8310879Z                     auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:41:26.8311067Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8311230Z                     tmp3_vec += tmp2;
2023-01-11T21:41:26.8311374Z                 }
2023-01-11T21:41:26.8311743Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:26.8311989Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:26.8312168Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8312299Z                 {
2023-01-11T21:41:26.8312498Z                     auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8312707Z                     auto tmp1 = static_cast<float>(3);
2023-01-11T21:41:26.8312874Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8313040Z                     tmp3 += tmp2;
2023-01-11T21:41:26.8313175Z                 }
2023-01-11T21:41:26.8313359Z                 out_ptr1[i0] = tmp3;
2023-01-11T21:41:26.8313493Z             }
2023-01-11T21:41:26.8313619Z         }
2023-01-11T21:41:26.8313770Z         #pragma omp for 
2023-01-11T21:41:26.8313947Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8314078Z         {
2023-01-11T21:41:26.8314356Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8314624Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(5));
2023-01-11T21:41:26.8314804Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8314997Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.8315126Z         }
2023-01-11T21:41:26.8315300Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8315465Z         for(long i0=16; i0<17; i0+=1)
2023-01-11T21:41:26.8315594Z         {
2023-01-11T21:41:26.8315763Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.8315963Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:41:26.8316144Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8316319Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8316430Z         }
2023-01-11T21:41:26.8316567Z     }
2023-01-11T21:41:26.8316689Z }
2023-01-11T21:41:26.8316892Z ''')
2023-01-11T21:41:26.8316906Z 
2023-01-11T21:41:26.8316913Z 
2023-01-11T21:41:26.8317097Z async_compile.wait(globals())
2023-01-11T21:41:26.8317255Z del async_compile
2023-01-11T21:41:26.8317265Z 
2023-01-11T21:41:26.8317397Z def call(args):
2023-01-11T21:41:26.8317521Z     arg0_1, = args
2023-01-11T21:41:26.8317667Z     args.clear()
2023-01-11T21:41:26.8318104Z     buf0 = empty_strided((1, 17, 8), (136, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8318497Z     buf1 = empty_strided((1, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8318678Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:41:26.8318994Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8319144Z     del arg0_1
2023-01-11T21:41:26.8319279Z     return (buf2, )
2023-01-11T21:41:26.8319293Z 
2023-01-11T21:41:26.8319323Z 
2023-01-11T21:41:26.8319462Z if __name__ == "__main__":
2023-01-11T21:41:26.8319794Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8320042Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8320473Z     arg0_1 = rand_strided((1, 17, 8, 9), (1224, 72, 9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8320682Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8320695Z 
2023-01-11T21:41:26.8320841Z ok (1.541s)
2023-01-11T21:41:26.8321742Z   test_sum_dtype_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8322078Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8322612Z [2023-01-11 21:38:36,045] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 452
2023-01-11T21:41:26.8323130Z [2023-01-11 21:38:36,063] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 452
2023-01-11T21:41:26.8323140Z 
2023-01-11T21:41:26.8323337Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8323491Z import torch
2023-01-11T21:41:26.8323640Z import random
2023-01-11T21:41:26.8323876Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8324108Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8324117Z 
2023-01-11T21:41:26.8324280Z aten = torch.ops.aten
2023-01-11T21:41:26.8324522Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8324716Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8324726Z 
2023-01-11T21:41:26.8324734Z 
2023-01-11T21:41:26.8325018Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8325413Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8325660Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8325869Z                        double* __restrict__ out_ptr0,
2023-01-11T21:41:26.8326064Z                        double* __restrict__ out_ptr1,
2023-01-11T21:41:26.8326268Z                        double* __restrict__ out_ptr2)
2023-01-11T21:41:26.8326381Z {
2023-01-11T21:41:26.8326583Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8326708Z     {
2023-01-11T21:41:26.8326867Z         #pragma omp for 
2023-01-11T21:41:26.8327038Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8327170Z         {
2023-01-11T21:41:26.8327306Z             {
2023-01-11T21:41:26.8327426Z                 {
2023-01-11T21:41:26.8327598Z                     double tmp2 = 0;
2023-01-11T21:41:26.8327796Z                     for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:26.8327935Z                     {
2023-01-11T21:41:26.8328084Z                         {
2023-01-11T21:41:26.8328306Z                             auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.8328538Z                             auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.8328687Z                             tmp2 += tmp1;
2023-01-11T21:41:26.8328830Z                         }
2023-01-11T21:41:26.8328966Z                     }
2023-01-11T21:41:26.8329281Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8329426Z                 }
2023-01-11T21:41:26.8329563Z             }
2023-01-11T21:41:26.8329694Z         }
2023-01-11T21:41:26.8329804Z     }
2023-01-11T21:41:26.8329919Z     {
2023-01-11T21:41:26.8330047Z         {
2023-01-11T21:41:26.8330208Z             double tmp2 = 0;
2023-01-11T21:41:26.8330428Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8330560Z             {
2023-01-11T21:41:26.8330759Z                 #pragma omp for reduction(+:tmp2) 
2023-01-11T21:41:26.8330938Z                 for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:41:26.8331219Z                 {
2023-01-11T21:41:26.8331363Z                     {
2023-01-11T21:41:26.8331557Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8331774Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.8331941Z                         tmp2 += tmp1;
2023-01-11T21:41:26.8332066Z                     }
2023-01-11T21:41:26.8332200Z                 }
2023-01-11T21:41:26.8332328Z             }
2023-01-11T21:41:26.8332501Z             out_ptr1[0] = tmp2;
2023-01-11T21:41:26.8332632Z         }
2023-01-11T21:41:26.8332751Z     }
2023-01-11T21:41:26.8332955Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8333062Z     {
2023-01-11T21:41:26.8333222Z         #pragma omp for 
2023-01-11T21:41:26.8333392Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8333526Z         {
2023-01-11T21:41:26.8333840Z             #pragma GCC ivdep
2023-01-11T21:41:26.8334033Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:41:26.8334151Z             {
2023-01-11T21:41:26.8334297Z                 {
2023-01-11T21:41:26.8334437Z                     {
2023-01-11T21:41:26.8334639Z                         auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:41:26.8334833Z                         auto tmp2 = out_ptr0[i1];
2023-01-11T21:41:26.8335023Z                         auto tmp4 = out_ptr1[0];
2023-01-11T21:41:26.8335247Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.8335422Z                         auto tmp3 = tmp1 * tmp2;
2023-01-11T21:41:26.8335598Z                         auto tmp5 = tmp3 + tmp4;
2023-01-11T21:41:26.8335791Z                         out_ptr2[i1 + (32*i0)] = tmp5;
2023-01-11T21:41:26.8335935Z                     }
2023-01-11T21:41:26.8336072Z                 }
2023-01-11T21:41:26.8336209Z             }
2023-01-11T21:41:26.8336339Z         }
2023-01-11T21:41:26.8336449Z     }
2023-01-11T21:41:26.8336572Z }
2023-01-11T21:41:26.8336789Z ''')
2023-01-11T21:41:26.8336803Z 
2023-01-11T21:41:26.8336812Z 
2023-01-11T21:41:26.8337000Z async_compile.wait(globals())
2023-01-11T21:41:26.8337154Z del async_compile
2023-01-11T21:41:26.8337163Z 
2023-01-11T21:41:26.8337312Z def call(args):
2023-01-11T21:41:26.8337459Z     arg0_1, = args
2023-01-11T21:41:26.8337585Z     args.clear()
2023-01-11T21:41:26.8337994Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.8338365Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.8338779Z     buf2 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.8339146Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.8339294Z     del arg0_1
2023-01-11T21:41:26.8339441Z     return (buf2, )
2023-01-11T21:41:26.8339452Z 
2023-01-11T21:41:26.8339459Z 
2023-01-11T21:41:26.8339627Z if __name__ == "__main__":
2023-01-11T21:41:26.8339863Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8340097Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8340503Z     arg0_1 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8340725Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8340738Z 
2023-01-11T21:41:26.8340878Z ok (0.039s)
2023-01-11T21:41:26.8341765Z   test_sum_int_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8342022Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8342558Z [2023-01-11 21:38:36,083] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 453
2023-01-11T21:41:26.8343267Z [2023-01-11 21:38:36,095] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 453
2023-01-11T21:41:26.8344086Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8344334Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8344836Z [2023-01-11 21:38:36,112] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 454
2023-01-11T21:41:26.8345369Z [2023-01-11 21:38:36,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 454
2023-01-11T21:41:26.8346126Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8346325Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8346769Z [2023-01-11 21:38:36,141] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 455
2023-01-11T21:41:26.8347302Z [2023-01-11 21:38:36,153] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 455
2023-01-11T21:41:26.8347314Z 
2023-01-11T21:41:26.8347509Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8347663Z import torch
2023-01-11T21:41:26.8347814Z import random
2023-01-11T21:41:26.8348033Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8348263Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8348276Z 
2023-01-11T21:41:26.8348444Z aten = torch.ops.aten
2023-01-11T21:41:26.8348715Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8348897Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8348906Z 
2023-01-11T21:41:26.8348913Z 
2023-01-11T21:41:26.8349190Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8349579Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8349804Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8349993Z                        const bool* __restrict__ in_ptr0,
2023-01-11T21:41:26.8350189Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.8350308Z {
2023-01-11T21:41:26.8350492Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.8350622Z     {
2023-01-11T21:41:26.8350743Z         {
2023-01-11T21:41:26.8350904Z             long tmp2 = 0;
2023-01-11T21:41:26.8351041Z             long tmp3 = 0;
2023-01-11T21:41:26.8351258Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8351401Z             {
2023-01-11T21:41:26.8351746Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:26.8351915Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.8352037Z                 {
2023-01-11T21:41:26.8352158Z                     {
2023-01-11T21:41:26.8352302Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8352483Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:26.8352648Z                         tmp2 += tmp1;
2023-01-11T21:41:26.8352791Z                         tmp3 += tmp1;
2023-01-11T21:41:26.8352914Z                     }
2023-01-11T21:41:26.8353031Z                 }
2023-01-11T21:41:26.8353141Z             }
2023-01-11T21:41:26.8353268Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.8353406Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:26.8353525Z         }
2023-01-11T21:41:26.8353645Z     }
2023-01-11T21:41:26.8353754Z     {
2023-01-11T21:41:26.8354006Z         {
2023-01-11T21:41:26.8354138Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.8354285Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:26.8354458Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:26.8354618Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8354772Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8354921Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:26.8355025Z         }
2023-01-11T21:41:26.8355117Z     }
2023-01-11T21:41:26.8355225Z }
2023-01-11T21:41:26.8355396Z ''')
2023-01-11T21:41:26.8355411Z 
2023-01-11T21:41:26.8355418Z 
2023-01-11T21:41:26.8355581Z async_compile.wait(globals())
2023-01-11T21:41:26.8355714Z del async_compile
2023-01-11T21:41:26.8355722Z 
2023-01-11T21:41:26.8355846Z def call(args):
2023-01-11T21:41:26.8355978Z     arg0_1, = args
2023-01-11T21:41:26.8356180Z     args.clear()
2023-01-11T21:41:26.8356513Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8356827Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8356983Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:26.8357270Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8357401Z     del arg0_1
2023-01-11T21:41:26.8357514Z     return (buf2, )
2023-01-11T21:41:26.8357525Z 
2023-01-11T21:41:26.8357531Z 
2023-01-11T21:41:26.8357669Z if __name__ == "__main__":
2023-01-11T21:41:26.8357845Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8358063Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8358396Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.8358588Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8358596Z 
2023-01-11T21:41:26.8358604Z 
2023-01-11T21:41:26.8358766Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8358895Z import torch
2023-01-11T21:41:26.8359022Z import random
2023-01-11T21:41:26.8359220Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8359413Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8359422Z 
2023-01-11T21:41:26.8359563Z aten = torch.ops.aten
2023-01-11T21:41:26.8359785Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8359946Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8359957Z 
2023-01-11T21:41:26.8359962Z 
2023-01-11T21:41:26.8360209Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8360546Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8360751Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8360947Z                        const unsigned char* __restrict__ in_ptr0,
2023-01-11T21:41:26.8361102Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.8361209Z {
2023-01-11T21:41:26.8361362Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.8361469Z     {
2023-01-11T21:41:26.8361587Z         {
2023-01-11T21:41:26.8361725Z             long tmp2 = 0;
2023-01-11T21:41:26.8361870Z             long tmp3 = 0;
2023-01-11T21:41:26.8362040Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8362157Z             {
2023-01-11T21:41:26.8362370Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:26.8362530Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.8362646Z                 {
2023-01-11T21:41:26.8362776Z                     {
2023-01-11T21:41:26.8362947Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8363112Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:26.8363254Z                         tmp2 += tmp1;
2023-01-11T21:41:26.8363396Z                         tmp3 += tmp1;
2023-01-11T21:41:26.8363521Z                     }
2023-01-11T21:41:26.8363642Z                 }
2023-01-11T21:41:26.8363752Z             }
2023-01-11T21:41:26.8363894Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.8364115Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:26.8364233Z         }
2023-01-11T21:41:26.8364346Z     }
2023-01-11T21:41:26.8364454Z     {
2023-01-11T21:41:26.8364562Z         {
2023-01-11T21:41:26.8364717Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.8364840Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:26.8365012Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:26.8365167Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8365318Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8365459Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:26.8365571Z         }
2023-01-11T21:41:26.8365676Z     }
2023-01-11T21:41:26.8365768Z }
2023-01-11T21:41:26.8365934Z ''')
2023-01-11T21:41:26.8365943Z 
2023-01-11T21:41:26.8365949Z 
2023-01-11T21:41:26.8366175Z async_compile.wait(globals())
2023-01-11T21:41:26.8366320Z del async_compile
2023-01-11T21:41:26.8366331Z 
2023-01-11T21:41:26.8366453Z def call(args):
2023-01-11T21:41:26.8366587Z     arg0_1, = args
2023-01-11T21:41:26.8366713Z     args.clear()
2023-01-11T21:41:26.8367022Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8367349Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8367502Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:26.8367775Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8367905Z     del arg0_1
2023-01-11T21:41:26.8368027Z     return (buf2, )
2023-01-11T21:41:26.8368036Z 
2023-01-11T21:41:26.8368042Z 
2023-01-11T21:41:26.8368184Z if __name__ == "__main__":
2023-01-11T21:41:26.8368386Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8368584Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8368918Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.uint8)
2023-01-11T21:41:26.8369235Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8369249Z 
2023-01-11T21:41:26.8369260Z 
2023-01-11T21:41:26.8369429Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8369558Z import torch
2023-01-11T21:41:26.8369693Z import random
2023-01-11T21:41:26.8369894Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8370104Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8370118Z 
2023-01-11T21:41:26.8370239Z aten = torch.ops.aten
2023-01-11T21:41:26.8370467Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8370623Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8370632Z 
2023-01-11T21:41:26.8370639Z 
2023-01-11T21:41:26.8370887Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8371229Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8371430Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8371613Z                        const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.8371787Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.8371879Z {
2023-01-11T21:41:26.8372032Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.8372148Z     {
2023-01-11T21:41:26.8372255Z         {
2023-01-11T21:41:26.8372398Z             long tmp2 = 0;
2023-01-11T21:41:26.8372536Z             long tmp3 = 0;
2023-01-11T21:41:26.8372702Z             #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8372816Z             {
2023-01-11T21:41:26.8373032Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:41:26.8373190Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.8373315Z                 {
2023-01-11T21:41:26.8373441Z                     {
2023-01-11T21:41:26.8373606Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8373784Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:41:26.8373921Z                         tmp2 += tmp1;
2023-01-11T21:41:26.8374062Z                         tmp3 += tmp1;
2023-01-11T21:41:26.8374321Z                     }
2023-01-11T21:41:26.8374441Z                 }
2023-01-11T21:41:26.8374553Z             }
2023-01-11T21:41:26.8374693Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.8374815Z             out_ptr1[0] = tmp3;
2023-01-11T21:41:26.8374932Z         }
2023-01-11T21:41:26.8375049Z     }
2023-01-11T21:41:26.8375159Z     {
2023-01-11T21:41:26.8375268Z         {
2023-01-11T21:41:26.8375421Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:41:26.8375570Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:41:26.8375728Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:26.8375880Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8376029Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8376180Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:41:26.8376286Z         }
2023-01-11T21:41:26.8376484Z     }
2023-01-11T21:41:26.8376583Z }
2023-01-11T21:41:26.8376755Z ''')
2023-01-11T21:41:26.8376767Z 
2023-01-11T21:41:26.8376779Z 
2023-01-11T21:41:26.8376941Z async_compile.wait(globals())
2023-01-11T21:41:26.8377074Z del async_compile
2023-01-11T21:41:26.8377082Z 
2023-01-11T21:41:26.8377210Z def call(args):
2023-01-11T21:41:26.8377334Z     arg0_1, = args
2023-01-11T21:41:26.8377465Z     args.clear()
2023-01-11T21:41:26.8377787Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8378075Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8378226Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:26.8378501Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8378629Z     del arg0_1
2023-01-11T21:41:26.8378752Z     return (buf2, )
2023-01-11T21:41:26.8378761Z 
2023-01-11T21:41:26.8378767Z 
2023-01-11T21:41:26.8378910Z if __name__ == "__main__":
2023-01-11T21:41:26.8379112Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8379327Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8379647Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.8379831Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8379841Z 
2023-01-11T21:41:26.8379964Z ok (0.090s)
2023-01-11T21:41:26.8380745Z   test_sum_keepdims_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8380964Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8381419Z [2023-01-11 21:38:36,169] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 456
2023-01-11T21:41:26.8381879Z [2023-01-11 21:38:36,178] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 456
2023-01-11T21:41:26.8381896Z 
2023-01-11T21:41:26.8382065Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8382193Z import torch
2023-01-11T21:41:26.8382299Z import random
2023-01-11T21:41:26.8382503Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8382703Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8382711Z 
2023-01-11T21:41:26.8382848Z aten = torch.ops.aten
2023-01-11T21:41:26.8383070Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8383320Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8383330Z 
2023-01-11T21:41:26.8383337Z 
2023-01-11T21:41:26.8383594Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8383933Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8384153Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8384343Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8384615Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8384733Z {
2023-01-11T21:41:26.8384903Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8385015Z     {
2023-01-11T21:41:26.8385156Z         #pragma omp for 
2023-01-11T21:41:26.8385281Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8385392Z         {
2023-01-11T21:41:26.8385512Z             {
2023-01-11T21:41:26.8385840Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8385985Z                 float tmp3 = 0;
2023-01-11T21:41:26.8386192Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:41:26.8386350Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8386456Z                 {
2023-01-11T21:41:26.8386772Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8387015Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8387185Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8387340Z                     tmp3_vec += tmp2;
2023-01-11T21:41:26.8387464Z                 }
2023-01-11T21:41:26.8387803Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:41:26.8388015Z                 #pragma omp simd simdlen(4)  reduction(+:tmp3)
2023-01-11T21:41:26.8388172Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8388270Z                 {
2023-01-11T21:41:26.8388437Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8388603Z                     auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.8388767Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8388913Z                     tmp3 += tmp2;
2023-01-11T21:41:26.8389026Z                 }
2023-01-11T21:41:26.8389178Z                 out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.8389277Z             }
2023-01-11T21:41:26.8389380Z         }
2023-01-11T21:41:26.8389494Z     }
2023-01-11T21:41:26.8389605Z }
2023-01-11T21:41:26.8389768Z ''')
2023-01-11T21:41:26.8389781Z 
2023-01-11T21:41:26.8389788Z 
2023-01-11T21:41:26.8389955Z async_compile.wait(globals())
2023-01-11T21:41:26.8390091Z del async_compile
2023-01-11T21:41:26.8390100Z 
2023-01-11T21:41:26.8390202Z def call(args):
2023-01-11T21:41:26.8390332Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8390460Z     args.clear()
2023-01-11T21:41:26.8390810Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8391089Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8391215Z     del arg0_1
2023-01-11T21:41:26.8391348Z     del arg1_1
2023-01-11T21:41:26.8391459Z     return (buf0, )
2023-01-11T21:41:26.8391469Z 
2023-01-11T21:41:26.8391482Z 
2023-01-11T21:41:26.8391619Z if __name__ == "__main__":
2023-01-11T21:41:26.8391821Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8392030Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8392379Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8392720Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8392926Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8392937Z 
2023-01-11T21:41:26.8393059Z ok (0.025s)
2023-01-11T21:41:26.8393822Z   test_tanh_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8394118Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8394581Z [2023-01-11 21:38:36,208] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 457
2023-01-11T21:41:26.8395042Z [2023-01-11 21:38:37,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 457
2023-01-11T21:41:26.8395051Z 
2023-01-11T21:41:26.8395222Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8395357Z import torch
2023-01-11T21:41:26.8395484Z import random
2023-01-11T21:41:26.8395687Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8395891Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8395901Z 
2023-01-11T21:41:26.8396023Z aten = torch.ops.aten
2023-01-11T21:41:26.8396325Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8396496Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8396507Z 
2023-01-11T21:41:26.8396513Z 
2023-01-11T21:41:26.8396766Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8397127Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8397340Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8397512Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8397687Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8397787Z {
2023-01-11T21:41:26.8397958Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8398070Z     {
2023-01-11T21:41:26.8398206Z         #pragma omp for 
2023-01-11T21:41:26.8398350Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8398473Z         {
2023-01-11T21:41:26.8398709Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8398850Z             auto tmp1 = tmp0.tanh();
2023-01-11T21:41:26.8399076Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.8399240Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.8399475Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.8399632Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.8399777Z             auto tmp6 = tmp5.tanh();
2023-01-11T21:41:26.8399933Z             tmp3.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8400086Z             tmp6.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8400198Z         }
2023-01-11T21:41:26.8400368Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8400517Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:41:26.8400624Z         {
2023-01-11T21:41:26.8400808Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8400982Z             auto tmp1 = std::tanh(tmp0);
2023-01-11T21:41:26.8401141Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:41:26.8401298Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.8401465Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:41:26.8401613Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:41:26.8401783Z             auto tmp6 = std::tanh(tmp5);
2023-01-11T21:41:26.8401935Z             out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.8402076Z             out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.8402176Z         }
2023-01-11T21:41:26.8402277Z     }
2023-01-11T21:41:26.8402391Z }
2023-01-11T21:41:26.8402553Z ''')
2023-01-11T21:41:26.8402565Z 
2023-01-11T21:41:26.8402570Z 
2023-01-11T21:41:26.8402726Z async_compile.wait(globals())
2023-01-11T21:41:26.8402862Z del async_compile
2023-01-11T21:41:26.8402875Z 
2023-01-11T21:41:26.8403002Z def call(args):
2023-01-11T21:41:26.8403120Z     arg0_1, = args
2023-01-11T21:41:26.8403237Z     args.clear()
2023-01-11T21:41:26.8403602Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8403951Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8404229Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8404448Z     del arg0_1
2023-01-11T21:41:26.8404589Z     return (buf0, buf1, )
2023-01-11T21:41:26.8404599Z 
2023-01-11T21:41:26.8404605Z 
2023-01-11T21:41:26.8404733Z if __name__ == "__main__":
2023-01-11T21:41:26.8404910Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8405128Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8405487Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8405664Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8405676Z 
2023-01-11T21:41:26.8405794Z ok (1.518s)
2023-01-11T21:41:26.8406638Z   test_tensor1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8406866Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8407328Z [2023-01-11 21:38:37,715] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 458
2023-01-11T21:41:26.8407788Z [2023-01-11 21:38:39,192] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 458
2023-01-11T21:41:26.8407798Z 
2023-01-11T21:41:26.8407950Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8408061Z import torch
2023-01-11T21:41:26.8408190Z import random
2023-01-11T21:41:26.8408394Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8408611Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8408621Z 
2023-01-11T21:41:26.8408754Z aten = torch.ops.aten
2023-01-11T21:41:26.8408990Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8409285Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8409303Z 
2023-01-11T21:41:26.8409310Z 
2023-01-11T21:41:26.8409561Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8409880Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8410087Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8410255Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8410424Z                        long* __restrict__ out_ptr1)
2023-01-11T21:41:26.8410522Z {
2023-01-11T21:41:26.8410703Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8410813Z     {
2023-01-11T21:41:26.8410939Z         #pragma omp for 
2023-01-11T21:41:26.8411088Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.8411209Z         {
2023-01-11T21:41:26.8411315Z             {
2023-01-11T21:41:26.8411431Z                 {
2023-01-11T21:41:26.8411597Z                     auto tmp2 = in_ptr0[i0];
2023-01-11T21:41:26.8411768Z                     auto tmp0 = static_cast<long>(1);
2023-01-11T21:41:26.8411955Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.8412114Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.8412261Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.8412374Z                 }
2023-01-11T21:41:26.8412492Z             }
2023-01-11T21:41:26.8412608Z         }
2023-01-11T21:41:26.8412748Z         #pragma omp single
2023-01-11T21:41:26.8412861Z         {
2023-01-11T21:41:26.8412967Z             {
2023-01-11T21:41:26.8413085Z                 {
2023-01-11T21:41:26.8413274Z                     auto tmp0 = static_cast<long>(5);
2023-01-11T21:41:26.8413426Z                     out_ptr1[0] = tmp0;
2023-01-11T21:41:26.8413540Z                 }
2023-01-11T21:41:26.8413636Z             }
2023-01-11T21:41:26.8413745Z         }
2023-01-11T21:41:26.8413846Z     }
2023-01-11T21:41:26.8413958Z }
2023-01-11T21:41:26.8414127Z ''')
2023-01-11T21:41:26.8414138Z 
2023-01-11T21:41:26.8414146Z 
2023-01-11T21:41:26.8414434Z async_compile.wait(globals())
2023-01-11T21:41:26.8414566Z del async_compile
2023-01-11T21:41:26.8414573Z 
2023-01-11T21:41:26.8414678Z def call(args):
2023-01-11T21:41:26.8414808Z     arg0_1, = args
2023-01-11T21:41:26.8414941Z     args.clear()
2023-01-11T21:41:26.8415302Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8415615Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8415901Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8416026Z     del arg0_1
2023-01-11T21:41:26.8416151Z     return (buf0, buf1, )
2023-01-11T21:41:26.8416174Z 
2023-01-11T21:41:26.8416180Z 
2023-01-11T21:41:26.8416296Z if __name__ == "__main__":
2023-01-11T21:41:26.8416491Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8416802Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8417152Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8417364Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8417372Z 
2023-01-11T21:41:26.8417493Z ok (1.497s)
2023-01-11T21:41:26.8418262Z   test_tensor2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8418485Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8418943Z [2023-01-11 21:38:39,212] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 459
2023-01-11T21:41:26.8419385Z [2023-01-11 21:38:40,688] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 459
2023-01-11T21:41:26.8419417Z 
2023-01-11T21:41:26.8419571Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8419697Z import torch
2023-01-11T21:41:26.8419828Z import random
2023-01-11T21:41:26.8420026Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8420222Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8420231Z 
2023-01-11T21:41:26.8420374Z aten = torch.ops.aten
2023-01-11T21:41:26.8420607Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8420761Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8421025Z constant0 = None  # 4ebd4ff1c68a89413a036eaaf84436373c4ec2939ac1d7f84e9908772a109281
2023-01-11T21:41:26.8421034Z 
2023-01-11T21:41:26.8421040Z 
2023-01-11T21:41:26.8421284Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8421628Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8421824Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.8422007Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8422178Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8422290Z {
2023-01-11T21:41:26.8422444Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8422555Z     {
2023-01-11T21:41:26.8422683Z         #pragma omp for 
2023-01-11T21:41:26.8422834Z         for(long i0=0; i0<19; i0+=1)
2023-01-11T21:41:26.8422956Z         {
2023-01-11T21:41:26.8423068Z             {
2023-01-11T21:41:26.8423237Z                 {
2023-01-11T21:41:26.8423407Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8423551Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:41:26.8423742Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.8423902Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.8424066Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.8424186Z                 }
2023-01-11T21:41:26.8424288Z             }
2023-01-11T21:41:26.8424394Z         }
2023-01-11T21:41:26.8424638Z     }
2023-01-11T21:41:26.8424745Z }
2023-01-11T21:41:26.8424914Z ''')
2023-01-11T21:41:26.8424927Z 
2023-01-11T21:41:26.8424934Z 
2023-01-11T21:41:26.8425095Z async_compile.wait(globals())
2023-01-11T21:41:26.8425224Z del async_compile
2023-01-11T21:41:26.8425237Z 
2023-01-11T21:41:26.8425347Z def call(args):
2023-01-11T21:41:26.8425483Z     arg0_1, = args
2023-01-11T21:41:26.8425613Z     args.clear()
2023-01-11T21:41:26.8425967Z     buf0 = empty_strided((19, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8426249Z     kernel_cpp_0(c_void_p(constant0.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8426379Z     del arg0_1
2023-01-11T21:41:26.8426502Z     return (buf0, )
2023-01-11T21:41:26.8426513Z 
2023-01-11T21:41:26.8426518Z 
2023-01-11T21:41:26.8426730Z if __name__ == "__main__":
2023-01-11T21:41:26.8426913Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8427135Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8427495Z     constant0 = rand_strided((19, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8427825Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8428013Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8428022Z 
2023-01-11T21:41:26.8428141Z ok (1.496s)
2023-01-11T21:41:26.8428909Z   test_tensor3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8429138Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8429583Z [2023-01-11 21:38:40,718] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 460
2023-01-11T21:41:26.8430038Z [2023-01-11 21:38:42,225] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 460
2023-01-11T21:41:26.8430070Z 
2023-01-11T21:41:26.8430222Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8430356Z import torch
2023-01-11T21:41:26.8430479Z import random
2023-01-11T21:41:26.8430682Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8430881Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8430891Z 
2023-01-11T21:41:26.8431037Z aten = torch.ops.aten
2023-01-11T21:41:26.8431280Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8431435Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8431443Z 
2023-01-11T21:41:26.8431449Z 
2023-01-11T21:41:26.8431681Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8432022Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8432239Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8432412Z                        long* __restrict__ out_ptr0,
2023-01-11T21:41:26.8432577Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.8432749Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.8432858Z {
2023-01-11T21:41:26.8432982Z     #pragma GCC ivdep
2023-01-11T21:41:26.8433129Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8433232Z     {
2023-01-11T21:41:26.8433345Z         {
2023-01-11T21:41:26.8433458Z             {
2023-01-11T21:41:26.8433638Z                 auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:26.8433802Z                 auto tmp1 = static_cast<long>(1);
2023-01-11T21:41:26.8433970Z                 auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.8434137Z                 auto tmp3 = static_cast<long>(2);
2023-01-11T21:41:26.8434323Z                 auto tmp4 = tmp2 ? tmp1 : tmp3;
2023-01-11T21:41:26.8434480Z                 auto tmp5 = tmp4 + tmp1;
2023-01-11T21:41:26.8434747Z                 out_ptr0[i0] = tmp5;
2023-01-11T21:41:26.8434863Z             }
2023-01-11T21:41:26.8434956Z         }
2023-01-11T21:41:26.8435068Z     }
2023-01-11T21:41:26.8435205Z     #pragma GCC ivdep
2023-01-11T21:41:26.8435352Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:41:26.8435462Z     {
2023-01-11T21:41:26.8435576Z         {
2023-01-11T21:41:26.8435697Z             {
2023-01-11T21:41:26.8435850Z                 auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:26.8436026Z                 auto tmp1 = static_cast<long>(1);
2023-01-11T21:41:26.8436187Z                 auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.8436355Z                 auto tmp3 = static_cast<long>(2);
2023-01-11T21:41:26.8436510Z                 auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.8436673Z                 auto tmp5 = static_cast<long>(3);
2023-01-11T21:41:26.8436924Z                 auto tmp6 = tmp4 ? tmp3 : tmp5;
2023-01-11T21:41:26.8437073Z                 auto tmp7 = tmp2 ? tmp1 : tmp6;
2023-01-11T21:41:26.8437232Z                 auto tmp8 = tmp7 + tmp3;
2023-01-11T21:41:26.8437372Z                 out_ptr1[i0] = tmp8;
2023-01-11T21:41:26.8437489Z             }
2023-01-11T21:41:26.8437602Z         }
2023-01-11T21:41:26.8437719Z     }
2023-01-11T21:41:26.8437893Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8437995Z     {
2023-01-11T21:41:26.8438136Z         #pragma omp for 
2023-01-11T21:41:26.8438284Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8438403Z         {
2023-01-11T21:41:26.8438519Z             {
2023-01-11T21:41:26.8438629Z                 {
2023-01-11T21:41:26.8438779Z                     auto tmp12 = in_ptr0[i0];
2023-01-11T21:41:26.8438954Z                     auto tmp0 = static_cast<long>(i0);
2023-01-11T21:41:26.8439122Z                     auto tmp1 = static_cast<long>(2);
2023-01-11T21:41:26.8439297Z                     auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.8439480Z                     auto tmp3 = static_cast<long>(1);
2023-01-11T21:41:26.8439645Z                     auto tmp4 = tmp0 < tmp3;
2023-01-11T21:41:26.8439814Z                     auto tmp5 = tmp4 ? tmp3 : tmp1;
2023-01-11T21:41:26.8439951Z                     auto tmp6 = static_cast<long>(3);
2023-01-11T21:41:26.8440114Z                     auto tmp7 = tmp0 < tmp6;
2023-01-11T21:41:26.8440294Z                     auto tmp8 = static_cast<long>(4);
2023-01-11T21:41:26.8440466Z                     auto tmp9 = tmp7 ? tmp6 : tmp8;
2023-01-11T21:41:26.8440640Z                     auto tmp10 = tmp2 ? tmp5 : tmp9;
2023-01-11T21:41:26.8440822Z                     auto tmp11 = static_cast<float>(tmp10);
2023-01-11T21:41:26.8440983Z                     auto tmp13 = tmp11 + tmp12;
2023-01-11T21:41:26.8441135Z                     out_ptr2[i0] = tmp13;
2023-01-11T21:41:26.8441236Z                 }
2023-01-11T21:41:26.8441355Z             }
2023-01-11T21:41:26.8441477Z         }
2023-01-11T21:41:26.8441580Z     }
2023-01-11T21:41:26.8441693Z }
2023-01-11T21:41:26.8441866Z ''')
2023-01-11T21:41:26.8441882Z 
2023-01-11T21:41:26.8441889Z 
2023-01-11T21:41:26.8442047Z async_compile.wait(globals())
2023-01-11T21:41:26.8442160Z del async_compile
2023-01-11T21:41:26.8442168Z 
2023-01-11T21:41:26.8442296Z def call(args):
2023-01-11T21:41:26.8442415Z     arg0_1, = args
2023-01-11T21:41:26.8442543Z     args.clear()
2023-01-11T21:41:26.8442886Z     buf0 = empty_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8443209Z     buf1 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8443556Z     buf2 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8443864Z     buf3 = empty_strided((0, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8444177Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.8444315Z     del arg0_1
2023-01-11T21:41:26.8444475Z     return (buf3, buf0, buf1, buf2, )
2023-01-11T21:41:26.8444483Z 
2023-01-11T21:41:26.8444589Z 
2023-01-11T21:41:26.8444725Z if __name__ == "__main__":
2023-01-11T21:41:26.8444921Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8445143Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8445497Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8445664Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8445697Z 
2023-01-11T21:41:26.8445808Z ok (1.537s)
2023-01-11T21:41:26.8446668Z   test_tmp_not_defined_issue1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8446900Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8447522Z Failed to collect metadata on function, produced code may be suboptimal.  Known situations this can occur are inference mode only compilation involving resize_ or prims (!schema.hasAnyAliasInfo() INTERNAL ASSERT FAILED); if your situation looks different please file a bug to PyTorch.
2023-01-11T21:41:26.8447687Z Traceback (most recent call last):
2023-01-11T21:41:26.8448176Z   File "/opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 1273, in aot_wrapper_dedupe
2023-01-11T21:41:26.8448463Z     fw_metadata, _out, _num_aliasing_metadata_outs = run_functionalized_fw_and_collect_metadata(
2023-01-11T21:41:26.8448903Z   File "/opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 289, in inner
2023-01-11T21:41:26.8449170Z     outs = f(*f_args)
2023-01-11T21:41:26.8449634Z   File "/opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 2327, in functional_call
2023-01-11T21:41:26.8449844Z     out = Interpreter(mod).run(*args[params_len:], **kwargs)
2023-01-11T21:41:26.8450251Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py", line 136, in run
2023-01-11T21:41:26.8450413Z     self.env[node] = self.run_node(node)
2023-01-11T21:41:26.8450819Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py", line 177, in run_node
2023-01-11T21:41:26.8451020Z     return getattr(self, n.op)(n.target, args, kwargs)
2023-01-11T21:41:26.8451441Z   File "/opt/conda/lib/python3.10/site-packages/torch/fx/interpreter.py", line 249, in call_function
2023-01-11T21:41:26.8451601Z     return target(*args, **kwargs)
2023-01-11T21:41:26.8451966Z   File "/opt/conda/lib/python3.10/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:41:26.8452140Z     return self._op(*args, **kwargs or {})
2023-01-11T21:41:26.8452602Z   File "/opt/conda/lib/python3.10/site-packages/torch/_inductor/overrides.py", line 36, in __torch_function__
2023-01-11T21:41:26.8452762Z     return func(*args, **kwargs)
2023-01-11T21:41:26.8453139Z   File "/opt/conda/lib/python3.10/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:41:26.8453311Z     return self._op(*args, **kwargs or {})
2023-01-11T21:41:26.8453734Z   File "/opt/conda/lib/python3.10/site-packages/torch/_prims/__init__.py", line 285, in _autograd_impl
2023-01-11T21:41:26.8453946Z     return backwards_not_supported(_prim)(*args, **kwargs)
2023-01-11T21:41:26.8454389Z   File "/opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py", line 309, in _autograd_impl
2023-01-11T21:41:26.8454563Z     return redispatch_prim(args, kwargs)
2023-01-11T21:41:26.8455020Z   File "/opt/conda/lib/python3.10/site-packages/torch/_prims_common/wrappers.py", line 279, in redispatch_prim
2023-01-11T21:41:26.8455176Z     return prim(*args, **kwargs)
2023-01-11T21:41:26.8455560Z   File "/opt/conda/lib/python3.10/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:41:26.8455736Z     return self._op(*args, **kwargs or {})
2023-01-11T21:41:26.8456715Z RuntimeError: !schema.hasAnyAliasInfo() INTERNAL ASSERT FAILED at "/var/lib/jenkins/workspace/aten/src/ATen/FunctionalizeFallbackKernel.cpp":32, please report a bug to PyTorch. mutating and aliasing ops should all have codegen'd kernels
2023-01-11T21:41:26.8456728Z 
2023-01-11T21:41:26.8457137Z While executing %broadcast_in_dim_default : [#users=1] = call_function[target=torch.ops.prims.broadcast_in_dim.default](args = (%var_default_1, [1, 512, 1], [0, 1]), kwargs = {})
2023-01-11T21:41:26.8457279Z Original traceback:
2023-01-11T21:41:26.8457400Z Module stack: {}
2023-01-11T21:41:26.8457667Z   File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 4723, in forward
2023-01-11T21:41:26.8457932Z     broadcast_in_dim_default_2 = torch.ops.prims.broadcast_in_dim.default(
2023-01-11T21:41:26.8458303Z  |   File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 318, in run
2023-01-11T21:41:26.8458457Z     return model(*ex, **kwargs)
2023-01-11T21:41:26.8458469Z 
2023-01-11T21:41:26.8458928Z [2023-01-11 21:38:42,445] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 461
2023-01-11T21:41:26.8458939Z 
2023-01-11T21:41:26.8459107Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8459239Z import torch
2023-01-11T21:41:26.8459335Z import random
2023-01-11T21:41:26.8459540Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8459755Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8459766Z 
2023-01-11T21:41:26.8459909Z aten = torch.ops.aten
2023-01-11T21:41:26.8460129Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8460290Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8460301Z 
2023-01-11T21:41:26.8460307Z 
2023-01-11T21:41:26.8460554Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8460896Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8461086Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8461266Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.8461450Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8461638Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8461810Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.8461990Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:41:26.8462169Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:41:26.8462348Z                        const float* __restrict__ in_ptr5,
2023-01-11T21:41:26.8462496Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8462664Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8462842Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.8463021Z                        float* __restrict__ out_ptr5)
2023-01-11T21:41:26.8463208Z {
2023-01-11T21:41:26.8463365Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:26.8463520Z     auto out_ptr4 = in_out_ptr1;
2023-01-11T21:41:26.8463676Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8463788Z     {
2023-01-11T21:41:26.8463922Z         #pragma omp for 
2023-01-11T21:41:26.8464076Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:26.8464188Z         {
2023-01-11T21:41:26.8464301Z             {
2023-01-11T21:41:26.8464644Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8464777Z                 float tmp1 = 0;
2023-01-11T21:41:26.8464987Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8465140Z                 for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.8465262Z                 {
2023-01-11T21:41:26.8465520Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (1024*i0));
2023-01-11T21:41:26.8465666Z                     tmp1_vec += tmp0;
2023-01-11T21:41:26.8465874Z                 }
2023-01-11T21:41:26.8466210Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.8466406Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.8466567Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:41:26.8466671Z                 {
2023-01-11T21:41:26.8466854Z                     auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:41:26.8466994Z                     tmp1 += tmp0;
2023-01-11T21:41:26.8467113Z                 }
2023-01-11T21:41:26.8467261Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.8467361Z             }
2023-01-11T21:41:26.8467464Z         }
2023-01-11T21:41:26.8467603Z         #pragma omp for 
2023-01-11T21:41:26.8467824Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:26.8467942Z         {
2023-01-11T21:41:26.8468062Z             {
2023-01-11T21:41:26.8468387Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8468529Z                 float tmp6 = 0;
2023-01-11T21:41:26.8468744Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8468897Z                 for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.8469010Z                 {
2023-01-11T21:41:26.8469251Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (1024*i0));
2023-01-11T21:41:26.8469483Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.8469723Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:41:26.8469885Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8470139Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8470311Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8470465Z                     tmp6_vec += tmp5;
2023-01-11T21:41:26.8470589Z                 }
2023-01-11T21:41:26.8470922Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8471131Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6)
2023-01-11T21:41:26.8471290Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:41:26.8471389Z                 {
2023-01-11T21:41:26.8471567Z                     auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:41:26.8471729Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:26.8471915Z                     auto tmp2 = static_cast<float>(1024);
2023-01-11T21:41:26.8472077Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8472325Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8472489Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8472634Z                     tmp6 += tmp5;
2023-01-11T21:41:26.8472736Z                 }
2023-01-11T21:41:26.8472888Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.8473006Z             }
2023-01-11T21:41:26.8473118Z         }
2023-01-11T21:41:26.8473252Z         #pragma omp for 
2023-01-11T21:41:26.8473393Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.8473491Z         {
2023-01-11T21:41:26.8473729Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8473962Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:41:26.8474116Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8474294Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.8474406Z         }
2023-01-11T21:41:26.8474573Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8474717Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:41:26.8474809Z         {
2023-01-11T21:41:26.8474968Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.8475156Z             auto tmp1 = static_cast<float>(1024);
2023-01-11T21:41:26.8475393Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8475536Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8475647Z         }
2023-01-11T21:41:26.8475772Z         #pragma omp for 
2023-01-11T21:41:26.8475920Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:26.8476033Z         {
2023-01-11T21:41:26.8476153Z             {
2023-01-11T21:41:26.8476469Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8476617Z                 float tmp9 = 0;
2023-01-11T21:41:26.8476829Z                 auto tmp9_vec = at::vec::Vectorized<float>(tmp9);
2023-01-11T21:41:26.8476986Z                 for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.8477088Z                 {
2023-01-11T21:41:26.8477414Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (1024*i0));
2023-01-11T21:41:26.8477636Z                     auto tmp1 = at::vec::Vectorized<float>(in_ptr2[i0]);
2023-01-11T21:41:26.8477863Z                     auto tmp3 = at::vec::Vectorized<float>(in_ptr3[i0]);
2023-01-11T21:41:26.8478097Z                     auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr4 + 8*i1);
2023-01-11T21:41:26.8478331Z                     auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr5 + 8*i1);
2023-01-11T21:41:26.8478603Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.8478768Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.8478901Z                     auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8479067Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.8479261Z                     tmp8.store(out_ptr2 + (8*i1) + (1024*i0));
2023-01-11T21:41:26.8479412Z                     tmp9_vec += tmp8;
2023-01-11T21:41:26.8479532Z                 }
2023-01-11T21:41:26.8479864Z                 tmp9 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp9_vec);
2023-01-11T21:41:26.8480074Z                 #pragma omp simd simdlen(4)  reduction(+:tmp9)
2023-01-11T21:41:26.8480230Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:41:26.8480334Z                 {
2023-01-11T21:41:26.8480501Z                     auto tmp0 = in_ptr1[i1 + (1024*i0)];
2023-01-11T21:41:26.8480656Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:41:26.8480817Z                     auto tmp3 = in_ptr3[i0];
2023-01-11T21:41:26.8480980Z                     auto tmp5 = in_ptr4[i1];
2023-01-11T21:41:26.8481143Z                     auto tmp7 = in_ptr5[i1];
2023-01-11T21:41:26.8481393Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:41:26.8481535Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.8481702Z                     auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8481864Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:41:26.8482040Z                     out_ptr2[i1 + (1024*i0)] = tmp8;
2023-01-11T21:41:26.8482176Z                     tmp9 += tmp8;
2023-01-11T21:41:26.8482296Z                 }
2023-01-11T21:41:26.8482440Z                 out_ptr3[i0] = tmp9;
2023-01-11T21:41:26.8482542Z             }
2023-01-11T21:41:26.8482658Z         }
2023-01-11T21:41:26.8482794Z         #pragma omp for 
2023-01-11T21:41:26.8482930Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:41:26.8483050Z         {
2023-01-11T21:41:26.8483168Z             {
2023-01-11T21:41:26.8483492Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8483622Z                 float tmp6 = 0;
2023-01-11T21:41:26.8483836Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8483978Z                 float tmp7 = 0;
2023-01-11T21:41:26.8484196Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.8484368Z                 for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.8484484Z                 {
2023-01-11T21:41:26.8484722Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + (8*i1) + (1024*i0));
2023-01-11T21:41:26.8485046Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr3[i0]);
2023-01-11T21:41:26.8485272Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:41:26.8485434Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8485697Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8485864Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8486014Z                     tmp6_vec += tmp5;
2023-01-11T21:41:26.8486157Z                     tmp7_vec += tmp0;
2023-01-11T21:41:26.8486270Z                 }
2023-01-11T21:41:26.8486602Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8486972Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:26.8487218Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:26.8487380Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:41:26.8487503Z                 {
2023-01-11T21:41:26.8487689Z                     auto tmp0 = out_ptr2[i1 + (1024*i0)];
2023-01-11T21:41:26.8487837Z                     auto tmp1 = out_ptr3[i0];
2023-01-11T21:41:26.8488023Z                     auto tmp2 = static_cast<float>(1024);
2023-01-11T21:41:26.8488184Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8488422Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8488576Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8488713Z                     tmp6 += tmp5;
2023-01-11T21:41:26.8488857Z                     tmp7 += tmp0;
2023-01-11T21:41:26.8488979Z                 }
2023-01-11T21:41:26.8489255Z                 out_ptr4[i0] = tmp6;
2023-01-11T21:41:26.8489400Z                 out_ptr5[i0] = tmp7;
2023-01-11T21:41:26.8489513Z             }
2023-01-11T21:41:26.8489618Z         }
2023-01-11T21:41:26.8489763Z         #pragma omp for 
2023-01-11T21:41:26.8489905Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:41:26.8490025Z         {
2023-01-11T21:41:26.8490263Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr4 + 8*i0);
2023-01-11T21:41:26.8490492Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:41:26.8490630Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8490997Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:41:26.8491157Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8491321Z             tmp4.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:26.8491439Z         }
2023-01-11T21:41:26.8491615Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8491764Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:41:26.8491860Z         {
2023-01-11T21:41:26.8492004Z             auto tmp0 = out_ptr4[i0];
2023-01-11T21:41:26.8492187Z             auto tmp1 = static_cast<float>(1024);
2023-01-11T21:41:26.8492343Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8492622Z             auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:41:26.8492774Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8492917Z             in_out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.8493013Z         }
2023-01-11T21:41:26.8493134Z     }
2023-01-11T21:41:26.8493248Z }
2023-01-11T21:41:26.8493393Z ''')
2023-01-11T21:41:26.8493405Z 
2023-01-11T21:41:26.8493413Z 
2023-01-11T21:41:26.8493575Z async_compile.wait(globals())
2023-01-11T21:41:26.8493702Z del async_compile
2023-01-11T21:41:26.8493714Z 
2023-01-11T21:41:26.8493842Z def call(args):
2023-01-11T21:41:26.8494015Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1 = args
2023-01-11T21:41:26.8494146Z     args.clear()
2023-01-11T21:41:26.8494515Z     buf0 = empty_strided((1, 512, 1), (512, 1, 512), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8495092Z     buf1 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8495235Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:41:26.8495613Z     buf3 = empty_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8495976Z     buf4 = empty_strided((1, 512, 1), (512, 1, 512), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8496324Z     buf5 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8496656Z     buf6 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8496856Z     buf7 = as_strided(buf5, (1, 512, 1), (512, 1, 1)); del buf5  # reuse
2023-01-11T21:41:26.8497561Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(arg3_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg5_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:41:26.8497690Z     del arg0_1
2023-01-11T21:41:26.8497811Z     del arg1_1
2023-01-11T21:41:26.8497938Z     del arg2_1
2023-01-11T21:41:26.8498057Z     del arg3_1
2023-01-11T21:41:26.8498175Z     del arg4_1
2023-01-11T21:41:26.8498281Z     del arg5_1
2023-01-11T21:41:26.8498437Z     return (buf2, buf6, buf7, )
2023-01-11T21:41:26.8498447Z 
2023-01-11T21:41:26.8498453Z 
2023-01-11T21:41:26.8498589Z if __name__ == "__main__":
2023-01-11T21:41:26.8498802Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8499016Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8499366Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8499715Z     arg1_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8500090Z     arg2_1 = rand_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8500466Z     arg3_1 = rand_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8500828Z     arg4_1 = rand_strided((1, 512, 1), (512, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8501186Z     arg5_1 = rand_strided((1, 512, 1), (512, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8501435Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1]))
2023-01-11T21:41:26.8501907Z [2023-01-11 21:38:44,193] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 461
2023-01-11T21:41:26.8501918Z 
2023-01-11T21:41:26.8502037Z ok (1.979s)
2023-01-11T21:41:26.8502827Z   test_tmp_not_defined_issue2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8503053Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8503574Z [2023-01-11 21:38:44,269] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 462
2023-01-11T21:41:26.8504040Z [2023-01-11 21:38:45,764] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 462
2023-01-11T21:41:26.8504054Z 
2023-01-11T21:41:26.8504196Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8504328Z import torch
2023-01-11T21:41:26.8504458Z import random
2023-01-11T21:41:26.8504663Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8504870Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8504879Z 
2023-01-11T21:41:26.8505011Z aten = torch.ops.aten
2023-01-11T21:41:26.8505251Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8505513Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8505541Z 
2023-01-11T21:41:26.8505550Z 
2023-01-11T21:41:26.8505771Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8506114Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8506323Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8506509Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8506679Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.8506852Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8506966Z {
2023-01-11T21:41:26.8507063Z     {
2023-01-11T21:41:26.8507380Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8507600Z         float tmp5 = 0;
2023-01-11T21:41:26.8507811Z         auto tmp5_vec = at::vec::Vectorized<float>(tmp5);
2023-01-11T21:41:26.8508000Z         #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8508122Z         {
2023-01-11T21:41:26.8508298Z             #pragma omp for reduction(+:tmp5_vec) 
2023-01-11T21:41:26.8508465Z             for(long i0=0; i0<17600; i0+=1)
2023-01-11T21:41:26.8508564Z             {
2023-01-11T21:41:26.8508799Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8509008Z                 auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:26.8509244Z                 auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i0);
2023-01-11T21:41:26.8509409Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8509562Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.8509712Z                 tmp5_vec += tmp4;
2023-01-11T21:41:26.8509800Z             }
2023-01-11T21:41:26.8510138Z             tmp5 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp5_vec);
2023-01-11T21:41:26.8510351Z             #pragma omp for simd simdlen(4) reduction(+:tmp5) 
2023-01-11T21:41:26.8510519Z             for(long i0=140800; i0<140800; i0+=1)
2023-01-11T21:41:26.8510631Z             {
2023-01-11T21:41:26.8510794Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8510946Z                 auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.8511105Z                 auto tmp3 = in_ptr2[i0];
2023-01-11T21:41:26.8511239Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8511386Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.8511520Z                 tmp5 += tmp4;
2023-01-11T21:41:26.8511631Z             }
2023-01-11T21:41:26.8511748Z         }
2023-01-11T21:41:26.8511891Z         out_ptr0[0] = tmp5;
2023-01-11T21:41:26.8511983Z     }
2023-01-11T21:41:26.8512093Z }
2023-01-11T21:41:26.8512250Z ''')
2023-01-11T21:41:26.8512261Z 
2023-01-11T21:41:26.8512273Z 
2023-01-11T21:41:26.8512436Z async_compile.wait(globals())
2023-01-11T21:41:26.8512569Z del async_compile
2023-01-11T21:41:26.8512583Z 
2023-01-11T21:41:26.8512716Z def call(args):
2023-01-11T21:41:26.8512897Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:41:26.8513022Z     args.clear()
2023-01-11T21:41:26.8513340Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8513683Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8513878Z     return (buf0, primals_1, primals_2, primals_3, )
2023-01-11T21:41:26.8513890Z 
2023-01-11T21:41:26.8513896Z 
2023-01-11T21:41:26.8514040Z if __name__ == "__main__":
2023-01-11T21:41:26.8514244Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8514457Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8514869Z     primals_1 = rand_strided((1, 88, 40, 40), (140800, 1600, 40, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8515208Z     primals_2 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8515711Z     primals_3 = rand_strided((1, 88, 40, 40), (140800, 1600, 40, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8515944Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:41:26.8515954Z 
2023-01-11T21:41:26.8516077Z ok (1.563s)
2023-01-11T21:41:26.8516329Z   test_to_device_constant_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:26.8516565Z   test_to_device_cpu (__main__.CpuTests) ... skip: requires cuda (0.000s)
2023-01-11T21:41:26.8517407Z   test_to_dtype_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8517632Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8518095Z [2023-01-11 21:38:45,864] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 463
2023-01-11T21:41:26.8518560Z [2023-01-11 21:38:47,629] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 463
2023-01-11T21:41:26.8518568Z 
2023-01-11T21:41:26.8518737Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8518854Z import torch
2023-01-11T21:41:26.8518976Z import random
2023-01-11T21:41:26.8519182Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8519394Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8519403Z 
2023-01-11T21:41:26.8519547Z aten = torch.ops.aten
2023-01-11T21:41:26.8519775Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8519944Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8519953Z 
2023-01-11T21:41:26.8519959Z 
2023-01-11T21:41:26.8520202Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8520535Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8520745Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.8520913Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8521071Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:26.8521183Z {
2023-01-11T21:41:26.8521360Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8521473Z     {
2023-01-11T21:41:26.8521593Z         #pragma omp for 
2023-01-11T21:41:26.8521737Z         for(long i0=0; i0<40; i0+=1)
2023-01-11T21:41:26.8521852Z         {
2023-01-11T21:41:26.8521975Z             {
2023-01-11T21:41:26.8522091Z                 {
2023-01-11T21:41:26.8522267Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8522451Z                     auto tmp1 = static_cast<double>(1);
2023-01-11T21:41:26.8522589Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8522791Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:41:26.8522980Z                     auto tmp4 = static_cast<bool>(tmp0);
2023-01-11T21:41:26.8523134Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.8523286Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.8523393Z                 }
2023-01-11T21:41:26.8523512Z             }
2023-01-11T21:41:26.8523605Z         }
2023-01-11T21:41:26.8523719Z     }
2023-01-11T21:41:26.8523828Z }
2023-01-11T21:41:26.8523986Z ''')
2023-01-11T21:41:26.8523994Z 
2023-01-11T21:41:26.8524001Z 
2023-01-11T21:41:26.8524162Z async_compile.wait(globals())
2023-01-11T21:41:26.8524289Z del async_compile
2023-01-11T21:41:26.8524298Z 
2023-01-11T21:41:26.8524426Z def call(args):
2023-01-11T21:41:26.8524547Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8524670Z     args.clear()
2023-01-11T21:41:26.8525035Z     buf0 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8525389Z     buf1 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.8525779Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8525935Z     return (arg0_1, buf0, arg1_1, buf1, )
2023-01-11T21:41:26.8525947Z 
2023-01-11T21:41:26.8525953Z 
2023-01-11T21:41:26.8526096Z if __name__ == "__main__":
2023-01-11T21:41:26.8526288Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8526493Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8526850Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8527201Z     arg1_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.8527395Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8527469Z 
2023-01-11T21:41:26.8527594Z ok (1.861s)
2023-01-11T21:41:26.8528366Z   test_topk_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8528596Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8529164Z [2023-01-11 21:38:47,645] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 464
2023-01-11T21:41:26.8529566Z [2023-01-11 21:38:47,651] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.topk
2023-01-11T21:41:26.8530011Z [2023-01-11 21:38:47,653] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 464
2023-01-11T21:41:26.8530041Z 
2023-01-11T21:41:26.8530194Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8530329Z import torch
2023-01-11T21:41:26.8530461Z import random
2023-01-11T21:41:26.8530665Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8530870Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8530880Z 
2023-01-11T21:41:26.8531016Z aten = torch.ops.aten
2023-01-11T21:41:26.8531244Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8531387Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8531397Z 
2023-01-11T21:41:26.8531423Z 
2023-01-11T21:41:26.8531562Z async_compile.wait(globals())
2023-01-11T21:41:26.8531684Z del async_compile
2023-01-11T21:41:26.8531695Z 
2023-01-11T21:41:26.8531824Z def call(args):
2023-01-11T21:41:26.8531951Z     arg0_1, = args
2023-01-11T21:41:26.8532077Z     args.clear()
2023-01-11T21:41:26.8532232Z     buf0 = aten.topk(arg0_1, 2)
2023-01-11T21:41:26.8532331Z     del arg0_1
2023-01-11T21:41:26.8532455Z     buf1 = buf0[0]
2023-01-11T21:41:26.8532656Z     assert_size_stride(buf1, (1, 1, 8, 2), (16, 16, 2, 1))
2023-01-11T21:41:26.8532781Z     buf2 = buf0[1]
2023-01-11T21:41:26.8532970Z     assert_size_stride(buf2, (1, 1, 8, 2), (16, 16, 2, 1))
2023-01-11T21:41:26.8533088Z     del buf0
2023-01-11T21:41:26.8533225Z     return (buf1, buf2, )
2023-01-11T21:41:26.8533234Z 
2023-01-11T21:41:26.8533240Z 
2023-01-11T21:41:26.8533375Z if __name__ == "__main__":
2023-01-11T21:41:26.8533560Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8533778Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8534161Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8534354Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8534361Z 
2023-01-11T21:41:26.8534484Z ok (0.023s)
2023-01-11T21:41:26.8535279Z   test_transpose_add_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8535640Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8536117Z [2023-01-11 21:38:47,668] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 465
2023-01-11T21:41:26.8536575Z [2023-01-11 21:38:49,312] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 465
2023-01-11T21:41:26.8536584Z 
2023-01-11T21:41:26.8536748Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8536858Z import torch
2023-01-11T21:41:26.8536987Z import random
2023-01-11T21:41:26.8537184Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8537399Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8537410Z 
2023-01-11T21:41:26.8537643Z aten = torch.ops.aten
2023-01-11T21:41:26.8537870Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8538044Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8538053Z 
2023-01-11T21:41:26.8538059Z 
2023-01-11T21:41:26.8538288Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8538626Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8538837Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8539027Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8539202Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8539319Z {
2023-01-11T21:41:26.8539488Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8539582Z     {
2023-01-11T21:41:26.8539731Z         #pragma omp for 
2023-01-11T21:41:26.8539879Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:41:26.8539994Z         {
2023-01-11T21:41:26.8540150Z             #pragma GCC ivdep
2023-01-11T21:41:26.8540292Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:41:26.8540414Z             {
2023-01-11T21:41:26.8540519Z                 {
2023-01-11T21:41:26.8540647Z                     {
2023-01-11T21:41:26.8540824Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:41:26.8541005Z                         auto tmp1 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:41:26.8541163Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8541339Z                         out_ptr0[i0 + (32*i1)] = tmp2;
2023-01-11T21:41:26.8541462Z                     }
2023-01-11T21:41:26.8541562Z                 }
2023-01-11T21:41:26.8541668Z             }
2023-01-11T21:41:26.8541786Z         }
2023-01-11T21:41:26.8541888Z     }
2023-01-11T21:41:26.8541991Z }
2023-01-11T21:41:26.8542157Z ''')
2023-01-11T21:41:26.8542166Z 
2023-01-11T21:41:26.8542172Z 
2023-01-11T21:41:26.8542329Z async_compile.wait(globals())
2023-01-11T21:41:26.8542451Z del async_compile
2023-01-11T21:41:26.8542459Z 
2023-01-11T21:41:26.8542584Z def call(args):
2023-01-11T21:41:26.8542719Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8542851Z     args.clear()
2023-01-11T21:41:26.8543298Z     buf0 = empty_strided((32, 16), (1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8543580Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8543704Z     del arg0_1
2023-01-11T21:41:26.8543808Z     del arg1_1
2023-01-11T21:41:26.8543942Z     return (buf0, )
2023-01-11T21:41:26.8543954Z 
2023-01-11T21:41:26.8543960Z 
2023-01-11T21:41:26.8544094Z if __name__ == "__main__":
2023-01-11T21:41:26.8544293Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8544507Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8544858Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8545207Z     arg1_1 = rand_strided((32, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8545406Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8545517Z 
2023-01-11T21:41:26.8545622Z ok (1.659s)
2023-01-11T21:41:26.8546397Z   test_transpose_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8546621Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8547081Z [2023-01-11 21:38:49,336] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 466
2023-01-11T21:41:26.8547540Z [2023-01-11 21:38:49,351] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 466
2023-01-11T21:41:26.8547623Z 
2023-01-11T21:41:26.8547799Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8547928Z import torch
2023-01-11T21:41:26.8548067Z import random
2023-01-11T21:41:26.8548271Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8548460Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8548489Z 
2023-01-11T21:41:26.8548610Z aten = torch.ops.aten
2023-01-11T21:41:26.8548835Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8548998Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8549006Z 
2023-01-11T21:41:26.8549013Z 
2023-01-11T21:41:26.8549258Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8549601Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8549810Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8549994Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8550168Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8550321Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8550434Z {
2023-01-11T21:41:26.8550610Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8550716Z     {
2023-01-11T21:41:26.8550855Z         #pragma omp for 
2023-01-11T21:41:26.8551001Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8551102Z         {
2023-01-11T21:41:26.8551256Z             #pragma GCC ivdep
2023-01-11T21:41:26.8551408Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:41:26.8551522Z             {
2023-01-11T21:41:26.8551640Z                 {
2023-01-11T21:41:26.8551764Z                     {
2023-01-11T21:41:26.8551941Z                         auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:41:26.8552108Z                         auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:41:26.8552276Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8552441Z                         out_ptr0[i0 + (8*i1)] = tmp2;
2023-01-11T21:41:26.8552570Z                     }
2023-01-11T21:41:26.8552690Z                 }
2023-01-11T21:41:26.8552812Z             }
2023-01-11T21:41:26.8552942Z         }
2023-01-11T21:41:26.8553069Z         #pragma omp for 
2023-01-11T21:41:26.8553214Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8553327Z         {
2023-01-11T21:41:26.8553569Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.8553808Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.8553960Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8554203Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:41:26.8554360Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8554509Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8554619Z         }
2023-01-11T21:41:26.8554787Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8554933Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.8555050Z         {
2023-01-11T21:41:26.8555201Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:41:26.8555366Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.8555599Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8555779Z             auto tmp3 = static_cast<float>(10);
2023-01-11T21:41:26.8555927Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8556075Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.8556188Z         }
2023-01-11T21:41:26.8556300Z     }
2023-01-11T21:41:26.8556383Z }
2023-01-11T21:41:26.8556552Z ''')
2023-01-11T21:41:26.8556566Z 
2023-01-11T21:41:26.8556572Z 
2023-01-11T21:41:26.8556730Z async_compile.wait(globals())
2023-01-11T21:41:26.8556864Z del async_compile
2023-01-11T21:41:26.8556875Z 
2023-01-11T21:41:26.8557005Z def call(args):
2023-01-11T21:41:26.8557140Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8557262Z     args.clear()
2023-01-11T21:41:26.8557670Z     buf0 = empty_strided((8, 8), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8558016Z     buf1 = empty_strided((8, 8), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8558355Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8558487Z     del arg0_1
2023-01-11T21:41:26.8558615Z     del arg1_1
2023-01-11T21:41:26.8558748Z     return (buf0, buf1, )
2023-01-11T21:41:26.8558759Z 
2023-01-11T21:41:26.8558764Z 
2023-01-11T21:41:26.8558905Z if __name__ == "__main__":
2023-01-11T21:41:26.8559106Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8559304Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8559647Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8559994Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8560197Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8560211Z 
2023-01-11T21:41:26.8560330Z ok (0.038s)
2023-01-11T21:41:26.8560932Z   test_transposed_propagates_cpu (__main__.CpuTests) ... [2023-01-11 21:38:49,364] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 467
2023-01-11T21:41:26.8561414Z [2023-01-11 21:38:50,991] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 467
2023-01-11T21:41:26.8561423Z 
2023-01-11T21:41:26.8561587Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8561721Z import torch
2023-01-11T21:41:26.8561834Z import random
2023-01-11T21:41:26.8562027Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8562238Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8562247Z 
2023-01-11T21:41:26.8562386Z aten = torch.ops.aten
2023-01-11T21:41:26.8562622Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8562778Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8562787Z 
2023-01-11T21:41:26.8562795Z 
2023-01-11T21:41:26.8563050Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8563388Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8563581Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8563770Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8563947Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8564059Z {
2023-01-11T21:41:26.8564205Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8564289Z     {
2023-01-11T21:41:26.8564396Z         #pragma omp for 
2023-01-11T21:41:26.8564493Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8564573Z         {
2023-01-11T21:41:26.8564755Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8564928Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.8565038Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8565160Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8565243Z         }
2023-01-11T21:41:26.8565353Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8565556Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:41:26.8565638Z         {
2023-01-11T21:41:26.8565749Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8565858Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.8565968Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8566074Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8566143Z         }
2023-01-11T21:41:26.8566221Z     }
2023-01-11T21:41:26.8566301Z }
2023-01-11T21:41:26.8566411Z ''')
2023-01-11T21:41:26.8566418Z 
2023-01-11T21:41:26.8566422Z 
2023-01-11T21:41:26.8566542Z async_compile.wait(globals())
2023-01-11T21:41:26.8566641Z del async_compile
2023-01-11T21:41:26.8566647Z 
2023-01-11T21:41:26.8566741Z def call(args):
2023-01-11T21:41:26.8566828Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8566921Z     args.clear()
2023-01-11T21:41:26.8567254Z     buf0 = empty_strided((1, 4, 4, 4), (64, 4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8567475Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8567567Z     del arg0_1
2023-01-11T21:41:26.8567656Z     del arg1_1
2023-01-11T21:41:26.8567752Z     return (buf0, )
2023-01-11T21:41:26.8567759Z 
2023-01-11T21:41:26.8567764Z 
2023-01-11T21:41:26.8567862Z if __name__ == "__main__":
2023-01-11T21:41:26.8567998Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8568158Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8568448Z     arg0_1 = rand_strided((1, 4, 4, 4), (64, 4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8568725Z     arg1_1 = rand_strided((4, 4, 4), (4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8568874Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8568881Z 
2023-01-11T21:41:26.8568970Z ok (1.640s)
2023-01-11T21:41:26.8569282Z   test_triton_conv_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:26.8569741Z   test_triton_mm2_cpu (__main__.CpuTests) ... [2023-01-11 21:38:51,026] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 468
2023-01-11T21:41:26.8570093Z [2023-01-11 21:38:52,502] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 468
2023-01-11T21:41:26.8570102Z 
2023-01-11T21:41:26.8570226Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8570317Z import torch
2023-01-11T21:41:26.8570411Z import random
2023-01-11T21:41:26.8570564Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8570722Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8570729Z 
2023-01-11T21:41:26.8570831Z aten = torch.ops.aten
2023-01-11T21:41:26.8571002Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8571114Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8571121Z 
2023-01-11T21:41:26.8571126Z 
2023-01-11T21:41:26.8571309Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8571574Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8571726Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:41:26.8571806Z {
2023-01-11T21:41:26.8571932Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8572012Z     {
2023-01-11T21:41:26.8572101Z         #pragma omp for 
2023-01-11T21:41:26.8572212Z         for(long i0=0; i0<131072; i0+=1)
2023-01-11T21:41:26.8572300Z         {
2023-01-11T21:41:26.8572483Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.8572652Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:41:26.8572777Z             tmp1.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.8572862Z         }
2023-01-11T21:41:26.8572976Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8573095Z         for(long i0=1048576; i0<1048576; i0+=1)
2023-01-11T21:41:26.8573180Z         {
2023-01-11T21:41:26.8573363Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:41:26.8573478Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:41:26.8573590Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.8573672Z         }
2023-01-11T21:41:26.8573740Z     }
2023-01-11T21:41:26.8573817Z }
2023-01-11T21:41:26.8573926Z ''')
2023-01-11T21:41:26.8573932Z 
2023-01-11T21:41:26.8573937Z 
2023-01-11T21:41:26.8574053Z async_compile.wait(globals())
2023-01-11T21:41:26.8574150Z del async_compile
2023-01-11T21:41:26.8574156Z 
2023-01-11T21:41:26.8574249Z def call(args):
2023-01-11T21:41:26.8574347Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.8574441Z     args.clear()
2023-01-11T21:41:26.8574712Z     buf0 = empty_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8574834Z     aten.mm.out(arg0_1, arg1_1, out=buf0)
2023-01-11T21:41:26.8574971Z     del arg0_1
2023-01-11T21:41:26.8575064Z     del arg1_1
2023-01-11T21:41:26.8575176Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:41:26.8575312Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8575395Z     return (buf1, )
2023-01-11T21:41:26.8575415Z 
2023-01-11T21:41:26.8575420Z 
2023-01-11T21:41:26.8575505Z if __name__ == "__main__":
2023-01-11T21:41:26.8575655Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8575815Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8576102Z     arg0_1 = rand_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8576378Z     arg1_1 = rand_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8576528Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.8576535Z 
2023-01-11T21:41:26.8576622Z ok (1.520s)
2023-01-11T21:41:26.8577236Z   test_triu_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8577405Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8577743Z [2023-01-11 21:38:52,554] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 469
2023-01-11T21:41:26.8578105Z [2023-01-11 21:38:54,224] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 469
2023-01-11T21:41:26.8578112Z 
2023-01-11T21:41:26.8578235Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8578327Z import torch
2023-01-11T21:41:26.8578421Z import random
2023-01-11T21:41:26.8578573Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8578737Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8578743Z 
2023-01-11T21:41:26.8578844Z aten = torch.ops.aten
2023-01-11T21:41:26.8579007Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8579131Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8579138Z 
2023-01-11T21:41:26.8579143Z 
2023-01-11T21:41:26.8579323Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8579585Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8579743Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8579877Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8580005Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8580132Z                        float* __restrict__ out_ptr2)
2023-01-11T21:41:26.8580199Z {
2023-01-11T21:41:26.8580328Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8580409Z     {
2023-01-11T21:41:26.8580531Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.8580640Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8580723Z         {
2023-01-11T21:41:26.8580857Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.8580942Z             {
2023-01-11T21:41:26.8581049Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8581166Z                 for(long i2=0; i2<10; i2+=1)
2023-01-11T21:41:26.8581254Z                 {
2023-01-11T21:41:26.8581341Z                     {
2023-01-11T21:41:26.8581432Z                         {
2023-01-11T21:41:26.8581559Z                             auto tmp3 = in_ptr0[i2 + (10*i1) + (100*i0)];
2023-01-11T21:41:26.8581814Z                             auto tmp0 = static_cast<int>((-1) + i2 + ((-1)*i1));
2023-01-11T21:41:26.8581953Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:41:26.8582080Z                             auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:41:26.8582220Z                             auto tmp4 = static_cast<float>(0);
2023-01-11T21:41:26.8582389Z                             auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:41:26.8582631Z                             auto tmp6 = static_cast<int>(i2 + ((-1)*i1));
2023-01-11T21:41:26.8582763Z                             auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:41:26.8582883Z                             auto tmp8 = tmp7 ? tmp3 : tmp4;
2023-01-11T21:41:26.8583217Z                             auto tmp9 = static_cast<int>((-2) + i2 + ((-1)*i1));
2023-01-11T21:41:26.8583349Z                             auto tmp10 = tmp9 >= tmp1;
2023-01-11T21:41:26.8583487Z                             auto tmp11 = tmp10 ? tmp3 : tmp4;
2023-01-11T21:41:26.8583625Z                             out_ptr0[i2 + (10*i1) + (100*i0)] = tmp5;
2023-01-11T21:41:26.8583761Z                             out_ptr1[i2 + (10*i1) + (100*i0)] = tmp8;
2023-01-11T21:41:26.8583897Z                             out_ptr2[i2 + (10*i1) + (100*i0)] = tmp11;
2023-01-11T21:41:26.8583987Z                         }
2023-01-11T21:41:26.8584063Z                     }
2023-01-11T21:41:26.8584151Z                 }
2023-01-11T21:41:26.8584235Z             }
2023-01-11T21:41:26.8584318Z         }
2023-01-11T21:41:26.8584405Z     }
2023-01-11T21:41:26.8584484Z }
2023-01-11T21:41:26.8584579Z ''')
2023-01-11T21:41:26.8584586Z 
2023-01-11T21:41:26.8584605Z 
2023-01-11T21:41:26.8584711Z async_compile.wait(globals())
2023-01-11T21:41:26.8584806Z del async_compile
2023-01-11T21:41:26.8584812Z 
2023-01-11T21:41:26.8584906Z def call(args):
2023-01-11T21:41:26.8584998Z     arg0_1, = args
2023-01-11T21:41:26.8585093Z     args.clear()
2023-01-11T21:41:26.8585380Z     buf0 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8585662Z     buf1 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8585934Z     buf2 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8586184Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:41:26.8586277Z     del arg0_1
2023-01-11T21:41:26.8586386Z     return (buf0, buf1, buf2, )
2023-01-11T21:41:26.8586397Z 
2023-01-11T21:41:26.8586401Z 
2023-01-11T21:41:26.8586502Z if __name__ == "__main__":
2023-01-11T21:41:26.8586652Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8586813Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8587097Z     arg0_1 = rand_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8587225Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8587232Z 
2023-01-11T21:41:26.8587318Z ok (1.712s)
2023-01-11T21:41:26.8587935Z   test_unbind_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8588100Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8588489Z [2023-01-11 21:38:54,246] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 470
2023-01-11T21:41:26.8588853Z [2023-01-11 21:38:54,253] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 470
2023-01-11T21:41:26.8588861Z 
2023-01-11T21:41:26.8588985Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8589079Z import torch
2023-01-11T21:41:26.8589173Z import random
2023-01-11T21:41:26.8589309Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8589467Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8589473Z 
2023-01-11T21:41:26.8589572Z aten = torch.ops.aten
2023-01-11T21:41:26.8589746Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8589900Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8589909Z 
2023-01-11T21:41:26.8589914Z 
2023-01-11T21:41:26.8590033Z async_compile.wait(globals())
2023-01-11T21:41:26.8590132Z del async_compile
2023-01-11T21:41:26.8590140Z 
2023-01-11T21:41:26.8590234Z def call(args):
2023-01-11T21:41:26.8590311Z     arg0_1, = args
2023-01-11T21:41:26.8590404Z     args.clear()
2023-01-11T21:41:26.8590762Z     return (as_strided(arg0_1, (4, 4), (4, 1)), as_strided(arg0_1, (4, 4), (4, 1), 16), as_strided(arg0_1, (4, 4), (4, 1), 32), as_strided(arg0_1, (4, 4), (4, 1), 48), as_strided(arg0_1, (4, 4), (16, 4)), as_strided(arg0_1, (4, 4), (16, 4), 1), as_strided(arg0_1, (4, 4), (16, 4), 2), as_strided(arg0_1, (4, 4), (16, 4), 3), )
2023-01-11T21:41:26.8590770Z 
2023-01-11T21:41:26.8590775Z 
2023-01-11T21:41:26.8590875Z if __name__ == "__main__":
2023-01-11T21:41:26.8591025Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8591187Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8591470Z     arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8591614Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8591624Z 
2023-01-11T21:41:26.8591712Z ok (0.029s)
2023-01-11T21:41:26.8592334Z   test_unroll_small_reduction_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8592486Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8592841Z [2023-01-11 21:38:54,294] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 471
2023-01-11T21:41:26.8593204Z [2023-01-11 21:38:55,848] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 471
2023-01-11T21:41:26.8593765Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8593933Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8594286Z [2023-01-11 21:38:55,890] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 472
2023-01-11T21:41:26.8594293Z 
2023-01-11T21:41:26.8594417Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8594510Z import torch
2023-01-11T21:41:26.8594604Z import random
2023-01-11T21:41:26.8594743Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8594900Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8594910Z 
2023-01-11T21:41:26.8595011Z aten = torch.ops.aten
2023-01-11T21:41:26.8595183Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8595338Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8595345Z 
2023-01-11T21:41:26.8595350Z 
2023-01-11T21:41:26.8595534Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8595795Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8595953Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8596085Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8596197Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.8596325Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8596448Z                        long* __restrict__ out_ptr3,
2023-01-11T21:41:26.8596576Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.8596732Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:41:26.8596858Z                        bool* __restrict__ out_ptr6,
2023-01-11T21:41:26.8596981Z                        long* __restrict__ out_ptr7,
2023-01-11T21:41:26.8597093Z                        long* __restrict__ out_ptr8,
2023-01-11T21:41:26.8597219Z                        float* __restrict__ out_ptr9,
2023-01-11T21:41:26.8597351Z                        float* __restrict__ out_ptr10)
2023-01-11T21:41:26.8597432Z {
2023-01-11T21:41:26.8597560Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8597642Z     {
2023-01-11T21:41:26.8597743Z         #pragma omp for 
2023-01-11T21:41:26.8597837Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8597919Z         {
2023-01-11T21:41:26.8598003Z             {
2023-01-11T21:41:26.8598088Z                 {
2023-01-11T21:41:26.8598211Z                     auto tmp0 = in_ptr0[3*i0];
2023-01-11T21:41:26.8598338Z                     auto tmp1 = in_ptr0[1 + (3*i0)];
2023-01-11T21:41:26.8598466Z                     auto tmp3 = in_ptr0[2 + (3*i0)];
2023-01-11T21:41:26.8598628Z                     auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:41:26.8598797Z                     auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:41:26.8598933Z                     auto tmp5 = static_cast<long>(0);
2023-01-11T21:41:26.8599063Z                     auto tmp6 = static_cast<long>(1);
2023-01-11T21:41:26.8599184Z                     auto tmp7 = tmp1 < tmp0;
2023-01-11T21:41:26.8599312Z                     auto tmp8 = tmp7 ? tmp6 : tmp5;
2023-01-11T21:41:26.8599443Z                     auto tmp9 = static_cast<long>(2);
2023-01-11T21:41:26.8599548Z                     auto tmp10 = tmp3 < tmp2;
2023-01-11T21:41:26.8599678Z                     auto tmp11 = tmp10 ? tmp9 : tmp8;
2023-01-11T21:41:26.8599847Z                     auto tmp12 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:26.8600014Z                     auto tmp13 = (tmp3 != tmp3) ? tmp3 : std::max(tmp12, tmp3);
2023-01-11T21:41:26.8600137Z                     auto tmp14 = tmp1 > tmp0;
2023-01-11T21:41:26.8600265Z                     auto tmp15 = tmp14 ? tmp6 : tmp5;
2023-01-11T21:41:26.8600389Z                     auto tmp16 = tmp3 > tmp12;
2023-01-11T21:41:26.8600519Z                     auto tmp17 = tmp16 ? tmp9 : tmp15;
2023-01-11T21:41:26.8600622Z                     auto tmp18 = tmp0 + tmp1;
2023-01-11T21:41:26.8600742Z                     auto tmp19 = tmp18 + tmp3;
2023-01-11T21:41:26.8600880Z                     auto tmp20 = static_cast<float>(1);
2023-01-11T21:41:26.8600999Z                     auto tmp21 = tmp0 > tmp20;
2023-01-11T21:41:26.8601138Z                     auto tmp22 = static_cast<long>(tmp21);
2023-01-11T21:41:26.8601277Z                     auto tmp23 = static_cast<bool>(tmp22);
2023-01-11T21:41:26.8601394Z                     auto tmp24 = tmp1 > tmp20;
2023-01-11T21:41:26.8601519Z                     auto tmp25 = static_cast<long>(tmp24);
2023-01-11T21:41:26.8601662Z                     auto tmp26 = static_cast<bool>(tmp25);
2023-01-11T21:41:26.8601784Z                     auto tmp27 = tmp23 || tmp26;
2023-01-11T21:41:26.8601904Z                     auto tmp28 = tmp3 > tmp20;
2023-01-11T21:41:26.8602076Z                     auto tmp29 = static_cast<long>(tmp28);
2023-01-11T21:41:26.8602214Z                     auto tmp30 = static_cast<bool>(tmp29);
2023-01-11T21:41:26.8602338Z                     auto tmp31 = tmp27 || tmp30;
2023-01-11T21:41:26.8602477Z                     auto tmp32 = static_cast<float>(0);
2023-01-11T21:41:26.8602583Z                     auto tmp33 = tmp0 > tmp32;
2023-01-11T21:41:26.8602700Z                     auto tmp34 = tmp33 == 0;
2023-01-11T21:41:26.8602837Z                     auto tmp35 = static_cast<long>(tmp34);
2023-01-11T21:41:26.8602974Z                     auto tmp36 = static_cast<bool>(tmp35);
2023-01-11T21:41:26.8603093Z                     auto tmp37 = tmp1 > tmp32;
2023-01-11T21:41:26.8603210Z                     auto tmp38 = tmp37 == 0;
2023-01-11T21:41:26.8603379Z                     auto tmp39 = static_cast<long>(tmp38);
2023-01-11T21:41:26.8603504Z                     auto tmp40 = static_cast<bool>(tmp39);
2023-01-11T21:41:26.8603632Z                     auto tmp41 = tmp36 || tmp40;
2023-01-11T21:41:26.8603749Z                     auto tmp42 = tmp3 > tmp32;
2023-01-11T21:41:26.8603867Z                     auto tmp43 = tmp42 == 0;
2023-01-11T21:41:26.8604004Z                     auto tmp44 = static_cast<long>(tmp43);
2023-01-11T21:41:26.8604141Z                     auto tmp45 = static_cast<bool>(tmp44);
2023-01-11T21:41:26.8604263Z                     auto tmp46 = tmp41 || tmp45;
2023-01-11T21:41:26.8604365Z                     auto tmp47 = tmp46 == 0;
2023-01-11T21:41:26.8604476Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.8604586Z                     out_ptr1[i0] = tmp11;
2023-01-11T21:41:26.8604698Z                     out_ptr2[i0] = tmp13;
2023-01-11T21:41:26.8604807Z                     out_ptr3[i0] = tmp17;
2023-01-11T21:41:26.8604921Z                     out_ptr4[i0] = tmp19;
2023-01-11T21:41:26.8605030Z                     out_ptr5[i0] = tmp31;
2023-01-11T21:41:26.8605124Z                     out_ptr6[i0] = tmp47;
2023-01-11T21:41:26.8605236Z                     out_ptr7[i0] = tmp11;
2023-01-11T21:41:26.8605342Z                     out_ptr8[i0] = tmp17;
2023-01-11T21:41:26.8605454Z                     out_ptr9[i0] = tmp4;
2023-01-11T21:41:26.8605568Z                     out_ptr10[i0] = tmp13;
2023-01-11T21:41:26.8605654Z                 }
2023-01-11T21:41:26.8605738Z             }
2023-01-11T21:41:26.8605806Z         }
2023-01-11T21:41:26.8605887Z     }
2023-01-11T21:41:26.8605966Z }
2023-01-11T21:41:26.8606077Z ''')
2023-01-11T21:41:26.8606084Z 
2023-01-11T21:41:26.8606089Z 
2023-01-11T21:41:26.8606208Z async_compile.wait(globals())
2023-01-11T21:41:26.8606305Z del async_compile
2023-01-11T21:41:26.8606312Z 
2023-01-11T21:41:26.8606403Z def call(args):
2023-01-11T21:41:26.8606492Z     arg0_1, = args
2023-01-11T21:41:26.8606571Z     args.clear()
2023-01-11T21:41:26.8606840Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8607096Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8607364Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8607617Z     buf3 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8607875Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8608122Z     buf5 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.8608351Z     buf6 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.8608603Z     buf7 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8608853Z     buf8 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8609272Z     buf9 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8609513Z     buf10 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8609888Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()))
2023-01-11T21:41:26.8610020Z     del arg0_1
2023-01-11T21:41:26.8610150Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, )
2023-01-11T21:41:26.8610155Z 
2023-01-11T21:41:26.8610159Z 
2023-01-11T21:41:26.8610234Z if __name__ == "__main__":
2023-01-11T21:41:26.8610335Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8610459Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8610653Z     arg0_1 = rand_strided((8, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8610795Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8610801Z 
2023-01-11T21:41:26.8611069Z [2023-01-11 21:38:57,427] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 472
2023-01-11T21:41:26.8611077Z 
2023-01-11T21:41:26.8611170Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8611239Z import torch
2023-01-11T21:41:26.8611308Z import random
2023-01-11T21:41:26.8611408Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8611524Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8611529Z 
2023-01-11T21:41:26.8611605Z aten = torch.ops.aten
2023-01-11T21:41:26.8611734Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8611823Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8611828Z 
2023-01-11T21:41:26.8611832Z 
2023-01-11T21:41:26.8611964Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8612168Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8612281Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8612375Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8612472Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8612565Z                        long* __restrict__ out_ptr1,
2023-01-11T21:41:26.8612659Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8612750Z                        long* __restrict__ out_ptr3,
2023-01-11T21:41:26.8612842Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.8612932Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:41:26.8613023Z                        long* __restrict__ out_ptr7,
2023-01-11T21:41:26.8613101Z                        long* __restrict__ out_ptr8,
2023-01-11T21:41:26.8613195Z                        float* __restrict__ out_ptr9,
2023-01-11T21:41:26.8613293Z                        float* __restrict__ out_ptr10)
2023-01-11T21:41:26.8613354Z {
2023-01-11T21:41:26.8613437Z     auto out_ptr6 = in_out_ptr0;
2023-01-11T21:41:26.8613532Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8613594Z     {
2023-01-11T21:41:26.8613657Z         #pragma omp for 
2023-01-11T21:41:26.8613737Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8613798Z         {
2023-01-11T21:41:26.8613860Z             {
2023-01-11T21:41:26.8613923Z                 {
2023-01-11T21:41:26.8614054Z                     float tmp1 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.8614173Z                     struct IndexValue_13 {size_t index; float value;};
2023-01-11T21:41:26.8614297Z                     IndexValue_13 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.8614436Z                     #pragma omp declare reduction(argmin : struct IndexValue_13 :\
2023-01-11T21:41:26.8614584Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.8614733Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.8614922Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.8615162Z                     float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.8615282Z                     struct IndexValue_14 {size_t index; float value;};
2023-01-11T21:41:26.8615516Z                     IndexValue_14 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.8615654Z                     #pragma omp declare reduction(argmax : struct IndexValue_14 :\
2023-01-11T21:41:26.8615790Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.8615934Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.8616205Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.8616289Z                     float tmp5 = 0;
2023-01-11T21:41:26.8616369Z                     bool tmp10 = 0;
2023-01-11T21:41:26.8616447Z                     bool tmp16 = 0;
2023-01-11T21:41:26.8616567Z                     struct IndexValue_15 {size_t index; float value;};
2023-01-11T21:41:26.8616703Z                     IndexValue_15 tmp17{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.8616829Z                     #pragma omp declare reduction(argmin : struct IndexValue_15 :\
2023-01-11T21:41:26.8616977Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.8617118Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.8617261Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.8617380Z                     struct IndexValue_16 {size_t index; float value;};
2023-01-11T21:41:26.8617617Z                     IndexValue_16 tmp18{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:41:26.8617756Z                     #pragma omp declare reduction(argmax : struct IndexValue_16 :\
2023-01-11T21:41:26.8617907Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:41:26.8618041Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:41:26.8618283Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:41:26.8618409Z                     float tmp19 = std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.8618621Z                     float tmp20 = -std::numeric_limits<float>::infinity();
2023-01-11T21:41:26.8618711Z                     for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.8618777Z                     {
2023-01-11T21:41:26.8618844Z                         {
2023-01-11T21:41:26.8618949Z                             auto tmp0 = in_ptr0[i1 + (3*i0)];
2023-01-11T21:41:26.8619043Z                             auto tmp6 = static_cast<float>(1);
2023-01-11T21:41:26.8619141Z                             auto tmp7 = tmp0 > tmp6;
2023-01-11T21:41:26.8619248Z                             auto tmp8 = static_cast<long>(tmp7);
2023-01-11T21:41:26.8619354Z                             auto tmp9 = static_cast<bool>(tmp8);
2023-01-11T21:41:26.8619460Z                             auto tmp11 = static_cast<float>(0);
2023-01-11T21:41:26.8619555Z                             auto tmp12 = tmp0 > tmp11;
2023-01-11T21:41:26.8619649Z                             auto tmp13 = tmp12 == 0;
2023-01-11T21:41:26.8619756Z                             auto tmp14 = static_cast<long>(tmp13);
2023-01-11T21:41:26.8619852Z                             auto tmp15 = static_cast<bool>(tmp14);
2023-01-11T21:41:26.8619954Z                             tmp1 = std::min(tmp1, tmp0);
2023-01-11T21:41:26.8620049Z                             if (tmp2.value > tmp0) {
2023-01-11T21:41:26.8620157Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:41:26.8620226Z                             }
2023-01-11T21:41:26.8620358Z                             tmp3 = std::max(tmp3, tmp0);
2023-01-11T21:41:26.8620452Z                             if (tmp4.value < tmp0) {
2023-01-11T21:41:26.8620549Z                                 tmp4.index = i1; tmp4.value = tmp0;
2023-01-11T21:41:26.8620618Z                             }
2023-01-11T21:41:26.8620699Z                             tmp5 += tmp0;
2023-01-11T21:41:26.8620788Z                             tmp10 = tmp10 || tmp9;
2023-01-11T21:41:26.8620877Z                             tmp16 = tmp16 || tmp15;
2023-01-11T21:41:26.8620970Z                             if (tmp17.value > tmp0) {
2023-01-11T21:41:26.8621079Z                                 tmp17.index = i1; tmp17.value = tmp0;
2023-01-11T21:41:26.8621148Z                             }
2023-01-11T21:41:26.8621276Z                             if (tmp18.value < tmp0) {
2023-01-11T21:41:26.8621390Z                                 tmp18.index = i1; tmp18.value = tmp0;
2023-01-11T21:41:26.8621460Z                             }
2023-01-11T21:41:26.8621564Z                             tmp19 = std::min(tmp19, tmp0);
2023-01-11T21:41:26.8621665Z                             tmp20 = std::max(tmp20, tmp0);
2023-01-11T21:41:26.8621731Z                         }
2023-01-11T21:41:26.8621795Z                     }
2023-01-11T21:41:26.8621868Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.8621960Z                     out_ptr1[i0] = tmp2.index;
2023-01-11T21:41:26.8622041Z                     out_ptr2[i0] = tmp3;
2023-01-11T21:41:26.8622129Z                     out_ptr3[i0] = tmp4.index;
2023-01-11T21:41:26.8622209Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:41:26.8622290Z                     out_ptr5[i0] = tmp10;
2023-01-11T21:41:26.8622371Z                     out_ptr6[i0] = tmp16;
2023-01-11T21:41:26.8622454Z                     out_ptr7[i0] = tmp17.index;
2023-01-11T21:41:26.8622545Z                     out_ptr8[i0] = tmp18.index;
2023-01-11T21:41:26.8622625Z                     out_ptr9[i0] = tmp19;
2023-01-11T21:41:26.8622711Z                     out_ptr10[i0] = tmp20;
2023-01-11T21:41:26.8622774Z                 }
2023-01-11T21:41:26.8622836Z             }
2023-01-11T21:41:26.8622896Z         }
2023-01-11T21:41:26.8622962Z         #pragma omp for 
2023-01-11T21:41:26.8623042Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8623102Z         {
2023-01-11T21:41:26.8623238Z             {
2023-01-11T21:41:26.8623306Z                 {
2023-01-11T21:41:26.8623399Z                     auto tmp0 = out_ptr6[i0];
2023-01-11T21:41:26.8623484Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:41:26.8623560Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.8623622Z                 }
2023-01-11T21:41:26.8623684Z             }
2023-01-11T21:41:26.8623746Z         }
2023-01-11T21:41:26.8623804Z     }
2023-01-11T21:41:26.8623864Z }
2023-01-11T21:41:26.8623936Z ''')
2023-01-11T21:41:26.8623942Z 
2023-01-11T21:41:26.8623958Z 
2023-01-11T21:41:26.8624036Z async_compile.wait(globals())
2023-01-11T21:41:26.8624108Z del async_compile
2023-01-11T21:41:26.8624113Z 
2023-01-11T21:41:26.8624182Z def call(args):
2023-01-11T21:41:26.8624250Z     arg0_1, = args
2023-01-11T21:41:26.8624319Z     args.clear()
2023-01-11T21:41:26.8624513Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8624700Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8624876Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8625059Z     buf3 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8625244Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8625426Z     buf5 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.8625603Z     buf6 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.8625789Z     buf8 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8626009Z     buf9 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.8626202Z     buf10 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8626379Z     buf11 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8626463Z     buf7 = buf6; del buf6  # reuse
2023-01-11T21:41:26.8626839Z     kernel_cpp_0(c_void_p(buf7.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()))
2023-01-11T21:41:26.8626908Z     del arg0_1
2023-01-11T21:41:26.8627066Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf8, buf9, buf10, buf11, )
2023-01-11T21:41:26.8627072Z 
2023-01-11T21:41:26.8627076Z 
2023-01-11T21:41:26.8627153Z if __name__ == "__main__":
2023-01-11T21:41:26.8627268Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8627390Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8627584Z     arg0_1 = rand_strided((8, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8627678Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8627683Z 
2023-01-11T21:41:26.8627748Z ok (3.175s)
2023-01-11T21:41:26.8627887Z   test_unspec_inputs_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:41:26.8628355Z   test_unsqueeze_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8628480Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8628746Z [2023-01-11 21:38:57,461] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 473
2023-01-11T21:41:26.8629010Z [2023-01-11 21:38:59,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 473
2023-01-11T21:41:26.8629015Z 
2023-01-11T21:41:26.8629107Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8629175Z import torch
2023-01-11T21:41:26.8629232Z import random
2023-01-11T21:41:26.8629343Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8629461Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8629466Z 
2023-01-11T21:41:26.8629542Z aten = torch.ops.aten
2023-01-11T21:41:26.8629676Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8629768Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8629773Z 
2023-01-11T21:41:26.8629777Z 
2023-01-11T21:41:26.8629909Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8630114Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8630219Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8630316Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8630411Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8630503Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8630596Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:26.8630698Z {
2023-01-11T21:41:26.8630795Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8630843Z     {
2023-01-11T21:41:26.8630916Z         #pragma omp for 
2023-01-11T21:41:26.8630997Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8631057Z         {
2023-01-11T21:41:26.8631193Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8631324Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.8631446Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8631574Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.8631645Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8631726Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:26.8631815Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8631902Z             tmp5.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8631988Z             tmp4.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.8632073Z             tmp5.store(out_ptr3 + 8*i0);
2023-01-11T21:41:26.8632134Z         }
2023-01-11T21:41:26.8632213Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8632294Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.8632353Z         {
2023-01-11T21:41:26.8632460Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8632560Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.8632642Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8632739Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.8632809Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8632889Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:41:26.8632968Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.8633044Z             out_ptr1[i0] = tmp5;
2023-01-11T21:41:26.8633118Z             out_ptr2[i0] = tmp4;
2023-01-11T21:41:26.8633192Z             out_ptr3[i0] = tmp5;
2023-01-11T21:41:26.8633241Z         }
2023-01-11T21:41:26.8633299Z     }
2023-01-11T21:41:26.8633357Z }
2023-01-11T21:41:26.8633435Z ''')
2023-01-11T21:41:26.8633441Z 
2023-01-11T21:41:26.8633445Z 
2023-01-11T21:41:26.8633532Z async_compile.wait(globals())
2023-01-11T21:41:26.8633603Z del async_compile
2023-01-11T21:41:26.8633609Z 
2023-01-11T21:41:26.8633677Z def call(args):
2023-01-11T21:41:26.8633746Z     arg0_1, = args
2023-01-11T21:41:26.8633804Z     args.clear()
2023-01-11T21:41:26.8634023Z     buf0 = empty_strided((2, 2, 2, 2, 1), (8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8634241Z     buf1 = empty_strided((2, 2, 1, 2, 2), (8, 4, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8634453Z     buf2 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8634658Z     buf3 = empty_strided((2, 2, 2, 1, 2), (8, 4, 2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8634870Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.8634937Z     del arg0_1
2023-01-11T21:41:26.8635030Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:41:26.8635034Z 
2023-01-11T21:41:26.8635039Z 
2023-01-11T21:41:26.8635101Z if __name__ == "__main__":
2023-01-11T21:41:26.8635215Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8635336Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8635542Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8635651Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8635656Z 
2023-01-11T21:41:26.8635720Z ok (1.696s)
2023-01-11T21:41:26.8636193Z   test_unsqueeze_inplace_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8636318Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8636577Z [2023-01-11 21:38:59,159] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 474
2023-01-11T21:41:26.8636830Z [2023-01-11 21:39:00,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 474
2023-01-11T21:41:26.8636880Z 
2023-01-11T21:41:26.8636962Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8637031Z import torch
2023-01-11T21:41:26.8637099Z import random
2023-01-11T21:41:26.8637212Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8637331Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8637336Z 
2023-01-11T21:41:26.8637412Z aten = torch.ops.aten
2023-01-11T21:41:26.8637543Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8637621Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8637625Z 
2023-01-11T21:41:26.8637641Z 
2023-01-11T21:41:26.8637761Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8637964Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8638113Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8638213Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8638312Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8638374Z {
2023-01-11T21:41:26.8638471Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8638518Z     {
2023-01-11T21:41:26.8638592Z         #pragma omp for 
2023-01-11T21:41:26.8638674Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8638734Z         {
2023-01-11T21:41:26.8638867Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.8639000Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.8639084Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8639200Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.8639285Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8639374Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8639465Z             tmp4.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8639526Z         }
2023-01-11T21:41:26.8639618Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8639701Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.8639750Z         {
2023-01-11T21:41:26.8639831Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8639928Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.8640008Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8640106Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:41:26.8640186Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8640263Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8640326Z             out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.8640385Z         }
2023-01-11T21:41:26.8640444Z     }
2023-01-11T21:41:26.8640500Z }
2023-01-11T21:41:26.8640579Z ''')
2023-01-11T21:41:26.8640584Z 
2023-01-11T21:41:26.8640588Z 
2023-01-11T21:41:26.8640675Z async_compile.wait(globals())
2023-01-11T21:41:26.8640748Z del async_compile
2023-01-11T21:41:26.8640753Z 
2023-01-11T21:41:26.8640809Z def call(args):
2023-01-11T21:41:26.8640877Z     arg0_1, = args
2023-01-11T21:41:26.8640946Z     args.clear()
2023-01-11T21:41:26.8641162Z     buf0 = empty_strided((2, 2, 1, 2, 2), (8, 4, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8641375Z     buf1 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8641536Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8641603Z     del arg0_1
2023-01-11T21:41:26.8641678Z     return (buf0, buf1, )
2023-01-11T21:41:26.8641684Z 
2023-01-11T21:41:26.8641688Z 
2023-01-11T21:41:26.8641750Z if __name__ == "__main__":
2023-01-11T21:41:26.8641862Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8641985Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8642193Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8642298Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8642333Z 
2023-01-11T21:41:26.8642401Z ok (1.570s)
2023-01-11T21:41:26.8642876Z   test_upsample_bicubic2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8643003Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8643262Z [2023-01-11 21:39:02,309] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 475
2023-01-11T21:41:26.8643268Z 
2023-01-11T21:41:26.8643348Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8643416Z import torch
2023-01-11T21:41:26.8643511Z import random
2023-01-11T21:41:26.8643626Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8643745Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8643751Z 
2023-01-11T21:41:26.8643826Z aten = torch.ops.aten
2023-01-11T21:41:26.8643957Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8644046Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8644051Z 
2023-01-11T21:41:26.8644055Z 
2023-01-11T21:41:26.8644176Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8644378Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8644495Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8644592Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8644687Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8644745Z {
2023-01-11T21:41:26.8644842Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8644890Z     {
2023-01-11T21:41:26.8644965Z         #pragma omp for 
2023-01-11T21:41:26.8645046Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.8645109Z         {
2023-01-11T21:41:26.8645186Z             #pragma GCC ivdep
2023-01-11T21:41:26.8645270Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.8652457Z             {
2023-01-11T21:41:26.8652691Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8652865Z                 for(long i2=0; i2<128; i2+=1)
2023-01-11T21:41:26.8652988Z                 {
2023-01-11T21:41:26.8653119Z                     {
2023-01-11T21:41:26.8653247Z                         {
2023-01-11T21:41:26.8653439Z                             auto tmp0 = static_cast<float>(i2);
2023-01-11T21:41:26.8653622Z                             auto tmp1 = 0.2440944881889764 * tmp0;
2023-01-11T21:41:26.8653800Z                             auto tmp2 = std::floor(tmp1);
2023-01-11T21:41:26.8654106Z                             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:41:26.8654315Z                             auto tmp4 = static_cast<float>(i1);
2023-01-11T21:41:26.8654503Z                             auto tmp5 = 0.49606299212598426 * tmp4;
2023-01-11T21:41:26.8654700Z                             auto tmp6 = std::floor(tmp5);
2023-01-11T21:41:26.8654979Z                             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:41:26.8655166Z                             auto tmp8 = static_cast<int>(tmp6);
2023-01-11T21:41:26.8655354Z                             auto tmp9 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8655598Z                             auto tmp10 = tmp8 + -1;
2023-01-11T21:41:26.8655760Z                             auto tmp11 = tmp8 + 0;
2023-01-11T21:41:26.8655923Z                             auto tmp12 = tmp8 + 1;
2023-01-11T21:41:26.8656079Z                             auto tmp13 = tmp8 + 2;
2023-01-11T21:41:26.8656336Z                             auto tmp14 = tmp9 + -1;
2023-01-11T21:41:26.8656497Z                             auto tmp15 = tmp9 + 0;
2023-01-11T21:41:26.8656663Z                             auto tmp16 = tmp9 + 1;
2023-01-11T21:41:26.8656816Z                             auto tmp17 = tmp9 + 2;
2023-01-11T21:41:26.8657207Z                             auto tmp18 = (tmp10 != tmp10) ? tmp10 : std::min(63, tmp10);
2023-01-11T21:41:26.8657431Z                             auto tmp19 = (tmp18 != tmp18) ? tmp18 : std::max(0, tmp18);
2023-01-11T21:41:26.8657663Z                             auto tmp20 = (tmp14 != tmp14) ? tmp14 : std::min(31, tmp14);
2023-01-11T21:41:26.8657895Z                             auto tmp21 = (tmp20 != tmp20) ? tmp20 : std::max(0, tmp20);
2023-01-11T21:41:26.8658102Z                             auto tmp22 = in_ptr0[tmp21 + (32*tmp19) + (2048*i0)];
2023-01-11T21:41:26.8658328Z                             auto tmp23 = (tmp15 != tmp15) ? tmp15 : std::min(31, tmp15);
2023-01-11T21:41:26.8658545Z                             auto tmp24 = (tmp23 != tmp23) ? tmp23 : std::max(0, tmp23);
2023-01-11T21:41:26.8658849Z                             auto tmp25 = in_ptr0[tmp24 + (32*tmp19) + (2048*i0)];
2023-01-11T21:41:26.8659058Z                             auto tmp26 = (tmp16 != tmp16) ? tmp16 : std::min(31, tmp16);
2023-01-11T21:41:26.8659290Z                             auto tmp27 = (tmp26 != tmp26) ? tmp26 : std::max(0, tmp26);
2023-01-11T21:41:26.8659489Z                             auto tmp28 = in_ptr0[tmp27 + (32*tmp19) + (2048*i0)];
2023-01-11T21:41:26.8659707Z                             auto tmp29 = (tmp17 != tmp17) ? tmp17 : std::min(31, tmp17);
2023-01-11T21:41:26.8659929Z                             auto tmp30 = (tmp29 != tmp29) ? tmp29 : std::max(0, tmp29);
2023-01-11T21:41:26.8660134Z                             auto tmp31 = in_ptr0[tmp30 + (32*tmp19) + (2048*i0)];
2023-01-11T21:41:26.8660304Z                             auto tmp32 = tmp3 + 1.0;
2023-01-11T21:41:26.8660597Z                             auto tmp33 = -0.75 * tmp32;
2023-01-11T21:41:26.8660862Z                             auto tmp34 = tmp33 - -3.75;
2023-01-11T21:41:26.8661025Z                             auto tmp35 = tmp34 * tmp32;
2023-01-11T21:41:26.8661298Z                             auto tmp36 = tmp35 + -6.0;
2023-01-11T21:41:26.8661477Z                             auto tmp37 = tmp36 * tmp32;
2023-01-11T21:41:26.8661752Z                             auto tmp38 = tmp37 - -3.0;
2023-01-11T21:41:26.8661923Z                             auto tmp39 = 1.25 * tmp3;
2023-01-11T21:41:26.8662193Z                             auto tmp40 = tmp39 - 2.25;
2023-01-11T21:41:26.8662363Z                             auto tmp41 = tmp40 * tmp3;
2023-01-11T21:41:26.8662520Z                             auto tmp42 = tmp41 * tmp3;
2023-01-11T21:41:26.8662683Z                             auto tmp43 = tmp42 + 1.0;
2023-01-11T21:41:26.8662949Z                             auto tmp44 = 1.0 - tmp3;
2023-01-11T21:41:26.8663116Z                             auto tmp45 = 1.25 * tmp44;
2023-01-11T21:41:26.8663477Z                             auto tmp46 = tmp45 - 2.25;
2023-01-11T21:41:26.8663664Z                             auto tmp47 = tmp46 * tmp44;
2023-01-11T21:41:26.8663836Z                             auto tmp48 = tmp47 * tmp44;
2023-01-11T21:41:26.8663990Z                             auto tmp49 = tmp48 + 1.0;
2023-01-11T21:41:26.8664161Z                             auto tmp50 = tmp44 + 1.0;
2023-01-11T21:41:26.8664422Z                             auto tmp51 = -0.75 * tmp50;
2023-01-11T21:41:26.8664694Z                             auto tmp52 = tmp51 - -3.75;
2023-01-11T21:41:26.8664866Z                             auto tmp53 = tmp52 * tmp50;
2023-01-11T21:41:26.8665135Z                             auto tmp54 = tmp53 + -6.0;
2023-01-11T21:41:26.8665300Z                             auto tmp55 = tmp54 * tmp50;
2023-01-11T21:41:26.8665581Z                             auto tmp56 = tmp55 - -3.0;
2023-01-11T21:41:26.8665732Z                             auto tmp57 = tmp22 * tmp38;
2023-01-11T21:41:26.8665896Z                             auto tmp58 = tmp25 * tmp43;
2023-01-11T21:41:26.8666066Z                             auto tmp59 = tmp28 * tmp49;
2023-01-11T21:41:26.8666235Z                             auto tmp60 = tmp31 * tmp56;
2023-01-11T21:41:26.8666522Z                             auto tmp61 = tmp59 + tmp60;
2023-01-11T21:41:26.8666694Z                             auto tmp62 = tmp58 + tmp61;
2023-01-11T21:41:26.8666859Z                             auto tmp63 = tmp57 + tmp62;
2023-01-11T21:41:26.8667075Z                             auto tmp64 = (tmp11 != tmp11) ? tmp11 : std::min(63, tmp11);
2023-01-11T21:41:26.8667298Z                             auto tmp65 = (tmp64 != tmp64) ? tmp64 : std::max(0, tmp64);
2023-01-11T21:41:26.8667503Z                             auto tmp66 = in_ptr0[tmp21 + (32*tmp65) + (2048*i0)];
2023-01-11T21:41:26.8667707Z                             auto tmp67 = in_ptr0[tmp24 + (32*tmp65) + (2048*i0)];
2023-01-11T21:41:26.8667910Z                             auto tmp68 = in_ptr0[tmp27 + (32*tmp65) + (2048*i0)];
2023-01-11T21:41:26.8668179Z                             auto tmp69 = in_ptr0[tmp30 + (32*tmp65) + (2048*i0)];
2023-01-11T21:41:26.8668356Z                             auto tmp70 = tmp66 * tmp38;
2023-01-11T21:41:26.8668535Z                             auto tmp71 = tmp67 * tmp43;
2023-01-11T21:41:26.8668707Z                             auto tmp72 = tmp68 * tmp49;
2023-01-11T21:41:26.8668851Z                             auto tmp73 = tmp69 * tmp56;
2023-01-11T21:41:26.8669028Z                             auto tmp74 = tmp72 + tmp73;
2023-01-11T21:41:26.8669187Z                             auto tmp75 = tmp71 + tmp74;
2023-01-11T21:41:26.8669362Z                             auto tmp76 = tmp70 + tmp75;
2023-01-11T21:41:26.8669583Z                             auto tmp77 = (tmp12 != tmp12) ? tmp12 : std::min(63, tmp12);
2023-01-11T21:41:26.8669798Z                             auto tmp78 = (tmp77 != tmp77) ? tmp77 : std::max(0, tmp77);
2023-01-11T21:41:26.8669997Z                             auto tmp79 = in_ptr0[tmp21 + (32*tmp78) + (2048*i0)];
2023-01-11T21:41:26.8670209Z                             auto tmp80 = in_ptr0[tmp24 + (32*tmp78) + (2048*i0)];
2023-01-11T21:41:26.8670396Z                             auto tmp81 = in_ptr0[tmp27 + (32*tmp78) + (2048*i0)];
2023-01-11T21:41:26.8670605Z                             auto tmp82 = in_ptr0[tmp30 + (32*tmp78) + (2048*i0)];
2023-01-11T21:41:26.8670777Z                             auto tmp83 = tmp79 * tmp38;
2023-01-11T21:41:26.8670945Z                             auto tmp84 = tmp80 * tmp43;
2023-01-11T21:41:26.8671112Z                             auto tmp85 = tmp81 * tmp49;
2023-01-11T21:41:26.8671279Z                             auto tmp86 = tmp82 * tmp56;
2023-01-11T21:41:26.8671452Z                             auto tmp87 = tmp85 + tmp86;
2023-01-11T21:41:26.8671599Z                             auto tmp88 = tmp84 + tmp87;
2023-01-11T21:41:26.8671769Z                             auto tmp89 = tmp83 + tmp88;
2023-01-11T21:41:26.8671990Z                             auto tmp90 = (tmp13 != tmp13) ? tmp13 : std::min(63, tmp13);
2023-01-11T21:41:26.8672215Z                             auto tmp91 = (tmp90 != tmp90) ? tmp90 : std::max(0, tmp90);
2023-01-11T21:41:26.8672414Z                             auto tmp92 = in_ptr0[tmp21 + (32*tmp91) + (2048*i0)];
2023-01-11T21:41:26.8672622Z                             auto tmp93 = in_ptr0[tmp24 + (32*tmp91) + (2048*i0)];
2023-01-11T21:41:26.8672818Z                             auto tmp94 = in_ptr0[tmp27 + (32*tmp91) + (2048*i0)];
2023-01-11T21:41:26.8673014Z                             auto tmp95 = in_ptr0[tmp30 + (32*tmp91) + (2048*i0)];
2023-01-11T21:41:26.8673188Z                             auto tmp96 = tmp92 * tmp38;
2023-01-11T21:41:26.8673337Z                             auto tmp97 = tmp93 * tmp43;
2023-01-11T21:41:26.8673499Z                             auto tmp98 = tmp94 * tmp49;
2023-01-11T21:41:26.8673668Z                             auto tmp99 = tmp95 * tmp56;
2023-01-11T21:41:26.8673849Z                             auto tmp100 = tmp98 + tmp99;
2023-01-11T21:41:26.8674021Z                             auto tmp101 = tmp97 + tmp100;
2023-01-11T21:41:26.8674196Z                             auto tmp102 = tmp96 + tmp101;
2023-01-11T21:41:26.8674460Z                             auto tmp103 = tmp7 + 1.0;
2023-01-11T21:41:26.8674735Z                             auto tmp104 = -0.75 * tmp103;
2023-01-11T21:41:26.8675009Z                             auto tmp105 = tmp104 - -3.75;
2023-01-11T21:41:26.8675189Z                             auto tmp106 = tmp105 * tmp103;
2023-01-11T21:41:26.8675469Z                             auto tmp107 = tmp106 + -6.0;
2023-01-11T21:41:26.8675643Z                             auto tmp108 = tmp107 * tmp103;
2023-01-11T21:41:26.8675921Z                             auto tmp109 = tmp108 - -3.0;
2023-01-11T21:41:26.8676086Z                             auto tmp110 = 1.25 * tmp7;
2023-01-11T21:41:26.8676334Z                             auto tmp111 = tmp110 - 2.25;
2023-01-11T21:41:26.8676498Z                             auto tmp112 = tmp111 * tmp7;
2023-01-11T21:41:26.8676750Z                             auto tmp113 = tmp112 * tmp7;
2023-01-11T21:41:26.8676931Z                             auto tmp114 = tmp113 + 1.0;
2023-01-11T21:41:26.8677193Z                             auto tmp115 = 1.0 - tmp7;
2023-01-11T21:41:26.8677366Z                             auto tmp116 = 1.25 * tmp115;
2023-01-11T21:41:26.8677632Z                             auto tmp117 = tmp116 - 2.25;
2023-01-11T21:41:26.8677812Z                             auto tmp118 = tmp117 * tmp115;
2023-01-11T21:41:26.8677960Z                             auto tmp119 = tmp118 * tmp115;
2023-01-11T21:41:26.8678132Z                             auto tmp120 = tmp119 + 1.0;
2023-01-11T21:41:26.8678299Z                             auto tmp121 = tmp115 + 1.0;
2023-01-11T21:41:26.8678570Z                             auto tmp122 = -0.75 * tmp121;
2023-01-11T21:41:26.8678835Z                             auto tmp123 = tmp122 - -3.75;
2023-01-11T21:41:26.8679009Z                             auto tmp124 = tmp123 * tmp121;
2023-01-11T21:41:26.8679285Z                             auto tmp125 = tmp124 + -6.0;
2023-01-11T21:41:26.8679441Z                             auto tmp126 = tmp125 * tmp121;
2023-01-11T21:41:26.8679712Z                             auto tmp127 = tmp126 - -3.0;
2023-01-11T21:41:26.8679885Z                             auto tmp128 = tmp63 * tmp109;
2023-01-11T21:41:26.8680047Z                             auto tmp129 = tmp76 * tmp114;
2023-01-11T21:41:26.8680222Z                             auto tmp130 = tmp89 * tmp120;
2023-01-11T21:41:26.8680394Z                             auto tmp131 = tmp102 * tmp127;
2023-01-11T21:41:26.8680560Z                             auto tmp132 = tmp130 + tmp131;
2023-01-11T21:41:26.8680731Z                             auto tmp133 = tmp129 + tmp132;
2023-01-11T21:41:26.8680887Z                             auto tmp134 = tmp128 + tmp133;
2023-01-11T21:41:26.8681072Z                             out_ptr0[i2 + (128*i1) + (16384*i0)] = tmp134;
2023-01-11T21:41:26.8681185Z                         }
2023-01-11T21:41:26.8681315Z                     }
2023-01-11T21:41:26.8681435Z                 }
2023-01-11T21:41:26.8681550Z             }
2023-01-11T21:41:26.8681670Z         }
2023-01-11T21:41:26.8681805Z         #pragma omp for 
2023-01-11T21:41:26.8681955Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.8682069Z         {
2023-01-11T21:41:26.8682209Z             #pragma GCC ivdep
2023-01-11T21:41:26.8682362Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:41:26.8682481Z             {
2023-01-11T21:41:26.8682613Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8682767Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:41:26.8682884Z                 {
2023-01-11T21:41:26.8683003Z                     {
2023-01-11T21:41:26.8683127Z                         {
2023-01-11T21:41:26.8683329Z                             auto tmp0 = static_cast<float>(i2);
2023-01-11T21:41:26.8683488Z                             auto tmp1 = tmp0 + 0.5;
2023-01-11T21:41:26.8683627Z                             auto tmp2 = 0.125 * tmp1;
2023-01-11T21:41:26.8683916Z                             auto tmp3 = tmp2 - 0.5;
2023-01-11T21:41:26.8684112Z                             auto tmp4 = std::floor(tmp3);
2023-01-11T21:41:26.8684472Z                             auto tmp5 = tmp3 - tmp4;
2023-01-11T21:41:26.8684669Z                             auto tmp6 = static_cast<float>(i1);
2023-01-11T21:41:26.8684842Z                             auto tmp7 = tmp6 + 0.5;
2023-01-11T21:41:26.8685009Z                             auto tmp8 = 0.5 * tmp7;
2023-01-11T21:41:26.8685252Z                             auto tmp9 = tmp8 - 0.5;
2023-01-11T21:41:26.8685423Z                             auto tmp10 = std::floor(tmp9);
2023-01-11T21:41:26.8685686Z                             auto tmp11 = tmp9 - tmp10;
2023-01-11T21:41:26.8685876Z                             auto tmp12 = static_cast<int>(tmp10);
2023-01-11T21:41:26.8686056Z                             auto tmp13 = static_cast<int>(tmp4);
2023-01-11T21:41:26.8686385Z                             auto tmp14 = tmp12 + -1;
2023-01-11T21:41:26.8686567Z                             auto tmp15 = tmp12 + 0;
2023-01-11T21:41:26.8686725Z                             auto tmp16 = tmp12 + 1;
2023-01-11T21:41:26.8686872Z                             auto tmp17 = tmp12 + 2;
2023-01-11T21:41:26.8687132Z                             auto tmp18 = tmp13 + -1;
2023-01-11T21:41:26.8687305Z                             auto tmp19 = tmp13 + 0;
2023-01-11T21:41:26.8687466Z                             auto tmp20 = tmp13 + 1;
2023-01-11T21:41:26.8687628Z                             auto tmp21 = tmp13 + 2;
2023-01-11T21:41:26.8687854Z                             auto tmp22 = (tmp14 != tmp14) ? tmp14 : std::min(63, tmp14);
2023-01-11T21:41:26.8688081Z                             auto tmp23 = (tmp22 != tmp22) ? tmp22 : std::max(0, tmp22);
2023-01-11T21:41:26.8688310Z                             auto tmp24 = (tmp18 != tmp18) ? tmp18 : std::min(31, tmp18);
2023-01-11T21:41:26.8688533Z                             auto tmp25 = (tmp24 != tmp24) ? tmp24 : std::max(0, tmp24);
2023-01-11T21:41:26.8688731Z                             auto tmp26 = in_ptr0[tmp25 + (32*tmp23) + (2048*i0)];
2023-01-11T21:41:26.8688956Z                             auto tmp27 = (tmp19 != tmp19) ? tmp19 : std::min(31, tmp19);
2023-01-11T21:41:26.8689311Z                             auto tmp28 = (tmp27 != tmp27) ? tmp27 : std::max(0, tmp27);
2023-01-11T21:41:26.8689512Z                             auto tmp29 = in_ptr0[tmp28 + (32*tmp23) + (2048*i0)];
2023-01-11T21:41:26.8689733Z                             auto tmp30 = (tmp20 != tmp20) ? tmp20 : std::min(31, tmp20);
2023-01-11T21:41:26.8689961Z                             auto tmp31 = (tmp30 != tmp30) ? tmp30 : std::max(0, tmp30);
2023-01-11T21:41:26.8690163Z                             auto tmp32 = in_ptr0[tmp31 + (32*tmp23) + (2048*i0)];
2023-01-11T21:41:26.8690384Z                             auto tmp33 = (tmp21 != tmp21) ? tmp21 : std::min(31, tmp21);
2023-01-11T21:41:26.8690608Z                             auto tmp34 = (tmp33 != tmp33) ? tmp33 : std::max(0, tmp33);
2023-01-11T21:41:26.8690788Z                             auto tmp35 = in_ptr0[tmp34 + (32*tmp23) + (2048*i0)];
2023-01-11T21:41:26.8690964Z                             auto tmp36 = tmp5 + 1.0;
2023-01-11T21:41:26.8691259Z                             auto tmp37 = -0.75 * tmp36;
2023-01-11T21:41:26.8691530Z                             auto tmp38 = tmp37 - -3.75;
2023-01-11T21:41:26.8691700Z                             auto tmp39 = tmp38 * tmp36;
2023-01-11T21:41:26.8691964Z                             auto tmp40 = tmp39 + -6.0;
2023-01-11T21:41:26.8692146Z                             auto tmp41 = tmp40 * tmp36;
2023-01-11T21:41:26.8692399Z                             auto tmp42 = tmp41 - -3.0;
2023-01-11T21:41:26.8692566Z                             auto tmp43 = 1.25 * tmp5;
2023-01-11T21:41:26.8692835Z                             auto tmp44 = tmp43 - 2.25;
2023-01-11T21:41:26.8693003Z                             auto tmp45 = tmp44 * tmp5;
2023-01-11T21:41:26.8693182Z                             auto tmp46 = tmp45 * tmp5;
2023-01-11T21:41:26.8693345Z                             auto tmp47 = tmp46 + 1.0;
2023-01-11T21:41:26.8693745Z                             auto tmp48 = 1.0 - tmp5;
2023-01-11T21:41:26.8693912Z                             auto tmp49 = 1.25 * tmp48;
2023-01-11T21:41:26.8694147Z                             auto tmp50 = tmp49 - 2.25;
2023-01-11T21:41:26.8694318Z                             auto tmp51 = tmp50 * tmp48;
2023-01-11T21:41:26.8694487Z                             auto tmp52 = tmp51 * tmp48;
2023-01-11T21:41:26.8694665Z                             auto tmp53 = tmp52 + 1.0;
2023-01-11T21:41:26.8694831Z                             auto tmp54 = tmp48 + 1.0;
2023-01-11T21:41:26.8695086Z                             auto tmp55 = -0.75 * tmp54;
2023-01-11T21:41:26.8695359Z                             auto tmp56 = tmp55 - -3.75;
2023-01-11T21:41:26.8695510Z                             auto tmp57 = tmp56 * tmp54;
2023-01-11T21:41:26.8695860Z                             auto tmp58 = tmp57 + -6.0;
2023-01-11T21:41:26.8696052Z                             auto tmp59 = tmp58 * tmp54;
2023-01-11T21:41:26.8696321Z                             auto tmp60 = tmp59 - -3.0;
2023-01-11T21:41:26.8696498Z                             auto tmp61 = tmp26 * tmp42;
2023-01-11T21:41:26.8696662Z                             auto tmp62 = tmp29 * tmp47;
2023-01-11T21:41:26.8696828Z                             auto tmp63 = tmp32 * tmp53;
2023-01-11T21:41:26.8696988Z                             auto tmp64 = tmp35 * tmp60;
2023-01-11T21:41:26.8697161Z                             auto tmp65 = tmp63 + tmp64;
2023-01-11T21:41:26.8697320Z                             auto tmp66 = tmp62 + tmp65;
2023-01-11T21:41:26.8697484Z                             auto tmp67 = tmp61 + tmp66;
2023-01-11T21:41:26.8697713Z                             auto tmp68 = (tmp15 != tmp15) ? tmp15 : std::min(63, tmp15);
2023-01-11T21:41:26.8697944Z                             auto tmp69 = (tmp68 != tmp68) ? tmp68 : std::max(0, tmp68);
2023-01-11T21:41:26.8698150Z                             auto tmp70 = in_ptr0[tmp25 + (32*tmp69) + (2048*i0)];
2023-01-11T21:41:26.8698359Z                             auto tmp71 = in_ptr0[tmp28 + (32*tmp69) + (2048*i0)];
2023-01-11T21:41:26.8698565Z                             auto tmp72 = in_ptr0[tmp31 + (32*tmp69) + (2048*i0)];
2023-01-11T21:41:26.8698748Z                             auto tmp73 = in_ptr0[tmp34 + (32*tmp69) + (2048*i0)];
2023-01-11T21:41:26.8698918Z                             auto tmp74 = tmp70 * tmp42;
2023-01-11T21:41:26.8699079Z                             auto tmp75 = tmp71 * tmp47;
2023-01-11T21:41:26.8699247Z                             auto tmp76 = tmp72 * tmp53;
2023-01-11T21:41:26.8699415Z                             auto tmp77 = tmp73 * tmp60;
2023-01-11T21:41:26.8699581Z                             auto tmp78 = tmp76 + tmp77;
2023-01-11T21:41:26.8699742Z                             auto tmp79 = tmp75 + tmp78;
2023-01-11T21:41:26.8699902Z                             auto tmp80 = tmp74 + tmp79;
2023-01-11T21:41:26.8700125Z                             auto tmp81 = (tmp16 != tmp16) ? tmp16 : std::min(63, tmp16);
2023-01-11T21:41:26.8700351Z                             auto tmp82 = (tmp81 != tmp81) ? tmp81 : std::max(0, tmp81);
2023-01-11T21:41:26.8700552Z                             auto tmp83 = in_ptr0[tmp25 + (32*tmp82) + (2048*i0)];
2023-01-11T21:41:26.8700751Z                             auto tmp84 = in_ptr0[tmp28 + (32*tmp82) + (2048*i0)];
2023-01-11T21:41:26.8700955Z                             auto tmp85 = in_ptr0[tmp31 + (32*tmp82) + (2048*i0)];
2023-01-11T21:41:26.8701154Z                             auto tmp86 = in_ptr0[tmp34 + (32*tmp82) + (2048*i0)];
2023-01-11T21:41:26.8701320Z                             auto tmp87 = tmp83 * tmp42;
2023-01-11T21:41:26.8701469Z                             auto tmp88 = tmp84 * tmp47;
2023-01-11T21:41:26.8701638Z                             auto tmp89 = tmp85 * tmp53;
2023-01-11T21:41:26.8701821Z                             auto tmp90 = tmp86 * tmp60;
2023-01-11T21:41:26.8701981Z                             auto tmp91 = tmp89 + tmp90;
2023-01-11T21:41:26.8702149Z                             auto tmp92 = tmp88 + tmp91;
2023-01-11T21:41:26.8702410Z                             auto tmp93 = tmp87 + tmp92;
2023-01-11T21:41:26.8702636Z                             auto tmp94 = (tmp17 != tmp17) ? tmp17 : std::min(63, tmp17);
2023-01-11T21:41:26.8702855Z                             auto tmp95 = (tmp94 != tmp94) ? tmp94 : std::max(0, tmp94);
2023-01-11T21:41:26.8703039Z                             auto tmp96 = in_ptr0[tmp25 + (32*tmp95) + (2048*i0)];
2023-01-11T21:41:26.8703329Z                             auto tmp97 = in_ptr0[tmp28 + (32*tmp95) + (2048*i0)];
2023-01-11T21:41:26.8703535Z                             auto tmp98 = in_ptr0[tmp31 + (32*tmp95) + (2048*i0)];
2023-01-11T21:41:26.8703738Z                             auto tmp99 = in_ptr0[tmp34 + (32*tmp95) + (2048*i0)];
2023-01-11T21:41:26.8703986Z                             auto tmp100 = tmp96 * tmp42;
2023-01-11T21:41:26.8704168Z                             auto tmp101 = tmp97 * tmp47;
2023-01-11T21:41:26.8704350Z                             auto tmp102 = tmp98 * tmp53;
2023-01-11T21:41:26.8704524Z                             auto tmp103 = tmp99 * tmp60;
2023-01-11T21:41:26.8704682Z                             auto tmp104 = tmp102 + tmp103;
2023-01-11T21:41:26.8704854Z                             auto tmp105 = tmp101 + tmp104;
2023-01-11T21:41:26.8705030Z                             auto tmp106 = tmp100 + tmp105;
2023-01-11T21:41:26.8705202Z                             auto tmp107 = tmp11 + 1.0;
2023-01-11T21:41:26.8705505Z                             auto tmp108 = -0.75 * tmp107;
2023-01-11T21:41:26.8705776Z                             auto tmp109 = tmp108 - -3.75;
2023-01-11T21:41:26.8705948Z                             auto tmp110 = tmp109 * tmp107;
2023-01-11T21:41:26.8706224Z                             auto tmp111 = tmp110 + -6.0;
2023-01-11T21:41:26.8706388Z                             auto tmp112 = tmp111 * tmp107;
2023-01-11T21:41:26.8706661Z                             auto tmp113 = tmp112 - -3.0;
2023-01-11T21:41:26.8706823Z                             auto tmp114 = 1.25 * tmp11;
2023-01-11T21:41:26.8707089Z                             auto tmp115 = tmp114 - 2.25;
2023-01-11T21:41:26.8707270Z                             auto tmp116 = tmp115 * tmp11;
2023-01-11T21:41:26.8707439Z                             auto tmp117 = tmp116 * tmp11;
2023-01-11T21:41:26.8707610Z                             auto tmp118 = tmp117 + 1.0;
2023-01-11T21:41:26.8707848Z                             auto tmp119 = 1.0 - tmp11;
2023-01-11T21:41:26.8708008Z                             auto tmp120 = 1.25 * tmp119;
2023-01-11T21:41:26.8708284Z                             auto tmp121 = tmp120 - 2.25;
2023-01-11T21:41:26.8708456Z                             auto tmp122 = tmp121 * tmp119;
2023-01-11T21:41:26.8708624Z                             auto tmp123 = tmp122 * tmp119;
2023-01-11T21:41:26.8708799Z                             auto tmp124 = tmp123 + 1.0;
2023-01-11T21:41:26.8708964Z                             auto tmp125 = tmp119 + 1.0;
2023-01-11T21:41:26.8709240Z                             auto tmp126 = -0.75 * tmp125;
2023-01-11T21:41:26.8709495Z                             auto tmp127 = tmp126 - -3.75;
2023-01-11T21:41:26.8709673Z                             auto tmp128 = tmp127 * tmp125;
2023-01-11T21:41:26.8709954Z                             auto tmp129 = tmp128 + -6.0;
2023-01-11T21:41:26.8710126Z                             auto tmp130 = tmp129 * tmp125;
2023-01-11T21:41:26.8710391Z                             auto tmp131 = tmp130 - -3.0;
2023-01-11T21:41:26.8710577Z                             auto tmp132 = tmp67 * tmp113;
2023-01-11T21:41:26.8710742Z                             auto tmp133 = tmp80 * tmp118;
2023-01-11T21:41:26.8710903Z                             auto tmp134 = tmp93 * tmp124;
2023-01-11T21:41:26.8711066Z                             auto tmp135 = tmp106 * tmp131;
2023-01-11T21:41:26.8711246Z                             auto tmp136 = tmp134 + tmp135;
2023-01-11T21:41:26.8711427Z                             auto tmp137 = tmp133 + tmp136;
2023-01-11T21:41:26.8711696Z                             auto tmp138 = tmp132 + tmp137;
2023-01-11T21:41:26.8711879Z                             out_ptr1[i2 + (256*i1) + (32768*i0)] = tmp138;
2023-01-11T21:41:26.8712009Z                         }
2023-01-11T21:41:26.8712112Z                     }
2023-01-11T21:41:26.8712230Z                 }
2023-01-11T21:41:26.8712351Z             }
2023-01-11T21:41:26.8712463Z         }
2023-01-11T21:41:26.8712579Z     }
2023-01-11T21:41:26.8712683Z }
2023-01-11T21:41:26.8712848Z ''')
2023-01-11T21:41:26.8712857Z 
2023-01-11T21:41:26.8712863Z 
2023-01-11T21:41:26.8713002Z async_compile.wait(globals())
2023-01-11T21:41:26.8713147Z del async_compile
2023-01-11T21:41:26.8713159Z 
2023-01-11T21:41:26.8713283Z def call(args):
2023-01-11T21:41:26.8713410Z     arg0_1, = args
2023-01-11T21:41:26.8713544Z     args.clear()
2023-01-11T21:41:26.8714020Z     buf0 = empty_strided((4, 3, 128, 128), (49152, 16384, 128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8714420Z     buf1 = empty_strided((4, 3, 128, 256), (98304, 32768, 256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8714701Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8714815Z     del arg0_1
2023-01-11T21:41:26.8714959Z     return (buf0, buf1, )
2023-01-11T21:41:26.8714970Z 
2023-01-11T21:41:26.8714978Z 
2023-01-11T21:41:26.8715106Z if __name__ == "__main__":
2023-01-11T21:41:26.8715306Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8715532Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8715916Z     arg0_1 = rand_strided((4, 3, 64, 32), (6144, 2048, 32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8716113Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8716581Z [2023-01-11 21:39:04,215] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 475
2023-01-11T21:41:26.8716591Z 
2023-01-11T21:41:26.8716697Z ok (3.529s)
2023-01-11T21:41:26.8717490Z   test_upsample_bilinear2d_a_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8717717Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8718177Z [2023-01-11 21:39:04,787] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 476
2023-01-11T21:41:26.8718186Z 
2023-01-11T21:41:26.8718355Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8718494Z import torch
2023-01-11T21:41:26.8718625Z import random
2023-01-11T21:41:26.8718836Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8719043Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8719062Z 
2023-01-11T21:41:26.8719185Z aten = torch.ops.aten
2023-01-11T21:41:26.8719429Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8719590Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8719603Z 
2023-01-11T21:41:26.8719608Z 
2023-01-11T21:41:26.8719855Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8720203Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8720406Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8720582Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.8720771Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8720929Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8721103Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:26.8721213Z {
2023-01-11T21:41:26.8721365Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.8721622Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:41:26.8721803Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8721914Z     {
2023-01-11T21:41:26.8722036Z         #pragma omp for 
2023-01-11T21:41:26.8722186Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8722290Z         {
2023-01-11T21:41:26.8722431Z             #pragma GCC ivdep
2023-01-11T21:41:26.8722596Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:26.8722706Z             {
2023-01-11T21:41:26.8722853Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8722988Z                 for(long i2=0; i2<45; i2+=1)
2023-01-11T21:41:26.8723099Z                 {
2023-01-11T21:41:26.8723219Z                     {
2023-01-11T21:41:26.8723342Z                         {
2023-01-11T21:41:26.8723532Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8723791Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:26.8723966Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8724167Z                             auto tmp3 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:26.8724325Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.8724611Z                             auto tmp5 = tmp4 - tmp1;
2023-01-11T21:41:26.8724799Z                             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:26.8725027Z                             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:41:26.8725212Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:41:26.8725405Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:41:26.8725584Z                             auto tmp10 = static_cast<float>(i2);
2023-01-11T21:41:26.8725747Z                             auto tmp11 = tmp10 + tmp1;
2023-01-11T21:41:26.8725963Z                             auto tmp12 = static_cast<float>(0.8444444444444444);
2023-01-11T21:41:26.8726136Z                             auto tmp13 = tmp11 * tmp12;
2023-01-11T21:41:26.8726419Z                             auto tmp14 = tmp13 - tmp1;
2023-01-11T21:41:26.8726646Z                             auto tmp15 = (tmp6 != tmp6) ? tmp6 : std::max(tmp14, tmp6);
2023-01-11T21:41:26.8726842Z                             auto tmp16 = std::floor(tmp15);
2023-01-11T21:41:26.8727034Z                             auto tmp17 = static_cast<long>(tmp16);
2023-01-11T21:41:26.8727222Z                             auto tmp18 = in_ptr0[tmp17 + (38*tmp9) + (1406*i0)];
2023-01-11T21:41:26.8727416Z                             auto tmp19 = static_cast<float>(1.0);
2023-01-11T21:41:26.8727615Z                             auto tmp20 = static_cast<float>(tmp9);
2023-01-11T21:41:26.8727883Z                             auto tmp21 = tmp7 - tmp20;
2023-01-11T21:41:26.8728158Z                             auto tmp22 = tmp19 - tmp21;
2023-01-11T21:41:26.8728337Z                             auto tmp23 = tmp18 * tmp22;
2023-01-11T21:41:26.8728526Z                             auto tmp24 = std::ceil(tmp7);
2023-01-11T21:41:26.8728718Z                             auto tmp25 = static_cast<float>(36.0);
2023-01-11T21:41:26.8728931Z                             auto tmp26 = (tmp25 != tmp25) ? tmp25 : std::min(tmp24, tmp25);
2023-01-11T21:41:26.8729247Z                             auto tmp27 = static_cast<long>(tmp26);
2023-01-11T21:41:26.8729459Z                             auto tmp28 = in_ptr0[tmp17 + (38*tmp27) + (1406*i0)];
2023-01-11T21:41:26.8729624Z                             auto tmp29 = tmp28 * tmp21;
2023-01-11T21:41:26.8729795Z                             auto tmp30 = tmp23 + tmp29;
2023-01-11T21:41:26.8729985Z                             out_ptr0[i2 + (45*i1) + (2025*i0)] = tmp30;
2023-01-11T21:41:26.8730113Z                         }
2023-01-11T21:41:26.8730233Z                     }
2023-01-11T21:41:26.8730330Z                 }
2023-01-11T21:41:26.8730444Z             }
2023-01-11T21:41:26.8730568Z         }
2023-01-11T21:41:26.8730718Z         #pragma omp for 
2023-01-11T21:41:26.8731021Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8731135Z         {
2023-01-11T21:41:26.8731262Z             #pragma GCC ivdep
2023-01-11T21:41:26.8731413Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:26.8731528Z             {
2023-01-11T21:41:26.8731680Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8731833Z                 for(long i2=0; i2<45; i2+=1)
2023-01-11T21:41:26.8731956Z                 {
2023-01-11T21:41:26.8732073Z                     {
2023-01-11T21:41:26.8732180Z                         {
2023-01-11T21:41:26.8732372Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8732565Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:26.8732738Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8733029Z                             auto tmp3 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:26.8733209Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:41:26.8733506Z                             auto tmp5 = tmp4 - tmp1;
2023-01-11T21:41:26.8733688Z                             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:41:26.8733917Z                             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:41:26.8734106Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:41:26.8734296Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:41:26.8734487Z                             auto tmp10 = static_cast<float>(i2);
2023-01-11T21:41:26.8734656Z                             auto tmp11 = tmp10 + tmp1;
2023-01-11T21:41:26.8734860Z                             auto tmp12 = static_cast<float>(0.8444444444444444);
2023-01-11T21:41:26.8735030Z                             auto tmp13 = tmp11 * tmp12;
2023-01-11T21:41:26.8735281Z                             auto tmp14 = tmp13 - tmp1;
2023-01-11T21:41:26.8735511Z                             auto tmp15 = (tmp6 != tmp6) ? tmp6 : std::max(tmp14, tmp6);
2023-01-11T21:41:26.8735706Z                             auto tmp16 = std::ceil(tmp15);
2023-01-11T21:41:26.8735904Z                             auto tmp17 = static_cast<float>(37.0);
2023-01-11T21:41:26.8736124Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:41:26.8736324Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:41:26.8736533Z                             auto tmp20 = in_ptr0[tmp19 + (38*tmp9) + (1406*i0)];
2023-01-11T21:41:26.8736729Z                             auto tmp21 = static_cast<float>(1.0);
2023-01-11T21:41:26.8736916Z                             auto tmp22 = static_cast<float>(tmp9);
2023-01-11T21:41:26.8737174Z                             auto tmp23 = tmp7 - tmp22;
2023-01-11T21:41:26.8737449Z                             auto tmp24 = tmp21 - tmp23;
2023-01-11T21:41:26.8737627Z                             auto tmp25 = tmp20 * tmp24;
2023-01-11T21:41:26.8737805Z                             auto tmp26 = std::ceil(tmp7);
2023-01-11T21:41:26.8738006Z                             auto tmp27 = static_cast<float>(36.0);
2023-01-11T21:41:26.8738238Z                             auto tmp28 = (tmp27 != tmp27) ? tmp27 : std::min(tmp26, tmp27);
2023-01-11T21:41:26.8738429Z                             auto tmp29 = static_cast<long>(tmp28);
2023-01-11T21:41:26.8738631Z                             auto tmp30 = in_ptr0[tmp19 + (38*tmp29) + (1406*i0)];
2023-01-11T21:41:26.8738788Z                             auto tmp31 = tmp30 * tmp23;
2023-01-11T21:41:26.8738952Z                             auto tmp32 = tmp25 + tmp31;
2023-01-11T21:41:26.8739139Z                             out_ptr1[i2 + (45*i1) + (2025*i0)] = tmp32;
2023-01-11T21:41:26.8739263Z                         }
2023-01-11T21:41:26.8739387Z                     }
2023-01-11T21:41:26.8739505Z                 }
2023-01-11T21:41:26.8739632Z             }
2023-01-11T21:41:26.8739731Z         }
2023-01-11T21:41:26.8739863Z         #pragma omp for 
2023-01-11T21:41:26.8740113Z         for(long i0=0; i0<360; i0+=1)
2023-01-11T21:41:26.8740229Z         {
2023-01-11T21:41:26.8740376Z             #pragma GCC ivdep
2023-01-11T21:41:26.8740535Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:26.8740633Z             {
2023-01-11T21:41:26.8740752Z                 {
2023-01-11T21:41:26.8740874Z                     {
2023-01-11T21:41:26.8741044Z                         auto tmp0 = out_ptr0[i1 + (45*i0)];
2023-01-11T21:41:26.8741226Z                         auto tmp16 = out_ptr1[i1 + (45*i0)];
2023-01-11T21:41:26.8741414Z                         auto tmp1 = static_cast<float>(1.0);
2023-01-11T21:41:26.8741613Z                         auto tmp2 = static_cast<float>(i1);
2023-01-11T21:41:26.8741778Z                         auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:41:26.8742026Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8742239Z                         auto tmp5 = static_cast<float>(0.8444444444444444);
2023-01-11T21:41:26.8742416Z                         auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8742689Z                         auto tmp7 = tmp6 - tmp3;
2023-01-11T21:41:26.8742873Z                         auto tmp8 = static_cast<float>(0.0);
2023-01-11T21:41:26.8743095Z                         auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::max(tmp7, tmp8);
2023-01-11T21:41:26.8743353Z                         auto tmp10 = std::floor(tmp9);
2023-01-11T21:41:26.8743515Z                         auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:41:26.8743699Z                         auto tmp12 = static_cast<float>(tmp11);
2023-01-11T21:41:26.8743969Z                         auto tmp13 = tmp9 - tmp12;
2023-01-11T21:41:26.8744219Z                         auto tmp14 = tmp1 - tmp13;
2023-01-11T21:41:26.8744389Z                         auto tmp15 = tmp0 * tmp14;
2023-01-11T21:41:26.8744558Z                         auto tmp17 = tmp16 * tmp13;
2023-01-11T21:41:26.8744731Z                         auto tmp18 = tmp15 + tmp17;
2023-01-11T21:41:26.8744908Z                         in_out_ptr0[i1 + (45*i0)] = tmp18;
2023-01-11T21:41:26.8745009Z                     }
2023-01-11T21:41:26.8745132Z                 }
2023-01-11T21:41:26.8745255Z             }
2023-01-11T21:41:26.8745371Z         }
2023-01-11T21:41:26.8745521Z         #pragma omp for 
2023-01-11T21:41:26.8745667Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8745763Z         {
2023-01-11T21:41:26.8745897Z             #pragma GCC ivdep
2023-01-11T21:41:26.8746051Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:26.8746173Z             {
2023-01-11T21:41:26.8746319Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8746470Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:41:26.8746589Z                 {
2023-01-11T21:41:26.8746682Z                     {
2023-01-11T21:41:26.8746808Z                         {
2023-01-11T21:41:26.8747003Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8747207Z                             auto tmp1 = static_cast<float>(0.4931506849315068);
2023-01-11T21:41:26.8747390Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8747573Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:26.8747774Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:41:26.8747940Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:41:26.8748150Z                             auto tmp6 = static_cast<float>(0.49333333333333335);
2023-01-11T21:41:26.8748312Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.8748503Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:41:26.8748693Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:41:26.8748903Z                             auto tmp10 = in_ptr0[tmp9 + (38*tmp4) + (1406*i0)];
2023-01-11T21:41:26.8749095Z                             auto tmp11 = static_cast<float>(1.0);
2023-01-11T21:41:26.8749302Z                             auto tmp12 = static_cast<float>(tmp4);
2023-01-11T21:41:26.8749673Z                             auto tmp13 = tmp2 - tmp12;
2023-01-11T21:41:26.8749936Z                             auto tmp14 = tmp11 - tmp13;
2023-01-11T21:41:26.8750104Z                             auto tmp15 = tmp10 * tmp14;
2023-01-11T21:41:26.8750296Z                             auto tmp16 = std::ceil(tmp2);
2023-01-11T21:41:26.8750487Z                             auto tmp17 = static_cast<float>(36.0);
2023-01-11T21:41:26.8750718Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:41:26.8750905Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:41:26.8751113Z                             auto tmp20 = in_ptr0[tmp9 + (38*tmp19) + (1406*i0)];
2023-01-11T21:41:26.8751339Z                             auto tmp21 = tmp20 * tmp13;
2023-01-11T21:41:26.8751521Z                             auto tmp22 = tmp15 + tmp21;
2023-01-11T21:41:26.8751718Z                             auto tmp23 = static_cast<float>(tmp9);
2023-01-11T21:41:26.8751993Z                             auto tmp24 = tmp7 - tmp23;
2023-01-11T21:41:26.8752252Z                             auto tmp25 = tmp11 - tmp24;
2023-01-11T21:41:26.8752417Z                             auto tmp26 = tmp22 * tmp25;
2023-01-11T21:41:26.8752595Z                             out_ptr2[i2 + (76*i1) + (5624*i0)] = tmp26;
2023-01-11T21:41:26.8752704Z                         }
2023-01-11T21:41:26.8752825Z                     }
2023-01-11T21:41:26.8752944Z                 }
2023-01-11T21:41:26.8753054Z             }
2023-01-11T21:41:26.8753166Z         }
2023-01-11T21:41:26.8753309Z         #pragma omp for 
2023-01-11T21:41:26.8753466Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8753563Z         {
2023-01-11T21:41:26.8753708Z             #pragma GCC ivdep
2023-01-11T21:41:26.8753859Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:26.8753970Z             {
2023-01-11T21:41:26.8754124Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8754275Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:41:26.8754400Z                 {
2023-01-11T21:41:26.8754502Z                     {
2023-01-11T21:41:26.8754630Z                         {
2023-01-11T21:41:26.8754820Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8755040Z                             auto tmp1 = static_cast<float>(0.4931506849315068);
2023-01-11T21:41:26.8755207Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8755402Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:26.8755586Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:41:26.8755762Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:41:26.8755975Z                             auto tmp6 = static_cast<float>(0.49333333333333335);
2023-01-11T21:41:26.8756147Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.8756336Z                             auto tmp8 = std::ceil(tmp7);
2023-01-11T21:41:26.8756526Z                             auto tmp9 = static_cast<float>(37.0);
2023-01-11T21:41:26.8756760Z                             auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp8, tmp9);
2023-01-11T21:41:26.8756964Z                             auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:41:26.8757162Z                             auto tmp12 = in_ptr0[tmp11 + (38*tmp4) + (1406*i0)];
2023-01-11T21:41:26.8757331Z                             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:41:26.8757527Z                             auto tmp14 = static_cast<float>(tmp4);
2023-01-11T21:41:26.8757816Z                             auto tmp15 = tmp2 - tmp14;
2023-01-11T21:41:26.8758086Z                             auto tmp16 = tmp13 - tmp15;
2023-01-11T21:41:26.8758268Z                             auto tmp17 = tmp12 * tmp16;
2023-01-11T21:41:26.8758458Z                             auto tmp18 = std::ceil(tmp2);
2023-01-11T21:41:26.8758773Z                             auto tmp19 = static_cast<float>(36.0);
2023-01-11T21:41:26.8759002Z                             auto tmp20 = (tmp19 != tmp19) ? tmp19 : std::min(tmp18, tmp19);
2023-01-11T21:41:26.8759174Z                             auto tmp21 = static_cast<long>(tmp20);
2023-01-11T21:41:26.8759375Z                             auto tmp22 = in_ptr0[tmp11 + (38*tmp21) + (1406*i0)];
2023-01-11T21:41:26.8759539Z                             auto tmp23 = tmp22 * tmp15;
2023-01-11T21:41:26.8759712Z                             auto tmp24 = tmp17 + tmp23;
2023-01-11T21:41:26.8759904Z                             auto tmp25 = std::floor(tmp7);
2023-01-11T21:41:26.8760099Z                             auto tmp26 = static_cast<long>(tmp25);
2023-01-11T21:41:26.8760291Z                             auto tmp27 = static_cast<float>(tmp26);
2023-01-11T21:41:26.8760638Z                             auto tmp28 = tmp7 - tmp27;
2023-01-11T21:41:26.8760798Z                             auto tmp29 = tmp24 * tmp28;
2023-01-11T21:41:26.8760989Z                             out_ptr3[i2 + (76*i1) + (5624*i0)] = tmp29;
2023-01-11T21:41:26.8761117Z                         }
2023-01-11T21:41:26.8761233Z                     }
2023-01-11T21:41:26.8761353Z                 }
2023-01-11T21:41:26.8761470Z             }
2023-01-11T21:41:26.8761580Z         }
2023-01-11T21:41:26.8761706Z         #pragma omp for 
2023-01-11T21:41:26.8761848Z         for(long i0=0; i0<5624; i0+=1)
2023-01-11T21:41:26.8761960Z         {
2023-01-11T21:41:26.8762188Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i0);
2023-01-11T21:41:26.8762426Z             auto tmp1 = at::vec::Vectorized<float>::loadu(out_ptr3 + 8*i0);
2023-01-11T21:41:26.8762580Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8762749Z             tmp2.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:26.8762831Z         }
2023-01-11T21:41:26.8763016Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8763179Z         for(long i0=44992; i0<44992; i0+=1)
2023-01-11T21:41:26.8763300Z         {
2023-01-11T21:41:26.8763460Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:26.8763608Z             auto tmp1 = out_ptr3[i0];
2023-01-11T21:41:26.8763756Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8763893Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.8764012Z         }
2023-01-11T21:41:26.8764125Z     }
2023-01-11T21:41:26.8764233Z }
2023-01-11T21:41:26.8764398Z ''')
2023-01-11T21:41:26.8764408Z 
2023-01-11T21:41:26.8764416Z 
2023-01-11T21:41:26.8764573Z async_compile.wait(globals())
2023-01-11T21:41:26.8764711Z del async_compile
2023-01-11T21:41:26.8764722Z 
2023-01-11T21:41:26.8764831Z def call(args):
2023-01-11T21:41:26.8764957Z     arg0_1, = args
2023-01-11T21:41:26.8765082Z     args.clear()
2023-01-11T21:41:26.8765473Z     buf0 = empty_strided((2, 4, 45, 45), (8100, 2025, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8765863Z     buf1 = empty_strided((2, 4, 45, 45), (8100, 2025, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8766015Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:26.8766391Z     buf3 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8766774Z     buf4 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8766902Z     buf5 = buf3; del buf3  # reuse
2023-01-11T21:41:26.8767265Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.8767395Z     del arg0_1
2023-01-11T21:41:26.8767525Z     return (buf2, buf5, )
2023-01-11T21:41:26.8767537Z 
2023-01-11T21:41:26.8767543Z 
2023-01-11T21:41:26.8767674Z if __name__ == "__main__":
2023-01-11T21:41:26.8767884Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8768110Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8768495Z     arg0_1 = rand_strided((2, 4, 37, 38), (5624, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8768760Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8769349Z [2023-01-11 21:39:06,707] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 476
2023-01-11T21:41:26.8769359Z 
2023-01-11T21:41:26.8769489Z ok (2.484s)
2023-01-11T21:41:26.8770295Z   test_upsample_bilinear2d_b_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8770628Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8771084Z [2023-01-11 21:39:06,990] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 477
2023-01-11T21:41:26.8771564Z [2023-01-11 21:39:08,580] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 477
2023-01-11T21:41:26.8771573Z 
2023-01-11T21:41:26.8771731Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8771870Z import torch
2023-01-11T21:41:26.8771983Z import random
2023-01-11T21:41:26.8772185Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8772405Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8772413Z 
2023-01-11T21:41:26.8772550Z aten = torch.ops.aten
2023-01-11T21:41:26.8772779Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8772940Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8772949Z 
2023-01-11T21:41:26.8772954Z 
2023-01-11T21:41:26.8773197Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8773543Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8773728Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8773920Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8774093Z                        float* __restrict__ out_ptr1)
2023-01-11T21:41:26.8774199Z {
2023-01-11T21:41:26.8774364Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:41:26.8774538Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8774656Z     {
2023-01-11T21:41:26.8774802Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.8774938Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8775048Z         {
2023-01-11T21:41:26.8775204Z             for(long i1=0; i1<80; i1+=1)
2023-01-11T21:41:26.8775323Z             {
2023-01-11T21:41:26.8775472Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8775629Z                 for(long i2=0; i2<118; i2+=1)
2023-01-11T21:41:26.8775728Z                 {
2023-01-11T21:41:26.8775846Z                     {
2023-01-11T21:41:26.8775969Z                         {
2023-01-11T21:41:26.8776164Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8776381Z                             auto tmp1 = static_cast<float>(0.4936708860759494);
2023-01-11T21:41:26.8776543Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8776726Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:26.8776903Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:41:26.8777095Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:41:26.8777295Z                             auto tmp6 = static_cast<float>(0.49572649572649574);
2023-01-11T21:41:26.8777465Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.8777661Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:41:26.8777856Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:41:26.8778065Z                             auto tmp10 = in_ptr0[tmp9 + (59*tmp4) + (2360*i0)];
2023-01-11T21:41:26.8778390Z                             auto tmp11 = static_cast<float>(1.0);
2023-01-11T21:41:26.8778573Z                             auto tmp12 = static_cast<float>(tmp4);
2023-01-11T21:41:26.8778872Z                             auto tmp13 = tmp2 - tmp12;
2023-01-11T21:41:26.8779137Z                             auto tmp14 = tmp11 - tmp13;
2023-01-11T21:41:26.8779314Z                             auto tmp15 = tmp10 * tmp14;
2023-01-11T21:41:26.8779495Z                             auto tmp16 = std::ceil(tmp2);
2023-01-11T21:41:26.8779683Z                             auto tmp17 = static_cast<float>(39.0);
2023-01-11T21:41:26.8779911Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:41:26.8780112Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:41:26.8780373Z                             auto tmp20 = in_ptr0[tmp9 + (59*tmp19) + (2360*i0)];
2023-01-11T21:41:26.8780552Z                             auto tmp21 = tmp20 * tmp13;
2023-01-11T21:41:26.8780725Z                             auto tmp22 = tmp15 + tmp21;
2023-01-11T21:41:26.8780921Z                             auto tmp23 = static_cast<float>(tmp9);
2023-01-11T21:41:26.8781195Z                             auto tmp24 = tmp7 - tmp23;
2023-01-11T21:41:26.8781453Z                             auto tmp25 = tmp11 - tmp24;
2023-01-11T21:41:26.8781617Z                             auto tmp26 = tmp22 * tmp25;
2023-01-11T21:41:26.8781804Z                             out_ptr0[i2 + (118*i1) + (9440*i0)] = tmp26;
2023-01-11T21:41:26.8781918Z                         }
2023-01-11T21:41:26.8782034Z                     }
2023-01-11T21:41:26.8782152Z                 }
2023-01-11T21:41:26.8782258Z             }
2023-01-11T21:41:26.8782371Z         }
2023-01-11T21:41:26.8782537Z         #pragma omp for  collapse(2)
2023-01-11T21:41:26.8782670Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.8782787Z         {
2023-01-11T21:41:26.8782935Z             for(long i1=0; i1<80; i1+=1)
2023-01-11T21:41:26.8783046Z             {
2023-01-11T21:41:26.8783302Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8783468Z                 for(long i2=0; i2<118; i2+=1)
2023-01-11T21:41:26.8783590Z                 {
2023-01-11T21:41:26.8783695Z                     {
2023-01-11T21:41:26.8783818Z                         {
2023-01-11T21:41:26.8784012Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8784213Z                             auto tmp1 = static_cast<float>(0.4936708860759494);
2023-01-11T21:41:26.8784388Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8784581Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:41:26.8784771Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:41:26.8784942Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:41:26.8785156Z                             auto tmp6 = static_cast<float>(0.49572649572649574);
2023-01-11T21:41:26.8785329Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:41:26.8785520Z                             auto tmp8 = std::ceil(tmp7);
2023-01-11T21:41:26.8785716Z                             auto tmp9 = static_cast<float>(58.0);
2023-01-11T21:41:26.8785944Z                             auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp8, tmp9);
2023-01-11T21:41:26.8786138Z                             auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:41:26.8786331Z                             auto tmp12 = in_ptr0[tmp11 + (59*tmp4) + (2360*i0)];
2023-01-11T21:41:26.8786498Z                             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:41:26.8786690Z                             auto tmp14 = static_cast<float>(tmp4);
2023-01-11T21:41:26.8786982Z                             auto tmp15 = tmp2 - tmp14;
2023-01-11T21:41:26.8787247Z                             auto tmp16 = tmp13 - tmp15;
2023-01-11T21:41:26.8787432Z                             auto tmp17 = tmp12 * tmp16;
2023-01-11T21:41:26.8787615Z                             auto tmp18 = std::ceil(tmp2);
2023-01-11T21:41:26.8787905Z                             auto tmp19 = static_cast<float>(39.0);
2023-01-11T21:41:26.8788135Z                             auto tmp20 = (tmp19 != tmp19) ? tmp19 : std::min(tmp18, tmp19);
2023-01-11T21:41:26.8788315Z                             auto tmp21 = static_cast<long>(tmp20);
2023-01-11T21:41:26.8788522Z                             auto tmp22 = in_ptr0[tmp11 + (59*tmp21) + (2360*i0)];
2023-01-11T21:41:26.8788694Z                             auto tmp23 = tmp22 * tmp15;
2023-01-11T21:41:26.8788862Z                             auto tmp24 = tmp17 + tmp23;
2023-01-11T21:41:26.8789052Z                             auto tmp25 = std::floor(tmp7);
2023-01-11T21:41:26.8789248Z                             auto tmp26 = static_cast<long>(tmp25);
2023-01-11T21:41:26.8789511Z                             auto tmp27 = static_cast<float>(tmp26);
2023-01-11T21:41:26.8789778Z                             auto tmp28 = tmp7 - tmp27;
2023-01-11T21:41:26.8789949Z                             auto tmp29 = tmp24 * tmp28;
2023-01-11T21:41:26.8790139Z                             out_ptr1[i2 + (118*i1) + (9440*i0)] = tmp29;
2023-01-11T21:41:26.8790264Z                         }
2023-01-11T21:41:26.8790388Z                     }
2023-01-11T21:41:26.8790492Z                 }
2023-01-11T21:41:26.8790608Z             }
2023-01-11T21:41:26.8790729Z         }
2023-01-11T21:41:26.8790851Z         #pragma omp for 
2023-01-11T21:41:26.8791004Z         for(long i0=0; i0<2360; i0+=1)
2023-01-11T21:41:26.8791120Z         {
2023-01-11T21:41:26.8791343Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 8*i0);
2023-01-11T21:41:26.8791581Z             auto tmp1 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8791732Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8791916Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.8792016Z         }
2023-01-11T21:41:26.8792186Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8792350Z         for(long i0=18880; i0<18880; i0+=1)
2023-01-11T21:41:26.8792470Z         {
2023-01-11T21:41:26.8792629Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:41:26.8792780Z             auto tmp1 = out_ptr1[i0];
2023-01-11T21:41:26.8792926Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.8793051Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8793170Z         }
2023-01-11T21:41:26.8793278Z     }
2023-01-11T21:41:26.8793392Z }
2023-01-11T21:41:26.8793556Z ''')
2023-01-11T21:41:26.8793566Z 
2023-01-11T21:41:26.8793577Z 
2023-01-11T21:41:26.8793727Z async_compile.wait(globals())
2023-01-11T21:41:26.8793856Z del async_compile
2023-01-11T21:41:26.8793867Z 
2023-01-11T21:41:26.8793979Z def call(args):
2023-01-11T21:41:26.8794109Z     arg0_1, = args
2023-01-11T21:41:26.8794239Z     args.clear()
2023-01-11T21:41:26.8794642Z     buf0 = empty_strided((1, 2, 80, 118), (18880, 9440, 118, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8795039Z     buf1 = empty_strided((1, 2, 80, 118), (18880, 9440, 118, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8795194Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:41:26.8795467Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8795595Z     del arg0_1
2023-01-11T21:41:26.8795709Z     return (buf2, )
2023-01-11T21:41:26.8795721Z 
2023-01-11T21:41:26.8795727Z 
2023-01-11T21:41:26.8795859Z if __name__ == "__main__":
2023-01-11T21:41:26.8796062Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8796275Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8796667Z     arg0_1 = rand_strided((1, 2, 40, 59), (4720, 2360, 59, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8796853Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8796863Z 
2023-01-11T21:41:26.8796986Z ok (1.872s)
2023-01-11T21:41:26.8797797Z   test_upsample_nearest1d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8798151Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8798590Z [2023-01-11 21:39:08,869] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 478
2023-01-11T21:41:26.8799057Z [2023-01-11 21:39:10,404] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 478
2023-01-11T21:41:26.8799070Z 
2023-01-11T21:41:26.8799229Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8799346Z import torch
2023-01-11T21:41:26.8799559Z import random
2023-01-11T21:41:26.8799764Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8799971Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8799989Z 
2023-01-11T21:41:26.8800124Z aten = torch.ops.aten
2023-01-11T21:41:26.8800340Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8800503Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8800513Z 
2023-01-11T21:41:26.8800520Z 
2023-01-11T21:41:26.8800767Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8801113Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8801326Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8801502Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8801670Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8801835Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8801991Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.8802159Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.8802275Z {
2023-01-11T21:41:26.8802450Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8802561Z     {
2023-01-11T21:41:26.8802700Z         #pragma omp for 
2023-01-11T21:41:26.8802850Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8802948Z         {
2023-01-11T21:41:26.8803100Z             #pragma GCC ivdep
2023-01-11T21:41:26.8803251Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:26.8803358Z             {
2023-01-11T21:41:26.8803480Z                 {
2023-01-11T21:41:26.8803608Z                     {
2023-01-11T21:41:26.8803778Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8803967Z                         auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:26.8804125Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8804325Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8804507Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:41:26.8804678Z                         out_ptr0[i1 + (74*i0)] = tmp4;
2023-01-11T21:41:26.8804845Z                         out_ptr1[i1 + (74*i0)] = tmp4;
2023-01-11T21:41:26.8804963Z                     }
2023-01-11T21:41:26.8805058Z                 }
2023-01-11T21:41:26.8805180Z             }
2023-01-11T21:41:26.8805299Z         }
2023-01-11T21:41:26.8805440Z         #pragma omp for 
2023-01-11T21:41:26.8805594Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8805707Z         {
2023-01-11T21:41:26.8805819Z             #pragma GCC ivdep
2023-01-11T21:41:26.8805977Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:41:26.8806102Z             {
2023-01-11T21:41:26.8806217Z                 {
2023-01-11T21:41:26.8806336Z                     {
2023-01-11T21:41:26.8806519Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8806715Z                         auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:41:26.8806863Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8807160Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8807346Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:41:26.8807510Z                         out_ptr2[i1 + (70*i0)] = tmp4;
2023-01-11T21:41:26.8807636Z                     }
2023-01-11T21:41:26.8807750Z                 }
2023-01-11T21:41:26.8807873Z             }
2023-01-11T21:41:26.8807972Z         }
2023-01-11T21:41:26.8808106Z         #pragma omp for 
2023-01-11T21:41:26.8808249Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8808363Z         {
2023-01-11T21:41:26.8808509Z             #pragma GCC ivdep
2023-01-11T21:41:26.8808664Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:26.8808782Z             {
2023-01-11T21:41:26.8808882Z                 {
2023-01-11T21:41:26.8809119Z                     {
2023-01-11T21:41:26.8809437Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8809644Z                         auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:26.8809816Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8810001Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8810176Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:41:26.8810328Z                         out_ptr3[i1 + (45*i0)] = tmp4;
2023-01-11T21:41:26.8810447Z                     }
2023-01-11T21:41:26.8810563Z                 }
2023-01-11T21:41:26.8810684Z             }
2023-01-11T21:41:26.8810798Z         }
2023-01-11T21:41:26.8810930Z         #pragma omp for 
2023-01-11T21:41:26.8811078Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8811177Z         {
2023-01-11T21:41:26.8811325Z             #pragma GCC ivdep
2023-01-11T21:41:26.8811468Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:41:26.8811587Z             {
2023-01-11T21:41:26.8811703Z                 {
2023-01-11T21:41:26.8811823Z                     {
2023-01-11T21:41:26.8811989Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8812202Z                         auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:41:26.8812370Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8812554Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8812738Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:41:26.8812915Z                         out_ptr4[i1 + (36*i0)] = tmp4;
2023-01-11T21:41:26.8813034Z                     }
2023-01-11T21:41:26.8813156Z                 }
2023-01-11T21:41:26.8813255Z             }
2023-01-11T21:41:26.8813355Z         }
2023-01-11T21:41:26.8813471Z     }
2023-01-11T21:41:26.8813581Z }
2023-01-11T21:41:26.8813766Z ''')
2023-01-11T21:41:26.8813779Z 
2023-01-11T21:41:26.8813785Z 
2023-01-11T21:41:26.8813956Z async_compile.wait(globals())
2023-01-11T21:41:26.8814093Z del async_compile
2023-01-11T21:41:26.8814103Z 
2023-01-11T21:41:26.8814198Z def call(args):
2023-01-11T21:41:26.8814335Z     arg0_1, = args
2023-01-11T21:41:26.8814456Z     args.clear()
2023-01-11T21:41:26.8814824Z     buf0 = empty_strided((2, 4, 74), (296, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8815178Z     buf4 = empty_strided((2, 4, 74), (296, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8815535Z     buf1 = empty_strided((2, 4, 70), (280, 70, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8815862Z     buf2 = empty_strided((2, 4, 45), (180, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8816150Z     buf3 = empty_strided((2, 4, 36), (144, 36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8816549Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.8816682Z     del arg0_1
2023-01-11T21:41:26.8816851Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.8816859Z 
2023-01-11T21:41:26.8816984Z 
2023-01-11T21:41:26.8817129Z if __name__ == "__main__":
2023-01-11T21:41:26.8817340Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8817552Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8817916Z     arg0_1 = rand_strided((2, 4, 37), (148, 37, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8818104Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8818116Z 
2023-01-11T21:41:26.8818214Z ok (1.823s)
2023-01-11T21:41:26.8819095Z   test_upsample_nearest2d_backward_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8819319Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8819788Z [2023-01-11 21:39:10,431] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 479
2023-01-11T21:41:26.8819797Z 
2023-01-11T21:41:26.8819971Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8820094Z import torch
2023-01-11T21:41:26.8820225Z import random
2023-01-11T21:41:26.8820432Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8820640Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8820649Z 
2023-01-11T21:41:26.8820777Z aten = torch.ops.aten
2023-01-11T21:41:26.8821003Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8821169Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8821181Z 
2023-01-11T21:41:26.8821187Z 
2023-01-11T21:41:26.8821435Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8821771Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8821988Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8822161Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8822330Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8822477Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8822652Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.8822821Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.8822938Z {
2023-01-11T21:41:26.8823116Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8823300Z     {
2023-01-11T21:41:26.8823432Z         #pragma omp for 
2023-01-11T21:41:26.8823562Z         for(long i0=0; i0<27; i0+=1)
2023-01-11T21:41:26.8823679Z         {
2023-01-11T21:41:26.8823826Z             #pragma GCC ivdep
2023-01-11T21:41:26.8823984Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:41:26.8824103Z             {
2023-01-11T21:41:26.8824215Z                 {
2023-01-11T21:41:26.8824314Z                     {
2023-01-11T21:41:26.8824502Z                         auto tmp0 = in_ptr0[(2*i1) + (24*i0)];
2023-01-11T21:41:26.8824691Z                         auto tmp1 = in_ptr0[1 + (2*i1) + (24*i0)];
2023-01-11T21:41:26.8824884Z                         auto tmp3 = in_ptr0[12 + (2*i1) + (24*i0)];
2023-01-11T21:41:26.8825055Z                         auto tmp5 = in_ptr0[13 + (2*i1) + (24*i0)];
2023-01-11T21:41:26.8825233Z                         auto tmp2 = tmp1 + tmp0;
2023-01-11T21:41:26.8825406Z                         auto tmp4 = tmp3 + tmp2;
2023-01-11T21:41:26.8825573Z                         auto tmp6 = tmp5 + tmp4;
2023-01-11T21:41:26.8825741Z                         auto tmp7 = static_cast<float>(1.0);
2023-01-11T21:41:26.8825898Z                         auto tmp8 = tmp6 * tmp7;
2023-01-11T21:41:26.8826066Z                         out_ptr0[i1 + (6*i0)] = tmp8;
2023-01-11T21:41:26.8826202Z                     }
2023-01-11T21:41:26.8826332Z                 }
2023-01-11T21:41:26.8826449Z             }
2023-01-11T21:41:26.8826665Z         }
2023-01-11T21:41:26.8826785Z         #pragma omp for 
2023-01-11T21:41:26.8826935Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:41:26.8827058Z         {
2023-01-11T21:41:26.8827205Z             #pragma GCC ivdep
2023-01-11T21:41:26.8827353Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.8827465Z             {
2023-01-11T21:41:26.8827600Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8827752Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:41:26.8827874Z                 {
2023-01-11T21:41:26.8827993Z                     {
2023-01-11T21:41:26.8828125Z                         {
2023-01-11T21:41:26.8828323Z                             auto tmp0 = static_cast<long>(((3 + (6*i1)) / 4));
2023-01-11T21:41:26.8828530Z                             auto tmp1 = static_cast<long>(((9 + (6*i1)) / 4));
2023-01-11T21:41:26.8828778Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.8828970Z                             auto tmp3 = static_cast<long>(((4 + (12*i2)) / 5));
2023-01-11T21:41:26.8829170Z                             auto tmp4 = static_cast<long>(((16 + (12*i2)) / 5));
2023-01-11T21:41:26.8829345Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:26.8829512Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:26.8829677Z                             float tmp7 = 0.0;
2023-01-11T21:41:26.8829817Z                             if(tmp6)
2023-01-11T21:41:26.8829939Z                             {
2023-01-11T21:41:26.8830140Z                                 auto tmp8 = in_ptr0[(12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:26.8830297Z                                 tmp7 = tmp8;
2023-01-11T21:41:26.8830426Z                             }
2023-01-11T21:41:26.8830632Z                             auto tmp9 = static_cast<long>(1 + (((4 + (12*i2)) / 5)));
2023-01-11T21:41:26.8830804Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:26.8830972Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:26.8831138Z                             float tmp12 = 0.0;
2023-01-11T21:41:26.8831259Z                             if(tmp11)
2023-01-11T21:41:26.8831387Z                             {
2023-01-11T21:41:26.8831602Z                                 auto tmp13 = in_ptr0[1 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:26.8831759Z                                 tmp12 = tmp13;
2023-01-11T21:41:26.8831885Z                             }
2023-01-11T21:41:26.8832060Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:26.8832266Z                             auto tmp15 = static_cast<long>(2 + (((4 + (12*i2)) / 5)));
2023-01-11T21:41:26.8832436Z                             auto tmp16 = tmp15 < tmp4;
2023-01-11T21:41:26.8832602Z                             auto tmp17 = tmp2 & tmp16;
2023-01-11T21:41:26.8832761Z                             float tmp18 = 0.0;
2023-01-11T21:41:26.8832908Z                             if(tmp17)
2023-01-11T21:41:26.8833033Z                             {
2023-01-11T21:41:26.8833251Z                                 auto tmp19 = in_ptr0[2 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:26.8833404Z                                 tmp18 = tmp19;
2023-01-11T21:41:26.8833529Z                             }
2023-01-11T21:41:26.8833686Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:26.8833892Z                             auto tmp21 = static_cast<long>(1 + (((3 + (6*i1)) / 4)));
2023-01-11T21:41:26.8834063Z                             auto tmp22 = tmp21 < tmp1;
2023-01-11T21:41:26.8834231Z                             auto tmp23 = tmp22 & tmp5;
2023-01-11T21:41:26.8834384Z                             float tmp24 = 0.0;
2023-01-11T21:41:26.8834525Z                             if(tmp23)
2023-01-11T21:41:26.8834659Z                             {
2023-01-11T21:41:26.8834877Z                                 auto tmp25 = in_ptr0[12 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:26.8835113Z                                 tmp24 = tmp25;
2023-01-11T21:41:26.8835247Z                             }
2023-01-11T21:41:26.8835420Z                             auto tmp26 = tmp24 + tmp20;
2023-01-11T21:41:26.8835584Z                             auto tmp27 = tmp22 & tmp10;
2023-01-11T21:41:26.8835760Z                             float tmp28 = 0.0;
2023-01-11T21:41:26.8835910Z                             if(tmp27)
2023-01-11T21:41:26.8836036Z                             {
2023-01-11T21:41:26.8836237Z                                 auto tmp29 = in_ptr0[13 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:26.8836387Z                                 tmp28 = tmp29;
2023-01-11T21:41:26.8836523Z                             }
2023-01-11T21:41:26.8836763Z                             auto tmp30 = tmp28 + tmp26;
2023-01-11T21:41:26.8836943Z                             auto tmp31 = tmp22 & tmp16;
2023-01-11T21:41:26.8837101Z                             float tmp32 = 0.0;
2023-01-11T21:41:26.8837240Z                             if(tmp31)
2023-01-11T21:41:26.8837349Z                             {
2023-01-11T21:41:26.8837567Z                                 auto tmp33 = in_ptr0[14 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:41:26.8837712Z                                 tmp32 = tmp33;
2023-01-11T21:41:26.8837838Z                             }
2023-01-11T21:41:26.8837997Z                             auto tmp34 = tmp32 + tmp30;
2023-01-11T21:41:26.8838177Z                             out_ptr1[i2 + (5*i1) + (20*i0)] = tmp34;
2023-01-11T21:41:26.8838306Z                         }
2023-01-11T21:41:26.8838431Z                     }
2023-01-11T21:41:26.8838535Z                 }
2023-01-11T21:41:26.8838656Z             }
2023-01-11T21:41:26.8838766Z         }
2023-01-11T21:41:26.8838915Z         #pragma omp for 
2023-01-11T21:41:26.8839069Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:41:26.8839188Z         {
2023-01-11T21:41:26.8839313Z             #pragma GCC ivdep
2023-01-11T21:41:26.8839464Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.8839569Z             {
2023-01-11T21:41:26.8839714Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8839877Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:41:26.8839994Z                 {
2023-01-11T21:41:26.8840117Z                     {
2023-01-11T21:41:26.8840230Z                         {
2023-01-11T21:41:26.8840419Z                             auto tmp0 = static_cast<long>(3*i1);
2023-01-11T21:41:26.8840624Z                             auto tmp1 = static_cast<long>(3 + (3*i1));
2023-01-11T21:41:26.8840791Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.8840995Z                             auto tmp3 = static_cast<long>(((7 + (12*i2)) / 8));
2023-01-11T21:41:26.8841203Z                             auto tmp4 = static_cast<long>(((19 + (12*i2)) / 8));
2023-01-11T21:41:26.8841379Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:26.8841554Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:26.8841707Z                             float tmp7 = 0.0;
2023-01-11T21:41:26.8841847Z                             if(tmp6)
2023-01-11T21:41:26.8841967Z                             {
2023-01-11T21:41:26.8842169Z                                 auto tmp8 = in_ptr0[(36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8842330Z                                 tmp7 = tmp8;
2023-01-11T21:41:26.8842459Z                             }
2023-01-11T21:41:26.8842662Z                             auto tmp9 = static_cast<long>(1 + (((7 + (12*i2)) / 8)));
2023-01-11T21:41:26.8842806Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:26.8842978Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:26.8843149Z                             float tmp12 = 0.0;
2023-01-11T21:41:26.8843293Z                             if(tmp11)
2023-01-11T21:41:26.8843513Z                             {
2023-01-11T21:41:26.8843721Z                                 auto tmp13 = in_ptr0[1 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8843883Z                                 tmp12 = tmp13;
2023-01-11T21:41:26.8843992Z                             }
2023-01-11T21:41:26.8844163Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:26.8844358Z                             auto tmp15 = static_cast<long>(1 + (3*i1));
2023-01-11T21:41:26.8844528Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:41:26.8844707Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:41:26.8844867Z                             float tmp18 = 0.0;
2023-01-11T21:41:26.8845013Z                             if(tmp17)
2023-01-11T21:41:26.8845121Z                             {
2023-01-11T21:41:26.8845397Z                                 auto tmp19 = in_ptr0[12 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8845564Z                                 tmp18 = tmp19;
2023-01-11T21:41:26.8845696Z                             }
2023-01-11T21:41:26.8845871Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:26.8846042Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:41:26.8846200Z                             float tmp22 = 0.0;
2023-01-11T21:41:26.8846339Z                             if(tmp21)
2023-01-11T21:41:26.8846450Z                             {
2023-01-11T21:41:26.8846661Z                                 auto tmp23 = in_ptr0[13 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8846809Z                                 tmp22 = tmp23;
2023-01-11T21:41:26.8846930Z                             }
2023-01-11T21:41:26.8847101Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:41:26.8847306Z                             auto tmp25 = static_cast<long>(2 + (3*i1));
2023-01-11T21:41:26.8847470Z                             auto tmp26 = tmp25 < tmp1;
2023-01-11T21:41:26.8847619Z                             auto tmp27 = tmp26 & tmp5;
2023-01-11T21:41:26.8847775Z                             float tmp28 = 0.0;
2023-01-11T21:41:26.8847923Z                             if(tmp27)
2023-01-11T21:41:26.8848048Z                             {
2023-01-11T21:41:26.8848255Z                                 auto tmp29 = in_ptr0[24 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8848394Z                                 tmp28 = tmp29;
2023-01-11T21:41:26.8848526Z                             }
2023-01-11T21:41:26.8848679Z                             auto tmp30 = tmp28 + tmp24;
2023-01-11T21:41:26.8848841Z                             auto tmp31 = tmp26 & tmp10;
2023-01-11T21:41:26.8849130Z                             float tmp32 = 0.0;
2023-01-11T21:41:26.8849278Z                             if(tmp31)
2023-01-11T21:41:26.8849408Z                             {
2023-01-11T21:41:26.8849616Z                                 auto tmp33 = in_ptr0[25 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8849781Z                                 tmp32 = tmp33;
2023-01-11T21:41:26.8849908Z                             }
2023-01-11T21:41:26.8850060Z                             auto tmp34 = tmp32 + tmp30;
2023-01-11T21:41:26.8850212Z                             float tmp35 = 0.0;
2023-01-11T21:41:26.8850355Z                             if(tmp6)
2023-01-11T21:41:26.8850482Z                             {
2023-01-11T21:41:26.8850697Z                                 auto tmp36 = in_ptr0[(36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8850848Z                                 tmp35 = tmp36;
2023-01-11T21:41:26.8850967Z                             }
2023-01-11T21:41:26.8851102Z                             float tmp37 = 0.0;
2023-01-11T21:41:26.8851246Z                             if(tmp11)
2023-01-11T21:41:26.8851373Z                             {
2023-01-11T21:41:26.8851591Z                                 auto tmp38 = in_ptr0[1 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8851862Z                                 tmp37 = tmp38;
2023-01-11T21:41:26.8851988Z                             }
2023-01-11T21:41:26.8852164Z                             auto tmp39 = tmp37 + tmp35;
2023-01-11T21:41:26.8852307Z                             float tmp40 = 0.0;
2023-01-11T21:41:26.8852452Z                             if(tmp17)
2023-01-11T21:41:26.8852576Z                             {
2023-01-11T21:41:26.8852788Z                                 auto tmp41 = in_ptr0[12 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8852937Z                                 tmp40 = tmp41;
2023-01-11T21:41:26.8853068Z                             }
2023-01-11T21:41:26.8853233Z                             auto tmp42 = tmp40 + tmp39;
2023-01-11T21:41:26.8853394Z                             float tmp43 = 0.0;
2023-01-11T21:41:26.8853600Z                             if(tmp21)
2023-01-11T21:41:26.8853730Z                             {
2023-01-11T21:41:26.8853945Z                                 auto tmp44 = in_ptr0[13 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8854104Z                                 tmp43 = tmp44;
2023-01-11T21:41:26.8854223Z                             }
2023-01-11T21:41:26.8854403Z                             auto tmp45 = tmp43 + tmp42;
2023-01-11T21:41:26.8854564Z                             float tmp46 = 0.0;
2023-01-11T21:41:26.8854685Z                             if(tmp27)
2023-01-11T21:41:26.8854818Z                             {
2023-01-11T21:41:26.8855021Z                                 auto tmp47 = in_ptr0[24 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8855182Z                                 tmp46 = tmp47;
2023-01-11T21:41:26.8855309Z                             }
2023-01-11T21:41:26.8855476Z                             auto tmp48 = tmp46 + tmp45;
2023-01-11T21:41:26.8855637Z                             float tmp49 = 0.0;
2023-01-11T21:41:26.8855754Z                             if(tmp31)
2023-01-11T21:41:26.8855886Z                             {
2023-01-11T21:41:26.8856092Z                                 auto tmp50 = in_ptr0[25 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:41:26.8856247Z                                 tmp49 = tmp50;
2023-01-11T21:41:26.8856371Z                             }
2023-01-11T21:41:26.8856532Z                             auto tmp51 = tmp49 + tmp48;
2023-01-11T21:41:26.8856709Z                             out_ptr2[i2 + (8*i1) + (16*i0)] = tmp34;
2023-01-11T21:41:26.8856873Z                             out_ptr3[i2 + (8*i1) + (16*i0)] = tmp51;
2023-01-11T21:41:26.8856999Z                         }
2023-01-11T21:41:26.8857127Z                     }
2023-01-11T21:41:26.8857243Z                 }
2023-01-11T21:41:26.8857354Z             }
2023-01-11T21:41:26.8857472Z         }
2023-01-11T21:41:26.8857629Z         #pragma omp for 
2023-01-11T21:41:26.8857772Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:41:26.8857888Z         {
2023-01-11T21:41:26.8858037Z             #pragma GCC ivdep
2023-01-11T21:41:26.8858182Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:41:26.8858302Z             {
2023-01-11T21:41:26.8858454Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8858614Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:41:26.8858716Z                 {
2023-01-11T21:41:26.8858840Z                     {
2023-01-11T21:41:26.8858964Z                         {
2023-01-11T21:41:26.8859169Z                             auto tmp0 = static_cast<long>(((3 + (6*i1)) / 4));
2023-01-11T21:41:26.8859374Z                             auto tmp1 = static_cast<long>(((9 + (6*i1)) / 4));
2023-01-11T21:41:26.8859543Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:41:26.8859749Z                             auto tmp3 = static_cast<long>(((6 + (12*i2)) / 7));
2023-01-11T21:41:26.8859927Z                             auto tmp4 = static_cast<long>(((18 + (12*i2)) / 7));
2023-01-11T21:41:26.8860102Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:41:26.8860394Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:41:26.8860551Z                             float tmp7 = 0.0;
2023-01-11T21:41:26.8860687Z                             if(tmp6)
2023-01-11T21:41:26.8860818Z                             {
2023-01-11T21:41:26.8861043Z                                 auto tmp8 = in_ptr0[(12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:41:26.8861192Z                                 tmp7 = tmp8;
2023-01-11T21:41:26.8861306Z                             }
2023-01-11T21:41:26.8861504Z                             auto tmp9 = static_cast<long>(1 + (((6 + (12*i2)) / 7)));
2023-01-11T21:41:26.8861678Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:41:26.8861847Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:41:26.8862064Z                             float tmp12 = 0.0;
2023-01-11T21:41:26.8862202Z                             if(tmp11)
2023-01-11T21:41:26.8862331Z                             {
2023-01-11T21:41:26.8862541Z                                 auto tmp13 = in_ptr0[1 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:41:26.8862697Z                                 tmp12 = tmp13;
2023-01-11T21:41:26.8862823Z                             }
2023-01-11T21:41:26.8862993Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:41:26.8863276Z                             auto tmp15 = static_cast<long>(1 + (((3 + (6*i1)) / 4)));
2023-01-11T21:41:26.8863452Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:41:26.8863617Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:41:26.8863760Z                             float tmp18 = 0.0;
2023-01-11T21:41:26.8863901Z                             if(tmp17)
2023-01-11T21:41:26.8864034Z                             {
2023-01-11T21:41:26.8864277Z                                 auto tmp19 = in_ptr0[12 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:41:26.8864436Z                                 tmp18 = tmp19;
2023-01-11T21:41:26.8864561Z                             }
2023-01-11T21:41:26.8864725Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:41:26.8864891Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:41:26.8865032Z                             float tmp22 = 0.0;
2023-01-11T21:41:26.8865172Z                             if(tmp21)
2023-01-11T21:41:26.8865298Z                             {
2023-01-11T21:41:26.8865525Z                                 auto tmp23 = in_ptr0[13 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:41:26.8865674Z                                 tmp22 = tmp23;
2023-01-11T21:41:26.8865771Z                             }
2023-01-11T21:41:26.8865898Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:41:26.8866023Z                             out_ptr4[i2 + (7*i1) + (28*i0)] = tmp24;
2023-01-11T21:41:26.8866117Z                         }
2023-01-11T21:41:26.8866208Z                     }
2023-01-11T21:41:26.8866294Z                 }
2023-01-11T21:41:26.8866381Z             }
2023-01-11T21:41:26.8866465Z         }
2023-01-11T21:41:26.8866553Z     }
2023-01-11T21:41:26.8866626Z }
2023-01-11T21:41:26.8866778Z ''')
2023-01-11T21:41:26.8866788Z 
2023-01-11T21:41:26.8866794Z 
2023-01-11T21:41:26.8866935Z async_compile.wait(globals())
2023-01-11T21:41:26.8867047Z del async_compile
2023-01-11T21:41:26.8867055Z 
2023-01-11T21:41:26.8867164Z def call(args):
2023-01-11T21:41:26.8867271Z     arg0_1, = args
2023-01-11T21:41:26.8867380Z     args.clear()
2023-01-11T21:41:26.8867720Z     buf0 = empty_strided((3, 3, 3, 6), (54, 18, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8868059Z     buf1 = empty_strided((3, 3, 4, 5), (60, 20, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8868400Z     buf2 = empty_strided((3, 3, 2, 8), (48, 16, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8868743Z     buf3 = empty_strided((3, 3, 2, 8), (48, 16, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8869176Z     buf4 = empty_strided((3, 3, 4, 7), (84, 28, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8869548Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:41:26.8869654Z     del arg0_1
2023-01-11T21:41:26.8869804Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.8869814Z 
2023-01-11T21:41:26.8869820Z 
2023-01-11T21:41:26.8869934Z if __name__ == "__main__":
2023-01-11T21:41:26.8870099Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8870293Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8870723Z     arg0_1 = rand_strided((3, 3, 6, 12), (216, 72, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8870903Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8871344Z [2023-01-11 21:39:12,370] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 479
2023-01-11T21:41:26.8871359Z 
2023-01-11T21:41:26.8871464Z ok (1.966s)
2023-01-11T21:41:26.8872235Z   test_upsample_nearest2d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8872439Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8872878Z [2023-01-11 21:39:12,799] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 480
2023-01-11T21:41:26.8873309Z [2023-01-11 21:39:14,369] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 480
2023-01-11T21:41:26.8873339Z 
2023-01-11T21:41:26.8873473Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8873582Z import torch
2023-01-11T21:41:26.8873691Z import random
2023-01-11T21:41:26.8873870Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8874063Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8874070Z 
2023-01-11T21:41:26.8874190Z aten = torch.ops.aten
2023-01-11T21:41:26.8874402Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8874529Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8874537Z 
2023-01-11T21:41:26.8874558Z 
2023-01-11T21:41:26.8874775Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8875104Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8875294Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8875453Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8875607Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8875761Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8875909Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.8876039Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.8876132Z {
2023-01-11T21:41:26.8876288Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8876383Z     {
2023-01-11T21:41:26.8876500Z         #pragma omp for 
2023-01-11T21:41:26.8876627Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8876723Z         {
2023-01-11T21:41:26.8876833Z             #pragma GCC ivdep
2023-01-11T21:41:26.8876969Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:26.8877067Z             {
2023-01-11T21:41:26.8877195Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8877332Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:41:26.8877438Z                 {
2023-01-11T21:41:26.8877527Z                     {
2023-01-11T21:41:26.8877634Z                         {
2023-01-11T21:41:26.8877909Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8878080Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:26.8878227Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8878393Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8878560Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:26.8878707Z                             auto tmp5 = tmp4 * tmp1;
2023-01-11T21:41:26.8878861Z                             auto tmp6 = static_cast<int>(tmp5);
2023-01-11T21:41:26.8879046Z                             auto tmp7 = in_ptr0[tmp6 + (38*tmp3) + (1406*i0)];
2023-01-11T21:41:26.8879210Z                             out_ptr0[i2 + (76*i1) + (5624*i0)] = tmp7;
2023-01-11T21:41:26.8879442Z                             out_ptr1[i2 + (76*i1) + (5624*i0)] = tmp7;
2023-01-11T21:41:26.8879567Z                         }
2023-01-11T21:41:26.8879678Z                     }
2023-01-11T21:41:26.8879776Z                 }
2023-01-11T21:41:26.8879859Z             }
2023-01-11T21:41:26.8879957Z         }
2023-01-11T21:41:26.8880079Z         #pragma omp for 
2023-01-11T21:41:26.8880204Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8880299Z         {
2023-01-11T21:41:26.8880422Z             #pragma GCC ivdep
2023-01-11T21:41:26.8880552Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:41:26.8880635Z             {
2023-01-11T21:41:26.8880761Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8880896Z                 for(long i2=0; i2<75; i2+=1)
2023-01-11T21:41:26.8880996Z                 {
2023-01-11T21:41:26.8881098Z                     {
2023-01-11T21:41:26.8881204Z                         {
2023-01-11T21:41:26.8881356Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8881545Z                             auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:41:26.8881694Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8881869Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8882033Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:26.8882212Z                             auto tmp5 = static_cast<float>(0.5066666666666667);
2023-01-11T21:41:26.8882355Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8882519Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:26.8882686Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:41:26.8882847Z                             out_ptr2[i2 + (75*i1) + (5250*i0)] = tmp8;
2023-01-11T21:41:26.8882952Z                         }
2023-01-11T21:41:26.8883054Z                     }
2023-01-11T21:41:26.8883154Z                 }
2023-01-11T21:41:26.8883255Z             }
2023-01-11T21:41:26.8883352Z         }
2023-01-11T21:41:26.8883459Z         #pragma omp for 
2023-01-11T21:41:26.8883588Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8883682Z         {
2023-01-11T21:41:26.8883812Z             #pragma GCC ivdep
2023-01-11T21:41:26.8883945Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:26.8884042Z             {
2023-01-11T21:41:26.8884169Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8884291Z                 for(long i2=0; i2<74; i2+=1)
2023-01-11T21:41:26.8884390Z                 {
2023-01-11T21:41:26.8884491Z                     {
2023-01-11T21:41:26.8884597Z                         {
2023-01-11T21:41:26.8884766Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8884944Z                             auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:26.8885094Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8885248Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8885414Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:26.8885694Z                             auto tmp5 = static_cast<float>(0.5135135135135135);
2023-01-11T21:41:26.8885839Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8886004Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:26.8886181Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:41:26.8886339Z                             out_ptr3[i2 + (74*i1) + (3330*i0)] = tmp8;
2023-01-11T21:41:26.8886431Z                         }
2023-01-11T21:41:26.8886533Z                     }
2023-01-11T21:41:26.8886631Z                 }
2023-01-11T21:41:26.8886728Z             }
2023-01-11T21:41:26.8886822Z         }
2023-01-11T21:41:26.8886941Z         #pragma omp for 
2023-01-11T21:41:26.8887068Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8887148Z         {
2023-01-11T21:41:26.8887339Z             #pragma GCC ivdep
2023-01-11T21:41:26.8887495Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:41:26.8887601Z             {
2023-01-11T21:41:26.8887726Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8887861Z                 for(long i2=0; i2<39; i2+=1)
2023-01-11T21:41:26.8887962Z                 {
2023-01-11T21:41:26.8888047Z                     {
2023-01-11T21:41:26.8888148Z                         {
2023-01-11T21:41:26.8888320Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8888501Z                             auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:41:26.8888649Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8888817Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8888984Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:26.8889284Z                             auto tmp5 = static_cast<float>(0.9743589743589743);
2023-01-11T21:41:26.8889444Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8889609Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:26.8889794Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:41:26.8889953Z                             out_ptr4[i2 + (39*i1) + (1404*i0)] = tmp8;
2023-01-11T21:41:26.8890059Z                         }
2023-01-11T21:41:26.8890162Z                     }
2023-01-11T21:41:26.8890247Z                 }
2023-01-11T21:41:26.8890348Z             }
2023-01-11T21:41:26.8890445Z         }
2023-01-11T21:41:26.8890538Z     }
2023-01-11T21:41:26.8890625Z }
2023-01-11T21:41:26.8890780Z ''')
2023-01-11T21:41:26.8890791Z 
2023-01-11T21:41:26.8890797Z 
2023-01-11T21:41:26.8890943Z async_compile.wait(globals())
2023-01-11T21:41:26.8891041Z del async_compile
2023-01-11T21:41:26.8891064Z 
2023-01-11T21:41:26.8891161Z def call(args):
2023-01-11T21:41:26.8891265Z     arg0_1, = args
2023-01-11T21:41:26.8891379Z     args.clear()
2023-01-11T21:41:26.8891760Z     buf0 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8892123Z     buf4 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8892481Z     buf1 = empty_strided((2, 4, 70, 75), (21000, 5250, 75, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8892830Z     buf2 = empty_strided((2, 4, 45, 74), (13320, 3330, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8893166Z     buf3 = empty_strided((2, 4, 36, 39), (5616, 1404, 39, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8893538Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.8893643Z     del arg0_1
2023-01-11T21:41:26.8893791Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.8893800Z 
2023-01-11T21:41:26.8893811Z 
2023-01-11T21:41:26.8893931Z if __name__ == "__main__":
2023-01-11T21:41:26.8894110Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8894434Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8894803Z     arg0_1 = rand_strided((2, 4, 37, 38), (5624, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8894972Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8894981Z 
2023-01-11T21:41:26.8895069Z ok (2.002s)
2023-01-11T21:41:26.8895845Z   test_upsample_nearest3d_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8896166Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8896626Z [2023-01-11 21:39:14,954] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 481
2023-01-11T21:41:26.8897068Z [2023-01-11 21:39:16,565] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 481
2023-01-11T21:41:26.8897077Z 
2023-01-11T21:41:26.8897221Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8897328Z import torch
2023-01-11T21:41:26.8897439Z import random
2023-01-11T21:41:26.8897619Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8897792Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8897801Z 
2023-01-11T21:41:26.8897925Z aten = torch.ops.aten
2023-01-11T21:41:26.8898136Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8898280Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8898288Z 
2023-01-11T21:41:26.8898294Z 
2023-01-11T21:41:26.8898525Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8898854Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8899046Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8899199Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8899335Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.8899480Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.8899627Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.8899771Z                        float* __restrict__ out_ptr4)
2023-01-11T21:41:26.8899866Z {
2023-01-11T21:41:26.8900020Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8900116Z     {
2023-01-11T21:41:26.8900219Z         #pragma omp for 
2023-01-11T21:41:26.8900344Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8900440Z         {
2023-01-11T21:41:26.8900562Z             #pragma GCC ivdep
2023-01-11T21:41:26.8900700Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:41:26.8900800Z             {
2023-01-11T21:41:26.8900927Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8901056Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:41:26.8901155Z                 {
2023-01-11T21:41:26.8901283Z                     #pragma GCC ivdep
2023-01-11T21:41:26.8901421Z                     for(long i3=0; i3<78; i3+=1)
2023-01-11T21:41:26.8901523Z                     {
2023-01-11T21:41:26.8901630Z                         {
2023-01-11T21:41:26.8901740Z                             {
2023-01-11T21:41:26.8901898Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8902075Z                                 auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:41:26.8902231Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8902401Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8902570Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:26.8902718Z                                 auto tmp5 = tmp4 * tmp1;
2023-01-11T21:41:26.8902890Z                                 auto tmp6 = static_cast<int>(tmp5);
2023-01-11T21:41:26.8903211Z                                 auto tmp7 = static_cast<float>(i3);
2023-01-11T21:41:26.8903363Z                                 auto tmp8 = tmp7 * tmp1;
2023-01-11T21:41:26.8903523Z                                 auto tmp9 = static_cast<int>(tmp8);
2023-01-11T21:41:26.8903724Z                                 auto tmp10 = in_ptr0[tmp9 + (39*tmp6) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:41:26.8903895Z                                 out_ptr0[i3 + (78*i2) + (5928*i1) + (438672*i0)] = tmp10;
2023-01-11T21:41:26.8904074Z                                 out_ptr1[i3 + (78*i2) + (5928*i1) + (438672*i0)] = tmp10;
2023-01-11T21:41:26.8904183Z                             }
2023-01-11T21:41:26.8904291Z                         }
2023-01-11T21:41:26.8904376Z                     }
2023-01-11T21:41:26.8904547Z                 }
2023-01-11T21:41:26.8904663Z             }
2023-01-11T21:41:26.8904762Z         }
2023-01-11T21:41:26.8904892Z         #pragma omp for 
2023-01-11T21:41:26.8905020Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8905116Z         {
2023-01-11T21:41:26.8905224Z             #pragma GCC ivdep
2023-01-11T21:41:26.8905353Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:41:26.8905450Z             {
2023-01-11T21:41:26.8905576Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8905710Z                 for(long i2=0; i2<75; i2+=1)
2023-01-11T21:41:26.8905809Z                 {
2023-01-11T21:41:26.8905920Z                     #pragma GCC ivdep
2023-01-11T21:41:26.8906061Z                     for(long i3=0; i3<80; i3+=1)
2023-01-11T21:41:26.8906163Z                     {
2023-01-11T21:41:26.8906267Z                         {
2023-01-11T21:41:26.8906373Z                             {
2023-01-11T21:41:26.8906549Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8906734Z                                 auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:41:26.8906872Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8907045Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8907215Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:26.8907399Z                                 auto tmp5 = static_cast<float>(0.5066666666666667);
2023-01-11T21:41:26.8907549Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8907718Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:26.8907884Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:41:26.8908060Z                                 auto tmp9 = static_cast<float>(0.4875);
2023-01-11T21:41:26.8908196Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:26.8908377Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:41:26.8908581Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:41:26.8908757Z                                 out_ptr2[i3 + (80*i2) + (6000*i1) + (420000*i0)] = tmp12;
2023-01-11T21:41:26.8908864Z                             }
2023-01-11T21:41:26.8908967Z                         }
2023-01-11T21:41:26.8909072Z                     }
2023-01-11T21:41:26.8909171Z                 }
2023-01-11T21:41:26.8909255Z             }
2023-01-11T21:41:26.8909351Z         }
2023-01-11T21:41:26.8909471Z         #pragma omp for 
2023-01-11T21:41:26.8909597Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8909697Z         {
2023-01-11T21:41:26.8909818Z             #pragma GCC ivdep
2023-01-11T21:41:26.8909934Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:41:26.8910032Z             {
2023-01-11T21:41:26.8910157Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8910299Z                 for(long i2=0; i2<74; i2+=1)
2023-01-11T21:41:26.8910399Z                 {
2023-01-11T21:41:26.8910528Z                     #pragma GCC ivdep
2023-01-11T21:41:26.8910767Z                     for(long i3=0; i3<103; i3+=1)
2023-01-11T21:41:26.8910856Z                     {
2023-01-11T21:41:26.8910963Z                         {
2023-01-11T21:41:26.8911069Z                             {
2023-01-11T21:41:26.8911237Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8911422Z                                 auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:41:26.8911574Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8911741Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8911894Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:26.8912073Z                                 auto tmp5 = static_cast<float>(0.5135135135135135);
2023-01-11T21:41:26.8912285Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8912472Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:26.8912642Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:41:26.8912821Z                                 auto tmp9 = static_cast<float>(0.3786407766990291);
2023-01-11T21:41:26.8912973Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:26.8913149Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:41:26.8913333Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:41:26.8913512Z                                 out_ptr3[i3 + (103*i2) + (7622*i1) + (342990*i0)] = tmp12;
2023-01-11T21:41:26.8913622Z                             }
2023-01-11T21:41:26.8913725Z                         }
2023-01-11T21:41:26.8913828Z                     }
2023-01-11T21:41:26.8913926Z                 }
2023-01-11T21:41:26.8914033Z             }
2023-01-11T21:41:26.8914112Z         }
2023-01-11T21:41:26.8914234Z         #pragma omp for 
2023-01-11T21:41:26.8914366Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8914462Z         {
2023-01-11T21:41:26.8914586Z             #pragma GCC ivdep
2023-01-11T21:41:26.8914713Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:41:26.8914809Z             {
2023-01-11T21:41:26.8914920Z                 #pragma GCC ivdep
2023-01-11T21:41:26.8915057Z                 for(long i2=0; i2<39; i2+=1)
2023-01-11T21:41:26.8915156Z                 {
2023-01-11T21:41:26.8915282Z                     #pragma GCC ivdep
2023-01-11T21:41:26.8915424Z                     for(long i3=0; i3<40; i3+=1)
2023-01-11T21:41:26.8915527Z                     {
2023-01-11T21:41:26.8915637Z                         {
2023-01-11T21:41:26.8915731Z                             {
2023-01-11T21:41:26.8915902Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:41:26.8916092Z                                 auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:41:26.8916248Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8916426Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:41:26.8916594Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:41:26.8916772Z                                 auto tmp5 = static_cast<float>(0.9743589743589743);
2023-01-11T21:41:26.8916907Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:41:26.8917079Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:41:26.8917243Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:41:26.8917419Z                                 auto tmp9 = static_cast<float>(0.975);
2023-01-11T21:41:26.8917569Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:26.8917746Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:41:26.8917943Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:41:26.8918239Z                                 out_ptr4[i3 + (40*i2) + (1560*i1) + (56160*i0)] = tmp12;
2023-01-11T21:41:26.8918332Z                             }
2023-01-11T21:41:26.8918433Z                         }
2023-01-11T21:41:26.8918535Z                     }
2023-01-11T21:41:26.8918632Z                 }
2023-01-11T21:41:26.8918731Z             }
2023-01-11T21:41:26.8918827Z         }
2023-01-11T21:41:26.8918919Z     }
2023-01-11T21:41:26.8918999Z }
2023-01-11T21:41:26.8919156Z ''')
2023-01-11T21:41:26.8919166Z 
2023-01-11T21:41:26.8919172Z 
2023-01-11T21:41:26.8919324Z async_compile.wait(globals())
2023-01-11T21:41:26.8919438Z del async_compile
2023-01-11T21:41:26.8919446Z 
2023-01-11T21:41:26.8919556Z def call(args):
2023-01-11T21:41:26.8919665Z     arg0_1, = args
2023-01-11T21:41:26.8919772Z     args.clear()
2023-01-11T21:41:26.8920223Z     buf0 = empty_strided((2, 4, 74, 76, 78), (1754688, 438672, 5928, 78, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8920639Z     buf4 = empty_strided((2, 4, 74, 76, 78), (1754688, 438672, 5928, 78, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8921024Z     buf1 = empty_strided((2, 4, 70, 75, 80), (1680000, 420000, 6000, 80, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8921412Z     buf2 = empty_strided((2, 4, 45, 74, 103), (1371960, 342990, 7622, 103, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8921788Z     buf3 = empty_strided((2, 4, 36, 39, 40), (224640, 56160, 1560, 40, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8922160Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:41:26.8922268Z     del arg0_1
2023-01-11T21:41:26.8922419Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:41:26.8922433Z 
2023-01-11T21:41:26.8922439Z 
2023-01-11T21:41:26.8922556Z if __name__ == "__main__":
2023-01-11T21:41:26.8922721Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8922925Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8923316Z     arg0_1 = rand_strided((2, 4, 37, 38, 39), (219336, 54834, 1482, 39, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8923487Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8923496Z 
2023-01-11T21:41:26.8923602Z ok (2.374s)
2023-01-11T21:41:26.8924353Z   test_var_mean_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8924557Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8925001Z [2023-01-11 21:39:16,769] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 482
2023-01-11T21:41:26.8925015Z 
2023-01-11T21:41:26.8925162Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8925273Z import torch
2023-01-11T21:41:26.8925369Z import random
2023-01-11T21:41:26.8925548Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8925737Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8925745Z 
2023-01-11T21:41:26.8925866Z aten = torch.ops.aten
2023-01-11T21:41:26.8926076Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8926219Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8926227Z 
2023-01-11T21:41:26.8926233Z 
2023-01-11T21:41:26.8926466Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8926790Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8926964Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:41:26.8927121Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:41:26.8927379Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:41:26.8927537Z                        float* __restrict__ in_out_ptr3,
2023-01-11T21:41:26.8927699Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8927850Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8928001Z                        float* __restrict__ out_ptr3)
2023-01-11T21:41:26.8928082Z {
2023-01-11T21:41:26.8928218Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:41:26.8928352Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:41:26.8928483Z     auto out_ptr4 = in_out_ptr2;
2023-01-11T21:41:26.8928611Z     auto out_ptr5 = in_out_ptr3;
2023-01-11T21:41:26.8928766Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8928864Z     {
2023-01-11T21:41:26.8928967Z         #pragma omp for 
2023-01-11T21:41:26.8929321Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8929429Z         {
2023-01-11T21:41:26.8929526Z             {
2023-01-11T21:41:26.8929843Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8929967Z                 float tmp1 = 0;
2023-01-11T21:41:26.8930160Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8930281Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8930381Z                 {
2023-01-11T21:41:26.8930605Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8930731Z                     tmp1_vec += tmp0;
2023-01-11T21:41:26.8930837Z                 }
2023-01-11T21:41:26.8931151Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.8931343Z                 #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.8931480Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8931568Z                 {
2023-01-11T21:41:26.8931717Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8931836Z                     tmp1 += tmp0;
2023-01-11T21:41:26.8931937Z                 }
2023-01-11T21:41:26.8932065Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.8932162Z             }
2023-01-11T21:41:26.8932258Z         }
2023-01-11T21:41:26.8932364Z         #pragma omp for 
2023-01-11T21:41:26.8932491Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:41:26.8932585Z         {
2023-01-11T21:41:26.8932685Z             {
2023-01-11T21:41:26.8932988Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8933111Z                 float tmp6 = 0;
2023-01-11T21:41:26.8933302Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8933417Z                 float tmp7 = 0;
2023-01-11T21:41:26.8933606Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.8933747Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.8933847Z                 {
2023-01-11T21:41:26.8934071Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i1));
2023-01-11T21:41:26.8934273Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:41:26.8934490Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:26.8934633Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8934871Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8935019Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8935147Z                     tmp6_vec += tmp5;
2023-01-11T21:41:26.8935271Z                     tmp7_vec += tmp0;
2023-01-11T21:41:26.8935371Z                 }
2023-01-11T21:41:26.8935687Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8936069Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:26.8936285Z                 #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:26.8936407Z                 for(long i1=8; i1<8; i1+=1)
2023-01-11T21:41:26.8936507Z                 {
2023-01-11T21:41:26.8936657Z                     auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:41:26.8936800Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:41:26.8936960Z                     auto tmp2 = static_cast<float>(8);
2023-01-11T21:41:26.8937099Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8937339Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8937529Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8937659Z                     tmp6 += tmp5;
2023-01-11T21:41:26.8937779Z                     tmp7 += tmp0;
2023-01-11T21:41:26.8937884Z                 }
2023-01-11T21:41:26.8938010Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:41:26.8938133Z                 out_ptr2[i0] = tmp7;
2023-01-11T21:41:26.8938232Z             }
2023-01-11T21:41:26.8938311Z         }
2023-01-11T21:41:26.8938431Z         #pragma omp for 
2023-01-11T21:41:26.8938558Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.8938658Z         {
2023-01-11T21:41:26.8938874Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 8*i0);
2023-01-11T21:41:26.8939082Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(7));
2023-01-11T21:41:26.8939215Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8939344Z             tmp2.store(in_out_ptr0 + 8*i0);
2023-01-11T21:41:26.8939443Z         }
2023-01-11T21:41:26.8939590Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8939722Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.8939820Z         {
2023-01-11T21:41:26.8939953Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:41:26.8940113Z             auto tmp1 = static_cast<float>(7);
2023-01-11T21:41:26.8940225Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8940352Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8940451Z         }
2023-01-11T21:41:26.8940570Z         #pragma omp for 
2023-01-11T21:41:26.8940695Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.8940792Z         {
2023-01-11T21:41:26.8941001Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 8*i0);
2023-01-11T21:41:26.8941195Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:41:26.8941329Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8941474Z             tmp2.store(in_out_ptr1 + 8*i0);
2023-01-11T21:41:26.8941570Z         }
2023-01-11T21:41:26.8941720Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.8941852Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.8941949Z         {
2023-01-11T21:41:26.8942066Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:41:26.8942228Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:41:26.8942358Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8942483Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.8942579Z         }
2023-01-11T21:41:26.8942698Z         #pragma omp for 
2023-01-11T21:41:26.8942824Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8942906Z         {
2023-01-11T21:41:26.8943006Z             {
2023-01-11T21:41:26.8943401Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8943530Z                 float tmp1 = 0;
2023-01-11T21:41:26.8943726Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:41:26.8943860Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.8943964Z                 {
2023-01-11T21:41:26.8944105Z                     for(long i2=0; i2<1; i2+=1)
2023-01-11T21:41:26.8944197Z                     {
2023-01-11T21:41:26.8944526Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i2) + (32*i1));
2023-01-11T21:41:26.8944659Z                         tmp1_vec += tmp0;
2023-01-11T21:41:26.8944762Z                     }
2023-01-11T21:41:26.8945076Z                     tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:41:26.8945266Z                     #pragma omp simd simdlen(4)  reduction(+:tmp1)
2023-01-11T21:41:26.8945406Z                     for(long i2=8; i2<8; i2+=1)
2023-01-11T21:41:26.8945508Z                     {
2023-01-11T21:41:26.8945658Z                         auto tmp0 = in_ptr0[i2 + (8*i0) + (32*i1)];
2023-01-11T21:41:26.8945781Z                         tmp1 += tmp0;
2023-01-11T21:41:26.8945884Z                     }
2023-01-11T21:41:26.8946051Z                 }
2023-01-11T21:41:26.8946194Z                 out_ptr3[i0] = tmp1;
2023-01-11T21:41:26.8946298Z             }
2023-01-11T21:41:26.8946381Z         }
2023-01-11T21:41:26.8946499Z         #pragma omp for 
2023-01-11T21:41:26.8946626Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8946725Z         {
2023-01-11T21:41:26.8946824Z             {
2023-01-11T21:41:26.8947120Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:41:26.8947244Z                 float tmp6 = 0;
2023-01-11T21:41:26.8947431Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:41:26.8947539Z                 float tmp7 = 0;
2023-01-11T21:41:26.8947726Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:41:26.8947862Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:41:26.8947964Z                 {
2023-01-11T21:41:26.8948107Z                     for(long i2=0; i2<1; i2+=1)
2023-01-11T21:41:26.8948212Z                     {
2023-01-11T21:41:26.8948445Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i0) + (8*i2) + (32*i1));
2023-01-11T21:41:26.8948641Z                         auto tmp1 = at::vec::Vectorized<float>(out_ptr3[i0]);
2023-01-11T21:41:26.8948859Z                         auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(16));
2023-01-11T21:41:26.8949009Z                         auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8949273Z                         auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8949421Z                         auto tmp5 = tmp4.pow(2);
2023-01-11T21:41:26.8949551Z                         tmp6_vec += tmp5;
2023-01-11T21:41:26.8949678Z                         tmp7_vec += tmp0;
2023-01-11T21:41:26.8949782Z                     }
2023-01-11T21:41:26.8950089Z                     tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:41:26.8950394Z                     tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:41:26.8950623Z                     #pragma omp simd simdlen(4)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:41:26.8950761Z                     for(long i2=8; i2<8; i2+=1)
2023-01-11T21:41:26.8950862Z                     {
2023-01-11T21:41:26.8951029Z                         auto tmp0 = in_ptr0[i2 + (8*i0) + (32*i1)];
2023-01-11T21:41:26.8951174Z                         auto tmp1 = out_ptr3[i0];
2023-01-11T21:41:26.8951339Z                         auto tmp2 = static_cast<float>(16);
2023-01-11T21:41:26.8951464Z                         auto tmp3 = tmp1 / tmp2;
2023-01-11T21:41:26.8951711Z                         auto tmp4 = tmp0 - tmp3;
2023-01-11T21:41:26.8951854Z                         auto tmp5 = tmp4 * tmp4;
2023-01-11T21:41:26.8951977Z                         tmp6 += tmp5;
2023-01-11T21:41:26.8952100Z                         tmp7 += tmp0;
2023-01-11T21:41:26.8952207Z                     }
2023-01-11T21:41:26.8952304Z                 }
2023-01-11T21:41:26.8952505Z                 out_ptr4[i0] = tmp6;
2023-01-11T21:41:26.8952631Z                 out_ptr5[i0] = tmp7;
2023-01-11T21:41:26.8952730Z             }
2023-01-11T21:41:26.8952829Z         }
2023-01-11T21:41:26.8952951Z         #pragma omp for 
2023-01-11T21:41:26.8953077Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8953174Z         {
2023-01-11T21:41:26.8953259Z             {
2023-01-11T21:41:26.8953358Z                 {
2023-01-11T21:41:26.8953501Z                     auto tmp0 = out_ptr4[i0];
2023-01-11T21:41:26.8953660Z                     auto tmp1 = static_cast<float>(15);
2023-01-11T21:41:26.8953803Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8953942Z                     in_out_ptr2[i0] = tmp2;
2023-01-11T21:41:26.8954042Z                 }
2023-01-11T21:41:26.8954126Z             }
2023-01-11T21:41:26.8954292Z         }
2023-01-11T21:41:26.8954430Z         #pragma omp for 
2023-01-11T21:41:26.8954557Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.8954660Z         {
2023-01-11T21:41:26.8954763Z             {
2023-01-11T21:41:26.8954848Z                 {
2023-01-11T21:41:26.8954990Z                     auto tmp0 = out_ptr5[i0];
2023-01-11T21:41:26.8955153Z                     auto tmp1 = static_cast<float>(16);
2023-01-11T21:41:26.8955294Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:41:26.8955433Z                     in_out_ptr3[i0] = tmp2;
2023-01-11T21:41:26.8955533Z                 }
2023-01-11T21:41:26.8955633Z             }
2023-01-11T21:41:26.8955716Z         }
2023-01-11T21:41:26.8955809Z     }
2023-01-11T21:41:26.8955898Z }
2023-01-11T21:41:26.8956045Z ''')
2023-01-11T21:41:26.8956057Z 
2023-01-11T21:41:26.8956062Z 
2023-01-11T21:41:26.8956206Z async_compile.wait(globals())
2023-01-11T21:41:26.8956320Z del async_compile
2023-01-11T21:41:26.8956329Z 
2023-01-11T21:41:26.8956441Z def call(args):
2023-01-11T21:41:26.8956548Z     arg0_1, = args
2023-01-11T21:41:26.8956647Z     args.clear()
2023-01-11T21:41:26.8957009Z     buf0 = empty_strided((1, 2, 4, 1), (8, 4, 1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8957342Z     buf1 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8957664Z     buf2 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8957796Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:41:26.8957925Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:41:26.8958257Z     buf5 = empty_strided((1, 1, 4, 1), (4, 4, 1, 4), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8958556Z     buf6 = empty_strided((1, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8958869Z     buf7 = empty_strided((1, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8959001Z     buf8 = buf6; del buf6  # reuse
2023-01-11T21:41:26.8959135Z     buf9 = buf7; del buf7  # reuse
2023-01-11T21:41:26.8959544Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:26.8959654Z     del arg0_1
2023-01-11T21:41:26.8959794Z     return (buf3, buf4, buf8, buf9, )
2023-01-11T21:41:26.8959803Z 
2023-01-11T21:41:26.8959809Z 
2023-01-11T21:41:26.8959924Z if __name__ == "__main__":
2023-01-11T21:41:26.8960088Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8960287Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8960639Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8960812Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.8961261Z [2023-01-11 21:39:18,384] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 482
2023-01-11T21:41:26.8961270Z 
2023-01-11T21:41:26.8961378Z ok (1.638s)
2023-01-11T21:41:26.8962131Z   test_vdd_clamp_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8962426Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8962864Z [2023-01-11 21:39:18,422] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 483
2023-01-11T21:41:26.8963303Z [2023-01-11 21:39:19,914] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 483
2023-01-11T21:41:26.8963314Z 
2023-01-11T21:41:26.8963446Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8963554Z import torch
2023-01-11T21:41:26.8963732Z import random
2023-01-11T21:41:26.8963924Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8964109Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8964123Z 
2023-01-11T21:41:26.8964244Z aten = torch.ops.aten
2023-01-11T21:41:26.8964455Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8964585Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8964610Z 
2023-01-11T21:41:26.8964616Z 
2023-01-11T21:41:26.8964833Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8965155Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8965343Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8965498Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.8965647Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:41:26.8965744Z {
2023-01-11T21:41:26.8965899Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8965985Z     {
2023-01-11T21:41:26.8966104Z         #pragma omp for 
2023-01-11T21:41:26.8966231Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:41:26.8966334Z         {
2023-01-11T21:41:26.8966431Z             {
2023-01-11T21:41:26.8966532Z                 {
2023-01-11T21:41:26.8966678Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.8966826Z                     auto tmp1 = static_cast<float>(3.0);
2023-01-11T21:41:26.8967034Z                     auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:41:26.8967194Z                     auto tmp3 = static_cast<float>(3);
2023-01-11T21:41:26.8967337Z                     auto tmp4 = tmp0 >= tmp3;
2023-01-11T21:41:26.8967468Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.8967599Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:41:26.8967703Z                 }
2023-01-11T21:41:26.8967788Z             }
2023-01-11T21:41:26.8967883Z         }
2023-01-11T21:41:26.8967978Z     }
2023-01-11T21:41:26.8968071Z }
2023-01-11T21:41:26.8968223Z ''')
2023-01-11T21:41:26.8968233Z 
2023-01-11T21:41:26.8968239Z 
2023-01-11T21:41:26.8968383Z async_compile.wait(globals())
2023-01-11T21:41:26.8968504Z del async_compile
2023-01-11T21:41:26.8968512Z 
2023-01-11T21:41:26.8968606Z def call(args):
2023-01-11T21:41:26.8968723Z     primals_1, = args
2023-01-11T21:41:26.8968832Z     args.clear()
2023-01-11T21:41:26.8969289Z     buf0 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8969600Z     buf1 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.8969864Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:41:26.8969976Z     del primals_1
2023-01-11T21:41:26.8970080Z     return (buf0, buf1, )
2023-01-11T21:41:26.8970106Z 
2023-01-11T21:41:26.8970112Z 
2023-01-11T21:41:26.8970211Z if __name__ == "__main__":
2023-01-11T21:41:26.8970390Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8970600Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8970938Z     primals_1 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8971236Z     print_performance(lambda: call([primals_1]))
2023-01-11T21:41:26.8971245Z 
2023-01-11T21:41:26.8971346Z ok (1.529s)
2023-01-11T21:41:26.8972107Z   test_vertical_fusion1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8972303Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8972742Z [2023-01-11 21:39:19,966] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 484
2023-01-11T21:41:26.8973251Z [2023-01-11 21:39:21,464] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 484
2023-01-11T21:41:26.8973287Z 
2023-01-11T21:41:26.8973424Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8973532Z import torch
2023-01-11T21:41:26.8973641Z import random
2023-01-11T21:41:26.8973821Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8974013Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8974022Z 
2023-01-11T21:41:26.8974143Z aten = torch.ops.aten
2023-01-11T21:41:26.8974349Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8974478Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8974486Z 
2023-01-11T21:41:26.8974492Z 
2023-01-11T21:41:26.8974723Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8975045Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8975240Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8975406Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8975563Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:41:26.8975721Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8975815Z {
2023-01-11T21:41:26.8975951Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8976047Z     {
2023-01-11T21:41:26.8976168Z         #pragma omp for 
2023-01-11T21:41:26.8976297Z         for(long i0=0; i0<41616; i0+=1)
2023-01-11T21:41:26.8976396Z         {
2023-01-11T21:41:26.8976521Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:41:26.8976608Z             {
2023-01-11T21:41:26.8976848Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (26*i0));
2023-01-11T21:41:26.8977069Z                 auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (26*i0));
2023-01-11T21:41:26.8977289Z                 auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr2 + 8*i1);
2023-01-11T21:41:26.8977699Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.061519070296458e-11));
2023-01-11T21:41:26.8977843Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8978219Z                 auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(-1.988366587925593e-08));
2023-01-11T21:41:26.8978355Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8978491Z                 auto tmp5 = tmp0 * tmp4;
2023-01-11T21:41:26.8978854Z                 auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(-3.087032500374211e-07));
2023-01-11T21:41:26.8978994Z                 auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.8979366Z                 auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1.55093272922008e-10));
2023-01-11T21:41:26.8979505Z                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:26.8979644Z                 auto tmp11 = tmp7 + tmp10;
2023-01-11T21:41:26.8979802Z                 auto tmp12 = tmp11.reciprocal();
2023-01-11T21:41:26.8980023Z                 auto tmp13 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:41:26.8980165Z                 auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:26.8980396Z                 auto tmp16 = tmp11 * tmp15;
2023-01-11T21:41:26.8980534Z                 auto tmp17 = tmp14 + tmp16;
2023-01-11T21:41:26.8980694Z                 tmp17.store(out_ptr0 + (8*i1) + (26*i0));
2023-01-11T21:41:26.8980792Z             }
2023-01-11T21:41:26.8980938Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.8981069Z             for(long i1=24; i1<26; i1+=1)
2023-01-11T21:41:26.8981167Z             {
2023-01-11T21:41:26.8981298Z                 auto tmp0 = in_ptr0[i1 + (26*i0)];
2023-01-11T21:41:26.8981445Z                 auto tmp8 = in_ptr1[i1 + (26*i0)];
2023-01-11T21:41:26.8981580Z                 auto tmp15 = in_ptr2[i1];
2023-01-11T21:41:26.8981890Z                 auto tmp1 = static_cast<float>(-1.061519070296458e-11);
2023-01-11T21:41:26.8982094Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.8982400Z                 auto tmp3 = static_cast<float>(-1.988366587925593e-08);
2023-01-11T21:41:26.8982541Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.8982657Z                 auto tmp5 = tmp0 * tmp4;
2023-01-11T21:41:26.8982947Z                 auto tmp6 = static_cast<float>(-3.087032500374211e-07);
2023-01-11T21:41:26.8983117Z                 auto tmp7 = tmp5 + tmp6;
2023-01-11T21:41:26.8983466Z                 auto tmp9 = static_cast<float>(1.55093272922008e-10);
2023-01-11T21:41:26.8983602Z                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:41:26.8983739Z                 auto tmp11 = tmp7 + tmp10;
2023-01-11T21:41:26.8983866Z                 auto tmp12 = 1 / tmp11;
2023-01-11T21:41:26.8984013Z                 auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:41:26.8984152Z                 auto tmp14 = tmp12 * tmp13;
2023-01-11T21:41:26.8984291Z                 auto tmp16 = tmp11 * tmp15;
2023-01-11T21:41:26.8984437Z                 auto tmp17 = tmp14 + tmp16;
2023-01-11T21:41:26.8984576Z                 out_ptr0[i1 + (26*i0)] = tmp17;
2023-01-11T21:41:26.8984673Z             }
2023-01-11T21:41:26.8984776Z         }
2023-01-11T21:41:26.8984855Z     }
2023-01-11T21:41:26.8984945Z }
2023-01-11T21:41:26.8985084Z ''')
2023-01-11T21:41:26.8985095Z 
2023-01-11T21:41:26.8985100Z 
2023-01-11T21:41:26.8985246Z async_compile.wait(globals())
2023-01-11T21:41:26.8985362Z del async_compile
2023-01-11T21:41:26.8985369Z 
2023-01-11T21:41:26.8985477Z def call(args):
2023-01-11T21:41:26.8985603Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:41:26.8985717Z     args.clear()
2023-01-11T21:41:26.8986066Z     buf0 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8986363Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.8986470Z     del arg0_1
2023-01-11T21:41:26.8986573Z     del arg1_1
2023-01-11T21:41:26.8986683Z     del arg2_1
2023-01-11T21:41:26.8986794Z     return (buf0, )
2023-01-11T21:41:26.8986802Z 
2023-01-11T21:41:26.8986809Z 
2023-01-11T21:41:26.8986933Z if __name__ == "__main__":
2023-01-11T21:41:26.8987098Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.8987297Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.8987657Z     arg0_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8988006Z     arg1_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8988315Z     arg2_1 = rand_strided((26, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.8988507Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:41:26.8988516Z 
2023-01-11T21:41:26.8988618Z ok (1.587s)
2023-01-11T21:41:26.8989370Z   test_views1_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8989669Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8990110Z [2023-01-11 21:39:21,520] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 485
2023-01-11T21:41:26.8990535Z [2023-01-11 21:39:23,014] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 485
2023-01-11T21:41:26.8991222Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8991483Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8991930Z [2023-01-11 21:39:23,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 486
2023-01-11T21:41:26.8992378Z [2023-01-11 21:39:24,667] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 486
2023-01-11T21:41:26.8993065Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8993260Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8993681Z [2023-01-11 21:39:24,685] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 487
2023-01-11T21:41:26.8994119Z [2023-01-11 21:39:26,323] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 487
2023-01-11T21:41:26.8994807Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8995002Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8995429Z [2023-01-11 21:39:26,345] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 488
2023-01-11T21:41:26.8995857Z [2023-01-11 21:39:27,974] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 488
2023-01-11T21:41:26.8996553Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.8996752Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.8997178Z [2023-01-11 21:39:27,993] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 489
2023-01-11T21:41:26.8997189Z 
2023-01-11T21:41:26.8997338Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.8997448Z import torch
2023-01-11T21:41:26.8997560Z import random
2023-01-11T21:41:26.8997745Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.8997933Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.8997942Z 
2023-01-11T21:41:26.8998049Z aten = torch.ops.aten
2023-01-11T21:41:26.8998261Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.8998410Z async_compile = AsyncCompile()
2023-01-11T21:41:26.8998418Z 
2023-01-11T21:41:26.8998425Z 
2023-01-11T21:41:26.8998662Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.8999087Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.8999276Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.8999438Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.8999594Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.8999674Z {
2023-01-11T21:41:26.8999826Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.8999921Z     {
2023-01-11T21:41:26.9000044Z         #pragma omp for 
2023-01-11T21:41:26.9000170Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.9000267Z         {
2023-01-11T21:41:26.9000482Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9000743Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9000890Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9001031Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9001134Z         }
2023-01-11T21:41:26.9001288Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9001416Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:41:26.9001513Z         {
2023-01-11T21:41:26.9001628Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9001757Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9001885Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9002010Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9002107Z         }
2023-01-11T21:41:26.9002201Z     }
2023-01-11T21:41:26.9002277Z }
2023-01-11T21:41:26.9002421Z ''')
2023-01-11T21:41:26.9002431Z 
2023-01-11T21:41:26.9002437Z 
2023-01-11T21:41:26.9002579Z async_compile.wait(globals())
2023-01-11T21:41:26.9002690Z del async_compile
2023-01-11T21:41:26.9002698Z 
2023-01-11T21:41:26.9002807Z def call(args):
2023-01-11T21:41:26.9002929Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9003039Z     args.clear()
2023-01-11T21:41:26.9003370Z     buf0 = empty_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9003617Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9003721Z     del arg0_1
2023-01-11T21:41:26.9003822Z     del arg1_1
2023-01-11T21:41:26.9003934Z     return (buf0, )
2023-01-11T21:41:26.9003943Z 
2023-01-11T21:41:26.9003949Z 
2023-01-11T21:41:26.9004066Z if __name__ == "__main__":
2023-01-11T21:41:26.9004244Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9004438Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9004773Z     arg0_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9005083Z     arg1_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9005267Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9005277Z 
2023-01-11T21:41:26.9005283Z 
2023-01-11T21:41:26.9005432Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9005544Z import torch
2023-01-11T21:41:26.9005644Z import random
2023-01-11T21:41:26.9005794Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9005952Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9005960Z 
2023-01-11T21:41:26.9006087Z aten = torch.ops.aten
2023-01-11T21:41:26.9006296Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9006442Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9006452Z 
2023-01-11T21:41:26.9006458Z 
2023-01-11T21:41:26.9006693Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9007027Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9007224Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9007398Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9007561Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9007658Z {
2023-01-11T21:41:26.9007885Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9007984Z     {
2023-01-11T21:41:26.9008111Z         #pragma omp for 
2023-01-11T21:41:26.9008247Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.9008348Z         {
2023-01-11T21:41:26.9008572Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9008784Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9008989Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9009271Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9009412Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9009567Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9009674Z         }
2023-01-11T21:41:26.9009831Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9010065Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:41:26.9010158Z         {
2023-01-11T21:41:26.9010299Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9010439Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9010608Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9010753Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9010897Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9011023Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9011116Z         }
2023-01-11T21:41:26.9011217Z     }
2023-01-11T21:41:26.9011313Z }
2023-01-11T21:41:26.9011466Z ''')
2023-01-11T21:41:26.9011474Z 
2023-01-11T21:41:26.9011480Z 
2023-01-11T21:41:26.9011625Z async_compile.wait(globals())
2023-01-11T21:41:26.9011748Z del async_compile
2023-01-11T21:41:26.9011759Z 
2023-01-11T21:41:26.9011871Z def call(args):
2023-01-11T21:41:26.9011980Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9012100Z     args.clear()
2023-01-11T21:41:26.9012431Z     buf0 = empty_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9012703Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9012825Z     del arg0_1
2023-01-11T21:41:26.9012933Z     del arg1_1
2023-01-11T21:41:26.9013056Z     return (buf0, )
2023-01-11T21:41:26.9013064Z 
2023-01-11T21:41:26.9013070Z 
2023-01-11T21:41:26.9013179Z if __name__ == "__main__":
2023-01-11T21:41:26.9013369Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9013567Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9013893Z     arg0_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9014226Z     arg1_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9014418Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9014426Z 
2023-01-11T21:41:26.9014432Z 
2023-01-11T21:41:26.9014588Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9014701Z import torch
2023-01-11T21:41:26.9014804Z import random
2023-01-11T21:41:26.9014994Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9015194Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9015202Z 
2023-01-11T21:41:26.9015330Z aten = torch.ops.aten
2023-01-11T21:41:26.9015546Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9015698Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9015709Z 
2023-01-11T21:41:26.9015715Z 
2023-01-11T21:41:26.9015934Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9016272Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9016453Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9016623Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9016785Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9016893Z {
2023-01-11T21:41:26.9017054Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9017156Z     {
2023-01-11T21:41:26.9017388Z         #pragma omp for 
2023-01-11T21:41:26.9017503Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:26.9017611Z         {
2023-01-11T21:41:26.9017835Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9018052Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9018192Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9018343Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9018455Z         }
2023-01-11T21:41:26.9018611Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9018734Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:26.9018840Z         {
2023-01-11T21:41:26.9018978Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9019120Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9019350Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9019485Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9019575Z         }
2023-01-11T21:41:26.9019689Z     }
2023-01-11T21:41:26.9019784Z }
2023-01-11T21:41:26.9019927Z ''')
2023-01-11T21:41:26.9019936Z 
2023-01-11T21:41:26.9019942Z 
2023-01-11T21:41:26.9020092Z async_compile.wait(globals())
2023-01-11T21:41:26.9020211Z del async_compile
2023-01-11T21:41:26.9020221Z 
2023-01-11T21:41:26.9020337Z def call(args):
2023-01-11T21:41:26.9020459Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9020561Z     args.clear()
2023-01-11T21:41:26.9020956Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9021223Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9021336Z     del arg0_1
2023-01-11T21:41:26.9021449Z     del arg1_1
2023-01-11T21:41:26.9021567Z     return (buf0, )
2023-01-11T21:41:26.9021576Z 
2023-01-11T21:41:26.9021582Z 
2023-01-11T21:41:26.9021715Z if __name__ == "__main__":
2023-01-11T21:41:26.9021890Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9022101Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9022431Z     arg0_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9022818Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9023005Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9023014Z 
2023-01-11T21:41:26.9023020Z 
2023-01-11T21:41:26.9023252Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9023371Z import torch
2023-01-11T21:41:26.9023490Z import random
2023-01-11T21:41:26.9023668Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9023867Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9023879Z 
2023-01-11T21:41:26.9024009Z aten = torch.ops.aten
2023-01-11T21:41:26.9024238Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9024398Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9024412Z 
2023-01-11T21:41:26.9024418Z 
2023-01-11T21:41:26.9024653Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9024988Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9025188Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9025340Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9025498Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9025601Z {
2023-01-11T21:41:26.9025759Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9025857Z     {
2023-01-11T21:41:26.9025988Z         #pragma omp for 
2023-01-11T21:41:26.9026123Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:26.9026214Z         {
2023-01-11T21:41:26.9026445Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9026659Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9026960Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9027103Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9027241Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9027392Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9027501Z         }
2023-01-11T21:41:26.9027637Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9027778Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:26.9027879Z         {
2023-01-11T21:41:26.9028020Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9028156Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9028317Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9028453Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9028576Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9028766Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9028881Z         }
2023-01-11T21:41:26.9028983Z     }
2023-01-11T21:41:26.9029079Z }
2023-01-11T21:41:26.9029225Z ''')
2023-01-11T21:41:26.9029234Z 
2023-01-11T21:41:26.9029239Z 
2023-01-11T21:41:26.9029387Z async_compile.wait(globals())
2023-01-11T21:41:26.9029498Z del async_compile
2023-01-11T21:41:26.9029509Z 
2023-01-11T21:41:26.9029623Z def call(args):
2023-01-11T21:41:26.9029746Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9029864Z     args.clear()
2023-01-11T21:41:26.9030266Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9030522Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9030639Z     del arg0_1
2023-01-11T21:41:26.9030734Z     del arg1_1
2023-01-11T21:41:26.9030850Z     return (buf0, )
2023-01-11T21:41:26.9030857Z 
2023-01-11T21:41:26.9030865Z 
2023-01-11T21:41:26.9030989Z if __name__ == "__main__":
2023-01-11T21:41:26.9031188Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9031396Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9031728Z     arg0_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9032121Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9032307Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9032316Z 
2023-01-11T21:41:26.9032761Z [2023-01-11 21:39:28,002] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 489
2023-01-11T21:41:26.9033473Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9033686Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9034131Z [2023-01-11 21:39:28,021] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 490
2023-01-11T21:41:26.9034588Z [2023-01-11 21:39:28,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 490
2023-01-11T21:41:26.9035300Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9035504Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9035943Z [2023-01-11 21:39:28,054] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 491
2023-01-11T21:41:26.9036404Z [2023-01-11 21:39:29,586] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 491
2023-01-11T21:41:26.9037210Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9037415Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9037856Z [2023-01-11 21:39:29,606] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 492
2023-01-11T21:41:26.9038276Z [2023-01-11 21:39:31,270] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 492
2023-01-11T21:41:26.9039043Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9039258Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9039689Z [2023-01-11 21:39:31,287] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 493
2023-01-11T21:41:26.9039699Z 
2023-01-11T21:41:26.9039855Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9039974Z import torch
2023-01-11T21:41:26.9040089Z import random
2023-01-11T21:41:26.9040284Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9040484Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9040492Z 
2023-01-11T21:41:26.9040601Z aten = torch.ops.aten
2023-01-11T21:41:26.9040830Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9040980Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9040989Z 
2023-01-11T21:41:26.9040996Z 
2023-01-11T21:41:26.9041235Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9041565Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9041765Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9041933Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9042094Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9042178Z {
2023-01-11T21:41:26.9042342Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9042440Z     {
2023-01-11T21:41:26.9042569Z         #pragma omp for 
2023-01-11T21:41:26.9042703Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:26.9042809Z         {
2023-01-11T21:41:26.9043029Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9043250Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9043375Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9043524Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9043632Z         }
2023-01-11T21:41:26.9043787Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9043929Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:26.9044039Z         {
2023-01-11T21:41:26.9044158Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9044301Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9044437Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9044570Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9044676Z         }
2023-01-11T21:41:26.9044777Z     }
2023-01-11T21:41:26.9044872Z }
2023-01-11T21:41:26.9045002Z ''')
2023-01-11T21:41:26.9045011Z 
2023-01-11T21:41:26.9045017Z 
2023-01-11T21:41:26.9045172Z async_compile.wait(globals())
2023-01-11T21:41:26.9045298Z del async_compile
2023-01-11T21:41:26.9045306Z 
2023-01-11T21:41:26.9045427Z def call(args):
2023-01-11T21:41:26.9045549Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9045671Z     args.clear()
2023-01-11T21:41:26.9046170Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9046434Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9046534Z     del arg0_1
2023-01-11T21:41:26.9046643Z     del arg1_1
2023-01-11T21:41:26.9046764Z     return (buf0, )
2023-01-11T21:41:26.9046773Z 
2023-01-11T21:41:26.9046779Z 
2023-01-11T21:41:26.9046903Z if __name__ == "__main__":
2023-01-11T21:41:26.9047092Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9047298Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9047670Z     arg0_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9048110Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9048293Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9048309Z 
2023-01-11T21:41:26.9048315Z 
2023-01-11T21:41:26.9048475Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9048587Z import torch
2023-01-11T21:41:26.9048707Z import random
2023-01-11T21:41:26.9048894Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9049238Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9049248Z 
2023-01-11T21:41:26.9049380Z aten = torch.ops.aten
2023-01-11T21:41:26.9049605Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9049738Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9049751Z 
2023-01-11T21:41:26.9049758Z 
2023-01-11T21:41:26.9049991Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9050320Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9050523Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9050697Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9050870Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9050969Z {
2023-01-11T21:41:26.9051118Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9051221Z     {
2023-01-11T21:41:26.9051349Z         #pragma omp for 
2023-01-11T21:41:26.9051483Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:26.9051587Z         {
2023-01-11T21:41:26.9051813Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9052024Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9052243Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9052371Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9052507Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9052664Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9052774Z         }
2023-01-11T21:41:26.9052928Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9053072Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:26.9053175Z         {
2023-01-11T21:41:26.9053292Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9053427Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9053590Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9053733Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9053870Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9054001Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9054102Z         }
2023-01-11T21:41:26.9054189Z     }
2023-01-11T21:41:26.9054292Z }
2023-01-11T21:41:26.9054436Z ''')
2023-01-11T21:41:26.9054446Z 
2023-01-11T21:41:26.9054451Z 
2023-01-11T21:41:26.9054596Z async_compile.wait(globals())
2023-01-11T21:41:26.9054717Z del async_compile
2023-01-11T21:41:26.9054726Z 
2023-01-11T21:41:26.9054841Z def call(args):
2023-01-11T21:41:26.9054966Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9055071Z     args.clear()
2023-01-11T21:41:26.9055467Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9055842Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9055958Z     del arg0_1
2023-01-11T21:41:26.9056070Z     del arg1_1
2023-01-11T21:41:26.9056186Z     return (buf0, )
2023-01-11T21:41:26.9056194Z 
2023-01-11T21:41:26.9056201Z 
2023-01-11T21:41:26.9056323Z if __name__ == "__main__":
2023-01-11T21:41:26.9056497Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9056699Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9057065Z     arg0_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9057516Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9057716Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9057724Z 
2023-01-11T21:41:26.9057735Z 
2023-01-11T21:41:26.9057890Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9058010Z import torch
2023-01-11T21:41:26.9058135Z import random
2023-01-11T21:41:26.9058307Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9058507Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9058515Z 
2023-01-11T21:41:26.9058647Z aten = torch.ops.aten
2023-01-11T21:41:26.9058869Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9059018Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9059027Z 
2023-01-11T21:41:26.9059033Z 
2023-01-11T21:41:26.9059257Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9059589Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9059785Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9059958Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9060104Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9060210Z {
2023-01-11T21:41:26.9060375Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9060477Z     {
2023-01-11T21:41:26.9060605Z         #pragma omp for 
2023-01-11T21:41:26.9060743Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9060831Z         {
2023-01-11T21:41:26.9061053Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9061275Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9061414Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9061566Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9061670Z         }
2023-01-11T21:41:26.9061833Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9061975Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9062067Z         {
2023-01-11T21:41:26.9062207Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9062344Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9062487Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9062620Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9062725Z         }
2023-01-11T21:41:26.9062808Z     }
2023-01-11T21:41:26.9062907Z }
2023-01-11T21:41:26.9063046Z ''')
2023-01-11T21:41:26.9063054Z 
2023-01-11T21:41:26.9063061Z 
2023-01-11T21:41:26.9063287Z async_compile.wait(globals())
2023-01-11T21:41:26.9063410Z del async_compile
2023-01-11T21:41:26.9063418Z 
2023-01-11T21:41:26.9063536Z def call(args):
2023-01-11T21:41:26.9063662Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9063786Z     args.clear()
2023-01-11T21:41:26.9064126Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9064395Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9064519Z     del arg0_1
2023-01-11T21:41:26.9064629Z     del arg1_1
2023-01-11T21:41:26.9064749Z     return (buf0, )
2023-01-11T21:41:26.9064837Z 
2023-01-11T21:41:26.9064843Z 
2023-01-11T21:41:26.9064968Z if __name__ == "__main__":
2023-01-11T21:41:26.9065156Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9065343Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9065694Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9066047Z     arg1_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9066236Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9066244Z 
2023-01-11T21:41:26.9066250Z 
2023-01-11T21:41:26.9066412Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9066534Z import torch
2023-01-11T21:41:26.9066650Z import random
2023-01-11T21:41:26.9066839Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9067071Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9067099Z 
2023-01-11T21:41:26.9067213Z aten = torch.ops.aten
2023-01-11T21:41:26.9067435Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9067588Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9067597Z 
2023-01-11T21:41:26.9067602Z 
2023-01-11T21:41:26.9067837Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9068170Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9068362Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9068532Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9068694Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9068781Z {
2023-01-11T21:41:26.9068943Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9069042Z     {
2023-01-11T21:41:26.9069170Z         #pragma omp for 
2023-01-11T21:41:26.9069311Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9069423Z         {
2023-01-11T21:41:26.9069627Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9069849Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9070065Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9070204Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9070344Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9070494Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9070597Z         }
2023-01-11T21:41:26.9070758Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9070884Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9070984Z         {
2023-01-11T21:41:26.9071122Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9071258Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9071422Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9071561Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9071701Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9071826Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9071926Z         }
2023-01-11T21:41:26.9072028Z     }
2023-01-11T21:41:26.9072130Z }
2023-01-11T21:41:26.9072270Z ''')
2023-01-11T21:41:26.9072279Z 
2023-01-11T21:41:26.9072286Z 
2023-01-11T21:41:26.9072431Z async_compile.wait(globals())
2023-01-11T21:41:26.9072553Z del async_compile
2023-01-11T21:41:26.9072561Z 
2023-01-11T21:41:26.9072658Z def call(args):
2023-01-11T21:41:26.9072782Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9072897Z     args.clear()
2023-01-11T21:41:26.9073242Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9073509Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9073624Z     del arg0_1
2023-01-11T21:41:26.9073731Z     del arg1_1
2023-01-11T21:41:26.9073838Z     return (buf0, )
2023-01-11T21:41:26.9073848Z 
2023-01-11T21:41:26.9073867Z 
2023-01-11T21:41:26.9073977Z if __name__ == "__main__":
2023-01-11T21:41:26.9074251Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9074451Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9074787Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9075142Z     arg1_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9075325Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9075333Z 
2023-01-11T21:41:26.9075781Z [2023-01-11 21:39:32,923] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 493
2023-01-11T21:41:26.9076544Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9076757Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9077185Z [2023-01-11 21:39:32,941] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 494
2023-01-11T21:41:26.9077624Z [2023-01-11 21:39:34,458] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 494
2023-01-11T21:41:26.9078331Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9078536Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9078976Z [2023-01-11 21:39:34,475] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 495
2023-01-11T21:41:26.9079423Z [2023-01-11 21:39:34,484] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 495
2023-01-11T21:41:26.9080125Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9080330Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9080773Z [2023-01-11 21:39:34,502] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 496
2023-01-11T21:41:26.9081222Z [2023-01-11 21:39:34,524] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 496
2023-01-11T21:41:26.9081922Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9082130Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9082546Z [2023-01-11 21:39:34,539] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 497
2023-01-11T21:41:26.9082560Z 
2023-01-11T21:41:26.9082712Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9082833Z import torch
2023-01-11T21:41:26.9082956Z import random
2023-01-11T21:41:26.9083149Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9083344Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9083352Z 
2023-01-11T21:41:26.9083483Z aten = torch.ops.aten
2023-01-11T21:41:26.9083685Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9083915Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9083923Z 
2023-01-11T21:41:26.9083931Z 
2023-01-11T21:41:26.9084154Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9084486Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9084681Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9084848Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9085011Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9085110Z {
2023-01-11T21:41:26.9085259Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9085362Z     {
2023-01-11T21:41:26.9085490Z         #pragma omp for 
2023-01-11T21:41:26.9085627Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9085729Z         {
2023-01-11T21:41:26.9086003Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9086226Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9086360Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9086510Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9086614Z         }
2023-01-11T21:41:26.9086773Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9086908Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9087009Z         {
2023-01-11T21:41:26.9087150Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9087265Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9087403Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9087541Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9087647Z         }
2023-01-11T21:41:26.9087748Z     }
2023-01-11T21:41:26.9087848Z }
2023-01-11T21:41:26.9087989Z ''')
2023-01-11T21:41:26.9087999Z 
2023-01-11T21:41:26.9088005Z 
2023-01-11T21:41:26.9088137Z async_compile.wait(globals())
2023-01-11T21:41:26.9088259Z del async_compile
2023-01-11T21:41:26.9088267Z 
2023-01-11T21:41:26.9088379Z def call(args):
2023-01-11T21:41:26.9088508Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9088626Z     args.clear()
2023-01-11T21:41:26.9088951Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9089343Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9089462Z     del arg0_1
2023-01-11T21:41:26.9089557Z     del arg1_1
2023-01-11T21:41:26.9089670Z     return (buf0, )
2023-01-11T21:41:26.9089683Z 
2023-01-11T21:41:26.9089689Z 
2023-01-11T21:41:26.9089812Z if __name__ == "__main__":
2023-01-11T21:41:26.9090004Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9090206Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9090527Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9090795Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9090933Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9090959Z 
2023-01-11T21:41:26.9090964Z 
2023-01-11T21:41:26.9091073Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9091164Z import torch
2023-01-11T21:41:26.9091257Z import random
2023-01-11T21:41:26.9091409Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9091566Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9091573Z 
2023-01-11T21:41:26.9091675Z aten = torch.ops.aten
2023-01-11T21:41:26.9091848Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9091954Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9091960Z 
2023-01-11T21:41:26.9091979Z 
2023-01-11T21:41:26.9092146Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9092410Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9092567Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9092703Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9092928Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9093014Z {
2023-01-11T21:41:26.9093144Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9093209Z     {
2023-01-11T21:41:26.9093311Z         #pragma omp for 
2023-01-11T21:41:26.9093422Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9093505Z         {
2023-01-11T21:41:26.9093688Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9093861Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9094035Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9094133Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9094243Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9094407Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9094494Z         }
2023-01-11T21:41:26.9094619Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9094732Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9094816Z         {
2023-01-11T21:41:26.9094912Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9095020Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9095149Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9095260Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9095369Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9095477Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9095561Z         }
2023-01-11T21:41:26.9095628Z     }
2023-01-11T21:41:26.9095706Z }
2023-01-11T21:41:26.9095818Z ''')
2023-01-11T21:41:26.9095824Z 
2023-01-11T21:41:26.9095829Z 
2023-01-11T21:41:26.9095947Z async_compile.wait(globals())
2023-01-11T21:41:26.9096045Z del async_compile
2023-01-11T21:41:26.9096051Z 
2023-01-11T21:41:26.9096148Z def call(args):
2023-01-11T21:41:26.9096248Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9096327Z     args.clear()
2023-01-11T21:41:26.9096593Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9096806Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9096896Z     del arg0_1
2023-01-11T21:41:26.9096984Z     del arg1_1
2023-01-11T21:41:26.9097078Z     return (buf0, )
2023-01-11T21:41:26.9097085Z 
2023-01-11T21:41:26.9097090Z 
2023-01-11T21:41:26.9097192Z if __name__ == "__main__":
2023-01-11T21:41:26.9097341Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9097488Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9097767Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9098029Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9098183Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9098190Z 
2023-01-11T21:41:26.9098195Z 
2023-01-11T21:41:26.9098317Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9098415Z import torch
2023-01-11T21:41:26.9098508Z import random
2023-01-11T21:41:26.9098658Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9098800Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9098807Z 
2023-01-11T21:41:26.9098907Z aten = torch.ops.aten
2023-01-11T21:41:26.9099079Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9099200Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9099207Z 
2023-01-11T21:41:26.9099212Z 
2023-01-11T21:41:26.9099395Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9099654Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9099808Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9099948Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9100065Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9100185Z {
2023-01-11T21:41:26.9100313Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9100395Z     {
2023-01-11T21:41:26.9100497Z         #pragma omp for 
2023-01-11T21:41:26.9100606Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9100688Z         {
2023-01-11T21:41:26.9100850Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9101019Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9101132Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9101250Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9101334Z         }
2023-01-11T21:41:26.9101459Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9101573Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9101640Z         {
2023-01-11T21:41:26.9101784Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9101896Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9102004Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9102113Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9102196Z         }
2023-01-11T21:41:26.9102276Z     }
2023-01-11T21:41:26.9102340Z }
2023-01-11T21:41:26.9102448Z ''')
2023-01-11T21:41:26.9102455Z 
2023-01-11T21:41:26.9102459Z 
2023-01-11T21:41:26.9102576Z async_compile.wait(globals())
2023-01-11T21:41:26.9102672Z del async_compile
2023-01-11T21:41:26.9102679Z 
2023-01-11T21:41:26.9102773Z def call(args):
2023-01-11T21:41:26.9102873Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9102967Z     args.clear()
2023-01-11T21:41:26.9103296Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9103511Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9103601Z     del arg0_1
2023-01-11T21:41:26.9103693Z     del arg1_1
2023-01-11T21:41:26.9103788Z     return (buf0, )
2023-01-11T21:41:26.9103794Z 
2023-01-11T21:41:26.9103799Z 
2023-01-11T21:41:26.9103902Z if __name__ == "__main__":
2023-01-11T21:41:26.9104052Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9104213Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9104515Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9104790Z     arg1_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9104942Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9104949Z 
2023-01-11T21:41:26.9104954Z 
2023-01-11T21:41:26.9105078Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9105169Z import torch
2023-01-11T21:41:26.9105261Z import random
2023-01-11T21:41:26.9105414Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9105573Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9105581Z 
2023-01-11T21:41:26.9105668Z aten = torch.ops.aten
2023-01-11T21:41:26.9105841Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9105966Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9105973Z 
2023-01-11T21:41:26.9105978Z 
2023-01-11T21:41:26.9106159Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9106417Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9106574Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9106708Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9106842Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9106908Z {
2023-01-11T21:41:26.9107035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9107116Z     {
2023-01-11T21:41:26.9107220Z         #pragma omp for 
2023-01-11T21:41:26.9107329Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9107416Z         {
2023-01-11T21:41:26.9107592Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9107810Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9107983Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9108097Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9108206Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9108325Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9108409Z         }
2023-01-11T21:41:26.9108532Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9108631Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9108713Z         {
2023-01-11T21:41:26.9108825Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9108934Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9109065Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9109207Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9109320Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9109412Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9109498Z         }
2023-01-11T21:41:26.9109579Z     }
2023-01-11T21:41:26.9109658Z }
2023-01-11T21:41:26.9109766Z ''')
2023-01-11T21:41:26.9109773Z 
2023-01-11T21:41:26.9109778Z 
2023-01-11T21:41:26.9109897Z async_compile.wait(globals())
2023-01-11T21:41:26.9109995Z del async_compile
2023-01-11T21:41:26.9110002Z 
2023-01-11T21:41:26.9110081Z def call(args):
2023-01-11T21:41:26.9110179Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9110270Z     args.clear()
2023-01-11T21:41:26.9110546Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9110758Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9110848Z     del arg0_1
2023-01-11T21:41:26.9110935Z     del arg1_1
2023-01-11T21:41:26.9111016Z     return (buf0, )
2023-01-11T21:41:26.9111039Z 
2023-01-11T21:41:26.9111044Z 
2023-01-11T21:41:26.9111130Z if __name__ == "__main__":
2023-01-11T21:41:26.9111278Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9111442Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9111751Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9112028Z     arg1_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9112177Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9112184Z 
2023-01-11T21:41:26.9112550Z [2023-01-11 21:39:36,068] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 497
2023-01-11T21:41:26.9113115Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9113282Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9113620Z [2023-01-11 21:39:36,087] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 498
2023-01-11T21:41:26.9113978Z [2023-01-11 21:39:37,836] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 498
2023-01-11T21:41:26.9114538Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9114702Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9115056Z [2023-01-11 21:39:37,855] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 499
2023-01-11T21:41:26.9115416Z [2023-01-11 21:39:37,865] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 499
2023-01-11T21:41:26.9116027Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9116190Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9116538Z [2023-01-11 21:39:37,884] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 500
2023-01-11T21:41:26.9116891Z [2023-01-11 21:39:37,899] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 500
2023-01-11T21:41:26.9117485Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9117655Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9117984Z [2023-01-11 21:39:37,917] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 501
2023-01-11T21:41:26.9118005Z 
2023-01-11T21:41:26.9118113Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9118208Z import torch
2023-01-11T21:41:26.9118300Z import random
2023-01-11T21:41:26.9118453Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9118610Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9118617Z 
2023-01-11T21:41:26.9118722Z aten = torch.ops.aten
2023-01-11T21:41:26.9118897Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9119006Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9119013Z 
2023-01-11T21:41:26.9119018Z 
2023-01-11T21:41:26.9119200Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9119460Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9119615Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9119751Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9119882Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9119961Z {
2023-01-11T21:41:26.9120090Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9120159Z     {
2023-01-11T21:41:26.9120258Z         #pragma omp for 
2023-01-11T21:41:26.9120364Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.9120446Z         {
2023-01-11T21:41:26.9120626Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9120802Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9120918Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9121026Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9121107Z         }
2023-01-11T21:41:26.9121230Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9121337Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.9121419Z         {
2023-01-11T21:41:26.9121529Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9121638Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9121734Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9121836Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9121917Z         }
2023-01-11T21:41:26.9121997Z     }
2023-01-11T21:41:26.9122073Z }
2023-01-11T21:41:26.9122181Z ''')
2023-01-11T21:41:26.9122188Z 
2023-01-11T21:41:26.9122193Z 
2023-01-11T21:41:26.9122298Z async_compile.wait(globals())
2023-01-11T21:41:26.9126671Z del async_compile
2023-01-11T21:41:26.9126679Z 
2023-01-11T21:41:26.9126774Z def call(args):
2023-01-11T21:41:26.9126948Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9127042Z     args.clear()
2023-01-11T21:41:26.9127327Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9127544Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9127633Z     del arg0_1
2023-01-11T21:41:26.9127707Z     del arg1_1
2023-01-11T21:41:26.9127806Z     return (buf0, )
2023-01-11T21:41:26.9127812Z 
2023-01-11T21:41:26.9127818Z 
2023-01-11T21:41:26.9127918Z if __name__ == "__main__":
2023-01-11T21:41:26.9128069Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9128233Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9128518Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9128819Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9128976Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9128987Z 
2023-01-11T21:41:26.9128992Z 
2023-01-11T21:41:26.9129242Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9129339Z import torch
2023-01-11T21:41:26.9129433Z import random
2023-01-11T21:41:26.9129584Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9129743Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9129750Z 
2023-01-11T21:41:26.9129855Z aten = torch.ops.aten
2023-01-11T21:41:26.9130031Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9130138Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9130159Z 
2023-01-11T21:41:26.9130164Z 
2023-01-11T21:41:26.9130333Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9130594Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9130752Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9130891Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9131024Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9131105Z {
2023-01-11T21:41:26.9131234Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9131301Z     {
2023-01-11T21:41:26.9131400Z         #pragma omp for 
2023-01-11T21:41:26.9131508Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.9131591Z         {
2023-01-11T21:41:26.9131774Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9131948Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9132122Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9132236Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9132333Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9132462Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9132546Z         }
2023-01-11T21:41:26.9132669Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9132783Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.9132866Z         {
2023-01-11T21:41:26.9132962Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9133069Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9133199Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9133310Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9133417Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9133524Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9133607Z         }
2023-01-11T21:41:26.9133676Z     }
2023-01-11T21:41:26.9133754Z }
2023-01-11T21:41:26.9133861Z ''')
2023-01-11T21:41:26.9133867Z 
2023-01-11T21:41:26.9133872Z 
2023-01-11T21:41:26.9133990Z async_compile.wait(globals())
2023-01-11T21:41:26.9134087Z del async_compile
2023-01-11T21:41:26.9134094Z 
2023-01-11T21:41:26.9134185Z def call(args):
2023-01-11T21:41:26.9134288Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9134382Z     args.clear()
2023-01-11T21:41:26.9134636Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9134922Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9135015Z     del arg0_1
2023-01-11T21:41:26.9135109Z     del arg1_1
2023-01-11T21:41:26.9135220Z     return (buf0, )
2023-01-11T21:41:26.9135226Z 
2023-01-11T21:41:26.9135230Z 
2023-01-11T21:41:26.9135310Z if __name__ == "__main__":
2023-01-11T21:41:26.9135425Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9135536Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9135752Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9135949Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9136107Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9136111Z 
2023-01-11T21:41:26.9136116Z 
2023-01-11T21:41:26.9136211Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9136283Z import torch
2023-01-11T21:41:26.9136351Z import random
2023-01-11T21:41:26.9136465Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9136571Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9136577Z 
2023-01-11T21:41:26.9136654Z aten = torch.ops.aten
2023-01-11T21:41:26.9136785Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9136875Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9136881Z 
2023-01-11T21:41:26.9136885Z 
2023-01-11T21:41:26.9137018Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9137221Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9137336Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9137442Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9137528Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9137590Z {
2023-01-11T21:41:26.9137686Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9137748Z     {
2023-01-11T21:41:26.9137824Z         #pragma omp for 
2023-01-11T21:41:26.9137904Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.9137965Z         {
2023-01-11T21:41:26.9138087Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9138215Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9138303Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9138394Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9138458Z         }
2023-01-11T21:41:26.9138551Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9138631Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:41:26.9138680Z         {
2023-01-11T21:41:26.9138763Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9138843Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9138922Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9139004Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9139063Z         }
2023-01-11T21:41:26.9139121Z     }
2023-01-11T21:41:26.9139167Z }
2023-01-11T21:41:26.9139246Z ''')
2023-01-11T21:41:26.9139251Z 
2023-01-11T21:41:26.9139255Z 
2023-01-11T21:41:26.9139342Z async_compile.wait(globals())
2023-01-11T21:41:26.9139413Z del async_compile
2023-01-11T21:41:26.9139418Z 
2023-01-11T21:41:26.9139486Z def call(args):
2023-01-11T21:41:26.9139559Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9139628Z     args.clear()
2023-01-11T21:41:26.9139810Z     buf0 = empty_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9139971Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9140040Z     del arg0_1
2023-01-11T21:41:26.9140104Z     del arg1_1
2023-01-11T21:41:26.9140175Z     return (buf0, )
2023-01-11T21:41:26.9140181Z 
2023-01-11T21:41:26.9140185Z 
2023-01-11T21:41:26.9140259Z if __name__ == "__main__":
2023-01-11T21:41:26.9140408Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9140529Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9140714Z     arg0_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9140907Z     arg1_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9141020Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9141025Z 
2023-01-11T21:41:26.9141028Z 
2023-01-11T21:41:26.9141119Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9141186Z import torch
2023-01-11T21:41:26.9141255Z import random
2023-01-11T21:41:26.9141367Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9141487Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9141492Z 
2023-01-11T21:41:26.9141585Z aten = torch.ops.aten
2023-01-11T21:41:26.9141720Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9141812Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9141818Z 
2023-01-11T21:41:26.9141822Z 
2023-01-11T21:41:26.9141954Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9142155Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9142272Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9142373Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9142470Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9142517Z {
2023-01-11T21:41:26.9142611Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9142674Z     {
2023-01-11T21:41:26.9142748Z         #pragma omp for 
2023-01-11T21:41:26.9142827Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:41:26.9142887Z         {
2023-01-11T21:41:26.9143023Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9143214Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9143354Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9143438Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9143521Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9143609Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9143670Z         }
2023-01-11T21:41:26.9143764Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9143834Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:41:26.9143895Z         {
2023-01-11T21:41:26.9143976Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9144058Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9144155Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9144236Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9144319Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9144386Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9144446Z         }
2023-01-11T21:41:26.9144508Z     }
2023-01-11T21:41:26.9144566Z }
2023-01-11T21:41:26.9144647Z ''')
2023-01-11T21:41:26.9144652Z 
2023-01-11T21:41:26.9144656Z 
2023-01-11T21:41:26.9144744Z async_compile.wait(globals())
2023-01-11T21:41:26.9144814Z del async_compile
2023-01-11T21:41:26.9144819Z 
2023-01-11T21:41:26.9144876Z def call(args):
2023-01-11T21:41:26.9144947Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9145016Z     args.clear()
2023-01-11T21:41:26.9145209Z     buf0 = empty_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9145369Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9145436Z     del arg0_1
2023-01-11T21:41:26.9145500Z     del arg1_1
2023-01-11T21:41:26.9145557Z     return (buf0, )
2023-01-11T21:41:26.9145574Z 
2023-01-11T21:41:26.9145578Z 
2023-01-11T21:41:26.9145643Z if __name__ == "__main__":
2023-01-11T21:41:26.9145755Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9145912Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9146109Z     arg0_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9146300Z     arg1_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9146413Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9146418Z 
2023-01-11T21:41:26.9146688Z [2023-01-11 21:39:37,925] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 501
2023-01-11T21:41:26.9147144Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9147273Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9147523Z [2023-01-11 21:39:37,945] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 502
2023-01-11T21:41:26.9147787Z [2023-01-11 21:39:37,976] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 502
2023-01-11T21:41:26.9148211Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9148336Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9148594Z [2023-01-11 21:39:37,995] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 503
2023-01-11T21:41:26.9148857Z [2023-01-11 21:39:38,008] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 503
2023-01-11T21:41:26.9149279Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9149401Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9149654Z [2023-01-11 21:39:38,030] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 504
2023-01-11T21:41:26.9149913Z [2023-01-11 21:39:38,081] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 504
2023-01-11T21:41:26.9150335Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9150460Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9150706Z [2023-01-11 21:39:38,101] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 505
2023-01-11T21:41:26.9150729Z 
2023-01-11T21:41:26.9150852Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9150961Z import torch
2023-01-11T21:41:26.9151072Z import random
2023-01-11T21:41:26.9151265Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9151457Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9151466Z 
2023-01-11T21:41:26.9151595Z aten = torch.ops.aten
2023-01-11T21:41:26.9151814Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9151944Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9151952Z 
2023-01-11T21:41:26.9151959Z 
2023-01-11T21:41:26.9152296Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9152623Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9152817Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9152982Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9153143Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9153241Z {
2023-01-11T21:41:26.9153396Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9153484Z     {
2023-01-11T21:41:26.9153611Z         #pragma omp for 
2023-01-11T21:41:26.9153740Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:26.9153848Z         {
2023-01-11T21:41:26.9154072Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9154377Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9154535Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9154670Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9154771Z         }
2023-01-11T21:41:26.9154925Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9155064Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:26.9155169Z         {
2023-01-11T21:41:26.9155307Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9155445Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9155564Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9155693Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9155791Z         }
2023-01-11T21:41:26.9155893Z     }
2023-01-11T21:41:26.9155987Z }
2023-01-11T21:41:26.9156146Z ''')
2023-01-11T21:41:26.9156160Z 
2023-01-11T21:41:26.9156165Z 
2023-01-11T21:41:26.9156312Z async_compile.wait(globals())
2023-01-11T21:41:26.9156416Z del async_compile
2023-01-11T21:41:26.9156424Z 
2023-01-11T21:41:26.9156544Z def call(args):
2023-01-11T21:41:26.9156677Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9156792Z     args.clear()
2023-01-11T21:41:26.9157144Z     buf0 = empty_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9157399Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9157515Z     del arg0_1
2023-01-11T21:41:26.9157604Z     del arg1_1
2023-01-11T21:41:26.9157717Z     return (buf0, )
2023-01-11T21:41:26.9157729Z 
2023-01-11T21:41:26.9157734Z 
2023-01-11T21:41:26.9157858Z if __name__ == "__main__":
2023-01-11T21:41:26.9158039Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9158238Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9158644Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9158982Z     arg1_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9159166Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9159176Z 
2023-01-11T21:41:26.9159188Z 
2023-01-11T21:41:26.9159323Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9159439Z import torch
2023-01-11T21:41:26.9159558Z import random
2023-01-11T21:41:26.9159744Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9159937Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9159945Z 
2023-01-11T21:41:26.9160071Z aten = torch.ops.aten
2023-01-11T21:41:26.9160277Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9160427Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9160436Z 
2023-01-11T21:41:26.9160442Z 
2023-01-11T21:41:26.9160673Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9161003Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9161198Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9161369Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9161527Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9161760Z {
2023-01-11T21:41:26.9161925Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9162007Z     {
2023-01-11T21:41:26.9162127Z         #pragma omp for 
2023-01-11T21:41:26.9162266Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:26.9162369Z         {
2023-01-11T21:41:26.9162590Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9162806Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9163015Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9163153Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9163272Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9163414Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9163520Z         }
2023-01-11T21:41:26.9163789Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9163950Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:26.9164064Z         {
2023-01-11T21:41:26.9164199Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9164322Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9164484Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9164618Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9164750Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9164877Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9164986Z         }
2023-01-11T21:41:26.9165069Z     }
2023-01-11T21:41:26.9165169Z }
2023-01-11T21:41:26.9165336Z ''')
2023-01-11T21:41:26.9165348Z 
2023-01-11T21:41:26.9165355Z 
2023-01-11T21:41:26.9165504Z async_compile.wait(globals())
2023-01-11T21:41:26.9165623Z del async_compile
2023-01-11T21:41:26.9165632Z 
2023-01-11T21:41:26.9165745Z def call(args):
2023-01-11T21:41:26.9165866Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9165989Z     args.clear()
2023-01-11T21:41:26.9166326Z     buf0 = empty_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9166594Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9166708Z     del arg0_1
2023-01-11T21:41:26.9166817Z     del arg1_1
2023-01-11T21:41:26.9166936Z     return (buf0, )
2023-01-11T21:41:26.9166946Z 
2023-01-11T21:41:26.9166952Z 
2023-01-11T21:41:26.9167075Z if __name__ == "__main__":
2023-01-11T21:41:26.9167256Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9167434Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9167844Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9168183Z     arg1_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9168372Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9168387Z 
2023-01-11T21:41:26.9168393Z 
2023-01-11T21:41:26.9168543Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9168673Z import torch
2023-01-11T21:41:26.9168791Z import random
2023-01-11T21:41:26.9168976Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9169288Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9169321Z 
2023-01-11T21:41:26.9169437Z aten = torch.ops.aten
2023-01-11T21:41:26.9169652Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9169801Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9169808Z 
2023-01-11T21:41:26.9169814Z 
2023-01-11T21:41:26.9170058Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9170381Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9170569Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9170743Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9170901Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9170983Z {
2023-01-11T21:41:26.9171300Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9171401Z     {
2023-01-11T21:41:26.9171526Z         #pragma omp for 
2023-01-11T21:41:26.9171657Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:26.9171761Z         {
2023-01-11T21:41:26.9171966Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9172185Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9172322Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9172474Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9172579Z         }
2023-01-11T21:41:26.9172730Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9172866Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:26.9172971Z         {
2023-01-11T21:41:26.9173093Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9173337Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9173489Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9173625Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9173732Z         }
2023-01-11T21:41:26.9173831Z     }
2023-01-11T21:41:26.9173910Z }
2023-01-11T21:41:26.9174064Z ''')
2023-01-11T21:41:26.9174073Z 
2023-01-11T21:41:26.9174079Z 
2023-01-11T21:41:26.9174233Z async_compile.wait(globals())
2023-01-11T21:41:26.9174354Z del async_compile
2023-01-11T21:41:26.9174362Z 
2023-01-11T21:41:26.9174477Z def call(args):
2023-01-11T21:41:26.9174601Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9174723Z     args.clear()
2023-01-11T21:41:26.9175104Z     buf0 = empty_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9175348Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9175460Z     del arg0_1
2023-01-11T21:41:26.9175570Z     del arg1_1
2023-01-11T21:41:26.9175697Z     return (buf0, )
2023-01-11T21:41:26.9175705Z 
2023-01-11T21:41:26.9175710Z 
2023-01-11T21:41:26.9175835Z if __name__ == "__main__":
2023-01-11T21:41:26.9176024Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9176221Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9176607Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9176980Z     arg1_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9177170Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9177180Z 
2023-01-11T21:41:26.9177186Z 
2023-01-11T21:41:26.9177335Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9177454Z import torch
2023-01-11T21:41:26.9177566Z import random
2023-01-11T21:41:26.9177752Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9177951Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9177960Z 
2023-01-11T21:41:26.9178084Z aten = torch.ops.aten
2023-01-11T21:41:26.9178284Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9178437Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9178446Z 
2023-01-11T21:41:26.9178452Z 
2023-01-11T21:41:26.9178704Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9179028Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9179219Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9179385Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9179544Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9179629Z {
2023-01-11T21:41:26.9179785Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9179885Z     {
2023-01-11T21:41:26.9180010Z         #pragma omp for 
2023-01-11T21:41:26.9180141Z         for(long i0=0; i0<630; i0+=1)
2023-01-11T21:41:26.9180249Z         {
2023-01-11T21:41:26.9180474Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9180812Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9181008Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9181143Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9181280Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9181427Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9181528Z         }
2023-01-11T21:41:26.9181680Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9181819Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:41:26.9181906Z         {
2023-01-11T21:41:26.9182042Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9182178Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9182341Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9182475Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9182690Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9182841Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9182934Z         }
2023-01-11T21:41:26.9183041Z     }
2023-01-11T21:41:26.9183211Z }
2023-01-11T21:41:26.9183363Z ''')
2023-01-11T21:41:26.9183372Z 
2023-01-11T21:41:26.9183378Z 
2023-01-11T21:41:26.9183526Z async_compile.wait(globals())
2023-01-11T21:41:26.9183648Z del async_compile
2023-01-11T21:41:26.9183655Z 
2023-01-11T21:41:26.9183764Z def call(args):
2023-01-11T21:41:26.9183872Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9183986Z     args.clear()
2023-01-11T21:41:26.9184366Z     buf0 = empty_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9184625Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9184734Z     del arg0_1
2023-01-11T21:41:26.9184840Z     del arg1_1
2023-01-11T21:41:26.9184964Z     return (buf0, )
2023-01-11T21:41:26.9184979Z 
2023-01-11T21:41:26.9184986Z 
2023-01-11T21:41:26.9185109Z if __name__ == "__main__":
2023-01-11T21:41:26.9185281Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9185484Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9185891Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9186255Z     arg1_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9186439Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9186448Z 
2023-01-11T21:41:26.9186902Z [2023-01-11 21:39:38,110] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 505
2023-01-11T21:41:26.9187593Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9187804Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9188254Z [2023-01-11 21:39:38,131] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 506
2023-01-11T21:41:26.9188689Z [2023-01-11 21:39:38,157] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 506
2023-01-11T21:41:26.9189381Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9189591Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9190053Z [2023-01-11 21:39:38,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 507
2023-01-11T21:41:26.9190629Z [2023-01-11 21:39:38,182] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 507
2023-01-11T21:41:26.9191314Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9191519Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9191969Z [2023-01-11 21:39:38,207] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 508
2023-01-11T21:41:26.9192496Z [2023-01-11 21:39:38,218] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 508
2023-01-11T21:41:26.9193198Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9193405Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9193853Z [2023-01-11 21:39:38,236] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 509
2023-01-11T21:41:26.9193864Z 
2023-01-11T21:41:26.9193997Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9194114Z import torch
2023-01-11T21:41:26.9194229Z import random
2023-01-11T21:41:26.9194415Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9194609Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9194617Z 
2023-01-11T21:41:26.9194747Z aten = torch.ops.aten
2023-01-11T21:41:26.9194965Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9195120Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9195130Z 
2023-01-11T21:41:26.9195136Z 
2023-01-11T21:41:26.9195368Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9195692Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9195883Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9196052Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9196215Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9196313Z {
2023-01-11T21:41:26.9196473Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9196555Z     {
2023-01-11T21:41:26.9196682Z         #pragma omp for 
2023-01-11T21:41:26.9196812Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9196917Z         {
2023-01-11T21:41:26.9197148Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9197362Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9197506Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9197647Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9197736Z         }
2023-01-11T21:41:26.9197894Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9198034Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9198136Z         {
2023-01-11T21:41:26.9198273Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9198411Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9198527Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9198658Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9198759Z         }
2023-01-11T21:41:26.9198858Z     }
2023-01-11T21:41:26.9198952Z }
2023-01-11T21:41:26.9199105Z ''')
2023-01-11T21:41:26.9199116Z 
2023-01-11T21:41:26.9199124Z 
2023-01-11T21:41:26.9199281Z async_compile.wait(globals())
2023-01-11T21:41:26.9199389Z del async_compile
2023-01-11T21:41:26.9199415Z 
2023-01-11T21:41:26.9199516Z def call(args):
2023-01-11T21:41:26.9199766Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9199885Z     args.clear()
2023-01-11T21:41:26.9200231Z     buf0 = empty_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9200488Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9200604Z     del arg0_1
2023-01-11T21:41:26.9200716Z     del arg1_1
2023-01-11T21:41:26.9200820Z     return (buf0, )
2023-01-11T21:41:26.9200829Z 
2023-01-11T21:41:26.9200834Z 
2023-01-11T21:41:26.9200958Z if __name__ == "__main__":
2023-01-11T21:41:26.9201140Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9201337Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9201700Z     arg0_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9202125Z     arg1_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9202324Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9202341Z 
2023-01-11T21:41:26.9202347Z 
2023-01-11T21:41:26.9202498Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9202596Z import torch
2023-01-11T21:41:26.9202711Z import random
2023-01-11T21:41:26.9202895Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9203090Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9203097Z 
2023-01-11T21:41:26.9203225Z aten = torch.ops.aten
2023-01-11T21:41:26.9203438Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9203584Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9203592Z 
2023-01-11T21:41:26.9203599Z 
2023-01-11T21:41:26.9203847Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9204159Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9204353Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9204519Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9204689Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9204785Z {
2023-01-11T21:41:26.9204943Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9205042Z     {
2023-01-11T21:41:26.9205155Z         #pragma omp for 
2023-01-11T21:41:26.9205291Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9205393Z         {
2023-01-11T21:41:26.9205614Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9205824Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9206036Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9206172Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9206304Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9206440Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9206541Z         }
2023-01-11T21:41:26.9206698Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9206842Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9206945Z         {
2023-01-11T21:41:26.9207077Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9207192Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9207354Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9207488Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9207621Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9207751Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9207846Z         }
2023-01-11T21:41:26.9207946Z     }
2023-01-11T21:41:26.9208023Z }
2023-01-11T21:41:26.9208181Z ''')
2023-01-11T21:41:26.9208192Z 
2023-01-11T21:41:26.9208198Z 
2023-01-11T21:41:26.9208348Z async_compile.wait(globals())
2023-01-11T21:41:26.9208465Z del async_compile
2023-01-11T21:41:26.9208472Z 
2023-01-11T21:41:26.9208593Z def call(args):
2023-01-11T21:41:26.9208715Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9208826Z     args.clear()
2023-01-11T21:41:26.9209431Z     buf0 = empty_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9209681Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9209799Z     del arg0_1
2023-01-11T21:41:26.9209909Z     del arg1_1
2023-01-11T21:41:26.9210028Z     return (buf0, )
2023-01-11T21:41:26.9210036Z 
2023-01-11T21:41:26.9210042Z 
2023-01-11T21:41:26.9210164Z if __name__ == "__main__":
2023-01-11T21:41:26.9210347Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9210547Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9210898Z     arg0_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9211236Z     arg1_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9211550Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9211567Z 
2023-01-11T21:41:26.9211580Z 
2023-01-11T21:41:26.9211744Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9211859Z import torch
2023-01-11T21:41:26.9211977Z import random
2023-01-11T21:41:26.9212162Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9212354Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9212365Z 
2023-01-11T21:41:26.9212470Z aten = torch.ops.aten
2023-01-11T21:41:26.9212688Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9212832Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9212840Z 
2023-01-11T21:41:26.9212846Z 
2023-01-11T21:41:26.9213101Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9213426Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9213615Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9213786Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9213951Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9214042Z {
2023-01-11T21:41:26.9214200Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9214298Z     {
2023-01-11T21:41:26.9214422Z         #pragma omp for 
2023-01-11T21:41:26.9214551Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9214653Z         {
2023-01-11T21:41:26.9214882Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9215075Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9215213Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9215355Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9215462Z         }
2023-01-11T21:41:26.9215617Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9215745Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9215849Z         {
2023-01-11T21:41:26.9215973Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9216113Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9216251Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9216382Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9216483Z         }
2023-01-11T21:41:26.9216583Z     }
2023-01-11T21:41:26.9216678Z }
2023-01-11T21:41:26.9216812Z ''')
2023-01-11T21:41:26.9216821Z 
2023-01-11T21:41:26.9216827Z 
2023-01-11T21:41:26.9216975Z async_compile.wait(globals())
2023-01-11T21:41:26.9217093Z del async_compile
2023-01-11T21:41:26.9217103Z 
2023-01-11T21:41:26.9217217Z def call(args):
2023-01-11T21:41:26.9217339Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9217455Z     args.clear()
2023-01-11T21:41:26.9217818Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9218072Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9218167Z     del arg0_1
2023-01-11T21:41:26.9218283Z     del arg1_1
2023-01-11T21:41:26.9218400Z     return (buf0, )
2023-01-11T21:41:26.9218408Z 
2023-01-11T21:41:26.9218532Z 
2023-01-11T21:41:26.9218668Z if __name__ == "__main__":
2023-01-11T21:41:26.9218853Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9219053Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9219404Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9219735Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9219920Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9219930Z 
2023-01-11T21:41:26.9219936Z 
2023-01-11T21:41:26.9220085Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9220195Z import torch
2023-01-11T21:41:26.9220310Z import random
2023-01-11T21:41:26.9220490Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9220763Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9220776Z 
2023-01-11T21:41:26.9220916Z aten = torch.ops.aten
2023-01-11T21:41:26.9221113Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9221267Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9221276Z 
2023-01-11T21:41:26.9221281Z 
2023-01-11T21:41:26.9221522Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9221843Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9222035Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9222196Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9222355Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9222456Z {
2023-01-11T21:41:26.9222597Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9222694Z     {
2023-01-11T21:41:26.9222823Z         #pragma omp for 
2023-01-11T21:41:26.9222959Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9223068Z         {
2023-01-11T21:41:26.9223368Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9223592Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9223788Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9223926Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9224063Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9224214Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9224313Z         }
2023-01-11T21:41:26.9224465Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9224595Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9224682Z         {
2023-01-11T21:41:26.9224821Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9224953Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9225116Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9225256Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9225390Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9225517Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9225609Z         }
2023-01-11T21:41:26.9225710Z     }
2023-01-11T21:41:26.9225807Z }
2023-01-11T21:41:26.9225965Z ''')
2023-01-11T21:41:26.9225976Z 
2023-01-11T21:41:26.9225982Z 
2023-01-11T21:41:26.9226132Z async_compile.wait(globals())
2023-01-11T21:41:26.9226248Z del async_compile
2023-01-11T21:41:26.9226256Z 
2023-01-11T21:41:26.9226371Z def call(args):
2023-01-11T21:41:26.9226477Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9226591Z     args.clear()
2023-01-11T21:41:26.9226947Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9227212Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9227329Z     del arg0_1
2023-01-11T21:41:26.9227436Z     del arg1_1
2023-01-11T21:41:26.9227549Z     return (buf0, )
2023-01-11T21:41:26.9227564Z 
2023-01-11T21:41:26.9227571Z 
2023-01-11T21:41:26.9227697Z if __name__ == "__main__":
2023-01-11T21:41:26.9227867Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9228203Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9228545Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9228887Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9229072Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9229082Z 
2023-01-11T21:41:26.9229533Z [2023-01-11 21:39:38,248] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 509
2023-01-11T21:41:26.9230316Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9230545Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9231007Z [2023-01-11 21:39:38,269] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 510
2023-01-11T21:41:26.9231393Z [2023-01-11 21:39:38,281] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 510
2023-01-11T21:41:26.9232096Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9232292Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9232734Z [2023-01-11 21:39:38,299] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 511
2023-01-11T21:41:26.9233171Z [2023-01-11 21:39:38,312] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 511
2023-01-11T21:41:26.9233879Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9234076Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9234505Z [2023-01-11 21:39:38,331] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 512
2023-01-11T21:41:26.9234936Z [2023-01-11 21:39:38,345] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 512
2023-01-11T21:41:26.9234945Z 
2023-01-11T21:41:26.9235101Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9235217Z import torch
2023-01-11T21:41:26.9235312Z import random
2023-01-11T21:41:26.9235500Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9235694Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9235702Z 
2023-01-11T21:41:26.9235825Z aten = torch.ops.aten
2023-01-11T21:41:26.9236037Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9236184Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9236192Z 
2023-01-11T21:41:26.9236198Z 
2023-01-11T21:41:26.9236420Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9236746Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9236920Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9237085Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9237246Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9237342Z {
2023-01-11T21:41:26.9237499Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9237675Z     {
2023-01-11T21:41:26.9237799Z         #pragma omp for 
2023-01-11T21:41:26.9237916Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9238016Z         {
2023-01-11T21:41:26.9238233Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9238445Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9238581Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9238724Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9238826Z         }
2023-01-11T21:41:26.9238962Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9239096Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9239193Z         {
2023-01-11T21:41:26.9239329Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9239461Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9239645Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9239780Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9239868Z         }
2023-01-11T21:41:26.9239962Z     }
2023-01-11T21:41:26.9240056Z }
2023-01-11T21:41:26.9240192Z ''')
2023-01-11T21:41:26.9240201Z 
2023-01-11T21:41:26.9240208Z 
2023-01-11T21:41:26.9240348Z async_compile.wait(globals())
2023-01-11T21:41:26.9240466Z del async_compile
2023-01-11T21:41:26.9240474Z 
2023-01-11T21:41:26.9240584Z def call(args):
2023-01-11T21:41:26.9240688Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9240800Z     args.clear()
2023-01-11T21:41:26.9241180Z     buf0 = empty_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9241439Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9241549Z     del arg0_1
2023-01-11T21:41:26.9241655Z     del arg1_1
2023-01-11T21:41:26.9241766Z     return (buf0, )
2023-01-11T21:41:26.9241779Z 
2023-01-11T21:41:26.9241785Z 
2023-01-11T21:41:26.9241903Z if __name__ == "__main__":
2023-01-11T21:41:26.9242071Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9242270Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9242606Z     arg0_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9242981Z     arg1_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9243165Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9243173Z 
2023-01-11T21:41:26.9243179Z 
2023-01-11T21:41:26.9243326Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9243437Z import torch
2023-01-11T21:41:26.9243550Z import random
2023-01-11T21:41:26.9243715Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9243908Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9243915Z 
2023-01-11T21:41:26.9244043Z aten = torch.ops.aten
2023-01-11T21:41:26.9244256Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9244403Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9244418Z 
2023-01-11T21:41:26.9244424Z 
2023-01-11T21:41:26.9244645Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9244972Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9245161Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9245310Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9245467Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9245566Z {
2023-01-11T21:41:26.9245727Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9245824Z     {
2023-01-11T21:41:26.9245947Z         #pragma omp for 
2023-01-11T21:41:26.9246078Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9246165Z         {
2023-01-11T21:41:26.9246388Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9246597Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9246883Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9247017Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9247151Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9247296Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9247394Z         }
2023-01-11T21:41:26.9247531Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9247668Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9247772Z         {
2023-01-11T21:41:26.9247909Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9248043Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9248201Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9248318Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9248448Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9248626Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9248732Z         }
2023-01-11T21:41:26.9248830Z     }
2023-01-11T21:41:26.9248928Z }
2023-01-11T21:41:26.9249214Z ''')
2023-01-11T21:41:26.9249224Z 
2023-01-11T21:41:26.9249230Z 
2023-01-11T21:41:26.9249361Z async_compile.wait(globals())
2023-01-11T21:41:26.9249476Z del async_compile
2023-01-11T21:41:26.9249484Z 
2023-01-11T21:41:26.9249594Z def call(args):
2023-01-11T21:41:26.9249711Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9249823Z     args.clear()
2023-01-11T21:41:26.9250201Z     buf0 = empty_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9250468Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9250580Z     del arg0_1
2023-01-11T21:41:26.9250671Z     del arg1_1
2023-01-11T21:41:26.9250781Z     return (buf0, )
2023-01-11T21:41:26.9250788Z 
2023-01-11T21:41:26.9250794Z 
2023-01-11T21:41:26.9250917Z if __name__ == "__main__":
2023-01-11T21:41:26.9251100Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9251304Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9251639Z     arg0_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9252009Z     arg1_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9252194Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9252203Z 
2023-01-11T21:41:26.9252209Z 
2023-01-11T21:41:26.9252345Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9252454Z import torch
2023-01-11T21:41:26.9252563Z import random
2023-01-11T21:41:26.9252750Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9252942Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9252951Z 
2023-01-11T21:41:26.9253075Z aten = torch.ops.aten
2023-01-11T21:41:26.9253293Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9253439Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9253449Z 
2023-01-11T21:41:26.9253459Z 
2023-01-11T21:41:26.9253665Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9253990Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9254181Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9254346Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9254502Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9254598Z {
2023-01-11T21:41:26.9254754Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9254836Z     {
2023-01-11T21:41:26.9254957Z         #pragma omp for 
2023-01-11T21:41:26.9255087Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.9255184Z         {
2023-01-11T21:41:26.9255405Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9255619Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9255757Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9256011Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9256095Z         }
2023-01-11T21:41:26.9256243Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9256373Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.9256471Z         {
2023-01-11T21:41:26.9256603Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9256734Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9256852Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9256977Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9257073Z         }
2023-01-11T21:41:26.9257169Z     }
2023-01-11T21:41:26.9257263Z }
2023-01-11T21:41:26.9257394Z ''')
2023-01-11T21:41:26.9257402Z 
2023-01-11T21:41:26.9257408Z 
2023-01-11T21:41:26.9257549Z async_compile.wait(globals())
2023-01-11T21:41:26.9257647Z del async_compile
2023-01-11T21:41:26.9257736Z 
2023-01-11T21:41:26.9257837Z def call(args):
2023-01-11T21:41:26.9257953Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9258070Z     args.clear()
2023-01-11T21:41:26.9258419Z     buf0 = empty_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9258679Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9258788Z     del arg0_1
2023-01-11T21:41:26.9258893Z     del arg1_1
2023-01-11T21:41:26.9258990Z     return (buf0, )
2023-01-11T21:41:26.9258999Z 
2023-01-11T21:41:26.9259005Z 
2023-01-11T21:41:26.9259126Z if __name__ == "__main__":
2023-01-11T21:41:26.9259307Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9259503Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9259833Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9260177Z     arg1_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9260359Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9260367Z 
2023-01-11T21:41:26.9260378Z 
2023-01-11T21:41:26.9260528Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9260626Z import torch
2023-01-11T21:41:26.9260735Z import random
2023-01-11T21:41:26.9260919Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9261112Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9261120Z 
2023-01-11T21:41:26.9261244Z aten = torch.ops.aten
2023-01-11T21:41:26.9261456Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9261603Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9261611Z 
2023-01-11T21:41:26.9261617Z 
2023-01-11T21:41:26.9261835Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9262141Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9262337Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9262502Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9262662Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9262759Z {
2023-01-11T21:41:26.9262915Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9263012Z     {
2023-01-11T21:41:26.9263195Z         #pragma omp for 
2023-01-11T21:41:26.9263332Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.9263433Z         {
2023-01-11T21:41:26.9263650Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9263860Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9264068Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9264203Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9264336Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9264469Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9264571Z         }
2023-01-11T21:41:26.9264721Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9264853Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.9265026Z         {
2023-01-11T21:41:26.9265163Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9265279Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:41:26.9265438Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9265568Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9265700Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9265829Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9265929Z         }
2023-01-11T21:41:26.9266023Z     }
2023-01-11T21:41:26.9266103Z }
2023-01-11T21:41:26.9266241Z ''')
2023-01-11T21:41:26.9266251Z 
2023-01-11T21:41:26.9266258Z 
2023-01-11T21:41:26.9266398Z async_compile.wait(globals())
2023-01-11T21:41:26.9266515Z del async_compile
2023-01-11T21:41:26.9266523Z 
2023-01-11T21:41:26.9266634Z def call(args):
2023-01-11T21:41:26.9266753Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9266918Z     args.clear()
2023-01-11T21:41:26.9267272Z     buf0 = empty_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9267520Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9267628Z     del arg0_1
2023-01-11T21:41:26.9267732Z     del arg1_1
2023-01-11T21:41:26.9267847Z     return (buf0, )
2023-01-11T21:41:26.9267854Z 
2023-01-11T21:41:26.9267860Z 
2023-01-11T21:41:26.9267979Z if __name__ == "__main__":
2023-01-11T21:41:26.9268160Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9268359Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9268667Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9269010Z     arg1_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9269191Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9269203Z 
2023-01-11T21:41:26.9269306Z ok (16.844s)
2023-01-11T21:41:26.9270069Z   test_views2_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9270279Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9270711Z [2023-01-11 21:39:38,362] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 513
2023-01-11T21:41:26.9271152Z [2023-01-11 21:39:39,936] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 513
2023-01-11T21:41:26.9271857Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9272059Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9272483Z [2023-01-11 21:39:39,956] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 514
2023-01-11T21:41:26.9272912Z [2023-01-11 21:39:41,549] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 514
2023-01-11T21:41:26.9273612Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9273816Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9274240Z [2023-01-11 21:39:41,565] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 515
2023-01-11T21:41:26.9274756Z [2023-01-11 21:39:43,095] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 515
2023-01-11T21:41:26.9275451Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9275647Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9276070Z [2023-01-11 21:39:43,114] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 516
2023-01-11T21:41:26.9276557Z [2023-01-11 21:39:44,796] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 516
2023-01-11T21:41:26.9277262Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9277460Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9277889Z [2023-01-11 21:39:44,813] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 517
2023-01-11T21:41:26.9277898Z 
2023-01-11T21:41:26.9278036Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9278148Z import torch
2023-01-11T21:41:26.9278258Z import random
2023-01-11T21:41:26.9278445Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9278642Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9278651Z 
2023-01-11T21:41:26.9278776Z aten = torch.ops.aten
2023-01-11T21:41:26.9278995Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9279142Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9279151Z 
2023-01-11T21:41:26.9279157Z 
2023-01-11T21:41:26.9279364Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9279689Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9279879Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9280036Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9280135Z {
2023-01-11T21:41:26.9280292Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9280389Z     {
2023-01-11T21:41:26.9280496Z         #pragma omp for 
2023-01-11T21:41:26.9280625Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.9280723Z         {
2023-01-11T21:41:26.9280945Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9281159Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9281297Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9281441Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9281526Z         }
2023-01-11T21:41:26.9281676Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9281806Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.9281908Z         {
2023-01-11T21:41:26.9282039Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9282197Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9282330Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9282445Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9282544Z         }
2023-01-11T21:41:26.9282640Z     }
2023-01-11T21:41:26.9282734Z }
2023-01-11T21:41:26.9282864Z ''')
2023-01-11T21:41:26.9282873Z 
2023-01-11T21:41:26.9282879Z 
2023-01-11T21:41:26.9283027Z async_compile.wait(globals())
2023-01-11T21:41:26.9283141Z del async_compile
2023-01-11T21:41:26.9283149Z 
2023-01-11T21:41:26.9283330Z def call(args):
2023-01-11T21:41:26.9283425Z     arg0_1, = args
2023-01-11T21:41:26.9283533Z     args.clear()
2023-01-11T21:41:26.9283860Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9284072Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9284179Z     del arg0_1
2023-01-11T21:41:26.9284291Z     return (buf0, )
2023-01-11T21:41:26.9284299Z 
2023-01-11T21:41:26.9284305Z 
2023-01-11T21:41:26.9284422Z if __name__ == "__main__":
2023-01-11T21:41:26.9284589Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9284785Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9285134Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9285360Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9285372Z 
2023-01-11T21:41:26.9285377Z 
2023-01-11T21:41:26.9285528Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9285647Z import torch
2023-01-11T21:41:26.9285759Z import random
2023-01-11T21:41:26.9285943Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9286121Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9286129Z 
2023-01-11T21:41:26.9286252Z aten = torch.ops.aten
2023-01-11T21:41:26.9286462Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9286607Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9286615Z 
2023-01-11T21:41:26.9286621Z 
2023-01-11T21:41:26.9286844Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9287170Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9287361Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9287522Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9287601Z {
2023-01-11T21:41:26.9287753Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9287852Z     {
2023-01-11T21:41:26.9287973Z         #pragma omp for 
2023-01-11T21:41:26.9288101Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.9288201Z         {
2023-01-11T21:41:26.9288417Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9288617Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.9288752Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.9288960Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9289221Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9289368Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9289467Z         }
2023-01-11T21:41:26.9289620Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9289736Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:41:26.9289839Z         {
2023-01-11T21:41:26.9289972Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9290131Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.9290268Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.9290424Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:26.9290559Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9290673Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9290770Z         }
2023-01-11T21:41:26.9290868Z     }
2023-01-11T21:41:26.9290963Z }
2023-01-11T21:41:26.9291096Z ''')
2023-01-11T21:41:26.9291104Z 
2023-01-11T21:41:26.9291110Z 
2023-01-11T21:41:26.9291253Z async_compile.wait(globals())
2023-01-11T21:41:26.9291370Z del async_compile
2023-01-11T21:41:26.9291378Z 
2023-01-11T21:41:26.9291475Z def call(args):
2023-01-11T21:41:26.9291583Z     arg0_1, = args
2023-01-11T21:41:26.9291695Z     args.clear()
2023-01-11T21:41:26.9292022Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9292241Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9292348Z     del arg0_1
2023-01-11T21:41:26.9292568Z     return (buf0, )
2023-01-11T21:41:26.9292575Z 
2023-01-11T21:41:26.9292580Z 
2023-01-11T21:41:26.9292685Z if __name__ == "__main__":
2023-01-11T21:41:26.9292827Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9292993Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9293318Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9293504Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9293514Z 
2023-01-11T21:41:26.9293521Z 
2023-01-11T21:41:26.9293683Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9293803Z import torch
2023-01-11T21:41:26.9293927Z import random
2023-01-11T21:41:26.9294111Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9294322Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9294394Z 
2023-01-11T21:41:26.9294533Z aten = torch.ops.aten
2023-01-11T21:41:26.9294766Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9294930Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9294938Z 
2023-01-11T21:41:26.9294945Z 
2023-01-11T21:41:26.9295191Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9295549Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9295755Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9295925Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9296015Z {
2023-01-11T21:41:26.9296183Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9296288Z     {
2023-01-11T21:41:26.9296422Z         #pragma omp for 
2023-01-11T21:41:26.9296564Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9296674Z         {
2023-01-11T21:41:26.9296898Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9297134Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9297283Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9297442Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9297551Z         }
2023-01-11T21:41:26.9297716Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9297866Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9297971Z         {
2023-01-11T21:41:26.9298101Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9298268Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9298417Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9298553Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9298661Z         }
2023-01-11T21:41:26.9298767Z     }
2023-01-11T21:41:26.9298854Z }
2023-01-11T21:41:26.9299001Z ''')
2023-01-11T21:41:26.9299010Z 
2023-01-11T21:41:26.9299017Z 
2023-01-11T21:41:26.9299175Z async_compile.wait(globals())
2023-01-11T21:41:26.9299304Z del async_compile
2023-01-11T21:41:26.9299313Z 
2023-01-11T21:41:26.9299435Z def call(args):
2023-01-11T21:41:26.9299557Z     arg0_1, = args
2023-01-11T21:41:26.9299684Z     args.clear()
2023-01-11T21:41:26.9300048Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9300269Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9300383Z     del arg0_1
2023-01-11T21:41:26.9300503Z     return (buf0, )
2023-01-11T21:41:26.9300512Z 
2023-01-11T21:41:26.9300519Z 
2023-01-11T21:41:26.9300646Z if __name__ == "__main__":
2023-01-11T21:41:26.9300846Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9301058Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9301471Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9301661Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9301670Z 
2023-01-11T21:41:26.9301681Z 
2023-01-11T21:41:26.9301831Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9301951Z import torch
2023-01-11T21:41:26.9302159Z import random
2023-01-11T21:41:26.9302359Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9302569Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9302577Z 
2023-01-11T21:41:26.9302709Z aten = torch.ops.aten
2023-01-11T21:41:26.9302940Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9303086Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9303112Z 
2023-01-11T21:41:26.9303188Z 
2023-01-11T21:41:26.9303417Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9303771Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9303978Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9304148Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9304253Z {
2023-01-11T21:41:26.9304486Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9304595Z     {
2023-01-11T21:41:26.9304712Z         #pragma omp for 
2023-01-11T21:41:26.9304859Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9304966Z         {
2023-01-11T21:41:26.9305200Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9305436Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.9305584Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.9305817Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9305962Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9306104Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9306214Z         }
2023-01-11T21:41:26.9306379Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9306528Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9306637Z         {
2023-01-11T21:41:26.9306786Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9306942Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.9307092Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.9307264Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:26.9307409Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9307547Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9307657Z         }
2023-01-11T21:41:26.9307764Z     }
2023-01-11T21:41:26.9307850Z }
2023-01-11T21:41:26.9308001Z ''')
2023-01-11T21:41:26.9308011Z 
2023-01-11T21:41:26.9308018Z 
2023-01-11T21:41:26.9308176Z async_compile.wait(globals())
2023-01-11T21:41:26.9308304Z del async_compile
2023-01-11T21:41:26.9308313Z 
2023-01-11T21:41:26.9308437Z def call(args):
2023-01-11T21:41:26.9308559Z     arg0_1, = args
2023-01-11T21:41:26.9308686Z     args.clear()
2023-01-11T21:41:26.9309051Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9309274Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9309389Z     del arg0_1
2023-01-11T21:41:26.9309510Z     return (buf0, )
2023-01-11T21:41:26.9309523Z 
2023-01-11T21:41:26.9309530Z 
2023-01-11T21:41:26.9309657Z if __name__ == "__main__":
2023-01-11T21:41:26.9309855Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9310073Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9310488Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9310662Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9310686Z 
2023-01-11T21:41:26.9311164Z [2023-01-11 21:39:44,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 517
2023-01-11T21:41:26.9311945Z /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9312272Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9312742Z [2023-01-11 21:39:44,838] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 518
2023-01-11T21:41:26.9313223Z [2023-01-11 21:39:44,846] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 518
2023-01-11T21:41:26.9313233Z 
2023-01-11T21:41:26.9313395Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9313523Z import torch
2023-01-11T21:41:26.9313650Z import random
2023-01-11T21:41:26.9313850Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9314047Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9314056Z 
2023-01-11T21:41:26.9314188Z aten = torch.ops.aten
2023-01-11T21:41:26.9314475Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9314643Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9314653Z 
2023-01-11T21:41:26.9314664Z 
2023-01-11T21:41:26.9314907Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9315263Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9315471Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9315644Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9315734Z {
2023-01-11T21:41:26.9315903Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9316010Z     {
2023-01-11T21:41:26.9316144Z         #pragma omp for 
2023-01-11T21:41:26.9316286Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9316397Z         {
2023-01-11T21:41:26.9316632Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9316851Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9317003Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9317161Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9317277Z         }
2023-01-11T21:41:26.9317441Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9317587Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9317695Z         {
2023-01-11T21:41:26.9317829Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9318000Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9318146Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9318284Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9318398Z         }
2023-01-11T21:41:26.9318500Z     }
2023-01-11T21:41:26.9318600Z }
2023-01-11T21:41:26.9318733Z ''')
2023-01-11T21:41:26.9318741Z 
2023-01-11T21:41:26.9318748Z 
2023-01-11T21:41:26.9318901Z async_compile.wait(globals())
2023-01-11T21:41:26.9319027Z del async_compile
2023-01-11T21:41:26.9319036Z 
2023-01-11T21:41:26.9319157Z def call(args):
2023-01-11T21:41:26.9319280Z     arg0_1, = args
2023-01-11T21:41:26.9319405Z     args.clear()
2023-01-11T21:41:26.9319790Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9320013Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9320134Z     del arg0_1
2023-01-11T21:41:26.9320258Z     return (buf0, )
2023-01-11T21:41:26.9320267Z 
2023-01-11T21:41:26.9320274Z 
2023-01-11T21:41:26.9320402Z if __name__ == "__main__":
2023-01-11T21:41:26.9320603Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9320816Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9321180Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9321367Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9321376Z 
2023-01-11T21:41:26.9321383Z 
2023-01-11T21:41:26.9321546Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9321651Z import torch
2023-01-11T21:41:26.9321776Z import random
2023-01-11T21:41:26.9321979Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9322268Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9322276Z 
2023-01-11T21:41:26.9322409Z aten = torch.ops.aten
2023-01-11T21:41:26.9322640Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9322798Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9322807Z 
2023-01-11T21:41:26.9322813Z 
2023-01-11T21:41:26.9323044Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9323403Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9323609Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9323779Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9323883Z {
2023-01-11T21:41:26.9324054Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9324158Z     {
2023-01-11T21:41:26.9324335Z         #pragma omp for 
2023-01-11T21:41:26.9324490Z         for(long i0=0; i0<125; i0+=1)
2023-01-11T21:41:26.9324597Z         {
2023-01-11T21:41:26.9324839Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9325071Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:41:26.9325219Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.9325452Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9325597Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9325742Z             tmp4.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9325850Z         }
2023-01-11T21:41:26.9326014Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9326162Z         for(long i0=1000; i0<1000; i0+=1)
2023-01-11T21:41:26.9326272Z         {
2023-01-11T21:41:26.9326416Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9326591Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:41:26.9326725Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:41:26.9326893Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:41:26.9327043Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:41:26.9327181Z             out_ptr0[i0] = tmp4;
2023-01-11T21:41:26.9327290Z         }
2023-01-11T21:41:26.9327394Z     }
2023-01-11T21:41:26.9327484Z }
2023-01-11T21:41:26.9327629Z ''')
2023-01-11T21:41:26.9327637Z 
2023-01-11T21:41:26.9327643Z 
2023-01-11T21:41:26.9327799Z async_compile.wait(globals())
2023-01-11T21:41:26.9327923Z del async_compile
2023-01-11T21:41:26.9327932Z 
2023-01-11T21:41:26.9328064Z def call(args):
2023-01-11T21:41:26.9328183Z     arg0_1, = args
2023-01-11T21:41:26.9328305Z     args.clear()
2023-01-11T21:41:26.9328681Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9328895Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9329137Z     del arg0_1
2023-01-11T21:41:26.9329268Z     return (buf0, )
2023-01-11T21:41:26.9329277Z 
2023-01-11T21:41:26.9329283Z 
2023-01-11T21:41:26.9329412Z if __name__ == "__main__":
2023-01-11T21:41:26.9329615Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9329828Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9330199Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9330385Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9330393Z 
2023-01-11T21:41:26.9330491Z ok (6.501s)
2023-01-11T21:41:26.9331324Z   test_views3_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9331547Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9332019Z [2023-01-11 21:39:44,907] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 519
2023-01-11T21:41:26.9332606Z [2023-01-11 21:39:46,486] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 519
2023-01-11T21:41:26.9332616Z 
2023-01-11T21:41:26.9332781Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9332902Z import torch
2023-01-11T21:41:26.9333027Z import random
2023-01-11T21:41:26.9333227Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9333421Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9333430Z 
2023-01-11T21:41:26.9333566Z aten = torch.ops.aten
2023-01-11T21:41:26.9333802Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9333964Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9333972Z 
2023-01-11T21:41:26.9333979Z 
2023-01-11T21:41:26.9334291Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9334653Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9334863Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:41:26.9335042Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9335199Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9335301Z {
2023-01-11T21:41:26.9335473Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9335579Z     {
2023-01-11T21:41:26.9335711Z         #pragma omp for 
2023-01-11T21:41:26.9335855Z         for(long i0=0; i0<744; i0+=1)
2023-01-11T21:41:26.9335966Z         {
2023-01-11T21:41:26.9336090Z             #pragma GCC ivdep
2023-01-11T21:41:26.9336235Z             for(long i1=0; i1<192; i1+=1)
2023-01-11T21:41:26.9336346Z             {
2023-01-11T21:41:26.9336462Z                 {
2023-01-11T21:41:26.9336577Z                     {
2023-01-11T21:41:26.9336758Z                         auto tmp0 = in_ptr0[(3*i0) + (i1 / 64)];
2023-01-11T21:41:26.9336946Z                         auto tmp1 = in_ptr1[(64*tmp0) + (i1 % 64)];
2023-01-11T21:41:26.9337101Z                         out_ptr0[i1 + (192*i0)] = tmp1;
2023-01-11T21:41:26.9337216Z                     }
2023-01-11T21:41:26.9337326Z                 }
2023-01-11T21:41:26.9337435Z             }
2023-01-11T21:41:26.9337545Z         }
2023-01-11T21:41:26.9337653Z     }
2023-01-11T21:41:26.9337742Z }
2023-01-11T21:41:26.9337888Z ''')
2023-01-11T21:41:26.9337897Z 
2023-01-11T21:41:26.9337903Z 
2023-01-11T21:41:26.9338061Z async_compile.wait(globals())
2023-01-11T21:41:26.9338186Z del async_compile
2023-01-11T21:41:26.9338194Z 
2023-01-11T21:41:26.9338313Z def call(args):
2023-01-11T21:41:26.9338443Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9338563Z     args.clear()
2023-01-11T21:41:26.9338978Z     buf0 = empty_strided((1, 12, 62, 192), (142848, 11904, 192, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9339247Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9339363Z     del arg0_1
2023-01-11T21:41:26.9339481Z     del arg1_1
2023-01-11T21:41:26.9339608Z     return (buf0, )
2023-01-11T21:41:26.9339617Z 
2023-01-11T21:41:26.9339623Z 
2023-01-11T21:41:26.9339753Z if __name__ == "__main__":
2023-01-11T21:41:26.9339950Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9340165Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9340533Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9340866Z     arg1_1 = rand_strided((2232, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:41:26.9341069Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9341078Z 
2023-01-11T21:41:26.9341191Z ok (1.641s)
2023-01-11T21:41:26.9341827Z   test_zero_dim_reductions_cpu (__main__.CpuTests) ... [2023-01-11 21:39:46,550] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 520
2023-01-11T21:41:26.9342305Z [2023-01-11 21:39:48,166] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 520
2023-01-11T21:41:26.9342806Z [2023-01-11 21:39:48,231] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 521
2023-01-11T21:41:26.9343247Z [2023-01-11 21:39:48,238] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 521
2023-01-11T21:41:26.9343254Z 
2023-01-11T21:41:26.9343386Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9343478Z import torch
2023-01-11T21:41:26.9343563Z import random
2023-01-11T21:41:26.9343718Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9343879Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9343886Z 
2023-01-11T21:41:26.9343993Z aten = torch.ops.aten
2023-01-11T21:41:26.9344167Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9344336Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9344344Z 
2023-01-11T21:41:26.9344349Z 
2023-01-11T21:41:26.9344538Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9344789Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9344936Z extern "C" void kernel(bool* __restrict__ out_ptr0)
2023-01-11T21:41:26.9345018Z {
2023-01-11T21:41:26.9345121Z     #pragma GCC ivdep
2023-01-11T21:41:26.9345227Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.9345308Z     {
2023-01-11T21:41:26.9345392Z         {
2023-01-11T21:41:26.9345461Z             {
2023-01-11T21:41:26.9345597Z                 auto tmp0 = static_cast<bool>(false);
2023-01-11T21:41:26.9345708Z                 auto tmp1 = tmp0 == 0;
2023-01-11T21:41:26.9345819Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.9345904Z             }
2023-01-11T21:41:26.9345986Z         }
2023-01-11T21:41:26.9346066Z     }
2023-01-11T21:41:26.9346130Z }
2023-01-11T21:41:26.9346234Z ''')
2023-01-11T21:41:26.9346241Z 
2023-01-11T21:41:26.9346249Z 
2023-01-11T21:41:26.9346369Z async_compile.wait(globals())
2023-01-11T21:41:26.9346468Z del async_compile
2023-01-11T21:41:26.9346478Z 
2023-01-11T21:41:26.9346570Z def call(args):
2023-01-11T21:41:26.9346661Z     arg0_1, = args
2023-01-11T21:41:26.9346758Z     args.clear()
2023-01-11T21:41:26.9347002Z     buf0 = empty_strided((2, 1), (1, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.9347138Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9347233Z     return (buf0, )
2023-01-11T21:41:26.9347240Z 
2023-01-11T21:41:26.9347245Z 
2023-01-11T21:41:26.9347343Z if __name__ == "__main__":
2023-01-11T21:41:26.9347491Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9347653Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9347920Z     arg0_1 = rand_strided((2, 0), (1, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.9348062Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9348069Z 
2023-01-11T21:41:26.9348078Z 
2023-01-11T21:41:26.9348200Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9348279Z import torch
2023-01-11T21:41:26.9348373Z import random
2023-01-11T21:41:26.9348525Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9348682Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9348688Z 
2023-01-11T21:41:26.9348789Z aten = torch.ops.aten
2023-01-11T21:41:26.9348963Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9349084Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9349090Z 
2023-01-11T21:41:26.9349095Z 
2023-01-11T21:41:26.9349261Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9349524Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9349671Z extern "C" void kernel(bool* __restrict__ out_ptr0)
2023-01-11T21:41:26.9349751Z {
2023-01-11T21:41:26.9349850Z     #pragma GCC ivdep
2023-01-11T21:41:26.9349959Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:41:26.9350040Z     {
2023-01-11T21:41:26.9350109Z         {
2023-01-11T21:41:26.9350193Z             {
2023-01-11T21:41:26.9350377Z                 auto tmp0 = static_cast<bool>(false);
2023-01-11T21:41:26.9350489Z                 auto tmp1 = tmp0 == 0;
2023-01-11T21:41:26.9350601Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:41:26.9350686Z             }
2023-01-11T21:41:26.9350769Z         }
2023-01-11T21:41:26.9350834Z     }
2023-01-11T21:41:26.9350912Z }
2023-01-11T21:41:26.9351024Z ''')
2023-01-11T21:41:26.9351031Z 
2023-01-11T21:41:26.9351036Z 
2023-01-11T21:41:26.9351160Z async_compile.wait(globals())
2023-01-11T21:41:26.9351255Z del async_compile
2023-01-11T21:41:26.9351262Z 
2023-01-11T21:41:26.9351352Z def call(args):
2023-01-11T21:41:26.9351443Z     arg0_1, = args
2023-01-11T21:41:26.9351524Z     args.clear()
2023-01-11T21:41:26.9351776Z     buf0 = empty_strided((2, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:41:26.9351942Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9352042Z     return (buf0, )
2023-01-11T21:41:26.9352049Z 
2023-01-11T21:41:26.9352054Z 
2023-01-11T21:41:26.9352157Z if __name__ == "__main__":
2023-01-11T21:41:26.9352306Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9352466Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9352733Z     arg0_1 = rand_strided((2, 0), (1, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:41:26.9352859Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9352866Z 
2023-01-11T21:41:26.9352953Z ok (1.751s)
2023-01-11T21:41:26.9353566Z   test_zeros_cpu (__main__.CpuTests) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9353733Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9354085Z [2023-01-11 21:39:48,348] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 522
2023-01-11T21:41:26.9354448Z [2023-01-11 21:39:49,959] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 522
2023-01-11T21:41:26.9354455Z 
2023-01-11T21:41:26.9354578Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9354670Z import torch
2023-01-11T21:41:26.9354763Z import random
2023-01-11T21:41:26.9354899Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9355056Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9355063Z 
2023-01-11T21:41:26.9355166Z aten = torch.ops.aten
2023-01-11T21:41:26.9355341Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9355462Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9355469Z 
2023-01-11T21:41:26.9355477Z 
2023-01-11T21:41:26.9355659Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9355919Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9356082Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9356198Z                        float* __restrict__ out_ptr0,
2023-01-11T21:41:26.9356330Z                        float* __restrict__ out_ptr1,
2023-01-11T21:41:26.9356455Z                        float* __restrict__ out_ptr2,
2023-01-11T21:41:26.9356580Z                        float* __restrict__ out_ptr3,
2023-01-11T21:41:26.9356704Z                        float* __restrict__ out_ptr4,
2023-01-11T21:41:26.9356826Z                        float* __restrict__ out_ptr5)
2023-01-11T21:41:26.9356906Z {
2023-01-11T21:41:26.9357020Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9357101Z     {
2023-01-11T21:41:26.9357205Z         #pragma omp for 
2023-01-11T21:41:26.9357316Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9357400Z         {
2023-01-11T21:41:26.9357583Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9357812Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:41:26.9357925Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9358033Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9358153Z             tmp2.store(out_ptr1 + 8*i0);
2023-01-11T21:41:26.9358235Z         }
2023-01-11T21:41:26.9358360Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9358467Z         for(long i0=8; i0<8; i0+=1)
2023-01-11T21:41:26.9358551Z         {
2023-01-11T21:41:26.9358649Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9358780Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:41:26.9358890Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9358998Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9359101Z             out_ptr1[i0] = tmp2;
2023-01-11T21:41:26.9359220Z         }
2023-01-11T21:41:26.9359328Z         #pragma omp for 
2023-01-11T21:41:26.9359423Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:26.9359509Z         {
2023-01-11T21:41:26.9359685Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:26.9359807Z             tmp0.store(out_ptr2 + 8*i0);
2023-01-11T21:41:26.9359891Z         }
2023-01-11T21:41:26.9360014Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9360130Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:41:26.9360198Z         {
2023-01-11T21:41:26.9360325Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:26.9360429Z             out_ptr2[i0] = tmp0;
2023-01-11T21:41:26.9360511Z         }
2023-01-11T21:41:26.9360611Z         #pragma omp for 
2023-01-11T21:41:26.9360718Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:41:26.9360800Z         {
2023-01-11T21:41:26.9360960Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:41:26.9361083Z             tmp0.store(out_ptr3 + 8*i0);
2023-01-11T21:41:26.9361168Z         }
2023-01-11T21:41:26.9361292Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9361412Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:41:26.9361495Z         {
2023-01-11T21:41:26.9361627Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:26.9361718Z             out_ptr3[i0] = tmp0;
2023-01-11T21:41:26.9361798Z         }
2023-01-11T21:41:26.9361898Z         #pragma omp for 
2023-01-11T21:41:26.9362008Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.9362091Z         {
2023-01-11T21:41:26.9362176Z             {
2023-01-11T21:41:26.9362261Z                 {
2023-01-11T21:41:26.9362384Z                     auto tmp0 = static_cast<float>(0);
2023-01-11T21:41:26.9362495Z                     out_ptr4[i0] = tmp0;
2023-01-11T21:41:26.9362582Z                 }
2023-01-11T21:41:26.9362666Z             }
2023-01-11T21:41:26.9362749Z         }
2023-01-11T21:41:26.9362854Z         #pragma omp for 
2023-01-11T21:41:26.9362946Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:41:26.9363028Z         {
2023-01-11T21:41:26.9363118Z             {
2023-01-11T21:41:26.9363204Z                 {
2023-01-11T21:41:26.9363344Z                     auto tmp0 = static_cast<float>(3.1416);
2023-01-11T21:41:26.9363455Z                     out_ptr5[i0] = tmp0;
2023-01-11T21:41:26.9363540Z                 }
2023-01-11T21:41:26.9363609Z             }
2023-01-11T21:41:26.9363689Z         }
2023-01-11T21:41:26.9363770Z     }
2023-01-11T21:41:26.9363848Z }
2023-01-11T21:41:26.9363955Z ''')
2023-01-11T21:41:26.9363963Z 
2023-01-11T21:41:26.9363968Z 
2023-01-11T21:41:26.9364085Z async_compile.wait(globals())
2023-01-11T21:41:26.9364182Z del async_compile
2023-01-11T21:41:26.9364189Z 
2023-01-11T21:41:26.9364267Z def call(args):
2023-01-11T21:41:26.9364357Z     arg0_1, = args
2023-01-11T21:41:26.9364451Z     args.clear()
2023-01-11T21:41:26.9364716Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9364975Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9365318Z     buf1 = empty_strided((1, 8, 64, 64), (32768, 4096, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9365618Z     buf2 = empty_strided((1, 8, 64, 64), (32768, 4096, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9365880Z     buf3 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9366132Z     buf5 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9366472Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:41:26.9366562Z     del arg0_1
2023-01-11T21:41:26.9366696Z     return (buf0, buf1, buf2, buf3, buf4, buf5, )
2023-01-11T21:41:26.9366704Z 
2023-01-11T21:41:26.9366709Z 
2023-01-11T21:41:26.9366839Z if __name__ == "__main__":
2023-01-11T21:41:26.9366993Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9367158Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9367417Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9367544Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:41:26.9367564Z 
2023-01-11T21:41:26.9367640Z ok (1.723s)
2023-01-11T21:41:26.9367802Z   test_print_pow (__main__.ExprPrinterTests) ... ok (0.004s)
2023-01-11T21:41:26.9368456Z   test_cpu_broadcast1_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9368623Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9368976Z [2023-01-11 21:39:49,980] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 523
2023-01-11T21:41:26.9369465Z [2023-01-11 21:39:49,987] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 523
2023-01-11T21:41:26.9369472Z 
2023-01-11T21:41:26.9369594Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9369686Z import torch
2023-01-11T21:41:26.9369780Z import random
2023-01-11T21:41:26.9369919Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9370076Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9370082Z 
2023-01-11T21:41:26.9370183Z aten = torch.ops.aten
2023-01-11T21:41:26.9370358Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9370480Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9370487Z 
2023-01-11T21:41:26.9370492Z 
2023-01-11T21:41:26.9370679Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9370938Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9371100Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9371225Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9371353Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9371431Z {
2023-01-11T21:41:26.9371557Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9371636Z     {
2023-01-11T21:41:26.9371738Z         #pragma omp for 
2023-01-11T21:41:26.9371846Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9371915Z         {
2023-01-11T21:41:26.9372091Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9372263Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9372374Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9372495Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9372577Z         }
2023-01-11T21:41:26.9372700Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9372856Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9372939Z         {
2023-01-11T21:41:26.9373049Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9373158Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9373268Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9373374Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9373456Z         }
2023-01-11T21:41:26.9373525Z     }
2023-01-11T21:41:26.9373603Z }
2023-01-11T21:41:26.9373711Z ''')
2023-01-11T21:41:26.9373717Z 
2023-01-11T21:41:26.9373722Z 
2023-01-11T21:41:26.9373838Z async_compile.wait(globals())
2023-01-11T21:41:26.9373937Z del async_compile
2023-01-11T21:41:26.9373943Z 
2023-01-11T21:41:26.9374035Z def call(args):
2023-01-11T21:41:26.9374137Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9374218Z     args.clear()
2023-01-11T21:41:26.9374523Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9374742Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9374836Z     del arg0_1
2023-01-11T21:41:26.9374924Z     del arg1_1
2023-01-11T21:41:26.9375019Z     return (buf0, )
2023-01-11T21:41:26.9375025Z 
2023-01-11T21:41:26.9375031Z 
2023-01-11T21:41:26.9375128Z if __name__ == "__main__":
2023-01-11T21:41:26.9375262Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9375424Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9375688Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9375953Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9376104Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9376110Z 
2023-01-11T21:41:26.9376198Z ok (0.021s)
2023-01-11T21:41:26.9376850Z   test_cpu_broadcast1_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9377021Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9377372Z [2023-01-11 21:39:50,000] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 524
2023-01-11T21:41:26.9377734Z [2023-01-11 21:39:51,535] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 524
2023-01-11T21:41:26.9377742Z 
2023-01-11T21:41:26.9377851Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9377944Z import torch
2023-01-11T21:41:26.9378039Z import random
2023-01-11T21:41:26.9378192Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9378354Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9378361Z 
2023-01-11T21:41:26.9378467Z aten = torch.ops.aten
2023-01-11T21:41:26.9378644Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9378749Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9378771Z 
2023-01-11T21:41:26.9378776Z 
2023-01-11T21:41:26.9378945Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9379205Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9379362Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9379497Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9379626Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9379708Z {
2023-01-11T21:41:26.9379836Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9379903Z     {
2023-01-11T21:41:26.9380007Z         #pragma omp for 
2023-01-11T21:41:26.9380117Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9380198Z         {
2023-01-11T21:41:26.9380349Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9380433Z             {
2023-01-11T21:41:26.9380610Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:26.9380763Z                 auto tmp1 = at::vec::Vectorized<float>(in_ptr1[i0]);
2023-01-11T21:41:26.9380878Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9381011Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9381095Z             }
2023-01-11T21:41:26.9381217Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9381328Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9381411Z             {
2023-01-11T21:41:26.9381509Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9381620Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9381730Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9381882Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9381969Z             }
2023-01-11T21:41:26.9382058Z         }
2023-01-11T21:41:26.9382140Z     }
2023-01-11T21:41:26.9382206Z }
2023-01-11T21:41:26.9382313Z ''')
2023-01-11T21:41:26.9382319Z 
2023-01-11T21:41:26.9382324Z 
2023-01-11T21:41:26.9382440Z async_compile.wait(globals())
2023-01-11T21:41:26.9382536Z del async_compile
2023-01-11T21:41:26.9382544Z 
2023-01-11T21:41:26.9382635Z def call(args):
2023-01-11T21:41:26.9382735Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9382828Z     args.clear()
2023-01-11T21:41:26.9383099Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9383405Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9383494Z     del arg0_1
2023-01-11T21:41:26.9383582Z     del arg1_1
2023-01-11T21:41:26.9383676Z     return (buf0, )
2023-01-11T21:41:26.9383682Z 
2023-01-11T21:41:26.9383690Z 
2023-01-11T21:41:26.9383790Z if __name__ == "__main__":
2023-01-11T21:41:26.9383941Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9384107Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9384357Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9384638Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9384791Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9384798Z 
2023-01-11T21:41:26.9384885Z ok (1.549s)
2023-01-11T21:41:26.9385542Z   test_cpu_broadcast1_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9385708Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9386063Z [2023-01-11 21:39:51,550] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 525
2023-01-11T21:41:26.9386424Z [2023-01-11 21:39:53,053] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 525
2023-01-11T21:41:26.9386431Z 
2023-01-11T21:41:26.9386555Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9386649Z import torch
2023-01-11T21:41:26.9386733Z import random
2023-01-11T21:41:26.9386884Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9387041Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9387048Z 
2023-01-11T21:41:26.9387150Z aten = torch.ops.aten
2023-01-11T21:41:26.9387325Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9387447Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9387457Z 
2023-01-11T21:41:26.9387462Z 
2023-01-11T21:41:26.9387644Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9387904Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9388088Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9388226Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9388354Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9388433Z {
2023-01-11T21:41:26.9388562Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9388641Z     {
2023-01-11T21:41:26.9388744Z         #pragma omp for 
2023-01-11T21:41:26.9388850Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9388931Z         {
2023-01-11T21:41:26.9389070Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9389188Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:26.9389308Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9389399Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9389461Z         }
2023-01-11T21:41:26.9389544Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9389623Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9389683Z         {
2023-01-11T21:41:26.9389767Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9389847Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.9389927Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9390004Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9390052Z         }
2023-01-11T21:41:26.9390110Z     }
2023-01-11T21:41:26.9390167Z }
2023-01-11T21:41:26.9390250Z ''')
2023-01-11T21:41:26.9390255Z 
2023-01-11T21:41:26.9390259Z 
2023-01-11T21:41:26.9390347Z async_compile.wait(globals())
2023-01-11T21:41:26.9390417Z del async_compile
2023-01-11T21:41:26.9390422Z 
2023-01-11T21:41:26.9390489Z def call(args):
2023-01-11T21:41:26.9390550Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9390620Z     args.clear()
2023-01-11T21:41:26.9390813Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9390976Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9391043Z     del arg0_1
2023-01-11T21:41:26.9391108Z     del arg1_1
2023-01-11T21:41:26.9391178Z     return (buf0, )
2023-01-11T21:41:26.9391183Z 
2023-01-11T21:41:26.9391188Z 
2023-01-11T21:41:26.9391249Z if __name__ == "__main__":
2023-01-11T21:41:26.9391361Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9391480Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9391672Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9391860Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9391972Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9391977Z 
2023-01-11T21:41:26.9392044Z ok (1.518s)
2023-01-11T21:41:26.9392534Z   test_cpu_broadcast1_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9392661Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9392916Z [2023-01-11 21:39:53,069] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 526
2023-01-11T21:41:26.9393167Z [2023-01-11 21:39:54,606] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 526
2023-01-11T21:41:26.9393172Z 
2023-01-11T21:41:26.9393264Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9393331Z import torch
2023-01-11T21:41:26.9393400Z import random
2023-01-11T21:41:26.9393514Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9393632Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9393665Z 
2023-01-11T21:41:26.9393742Z aten = torch.ops.aten
2023-01-11T21:41:26.9393860Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9393949Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9393954Z 
2023-01-11T21:41:26.9393958Z 
2023-01-11T21:41:26.9394091Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9394291Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9394405Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9394507Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9394602Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9394660Z {
2023-01-11T21:41:26.9394743Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9394832Z     {
2023-01-11T21:41:26.9394909Z         #pragma omp for 
2023-01-11T21:41:26.9394989Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9395051Z         {
2023-01-11T21:41:26.9395131Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9395194Z             {
2023-01-11T21:41:26.9395316Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:26.9395455Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9395541Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9395641Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9395702Z             }
2023-01-11T21:41:26.9395791Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9395873Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9395922Z             {
2023-01-11T21:41:26.9396005Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9396100Z                 auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9396183Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9396273Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9396336Z             }
2023-01-11T21:41:26.9396398Z         }
2023-01-11T21:41:26.9396445Z     }
2023-01-11T21:41:26.9396501Z }
2023-01-11T21:41:26.9396581Z ''')
2023-01-11T21:41:26.9396585Z 
2023-01-11T21:41:26.9396589Z 
2023-01-11T21:41:26.9396676Z async_compile.wait(globals())
2023-01-11T21:41:26.9396747Z del async_compile
2023-01-11T21:41:26.9396753Z 
2023-01-11T21:41:26.9396821Z def call(args):
2023-01-11T21:41:26.9396894Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9396952Z     args.clear()
2023-01-11T21:41:26.9397148Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9397306Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9397373Z     del arg0_1
2023-01-11T21:41:26.9397438Z     del arg1_1
2023-01-11T21:41:26.9397509Z     return (buf0, )
2023-01-11T21:41:26.9397514Z 
2023-01-11T21:41:26.9397518Z 
2023-01-11T21:41:26.9397592Z if __name__ == "__main__":
2023-01-11T21:41:26.9397705Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9397814Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9398004Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9398199Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9398312Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9398317Z 
2023-01-11T21:41:26.9398380Z ok (1.553s)
2023-01-11T21:41:26.9398873Z   test_cpu_broadcast1_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9398998Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9399292Z [2023-01-11 21:39:54,621] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 527
2023-01-11T21:41:26.9399556Z [2023-01-11 21:39:56,162] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 527
2023-01-11T21:41:26.9399562Z 
2023-01-11T21:41:26.9399654Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9399711Z import torch
2023-01-11T21:41:26.9399778Z import random
2023-01-11T21:41:26.9399889Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9400008Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9400013Z 
2023-01-11T21:41:26.9400088Z aten = torch.ops.aten
2023-01-11T21:41:26.9400218Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9400335Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9400340Z 
2023-01-11T21:41:26.9400344Z 
2023-01-11T21:41:26.9400479Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9400672Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9400788Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9400890Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:26.9400989Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9401047Z {
2023-01-11T21:41:26.9401142Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9401201Z     {
2023-01-11T21:41:26.9401263Z         #pragma omp for 
2023-01-11T21:41:26.9401343Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9401404Z         {
2023-01-11T21:41:26.9401481Z             #pragma GCC ivdep
2023-01-11T21:41:26.9401564Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9401626Z             {
2023-01-11T21:41:26.9401678Z                 {
2023-01-11T21:41:26.9401742Z                     {
2023-01-11T21:41:26.9401834Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9401936Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9402045Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.9402136Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9402230Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9402295Z                     }
2023-01-11T21:41:26.9402345Z                 }
2023-01-11T21:41:26.9402405Z             }
2023-01-11T21:41:26.9402464Z         }
2023-01-11T21:41:26.9402522Z     }
2023-01-11T21:41:26.9402579Z }
2023-01-11T21:41:26.9402656Z ''')
2023-01-11T21:41:26.9402662Z 
2023-01-11T21:41:26.9402666Z 
2023-01-11T21:41:26.9402753Z async_compile.wait(globals())
2023-01-11T21:41:26.9402811Z del async_compile
2023-01-11T21:41:26.9402816Z 
2023-01-11T21:41:26.9402885Z def call(args):
2023-01-11T21:41:26.9402958Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9403027Z     args.clear()
2023-01-11T21:41:26.9403229Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9403391Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9403458Z     del arg0_1
2023-01-11T21:41:26.9403511Z     del arg1_1
2023-01-11T21:41:26.9403580Z     return (buf0, )
2023-01-11T21:41:26.9403585Z 
2023-01-11T21:41:26.9403589Z 
2023-01-11T21:41:26.9403662Z if __name__ == "__main__":
2023-01-11T21:41:26.9403773Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9403897Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9404088Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9404285Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9404399Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9404403Z 
2023-01-11T21:41:26.9404456Z ok (1.555s)
2023-01-11T21:41:26.9404985Z   test_cpu_broadcast1_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9405111Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9405367Z [2023-01-11 21:39:56,176] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 528
2023-01-11T21:41:26.9405629Z [2023-01-11 21:39:57,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 528
2023-01-11T21:41:26.9405634Z 
2023-01-11T21:41:26.9405754Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9405824Z import torch
2023-01-11T21:41:26.9405892Z import random
2023-01-11T21:41:26.9406005Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9406113Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9406120Z 
2023-01-11T21:41:26.9406194Z aten = torch.ops.aten
2023-01-11T21:41:26.9406325Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9406413Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9406418Z 
2023-01-11T21:41:26.9406423Z 
2023-01-11T21:41:26.9406555Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9406756Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9406872Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9406972Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.9407056Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9407115Z {
2023-01-11T21:41:26.9407211Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9407270Z     {
2023-01-11T21:41:26.9407345Z         #pragma omp for 
2023-01-11T21:41:26.9407427Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9407487Z         {
2023-01-11T21:41:26.9407537Z             {
2023-01-11T21:41:26.9407599Z                 {
2023-01-11T21:41:26.9407688Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9407777Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9407881Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.9407970Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9408053Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.9408106Z                 }
2023-01-11T21:41:26.9408165Z             }
2023-01-11T21:41:26.9408224Z         }
2023-01-11T21:41:26.9408282Z     }
2023-01-11T21:41:26.9408338Z }
2023-01-11T21:41:26.9408417Z ''')
2023-01-11T21:41:26.9408422Z 
2023-01-11T21:41:26.9408426Z 
2023-01-11T21:41:26.9408514Z async_compile.wait(globals())
2023-01-11T21:41:26.9408573Z del async_compile
2023-01-11T21:41:26.9408578Z 
2023-01-11T21:41:26.9408647Z def call(args):
2023-01-11T21:41:26.9408720Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9408788Z     args.clear()
2023-01-11T21:41:26.9408982Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9409330Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9409434Z     del arg0_1
2023-01-11T21:41:26.9409501Z     del arg1_1
2023-01-11T21:41:26.9409574Z     return (buf0, )
2023-01-11T21:41:26.9409579Z 
2023-01-11T21:41:26.9409583Z 
2023-01-11T21:41:26.9409658Z if __name__ == "__main__":
2023-01-11T21:41:26.9409772Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9409893Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9410092Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9410281Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9410456Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9410462Z 
2023-01-11T21:41:26.9410514Z ok (1.571s)
2023-01-11T21:41:26.9411002Z   test_cpu_broadcast1_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9411129Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9411389Z [2023-01-11 21:39:57,756] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 529
2023-01-11T21:41:26.9411689Z [2023-01-11 21:39:59,473] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 529
2023-01-11T21:41:26.9411695Z 
2023-01-11T21:41:26.9411792Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9411861Z import torch
2023-01-11T21:41:26.9411930Z import random
2023-01-11T21:41:26.9412044Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9412151Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9412156Z 
2023-01-11T21:41:26.9412232Z aten = torch.ops.aten
2023-01-11T21:41:26.9412364Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9412454Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9412460Z 
2023-01-11T21:41:26.9412464Z 
2023-01-11T21:41:26.9412600Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9412802Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9412919Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9413025Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9413111Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9413173Z {
2023-01-11T21:41:26.9413269Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9413330Z     {
2023-01-11T21:41:26.9413407Z         #pragma omp for 
2023-01-11T21:41:26.9413488Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9413550Z         {
2023-01-11T21:41:26.9413618Z             #pragma GCC ivdep
2023-01-11T21:41:26.9413702Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9413765Z             {
2023-01-11T21:41:26.9413828Z                 {
2023-01-11T21:41:26.9413894Z                     {
2023-01-11T21:41:26.9413987Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9414092Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9414174Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9414271Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9414336Z                     }
2023-01-11T21:41:26.9414398Z                 }
2023-01-11T21:41:26.9414461Z             }
2023-01-11T21:41:26.9414521Z         }
2023-01-11T21:41:26.9414567Z     }
2023-01-11T21:41:26.9414625Z }
2023-01-11T21:41:26.9414704Z ''')
2023-01-11T21:41:26.9414709Z 
2023-01-11T21:41:26.9414713Z 
2023-01-11T21:41:26.9414799Z async_compile.wait(globals())
2023-01-11T21:41:26.9414868Z del async_compile
2023-01-11T21:41:26.9414873Z 
2023-01-11T21:41:26.9414941Z def call(args):
2023-01-11T21:41:26.9415013Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9415083Z     args.clear()
2023-01-11T21:41:26.9415269Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9415432Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9415497Z     del arg0_1
2023-01-11T21:41:26.9415562Z     del arg1_1
2023-01-11T21:41:26.9415631Z     return (buf0, )
2023-01-11T21:41:26.9415639Z 
2023-01-11T21:41:26.9415643Z 
2023-01-11T21:41:26.9415717Z if __name__ == "__main__":
2023-01-11T21:41:26.9415827Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9415966Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9416163Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9416357Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9416470Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9416475Z 
2023-01-11T21:41:26.9416541Z ok (1.741s)
2023-01-11T21:41:26.9417067Z   test_cpu_broadcast1_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9417195Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9417456Z [2023-01-11 21:39:59,496] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 530
2023-01-11T21:41:26.9417717Z [2023-01-11 21:40:01,048] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 530
2023-01-11T21:41:26.9417723Z 
2023-01-11T21:41:26.9417815Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9417870Z import torch
2023-01-11T21:41:26.9417938Z import random
2023-01-11T21:41:26.9418049Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9418173Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9418178Z 
2023-01-11T21:41:26.9418254Z aten = torch.ops.aten
2023-01-11T21:41:26.9418385Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9418474Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9418481Z 
2023-01-11T21:41:26.9418485Z 
2023-01-11T21:41:26.9418618Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9418812Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9418927Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9419028Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9419123Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9419180Z {
2023-01-11T21:41:26.9419276Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9419334Z     {
2023-01-11T21:41:26.9419397Z         #pragma omp for 
2023-01-11T21:41:26.9419478Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9419538Z         {
2023-01-11T21:41:26.9419618Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9419679Z             {
2023-01-11T21:41:26.9419808Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:41:26.9419950Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9420025Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9420128Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9420189Z             }
2023-01-11T21:41:26.9420278Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9420359Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9420419Z             {
2023-01-11T21:41:26.9420501Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9420583Z                 auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9420666Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9420755Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9420819Z             }
2023-01-11T21:41:26.9420878Z         }
2023-01-11T21:41:26.9420940Z     }
2023-01-11T21:41:26.9420996Z }
2023-01-11T21:41:26.9421062Z ''')
2023-01-11T21:41:26.9421067Z 
2023-01-11T21:41:26.9421071Z 
2023-01-11T21:41:26.9421161Z async_compile.wait(globals())
2023-01-11T21:41:26.9421232Z del async_compile
2023-01-11T21:41:26.9421237Z 
2023-01-11T21:41:26.9421338Z def call(args):
2023-01-11T21:41:26.9421411Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9421481Z     args.clear()
2023-01-11T21:41:26.9421680Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9421840Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9421895Z     del arg0_1
2023-01-11T21:41:26.9421958Z     del arg1_1
2023-01-11T21:41:26.9422027Z     return (buf0, )
2023-01-11T21:41:26.9422032Z 
2023-01-11T21:41:26.9422036Z 
2023-01-11T21:41:26.9422109Z if __name__ == "__main__":
2023-01-11T21:41:26.9422218Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9422338Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9422584Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9422772Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9422888Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9422893Z 
2023-01-11T21:41:26.9422957Z ok (1.574s)
2023-01-11T21:41:26.9423543Z   test_cpu_broadcast2_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9423672Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9423934Z [2023-01-11 21:40:01,063] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 531
2023-01-11T21:41:26.9424199Z [2023-01-11 21:40:02,597] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 531
2023-01-11T21:41:26.9424205Z 
2023-01-11T21:41:26.9424298Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9424367Z import torch
2023-01-11T21:41:26.9424435Z import random
2023-01-11T21:41:26.9424534Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9424653Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9424658Z 
2023-01-11T21:41:26.9424734Z aten = torch.ops.aten
2023-01-11T21:41:26.9424866Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9424955Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9424960Z 
2023-01-11T21:41:26.9424964Z 
2023-01-11T21:41:26.9425096Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9425298Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9425413Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9425507Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9425604Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9425667Z {
2023-01-11T21:41:26.9425761Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9425820Z     {
2023-01-11T21:41:26.9425894Z         #pragma omp for 
2023-01-11T21:41:26.9425974Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9426023Z         {
2023-01-11T21:41:26.9426103Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9426165Z             {
2023-01-11T21:41:26.9426294Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:41:26.9426424Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:26.9426510Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9426612Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9426662Z             }
2023-01-11T21:41:26.9426754Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9426836Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9426900Z             {
2023-01-11T21:41:26.9427019Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9427101Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9427184Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9427262Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9427323Z             }
2023-01-11T21:41:26.9427383Z         }
2023-01-11T21:41:26.9427444Z     }
2023-01-11T21:41:26.9427501Z }
2023-01-11T21:41:26.9427580Z ''')
2023-01-11T21:41:26.9427586Z 
2023-01-11T21:41:26.9427590Z 
2023-01-11T21:41:26.9427676Z async_compile.wait(globals())
2023-01-11T21:41:26.9427736Z del async_compile
2023-01-11T21:41:26.9427741Z 
2023-01-11T21:41:26.9427809Z def call(args):
2023-01-11T21:41:26.9427882Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9427949Z     args.clear()
2023-01-11T21:41:26.9428187Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9428349Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9428418Z     del arg0_1
2023-01-11T21:41:26.9428471Z     del arg1_1
2023-01-11T21:41:26.9428539Z     return (buf0, )
2023-01-11T21:41:26.9428544Z 
2023-01-11T21:41:26.9428548Z 
2023-01-11T21:41:26.9428621Z if __name__ == "__main__":
2023-01-11T21:41:26.9428732Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9428850Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9429054Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9429292Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9429405Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9429410Z 
2023-01-11T21:41:26.9429463Z ok (1.548s)
2023-01-11T21:41:26.9429957Z   test_cpu_broadcast2_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9430085Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9430340Z [2023-01-11 21:40:02,611] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 532
2023-01-11T21:41:26.9430601Z [2023-01-11 21:40:02,619] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 532
2023-01-11T21:41:26.9430606Z 
2023-01-11T21:41:26.9430697Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9430764Z import torch
2023-01-11T21:41:26.9430832Z import random
2023-01-11T21:41:26.9430945Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9431053Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9431069Z 
2023-01-11T21:41:26.9431133Z aten = torch.ops.aten
2023-01-11T21:41:26.9431266Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9431355Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9431360Z 
2023-01-11T21:41:26.9431365Z 
2023-01-11T21:41:26.9431498Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9431700Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9431815Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9431916Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9432001Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9432059Z {
2023-01-11T21:41:26.9432153Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9432211Z     {
2023-01-11T21:41:26.9432284Z         #pragma omp for 
2023-01-11T21:41:26.9432366Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9432426Z         {
2023-01-11T21:41:26.9432546Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9432707Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9432790Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9432879Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9432941Z         }
2023-01-11T21:41:26.9433033Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9433111Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9433160Z         {
2023-01-11T21:41:26.9433240Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9433319Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9433399Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9433476Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9433536Z         }
2023-01-11T21:41:26.9433594Z     }
2023-01-11T21:41:26.9433638Z }
2023-01-11T21:41:26.9433748Z ''')
2023-01-11T21:41:26.9433754Z 
2023-01-11T21:41:26.9433758Z 
2023-01-11T21:41:26.9433846Z async_compile.wait(globals())
2023-01-11T21:41:26.9433918Z del async_compile
2023-01-11T21:41:26.9433923Z 
2023-01-11T21:41:26.9433992Z def call(args):
2023-01-11T21:41:26.9434064Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9434133Z     args.clear()
2023-01-11T21:41:26.9434327Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9434487Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9434556Z     del arg0_1
2023-01-11T21:41:26.9434621Z     del arg1_1
2023-01-11T21:41:26.9434689Z     return (buf0, )
2023-01-11T21:41:26.9434694Z 
2023-01-11T21:41:26.9434698Z 
2023-01-11T21:41:26.9434771Z if __name__ == "__main__":
2023-01-11T21:41:26.9434880Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9434999Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9435194Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9435394Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9435507Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9435512Z 
2023-01-11T21:41:26.9435576Z ok (0.021s)
2023-01-11T21:41:26.9436067Z   test_cpu_broadcast2_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9436192Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9436448Z [2023-01-11 21:40:02,631] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 533
2023-01-11T21:41:26.9436713Z [2023-01-11 21:40:02,639] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 533
2023-01-11T21:41:26.9436720Z 
2023-01-11T21:41:26.9436812Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9436879Z import torch
2023-01-11T21:41:26.9436935Z import random
2023-01-11T21:41:26.9437047Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9437164Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9437169Z 
2023-01-11T21:41:26.9437244Z aten = torch.ops.aten
2023-01-11T21:41:26.9437373Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9437462Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9437467Z 
2023-01-11T21:41:26.9437472Z 
2023-01-11T21:41:26.9437603Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9437806Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9437913Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9438015Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9438143Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9438201Z {
2023-01-11T21:41:26.9438295Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9438353Z     {
2023-01-11T21:41:26.9438428Z         #pragma omp for 
2023-01-11T21:41:26.9438496Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9438556Z         {
2023-01-11T21:41:26.9438688Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9438806Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:26.9438888Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9438979Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9439040Z         }
2023-01-11T21:41:26.9439120Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9439227Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9439290Z         {
2023-01-11T21:41:26.9439372Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9439453Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.9439532Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9439609Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9439658Z         }
2023-01-11T21:41:26.9439716Z     }
2023-01-11T21:41:26.9439776Z }
2023-01-11T21:41:26.9439854Z ''')
2023-01-11T21:41:26.9439859Z 
2023-01-11T21:41:26.9439863Z 
2023-01-11T21:41:26.9439951Z async_compile.wait(globals())
2023-01-11T21:41:26.9440021Z del async_compile
2023-01-11T21:41:26.9440026Z 
2023-01-11T21:41:26.9440094Z def call(args):
2023-01-11T21:41:26.9440155Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9440223Z     args.clear()
2023-01-11T21:41:26.9440426Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9440588Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9440654Z     del arg0_1
2023-01-11T21:41:26.9440719Z     del arg1_1
2023-01-11T21:41:26.9440791Z     return (buf0, )
2023-01-11T21:41:26.9440796Z 
2023-01-11T21:41:26.9440801Z 
2023-01-11T21:41:26.9440874Z if __name__ == "__main__":
2023-01-11T21:41:26.9440972Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9441092Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9441298Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9441486Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9441598Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9441603Z 
2023-01-11T21:41:26.9441668Z ok (0.020s)
2023-01-11T21:41:26.9442156Z   test_cpu_broadcast2_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9442282Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9442538Z [2023-01-11 21:40:02,651] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 534
2023-01-11T21:41:26.9442791Z [2023-01-11 21:40:02,659] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 534
2023-01-11T21:41:26.9442796Z 
2023-01-11T21:41:26.9442887Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9442955Z import torch
2023-01-11T21:41:26.9443023Z import random
2023-01-11T21:41:26.9443136Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9443254Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9443259Z 
2023-01-11T21:41:26.9443340Z aten = torch.ops.aten
2023-01-11T21:41:26.9443471Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9443593Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9443598Z 
2023-01-11T21:41:26.9443603Z 
2023-01-11T21:41:26.9443736Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9443937Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9444051Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9444153Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9444248Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9444307Z {
2023-01-11T21:41:26.9444390Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9444449Z     {
2023-01-11T21:41:26.9444524Z         #pragma omp for 
2023-01-11T21:41:26.9444604Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9444665Z         {
2023-01-11T21:41:26.9444774Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9444837Z             {
2023-01-11T21:41:26.9444953Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:41:26.9445095Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9445181Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9445283Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9445346Z             }
2023-01-11T21:41:26.9445436Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9445517Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9445566Z             {
2023-01-11T21:41:26.9445648Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9445742Z                 auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9445825Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9445914Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9445975Z             }
2023-01-11T21:41:26.9446038Z         }
2023-01-11T21:41:26.9446087Z     }
2023-01-11T21:41:26.9446143Z }
2023-01-11T21:41:26.9446222Z ''')
2023-01-11T21:41:26.9446230Z 
2023-01-11T21:41:26.9446234Z 
2023-01-11T21:41:26.9446320Z async_compile.wait(globals())
2023-01-11T21:41:26.9446391Z del async_compile
2023-01-11T21:41:26.9446396Z 
2023-01-11T21:41:26.9446464Z def call(args):
2023-01-11T21:41:26.9446537Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9446606Z     args.clear()
2023-01-11T21:41:26.9446801Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9446962Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9447028Z     del arg0_1
2023-01-11T21:41:26.9447092Z     del arg1_1
2023-01-11T21:41:26.9447162Z     return (buf0, )
2023-01-11T21:41:26.9447167Z 
2023-01-11T21:41:26.9447171Z 
2023-01-11T21:41:26.9447245Z if __name__ == "__main__":
2023-01-11T21:41:26.9447358Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9447466Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9447672Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9447872Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9447984Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9447990Z 
2023-01-11T21:41:26.9448055Z ok (0.020s)
2023-01-11T21:41:26.9448547Z   test_cpu_broadcast2_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9448673Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9448932Z [2023-01-11 21:40:02,672] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 535
2023-01-11T21:41:26.9449453Z [2023-01-11 21:40:04,181] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 535
2023-01-11T21:41:26.9449460Z 
2023-01-11T21:41:26.9449555Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9449612Z import torch
2023-01-11T21:41:26.9449681Z import random
2023-01-11T21:41:26.9449793Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9449912Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9449916Z 
2023-01-11T21:41:26.9449991Z aten = torch.ops.aten
2023-01-11T21:41:26.9450122Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9450211Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9450217Z 
2023-01-11T21:41:26.9450221Z 
2023-01-11T21:41:26.9450357Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9450605Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9450729Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9450835Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:26.9450933Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9450993Z {
2023-01-11T21:41:26.9451087Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9451147Z     {
2023-01-11T21:41:26.9451210Z         #pragma omp for 
2023-01-11T21:41:26.9451290Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9451350Z         {
2023-01-11T21:41:26.9451427Z             #pragma GCC ivdep
2023-01-11T21:41:26.9451511Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9451571Z             {
2023-01-11T21:41:26.9451636Z                 {
2023-01-11T21:41:26.9451690Z                     {
2023-01-11T21:41:26.9451782Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9451886Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9451994Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.9452087Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9452181Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9452243Z                     }
2023-01-11T21:41:26.9452295Z                 }
2023-01-11T21:41:26.9452355Z             }
2023-01-11T21:41:26.9452415Z         }
2023-01-11T21:41:26.9452475Z     }
2023-01-11T21:41:26.9452531Z }
2023-01-11T21:41:26.9452609Z ''')
2023-01-11T21:41:26.9452614Z 
2023-01-11T21:41:26.9452618Z 
2023-01-11T21:41:26.9452706Z async_compile.wait(globals())
2023-01-11T21:41:26.9452765Z del async_compile
2023-01-11T21:41:26.9452770Z 
2023-01-11T21:41:26.9452838Z def call(args):
2023-01-11T21:41:26.9452913Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9452980Z     args.clear()
2023-01-11T21:41:26.9453190Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9453352Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9453423Z     del arg0_1
2023-01-11T21:41:26.9453475Z     del arg1_1
2023-01-11T21:41:26.9453544Z     return (buf0, )
2023-01-11T21:41:26.9453549Z 
2023-01-11T21:41:26.9453553Z 
2023-01-11T21:41:26.9453627Z if __name__ == "__main__":
2023-01-11T21:41:26.9453738Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9453856Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9454059Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9454253Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9454366Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9454370Z 
2023-01-11T21:41:26.9454423Z ok (1.523s)
2023-01-11T21:41:26.9454909Z   test_cpu_broadcast2_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9455089Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9455346Z [2023-01-11 21:40:04,202] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 536
2023-01-11T21:41:26.9455610Z [2023-01-11 21:40:05,764] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 536
2023-01-11T21:41:26.9455616Z 
2023-01-11T21:41:26.9455708Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9455776Z import torch
2023-01-11T21:41:26.9455845Z import random
2023-01-11T21:41:26.9455958Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9456092Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9456097Z 
2023-01-11T21:41:26.9456176Z aten = torch.ops.aten
2023-01-11T21:41:26.9456308Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9456398Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9456403Z 
2023-01-11T21:41:26.9456407Z 
2023-01-11T21:41:26.9456539Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9456742Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9456859Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9456958Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.9457043Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9457102Z {
2023-01-11T21:41:26.9457197Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9457256Z     {
2023-01-11T21:41:26.9457330Z         #pragma omp for 
2023-01-11T21:41:26.9457412Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9457494Z         {
2023-01-11T21:41:26.9457594Z             #pragma GCC ivdep
2023-01-11T21:41:26.9457739Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9457848Z             {
2023-01-11T21:41:26.9457957Z                 {
2023-01-11T21:41:26.9458068Z                     {
2023-01-11T21:41:26.9458226Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9458382Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9458551Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.9458706Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9458867Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9458982Z                     }
2023-01-11T21:41:26.9459093Z                 }
2023-01-11T21:41:26.9459201Z             }
2023-01-11T21:41:26.9459307Z         }
2023-01-11T21:41:26.9459395Z     }
2023-01-11T21:41:26.9459500Z }
2023-01-11T21:41:26.9459643Z ''')
2023-01-11T21:41:26.9459651Z 
2023-01-11T21:41:26.9459659Z 
2023-01-11T21:41:26.9459816Z async_compile.wait(globals())
2023-01-11T21:41:26.9459949Z del async_compile
2023-01-11T21:41:26.9459957Z 
2023-01-11T21:41:26.9460077Z def call(args):
2023-01-11T21:41:26.9460206Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9460314Z     args.clear()
2023-01-11T21:41:26.9460683Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9460965Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9461081Z     del arg0_1
2023-01-11T21:41:26.9461196Z     del arg1_1
2023-01-11T21:41:26.9461317Z     return (buf0, )
2023-01-11T21:41:26.9461325Z 
2023-01-11T21:41:26.9461332Z 
2023-01-11T21:41:26.9461460Z if __name__ == "__main__":
2023-01-11T21:41:26.9461655Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9461858Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9462229Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9462624Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9462825Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9462834Z 
2023-01-11T21:41:26.9462946Z ok (1.610s)
2023-01-11T21:41:26.9463887Z   test_cpu_broadcast2_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9464101Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9464605Z [2023-01-11 21:40:05,810] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 537
2023-01-11T21:41:26.9465093Z [2023-01-11 21:40:07,481] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 537
2023-01-11T21:41:26.9465107Z 
2023-01-11T21:41:26.9465257Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9465379Z import torch
2023-01-11T21:41:26.9465500Z import random
2023-01-11T21:41:26.9465704Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9465918Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9465927Z 
2023-01-11T21:41:26.9466061Z aten = torch.ops.aten
2023-01-11T21:41:26.9466299Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9466445Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9466469Z 
2023-01-11T21:41:26.9466476Z 
2023-01-11T21:41:26.9466706Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9467070Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9467283Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9467463Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9467635Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9467736Z {
2023-01-11T21:41:26.9467907Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9467996Z     {
2023-01-11T21:41:26.9468129Z         #pragma omp for 
2023-01-11T21:41:26.9468269Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9468375Z         {
2023-01-11T21:41:26.9468511Z             #pragma GCC ivdep
2023-01-11T21:41:26.9468656Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9468768Z             {
2023-01-11T21:41:26.9468863Z                 {
2023-01-11T21:41:26.9468975Z                     {
2023-01-11T21:41:26.9469141Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9469318Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9469481Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9469640Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9469752Z                     }
2023-01-11T21:41:26.9469853Z                 }
2023-01-11T21:41:26.9469958Z             }
2023-01-11T21:41:26.9470065Z         }
2023-01-11T21:41:26.9470169Z     }
2023-01-11T21:41:26.9470270Z }
2023-01-11T21:41:26.9470410Z ''')
2023-01-11T21:41:26.9470419Z 
2023-01-11T21:41:26.9470426Z 
2023-01-11T21:41:26.9470582Z async_compile.wait(globals())
2023-01-11T21:41:26.9470691Z del async_compile
2023-01-11T21:41:26.9470699Z 
2023-01-11T21:41:26.9470819Z def call(args):
2023-01-11T21:41:26.9470946Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9471067Z     args.clear()
2023-01-11T21:41:26.9471443Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9471722Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9471837Z     del arg0_1
2023-01-11T21:41:26.9471941Z     del arg1_1
2023-01-11T21:41:26.9472063Z     return (buf0, )
2023-01-11T21:41:26.9472072Z 
2023-01-11T21:41:26.9472140Z 
2023-01-11T21:41:26.9472276Z if __name__ == "__main__":
2023-01-11T21:41:26.9472474Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9472691Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9473065Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9473424Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9473626Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9473636Z 
2023-01-11T21:41:26.9473735Z ok (1.688s)
2023-01-11T21:41:26.9474705Z   test_cpu_broadcast2_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9474932Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9475404Z [2023-01-11 21:40:07,496] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 538
2023-01-11T21:41:26.9475890Z [2023-01-11 21:40:07,506] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 538
2023-01-11T21:41:26.9475899Z 
2023-01-11T21:41:26.9476062Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9476182Z import torch
2023-01-11T21:41:26.9476303Z import random
2023-01-11T21:41:26.9476503Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9476700Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9476724Z 
2023-01-11T21:41:26.9476844Z aten = torch.ops.aten
2023-01-11T21:41:26.9477086Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9477246Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9477261Z 
2023-01-11T21:41:26.9477268Z 
2023-01-11T21:41:26.9477508Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9477874Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9478082Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9478258Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9478425Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9478516Z {
2023-01-11T21:41:26.9478690Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9478797Z     {
2023-01-11T21:41:26.9478929Z         #pragma omp for 
2023-01-11T21:41:26.9479068Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9479180Z         {
2023-01-11T21:41:26.9479309Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9479416Z             {
2023-01-11T21:41:26.9479663Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i1);
2023-01-11T21:41:26.9479911Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9480069Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9480242Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9480351Z             }
2023-01-11T21:41:26.9480510Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9480641Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9480748Z             {
2023-01-11T21:41:26.9480895Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9481058Z                 auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9481207Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9481361Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9481469Z             }
2023-01-11T21:41:26.9481563Z         }
2023-01-11T21:41:26.9481666Z     }
2023-01-11T21:41:26.9481770Z }
2023-01-11T21:41:26.9481910Z ''')
2023-01-11T21:41:26.9481919Z 
2023-01-11T21:41:26.9481926Z 
2023-01-11T21:41:26.9482145Z async_compile.wait(globals())
2023-01-11T21:41:26.9482272Z del async_compile
2023-01-11T21:41:26.9482280Z 
2023-01-11T21:41:26.9482401Z def call(args):
2023-01-11T21:41:26.9482513Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9482635Z     args.clear()
2023-01-11T21:41:26.9483013Z     buf0 = empty_strided((1, 10, 10), (100, 1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9483295Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9483414Z     del arg0_1
2023-01-11T21:41:26.9483528Z     del arg1_1
2023-01-11T21:41:26.9483650Z     return (buf0, )
2023-01-11T21:41:26.9483658Z 
2023-01-11T21:41:26.9483665Z 
2023-01-11T21:41:26.9483780Z if __name__ == "__main__":
2023-01-11T21:41:26.9483980Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9484241Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9484617Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9484977Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9485178Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9485187Z 
2023-01-11T21:41:26.9485302Z ok (0.024s)
2023-01-11T21:41:26.9486218Z   test_cpu_broadcast3_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9486439Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9486896Z [2023-01-11 21:40:07,518] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 539
2023-01-11T21:41:26.9487380Z [2023-01-11 21:40:09,165] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 539
2023-01-11T21:41:26.9487393Z 
2023-01-11T21:41:26.9487558Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9487679Z import torch
2023-01-11T21:41:26.9487801Z import random
2023-01-11T21:41:26.9488002Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9488215Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9488224Z 
2023-01-11T21:41:26.9488358Z aten = torch.ops.aten
2023-01-11T21:41:26.9488579Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9488738Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9488747Z 
2023-01-11T21:41:26.9488753Z 
2023-01-11T21:41:26.9488994Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9489477Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9489681Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9489855Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9490020Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9490122Z {
2023-01-11T21:41:26.9490268Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9490371Z     {
2023-01-11T21:41:26.9490499Z         #pragma omp for 
2023-01-11T21:41:26.9490637Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9490743Z         {
2023-01-11T21:41:26.9490961Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:26.9491191Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9491322Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9491478Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9491586Z         }
2023-01-11T21:41:26.9491752Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9491896Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9492003Z         {
2023-01-11T21:41:26.9492146Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.9492375Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9492520Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9492657Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9492763Z         }
2023-01-11T21:41:26.9492867Z     }
2023-01-11T21:41:26.9492968Z }
2023-01-11T21:41:26.9493112Z ''')
2023-01-11T21:41:26.9493122Z 
2023-01-11T21:41:26.9493129Z 
2023-01-11T21:41:26.9493272Z async_compile.wait(globals())
2023-01-11T21:41:26.9493399Z del async_compile
2023-01-11T21:41:26.9493408Z 
2023-01-11T21:41:26.9493526Z def call(args):
2023-01-11T21:41:26.9493655Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9493776Z     args.clear()
2023-01-11T21:41:26.9494126Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9494464Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9494571Z     del arg0_1
2023-01-11T21:41:26.9494686Z     del arg1_1
2023-01-11T21:41:26.9494816Z     return (buf0, )
2023-01-11T21:41:26.9494825Z 
2023-01-11T21:41:26.9494833Z 
2023-01-11T21:41:26.9494966Z if __name__ == "__main__":
2023-01-11T21:41:26.9495164Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9495381Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9495728Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9496076Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9496260Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9496283Z 
2023-01-11T21:41:26.9496383Z ok (1.660s)
2023-01-11T21:41:26.9497303Z   test_cpu_broadcast3_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9497525Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9497998Z [2023-01-11 21:40:09,186] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 540
2023-01-11T21:41:26.9498475Z [2023-01-11 21:40:09,197] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 540
2023-01-11T21:41:26.9498484Z 
2023-01-11T21:41:26.9498647Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9498769Z import torch
2023-01-11T21:41:26.9498891Z import random
2023-01-11T21:41:26.9499078Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9499287Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9499296Z 
2023-01-11T21:41:26.9499436Z aten = torch.ops.aten
2023-01-11T21:41:26.9499671Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9499834Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9499843Z 
2023-01-11T21:41:26.9499850Z 
2023-01-11T21:41:26.9500091Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9500458Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9500666Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9500844Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9500999Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9501102Z {
2023-01-11T21:41:26.9501275Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9501380Z     {
2023-01-11T21:41:26.9501512Z         #pragma omp for 
2023-01-11T21:41:26.9501650Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:41:26.9501745Z         {
2023-01-11T21:41:26.9501965Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:26.9502191Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9502422Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9502580Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9502724Z         }
2023-01-11T21:41:26.9502888Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9503028Z         for(long i0=8; i0<10; i0+=1)
2023-01-11T21:41:26.9503183Z         {
2023-01-11T21:41:26.9503329Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.9503469Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9503608Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9503741Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9503847Z         }
2023-01-11T21:41:26.9503933Z     }
2023-01-11T21:41:26.9504033Z }
2023-01-11T21:41:26.9504170Z ''')
2023-01-11T21:41:26.9504179Z 
2023-01-11T21:41:26.9504186Z 
2023-01-11T21:41:26.9504383Z async_compile.wait(globals())
2023-01-11T21:41:26.9504512Z del async_compile
2023-01-11T21:41:26.9504522Z 
2023-01-11T21:41:26.9504643Z def call(args):
2023-01-11T21:41:26.9504776Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9504900Z     args.clear()
2023-01-11T21:41:26.9505258Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9505541Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9505660Z     del arg0_1
2023-01-11T21:41:26.9505774Z     del arg1_1
2023-01-11T21:41:26.9505895Z     return (buf0, )
2023-01-11T21:41:26.9505904Z 
2023-01-11T21:41:26.9505912Z 
2023-01-11T21:41:26.9506041Z if __name__ == "__main__":
2023-01-11T21:41:26.9506241Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9506442Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9506786Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9507155Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9507357Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9507369Z 
2023-01-11T21:41:26.9507482Z ok (0.031s)
2023-01-11T21:41:26.9508395Z   test_cpu_broadcast3_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9508612Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9509089Z [2023-01-11 21:40:09,211] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 541
2023-01-11T21:41:26.9509569Z [2023-01-11 21:40:10,723] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 541
2023-01-11T21:41:26.9509579Z 
2023-01-11T21:41:26.9509742Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9509850Z import torch
2023-01-11T21:41:26.9509971Z import random
2023-01-11T21:41:26.9510171Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9510384Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9510393Z 
2023-01-11T21:41:26.9510528Z aten = torch.ops.aten
2023-01-11T21:41:26.9510764Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9510890Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9510898Z 
2023-01-11T21:41:26.9510903Z 
2023-01-11T21:41:26.9511085Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9511332Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9511486Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9511629Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9511758Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9511896Z {
2023-01-11T21:41:26.9511977Z     {
2023-01-11T21:41:26.9512059Z         {
2023-01-11T21:41:26.9512156Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.9512264Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.9512375Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9512480Z             out_ptr0[0] = tmp2;
2023-01-11T21:41:26.9512563Z         }
2023-01-11T21:41:26.9512645Z     }
2023-01-11T21:41:26.9512724Z }
2023-01-11T21:41:26.9512817Z ''')
2023-01-11T21:41:26.9512823Z 
2023-01-11T21:41:26.9512828Z 
2023-01-11T21:41:26.9512943Z async_compile.wait(globals())
2023-01-11T21:41:26.9513039Z del async_compile
2023-01-11T21:41:26.9513045Z 
2023-01-11T21:41:26.9513137Z def call(args):
2023-01-11T21:41:26.9513236Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9513329Z     args.clear()
2023-01-11T21:41:26.9513631Z     buf0 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9513830Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9513926Z     del arg0_1
2023-01-11T21:41:26.9514015Z     del arg1_1
2023-01-11T21:41:26.9514107Z     return (buf0, )
2023-01-11T21:41:26.9514114Z 
2023-01-11T21:41:26.9514119Z 
2023-01-11T21:41:26.9514218Z if __name__ == "__main__":
2023-01-11T21:41:26.9514367Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9514528Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9514790Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9515038Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9515187Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9515194Z 
2023-01-11T21:41:26.9515280Z ok (1.526s)
2023-01-11T21:41:26.9515928Z   test_cpu_broadcast3_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9516096Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9516448Z [2023-01-11 21:40:10,737] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 542
2023-01-11T21:41:26.9516810Z [2023-01-11 21:40:12,425] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 542
2023-01-11T21:41:26.9516817Z 
2023-01-11T21:41:26.9516941Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9517035Z import torch
2023-01-11T21:41:26.9517114Z import random
2023-01-11T21:41:26.9517265Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9517424Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9517431Z 
2023-01-11T21:41:26.9517540Z aten = torch.ops.aten
2023-01-11T21:41:26.9517715Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9517836Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9517843Z 
2023-01-11T21:41:26.9517847Z 
2023-01-11T21:41:26.9518030Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9518291Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9518433Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9518567Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9518698Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9518775Z {
2023-01-11T21:41:26.9518906Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9518989Z     {
2023-01-11T21:41:26.9519092Z         #pragma omp for 
2023-01-11T21:41:26.9519190Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.9519273Z         {
2023-01-11T21:41:26.9519439Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:26.9519650Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9519761Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9519881Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9519964Z         }
2023-01-11T21:41:26.9520074Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9520185Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.9520268Z         {
2023-01-11T21:41:26.9520378Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.9520488Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9520597Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9520702Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9520769Z         }
2023-01-11T21:41:26.9520847Z     }
2023-01-11T21:41:26.9520924Z }
2023-01-11T21:41:26.9521065Z ''')
2023-01-11T21:41:26.9521073Z 
2023-01-11T21:41:26.9521078Z 
2023-01-11T21:41:26.9521197Z async_compile.wait(globals())
2023-01-11T21:41:26.9521297Z del async_compile
2023-01-11T21:41:26.9521304Z 
2023-01-11T21:41:26.9521395Z def call(args):
2023-01-11T21:41:26.9521480Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9521572Z     args.clear()
2023-01-11T21:41:26.9521844Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9522057Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9522147Z     del arg0_1
2023-01-11T21:41:26.9522233Z     del arg1_1
2023-01-11T21:41:26.9522326Z     return (buf0, )
2023-01-11T21:41:26.9522332Z 
2023-01-11T21:41:26.9522337Z 
2023-01-11T21:41:26.9522435Z if __name__ == "__main__":
2023-01-11T21:41:26.9522571Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9522730Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9522997Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9523268Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9523423Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9523429Z 
2023-01-11T21:41:26.9523516Z ok (1.702s)
2023-01-11T21:41:26.9524160Z   test_cpu_broadcast3_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9524323Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9524680Z [2023-01-11 21:40:12,441] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 543
2023-01-11T21:41:26.9525028Z [2023-01-11 21:40:14,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 543
2023-01-11T21:41:26.9525054Z 
2023-01-11T21:41:26.9525164Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9525255Z import torch
2023-01-11T21:41:26.9525347Z import random
2023-01-11T21:41:26.9525498Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9525655Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9525662Z 
2023-01-11T21:41:26.9525764Z aten = torch.ops.aten
2023-01-11T21:41:26.9525942Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9526048Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9526054Z 
2023-01-11T21:41:26.9526074Z 
2023-01-11T21:41:26.9526244Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9526506Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9526666Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9526806Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:26.9526977Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9527057Z {
2023-01-11T21:41:26.9527187Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9527254Z     {
2023-01-11T21:41:26.9527355Z         #pragma omp for 
2023-01-11T21:41:26.9527465Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:26.9527549Z         {
2023-01-11T21:41:26.9527633Z             {
2023-01-11T21:41:26.9527726Z                 {
2023-01-11T21:41:26.9527829Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.9527948Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.9528087Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.9528210Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9528320Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.9528442Z                 }
2023-01-11T21:41:26.9528531Z             }
2023-01-11T21:41:26.9528598Z         }
2023-01-11T21:41:26.9528679Z     }
2023-01-11T21:41:26.9528761Z }
2023-01-11T21:41:26.9528866Z ''')
2023-01-11T21:41:26.9528873Z 
2023-01-11T21:41:26.9528878Z 
2023-01-11T21:41:26.9528996Z async_compile.wait(globals())
2023-01-11T21:41:26.9529311Z del async_compile
2023-01-11T21:41:26.9529319Z 
2023-01-11T21:41:26.9529411Z def call(args):
2023-01-11T21:41:26.9529512Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9529591Z     args.clear()
2023-01-11T21:41:26.9529864Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9530078Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9530166Z     del arg0_1
2023-01-11T21:41:26.9530254Z     del arg1_1
2023-01-11T21:41:26.9530349Z     return (buf0, )
2023-01-11T21:41:26.9530355Z 
2023-01-11T21:41:26.9530360Z 
2023-01-11T21:41:26.9530460Z if __name__ == "__main__":
2023-01-11T21:41:26.9530602Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9530764Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9531032Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9531300Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9531453Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9531459Z 
2023-01-11T21:41:26.9531547Z ok (1.698s)
2023-01-11T21:41:26.9532188Z   test_cpu_broadcast3_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9532360Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9532710Z [2023-01-11 21:40:14,139] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 544
2023-01-11T21:41:26.9533075Z [2023-01-11 21:40:15,839] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 544
2023-01-11T21:41:26.9533083Z 
2023-01-11T21:41:26.9533194Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9533285Z import torch
2023-01-11T21:41:26.9533380Z import random
2023-01-11T21:41:26.9533531Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9533687Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9533693Z 
2023-01-11T21:41:26.9533795Z aten = torch.ops.aten
2023-01-11T21:41:26.9533970Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9534074Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9534080Z 
2023-01-11T21:41:26.9534097Z 
2023-01-11T21:41:26.9534270Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9534529Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9534750Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9534885Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.9535013Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9535093Z {
2023-01-11T21:41:26.9535255Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9535324Z     {
2023-01-11T21:41:26.9535424Z         #pragma omp for 
2023-01-11T21:41:26.9535534Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9535618Z         {
2023-01-11T21:41:26.9535705Z             {
2023-01-11T21:41:26.9535793Z                 {
2023-01-11T21:41:26.9535914Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.9536021Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9542382Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.9542612Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9542733Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.9542827Z                 }
2023-01-11T21:41:26.9542912Z             }
2023-01-11T21:41:26.9542981Z         }
2023-01-11T21:41:26.9543064Z     }
2023-01-11T21:41:26.9543220Z }
2023-01-11T21:41:26.9543347Z ''')
2023-01-11T21:41:26.9543355Z 
2023-01-11T21:41:26.9543360Z 
2023-01-11T21:41:26.9543481Z async_compile.wait(globals())
2023-01-11T21:41:26.9543579Z del async_compile
2023-01-11T21:41:26.9543586Z 
2023-01-11T21:41:26.9543678Z def call(args):
2023-01-11T21:41:26.9543778Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9543857Z     args.clear()
2023-01-11T21:41:26.9544129Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9544347Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9544439Z     del arg0_1
2023-01-11T21:41:26.9544527Z     del arg1_1
2023-01-11T21:41:26.9544624Z     return (buf0, )
2023-01-11T21:41:26.9544631Z 
2023-01-11T21:41:26.9544636Z 
2023-01-11T21:41:26.9544739Z if __name__ == "__main__":
2023-01-11T21:41:26.9544880Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9545042Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9545307Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9545567Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9545720Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9545728Z 
2023-01-11T21:41:26.9545814Z ok (1.715s)
2023-01-11T21:41:26.9546468Z   test_cpu_broadcast3_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9546639Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9546995Z [2023-01-11 21:40:15,855] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 545
2023-01-11T21:41:26.9547359Z [2023-01-11 21:40:17,390] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 545
2023-01-11T21:41:26.9547367Z 
2023-01-11T21:41:26.9547477Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9547567Z import torch
2023-01-11T21:41:26.9547657Z import random
2023-01-11T21:41:26.9547810Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9547967Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9547974Z 
2023-01-11T21:41:26.9548076Z aten = torch.ops.aten
2023-01-11T21:41:26.9548251Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9548361Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9548381Z 
2023-01-11T21:41:26.9548387Z 
2023-01-11T21:41:26.9548659Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9548919Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9549077Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9549214Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9549346Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9549428Z {
2023-01-11T21:41:26.9549556Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9549624Z     {
2023-01-11T21:41:26.9549725Z         #pragma omp for 
2023-01-11T21:41:26.9549837Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9549921Z         {
2023-01-11T21:41:26.9550027Z             #pragma GCC ivdep
2023-01-11T21:41:26.9550138Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9550225Z             {
2023-01-11T21:41:26.9550332Z                 {
2023-01-11T21:41:26.9550426Z                     {
2023-01-11T21:41:26.9550550Z                         auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.9550691Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9550814Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9550939Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9551028Z                     }
2023-01-11T21:41:26.9551100Z                 }
2023-01-11T21:41:26.9551183Z             }
2023-01-11T21:41:26.9551265Z         }
2023-01-11T21:41:26.9551345Z     }
2023-01-11T21:41:26.9551422Z }
2023-01-11T21:41:26.9551528Z ''')
2023-01-11T21:41:26.9551534Z 
2023-01-11T21:41:26.9551539Z 
2023-01-11T21:41:26.9551657Z async_compile.wait(globals())
2023-01-11T21:41:26.9551739Z del async_compile
2023-01-11T21:41:26.9551746Z 
2023-01-11T21:41:26.9551839Z def call(args):
2023-01-11T21:41:26.9551940Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9552035Z     args.clear()
2023-01-11T21:41:26.9552308Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9552527Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9552616Z     del arg0_1
2023-01-11T21:41:26.9552693Z     del arg1_1
2023-01-11T21:41:26.9552785Z     return (buf0, )
2023-01-11T21:41:26.9552791Z 
2023-01-11T21:41:26.9552796Z 
2023-01-11T21:41:26.9552894Z if __name__ == "__main__":
2023-01-11T21:41:26.9553046Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9553206Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9553465Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9553735Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9553886Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9553894Z 
2023-01-11T21:41:26.9553971Z ok (1.551s)
2023-01-11T21:41:26.9554629Z   test_cpu_broadcast3_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9554798Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9555153Z [2023-01-11 21:40:17,405] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 546
2023-01-11T21:41:26.9555533Z [2023-01-11 21:40:17,413] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 546
2023-01-11T21:41:26.9555541Z 
2023-01-11T21:41:26.9555641Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9555709Z import torch
2023-01-11T21:41:26.9555779Z import random
2023-01-11T21:41:26.9555892Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9555999Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9556040Z 
2023-01-11T21:41:26.9556119Z aten = torch.ops.aten
2023-01-11T21:41:26.9556252Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9556342Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9556347Z 
2023-01-11T21:41:26.9556351Z 
2023-01-11T21:41:26.9556488Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9556690Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9556808Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9556909Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9556996Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9557055Z {
2023-01-11T21:41:26.9557151Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9557244Z     {
2023-01-11T21:41:26.9557323Z         #pragma omp for 
2023-01-11T21:41:26.9557404Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.9557467Z         {
2023-01-11T21:41:26.9557582Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:41:26.9557713Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9557797Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9557886Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9557950Z         }
2023-01-11T21:41:26.9558042Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9558122Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.9558171Z         {
2023-01-11T21:41:26.9558251Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:41:26.9558333Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9558414Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9558492Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9558556Z         }
2023-01-11T21:41:26.9558614Z     }
2023-01-11T21:41:26.9558660Z }
2023-01-11T21:41:26.9558738Z ''')
2023-01-11T21:41:26.9558745Z 
2023-01-11T21:41:26.9558750Z 
2023-01-11T21:41:26.9558836Z async_compile.wait(globals())
2023-01-11T21:41:26.9558908Z del async_compile
2023-01-11T21:41:26.9558913Z 
2023-01-11T21:41:26.9558982Z def call(args):
2023-01-11T21:41:26.9559056Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9559124Z     args.clear()
2023-01-11T21:41:26.9559312Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9559474Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9559539Z     del arg0_1
2023-01-11T21:41:26.9559603Z     del arg1_1
2023-01-11T21:41:26.9559673Z     return (buf0, )
2023-01-11T21:41:26.9559679Z 
2023-01-11T21:41:26.9559683Z 
2023-01-11T21:41:26.9559756Z if __name__ == "__main__":
2023-01-11T21:41:26.9559869Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9559991Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9560174Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9560377Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9560488Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9560493Z 
2023-01-11T21:41:26.9560557Z ok (0.023s)
2023-01-11T21:41:26.9561046Z   test_cpu_dense_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9561170Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9561429Z [2023-01-11 21:40:17,427] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 547
2023-01-11T21:41:26.9561745Z [2023-01-11 21:40:18,949] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 547
2023-01-11T21:41:26.9561751Z 
2023-01-11T21:41:26.9561844Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9561901Z import torch
2023-01-11T21:41:26.9561969Z import random
2023-01-11T21:41:26.9562082Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9562200Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9562205Z 
2023-01-11T21:41:26.9562279Z aten = torch.ops.aten
2023-01-11T21:41:26.9562410Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9562499Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9562503Z 
2023-01-11T21:41:26.9562508Z 
2023-01-11T21:41:26.9562639Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9562857Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9562977Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9563082Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9563179Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9563239Z {
2023-01-11T21:41:26.9563334Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9563394Z     {
2023-01-11T21:41:26.9563457Z         #pragma omp for 
2023-01-11T21:41:26.9563538Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9563599Z         {
2023-01-11T21:41:26.9563681Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9563743Z             {
2023-01-11T21:41:26.9563884Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9564018Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:26.9564107Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9564197Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9564260Z             }
2023-01-11T21:41:26.9564353Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9564435Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9564497Z             {
2023-01-11T21:41:26.9564591Z                 auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9564675Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9564746Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9564836Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9564896Z             }
2023-01-11T21:41:26.9564957Z         }
2023-01-11T21:41:26.9565016Z     }
2023-01-11T21:41:26.9565074Z }
2023-01-11T21:41:26.9565140Z ''')
2023-01-11T21:41:26.9565145Z 
2023-01-11T21:41:26.9565162Z 
2023-01-11T21:41:26.9565237Z async_compile.wait(globals())
2023-01-11T21:41:26.9565307Z del async_compile
2023-01-11T21:41:26.9565312Z 
2023-01-11T21:41:26.9565383Z def call(args):
2023-01-11T21:41:26.9565457Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9565527Z     args.clear()
2023-01-11T21:41:26.9565727Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9565888Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9565943Z     del arg0_1
2023-01-11T21:41:26.9566008Z     del arg1_1
2023-01-11T21:41:26.9566077Z     return (buf0, )
2023-01-11T21:41:26.9566083Z 
2023-01-11T21:41:26.9566087Z 
2023-01-11T21:41:26.9566161Z if __name__ == "__main__":
2023-01-11T21:41:26.9566271Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9566392Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9566591Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9566784Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9566887Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9566893Z 
2023-01-11T21:41:26.9566958Z ok (1.536s)
2023-01-11T21:41:26.9567475Z   test_cpu_dense_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9567600Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9567860Z [2023-01-11 21:40:18,966] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 548
2023-01-11T21:41:26.9568125Z [2023-01-11 21:40:20,826] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 548
2023-01-11T21:41:26.9568131Z 
2023-01-11T21:41:26.9568249Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9568319Z import torch
2023-01-11T21:41:26.9568389Z import random
2023-01-11T21:41:26.9568490Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9568609Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9568614Z 
2023-01-11T21:41:26.9568689Z aten = torch.ops.aten
2023-01-11T21:41:26.9568819Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9568907Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9568912Z 
2023-01-11T21:41:26.9568917Z 
2023-01-11T21:41:26.9569202Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9569475Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9569596Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9569686Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9569782Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9569844Z {
2023-01-11T21:41:26.9569940Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9570001Z     {
2023-01-11T21:41:26.9570078Z         #pragma omp for 
2023-01-11T21:41:26.9570159Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9570209Z         {
2023-01-11T21:41:26.9570289Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9570351Z             {
2023-01-11T21:41:26.9570493Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9570617Z                 auto tmp1 = at::vec::Vectorized<float>(in_ptr1[i0]);
2023-01-11T21:41:26.9570702Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9570803Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9570854Z             }
2023-01-11T21:41:26.9570944Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9571024Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9571084Z             {
2023-01-11T21:41:26.9571181Z                 auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9571263Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9571348Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9571426Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9571486Z             }
2023-01-11T21:41:26.9571546Z         }
2023-01-11T21:41:26.9571605Z     }
2023-01-11T21:41:26.9571661Z }
2023-01-11T21:41:26.9571743Z ''')
2023-01-11T21:41:26.9571748Z 
2023-01-11T21:41:26.9571752Z 
2023-01-11T21:41:26.9571838Z async_compile.wait(globals())
2023-01-11T21:41:26.9571897Z del async_compile
2023-01-11T21:41:26.9571912Z 
2023-01-11T21:41:26.9571969Z def call(args):
2023-01-11T21:41:26.9572041Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9572109Z     args.clear()
2023-01-11T21:41:26.9572317Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9572480Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9572550Z     del arg0_1
2023-01-11T21:41:26.9572615Z     del arg1_1
2023-01-11T21:41:26.9572673Z     return (buf0, )
2023-01-11T21:41:26.9572733Z 
2023-01-11T21:41:26.9572737Z 
2023-01-11T21:41:26.9572816Z if __name__ == "__main__":
2023-01-11T21:41:26.9572931Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9573053Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9573254Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9573459Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9573572Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9573577Z 
2023-01-11T21:41:26.9573641Z ok (1.878s)
2023-01-11T21:41:26.9574154Z   test_cpu_dense_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9574285Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9574545Z [2023-01-11 21:40:20,849] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 549
2023-01-11T21:41:26.9574809Z [2023-01-11 21:40:22,566] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 549
2023-01-11T21:41:26.9574814Z 
2023-01-11T21:41:26.9574906Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9574974Z import torch
2023-01-11T21:41:26.9575044Z import random
2023-01-11T21:41:26.9575157Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9575275Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9575280Z 
2023-01-11T21:41:26.9575345Z aten = torch.ops.aten
2023-01-11T21:41:26.9575477Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9575568Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9575576Z 
2023-01-11T21:41:26.9575580Z 
2023-01-11T21:41:26.9575715Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9575917Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9576034Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9576134Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9576230Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9576277Z {
2023-01-11T21:41:26.9576372Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9576431Z     {
2023-01-11T21:41:26.9576506Z         #pragma omp for 
2023-01-11T21:41:26.9576585Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.9576646Z         {
2023-01-11T21:41:26.9576782Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9576888Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:26.9576973Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9577061Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9577122Z         }
2023-01-11T21:41:26.9577214Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9577293Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.9577353Z         {
2023-01-11T21:41:26.9577422Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9577501Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.9577580Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9577658Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9577717Z         }
2023-01-11T21:41:26.9577777Z     }
2023-01-11T21:41:26.9577822Z }
2023-01-11T21:41:26.9577899Z ''')
2023-01-11T21:41:26.9577903Z 
2023-01-11T21:41:26.9577907Z 
2023-01-11T21:41:26.9577995Z async_compile.wait(globals())
2023-01-11T21:41:26.9578068Z del async_compile
2023-01-11T21:41:26.9578072Z 
2023-01-11T21:41:26.9578140Z def call(args):
2023-01-11T21:41:26.9578213Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9578319Z     args.clear()
2023-01-11T21:41:26.9578519Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9578666Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9578732Z     del arg0_1
2023-01-11T21:41:26.9578796Z     del arg1_1
2023-01-11T21:41:26.9578866Z     return (buf0, )
2023-01-11T21:41:26.9578871Z 
2023-01-11T21:41:26.9578875Z 
2023-01-11T21:41:26.9578947Z if __name__ == "__main__":
2023-01-11T21:41:26.9579059Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9579179Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9579382Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9579589Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9579705Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9579712Z 
2023-01-11T21:41:26.9579776Z ok (1.739s)
2023-01-11T21:41:26.9580259Z   test_cpu_dense_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9580383Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9580638Z [2023-01-11 21:40:22,581] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 550
2023-01-11T21:41:26.9580902Z [2023-01-11 21:40:22,589] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 550
2023-01-11T21:41:26.9580910Z 
2023-01-11T21:41:26.9581000Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9581068Z import torch
2023-01-11T21:41:26.9581127Z import random
2023-01-11T21:41:26.9581239Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9581357Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9581361Z 
2023-01-11T21:41:26.9581438Z aten = torch.ops.aten
2023-01-11T21:41:26.9581567Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9581656Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9581662Z 
2023-01-11T21:41:26.9581666Z 
2023-01-11T21:41:26.9581799Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9582000Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9582105Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9582206Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9582307Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9582364Z {
2023-01-11T21:41:26.9582458Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9582519Z     {
2023-01-11T21:41:26.9582593Z         #pragma omp for 
2023-01-11T21:41:26.9582662Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.9582721Z         {
2023-01-11T21:41:26.9582854Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9582981Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9583065Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9583233Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9583300Z         }
2023-01-11T21:41:26.9583383Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9583465Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.9583525Z         {
2023-01-11T21:41:26.9583606Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9583688Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9583765Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9583843Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9583929Z         }
2023-01-11T21:41:26.9583988Z     }
2023-01-11T21:41:26.9584044Z }
2023-01-11T21:41:26.9584125Z ''')
2023-01-11T21:41:26.9584130Z 
2023-01-11T21:41:26.9584134Z 
2023-01-11T21:41:26.9584221Z async_compile.wait(globals())
2023-01-11T21:41:26.9584291Z del async_compile
2023-01-11T21:41:26.9584296Z 
2023-01-11T21:41:26.9584364Z def call(args):
2023-01-11T21:41:26.9584425Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9584492Z     args.clear()
2023-01-11T21:41:26.9584692Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9584853Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9584920Z     del arg0_1
2023-01-11T21:41:26.9584985Z     del arg1_1
2023-01-11T21:41:26.9585055Z     return (buf0, )
2023-01-11T21:41:26.9585090Z 
2023-01-11T21:41:26.9585094Z 
2023-01-11T21:41:26.9585169Z if __name__ == "__main__":
2023-01-11T21:41:26.9585271Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9585393Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9585591Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9585790Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9585903Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9585908Z 
2023-01-11T21:41:26.9585973Z ok (0.022s)
2023-01-11T21:41:26.9586458Z   test_cpu_dense_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9586585Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9586846Z [2023-01-11 21:40:22,604] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 551
2023-01-11T21:41:26.9587096Z [2023-01-11 21:40:24,150] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 551
2023-01-11T21:41:26.9587113Z 
2023-01-11T21:41:26.9587193Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9587260Z import torch
2023-01-11T21:41:26.9587328Z import random
2023-01-11T21:41:26.9587440Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9587559Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9587564Z 
2023-01-11T21:41:26.9587640Z aten = torch.ops.aten
2023-01-11T21:41:26.9587771Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9587848Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9587856Z 
2023-01-11T21:41:26.9587870Z 
2023-01-11T21:41:26.9587990Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9588195Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9588311Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9588415Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:26.9588514Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9588572Z {
2023-01-11T21:41:26.9588668Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9588715Z     {
2023-01-11T21:41:26.9588790Z         #pragma omp for 
2023-01-11T21:41:26.9588869Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:26.9588929Z         {
2023-01-11T21:41:26.9588990Z             {
2023-01-11T21:41:26.9589053Z                 {
2023-01-11T21:41:26.9589132Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9589224Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.9589331Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.9589419Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9589533Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.9589595Z                 }
2023-01-11T21:41:26.9589657Z             }
2023-01-11T21:41:26.9589704Z         }
2023-01-11T21:41:26.9589763Z     }
2023-01-11T21:41:26.9589819Z }
2023-01-11T21:41:26.9589897Z ''')
2023-01-11T21:41:26.9589902Z 
2023-01-11T21:41:26.9589907Z 
2023-01-11T21:41:26.9589993Z async_compile.wait(globals())
2023-01-11T21:41:26.9590063Z del async_compile
2023-01-11T21:41:26.9590068Z 
2023-01-11T21:41:26.9590136Z def call(args):
2023-01-11T21:41:26.9590208Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9590267Z     args.clear()
2023-01-11T21:41:26.9590465Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9590653Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9590723Z     del arg0_1
2023-01-11T21:41:26.9590793Z     del arg1_1
2023-01-11T21:41:26.9590863Z     return (buf0, )
2023-01-11T21:41:26.9590868Z 
2023-01-11T21:41:26.9590872Z 
2023-01-11T21:41:26.9590946Z if __name__ == "__main__":
2023-01-11T21:41:26.9591045Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9591166Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9591364Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9591560Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9591672Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9591676Z 
2023-01-11T21:41:26.9591741Z ok (1.561s)
2023-01-11T21:41:26.9592222Z   test_cpu_dense_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9592349Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9592608Z [2023-01-11 21:40:24,165] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 552
2023-01-11T21:41:26.9592871Z [2023-01-11 21:40:25,716] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 552
2023-01-11T21:41:26.9592876Z 
2023-01-11T21:41:26.9592956Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9593025Z import torch
2023-01-11T21:41:26.9593092Z import random
2023-01-11T21:41:26.9593204Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9593321Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9593326Z 
2023-01-11T21:41:26.9593404Z aten = torch.ops.aten
2023-01-11T21:41:26.9593535Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9593612Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9593619Z 
2023-01-11T21:41:26.9593635Z 
2023-01-11T21:41:26.9593757Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9593959Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9594075Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9594174Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.9594272Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9594331Z {
2023-01-11T21:41:26.9594426Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9594473Z     {
2023-01-11T21:41:26.9594548Z         #pragma omp for 
2023-01-11T21:41:26.9594628Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9594689Z         {
2023-01-11T21:41:26.9594766Z             #pragma GCC ivdep
2023-01-11T21:41:26.9594851Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9594912Z             {
2023-01-11T21:41:26.9594997Z                 {
2023-01-11T21:41:26.9595061Z                     {
2023-01-11T21:41:26.9595164Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9595255Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9595361Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.9595450Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9595544Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9595596Z                     }
2023-01-11T21:41:26.9595657Z                 }
2023-01-11T21:41:26.9595718Z             }
2023-01-11T21:41:26.9595777Z         }
2023-01-11T21:41:26.9595836Z     }
2023-01-11T21:41:26.9595892Z }
2023-01-11T21:41:26.9595958Z ''')
2023-01-11T21:41:26.9595963Z 
2023-01-11T21:41:26.9595977Z 
2023-01-11T21:41:26.9596081Z async_compile.wait(globals())
2023-01-11T21:41:26.9596154Z del async_compile
2023-01-11T21:41:26.9596159Z 
2023-01-11T21:41:26.9596227Z def call(args):
2023-01-11T21:41:26.9596303Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9596371Z     args.clear()
2023-01-11T21:41:26.9596569Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9596729Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9596784Z     del arg0_1
2023-01-11T21:41:26.9596845Z     del arg1_1
2023-01-11T21:41:26.9596914Z     return (buf0, )
2023-01-11T21:41:26.9596919Z 
2023-01-11T21:41:26.9596923Z 
2023-01-11T21:41:26.9596997Z if __name__ == "__main__":
2023-01-11T21:41:26.9597109Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9597228Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9597427Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9597618Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9597719Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9597726Z 
2023-01-11T21:41:26.9597791Z ok (1.567s)
2023-01-11T21:41:26.9598271Z   test_cpu_dense_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9598395Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9598651Z [2023-01-11 21:40:25,734] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 553
2023-01-11T21:41:26.9598916Z [2023-01-11 21:40:27,577] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 553
2023-01-11T21:41:26.9598923Z 
2023-01-11T21:41:26.9599013Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9599084Z import torch
2023-01-11T21:41:26.9599151Z import random
2023-01-11T21:41:26.9599252Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9599371Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9599377Z 
2023-01-11T21:41:26.9599452Z aten = torch.ops.aten
2023-01-11T21:41:26.9599582Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9599671Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9599676Z 
2023-01-11T21:41:26.9599681Z 
2023-01-11T21:41:26.9599813Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9600013Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9600128Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9600219Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9600316Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9600376Z {
2023-01-11T21:41:26.9600612Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9600670Z     {
2023-01-11T21:41:26.9600745Z         #pragma omp for 
2023-01-11T21:41:26.9600828Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9600877Z         {
2023-01-11T21:41:26.9600955Z             #pragma GCC ivdep
2023-01-11T21:41:26.9601037Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9601098Z             {
2023-01-11T21:41:26.9601160Z                 {
2023-01-11T21:41:26.9601224Z                     {
2023-01-11T21:41:26.9601325Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9601417Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9601510Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9601604Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9601697Z                     }
2023-01-11T21:41:26.9601761Z                 }
2023-01-11T21:41:26.9601822Z             }
2023-01-11T21:41:26.9601873Z         }
2023-01-11T21:41:26.9601932Z     }
2023-01-11T21:41:26.9601989Z }
2023-01-11T21:41:26.9602067Z ''')
2023-01-11T21:41:26.9602072Z 
2023-01-11T21:41:26.9602076Z 
2023-01-11T21:41:26.9602164Z async_compile.wait(globals())
2023-01-11T21:41:26.9602233Z del async_compile
2023-01-11T21:41:26.9602239Z 
2023-01-11T21:41:26.9602306Z def call(args):
2023-01-11T21:41:26.9602379Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9602436Z     args.clear()
2023-01-11T21:41:26.9602634Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9602793Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9602860Z     del arg0_1
2023-01-11T21:41:26.9602925Z     del arg1_1
2023-01-11T21:41:26.9602994Z     return (buf0, )
2023-01-11T21:41:26.9602998Z 
2023-01-11T21:41:26.9603005Z 
2023-01-11T21:41:26.9603081Z if __name__ == "__main__":
2023-01-11T21:41:26.9603180Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9603302Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9603500Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9603693Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9603805Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9603810Z 
2023-01-11T21:41:26.9603875Z ok (1.861s)
2023-01-11T21:41:26.9604367Z   test_cpu_dense_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9604490Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9604749Z [2023-01-11 21:40:27,593] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 554
2023-01-11T21:41:26.9605012Z [2023-01-11 21:40:29,165] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 554
2023-01-11T21:41:26.9605018Z 
2023-01-11T21:41:26.9605097Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9605163Z import torch
2023-01-11T21:41:26.9605233Z import random
2023-01-11T21:41:26.9605343Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9605460Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9605465Z 
2023-01-11T21:41:26.9605540Z aten = torch.ops.aten
2023-01-11T21:41:26.9605670Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9605748Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9605764Z 
2023-01-11T21:41:26.9605771Z 
2023-01-11T21:41:26.9605891Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9606092Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9606239Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9606340Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9606438Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9606496Z {
2023-01-11T21:41:26.9606592Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9606639Z     {
2023-01-11T21:41:26.9606717Z         #pragma omp for 
2023-01-11T21:41:26.9606795Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9606855Z         {
2023-01-11T21:41:26.9606932Z             #pragma GCC ivdep
2023-01-11T21:41:26.9607014Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9607075Z             {
2023-01-11T21:41:26.9607126Z                 {
2023-01-11T21:41:26.9607189Z                     {
2023-01-11T21:41:26.9607316Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9607418Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:41:26.9607512Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9607609Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9607673Z                     }
2023-01-11T21:41:26.9607723Z                 }
2023-01-11T21:41:26.9607783Z             }
2023-01-11T21:41:26.9607843Z         }
2023-01-11T21:41:26.9607902Z     }
2023-01-11T21:41:26.9607958Z }
2023-01-11T21:41:26.9608033Z ''')
2023-01-11T21:41:26.9608038Z 
2023-01-11T21:41:26.9608042Z 
2023-01-11T21:41:26.9608127Z async_compile.wait(globals())
2023-01-11T21:41:26.9608186Z del async_compile
2023-01-11T21:41:26.9608191Z 
2023-01-11T21:41:26.9608258Z def call(args):
2023-01-11T21:41:26.9608330Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9608399Z     args.clear()
2023-01-11T21:41:26.9608599Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9608760Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9608828Z     del arg0_1
2023-01-11T21:41:26.9608882Z     del arg1_1
2023-01-11T21:41:26.9608951Z     return (buf0, )
2023-01-11T21:41:26.9608956Z 
2023-01-11T21:41:26.9608960Z 
2023-01-11T21:41:26.9609168Z if __name__ == "__main__":
2023-01-11T21:41:26.9609349Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9609480Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9609686Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9609882Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9609997Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9610003Z 
2023-01-11T21:41:26.9610056Z ok (1.588s)
2023-01-11T21:41:26.9610548Z   test_cpu_double_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9610678Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9610937Z [2023-01-11 21:40:29,187] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 555
2023-01-11T21:41:26.9611201Z [2023-01-11 21:40:31,145] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 555
2023-01-11T21:41:26.9611206Z 
2023-01-11T21:41:26.9611299Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9611368Z import torch
2023-01-11T21:41:26.9611440Z import random
2023-01-11T21:41:26.9611556Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9611662Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9611667Z 
2023-01-11T21:41:26.9611804Z aten = torch.ops.aten
2023-01-11T21:41:26.9611935Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9612025Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9612031Z 
2023-01-11T21:41:26.9612035Z 
2023-01-11T21:41:26.9612168Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9612372Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9612491Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.9612593Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9612680Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9612739Z {
2023-01-11T21:41:26.9612835Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9612895Z     {
2023-01-11T21:41:26.9612969Z         #pragma omp for 
2023-01-11T21:41:26.9613085Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9613149Z         {
2023-01-11T21:41:26.9613216Z             #pragma GCC ivdep
2023-01-11T21:41:26.9613302Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9613365Z             {
2023-01-11T21:41:26.9613429Z                 {
2023-01-11T21:41:26.9613503Z                     {
2023-01-11T21:41:26.9613603Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9613759Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9613856Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:26.9613948Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9614041Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9614105Z                     }
2023-01-11T21:41:26.9614166Z                 }
2023-01-11T21:41:26.9614227Z             }
2023-01-11T21:41:26.9614286Z         }
2023-01-11T21:41:26.9614333Z     }
2023-01-11T21:41:26.9614391Z }
2023-01-11T21:41:26.9614470Z ''')
2023-01-11T21:41:26.9614475Z 
2023-01-11T21:41:26.9614479Z 
2023-01-11T21:41:26.9614568Z async_compile.wait(globals())
2023-01-11T21:41:26.9614636Z del async_compile
2023-01-11T21:41:26.9614642Z 
2023-01-11T21:41:26.9614712Z def call(args):
2023-01-11T21:41:26.9614783Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9614841Z     args.clear()
2023-01-11T21:41:26.9615040Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9615200Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9615267Z     del arg0_1
2023-01-11T21:41:26.9615331Z     del arg1_1
2023-01-11T21:41:26.9615400Z     return (buf0, )
2023-01-11T21:41:26.9615405Z 
2023-01-11T21:41:26.9615409Z 
2023-01-11T21:41:26.9615483Z if __name__ == "__main__":
2023-01-11T21:41:26.9615593Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9615705Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9615902Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9616096Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9616208Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9616212Z 
2023-01-11T21:41:26.9616277Z ok (1.979s)
2023-01-11T21:41:26.9616768Z   test_cpu_double_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9616893Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9617154Z [2023-01-11 21:40:31,165] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 556
2023-01-11T21:41:26.9617417Z [2023-01-11 21:40:33,228] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 556
2023-01-11T21:41:26.9617450Z 
2023-01-11T21:41:26.9617532Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9617601Z import torch
2023-01-11T21:41:26.9617668Z import random
2023-01-11T21:41:26.9617781Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9617898Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9617903Z 
2023-01-11T21:41:26.9617980Z aten = torch.ops.aten
2023-01-11T21:41:26.9618108Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9618200Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9618205Z 
2023-01-11T21:41:26.9618209Z 
2023-01-11T21:41:26.9618330Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9618562Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9618683Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.9618785Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9618884Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9618943Z {
2023-01-11T21:41:26.9619037Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9619084Z     {
2023-01-11T21:41:26.9619159Z         #pragma omp for 
2023-01-11T21:41:26.9619238Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9619299Z         {
2023-01-11T21:41:26.9619377Z             #pragma GCC ivdep
2023-01-11T21:41:26.9619461Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9619522Z             {
2023-01-11T21:41:26.9619573Z                 {
2023-01-11T21:41:26.9619637Z                     {
2023-01-11T21:41:26.9619739Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9619831Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9619941Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:26.9620031Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9620126Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9620178Z                     }
2023-01-11T21:41:26.9620239Z                 }
2023-01-11T21:41:26.9620300Z             }
2023-01-11T21:41:26.9620360Z         }
2023-01-11T21:41:26.9620419Z     }
2023-01-11T21:41:26.9620476Z }
2023-01-11T21:41:26.9620541Z ''')
2023-01-11T21:41:26.9620557Z 
2023-01-11T21:41:26.9620561Z 
2023-01-11T21:41:26.9620636Z async_compile.wait(globals())
2023-01-11T21:41:26.9620705Z del async_compile
2023-01-11T21:41:26.9620710Z 
2023-01-11T21:41:26.9620777Z def call(args):
2023-01-11T21:41:26.9620849Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9620918Z     args.clear()
2023-01-11T21:41:26.9621128Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9621294Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9621349Z     del arg0_1
2023-01-11T21:41:26.9621416Z     del arg1_1
2023-01-11T21:41:26.9621485Z     return (buf0, )
2023-01-11T21:41:26.9621490Z 
2023-01-11T21:41:26.9621494Z 
2023-01-11T21:41:26.9621569Z if __name__ == "__main__":
2023-01-11T21:41:26.9621679Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9621800Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9621996Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9622202Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9622303Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9622308Z 
2023-01-11T21:41:26.9622373Z ok (2.083s)
2023-01-11T21:41:26.9622863Z   test_cpu_double_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9623017Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9623354Z [2023-01-11 21:40:33,247] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 557
2023-01-11T21:41:26.9623625Z [2023-01-11 21:40:35,200] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 557
2023-01-11T21:41:26.9623631Z 
2023-01-11T21:41:26.9623723Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9623793Z import torch
2023-01-11T21:41:26.9623862Z import random
2023-01-11T21:41:26.9623964Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9624083Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9624124Z 
2023-01-11T21:41:26.9624203Z aten = torch.ops.aten
2023-01-11T21:41:26.9624334Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9624427Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9624431Z 
2023-01-11T21:41:26.9624436Z 
2023-01-11T21:41:26.9624569Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9624773Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9624890Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.9624993Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9625080Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9625137Z {
2023-01-11T21:41:26.9625232Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9625291Z     {
2023-01-11T21:41:26.9625367Z         #pragma omp for 
2023-01-11T21:41:26.9625448Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:26.9625496Z         {
2023-01-11T21:41:26.9625560Z             {
2023-01-11T21:41:26.9625621Z                 {
2023-01-11T21:41:26.9625711Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9625802Z                     auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.9625908Z                     auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:26.9625997Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9626068Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.9626130Z                 }
2023-01-11T21:41:26.9626194Z             }
2023-01-11T21:41:26.9626254Z         }
2023-01-11T21:41:26.9626312Z     }
2023-01-11T21:41:26.9626370Z }
2023-01-11T21:41:26.9626446Z ''')
2023-01-11T21:41:26.9626451Z 
2023-01-11T21:41:26.9626456Z 
2023-01-11T21:41:26.9626531Z async_compile.wait(globals())
2023-01-11T21:41:26.9626601Z del async_compile
2023-01-11T21:41:26.9626606Z 
2023-01-11T21:41:26.9626675Z def call(args):
2023-01-11T21:41:26.9626747Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9626818Z     args.clear()
2023-01-11T21:41:26.9627018Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9627180Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9627235Z     del arg0_1
2023-01-11T21:41:26.9627300Z     del arg1_1
2023-01-11T21:41:26.9627369Z     return (buf0, )
2023-01-11T21:41:26.9627374Z 
2023-01-11T21:41:26.9627378Z 
2023-01-11T21:41:26.9627451Z if __name__ == "__main__":
2023-01-11T21:41:26.9627562Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9627682Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9627881Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9628070Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9628171Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9628187Z 
2023-01-11T21:41:26.9628243Z ok (1.972s)
2023-01-11T21:41:26.9628726Z   test_cpu_double_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9628882Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9629144Z [2023-01-11 21:40:35,215] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 558
2023-01-11T21:41:26.9629407Z [2023-01-11 21:40:36,722] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 558
2023-01-11T21:41:26.9629413Z 
2023-01-11T21:41:26.9629504Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9629572Z import torch
2023-01-11T21:41:26.9629640Z import random
2023-01-11T21:41:26.9629767Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9629887Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9629894Z 
2023-01-11T21:41:26.9629971Z aten = torch.ops.aten
2023-01-11T21:41:26.9630103Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9630193Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9630197Z 
2023-01-11T21:41:26.9630202Z 
2023-01-11T21:41:26.9630336Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9630541Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9630659Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.9630762Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9630848Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9630907Z {
2023-01-11T21:41:26.9631003Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9631064Z     {
2023-01-11T21:41:26.9631139Z         #pragma omp for 
2023-01-11T21:41:26.9631220Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:26.9631272Z         {
2023-01-11T21:41:26.9631333Z             {
2023-01-11T21:41:26.9631394Z                 {
2023-01-11T21:41:26.9631484Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9631572Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9631679Z                     auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:26.9631767Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9631841Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.9631904Z                 }
2023-01-11T21:41:26.9631966Z             }
2023-01-11T21:41:26.9632027Z         }
2023-01-11T21:41:26.9632085Z     }
2023-01-11T21:41:26.9632142Z }
2023-01-11T21:41:26.9632219Z ''')
2023-01-11T21:41:26.9632225Z 
2023-01-11T21:41:26.9632229Z 
2023-01-11T21:41:26.9632303Z async_compile.wait(globals())
2023-01-11T21:41:26.9632376Z del async_compile
2023-01-11T21:41:26.9632381Z 
2023-01-11T21:41:26.9632450Z def call(args):
2023-01-11T21:41:26.9632525Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9632598Z     args.clear()
2023-01-11T21:41:26.9632796Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9632958Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9633027Z     del arg0_1
2023-01-11T21:41:26.9633080Z     del arg1_1
2023-01-11T21:41:26.9633147Z     return (buf0, )
2023-01-11T21:41:26.9633152Z 
2023-01-11T21:41:26.9633156Z 
2023-01-11T21:41:26.9633228Z if __name__ == "__main__":
2023-01-11T21:41:26.9633340Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9633460Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9633658Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9633856Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9633959Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9634002Z 
2023-01-11T21:41:26.9634056Z ok (1.521s)
2023-01-11T21:41:26.9634543Z   test_cpu_double_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9634666Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9634923Z [2023-01-11 21:40:36,737] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 559
2023-01-11T21:41:26.9635190Z [2023-01-11 21:40:38,303] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 559
2023-01-11T21:41:26.9635245Z 
2023-01-11T21:41:26.9635340Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9635409Z import torch
2023-01-11T21:41:26.9635481Z import random
2023-01-11T21:41:26.9635595Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9635702Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9635707Z 
2023-01-11T21:41:26.9635784Z aten = torch.ops.aten
2023-01-11T21:41:26.9635915Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9636006Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9636011Z 
2023-01-11T21:41:26.9636015Z 
2023-01-11T21:41:26.9636150Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9636352Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9636473Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.9636576Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:26.9636666Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9636726Z {
2023-01-11T21:41:26.9636822Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9636884Z     {
2023-01-11T21:41:26.9636961Z         #pragma omp for 
2023-01-11T21:41:26.9637043Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:41:26.9637109Z         {
2023-01-11T21:41:26.9637160Z             {
2023-01-11T21:41:26.9637223Z                 {
2023-01-11T21:41:26.9637314Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9637401Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9637487Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9637569Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9637621Z                 }
2023-01-11T21:41:26.9637684Z             }
2023-01-11T21:41:26.9637743Z         }
2023-01-11T21:41:26.9637802Z     }
2023-01-11T21:41:26.9637859Z }
2023-01-11T21:41:26.9637936Z ''')
2023-01-11T21:41:26.9637941Z 
2023-01-11T21:41:26.9637948Z 
2023-01-11T21:41:26.9638039Z async_compile.wait(globals())
2023-01-11T21:41:26.9638096Z del async_compile
2023-01-11T21:41:26.9638114Z 
2023-01-11T21:41:26.9638171Z def call(args):
2023-01-11T21:41:26.9638244Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9638315Z     args.clear()
2023-01-11T21:41:26.9638514Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9638675Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9638742Z     del arg0_1
2023-01-11T21:41:26.9638806Z     del arg1_1
2023-01-11T21:41:26.9638865Z     return (buf0, )
2023-01-11T21:41:26.9638870Z 
2023-01-11T21:41:26.9638874Z 
2023-01-11T21:41:26.9638946Z if __name__ == "__main__":
2023-01-11T21:41:26.9639058Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9639179Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9639379Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9639575Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9639729Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9639734Z 
2023-01-11T21:41:26.9639797Z ok (1.582s)
2023-01-11T21:41:26.9640267Z   test_cpu_double_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9640393Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9640649Z [2023-01-11 21:40:38,322] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 560
2023-01-11T21:41:26.9640940Z [2023-01-11 21:40:39,853] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 560
2023-01-11T21:41:26.9640946Z 
2023-01-11T21:41:26.9641041Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9641111Z import torch
2023-01-11T21:41:26.9641180Z import random
2023-01-11T21:41:26.9641292Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9641410Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9641415Z 
2023-01-11T21:41:26.9641480Z aten = torch.ops.aten
2023-01-11T21:41:26.9641608Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9641698Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9641703Z 
2023-01-11T21:41:26.9641707Z 
2023-01-11T21:41:26.9641841Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9642041Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9642158Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.9642261Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.9642358Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9642407Z {
2023-01-11T21:41:26.9642501Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9642562Z     {
2023-01-11T21:41:26.9642637Z         #pragma omp for 
2023-01-11T21:41:26.9642716Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9642777Z         {
2023-01-11T21:41:26.9642853Z             #pragma GCC ivdep
2023-01-11T21:41:26.9642926Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9642986Z             {
2023-01-11T21:41:26.9643049Z                 {
2023-01-11T21:41:26.9643115Z                     {
2023-01-11T21:41:26.9643217Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9643310Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9643407Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:26.9643500Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9643594Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9643660Z                     }
2023-01-11T21:41:26.9643722Z                 }
2023-01-11T21:41:26.9643784Z             }
2023-01-11T21:41:26.9643844Z         }
2023-01-11T21:41:26.9643891Z     }
2023-01-11T21:41:26.9643948Z }
2023-01-11T21:41:26.9644025Z ''')
2023-01-11T21:41:26.9644030Z 
2023-01-11T21:41:26.9644034Z 
2023-01-11T21:41:26.9644122Z async_compile.wait(globals())
2023-01-11T21:41:26.9644191Z del async_compile
2023-01-11T21:41:26.9644196Z 
2023-01-11T21:41:26.9644265Z def call(args):
2023-01-11T21:41:26.9644339Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9644396Z     args.clear()
2023-01-11T21:41:26.9644595Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9644756Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9644823Z     del arg0_1
2023-01-11T21:41:26.9644891Z     del arg1_1
2023-01-11T21:41:26.9644962Z     return (buf0, )
2023-01-11T21:41:26.9644967Z 
2023-01-11T21:41:26.9645001Z 
2023-01-11T21:41:26.9645077Z if __name__ == "__main__":
2023-01-11T21:41:26.9645189Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9645299Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9645497Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9645686Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9645798Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9645803Z 
2023-01-11T21:41:26.9645868Z ok (1.550s)
2023-01-11T21:41:26.9646390Z   test_cpu_double_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9646520Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9646779Z [2023-01-11 21:40:39,870] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 561
2023-01-11T21:41:26.9647041Z [2023-01-11 21:40:41,767] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 561
2023-01-11T21:41:26.9647046Z 
2023-01-11T21:41:26.9647138Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9647195Z import torch
2023-01-11T21:41:26.9647265Z import random
2023-01-11T21:41:26.9647378Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9647498Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9647503Z 
2023-01-11T21:41:26.9647579Z aten = torch.ops.aten
2023-01-11T21:41:26.9647712Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9647800Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9647805Z 
2023-01-11T21:41:26.9647811Z 
2023-01-11T21:41:26.9647943Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9648134Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9648251Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.9648353Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9648448Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9648507Z {
2023-01-11T21:41:26.9648602Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9648662Z     {
2023-01-11T21:41:26.9648725Z         #pragma omp for 
2023-01-11T21:41:26.9648805Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9648866Z         {
2023-01-11T21:41:26.9648943Z             #pragma GCC ivdep
2023-01-11T21:41:26.9649150Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9649250Z             {
2023-01-11T21:41:26.9649331Z                 {
2023-01-11T21:41:26.9649401Z                     {
2023-01-11T21:41:26.9649507Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9649609Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9649718Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:26.9649809Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9649903Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9649967Z                     }
2023-01-11T21:41:26.9650017Z                 }
2023-01-11T21:41:26.9650077Z             }
2023-01-11T21:41:26.9650138Z         }
2023-01-11T21:41:26.9650196Z     }
2023-01-11T21:41:26.9650252Z }
2023-01-11T21:41:26.9650333Z ''')
2023-01-11T21:41:26.9650339Z 
2023-01-11T21:41:26.9650343Z 
2023-01-11T21:41:26.9650430Z async_compile.wait(globals())
2023-01-11T21:41:26.9650488Z del async_compile
2023-01-11T21:41:26.9650495Z 
2023-01-11T21:41:26.9650563Z def call(args):
2023-01-11T21:41:26.9650636Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9650762Z     args.clear()
2023-01-11T21:41:26.9650963Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9651122Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9651189Z     del arg0_1
2023-01-11T21:41:26.9651242Z     del arg1_1
2023-01-11T21:41:26.9651311Z     return (buf0, )
2023-01-11T21:41:26.9651317Z 
2023-01-11T21:41:26.9651321Z 
2023-01-11T21:41:26.9651396Z if __name__ == "__main__":
2023-01-11T21:41:26.9651507Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9651628Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9651823Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9652054Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9652168Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9652174Z 
2023-01-11T21:41:26.9652230Z ok (1.914s)
2023-01-11T21:41:26.9652719Z   test_cpu_double_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9652842Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9653099Z [2023-01-11 21:40:41,787] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 562
2023-01-11T21:41:26.9653362Z [2023-01-11 21:40:43,294] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 562
2023-01-11T21:41:26.9653369Z 
2023-01-11T21:41:26.9653460Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9653528Z import torch
2023-01-11T21:41:26.9653597Z import random
2023-01-11T21:41:26.9653710Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9653816Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9653821Z 
2023-01-11T21:41:26.9653896Z aten = torch.ops.aten
2023-01-11T21:41:26.9654026Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9654118Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9654123Z 
2023-01-11T21:41:26.9654127Z 
2023-01-11T21:41:26.9654259Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9654460Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9654577Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:41:26.9654679Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9654767Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9654824Z {
2023-01-11T21:41:26.9654922Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9654985Z     {
2023-01-11T21:41:26.9655060Z         #pragma omp for 
2023-01-11T21:41:26.9655140Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9655201Z         {
2023-01-11T21:41:26.9655266Z             #pragma GCC ivdep
2023-01-11T21:41:26.9655348Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9655410Z             {
2023-01-11T21:41:26.9655472Z                 {
2023-01-11T21:41:26.9655536Z                     {
2023-01-11T21:41:26.9655635Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9655734Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:41:26.9655832Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:41:26.9655923Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9656019Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9656084Z                     }
2023-01-11T21:41:26.9656145Z                 }
2023-01-11T21:41:26.9656236Z             }
2023-01-11T21:41:26.9656296Z         }
2023-01-11T21:41:26.9656343Z     }
2023-01-11T21:41:26.9656400Z }
2023-01-11T21:41:26.9656479Z ''')
2023-01-11T21:41:26.9656485Z 
2023-01-11T21:41:26.9656489Z 
2023-01-11T21:41:26.9656577Z async_compile.wait(globals())
2023-01-11T21:41:26.9656646Z del async_compile
2023-01-11T21:41:26.9656651Z 
2023-01-11T21:41:26.9656718Z def call(args):
2023-01-11T21:41:26.9656791Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9656849Z     args.clear()
2023-01-11T21:41:26.9657044Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9657205Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9657271Z     del arg0_1
2023-01-11T21:41:26.9657337Z     del arg1_1
2023-01-11T21:41:26.9657405Z     return (buf0, )
2023-01-11T21:41:26.9657438Z 
2023-01-11T21:41:26.9657443Z 
2023-01-11T21:41:26.9657519Z if __name__ == "__main__":
2023-01-11T21:41:26.9657621Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9657743Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9657943Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9658136Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9658250Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9658255Z 
2023-01-11T21:41:26.9658319Z ok (1.528s)
2023-01-11T21:41:26.9658803Z   test_cpu_int_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9658928Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9659190Z [2023-01-11 21:40:43,314] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 563
2023-01-11T21:41:26.9659454Z [2023-01-11 21:40:44,788] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 563
2023-01-11T21:41:26.9659459Z 
2023-01-11T21:41:26.9659539Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9659606Z import torch
2023-01-11T21:41:26.9659673Z import random
2023-01-11T21:41:26.9659786Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9659903Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9659908Z 
2023-01-11T21:41:26.9659984Z aten = torch.ops.aten
2023-01-11T21:41:26.9660114Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9660191Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9660210Z 
2023-01-11T21:41:26.9660214Z 
2023-01-11T21:41:26.9660335Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9660539Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9660652Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.9660757Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9660852Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9660912Z {
2023-01-11T21:41:26.9661006Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9661054Z     {
2023-01-11T21:41:26.9661128Z         #pragma omp for 
2023-01-11T21:41:26.9661207Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9661267Z         {
2023-01-11T21:41:26.9661327Z             {
2023-01-11T21:41:26.9661388Z                 {
2023-01-11T21:41:26.9661478Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9661558Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.9661661Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.9661752Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9661865Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.9661928Z                 }
2023-01-11T21:41:26.9661990Z             }
2023-01-11T21:41:26.9662049Z         }
2023-01-11T21:41:26.9662097Z     }
2023-01-11T21:41:26.9662152Z }
2023-01-11T21:41:26.9662230Z ''')
2023-01-11T21:41:26.9662236Z 
2023-01-11T21:41:26.9662240Z 
2023-01-11T21:41:26.9662327Z async_compile.wait(globals())
2023-01-11T21:41:26.9662396Z del async_compile
2023-01-11T21:41:26.9662401Z 
2023-01-11T21:41:26.9662469Z def call(args):
2023-01-11T21:41:26.9662542Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9662598Z     args.clear()
2023-01-11T21:41:26.9662792Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9662980Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9663050Z     del arg0_1
2023-01-11T21:41:26.9663115Z     del arg1_1
2023-01-11T21:41:26.9663270Z     return (buf0, )
2023-01-11T21:41:26.9663279Z 
2023-01-11T21:41:26.9663283Z 
2023-01-11T21:41:26.9663359Z if __name__ == "__main__":
2023-01-11T21:41:26.9663473Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9663583Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9663776Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9663966Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9664079Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9664084Z 
2023-01-11T21:41:26.9664146Z ok (1.493s)
2023-01-11T21:41:26.9664630Z   test_cpu_int_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9664756Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9665012Z [2023-01-11 21:40:44,803] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 564
2023-01-11T21:41:26.9665277Z [2023-01-11 21:40:46,674] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 564
2023-01-11T21:41:26.9665282Z 
2023-01-11T21:41:26.9665362Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9665429Z import torch
2023-01-11T21:41:26.9665498Z import random
2023-01-11T21:41:26.9665609Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9665724Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9665729Z 
2023-01-11T21:41:26.9665805Z aten = torch.ops.aten
2023-01-11T21:41:26.9665937Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9666025Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9666032Z 
2023-01-11T21:41:26.9666036Z 
2023-01-11T21:41:26.9666155Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9666358Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9666469Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.9666571Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9666668Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9666725Z {
2023-01-11T21:41:26.9666820Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9666868Z     {
2023-01-11T21:41:26.9666941Z         #pragma omp for 
2023-01-11T21:41:26.9667020Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9667082Z         {
2023-01-11T21:41:26.9667159Z             #pragma GCC ivdep
2023-01-11T21:41:26.9667243Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9667304Z             {
2023-01-11T21:41:26.9667355Z                 {
2023-01-11T21:41:26.9667459Z                     {
2023-01-11T21:41:26.9667550Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9667640Z                         auto tmp2 = in_ptr1[i0];
2023-01-11T21:41:26.9667745Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.9667834Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9667928Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9667981Z                     }
2023-01-11T21:41:26.9668042Z                 }
2023-01-11T21:41:26.9668102Z             }
2023-01-11T21:41:26.9668163Z         }
2023-01-11T21:41:26.9668220Z     }
2023-01-11T21:41:26.9668276Z }
2023-01-11T21:41:26.9668341Z ''')
2023-01-11T21:41:26.9668358Z 
2023-01-11T21:41:26.9668362Z 
2023-01-11T21:41:26.9668436Z async_compile.wait(globals())
2023-01-11T21:41:26.9668534Z del async_compile
2023-01-11T21:41:26.9668540Z 
2023-01-11T21:41:26.9668610Z def call(args):
2023-01-11T21:41:26.9668682Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9668751Z     args.clear()
2023-01-11T21:41:26.9668959Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9669120Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9669174Z     del arg0_1
2023-01-11T21:41:26.9669238Z     del arg1_1
2023-01-11T21:41:26.9669307Z     return (buf0, )
2023-01-11T21:41:26.9669312Z 
2023-01-11T21:41:26.9669316Z 
2023-01-11T21:41:26.9669389Z if __name__ == "__main__":
2023-01-11T21:41:26.9669499Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9669618Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9669806Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9670014Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9670115Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9670121Z 
2023-01-11T21:41:26.9670186Z ok (1.886s)
2023-01-11T21:41:26.9670669Z   test_cpu_int_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9670794Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9671054Z [2023-01-11 21:40:46,689] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 565
2023-01-11T21:41:26.9671316Z [2023-01-11 21:40:48,456] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 565
2023-01-11T21:41:26.9671322Z 
2023-01-11T21:41:26.9671413Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9671483Z import torch
2023-01-11T21:41:26.9671552Z import random
2023-01-11T21:41:26.9671652Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9671770Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9671774Z 
2023-01-11T21:41:26.9671850Z aten = torch.ops.aten
2023-01-11T21:41:26.9671979Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9672067Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9672072Z 
2023-01-11T21:41:26.9672077Z 
2023-01-11T21:41:26.9672209Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9672409Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9672522Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.9672614Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9672714Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9672771Z {
2023-01-11T21:41:26.9672897Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9672955Z     {
2023-01-11T21:41:26.9673029Z         #pragma omp for 
2023-01-11T21:41:26.9673109Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9673159Z         {
2023-01-11T21:41:26.9673217Z             {
2023-01-11T21:41:26.9673280Z                 {
2023-01-11T21:41:26.9673369Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9673456Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:41:26.9673558Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.9673647Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9673717Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:41:26.9673779Z                 }
2023-01-11T21:41:26.9673839Z             }
2023-01-11T21:41:26.9673898Z         }
2023-01-11T21:41:26.9673956Z     }
2023-01-11T21:41:26.9674040Z }
2023-01-11T21:41:26.9674119Z ''')
2023-01-11T21:41:26.9674124Z 
2023-01-11T21:41:26.9674128Z 
2023-01-11T21:41:26.9674204Z async_compile.wait(globals())
2023-01-11T21:41:26.9674275Z del async_compile
2023-01-11T21:41:26.9674280Z 
2023-01-11T21:41:26.9674347Z def call(args):
2023-01-11T21:41:26.9674419Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9674485Z     args.clear()
2023-01-11T21:41:26.9674677Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9674835Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9674890Z     del arg0_1
2023-01-11T21:41:26.9674955Z     del arg1_1
2023-01-11T21:41:26.9675025Z     return (buf0, )
2023-01-11T21:41:26.9675030Z 
2023-01-11T21:41:26.9675034Z 
2023-01-11T21:41:26.9675106Z if __name__ == "__main__":
2023-01-11T21:41:26.9675216Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9675337Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9675525Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9675718Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9675820Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9675825Z 
2023-01-11T21:41:26.9675889Z ok (1.782s)
2023-01-11T21:41:26.9676367Z   test_cpu_int_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9676492Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9676753Z [2023-01-11 21:40:48,472] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 566
2023-01-11T21:41:26.9677011Z [2023-01-11 21:40:50,800] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 566
2023-01-11T21:41:26.9677018Z 
2023-01-11T21:41:26.9677111Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9677178Z import torch
2023-01-11T21:41:26.9677246Z import random
2023-01-11T21:41:26.9677346Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9677463Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9677467Z 
2023-01-11T21:41:26.9677543Z aten = torch.ops.aten
2023-01-11T21:41:26.9677671Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9677760Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9677765Z 
2023-01-11T21:41:26.9677769Z 
2023-01-11T21:41:26.9677899Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9678103Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9678217Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.9678309Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9678447Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9678504Z {
2023-01-11T21:41:26.9678603Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9678663Z     {
2023-01-11T21:41:26.9678736Z         #pragma omp for 
2023-01-11T21:41:26.9678814Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9678864Z         {
2023-01-11T21:41:26.9678940Z             #pragma GCC ivdep
2023-01-11T21:41:26.9679023Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9679085Z             {
2023-01-11T21:41:26.9679149Z                 {
2023-01-11T21:41:26.9679212Z                     {
2023-01-11T21:41:26.9679304Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9679393Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9679524Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.9679619Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9679714Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9679777Z                     }
2023-01-11T21:41:26.9679840Z                 }
2023-01-11T21:41:26.9679900Z             }
2023-01-11T21:41:26.9679949Z         }
2023-01-11T21:41:26.9680005Z     }
2023-01-11T21:41:26.9680063Z }
2023-01-11T21:41:26.9680138Z ''')
2023-01-11T21:41:26.9680143Z 
2023-01-11T21:41:26.9680148Z 
2023-01-11T21:41:26.9680235Z async_compile.wait(globals())
2023-01-11T21:41:26.9680304Z del async_compile
2023-01-11T21:41:26.9680309Z 
2023-01-11T21:41:26.9680376Z def call(args):
2023-01-11T21:41:26.9680438Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9680505Z     args.clear()
2023-01-11T21:41:26.9680702Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9680863Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9680930Z     del arg0_1
2023-01-11T21:41:26.9680993Z     del arg1_1
2023-01-11T21:41:26.9681064Z     return (buf0, )
2023-01-11T21:41:26.9681069Z 
2023-01-11T21:41:26.9681076Z 
2023-01-11T21:41:26.9681149Z if __name__ == "__main__":
2023-01-11T21:41:26.9681248Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9681366Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9681557Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9681756Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9681868Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9681873Z 
2023-01-11T21:41:26.9681937Z ok (2.345s)
2023-01-11T21:41:26.9682420Z   test_cpu_int_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9682547Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9682805Z [2023-01-11 21:40:50,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 567
2023-01-11T21:41:26.9683056Z [2023-01-11 21:40:52,932] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 567
2023-01-11T21:41:26.9683073Z 
2023-01-11T21:41:26.9683152Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9683221Z import torch
2023-01-11T21:41:26.9683294Z import random
2023-01-11T21:41:26.9683406Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9683524Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9683529Z 
2023-01-11T21:41:26.9683608Z aten = torch.ops.aten
2023-01-11T21:41:26.9683737Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9683815Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9683848Z 
2023-01-11T21:41:26.9683852Z 
2023-01-11T21:41:26.9683988Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9684191Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9684303Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.9684408Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:26.9684504Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9684562Z {
2023-01-11T21:41:26.9684645Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9684703Z     {
2023-01-11T21:41:26.9684778Z         #pragma omp for 
2023-01-11T21:41:26.9684856Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9684916Z         {
2023-01-11T21:41:26.9685033Z             #pragma GCC ivdep
2023-01-11T21:41:26.9685117Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9685167Z             {
2023-01-11T21:41:26.9685232Z                 {
2023-01-11T21:41:26.9685296Z                     {
2023-01-11T21:41:26.9685388Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9685489Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9685597Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.9685688Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9685769Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9685832Z                     }
2023-01-11T21:41:26.9685894Z                 }
2023-01-11T21:41:26.9685955Z             }
2023-01-11T21:41:26.9686015Z         }
2023-01-11T21:41:26.9686077Z     }
2023-01-11T21:41:26.9686134Z }
2023-01-11T21:41:26.9686200Z ''')
2023-01-11T21:41:26.9686205Z 
2023-01-11T21:41:26.9686209Z 
2023-01-11T21:41:26.9686299Z async_compile.wait(globals())
2023-01-11T21:41:26.9686369Z del async_compile
2023-01-11T21:41:26.9686375Z 
2023-01-11T21:41:26.9686443Z def call(args):
2023-01-11T21:41:26.9686518Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9686588Z     args.clear()
2023-01-11T21:41:26.9686785Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9686933Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9686999Z     del arg0_1
2023-01-11T21:41:26.9687065Z     del arg1_1
2023-01-11T21:41:26.9687134Z     return (buf0, )
2023-01-11T21:41:26.9687138Z 
2023-01-11T21:41:26.9687143Z 
2023-01-11T21:41:26.9687215Z if __name__ == "__main__":
2023-01-11T21:41:26.9687325Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9687443Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9687634Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9687822Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9687934Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9687942Z 
2023-01-11T21:41:26.9688005Z ok (2.131s)
2023-01-11T21:41:26.9688481Z   test_cpu_int_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9688605Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9688861Z [2023-01-11 21:40:52,948] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 568
2023-01-11T21:41:26.9689317Z [2023-01-11 21:40:55,152] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 568
2023-01-11T21:41:26.9689326Z 
2023-01-11T21:41:26.9689433Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9689564Z import torch
2023-01-11T21:41:26.9689621Z import random
2023-01-11T21:41:26.9689734Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9689850Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9689855Z 
2023-01-11T21:41:26.9689933Z aten = torch.ops.aten
2023-01-11T21:41:26.9690063Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9690152Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9690158Z 
2023-01-11T21:41:26.9690162Z 
2023-01-11T21:41:26.9690296Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9690497Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9690613Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.9690702Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.9690832Z                        int* __restrict__ out_ptr0)
2023-01-11T21:41:26.9690894Z {
2023-01-11T21:41:26.9690991Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9691049Z     {
2023-01-11T21:41:26.9691124Z         #pragma omp for 
2023-01-11T21:41:26.9691192Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9691252Z         {
2023-01-11T21:41:26.9691312Z             {
2023-01-11T21:41:26.9691373Z                 {
2023-01-11T21:41:26.9691462Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9691550Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9691637Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9691708Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9691770Z                 }
2023-01-11T21:41:26.9691830Z             }
2023-01-11T21:41:26.9691887Z         }
2023-01-11T21:41:26.9691945Z     }
2023-01-11T21:41:26.9692001Z }
2023-01-11T21:41:26.9692078Z ''')
2023-01-11T21:41:26.9692084Z 
2023-01-11T21:41:26.9692090Z 
2023-01-11T21:41:26.9692165Z async_compile.wait(globals())
2023-01-11T21:41:26.9692235Z del async_compile
2023-01-11T21:41:26.9692242Z 
2023-01-11T21:41:26.9692309Z def call(args):
2023-01-11T21:41:26.9692382Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9692450Z     args.clear()
2023-01-11T21:41:26.9692640Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9692801Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9692856Z     del arg0_1
2023-01-11T21:41:26.9692920Z     del arg1_1
2023-01-11T21:41:26.9692990Z     return (buf0, )
2023-01-11T21:41:26.9692996Z 
2023-01-11T21:41:26.9693000Z 
2023-01-11T21:41:26.9693072Z if __name__ == "__main__":
2023-01-11T21:41:26.9693180Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9693299Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9693489Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9693676Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9693781Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9693796Z 
2023-01-11T21:41:26.9693849Z ok (2.219s)
2023-01-11T21:41:26.9694327Z   test_cpu_int_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9694451Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9694714Z [2023-01-11 21:40:55,172] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 569
2023-01-11T21:41:26.9694978Z [2023-01-11 21:40:56,810] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 569
2023-01-11T21:41:26.9694984Z 
2023-01-11T21:41:26.9695107Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9695176Z import torch
2023-01-11T21:41:26.9695246Z import random
2023-01-11T21:41:26.9695347Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9695467Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9695473Z 
2023-01-11T21:41:26.9695550Z aten = torch.ops.aten
2023-01-11T21:41:26.9695680Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9695769Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9695775Z 
2023-01-11T21:41:26.9695779Z 
2023-01-11T21:41:26.9695913Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9696115Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9696227Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.9696364Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9696452Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9696514Z {
2023-01-11T21:41:26.9696607Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9696667Z     {
2023-01-11T21:41:26.9696741Z         #pragma omp for 
2023-01-11T21:41:26.9696822Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9696873Z         {
2023-01-11T21:41:26.9696951Z             #pragma GCC ivdep
2023-01-11T21:41:26.9697035Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9697097Z             {
2023-01-11T21:41:26.9697160Z                 {
2023-01-11T21:41:26.9697226Z                     {
2023-01-11T21:41:26.9697318Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:41:26.9697410Z                         auto tmp2 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9697517Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.9697611Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9697705Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9697772Z                     }
2023-01-11T21:41:26.9697834Z                 }
2023-01-11T21:41:26.9697895Z             }
2023-01-11T21:41:26.9697945Z         }
2023-01-11T21:41:26.9698004Z     }
2023-01-11T21:41:26.9698061Z }
2023-01-11T21:41:26.9698139Z ''')
2023-01-11T21:41:26.9698144Z 
2023-01-11T21:41:26.9698148Z 
2023-01-11T21:41:26.9698234Z async_compile.wait(globals())
2023-01-11T21:41:26.9698305Z del async_compile
2023-01-11T21:41:26.9698310Z 
2023-01-11T21:41:26.9698378Z def call(args):
2023-01-11T21:41:26.9698441Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9698509Z     args.clear()
2023-01-11T21:41:26.9698708Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9698869Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9698935Z     del arg0_1
2023-01-11T21:41:26.9699002Z     del arg1_1
2023-01-11T21:41:26.9699071Z     return (buf0, )
2023-01-11T21:41:26.9699076Z 
2023-01-11T21:41:26.9699083Z 
2023-01-11T21:41:26.9699156Z if __name__ == "__main__":
2023-01-11T21:41:26.9699256Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9699373Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9699563Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9699760Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9699871Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9699876Z 
2023-01-11T21:41:26.9699941Z ok (1.658s)
2023-01-11T21:41:26.9700426Z   test_cpu_int_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9700581Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9700837Z [2023-01-11 21:40:56,825] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 570
2023-01-11T21:41:26.9701086Z [2023-01-11 21:40:58,437] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 570
2023-01-11T21:41:26.9701103Z 
2023-01-11T21:41:26.9701182Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9701248Z import torch
2023-01-11T21:41:26.9701316Z import random
2023-01-11T21:41:26.9701426Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9701543Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9701548Z 
2023-01-11T21:41:26.9701625Z aten = torch.ops.aten
2023-01-11T21:41:26.9701782Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9701862Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9701867Z 
2023-01-11T21:41:26.9701873Z 
2023-01-11T21:41:26.9702003Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9702204Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9702316Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:41:26.9702419Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9702514Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9702573Z {
2023-01-11T21:41:26.9702666Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9702714Z     {
2023-01-11T21:41:26.9702788Z         #pragma omp for 
2023-01-11T21:41:26.9702869Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9702928Z         {
2023-01-11T21:41:26.9703004Z             #pragma GCC ivdep
2023-01-11T21:41:26.9703086Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9703213Z             {
2023-01-11T21:41:26.9703284Z                 {
2023-01-11T21:41:26.9703348Z                     {
2023-01-11T21:41:26.9703445Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9703545Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9703652Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:41:26.9703741Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9703823Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9703887Z                     }
2023-01-11T21:41:26.9703948Z                 }
2023-01-11T21:41:26.9704009Z             }
2023-01-11T21:41:26.9704068Z         }
2023-01-11T21:41:26.9704126Z     }
2023-01-11T21:41:26.9704182Z }
2023-01-11T21:41:26.9704251Z ''')
2023-01-11T21:41:26.9704256Z 
2023-01-11T21:41:26.9704261Z 
2023-01-11T21:41:26.9704349Z async_compile.wait(globals())
2023-01-11T21:41:26.9704419Z del async_compile
2023-01-11T21:41:26.9704426Z 
2023-01-11T21:41:26.9704498Z def call(args):
2023-01-11T21:41:26.9704571Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9704640Z     args.clear()
2023-01-11T21:41:26.9704842Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9705001Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9705055Z     del arg0_1
2023-01-11T21:41:26.9705120Z     del arg1_1
2023-01-11T21:41:26.9705191Z     return (buf0, )
2023-01-11T21:41:26.9705196Z 
2023-01-11T21:41:26.9705200Z 
2023-01-11T21:41:26.9705274Z if __name__ == "__main__":
2023-01-11T21:41:26.9705383Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9705502Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9705691Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9705876Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9705992Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9705997Z 
2023-01-11T21:41:26.9706099Z ok (1.627s)
2023-01-11T21:41:26.9706588Z   test_cpu_strided_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9706711Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9706966Z [2023-01-11 21:40:58,451] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 571
2023-01-11T21:41:26.9707225Z [2023-01-11 21:40:59,965] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 571
2023-01-11T21:41:26.9707230Z 
2023-01-11T21:41:26.9707349Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9707421Z import torch
2023-01-11T21:41:26.9707488Z import random
2023-01-11T21:41:26.9707590Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9707707Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9707713Z 
2023-01-11T21:41:26.9707789Z aten = torch.ops.aten
2023-01-11T21:41:26.9707918Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9708004Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9708009Z 
2023-01-11T21:41:26.9708014Z 
2023-01-11T21:41:26.9708147Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9708349Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9708465Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9708556Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9708654Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9708712Z {
2023-01-11T21:41:26.9708809Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9708870Z     {
2023-01-11T21:41:26.9708944Z         #pragma omp for 
2023-01-11T21:41:26.9709023Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9709073Z         {
2023-01-11T21:41:26.9709149Z             #pragma GCC ivdep
2023-01-11T21:41:26.9709232Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9709294Z             {
2023-01-11T21:41:26.9709355Z                 {
2023-01-11T21:41:26.9709418Z                     {
2023-01-11T21:41:26.9709511Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9709602Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9709693Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9709786Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9709850Z                     }
2023-01-11T21:41:26.9709911Z                 }
2023-01-11T21:41:26.9709973Z             }
2023-01-11T21:41:26.9710022Z         }
2023-01-11T21:41:26.9710080Z     }
2023-01-11T21:41:26.9710137Z }
2023-01-11T21:41:26.9710219Z ''')
2023-01-11T21:41:26.9710225Z 
2023-01-11T21:41:26.9710229Z 
2023-01-11T21:41:26.9710316Z async_compile.wait(globals())
2023-01-11T21:41:26.9710387Z del async_compile
2023-01-11T21:41:26.9710392Z 
2023-01-11T21:41:26.9710458Z def call(args):
2023-01-11T21:41:26.9710520Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9710588Z     args.clear()
2023-01-11T21:41:26.9710788Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9710946Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9711016Z     del arg0_1
2023-01-11T21:41:26.9711081Z     del arg1_1
2023-01-11T21:41:26.9711150Z     return (buf0, )
2023-01-11T21:41:26.9711156Z 
2023-01-11T21:41:26.9711159Z 
2023-01-11T21:41:26.9711233Z if __name__ == "__main__":
2023-01-11T21:41:26.9711336Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9711456Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9711687Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9711877Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9711988Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9711993Z 
2023-01-11T21:41:26.9712058Z ok (1.528s)
2023-01-11T21:41:26.9712544Z   test_cpu_strided_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9712693Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9712952Z [2023-01-11 21:40:59,980] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 572
2023-01-11T21:41:26.9713205Z [2023-01-11 21:41:01,690] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 572
2023-01-11T21:41:26.9713221Z 
2023-01-11T21:41:26.9713300Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9713367Z import torch
2023-01-11T21:41:26.9713433Z import random
2023-01-11T21:41:26.9713542Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9713659Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9713664Z 
2023-01-11T21:41:26.9713739Z aten = torch.ops.aten
2023-01-11T21:41:26.9713869Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9713946Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9713950Z 
2023-01-11T21:41:26.9713965Z 
2023-01-11T21:41:26.9714084Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9714286Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9714403Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9714508Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9714602Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9714659Z {
2023-01-11T21:41:26.9714754Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9714802Z     {
2023-01-11T21:41:26.9714875Z         #pragma omp for 
2023-01-11T21:41:26.9714955Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9715014Z         {
2023-01-11T21:41:26.9715089Z             #pragma GCC ivdep
2023-01-11T21:41:26.9715171Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9715221Z             {
2023-01-11T21:41:26.9715283Z                 {
2023-01-11T21:41:26.9715348Z                     {
2023-01-11T21:41:26.9715451Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9715542Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9715633Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9715729Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9715782Z                     }
2023-01-11T21:41:26.9715840Z                 }
2023-01-11T21:41:26.9715901Z             }
2023-01-11T21:41:26.9715960Z         }
2023-01-11T21:41:26.9716018Z     }
2023-01-11T21:41:26.9716073Z }
2023-01-11T21:41:26.9716149Z ''')
2023-01-11T21:41:26.9716155Z 
2023-01-11T21:41:26.9716159Z 
2023-01-11T21:41:26.9716234Z async_compile.wait(globals())
2023-01-11T21:41:26.9716303Z del async_compile
2023-01-11T21:41:26.9716309Z 
2023-01-11T21:41:26.9716376Z def call(args):
2023-01-11T21:41:26.9716453Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9716521Z     args.clear()
2023-01-11T21:41:26.9716727Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9716891Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9716957Z     del arg0_1
2023-01-11T21:41:26.9717051Z     del arg1_1
2023-01-11T21:41:26.9717119Z     return (buf0, )
2023-01-11T21:41:26.9717125Z 
2023-01-11T21:41:26.9717129Z 
2023-01-11T21:41:26.9717202Z if __name__ == "__main__":
2023-01-11T21:41:26.9717312Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9717432Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9717629Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9717836Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9717937Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9717953Z 
2023-01-11T21:41:26.9718006Z ok (1.725s)
2023-01-11T21:41:26.9718529Z   test_cpu_strided_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9718657Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9718915Z [2023-01-11 21:41:01,705] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 573
2023-01-11T21:41:26.9719175Z [2023-01-11 21:41:03,388] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 573
2023-01-11T21:41:26.9719181Z 
2023-01-11T21:41:26.9719271Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9719338Z import torch
2023-01-11T21:41:26.9719407Z import random
2023-01-11T21:41:26.9719519Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9719628Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9719633Z 
2023-01-11T21:41:26.9719709Z aten = torch.ops.aten
2023-01-11T21:41:26.9719841Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9719932Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9719937Z 
2023-01-11T21:41:26.9719941Z 
2023-01-11T21:41:26.9720074Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9720274Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9720387Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9720489Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9720575Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9720633Z {
2023-01-11T21:41:26.9720727Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9720784Z     {
2023-01-11T21:41:26.9720856Z         #pragma omp for 
2023-01-11T21:41:26.9720937Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9721000Z         {
2023-01-11T21:41:26.9721065Z             #pragma GCC ivdep
2023-01-11T21:41:26.9721147Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9721215Z             {
2023-01-11T21:41:26.9721277Z                 {
2023-01-11T21:41:26.9721341Z                     {
2023-01-11T21:41:26.9721445Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9721524Z                         auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.9721616Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9721709Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9721778Z                     }
2023-01-11T21:41:26.9721838Z                 }
2023-01-11T21:41:26.9721897Z             }
2023-01-11T21:41:26.9721957Z         }
2023-01-11T21:41:26.9722004Z     }
2023-01-11T21:41:26.9722060Z }
2023-01-11T21:41:26.9722137Z ''')
2023-01-11T21:41:26.9722142Z 
2023-01-11T21:41:26.9722146Z 
2023-01-11T21:41:26.9722234Z async_compile.wait(globals())
2023-01-11T21:41:26.9722304Z del async_compile
2023-01-11T21:41:26.9722310Z 
2023-01-11T21:41:26.9722379Z def call(args):
2023-01-11T21:41:26.9722501Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9722569Z     args.clear()
2023-01-11T21:41:26.9722758Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9722919Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9722987Z     del arg0_1
2023-01-11T21:41:26.9723051Z     del arg1_1
2023-01-11T21:41:26.9723121Z     return (buf0, )
2023-01-11T21:41:26.9723125Z 
2023-01-11T21:41:26.9723129Z 
2023-01-11T21:41:26.9723204Z if __name__ == "__main__":
2023-01-11T21:41:26.9723316Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9723425Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9723623Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9723846Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9723961Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9723969Z 
2023-01-11T21:41:26.9724033Z ok (1.698s)
2023-01-11T21:41:26.9724513Z   test_cpu_strided_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9724638Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9724894Z [2023-01-11 21:41:03,403] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 574
2023-01-11T21:41:26.9725160Z [2023-01-11 21:41:05,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 574
2023-01-11T21:41:26.9725165Z 
2023-01-11T21:41:26.9725257Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9725315Z import torch
2023-01-11T21:41:26.9725383Z import random
2023-01-11T21:41:26.9725495Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9725611Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9725617Z 
2023-01-11T21:41:26.9725693Z aten = torch.ops.aten
2023-01-11T21:41:26.9725822Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9725911Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9725916Z 
2023-01-11T21:41:26.9725920Z 
2023-01-11T21:41:26.9726052Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9726241Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9726371Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9726474Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9726567Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9726625Z {
2023-01-11T21:41:26.9726721Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9726779Z     {
2023-01-11T21:41:26.9726842Z         #pragma omp for 
2023-01-11T21:41:26.9726922Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9726982Z         {
2023-01-11T21:41:26.9727060Z             #pragma GCC ivdep
2023-01-11T21:41:26.9727142Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9727203Z             {
2023-01-11T21:41:26.9727254Z                 {
2023-01-11T21:41:26.9727313Z                     {
2023-01-11T21:41:26.9727412Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9727512Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9727603Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9727697Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9727762Z                     }
2023-01-11T21:41:26.9727824Z                 }
2023-01-11T21:41:26.9727874Z             }
2023-01-11T21:41:26.9727968Z         }
2023-01-11T21:41:26.9728027Z     }
2023-01-11T21:41:26.9728083Z }
2023-01-11T21:41:26.9728163Z ''')
2023-01-11T21:41:26.9728168Z 
2023-01-11T21:41:26.9728172Z 
2023-01-11T21:41:26.9728258Z async_compile.wait(globals())
2023-01-11T21:41:26.9728316Z del async_compile
2023-01-11T21:41:26.9728333Z 
2023-01-11T21:41:26.9728389Z def call(args):
2023-01-11T21:41:26.9728462Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9728530Z     args.clear()
2023-01-11T21:41:26.9728728Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9728890Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9728956Z     del arg0_1
2023-01-11T21:41:26.9729144Z     del arg1_1
2023-01-11T21:41:26.9729236Z     return (buf0, )
2023-01-11T21:41:26.9729247Z 
2023-01-11T21:41:26.9729320Z 
2023-01-11T21:41:26.9729411Z if __name__ == "__main__":
2023-01-11T21:41:26.9729526Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9729649Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9729852Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9730045Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9730157Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9730162Z 
2023-01-11T21:41:26.9730226Z ok (2.435s)
2023-01-11T21:41:26.9730704Z   test_cpu_strided_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9730830Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9731094Z [2023-01-11 21:41:05,848] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 575
2023-01-11T21:41:26.9731356Z [2023-01-11 21:41:07,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 575
2023-01-11T21:41:26.9731362Z 
2023-01-11T21:41:26.9731454Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9731526Z import torch
2023-01-11T21:41:26.9731593Z import random
2023-01-11T21:41:26.9731706Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9731824Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9731829Z 
2023-01-11T21:41:26.9731893Z aten = torch.ops.aten
2023-01-11T21:41:26.9732023Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9732112Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9732117Z 
2023-01-11T21:41:26.9732124Z 
2023-01-11T21:41:26.9732259Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9732460Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9732579Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9732684Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:26.9732782Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9732829Z {
2023-01-11T21:41:26.9732923Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9732981Z     {
2023-01-11T21:41:26.9733055Z         #pragma omp for 
2023-01-11T21:41:26.9733135Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9733195Z         {
2023-01-11T21:41:26.9733271Z             #pragma GCC ivdep
2023-01-11T21:41:26.9733343Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9733404Z             {
2023-01-11T21:41:26.9733468Z                 {
2023-01-11T21:41:26.9733534Z                     {
2023-01-11T21:41:26.9733640Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9733737Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9733882Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.9733964Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9734056Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9734119Z                     }
2023-01-11T21:41:26.9734181Z                 }
2023-01-11T21:41:26.9734241Z             }
2023-01-11T21:41:26.9734300Z         }
2023-01-11T21:41:26.9734347Z     }
2023-01-11T21:41:26.9734406Z }
2023-01-11T21:41:26.9734487Z ''')
2023-01-11T21:41:26.9734492Z 
2023-01-11T21:41:26.9734496Z 
2023-01-11T21:41:26.9734582Z async_compile.wait(globals())
2023-01-11T21:41:26.9734652Z del async_compile
2023-01-11T21:41:26.9734656Z 
2023-01-11T21:41:26.9734724Z def call(args):
2023-01-11T21:41:26.9734796Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9734903Z     args.clear()
2023-01-11T21:41:26.9735095Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9735262Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9735329Z     del arg0_1
2023-01-11T21:41:26.9735393Z     del arg1_1
2023-01-11T21:41:26.9735463Z     return (buf0, )
2023-01-11T21:41:26.9735468Z 
2023-01-11T21:41:26.9735472Z 
2023-01-11T21:41:26.9735545Z if __name__ == "__main__":
2023-01-11T21:41:26.9735656Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9735776Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9735962Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9736155Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9736269Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9736273Z 
2023-01-11T21:41:26.9736340Z ok (1.911s)
2023-01-11T21:41:26.9736822Z   test_cpu_strided_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9736947Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9737205Z [2023-01-11 21:41:07,760] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 576
2023-01-11T21:41:26.9737467Z [2023-01-11 21:41:10,205] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 576
2023-01-11T21:41:26.9737473Z 
2023-01-11T21:41:26.9737564Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9737621Z import torch
2023-01-11T21:41:26.9737691Z import random
2023-01-11T21:41:26.9737801Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9737919Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9737927Z 
2023-01-11T21:41:26.9738003Z aten = torch.ops.aten
2023-01-11T21:41:26.9738133Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9738224Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9738229Z 
2023-01-11T21:41:26.9738233Z 
2023-01-11T21:41:26.9738364Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9738556Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9738673Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9738773Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.9738869Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9738927Z {
2023-01-11T21:41:26.9739023Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9739086Z     {
2023-01-11T21:41:26.9739149Z         #pragma omp for 
2023-01-11T21:41:26.9739230Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9739322Z         {
2023-01-11T21:41:26.9739399Z             #pragma GCC ivdep
2023-01-11T21:41:26.9739482Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9739542Z             {
2023-01-11T21:41:26.9739603Z                 {
2023-01-11T21:41:26.9739656Z                     {
2023-01-11T21:41:26.9739760Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9739851Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9739958Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.9740048Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9740141Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9740205Z                     }
2023-01-11T21:41:26.9740255Z                 }
2023-01-11T21:41:26.9740316Z             }
2023-01-11T21:41:26.9740402Z         }
2023-01-11T21:41:26.9740462Z     }
2023-01-11T21:41:26.9740519Z }
2023-01-11T21:41:26.9740597Z ''')
2023-01-11T21:41:26.9740604Z 
2023-01-11T21:41:26.9740608Z 
2023-01-11T21:41:26.9740696Z async_compile.wait(globals())
2023-01-11T21:41:26.9740759Z del async_compile
2023-01-11T21:41:26.9740766Z 
2023-01-11T21:41:26.9740871Z def call(args):
2023-01-11T21:41:26.9740995Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9741121Z     args.clear()
2023-01-11T21:41:26.9741492Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9741786Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9741907Z     del arg0_1
2023-01-11T21:41:26.9742013Z     del arg1_1
2023-01-11T21:41:26.9742139Z     return (buf0, )
2023-01-11T21:41:26.9742148Z 
2023-01-11T21:41:26.9742155Z 
2023-01-11T21:41:26.9742288Z if __name__ == "__main__":
2023-01-11T21:41:26.9742504Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9742727Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9743114Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9743546Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9743753Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9743763Z 
2023-01-11T21:41:26.9743864Z ok (2.473s)
2023-01-11T21:41:26.9744796Z   test_cpu_strided_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9745025Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9745547Z [2023-01-11 21:41:10,225] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 577
2023-01-11T21:41:26.9746062Z [2023-01-11 21:41:13,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 577
2023-01-11T21:41:26.9746073Z 
2023-01-11T21:41:26.9746241Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9746367Z import torch
2023-01-11T21:41:26.9746495Z import random
2023-01-11T21:41:26.9746703Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9746908Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9746931Z 
2023-01-11T21:41:26.9747064Z aten = torch.ops.aten
2023-01-11T21:41:26.9747304Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9747469Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9747479Z 
2023-01-11T21:41:26.9747487Z 
2023-01-11T21:41:26.9747752Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9748120Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9748289Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9748540Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9748656Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9748737Z {
2023-01-11T21:41:26.9748867Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9748947Z     {
2023-01-11T21:41:26.9749048Z         #pragma omp for 
2023-01-11T21:41:26.9749156Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9749240Z         {
2023-01-11T21:41:26.9749331Z             #pragma GCC ivdep
2023-01-11T21:41:26.9749440Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9749526Z             {
2023-01-11T21:41:26.9749612Z                 {
2023-01-11T21:41:26.9749699Z                     {
2023-01-11T21:41:26.9749838Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9750014Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9750127Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9750254Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9750344Z                     }
2023-01-11T21:41:26.9750429Z                 }
2023-01-11T21:41:26.9750514Z             }
2023-01-11T21:41:26.9750596Z         }
2023-01-11T21:41:26.9750677Z     }
2023-01-11T21:41:26.9750741Z }
2023-01-11T21:41:26.9750856Z ''')
2023-01-11T21:41:26.9750863Z 
2023-01-11T21:41:26.9750868Z 
2023-01-11T21:41:26.9750987Z async_compile.wait(globals())
2023-01-11T21:41:26.9751082Z del async_compile
2023-01-11T21:41:26.9751089Z 
2023-01-11T21:41:26.9751181Z def call(args):
2023-01-11T21:41:26.9751278Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9751371Z     args.clear()
2023-01-11T21:41:26.9751634Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9751849Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9751940Z     del arg0_1
2023-01-11T21:41:26.9752031Z     del arg1_1
2023-01-11T21:41:26.9752125Z     return (buf0, )
2023-01-11T21:41:26.9752132Z 
2023-01-11T21:41:26.9752137Z 
2023-01-11T21:41:26.9752235Z if __name__ == "__main__":
2023-01-11T21:41:26.9752387Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9752546Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9752805Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9753079Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9753253Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9753262Z 
2023-01-11T21:41:26.9753366Z ok (2.920s)
2023-01-11T21:41:26.9754125Z   test_cpu_strided_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9754351Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9754796Z [2023-01-11 21:41:13,147] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 578
2023-01-11T21:41:26.9755235Z [2023-01-11 21:41:15,587] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 578
2023-01-11T21:41:26.9755247Z 
2023-01-11T21:41:26.9755388Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9755478Z import torch
2023-01-11T21:41:26.9755581Z import random
2023-01-11T21:41:26.9755744Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9755918Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9755935Z 
2023-01-11T21:41:26.9756050Z aten = torch.ops.aten
2023-01-11T21:41:26.9756242Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9756497Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9756507Z 
2023-01-11T21:41:26.9756513Z 
2023-01-11T21:41:26.9756744Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9757010Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9757181Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9757329Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9757472Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9757561Z {
2023-01-11T21:41:26.9757706Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9757797Z     {
2023-01-11T21:41:26.9757895Z         #pragma omp for 
2023-01-11T21:41:26.9758018Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9758107Z         {
2023-01-11T21:41:26.9758320Z             #pragma GCC ivdep
2023-01-11T21:41:26.9758467Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9758571Z             {
2023-01-11T21:41:26.9758664Z                 {
2023-01-11T21:41:26.9758755Z                     {
2023-01-11T21:41:26.9758929Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9759099Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:41:26.9759255Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9759407Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9759515Z                     }
2023-01-11T21:41:26.9759618Z                 }
2023-01-11T21:41:26.9759703Z             }
2023-01-11T21:41:26.9759801Z         }
2023-01-11T21:41:26.9759900Z     }
2023-01-11T21:41:26.9759999Z }
2023-01-11T21:41:26.9760183Z ''')
2023-01-11T21:41:26.9760193Z 
2023-01-11T21:41:26.9760200Z 
2023-01-11T21:41:26.9760369Z async_compile.wait(globals())
2023-01-11T21:41:26.9760502Z del async_compile
2023-01-11T21:41:26.9760513Z 
2023-01-11T21:41:26.9760614Z def call(args):
2023-01-11T21:41:26.9760745Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9760874Z     args.clear()
2023-01-11T21:41:26.9761274Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9761561Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9761683Z     del arg0_1
2023-01-11T21:41:26.9761793Z     del arg1_1
2023-01-11T21:41:26.9761892Z     return (buf0, )
2023-01-11T21:41:26.9761920Z 
2023-01-11T21:41:26.9761928Z 
2023-01-11T21:41:26.9762038Z if __name__ == "__main__":
2023-01-11T21:41:26.9762231Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9762437Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9762872Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9763313Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9763538Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9763560Z 
2023-01-11T21:41:26.9763679Z ok (2.463s)
2023-01-11T21:41:26.9764553Z   test_cpu_transposed_broadcast1 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9764828Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9765350Z [2023-01-11 21:41:15,607] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 579
2023-01-11T21:41:26.9765761Z [2023-01-11 21:41:15,617] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 579
2023-01-11T21:41:26.9765770Z 
2023-01-11T21:41:26.9765896Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9766100Z import torch
2023-01-11T21:41:26.9766195Z import random
2023-01-11T21:41:26.9766349Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9766509Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9766516Z 
2023-01-11T21:41:26.9766618Z aten = torch.ops.aten
2023-01-11T21:41:26.9766780Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9766901Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9766909Z 
2023-01-11T21:41:26.9766914Z 
2023-01-11T21:41:26.9767102Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9767364Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9767519Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9767655Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9767835Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9767917Z {
2023-01-11T21:41:26.9768043Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9768127Z     {
2023-01-11T21:41:26.9768234Z         #pragma omp for 
2023-01-11T21:41:26.9768345Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9768433Z         {
2023-01-11T21:41:26.9768546Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9768623Z             {
2023-01-11T21:41:26.9768828Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9769154Z                 auto tmp1 = at::vec::Vectorized<float>(in_ptr1[i0]);
2023-01-11T21:41:26.9769453Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9769655Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9769776Z             }
2023-01-11T21:41:26.9769965Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9770256Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9770395Z             {
2023-01-11T21:41:26.9770623Z                 auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9770839Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9771053Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9771273Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9771430Z             }
2023-01-11T21:41:26.9771565Z         }
2023-01-11T21:41:26.9771668Z     }
2023-01-11T21:41:26.9771784Z }
2023-01-11T21:41:26.9771949Z ''')
2023-01-11T21:41:26.9771959Z 
2023-01-11T21:41:26.9771966Z 
2023-01-11T21:41:26.9772128Z async_compile.wait(globals())
2023-01-11T21:41:26.9772258Z del async_compile
2023-01-11T21:41:26.9772267Z 
2023-01-11T21:41:26.9772390Z def call(args):
2023-01-11T21:41:26.9772524Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9772637Z     args.clear()
2023-01-11T21:41:26.9773001Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9773298Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9773445Z     del arg0_1
2023-01-11T21:41:26.9773612Z     del arg1_1
2023-01-11T21:41:26.9773779Z     return (buf0, )
2023-01-11T21:41:26.9773789Z 
2023-01-11T21:41:26.9773797Z 
2023-01-11T21:41:26.9773980Z if __name__ == "__main__":
2023-01-11T21:41:26.9774225Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9774518Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9774990Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9775410Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9775617Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9775626Z 
2023-01-11T21:41:26.9775741Z ok (0.027s)
2023-01-11T21:41:26.9776653Z   test_cpu_transposed_broadcast2 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9777034Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9777642Z [2023-01-11 21:41:15,632] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 580
2023-01-11T21:41:26.9778284Z [2023-01-11 21:41:15,641] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 580
2023-01-11T21:41:26.9778297Z 
2023-01-11T21:41:26.9778493Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9778647Z import torch
2023-01-11T21:41:26.9778790Z import random
2023-01-11T21:41:26.9778994Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9779283Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9779295Z 
2023-01-11T21:41:26.9779439Z aten = torch.ops.aten
2023-01-11T21:41:26.9779676Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9779826Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9779852Z 
2023-01-11T21:41:26.9779859Z 
2023-01-11T21:41:26.9780094Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9780506Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9780782Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9781016Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9781234Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9781377Z {
2023-01-11T21:41:26.9781610Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9781734Z     {
2023-01-11T21:41:26.9781920Z         #pragma omp for 
2023-01-11T21:41:26.9782120Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9782259Z         {
2023-01-11T21:41:26.9782406Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:41:26.9782524Z             {
2023-01-11T21:41:26.9782791Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9783020Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i1);
2023-01-11T21:41:26.9783259Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9783445Z                 tmp2.store(out_ptr0 + (8*i1) + (10*i0));
2023-01-11T21:41:26.9783564Z             }
2023-01-11T21:41:26.9783729Z             #pragma omp simd simdlen(4) 
2023-01-11T21:41:26.9783880Z             for(long i1=8; i1<10; i1+=1)
2023-01-11T21:41:26.9784025Z             {
2023-01-11T21:41:26.9784219Z                 auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9784426Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:41:26.9784627Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9784849Z                 out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:41:26.9785000Z             }
2023-01-11T21:41:26.9785145Z         }
2023-01-11T21:41:26.9785293Z     }
2023-01-11T21:41:26.9785424Z }
2023-01-11T21:41:26.9785617Z ''')
2023-01-11T21:41:26.9785628Z 
2023-01-11T21:41:26.9785638Z 
2023-01-11T21:41:26.9785832Z async_compile.wait(globals())
2023-01-11T21:41:26.9785967Z del async_compile
2023-01-11T21:41:26.9785976Z 
2023-01-11T21:41:26.9786105Z def call(args):
2023-01-11T21:41:26.9786239Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9786368Z     args.clear()
2023-01-11T21:41:26.9786764Z     buf0 = empty_strided((1, 10, 10), (100, 1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9787040Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9787160Z     del arg0_1
2023-01-11T21:41:26.9787283Z     del arg1_1
2023-01-11T21:41:26.9787416Z     return (buf0, )
2023-01-11T21:41:26.9787427Z 
2023-01-11T21:41:26.9787433Z 
2023-01-11T21:41:26.9787616Z if __name__ == "__main__":
2023-01-11T21:41:26.9787876Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9788153Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9788703Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9789191Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9789419Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9789430Z 
2023-01-11T21:41:26.9789549Z ok (0.024s)
2023-01-11T21:41:26.9790455Z   test_cpu_transposed_broadcast3 (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9790737Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9791306Z [2023-01-11 21:41:15,655] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 581
2023-01-11T21:41:26.9791954Z [2023-01-11 21:41:15,663] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 581
2023-01-11T21:41:26.9791967Z 
2023-01-11T21:41:26.9792200Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9792366Z import torch
2023-01-11T21:41:26.9792513Z import random
2023-01-11T21:41:26.9792764Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9792980Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9792989Z 
2023-01-11T21:41:26.9793131Z aten = torch.ops.aten
2023-01-11T21:41:26.9793371Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9793537Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9793546Z 
2023-01-11T21:41:26.9793554Z 
2023-01-11T21:41:26.9793815Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9794178Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9794394Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9794644Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9794865Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9795014Z {
2023-01-11T21:41:26.9795247Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9795395Z     {
2023-01-11T21:41:26.9795582Z         #pragma omp for 
2023-01-11T21:41:26.9795755Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.9795902Z         {
2023-01-11T21:41:26.9796215Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9796444Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:41:26.9796598Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9796769Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9796883Z         }
2023-01-11T21:41:26.9797033Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9797180Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.9797302Z         {
2023-01-11T21:41:26.9797455Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9797601Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:41:26.9797753Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9797910Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9798024Z         }
2023-01-11T21:41:26.9798169Z     }
2023-01-11T21:41:26.9798316Z }
2023-01-11T21:41:26.9798519Z ''')
2023-01-11T21:41:26.9798534Z 
2023-01-11T21:41:26.9798541Z 
2023-01-11T21:41:26.9798756Z async_compile.wait(globals())
2023-01-11T21:41:26.9798929Z del async_compile
2023-01-11T21:41:26.9798940Z 
2023-01-11T21:41:26.9799111Z def call(args):
2023-01-11T21:41:26.9799265Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9799423Z     args.clear()
2023-01-11T21:41:26.9799877Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9800174Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9800382Z     del arg0_1
2023-01-11T21:41:26.9800497Z     del arg1_1
2023-01-11T21:41:26.9800626Z     return (buf0, )
2023-01-11T21:41:26.9800635Z 
2023-01-11T21:41:26.9800642Z 
2023-01-11T21:41:26.9800775Z if __name__ == "__main__":
2023-01-11T21:41:26.9800964Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9801186Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9801616Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9802074Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9802330Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9802340Z 
2023-01-11T21:41:26.9802493Z ok (0.022s)
2023-01-11T21:41:26.9803629Z   test_cpu_transposed_dense (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9803866Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9804349Z [2023-01-11 21:41:15,679] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 582
2023-01-11T21:41:26.9804826Z [2023-01-11 21:41:17,263] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 582
2023-01-11T21:41:26.9804836Z 
2023-01-11T21:41:26.9805038Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9805208Z import torch
2023-01-11T21:41:26.9805380Z import random
2023-01-11T21:41:26.9805643Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9805928Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9805946Z 
2023-01-11T21:41:26.9806124Z aten = torch.ops.aten
2023-01-11T21:41:26.9806429Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9806625Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9806635Z 
2023-01-11T21:41:26.9806646Z 
2023-01-11T21:41:26.9806923Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9807276Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9807491Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9807680Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9807855Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9807963Z {
2023-01-11T21:41:26.9808112Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9808227Z     {
2023-01-11T21:41:26.9808371Z         #pragma omp for 
2023-01-11T21:41:26.9808569Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9808703Z         {
2023-01-11T21:41:26.9808903Z             #pragma GCC ivdep
2023-01-11T21:41:26.9809253Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9809386Z             {
2023-01-11T21:41:26.9809538Z                 {
2023-01-11T21:41:26.9809692Z                     {
2023-01-11T21:41:26.9809933Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:41:26.9810166Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9810367Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9810540Z                         out_ptr0[i0 + (10*i1)] = tmp2;
2023-01-11T21:41:26.9810646Z                     }
2023-01-11T21:41:26.9810762Z                 }
2023-01-11T21:41:26.9810874Z             }
2023-01-11T21:41:26.9810987Z         }
2023-01-11T21:41:26.9811093Z     }
2023-01-11T21:41:26.9811201Z }
2023-01-11T21:41:26.9811356Z ''')
2023-01-11T21:41:26.9811372Z 
2023-01-11T21:41:26.9811379Z 
2023-01-11T21:41:26.9811513Z async_compile.wait(globals())
2023-01-11T21:41:26.9811642Z del async_compile
2023-01-11T21:41:26.9811754Z 
2023-01-11T21:41:26.9811882Z def call(args):
2023-01-11T21:41:26.9812025Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9812193Z     args.clear()
2023-01-11T21:41:26.9812672Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9813046Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9813180Z     del arg0_1
2023-01-11T21:41:26.9813346Z     del arg1_1
2023-01-11T21:41:26.9813513Z     return (buf0, )
2023-01-11T21:41:26.9813528Z 
2023-01-11T21:41:26.9813538Z 
2023-01-11T21:41:26.9813712Z if __name__ == "__main__":
2023-01-11T21:41:26.9813955Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9814178Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9814627Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9814993Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9815191Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9815201Z 
2023-01-11T21:41:26.9815318Z ok (1.601s)
2023-01-11T21:41:26.9816389Z   test_cpu_transposed_double (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9816682Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9817302Z [2023-01-11 21:41:17,285] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 583
2023-01-11T21:41:26.9817816Z [2023-01-11 21:41:19,792] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 583
2023-01-11T21:41:26.9817833Z 
2023-01-11T21:41:26.9818002Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9818129Z import torch
2023-01-11T21:41:26.9818263Z import random
2023-01-11T21:41:26.9818460Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9818678Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9818688Z 
2023-01-11T21:41:26.9818823Z aten = torch.ops.aten
2023-01-11T21:41:26.9819105Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9819326Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9819337Z 
2023-01-11T21:41:26.9819347Z 
2023-01-11T21:41:26.9819662Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9820137Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9820422Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9820663Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:41:26.9820867Z                        double* __restrict__ out_ptr0)
2023-01-11T21:41:26.9820976Z {
2023-01-11T21:41:26.9821150Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9821263Z     {
2023-01-11T21:41:26.9821400Z         #pragma omp for 
2023-01-11T21:41:26.9821549Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9821648Z         {
2023-01-11T21:41:26.9821791Z             #pragma GCC ivdep
2023-01-11T21:41:26.9821949Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9822064Z             {
2023-01-11T21:41:26.9822180Z                 {
2023-01-11T21:41:26.9822300Z                     {
2023-01-11T21:41:26.9822527Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:41:26.9822731Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:41:26.9823009Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:41:26.9823312Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:41:26.9823537Z                         out_ptr0[i0 + (10*i1)] = tmp3;
2023-01-11T21:41:26.9823797Z                     }
2023-01-11T21:41:26.9823947Z                 }
2023-01-11T21:41:26.9824080Z             }
2023-01-11T21:41:26.9824213Z         }
2023-01-11T21:41:26.9824337Z     }
2023-01-11T21:41:26.9824457Z }
2023-01-11T21:41:26.9824617Z ''')
2023-01-11T21:41:26.9824628Z 
2023-01-11T21:41:26.9824635Z 
2023-01-11T21:41:26.9824797Z async_compile.wait(globals())
2023-01-11T21:41:26.9824929Z del async_compile
2023-01-11T21:41:26.9824939Z 
2023-01-11T21:41:26.9825062Z def call(args):
2023-01-11T21:41:26.9825180Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9825300Z     args.clear()
2023-01-11T21:41:26.9825675Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9826042Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9826208Z     del arg0_1
2023-01-11T21:41:26.9826370Z     del arg1_1
2023-01-11T21:41:26.9826528Z     return (buf0, )
2023-01-11T21:41:26.9826549Z 
2023-01-11T21:41:26.9826557Z 
2023-01-11T21:41:26.9826741Z if __name__ == "__main__":
2023-01-11T21:41:26.9826985Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9827270Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9827742Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9828118Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:41:26.9828323Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9828333Z 
2023-01-11T21:41:26.9828452Z ok (2.528s)
2023-01-11T21:41:26.9829357Z   test_cpu_transposed_int (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9829646Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9830243Z [2023-01-11 21:41:19,810] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 584
2023-01-11T21:41:26.9830868Z [2023-01-11 21:41:22,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 584
2023-01-11T21:41:26.9830902Z 
2023-01-11T21:41:26.9831106Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9831251Z import torch
2023-01-11T21:41:26.9831389Z import random
2023-01-11T21:41:26.9831600Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9831813Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9831823Z 
2023-01-11T21:41:26.9831966Z aten = torch.ops.aten
2023-01-11T21:41:26.9832202Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9832353Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9832368Z 
2023-01-11T21:41:26.9832391Z 
2023-01-11T21:41:26.9832627Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9833039Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9833314Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9833556Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:41:26.9833792Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9833936Z {
2023-01-11T21:41:26.9834168Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9834294Z     {
2023-01-11T21:41:26.9834478Z         #pragma omp for 
2023-01-11T21:41:26.9834663Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9834789Z         {
2023-01-11T21:41:26.9834935Z             #pragma GCC ivdep
2023-01-11T21:41:26.9835099Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9835199Z             {
2023-01-11T21:41:26.9835313Z                 {
2023-01-11T21:41:26.9835503Z                     {
2023-01-11T21:41:26.9835688Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:41:26.9835858Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9836055Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:41:26.9836218Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:41:26.9836382Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:41:26.9836539Z                     }
2023-01-11T21:41:26.9836680Z                 }
2023-01-11T21:41:26.9836832Z             }
2023-01-11T21:41:26.9836979Z         }
2023-01-11T21:41:26.9837125Z     }
2023-01-11T21:41:26.9837260Z }
2023-01-11T21:41:26.9837446Z ''')
2023-01-11T21:41:26.9837463Z 
2023-01-11T21:41:26.9837473Z 
2023-01-11T21:41:26.9837682Z async_compile.wait(globals())
2023-01-11T21:41:26.9837921Z del async_compile
2023-01-11T21:41:26.9837940Z 
2023-01-11T21:41:26.9838099Z def call(args):
2023-01-11T21:41:26.9838265Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9838406Z     args.clear()
2023-01-11T21:41:26.9838784Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9839074Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9839180Z     del arg0_1
2023-01-11T21:41:26.9839298Z     del arg1_1
2023-01-11T21:41:26.9839427Z     return (buf0, )
2023-01-11T21:41:26.9839437Z 
2023-01-11T21:41:26.9839444Z 
2023-01-11T21:41:26.9839578Z if __name__ == "__main__":
2023-01-11T21:41:26.9839772Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9840043Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9840513Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9840939Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:41:26.9841200Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9841220Z 
2023-01-11T21:41:26.9841372Z ok (2.353s)
2023-01-11T21:41:26.9842361Z   test_cpu_transposed_strided (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9842591Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9843082Z [2023-01-11 21:41:22,168] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 585
2023-01-11T21:41:26.9843659Z [2023-01-11 21:41:24,551] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 585
2023-01-11T21:41:26.9843674Z 
2023-01-11T21:41:26.9843897Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9844063Z import torch
2023-01-11T21:41:26.9844229Z import random
2023-01-11T21:41:26.9844469Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9844754Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9844766Z 
2023-01-11T21:41:26.9844955Z aten = torch.ops.aten
2023-01-11T21:41:26.9845252Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9845427Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9845437Z 
2023-01-11T21:41:26.9845443Z 
2023-01-11T21:41:26.9845695Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9846052Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9846268Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9846434Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9846613Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9846723Z {
2023-01-11T21:41:26.9847045Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9847189Z     {
2023-01-11T21:41:26.9847369Z         #pragma omp for 
2023-01-11T21:41:26.9847560Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:41:26.9847696Z         {
2023-01-11T21:41:26.9847884Z             #pragma GCC ivdep
2023-01-11T21:41:26.9848076Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:41:26.9848226Z             {
2023-01-11T21:41:26.9848375Z                 {
2023-01-11T21:41:26.9848537Z                     {
2023-01-11T21:41:26.9848730Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:41:26.9848926Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:41:26.9849268Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9849446Z                         out_ptr0[i0 + (10*i1)] = tmp2;
2023-01-11T21:41:26.9849661Z                     }
2023-01-11T21:41:26.9849781Z                 }
2023-01-11T21:41:26.9849885Z             }
2023-01-11T21:41:26.9849981Z         }
2023-01-11T21:41:26.9850092Z     }
2023-01-11T21:41:26.9850200Z }
2023-01-11T21:41:26.9850360Z ''')
2023-01-11T21:41:26.9850371Z 
2023-01-11T21:41:26.9850378Z 
2023-01-11T21:41:26.9850548Z async_compile.wait(globals())
2023-01-11T21:41:26.9850703Z del async_compile
2023-01-11T21:41:26.9850720Z 
2023-01-11T21:41:26.9850877Z def call(args):
2023-01-11T21:41:26.9851057Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9851208Z     args.clear()
2023-01-11T21:41:26.9851688Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9852060Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9852225Z     del arg0_1
2023-01-11T21:41:26.9852368Z     del arg1_1
2023-01-11T21:41:26.9852506Z     return (buf0, )
2023-01-11T21:41:26.9852516Z 
2023-01-11T21:41:26.9852523Z 
2023-01-11T21:41:26.9852668Z if __name__ == "__main__":
2023-01-11T21:41:26.9852860Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9853087Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9853462Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9853835Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9854064Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9854075Z 
2023-01-11T21:41:26.9854233Z ok (2.408s)
2023-01-11T21:41:26.9855387Z   test_cpu_transposed_transposed (__main__.SweepInputsCpuTest) ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:26.9855689Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:41:26.9856220Z [2023-01-11 21:41:24,579] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 586
2023-01-11T21:41:26.9856691Z [2023-01-11 21:41:24,594] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 586
2023-01-11T21:41:26.9856719Z 
2023-01-11T21:41:26.9856872Z from ctypes import c_void_p, c_long
2023-01-11T21:41:26.9856997Z import torch
2023-01-11T21:41:26.9857123Z import random
2023-01-11T21:41:26.9857330Z from torch import empty_strided, as_strided, device
2023-01-11T21:41:26.9857566Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:41:26.9857578Z 
2023-01-11T21:41:26.9857755Z aten = torch.ops.aten
2023-01-11T21:41:26.9858053Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:41:26.9858248Z async_compile = AsyncCompile()
2023-01-11T21:41:26.9858260Z 
2023-01-11T21:41:26.9858295Z 
2023-01-11T21:41:26.9858599Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:41:26.9859075Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:41:26.9859447Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:41:26.9859635Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:41:26.9859810Z                        float* __restrict__ out_ptr0)
2023-01-11T21:41:26.9859917Z {
2023-01-11T21:41:26.9860093Z     #pragma omp parallel num_threads(4)
2023-01-11T21:41:26.9860195Z     {
2023-01-11T21:41:26.9860330Z         #pragma omp for 
2023-01-11T21:41:26.9860480Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:41:26.9860592Z         {
2023-01-11T21:41:26.9860846Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 8*i0);
2023-01-11T21:41:26.9861140Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 8*i0);
2023-01-11T21:41:26.9861394Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9861594Z             tmp2.store(out_ptr0 + 8*i0);
2023-01-11T21:41:26.9861741Z         }
2023-01-11T21:41:26.9861972Z         #pragma omp for simd simdlen(4) 
2023-01-11T21:41:26.9862167Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:41:26.9862314Z         {
2023-01-11T21:41:26.9862519Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:41:26.9862713Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:41:26.9862868Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:41:26.9863022Z             out_ptr0[i0] = tmp2;
2023-01-11T21:41:26.9863211Z         }
2023-01-11T21:41:26.9863327Z     }
2023-01-11T21:41:26.9863435Z }
2023-01-11T21:41:26.9863605Z ''')
2023-01-11T21:41:26.9863616Z 
2023-01-11T21:41:26.9863623Z 
2023-01-11T21:41:26.9863784Z async_compile.wait(globals())
2023-01-11T21:41:26.9863899Z del async_compile
2023-01-11T21:41:26.9863909Z 
2023-01-11T21:41:26.9864033Z def call(args):
2023-01-11T21:41:26.9864166Z     arg0_1, arg1_1 = args
2023-01-11T21:41:26.9864297Z     args.clear()
2023-01-11T21:41:26.9864719Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9865109Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:41:26.9865273Z     del arg0_1
2023-01-11T21:41:26.9865411Z     del arg1_1
2023-01-11T21:41:26.9865575Z     return (buf0, )
2023-01-11T21:41:26.9865587Z 
2023-01-11T21:41:26.9865598Z 
2023-01-11T21:41:26.9865770Z if __name__ == "__main__":
2023-01-11T21:41:26.9866028Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:41:26.9866313Z     from torch._inductor.utils import print_performance
2023-01-11T21:41:26.9866701Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9867066Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:41:26.9867269Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:41:26.9867280Z 
2023-01-11T21:41:26.9867385Z ok (0.043s)
2023-01-11T21:41:26.9867651Z   test_indexing_join (__main__.TestIndexingSimplification) ... ok (0.112s)
2023-01-11T21:41:26.9867949Z   test_indexing_simplification (__main__.TestIndexingSimplification) ... ok (0.104s)
2023-01-11T21:41:26.9867958Z 
2023-01-11T21:41:26.9868433Z ----------------------------------------------------------------------
2023-01-11T21:41:26.9868635Z Ran 362 tests in 1070.250s
2023-01-11T21:41:26.9868649Z 
2023-01-11T21:41:26.9868810Z OK (skipped=17)
2023-01-11T21:41:26.9868822Z 
2023-01-11T21:41:26.9869018Z Generating XML reports...
2023-01-11T21:41:26.9869757Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-CPUReproTests-20230111212334.xml
2023-01-11T21:41:26.9870313Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-CpuTests-20230111212334.xml
2023-01-11T21:41:26.9870889Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-ExprPrinterTests-20230111212334.xml
2023-01-11T21:41:26.9871495Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-SweepInputsCpuTest-20230111212334.xml
2023-01-11T21:41:26.9872375Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-TestIndexingSimplification-20230111212334.xml
2023-01-11T21:41:26.9872389Z 
2023-01-11T21:41:26.9873140Z ##[endgroup]
2023-01-11T21:41:26.9873812Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_9_sfivr0)
2023-01-11T21:41:26.9873822Z 
2023-01-11T21:41:29.2129251Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:29.3188395Z Ignoring disabled issues:  []
2023-01-11T21:41:29.3480709Z Running test_mkldnn_verbose ... [2023-01-11 21:41:29.347613]
2023-01-11T21:41:29.3483656Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkldnn_verbose.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:29.348022]
2023-01-11T21:41:35.4445714Z 
2023-01-11T21:41:35.4446226Z Expand the folded group to see the log file of test_mkldnn_verbose
2023-01-11T21:41:35.4447365Z ##[group]PRINTING LOG FILE of test_mkldnn_verbose (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_verbose_h35cuz8n)
2023-01-11T21:41:35.4447815Z 
2023-01-11T21:41:35.4447951Z Running tests...
2023-01-11T21:41:35.4448636Z ----------------------------------------------------------------------
2023-01-11T21:41:35.4449469Z Test results will be stored in test-reports/python-unittest/test_mkldnn_verbose
2023-01-11T21:41:35.4449949Z   test_verbose_off (__main__.TestMKLDNNVerbose) ... ok (1.889s)
2023-01-11T21:41:35.4450244Z   test_verbose_on (__main__.TestMKLDNNVerbose) ... ok (1.462s)
2023-01-11T21:41:35.4450454Z 
2023-01-11T21:41:35.4450727Z ----------------------------------------------------------------------
2023-01-11T21:41:35.4450963Z Ran 2 tests in 3.351s
2023-01-11T21:41:35.4451079Z 
2023-01-11T21:41:35.4451140Z OK
2023-01-11T21:41:35.4451230Z 
2023-01-11T21:41:35.4451317Z Generating XML reports...
2023-01-11T21:41:35.4451734Z Generated XML report: test-reports/python-unittest/test_mkldnn_verbose/TEST-TestMKLDNNVerbose-20230111214131.xml
2023-01-11T21:41:35.4451982Z 
2023-01-11T21:41:35.4452225Z ##[endgroup]
2023-01-11T21:41:35.4452621Z FINISHED PRINTING LOG FILE of test_mkldnn_verbose (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_verbose_h35cuz8n)
2023-01-11T21:41:35.4452843Z 
2023-01-11T21:41:37.7417542Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:37.8266691Z Ignoring disabled issues:  []
2023-01-11T21:41:37.8425642Z Running test_model_dump ... [2023-01-11 21:41:37.842219]
2023-01-11T21:41:37.8427507Z Executing ['/opt/conda/bin/python', '-bb', 'test_model_dump.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:37.842512]
2023-01-11T21:41:40.9781298Z 
2023-01-11T21:41:40.9781812Z Expand the folded group to see the log file of test_model_dump
2023-01-11T21:41:40.9783068Z ##[group]PRINTING LOG FILE of test_model_dump (/var/lib/jenkins/workspace/test/test-reports/test_model_dump_1n2pnhij)
2023-01-11T21:41:40.9783681Z 
2023-01-11T21:41:40.9783862Z Running tests...
2023-01-11T21:41:40.9784689Z ----------------------------------------------------------------------
2023-01-11T21:41:40.9785577Z Test results will be stored in test-reports/python-unittest/test_model_dump
2023-01-11T21:41:40.9786272Z   test_inline_skeleton (__main__.TestModelDump) ... ok (0.232s)
2023-01-11T21:41:40.9786886Z   test_invalid_json (__main__.TestModelDump) ... ok (0.019s)
2023-01-11T21:41:40.9787453Z   test_main (__main__.TestModelDump) ... ok (0.015s)
2023-01-11T21:41:40.9788042Z   test_memory_computation (__main__.TestModelDump) ... skip: Webdriver not requested (0.001s)
2023-01-11T21:41:40.9788690Z   test_model_with_lists (__main__.TestModelDump) ... ok (0.004s)
2023-01-11T21:41:40.9789274Z   test_optimized_quantized_model (__main__.TestModelDump) ... ok (0.478s)
2023-01-11T21:41:40.9789896Z   test_quantized_model (__main__.TestModelDump) ... ok (0.348s)
2023-01-11T21:41:40.9790522Z   test_scripted_model (__main__.TestModelDump) ... ok (0.020s)
2023-01-11T21:41:40.9791426Z   test_traced_model (__main__.TestModelDump) ... ok (0.032s)
2023-01-11T21:41:40.9791757Z 
2023-01-11T21:41:40.9792253Z ----------------------------------------------------------------------
2023-01-11T21:41:40.9792817Z Ran 9 tests in 1.149s
2023-01-11T21:41:40.9793078Z 
2023-01-11T21:41:40.9793241Z OK (skipped=1)
2023-01-11T21:41:40.9793489Z 
2023-01-11T21:41:40.9793666Z Generating XML reports...
2023-01-11T21:41:40.9794544Z Generated XML report: test-reports/python-unittest/test_model_dump/TEST-TestModelDump-20230111214139.xml
2023-01-11T21:41:40.9794942Z 
2023-01-11T21:41:40.9795363Z ##[endgroup]
2023-01-11T21:41:40.9796035Z FINISHED PRINTING LOG FILE of test_model_dump (/var/lib/jenkins/workspace/test/test-reports/test_model_dump_1n2pnhij)
2023-01-11T21:41:40.9796415Z 
2023-01-11T21:41:44.0227795Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:44.1238069Z Ignoring disabled issues:  []
2023-01-11T21:41:44.1562070Z Running test_module_init ... [2023-01-11 21:41:44.155755]
2023-01-11T21:41:44.1564167Z Executing ['/opt/conda/bin/python', '-bb', 'test_module_init.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:44.156123]
2023-01-11T21:41:46.2773412Z 
2023-01-11T21:41:46.2773939Z Expand the folded group to see the log file of test_module_init
2023-01-11T21:41:46.2775091Z ##[group]PRINTING LOG FILE of test_module_init (/var/lib/jenkins/workspace/test/test-reports/test_module_init_s9462x0e)
2023-01-11T21:41:46.2775448Z 
2023-01-11T21:41:46.2775584Z Running tests...
2023-01-11T21:41:46.2776265Z ----------------------------------------------------------------------
2023-01-11T21:41:46.2776573Z 
2023-01-11T21:41:46.2776917Z ----------------------------------------------------------------------
2023-01-11T21:41:46.2777353Z Ran 0 tests in 0.000s
2023-01-11T21:41:46.2777546Z 
2023-01-11T21:41:46.2777654Z OK
2023-01-11T21:41:46.2777854Z 
2023-01-11T21:41:46.2778009Z Generating XML reports...
2023-01-11T21:41:46.2778555Z Test results will be stored in test-reports/python-unittest/test_module_init
2023-01-11T21:41:46.2778832Z 
2023-01-11T21:41:46.2779176Z ##[endgroup]
2023-01-11T21:41:46.2779574Z FINISHED PRINTING LOG FILE of test_module_init (/var/lib/jenkins/workspace/test/test-reports/test_module_init_s9462x0e)
2023-01-11T21:41:46.2779790Z 
2023-01-11T21:41:49.2136405Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:49.3352685Z Ignoring disabled issues:  []
2023-01-11T21:41:49.3658169Z Running test_monitor ... [2023-01-11 21:41:49.365292]
2023-01-11T21:41:49.3660338Z Executing ['/opt/conda/bin/python', '-bb', 'test_monitor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:49.365721]
2023-01-11T21:41:51.8227624Z 
2023-01-11T21:41:51.8228115Z Expand the folded group to see the log file of test_monitor
2023-01-11T21:41:51.8229182Z ##[group]PRINTING LOG FILE of test_monitor (/var/lib/jenkins/workspace/test/test-reports/test_monitor_56qdajuw)
2023-01-11T21:41:51.8229596Z 
2023-01-11T21:41:51.8229723Z Running tests...
2023-01-11T21:41:51.8230320Z ----------------------------------------------------------------------
2023-01-11T21:41:51.8230996Z Test results will be stored in test-reports/python-unittest/test_monitor
2023-01-11T21:41:51.8231516Z   test_event_handler (__main__.TestMonitor) ... ok (0.234s)
2023-01-11T21:41:51.8231952Z   test_fixed_count_stat (__main__.TestMonitor) ... ok (0.001s)
2023-01-11T21:41:51.8232420Z   test_interval_stat (__main__.TestMonitor) ... ok (0.002s)
2023-01-11T21:41:51.8232829Z   test_log_event (__main__.TestMonitor) ... ok (0.001s)
2023-01-11T21:41:51.8233319Z   test_event_handler (__main__.TestMonitorTensorboard) ... ok (0.180s)
2023-01-11T21:41:51.8233628Z 
2023-01-11T21:41:51.8233977Z ----------------------------------------------------------------------
2023-01-11T21:41:51.8234399Z Ran 5 tests in 0.419s
2023-01-11T21:41:51.8234607Z 
2023-01-11T21:41:51.8234718Z OK
2023-01-11T21:41:51.8234880Z 
2023-01-11T21:41:51.8235018Z Generating XML reports...
2023-01-11T21:41:51.8236001Z Generated XML report: test-reports/python-unittest/test_monitor/TEST-TestMonitor-20230111214151.xml
2023-01-11T21:41:51.8236931Z Generated XML report: test-reports/python-unittest/test_monitor/TEST-TestMonitorTensorboard-20230111214151.xml
2023-01-11T21:41:51.8237359Z 
2023-01-11T21:41:51.8237742Z ##[endgroup]
2023-01-11T21:41:51.8238259Z FINISHED PRINTING LOG FILE of test_monitor (/var/lib/jenkins/workspace/test/test-reports/test_monitor_56qdajuw)
2023-01-11T21:41:51.8238554Z 
2023-01-11T21:41:54.6854123Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:41:54.8107327Z Ignoring disabled issues:  []
2023-01-11T21:41:54.8365912Z Running test_namedtensor ... [2023-01-11 21:41:54.836243]
2023-01-11T21:41:54.8368768Z Executing ['/opt/conda/bin/python', '-bb', 'test_namedtensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:41:54.836567]
2023-01-11T21:41:57.9884657Z 
2023-01-11T21:41:57.9888748Z Expand the folded group to see the log file of test_namedtensor
2023-01-11T21:41:57.9889882Z ##[group]PRINTING LOG FILE of test_namedtensor (/var/lib/jenkins/workspace/test/test-reports/test_namedtensor_79wml2do)
2023-01-11T21:41:57.9890276Z 
2023-01-11T21:41:57.9890500Z Running tests...
2023-01-11T21:41:57.9891172Z ----------------------------------------------------------------------
2023-01-11T21:41:57.9891855Z Test results will be stored in test-reports/python-unittest/test_namedtensor
2023-01-11T21:41:57.9892468Z   test_aaa_must_run_first_check_experimental_warning (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:41:57.9893020Z   test_addcmul_addcdiv (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:41:57.9893418Z   test_addmm (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:41:57.9893802Z   test_addmv (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:41:57.9894178Z   test_align_as (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9894576Z   test_align_tensors (__main__.TestNamedTensor) ... skip: Not implemented yet (0.001s)
2023-01-11T21:41:57.9895058Z   test_align_tensors_two_inputs (__main__.TestNamedTensor) ... skip: Not implemented yet (0.002s)
2023-01-11T21:41:57.9895479Z   test_align_to (__main__.TestNamedTensor) ... ok (0.009s)
2023-01-11T21:41:57.9895870Z   test_align_to_ellipsis (__main__.TestNamedTensor) ... ok (0.010s)
2023-01-11T21:41:57.9896240Z   test_any_all (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9896607Z   test_as_strided (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9897019Z   test_as_strided_cuda (__main__.TestNamedTensor) ... skip: no CUDA (0.000s)
2023-01-11T21:41:57.9897684Z   test_autograd_ignores_names (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9898360Z   test_autograd_smoke (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9899064Z   test_autograd_warns_named_grad (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9899759Z   test_bernoulli (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9900408Z   test_big_tensor_repr_has_names (__main__.TestNamedTensor) ... ok (0.018s)
2023-01-11T21:41:57.9901078Z   test_binary_ops (__main__.TestNamedTensor) ... ok (0.340s)
2023-01-11T21:41:57.9901698Z   test_bitwise_not (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9902305Z   test_bmm (__main__.TestNamedTensor) ... ok (0.019s)
2023-01-11T21:41:57.9902880Z   test_cat (__main__.TestNamedTensor) ... ok (0.015s)
2023-01-11T21:41:57.9903551Z   test_cdist (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:41:57.9904152Z   test_comparison_ops (__main__.TestNamedTensor) ... ok (0.005s)
2023-01-11T21:41:57.9904809Z   test_copy_transpose (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9905444Z   test_cummax_cummin (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9906043Z   test_detach (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9906650Z   test_diagonal (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9907225Z   test_dot (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9908074Z   test_equal (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9908655Z   test_expand (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9909295Z   test_factory_coverage (__main__.TestNamedTensor) ... ok (0.004s)
2023-01-11T21:41:57.9909964Z   test_factory_edge_cases (__main__.TestNamedTensor) ... ok (0.020s)
2023-01-11T21:41:57.9910553Z   test_flatten (__main__.TestNamedTensor) ... ok (0.010s)
2023-01-11T21:41:57.9911167Z   test_flatten_nodims (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:41:57.9911790Z   test_has_names (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9912389Z   test_index_fill (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9914121Z   test_info_smoke (__main__.TestNamedTensor) ... /var/lib/jenkins/workspace/test/test_namedtensor.py:617: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:57.9915537Z   tensor.storage()
2023-01-11T21:41:57.9916895Z /var/lib/jenkins/workspace/test/test_namedtensor.py:619: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:57.9918154Z   tensor.storage_type()
2023-01-11T21:41:57.9920136Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:57.9921621Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:41:57.9923535Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:41:57.9925057Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:41:57.9925576Z ok (0.001s)
2023-01-11T21:41:57.9926093Z   test_logcumsumexp (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9926719Z   test_logical_not (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9927352Z   test_logical_ops (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:41:57.9927999Z   test_masked_fill (__main__.TestNamedTensor) ... ok (0.014s)
2023-01-11T21:41:57.9928629Z   test_masked_select (__main__.TestNamedTensor) ... ok (0.006s)
2023-01-11T21:41:57.9929301Z   test_matmul (__main__.TestNamedTensor) ... ok (0.021s)
2023-01-11T21:41:57.9929912Z   test_max_pooling (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:41:57.9930627Z   test_max_pooling_without_names_does_not_warn (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9931302Z   test_mm (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:41:57.9931864Z   test_mv (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9932499Z   test_no_jit_script_support (__main__.TestNamedTensor) ... ok (0.047s)
2023-01-11T21:41:57.9933175Z   test_no_jit_tracer_support (__main__.TestNamedTensor) ... ok (0.012s)
2023-01-11T21:41:57.9933888Z   test_no_multiprocessing_support (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9934585Z   test_no_pickle_support (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9935256Z   test_no_save_support (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9935946Z   test_noncontig_contiguous (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9936785Z   test_none_names_refcount (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9937484Z   test_nyi_dimname_overload_msg (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:41:57.9938167Z   test_out_fn_semantics (__main__.TestNamedTensor) ... ok (0.021s)
2023-01-11T21:41:57.9938852Z   test_pow_special (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9939502Z   test_py3_ellipsis (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9940153Z   test_reduction_fns (__main__.TestNamedTensor) ... ok (0.106s)
2023-01-11T21:41:57.9940788Z   test_refine_names (__main__.TestNamedTensor) ... ok (0.009s)
2023-01-11T21:41:57.9941390Z   test_rename (__main__.TestNamedTensor) ... ok (0.008s)
2023-01-11T21:41:57.9941994Z   test_rename_ (__main__.TestNamedTensor) ... ok (0.006s)
2023-01-11T21:41:57.9942760Z   test_rename_globber (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:41:57.9943504Z   test_rename_rename_map (__main__.TestNamedTensor) ... ok (0.002s)
2023-01-11T21:41:57.9944119Z   test_repr (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9944711Z   test_resize (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:41:57.9945318Z   test_select (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:41:57.9945953Z   test_select_cuda (__main__.TestNamedTensor) ... skip: no CUDA (0.000s)
2023-01-11T21:41:57.9946662Z   test_set_names_property (__main__.TestNamedTensor) ... ok (0.005s)
2023-01-11T21:41:57.9947287Z   test_size (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:41:57.9947932Z   test_split_fns_propagates_names (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9948593Z   test_squeeze (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9949183Z   test_stride (__main__.TestNamedTensor) ... ok (0.007s)
2023-01-11T21:41:57.9949814Z   test_tensor_from_lists (__main__.TestNamedTensor) ... ok (0.003s)
2023-01-11T21:41:57.9951152Z   test_tensor_from_named_tensor (__main__.TestNamedTensor) ... /var/lib/jenkins/workspace/test/test_namedtensor.py:516: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:41:57.9952363Z   tensor = torch.tensor(x)
2023-01-11T21:41:57.9953498Z /var/lib/jenkins/workspace/test/test_namedtensor.py:522: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:41:57.9954579Z   tensor = torch.tensor(x, names=None)
2023-01-11T21:41:57.9955690Z /var/lib/jenkins/workspace/test/test_namedtensor.py:527: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:41:57.9956903Z   tensor = torch.tensor(x, names=('N', 'C'))
2023-01-11T21:41:57.9957369Z ok (0.003s)
2023-01-11T21:41:57.9957893Z   test_tensor_from_numpy (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9959240Z   test_tensor_from_tensor (__main__.TestNamedTensor) ... /var/lib/jenkins/workspace/test/test_namedtensor.py:511: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:41:57.9960459Z   tensor = torch.tensor(x, names=names)
2023-01-11T21:41:57.9960938Z ok (0.001s)
2023-01-11T21:41:57.9961461Z   test_tensor_grad_is_unnamed (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9962149Z   test_transpose_variants (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9962792Z   test_trivial (__main__.TestNamedTensor) ... ok (0.000s)
2023-01-11T21:41:57.9963486Z   test_unary_propagate_names_fns (__main__.TestNamedTensor) ... ok (0.018s)
2023-01-11T21:41:57.9964132Z   test_unflatten (__main__.TestNamedTensor) ... ok (0.016s)
2023-01-11T21:41:57.9964905Z   test_unsupported_op_error_msg (__main__.TestNamedTensor) ... ok (0.010s)
2023-01-11T21:41:57.9965685Z   test_using_seen_interned_string_doesnt_bump_refcount (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9966538Z   test_using_unseen_interned_string_bumps_refcount_permanently (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9967376Z   test_using_unseen_uninterned_string_refcounts (__main__.TestNamedTensor) ... ok (0.001s)
2023-01-11T21:41:57.9967800Z 
2023-01-11T21:41:57.9968255Z ----------------------------------------------------------------------
2023-01-11T21:41:57.9968804Z Ran 86 tests in 0.857s
2023-01-11T21:41:57.9969030Z 
2023-01-11T21:41:57.9969293Z OK (skipped=4)
2023-01-11T21:41:57.9969540Z 
2023-01-11T21:41:57.9969737Z Generating XML reports...
2023-01-11T21:41:57.9970838Z Generated XML report: test-reports/python-unittest/test_namedtensor/TEST-TestNamedTensor-20230111214156.xml
2023-01-11T21:41:57.9971424Z 
2023-01-11T21:41:57.9971949Z ##[endgroup]
2023-01-11T21:41:57.9972799Z FINISHED PRINTING LOG FILE of test_namedtensor (/var/lib/jenkins/workspace/test/test-reports/test_namedtensor_79wml2do)
2023-01-11T21:41:57.9973310Z 
2023-01-11T21:42:00.0887760Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:00.1737578Z Ignoring disabled issues:  []
2023-01-11T21:42:00.1896108Z Running test_native_functions ... [2023-01-11 21:42:00.189268]
2023-01-11T21:42:00.1898290Z Executing ['/opt/conda/bin/python', '-bb', 'test_native_functions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:00.189508]
2023-01-11T21:42:02.6648501Z 
2023-01-11T21:42:02.6649947Z Expand the folded group to see the log file of test_native_functions
2023-01-11T21:42:02.6651383Z ##[group]PRINTING LOG FILE of test_native_functions (/var/lib/jenkins/workspace/test/test-reports/test_native_functions_z3hrc8il)
2023-01-11T21:42:02.6651811Z 
2023-01-11T21:42:02.6652073Z Running tests...
2023-01-11T21:42:02.6652787Z ----------------------------------------------------------------------
2023-01-11T21:42:02.6653544Z Test results will be stored in test-reports/python-unittest/test_native_functions
2023-01-11T21:42:02.6654164Z   test_intlist_error_with_overload (__main__.TestNativeFunctions) ... ok (0.258s)
2023-01-11T21:42:02.6655152Z   test_optional_filled_intlist (__main__.TestNativeFunctions) ... pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6656043Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6656824Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6657595Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6658374Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6659150Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6659833Z pad(): argument 'pad' (position 2) must be tuple of ints, not str
2023-01-11T21:42:02.6660230Z ok (0.056s)
2023-01-11T21:42:02.6660688Z   test_optional_floatlist (__main__.TestNativeFunctions) ... ok (0.011s)
2023-01-11T21:42:02.6661267Z   test_optional_floatlist_invalid (__main__.TestNativeFunctions) ... ok (0.010s)
2023-01-11T21:42:02.6661832Z   test_optional_intlist (__main__.TestNativeFunctions) ... ok (0.010s)
2023-01-11T21:42:02.6662411Z   test_optional_intlist_invalid (__main__.TestNativeFunctions) ... ok (0.009s)
2023-01-11T21:42:02.6662975Z   test_string_defaults (__main__.TestNativeFunctions) ... ok (0.006s)
2023-01-11T21:42:02.6664059Z   test_symintlist_error (__main__.TestNativeFunctions) ... pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6664905Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6665994Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6666787Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6667517Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6668281Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6668977Z pad(): argument 'pad' (position 2) must be tuple of ints, not str
2023-01-11T21:42:02.6669405Z ok (0.016s)
2023-01-11T21:42:02.6670234Z   test_symintlist_error_with_overload (__main__.TestNativeFunctions) ... view() received an invalid combination of arguments - got (tuple), but expected one of:
2023-01-11T21:42:02.6671027Z  * (torch.dtype dtype)
2023-01-11T21:42:02.6671669Z       didn't match because some of the arguments have invalid types: (!tuple of (str,)!)
2023-01-11T21:42:02.6672132Z  * (tuple of ints size)
2023-01-11T21:42:02.6672755Z       didn't match because some of the arguments have invalid types: (!tuple of (str,)!)
2023-01-11T21:42:02.6673105Z 
2023-01-11T21:42:02.6673518Z view(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6674267Z view() received an invalid combination of arguments - got (tuple), but expected one of:
2023-01-11T21:42:02.6674739Z  * (torch.dtype dtype)
2023-01-11T21:42:02.6675366Z       didn't match because some of the arguments have invalid types: (!tuple of (str, int)!)
2023-01-11T21:42:02.6675844Z  * (tuple of ints size)
2023-01-11T21:42:02.6676467Z       didn't match because some of the arguments have invalid types: (!tuple of (str, int)!)
2023-01-11T21:42:02.6676811Z 
2023-01-11T21:42:02.6677243Z view() received an invalid combination of arguments - got (list), but expected one of:
2023-01-11T21:42:02.6677722Z  * (torch.dtype dtype)
2023-01-11T21:42:02.6678343Z       didn't match because some of the arguments have invalid types: (!list of [str]!)
2023-01-11T21:42:02.6678797Z  * (tuple of ints size)
2023-01-11T21:42:02.6679399Z       didn't match because some of the arguments have invalid types: (!list of [str]!)
2023-01-11T21:42:02.6679733Z 
2023-01-11T21:42:02.6680154Z view(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6680872Z view() received an invalid combination of arguments - got (list), but expected one of:
2023-01-11T21:42:02.6681349Z  * (torch.dtype dtype)
2023-01-11T21:42:02.6681991Z       didn't match because some of the arguments have invalid types: (!list of [str, int]!)
2023-01-11T21:42:02.6682442Z  * (tuple of ints size)
2023-01-11T21:42:02.6683071Z       didn't match because some of the arguments have invalid types: (!list of [str, int]!)
2023-01-11T21:42:02.6683414Z 
2023-01-11T21:42:02.6683847Z view() received an invalid combination of arguments - got (str), but expected one of:
2023-01-11T21:42:02.6684332Z  * (torch.dtype dtype)
2023-01-11T21:42:02.6684918Z       didn't match because some of the arguments have invalid types: (!str!)
2023-01-11T21:42:02.6685377Z  * (tuple of ints size)
2023-01-11T21:42:02.6685963Z       didn't match because some of the arguments have invalid types: (!str!)
2023-01-11T21:42:02.6686274Z 
2023-01-11T21:42:02.6686387Z ok (0.016s)
2023-01-11T21:42:02.6687307Z   test_symintlist_error_with_overload_but_is_unique (__main__.TestNativeFunctions) ... set_() received an invalid combination of arguments - got (Tensor, int, tuple), but expected one of:
2023-01-11T21:42:02.6687924Z  * ()
2023-01-11T21:42:02.6688244Z  * (torch.Storage source)
2023-01-11T21:42:02.6688728Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6689360Z  * (Tensor source)
2023-01-11T21:42:02.6689838Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6690156Z 
2023-01-11T21:42:02.6690581Z set_(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6691549Z set_() received an invalid combination of arguments - got (Tensor, int, tuple), but expected one of:
2023-01-11T21:42:02.6692020Z  * ()
2023-01-11T21:42:02.6692341Z  * (torch.Storage source)
2023-01-11T21:42:02.6692825Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6693298Z  * (Tensor source)
2023-01-11T21:42:02.6693770Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6694064Z 
2023-01-11T21:42:02.6694542Z set_() received an invalid combination of arguments - got (Tensor, int, list), but expected one of:
2023-01-11T21:42:02.6695005Z  * ()
2023-01-11T21:42:02.6695323Z  * (torch.Storage source)
2023-01-11T21:42:02.6695938Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6696405Z  * (Tensor source)
2023-01-11T21:42:02.6696874Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6697196Z 
2023-01-11T21:42:02.6697618Z set_(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6698382Z set_() received an invalid combination of arguments - got (Tensor, int, list), but expected one of:
2023-01-11T21:42:02.6698846Z  * ()
2023-01-11T21:42:02.6699169Z  * (torch.Storage source)
2023-01-11T21:42:02.6699644Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6700105Z  * (Tensor source)
2023-01-11T21:42:02.6700567Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6700888Z 
2023-01-11T21:42:02.6701335Z set_() received an invalid combination of arguments - got (Tensor, int, str), but expected one of:
2023-01-11T21:42:02.6701806Z  * ()
2023-01-11T21:42:02.6702142Z  * (torch.Storage source)
2023-01-11T21:42:02.6702644Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6703175Z  * (Tensor source)
2023-01-11T21:42:02.6703628Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T21:42:02.6703958Z 
2023-01-11T21:42:02.6704087Z ok (0.016s)
2023-01-11T21:42:02.6704929Z   test_vararg_symintlist_error (__main__.TestNativeFunctions) ... rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6705780Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6706550Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6707338Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6708086Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6708855Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6709659Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6710437Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6711188Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6711936Z rand() received an invalid combination of arguments - got (str, int), but expected one of:
2023-01-11T21:42:02.6712763Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6713693Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6714545Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6715496Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6715935Z 
2023-01-11T21:42:02.6716406Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T21:42:02.6717141Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T21:42:02.6717896Z rand() received an invalid combination of arguments - got (str, int), but expected one of:
2023-01-11T21:42:02.6718710Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6719738Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6720581Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6721391Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6721832Z 
2023-01-11T21:42:02.6722295Z rand() received an invalid combination of arguments - got (str, str, str), but expected one of:
2023-01-11T21:42:02.6723104Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6724025Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6724852Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6725682Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T21:42:02.6726125Z 
2023-01-11T21:42:02.6726247Z ok (0.032s)
2023-01-11T21:42:02.6726441Z 
2023-01-11T21:42:02.6726834Z ----------------------------------------------------------------------
2023-01-11T21:42:02.6727257Z Ran 11 tests in 0.440s
2023-01-11T21:42:02.6727469Z 
2023-01-11T21:42:02.6727586Z OK
2023-01-11T21:42:02.6727761Z 
2023-01-11T21:42:02.6727918Z Generating XML reports...
2023-01-11T21:42:02.6728747Z Generated XML report: test-reports/python-unittest/test_native_functions/TEST-TestNativeFunctions-20230111214201.xml
2023-01-11T21:42:02.6729367Z 
2023-01-11T21:42:02.6729910Z ##[endgroup]
2023-01-11T21:42:02.6730692Z FINISHED PRINTING LOG FILE of test_native_functions (/var/lib/jenkins/workspace/test/test-reports/test_native_functions_z3hrc8il)
2023-01-11T21:42:02.6731130Z 
2023-01-11T21:42:04.6942486Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:04.7768527Z Ignoring disabled issues:  []
2023-01-11T21:42:04.7929821Z Running test_native_mha ... [2023-01-11 21:42:04.792734]
2023-01-11T21:42:04.7932147Z Executing ['/opt/conda/bin/python', '-bb', 'test_native_mha.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:04.792998]
2023-01-11T21:42:06.7780412Z 
2023-01-11T21:42:06.7781004Z Expand the folded group to see the log file of test_native_mha
2023-01-11T21:42:06.7782215Z ##[group]PRINTING LOG FILE of test_native_mha (/var/lib/jenkins/workspace/test/test-reports/test_native_mha_mbkhf_t8)
2023-01-11T21:42:06.7782770Z 
2023-01-11T21:42:06.7782937Z Running tests...
2023-01-11T21:42:06.7783836Z ----------------------------------------------------------------------
2023-01-11T21:42:06.7784270Z 
2023-01-11T21:42:06.7784735Z ----------------------------------------------------------------------
2023-01-11T21:42:06.7785523Z Ran 0 tests in 0.000s
2023-01-11T21:42:06.7785789Z 
2023-01-11T21:42:06.7785924Z OK
2023-01-11T21:42:06.7786134Z 
2023-01-11T21:42:06.7786333Z Generating XML reports...
2023-01-11T21:42:06.7787104Z Test results will be stored in test-reports/python-unittest/test_native_mha
2023-01-11T21:42:06.7787548Z 
2023-01-11T21:42:06.7788083Z ##[endgroup]
2023-01-11T21:42:06.7789009Z FINISHED PRINTING LOG FILE of test_native_mha (/var/lib/jenkins/workspace/test/test-reports/test_native_mha_mbkhf_t8)
2023-01-11T21:42:06.7789530Z 
2023-01-11T21:42:08.9163846Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:09.0019228Z Ignoring disabled issues:  []
2023-01-11T21:42:09.0180248Z Running test_nestedtensor ... [2023-01-11 21:42:09.017620]
2023-01-11T21:42:09.0182643Z Executing ['/opt/conda/bin/python', '-bb', 'test_nestedtensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:09.017903]
2023-01-11T21:42:11.4659616Z 
2023-01-11T21:42:11.4660102Z Expand the folded group to see the log file of test_mkldnn_fusion
2023-01-11T21:42:11.4661094Z ##[group]PRINTING LOG FILE of test_mkldnn_fusion (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_fusion_vjzokc9j)
2023-01-11T21:42:11.4661337Z 
2023-01-11T21:42:11.4661412Z Running tests...
2023-01-11T21:42:11.4661866Z ----------------------------------------------------------------------
2023-01-11T21:42:11.4662267Z Test results will be stored in test-reports/python-unittest/test_mkldnn_fusion
2023-01-11T21:42:11.4662677Z   test_conv_binary_fusion_ops (__main__.TestMkldnnFusion) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:42:11.4663049Z   test_conv_unary_fusion_nnc (__main__.TestMkldnnFusion) ... ok (0.791s)
2023-01-11T21:42:11.4663540Z   test_conv_unary_fusion_ops (__main__.TestMkldnnFusion) ... ok (54.549s)
2023-01-11T21:42:11.4664022Z   test_linear_binary_fusion_ops (__main__.TestMkldnnFusion) ... ok (0.027s)
2023-01-11T21:42:11.4664557Z   test_linear_unary_fusion_ops (__main__.TestMkldnnFusion) ... ok (0.022s)
2023-01-11T21:42:11.4665031Z   test_single_conv (__main__.TestMkldnnFusion) ... ok (1.268s)
2023-01-11T21:42:11.4665520Z   test_unsupported_conv (__main__.TestMkldnnFusion) ... ok (11.138s)
2023-01-11T21:42:11.4665809Z 
2023-01-11T21:42:11.4666169Z ----------------------------------------------------------------------
2023-01-11T21:42:11.4666600Z Ran 7 tests in 67.795s
2023-01-11T21:42:11.4666819Z 
2023-01-11T21:42:11.4666938Z OK (skipped=1)
2023-01-11T21:42:11.4667047Z 
2023-01-11T21:42:11.4667131Z Generating XML reports...
2023-01-11T21:42:11.4667564Z Generated XML report: test-reports/python-unittest/test_mkldnn_fusion/TEST-TestMkldnnFusion-20230111214103.xml
2023-01-11T21:42:11.4667796Z 
2023-01-11T21:42:11.4668029Z ##[endgroup]
2023-01-11T21:42:11.4668408Z FINISHED PRINTING LOG FILE of test_mkldnn_fusion (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_fusion_vjzokc9j)
2023-01-11T21:42:11.4668628Z 
2023-01-11T21:42:11.5399377Z 
2023-01-11T21:42:11.5399885Z Expand the folded group to see the log file of test_nestedtensor
2023-01-11T21:42:11.5400882Z ##[group]PRINTING LOG FILE of test_nestedtensor (/var/lib/jenkins/workspace/test/test-reports/test_nestedtensor_90mlron8)
2023-01-11T21:42:11.5401273Z 
2023-01-11T21:42:11.5401404Z Running tests...
2023-01-11T21:42:11.5402060Z ----------------------------------------------------------------------
2023-01-11T21:42:11.5402763Z Test results will be stored in test-reports/python-unittest/test_nestedtensor
2023-01-11T21:42:11.5403752Z   test_2d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... /var/lib/jenkins/workspace/test/test_nestedtensor.py:113: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:42:11.5404420Z   nested_tensor = torch.nested.nested_tensor(data, dtype=torch.int64)
2023-01-11T21:42:11.5404670Z ok (0.003s)
2023-01-11T21:42:11.5405137Z   test_2d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5405548Z   test_2d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5405897Z   test_2d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5406280Z   test_2d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5406628Z   test_2d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5407016Z   test_2d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5407427Z   test_2d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5407803Z   test_3d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5408153Z   test_3d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5408535Z   test_3d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5408863Z   test_3d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5409433Z   test_3d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5409778Z   test_3d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5410173Z   test_3d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5410501Z   test_3d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5410938Z   test_3d_nested_tensor_float_batch_size_2_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5411300Z   test_3d_nested_tensor_float_batch_size_2_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5411709Z   test_3d_nested_tensor_float_batch_size_2_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5412047Z   test_3d_nested_tensor_float_batch_size_2_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5412449Z   test_3d_nested_tensor_float_batch_size_4_max_seq_len_3_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5412802Z   test_3d_nested_tensor_float_batch_size_4_max_seq_len_3_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5413209Z   test_3d_nested_tensor_float_batch_size_4_max_seq_len_5_vocab_size_10 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5413547Z   test_3d_nested_tensor_float_batch_size_4_max_seq_len_5_vocab_size_20 (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5413905Z   test_copy_ (__main__.TestNestedTensor) ... ok (0.009s)
2023-01-11T21:42:11.5414178Z   test_default_nested_tensor (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:11.5414496Z   test_dim (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:11.5414737Z   test_fill_ (__main__.TestNestedTensor) ... ok (0.006s)
2023-01-11T21:42:11.5414995Z   test_is_contiguous (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5415314Z   test_nested_namespace (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:11.5415581Z   test_nested_tensor (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:11.5415874Z   test_nested_tensor_matching_dim (__main__.TestNestedTensor) ... ok (0.006s)
2023-01-11T21:42:11.5416188Z   test_numel (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5416430Z   test_ones_like (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5416804Z   test_repr_string (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:11.5417063Z   test_size (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5417315Z   test_size_dim (__main__.TestNestedTensor) ... ok (0.006s)
2023-01-11T21:42:11.5417608Z   test_stride (__main__.TestNestedTensor) ... ok (0.002s)
2023-01-11T21:42:11.5417855Z   test_to (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:11.5418138Z   test_to_padded_tensor_on_empty_tensor (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:11.5418452Z   test_unbind_0 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:11.5418705Z   test_unbind_1 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:11.5418999Z   test_unbind_3 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:11.5419290Z   test_unbind_4 (__main__.TestNestedTensor) ... ok (0.001s)
2023-01-11T21:42:11.5419546Z   test_unbind_dim (__main__.TestNestedTensor) ... ok (0.003s)
2023-01-11T21:42:11.5419733Z 
2023-01-11T21:42:11.5419967Z ----------------------------------------------------------------------
2023-01-11T21:42:11.5420209Z Ran 45 tests in 0.109s
2023-01-11T21:42:11.5420310Z 
2023-01-11T21:42:11.5420369Z OK
2023-01-11T21:42:11.5420499Z 
2023-01-11T21:42:11.5420590Z Generating XML reports...
2023-01-11T21:42:11.5421019Z Generated XML report: test-reports/python-unittest/test_nestedtensor/TEST-TestNestedTensor-20230111214211.xml
2023-01-11T21:42:11.5421301Z 
2023-01-11T21:42:11.5421539Z ##[endgroup]
2023-01-11T21:42:11.5421938Z FINISHED PRINTING LOG FILE of test_nestedtensor (/var/lib/jenkins/workspace/test/test-reports/test_nestedtensor_90mlron8)
2023-01-11T21:42:11.5422190Z 
2023-01-11T21:42:13.3659388Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:13.4131516Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:13.4311528Z Ignoring disabled issues:  []
2023-01-11T21:42:13.4469821Z Running test_numba_integration ... [2023-01-11 21:42:13.446715]
2023-01-11T21:42:13.4472469Z Executing ['/opt/conda/bin/python', '-bb', 'test_numba_integration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:13.446951]
2023-01-11T21:42:13.4780464Z Ignoring disabled issues:  []
2023-01-11T21:42:13.4940160Z Running test_numpy_interop ... [2023-01-11 21:42:13.493680]
2023-01-11T21:42:13.4941334Z Executing ['/opt/conda/bin/python', '-bb', 'test_numpy_interop.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:13.493927]
2023-01-11T21:42:15.3924022Z 
2023-01-11T21:42:15.3924424Z Expand the folded group to see the log file of test_numpy_interop
2023-01-11T21:42:15.3925076Z ##[group]PRINTING LOG FILE of test_numpy_interop (/var/lib/jenkins/workspace/test/test-reports/test_numpy_interop_ylhwuspw)
2023-01-11T21:42:15.3925321Z 
2023-01-11T21:42:15.3925410Z Running tests...
2023-01-11T21:42:15.3925824Z ----------------------------------------------------------------------
2023-01-11T21:42:15.3926007Z 
2023-01-11T21:42:15.3926190Z ----------------------------------------------------------------------
2023-01-11T21:42:15.3926428Z Ran 0 tests in 0.000s
2023-01-11T21:42:15.3926541Z 
2023-01-11T21:42:15.3926602Z OK
2023-01-11T21:42:15.3926692Z 
2023-01-11T21:42:15.3926779Z Generating XML reports...
2023-01-11T21:42:15.3927097Z Test results will be stored in test-reports/python-unittest/test_numpy_interop
2023-01-11T21:42:15.3927277Z 
2023-01-11T21:42:15.3927491Z ##[endgroup]
2023-01-11T21:42:15.3927881Z FINISHED PRINTING LOG FILE of test_numpy_interop (/var/lib/jenkins/workspace/test/test-reports/test_numpy_interop_ylhwuspw)
2023-01-11T21:42:15.3928086Z 
2023-01-11T21:42:15.3940041Z 
2023-01-11T21:42:15.3940335Z Expand the folded group to see the log file of test_numba_integration
2023-01-11T21:42:15.3941192Z ##[group]PRINTING LOG FILE of test_numba_integration (/var/lib/jenkins/workspace/test/test-reports/test_numba_integration_c0b5lclq)
2023-01-11T21:42:15.3941545Z 
2023-01-11T21:42:15.3941814Z Running tests...
2023-01-11T21:42:15.3942158Z ----------------------------------------------------------------------
2023-01-11T21:42:15.3942536Z Test results will be stored in test-reports/python-unittest/test_numba_integration
2023-01-11T21:42:15.3942850Z   test_active_device (__main__.TestNumbaIntegration)
2023-01-11T21:42:15.3943298Z 'as_cuda_array' tensor device must match active numba context. ... skip: No cuda (0.001s)
2023-01-11T21:42:15.3943581Z   test_array_adaptor (__main__.TestNumbaIntegration)
2023-01-11T21:42:15.3943879Z Torch __cuda_array_adaptor__ exposes tensor data to numba.cuda. ... skip: No cuda (0.001s)
2023-01-11T21:42:15.3944180Z   test_conversion_errors (__main__.TestNumbaIntegration)
2023-01-11T21:42:15.3944490Z Numba properly detects array interface for tensor.Tensor variants. ... skip: No cuda (0.001s)
2023-01-11T21:42:15.3944863Z   test_cuda_array_interface (__main__.TestNumbaIntegration)
2023-01-11T21:42:15.3945169Z torch.Tensor exposes __cuda_array_interface__ for cuda tensors. ... skip: No cuda (0.001s)
2023-01-11T21:42:15.3945476Z   test_from_cuda_array_interface (__main__.TestNumbaIntegration)
2023-01-11T21:42:15.3945891Z torch.as_tensor() and torch.tensor() supports the __cuda_array_interface__ protocol. ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.001s)
2023-01-11T21:42:15.3946306Z   test_from_cuda_array_interface_active_device (__main__.TestNumbaIntegration)
2023-01-11T21:42:15.3946710Z torch.as_tensor() tensor device must match active numba context. ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.001s)
2023-01-11T21:42:15.3947108Z   test_from_cuda_array_interface_inferred_strides (__main__.TestNumbaIntegration)
2023-01-11T21:42:15.3947442Z torch.as_tensor(numba_ary) should have correct inferred (contiguous) strides ... skip: No cuda (0.000s)
2023-01-11T21:42:15.3947771Z   test_from_cuda_array_interface_lifetime (__main__.TestNumbaIntegration)
2023-01-11T21:42:15.3948203Z torch.as_tensor(obj) tensor grabs a reference to obj so that the lifetime of obj exceeds the tensor ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.000s)
2023-01-11T21:42:15.3948481Z 
2023-01-11T21:42:15.3948686Z ----------------------------------------------------------------------
2023-01-11T21:42:15.3948914Z Ran 8 tests in 0.007s
2023-01-11T21:42:15.3949024Z 
2023-01-11T21:42:15.3949096Z OK (skipped=8)
2023-01-11T21:42:15.3949201Z 
2023-01-11T21:42:15.3949284Z Generating XML reports...
2023-01-11T21:42:15.3949720Z Generated XML report: test-reports/python-unittest/test_numba_integration/TEST-TestNumbaIntegration-20230111214215.xml
2023-01-11T21:42:15.3949956Z 
2023-01-11T21:42:15.3950180Z ##[endgroup]
2023-01-11T21:42:15.3950589Z FINISHED PRINTING LOG FILE of test_numba_integration (/var/lib/jenkins/workspace/test/test-reports/test_numba_integration_c0b5lclq)
2023-01-11T21:42:15.3950818Z 
2023-01-11T21:42:17.2298805Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:17.2602644Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:17.2948587Z Ignoring disabled issues:  []
2023-01-11T21:42:17.3106632Z Running test_nvfuser_dynamo ... [2023-01-11 21:42:17.310412]
2023-01-11T21:42:17.3109400Z Executing ['/opt/conda/bin/python', '-bb', 'test_nvfuser_dynamo.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:17.310665]
2023-01-11T21:42:17.3246190Z Ignoring disabled issues:  []
2023-01-11T21:42:17.3403243Z Running test_nvfuser_frontend ... [2023-01-11 21:42:17.340128]
2023-01-11T21:42:17.3405892Z Executing ['/opt/conda/bin/python', '-bb', 'test_nvfuser_frontend.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:17.340376]
2023-01-11T21:42:18.9293422Z 
2023-01-11T21:42:18.9293836Z Expand the folded group to see the log file of test_nvfuser_frontend
2023-01-11T21:42:18.9294574Z ##[group]PRINTING LOG FILE of test_nvfuser_frontend (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_frontend_2by9bfbt)
2023-01-11T21:42:18.9295025Z 
2023-01-11T21:42:18.9295101Z Running tests...
2023-01-11T21:42:18.9295496Z ----------------------------------------------------------------------
2023-01-11T21:42:18.9295970Z   test_basic (__main__.TestNvFuserFrontend) ... Test results will be stored in test-reports/python-unittest/test_nvfuser_frontend
2023-01-11T21:42:18.9296267Z skip: requires CUDA (0.001s)
2023-01-11T21:42:18.9296536Z   test_basic_fp16 (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:18.9296863Z   test_broadcast_mixing (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:18.9297194Z   test_cast_double_to_half (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:18.9297591Z   test_explicit_broadcast_input (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:18.9297941Z   test_implicit_broadcast_input (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:18.9298283Z   test_ops_broadcast (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.000s)
2023-01-11T21:42:18.9298595Z   test_prim_layer_norm_fwd (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.003s)
2023-01-11T21:42:18.9298920Z   test_prim_rms_norm_fwd (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:18.9299244Z   test_promote_to_double (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.000s)
2023-01-11T21:42:18.9299423Z 
2023-01-11T21:42:18.9299626Z ----------------------------------------------------------------------
2023-01-11T21:42:18.9299851Z Ran 10 tests in 0.009s
2023-01-11T21:42:18.9299961Z 
2023-01-11T21:42:18.9300033Z OK (skipped=10)
2023-01-11T21:42:18.9300138Z 
2023-01-11T21:42:18.9300221Z Generating XML reports...
2023-01-11T21:42:18.9300650Z Generated XML report: test-reports/python-unittest/test_nvfuser_frontend/TEST-TestNvFuserFrontend-20230111214218.xml
2023-01-11T21:42:18.9300897Z 
2023-01-11T21:42:18.9301126Z ##[endgroup]
2023-01-11T21:42:18.9301527Z FINISHED PRINTING LOG FILE of test_nvfuser_frontend (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_frontend_2by9bfbt)
2023-01-11T21:42:18.9301750Z 
2023-01-11T21:42:19.1210499Z 
2023-01-11T21:42:19.1210938Z Expand the folded group to see the log file of test_nvfuser_dynamo
2023-01-11T21:42:19.1211819Z ##[group]PRINTING LOG FILE of test_nvfuser_dynamo (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_dynamo_ppkzyqgh)
2023-01-11T21:42:19.1212384Z 
2023-01-11T21:42:19.1212548Z Running tests...
2023-01-11T21:42:19.1213352Z ----------------------------------------------------------------------
2023-01-11T21:42:19.1214465Z   test_basic (__main__.TestNvFuserDynamo) ... Test results will be stored in test-reports/python-unittest/test_nvfuser_dynamo
2023-01-11T21:42:19.1215183Z skip: requires CUDA (0.001s)
2023-01-11T21:42:19.1215899Z   test_batch_norm_implicit_dtype_promotion (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:19.1216747Z   test_dtype_correctness (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.000s)
2023-01-11T21:42:19.1217499Z   test_min_cut (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.001s)
2023-01-11T21:42:19.1217907Z 
2023-01-11T21:42:19.1218378Z ----------------------------------------------------------------------
2023-01-11T21:42:19.1218959Z Ran 4 tests in 0.003s
2023-01-11T21:42:19.1219226Z 
2023-01-11T21:42:19.1219387Z OK (skipped=4)
2023-01-11T21:42:19.1219647Z 
2023-01-11T21:42:19.1219835Z Generating XML reports...
2023-01-11T21:42:19.1220834Z Generated XML report: test-reports/python-unittest/test_nvfuser_dynamo/TEST-TestNvFuserDynamo-20230111214218.xml
2023-01-11T21:42:19.1221439Z 
2023-01-11T21:42:19.1221963Z ##[endgroup]
2023-01-11T21:42:19.1222921Z FINISHED PRINTING LOG FILE of test_nvfuser_dynamo (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_dynamo_ppkzyqgh)
2023-01-11T21:42:19.1223573Z 
2023-01-11T21:42:20.8025328Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:20.8672013Z Ignoring disabled issues:  []
2023-01-11T21:42:20.8832150Z Running test_openmp ... [2023-01-11 21:42:20.882882]
2023-01-11T21:42:20.8833545Z Executing ['/opt/conda/bin/python', '-bb', 'test_openmp.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:20.883132]
2023-01-11T21:42:21.0119035Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:21.0763855Z Ignoring disabled issues:  []
2023-01-11T21:42:21.0921496Z Running test_optim ... [2023-01-11 21:42:21.091860]
2023-01-11T21:42:21.0923774Z Executing ['/opt/conda/bin/python', '-bb', 'test_optim.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:21.092115]
2023-01-11T21:42:26.0681016Z 
2023-01-11T21:42:26.0681631Z Expand the folded group to see the log file of test_openmp
2023-01-11T21:42:26.0682623Z ##[group]PRINTING LOG FILE of test_openmp (/var/lib/jenkins/workspace/test/test-reports/test_openmp_po8kp7nj)
2023-01-11T21:42:26.0682875Z 
2023-01-11T21:42:26.0682939Z Running tests...
2023-01-11T21:42:26.0683397Z ----------------------------------------------------------------------
2023-01-11T21:42:26.0683776Z Test results will be stored in test-reports/python-unittest/test_openmp
2023-01-11T21:42:26.0684105Z   test_n_threads (__main__.TestOpenMP_ParallelFor)
2023-01-11T21:42:26.0684363Z Make sure there is no memory leak with many threads ... ok (1.396s)
2023-01-11T21:42:26.0684626Z   test_one_thread (__main__.TestOpenMP_ParallelFor)
2023-01-11T21:42:26.0685048Z Make sure there is no memory leak with one thread: issue gh-32284 ... ok (2.093s)
2023-01-11T21:42:26.0685220Z 
2023-01-11T21:42:26.0685407Z ----------------------------------------------------------------------
2023-01-11T21:42:26.0685698Z Ran 2 tests in 3.489s
2023-01-11T21:42:26.0685810Z 
2023-01-11T21:42:26.0685869Z OK
2023-01-11T21:42:26.0685963Z 
2023-01-11T21:42:26.0686046Z Generating XML reports...
2023-01-11T21:42:26.0686490Z Generated XML report: test-reports/python-unittest/test_openmp/TEST-TestOpenMP_ParallelFor-20230111214222.xml
2023-01-11T21:42:26.0686722Z 
2023-01-11T21:42:26.0686948Z ##[endgroup]
2023-01-11T21:42:26.0687365Z FINISHED PRINTING LOG FILE of test_openmp (/var/lib/jenkins/workspace/test/test-reports/test_openmp_po8kp7nj)
2023-01-11T21:42:26.0687564Z 
2023-01-11T21:42:27.8904212Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:27.9542884Z Ignoring disabled issues:  []
2023-01-11T21:42:27.9700781Z Running test_package ... [2023-01-11 21:42:27.969831]
2023-01-11T21:42:27.9703112Z Executing ['/opt/conda/bin/python', '-bb', 'test_package.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:27.970059]
2023-01-11T21:42:31.8324364Z 
2023-01-11T21:42:31.8325127Z Expand the folded group to see the log file of test_package
2023-01-11T21:42:31.8326187Z ##[group]PRINTING LOG FILE of test_package (/var/lib/jenkins/workspace/test/test-reports/test_package_0slox5a_)
2023-01-11T21:42:31.8326693Z 
2023-01-11T21:42:31.8326842Z Running tests...
2023-01-11T21:42:31.8327432Z ----------------------------------------------------------------------
2023-01-11T21:42:31.8328209Z Test results will be stored in test-reports/python-unittest/test_package
2023-01-11T21:42:31.8330817Z   test_trace_dependencies (test_analyze.TestAnalyze) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/81213 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.205s)
2023-01-11T21:42:31.8332052Z   test_allow_empty_with_error (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8332672Z If an error occurs during packaging, it should not be shadowed by the allow_empty error. ... ok (0.002s)
2023-01-11T21:42:31.8333295Z   test_broken_dependency (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8333882Z A unpackageable dependency should raise a PackagingError. ... ok (0.002s)
2023-01-11T21:42:31.8334717Z   test_deny (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8335147Z Test marking packages as "deny" during export. ... ok (0.002s)
2023-01-11T21:42:31.8335679Z   test_deny_glob (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8336267Z Test marking packages as "deny" using globs instead of package names. ... ok (0.002s)
2023-01-11T21:42:31.8336881Z   test_extern (test_dependency_api.TestDependencyAPI) ... ok (0.001s)
2023-01-11T21:42:31.8337414Z   test_extern_glob (test_dependency_api.TestDependencyAPI) ... ok (0.002s)
2023-01-11T21:42:31.8337988Z   test_extern_glob_allow_empty (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8338576Z Test that an error is thrown when a extern glob is specified with allow_empty=True ... ok (0.001s)
2023-01-11T21:42:31.8339371Z   test_externing_c_extension (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8341425Z Externing c extensions modules should allow us to still access them especially those found in torch._C. ... /opt/conda/lib/python3.10/site-packages/torch/package/package_exporter.py:900: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8342753Z   storage_type_str = obj.pickle_storage_type()
2023-01-11T21:42:31.8344250Z /opt/conda/lib/python3.10/site-packages/torch/package/package_exporter.py:903: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8345101Z   storage_numel = obj.size()
2023-01-11T21:42:31.8346541Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8347538Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:42:31.8347889Z ok (0.007s)
2023-01-11T21:42:31.8348320Z   test_implicit_intern (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8348882Z The save_module APIs should implicitly intern the module being saved. ... ok (0.001s)
2023-01-11T21:42:31.8349475Z   test_intern_error (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8350058Z Failure to handle all dependencies should lead to an error. ... ok (0.002s)
2023-01-11T21:42:31.8350618Z   test_invalid_import (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8351347Z An incorrectly-formed import should raise a PackagingError. ... ok (0.001s)
2023-01-11T21:42:31.8351940Z   test_mock (test_dependency_api.TestDependencyAPI) ... ok (0.002s)
2023-01-11T21:42:31.8352490Z   test_mock_glob (test_dependency_api.TestDependencyAPI) ... ok (0.002s)
2023-01-11T21:42:31.8353076Z   test_mock_glob_allow_empty (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8353584Z Test that an error is thrown when a mock glob is specified with allow_empty=True ... ok (0.001s)
2023-01-11T21:42:31.8354299Z   test_pickle_mocked (test_dependency_api.TestDependencyAPI) ... ok (0.001s)
2023-01-11T21:42:31.8354941Z   test_pickle_mocked_all (test_dependency_api.TestDependencyAPI) ... ok (0.001s)
2023-01-11T21:42:31.8355504Z   test_repackage_mocked_module (test_dependency_api.TestDependencyAPI)
2023-01-11T21:42:31.8356302Z Re-packaging a package that contains a mocked module should work correctly. ... ok (0.003s)
2023-01-11T21:42:31.8356958Z   test_extern_and_mock_hook (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:42:31.8357557Z   test_multiple_extern_hooks (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:42:31.8358354Z   test_multiple_mock_hooks (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:42:31.8358982Z   test_remove_hooks (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:42:31.8359630Z   test_single_hook (test_dependency_hooks.TestDependencyHooks) ... ok (0.001s)
2023-01-11T21:42:31.8360171Z   test_all_paths (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8360708Z   test_contains (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8361174Z   test_contains_non_hashable (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8361607Z   test_edges (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8362040Z   test_forward_closure (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8362583Z   test_iter (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8362998Z   test_node_attr_update (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8363454Z   test_node_attrs (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8363920Z   test_predecessor_not_in_graph (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8364560Z   test_predecessors (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8365056Z   test_successor_not_in_graph (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8365598Z   test_successors (test_digraph.TestDiGraph) ... ok (0.001s)
2023-01-11T21:42:31.8366144Z   test_importer_access (test_directory_reader.DirectoryReaderTest) ... ok (0.003s)
2023-01-11T21:42:31.8366725Z   test_loading_has_record (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:42:31.8367409Z Test DirectoryReader's has_record(). ... ok (0.003s)
2023-01-11T21:42:31.8367969Z   test_loading_module (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:42:31.8368547Z Test basic saving and loading of a packages from a DirectoryReader. ... ok (0.003s)
2023-01-11T21:42:31.8369327Z   test_loading_pickle (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:42:31.8370142Z Test basic saving and loading of modules and pickles from a DirectoryReader. ... skip: Does not work with latest TorchVision, see https://github.com/pytorch/pytorch/issues/81115 (0.001s)
2023-01-11T21:42:31.8370987Z   test_package_resource_access (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:42:31.8371510Z Packaged modules should be able to use the importlib.resources API to access ... ok (0.003s)
2023-01-11T21:42:31.8372176Z   test_resource_access_by_path (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:42:31.8372832Z Tests that packaged code can used importlib.resources.path. ... ok (0.004s)
2023-01-11T21:42:31.8373420Z   test_resource_reader (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:42:31.8373960Z Tests DirectoryReader as the base for get_resource_reader. ... ok (0.005s)
2023-01-11T21:42:31.8374556Z   test_scriptobject_failure_message (test_directory_reader.DirectoryReaderTest)
2023-01-11T21:42:31.8375162Z Test basic saving and loading of a ScriptModule in a directory. ... ok (0.011s)
2023-01-11T21:42:31.8375676Z   test_exclude (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8376200Z   test_exclude_from_all (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8376728Z   test_invalid_raw (test_glob_group.TestGlobGroup) ... ok (0.000s)
2023-01-11T21:42:31.8377297Z   test_list_include_exclude (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8377854Z   test_one_star (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8378473Z   test_one_star_middle (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8378977Z   test_one_star_multiple_in_component (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8379473Z   test_one_star_partial (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8379975Z   test_one_star_partial_extension (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8380621Z   test_raw_two_star (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8381040Z   test_two_star (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8381481Z   test_two_star_end (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8381947Z   test_two_star_middle (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8382561Z   test_two_star_multiple (test_glob_group.TestGlobGroup) ... ok (0.001s)
2023-01-11T21:42:31.8383182Z   test_ordered_importer_basic (test_importer.TestImporter) ... ok (0.001s)
2023-01-11T21:42:31.8383745Z   test_ordered_importer_whichmodule (test_importer.TestImporter)
2023-01-11T21:42:31.8384480Z OrderedImporter's implementation of whichmodule should try each ... ok (0.001s)
2023-01-11T21:42:31.8385253Z   test_package_importer_whichmodule_no_dunder_module (test_importer.TestImporter)
2023-01-11T21:42:31.8385892Z Exercise corner case where we try to pickle an object whose ... ok (0.001s)
2023-01-11T21:42:31.8386489Z   test_single_ordered_importer (test_importer.TestImporter) ... ok (0.001s)
2023-01-11T21:42:31.8387032Z   test_sys_importer (test_importer.TestImporter) ... ok (0.001s)
2023-01-11T21:42:31.8387590Z   test_sys_importer_roundtrip (test_importer.TestImporter) ... ok (0.001s)
2023-01-11T21:42:31.8388164Z   test_load_bc_packages_fx_module (test_load_bc_packages.TestLoadBCPackages)
2023-01-11T21:42:31.8388728Z Tests for backwards compatible fx module ... ok (0.012s)
2023-01-11T21:42:31.8389281Z   test_load_bc_packages_nn_module (test_load_bc_packages.TestLoadBCPackages)
2023-01-11T21:42:31.8389740Z Tests for backwards compatible nn module ... ok (0.008s)
2023-01-11T21:42:31.8390364Z   test_load_bc_packages_torchscript_module (test_load_bc_packages.TestLoadBCPackages)
2023-01-11T21:42:31.8391002Z Tests for backwards compatible torchscript module ... ok (0.041s)
2023-01-11T21:42:31.8391523Z   test_demangle_base (test_mangling.TestMangling)
2023-01-11T21:42:31.8392077Z Demangling a mangle parent directly should currently return an empty string. ... ok (0.001s)
2023-01-11T21:42:31.8392676Z   test_demangler_multiple_manglers (test_mangling.TestMangling)
2023-01-11T21:42:31.8393276Z PackageDemangler should be able to demangle name generated by any PackageMangler. ... ok (0.001s)
2023-01-11T21:42:31.8393855Z   test_is_mangled (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:42:31.8394391Z   test_mangle_empty_errors (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:42:31.8394903Z   test_mangle_prefix (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:42:31.8395441Z   test_mangler_is_consistent (test_mangling.TestMangling)
2023-01-11T21:42:31.8396017Z Mangling the same name twice should produce the same result. ... ok (0.001s)
2023-01-11T21:42:31.8396561Z   test_package_mangler (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:42:31.8397139Z   test_roundtrip_mangling (test_mangling.TestMangling) ... ok (0.001s)
2023-01-11T21:42:31.8397593Z   test_unique_manglers (test_mangling.TestMangling)
2023-01-11T21:42:31.8398095Z Each mangler instance should generate a unique mangled name for a given input. ... ok (0.001s)
2023-01-11T21:42:31.8398580Z   test_unique_module_names (test_mangling.TestMangling) ... ok (0.002s)
2023-01-11T21:42:31.8399017Z   test_dunder_package_present (test_misc.TestMisc)
2023-01-11T21:42:31.8399649Z The attribute '__torch_package__' should be populated on imported modules. ... ok (0.001s)
2023-01-11T21:42:31.8400123Z   test_dunder_package_works_from_package (test_misc.TestMisc)
2023-01-11T21:42:31.8400905Z The attribute '__torch_package__' should be accessible from within ... ok (0.002s)
2023-01-11T21:42:31.8401411Z   test_exporter_content_lists (test_misc.TestMisc)
2023-01-11T21:42:31.8402075Z Test content list API for PackageExporter's contained modules. ... ok (0.003s)
2023-01-11T21:42:31.8402558Z   test_file_structure (test_misc.TestMisc)
2023-01-11T21:42:31.8403245Z Tests package's Directory structure representation of a zip file. Ensures ... ok (0.003s)
2023-01-11T21:42:31.8403959Z   test_file_structure_has_file (test_misc.TestMisc)
2023-01-11T21:42:31.8404520Z Test Directory's has_file() method. ... ok (0.001s)
2023-01-11T21:42:31.8404933Z   test_inspect_class (test_misc.TestMisc)
2023-01-11T21:42:31.8405422Z Should be able to retrieve source for a packaged class. ... ok (0.002s)
2023-01-11T21:42:31.8405886Z   test_is_from_package (test_misc.TestMisc)
2023-01-11T21:42:31.8406361Z is_from_package should work for objects and modules ... ok (0.001s)
2023-01-11T21:42:31.8406866Z   test_load_python_version_from_package (test_misc.TestMisc)
2023-01-11T21:42:31.8407418Z Tests loading a package with a python version embdded ... ok (0.002s)
2023-01-11T21:42:31.8407868Z   test_loaders_that_remap_files_work_ok (test_misc.TestMisc) ... ok (0.002s)
2023-01-11T21:42:31.8408409Z   test_python_version (test_misc.TestMisc)
2023-01-11T21:42:31.8409311Z Tests that the current python version is stored in the package and is available ... ok (0.002s)
2023-01-11T21:42:31.8409854Z   test_std_lib_sys_hackery_checks (test_misc.TestMisc)
2023-01-11T21:42:31.8410379Z The standard library performs sys.module assignment hackery which ... ok (0.003s)
2023-01-11T21:42:31.8411128Z   test_model_save (test_model.ModelTest) ... skip: Does not work with recent torchvision, see https://github.com/pytorch/pytorch/issues/81115 (0.001s)
2023-01-11T21:42:31.8411957Z   test_resnet (test_model.ModelTest) ... skip: Does not work with recent torchvision, see https://github.com/pytorch/pytorch/issues/81115 (0.001s)
2023-01-11T21:42:31.8412797Z   test_script_resnet (test_model.ModelTest) ... skip: Does not work with recent torchvision, see https://github.com/pytorch/pytorch/issues/81115 (0.001s)
2023-01-11T21:42:31.8413517Z   test_package_fx_custom_tracer (test_package_fx.TestPackageFX) ... ok (0.009s)
2023-01-11T21:42:31.8414131Z   test_package_fx_package (test_package_fx.TestPackageFX) ... ok (0.017s)
2023-01-11T21:42:31.8414783Z   test_package_fx_simple (test_package_fx.TestPackageFX) ... ok (0.005s)
2023-01-11T21:42:31.8415256Z   test_package_fx_with_imports (test_package_fx.TestPackageFX) ... ok (0.005s)
2023-01-11T21:42:31.8415744Z   test_package_then_fx (test_package_fx.TestPackageFX) ... ok (0.004s)
2023-01-11T21:42:31.8416253Z   test_different_package_interface (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8416730Z Test a case where the interface defined in the package is ... ok (0.012s)
2023-01-11T21:42:31.8417229Z   test_different_package_script_class (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8417732Z Test a case where the script class defined in the package is ... ok (0.006s)
2023-01-11T21:42:31.8418247Z   test_load_shared_scriptmodules (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8418884Z Test loading of single ScriptModule shared by multiple eager ... ok (0.008s)
2023-01-11T21:42:31.8419460Z   test_load_shared_tensors (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8420682Z Test tensors shared across eager and ScriptModules on load ... /var/lib/jenkins/workspace/test/package/test_package_script.py:547: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8421799Z   shared_tensor.storage()._cdata,
2023-01-11T21:42:31.8422864Z /var/lib/jenkins/workspace/test/package/test_package_script.py:548: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8423977Z   scripted_mod_0.tensor.storage()._cdata,
2023-01-11T21:42:31.8425032Z /var/lib/jenkins/workspace/test/package/test_package_script.py:551: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8426154Z   shared_tensor.storage()._cdata,
2023-01-11T21:42:31.8427337Z /var/lib/jenkins/workspace/test/package/test_package_script.py:552: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8428298Z   scripted_mod_1.tensor.storage()._cdata,
2023-01-11T21:42:31.8429453Z /var/lib/jenkins/workspace/test/package/test_package_script.py:565: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8430448Z   loaded_mod_1.tensor.storage()._cdata,
2023-01-11T21:42:31.8431500Z /var/lib/jenkins/workspace/test/package/test_package_script.py:566: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8432572Z   loaded_mod_1.sub_mod_0.tensor.storage()._cdata,
2023-01-11T21:42:31.8433616Z /var/lib/jenkins/workspace/test/package/test_package_script.py:569: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8434470Z   loaded_mod_1.tensor.storage()._cdata,
2023-01-11T21:42:31.8435355Z /var/lib/jenkins/workspace/test/package/test_package_script.py:570: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8436238Z   loaded_mod_1.sub_mod_1.tensor.storage()._cdata,
2023-01-11T21:42:31.8436656Z ok (0.008s)
2023-01-11T21:42:31.8437114Z   test_load_shared_tensors_repackaged (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8438371Z Test tensors shared across eager and ScriptModules on load ... /var/lib/jenkins/workspace/test/package/test_package_script.py:619: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8439486Z   loaded_mod_1.tensor.storage()._cdata,
2023-01-11T21:42:31.8440590Z /var/lib/jenkins/workspace/test/package/test_package_script.py:620: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8441602Z   loaded_mod_1.sub_mod_0.tensor.storage()._cdata,
2023-01-11T21:42:31.8442668Z /var/lib/jenkins/workspace/test/package/test_package_script.py:623: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8443644Z   loaded_mod_1.tensor.storage()._cdata,
2023-01-11T21:42:31.8444977Z /var/lib/jenkins/workspace/test/package/test_package_script.py:624: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:42:31.8445958Z   loaded_mod_1.sub_mod_1.tensor.storage()._cdata,
2023-01-11T21:42:31.8446329Z ok (0.011s)
2023-01-11T21:42:31.8446790Z   test_mixing_packaged_and_inline_modules (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8447397Z Test saving inline and imported modules in same package with ... ok (0.047s)
2023-01-11T21:42:31.8447990Z   test_mixing_packaged_and_inline_modules_shared_code (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8448740Z Test saving inline and imported modules in same package that ... ok (0.854s)
2023-01-11T21:42:31.8449496Z   test_package_interface (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8450102Z Packaging an interface class should work correctly. ... ok (0.040s)
2023-01-11T21:42:31.8450732Z   test_package_script_class (test_package_script.TestPackageScript) ... ok (0.004s)
2023-01-11T21:42:31.8451333Z   test_package_script_class_referencing_self (test_package_script.TestPackageScript) ... ok (0.013s)
2023-01-11T21:42:31.8451909Z   test_save_eager_mods_sharing_scriptmodule (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8452420Z Test saving of single ScriptModule shared by multiple ... ok (0.005s)
2023-01-11T21:42:31.8452917Z   test_save_independent_scriptmodules (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8453419Z Test to verify saving multiple ScriptModules with completely ... ok (0.008s)
2023-01-11T21:42:31.8453934Z   test_save_repeat_scriptmodules (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8454515Z Test to verify saving multiple different modules and ... ok (0.042s)
2023-01-11T21:42:31.8455061Z   test_save_scriptmodule (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8455570Z Test basic saving of ScriptModule. ... ok (0.004s)
2023-01-11T21:42:31.8456062Z   test_save_scriptmodule_file (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8456585Z Test basic saving of ScriptModule in file. ... ok (0.004s)
2023-01-11T21:42:31.8457147Z   test_save_scriptmodule_only_necessary_code (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8457761Z Test to verify when saving multiple packages with same CU ... ok (0.332s)
2023-01-11T21:42:31.8458384Z   test_save_scriptmodule_with_submods (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8458954Z Test basic saving of ScriptModule with submodule. ... ok (0.007s)
2023-01-11T21:42:31.8459543Z   test_save_scriptmodules_in_container (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8460142Z Test saving of ScriptModules inside of container. Checks that relations ... ok (0.035s)
2023-01-11T21:42:31.8460790Z   test_save_scriptmodules_submod_redefinition (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8461453Z Test to verify saving multiple ScriptModules with same top module ... ok (0.012s)
2023-01-11T21:42:31.8461925Z   test_save_shared_tensors (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8462617Z Test tensors shared across eager and ScriptModules are serialized once. ... ok (0.007s)
2023-01-11T21:42:31.8463387Z   test_saving_and_scripting_packaged_mod (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8463947Z Test scripting a module loaded from a package ... ok (0.008s)
2023-01-11T21:42:31.8464475Z   test_scriptmodules_repeat_save (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8465056Z Test to verify saving and loading same ScriptModule object works ... ok (0.010s)
2023-01-11T21:42:31.8465634Z   test_tensor_sharing_pickle (test_package_script.TestPackageScript)
2023-01-11T21:42:31.8466178Z Test that saving a ScriptModule and a separately saving a tensor ... ok (0.004s)
2023-01-11T21:42:31.8466988Z   test_repackage_import_indirectly_via_parent_module (test_repackage.TestRepackage) ... ok (0.005s)
2023-01-11T21:42:31.8467645Z   test_importer_access (test_resources.TestResources) ... ok (0.002s)
2023-01-11T21:42:31.8468233Z   test_package_resource_access (test_resources.TestResources)
2023-01-11T21:42:31.8468876Z Packaged modules should be able to use the importlib.resources API to access ... ok (0.001s)
2023-01-11T21:42:31.8469402Z   test_resource_access_by_path (test_resources.TestResources)
2023-01-11T21:42:31.8469896Z Tests that packaged code can used importlib.resources.path. ... ok (0.002s)
2023-01-11T21:42:31.8470357Z   test_resource_reader (test_resources.TestResources)
2023-01-11T21:42:31.8470818Z Test compliance with the get_resource_reader importlib API. ... ok (0.003s)
2023-01-11T21:42:31.8471429Z   test_bad_dunder_imports (test_save_load.TestSaveLoad)
2023-01-11T21:42:31.8472043Z Test to ensure bad __imports__ don't cause PackageExporter to fail. ... ok (0.001s)
2023-01-11T21:42:31.8472633Z   test_dunder_imports (test_save_load.TestSaveLoad) ... ok (0.003s)
2023-01-11T21:42:31.8473163Z   test_exporting_mismatched_code (test_save_load.TestSaveLoad)
2023-01-11T21:42:31.8473772Z If an object with the same qualified name is loaded from different ... ok (0.004s)
2023-01-11T21:42:31.8474285Z   test_pickle (test_save_load.TestSaveLoad) ... ok (0.002s)
2023-01-11T21:42:31.8474777Z   test_save_imported_module (test_save_load.TestSaveLoad)
2023-01-11T21:42:31.8475318Z Saving a module that came from another PackageImporter should work. ... ok (0.002s)
2023-01-11T21:42:31.8475950Z   test_save_imported_module_using_package_importer (test_save_load.TestSaveLoad)
2023-01-11T21:42:31.8476796Z Exercise a corner case: re-packaging a module that uses `torch_package_importer` ... ok (0.002s)
2023-01-11T21:42:31.8477377Z   test_save_module (test_save_load.TestSaveLoad) ... ok (0.002s)
2023-01-11T21:42:31.8477917Z   test_save_module_binary (test_save_load.TestSaveLoad) ... ok (0.001s)
2023-01-11T21:42:31.8478433Z   test_saving_source (test_save_load.TestSaveLoad) ... ok (0.004s)
2023-01-11T21:42:31.8478948Z   test_saving_string (test_save_load.TestSaveLoad) ... ok (0.001s)
2023-01-11T21:42:31.8479310Z 
2023-01-11T21:42:31.8479658Z ----------------------------------------------------------------------
2023-01-11T21:42:31.8480032Z Ran 133 tests in 1.949s
2023-01-11T21:42:31.8480298Z 
2023-01-11T21:42:31.8480422Z OK (skipped=5)
2023-01-11T21:42:31.8480622Z 
2023-01-11T21:42:31.8480782Z Generating XML reports...
2023-01-11T21:42:31.8481671Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_dependency_api.TestDependencyAPI-20230111214229.xml
2023-01-11T21:42:31.8482746Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_dependency_hooks.TestDependencyHooks-20230111214229.xml
2023-01-11T21:42:31.8483782Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_digraph.TestDiGraph-20230111214229.xml
2023-01-11T21:42:31.8484835Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_directory_reader.DirectoryReaderTest-20230111214229.xml
2023-01-11T21:42:31.8485937Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_glob_group.TestGlobGroup-20230111214229.xml
2023-01-11T21:42:31.8487029Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_importer.TestImporter-20230111214229.xml
2023-01-11T21:42:31.8487979Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_load_bc_packages.TestLoadBCPackages-20230111214229.xml
2023-01-11T21:42:31.8488900Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_mangling.TestMangling-20230111214229.xml
2023-01-11T21:42:31.8489877Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_misc.TestMisc-20230111214229.xml
2023-01-11T21:42:31.8490811Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_package_fx.TestPackageFX-20230111214229.xml
2023-01-11T21:42:31.8492050Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_package_script.TestPackageScript-20230111214229.xml
2023-01-11T21:42:31.8493088Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_repackage.TestRepackage-20230111214229.xml
2023-01-11T21:42:31.8494131Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_resources.TestResources-20230111214229.xml
2023-01-11T21:42:31.8495157Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_save_load.TestSaveLoad-20230111214229.xml
2023-01-11T21:42:31.8496162Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_analyze.TestAnalyze-20230111214229.xml
2023-01-11T21:42:31.8497122Z Generated XML report: test-reports/python-unittest/test_package/TEST-test_model.ModelTest-20230111214229.xml
2023-01-11T21:42:31.8497596Z 
2023-01-11T21:42:31.8498152Z ##[endgroup]
2023-01-11T21:42:31.8498994Z FINISHED PRINTING LOG FILE of test_package (/var/lib/jenkins/workspace/test/test-reports/test_package_0slox5a_)
2023-01-11T21:42:31.8499431Z 
2023-01-11T21:42:33.9652039Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:34.0589807Z Ignoring disabled issues:  []
2023-01-11T21:42:34.0785446Z Running test_per_overload_api ... [2023-01-11 21:42:34.078173]
2023-01-11T21:42:34.0786789Z Executing ['/opt/conda/bin/python', '-bb', 'test_per_overload_api.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:34.078422]
2023-01-11T21:42:36.4365184Z 
2023-01-11T21:42:36.4366342Z Expand the folded group to see the log file of test_per_overload_api
2023-01-11T21:42:36.4367188Z ##[group]PRINTING LOG FILE of test_per_overload_api (/var/lib/jenkins/workspace/test/test-reports/test_per_overload_api_ksrmkocj)
2023-01-11T21:42:36.4367513Z 
2023-01-11T21:42:36.4367608Z Running tests...
2023-01-11T21:42:36.4368113Z ----------------------------------------------------------------------
2023-01-11T21:42:36.4368859Z Test results will be stored in test-reports/python-unittest/test_per_overload_api
2023-01-11T21:42:36.4369456Z   test_basics_opoverload (__main__.TestPerOverloadAPI) ... ok (0.356s)
2023-01-11T21:42:36.4369840Z   test_basics_opoverloadpacket (__main__.TestPerOverloadAPI) ... ok (0.002s)
2023-01-11T21:42:36.4370223Z   test_decompose (__main__.TestPerOverloadAPI) ... ok (0.002s)
2023-01-11T21:42:36.4370424Z 
2023-01-11T21:42:36.4370686Z ----------------------------------------------------------------------
2023-01-11T21:42:36.4370992Z Ran 3 tests in 0.360s
2023-01-11T21:42:36.4371150Z 
2023-01-11T21:42:36.4371214Z OK
2023-01-11T21:42:36.4371330Z 
2023-01-11T21:42:36.4371435Z Generating XML reports...
2023-01-11T21:42:36.4371999Z Generated XML report: test-reports/python-unittest/test_per_overload_api/TEST-TestPerOverloadAPI-20230111214235.xml
2023-01-11T21:42:36.4372316Z 
2023-01-11T21:42:36.4372581Z ##[endgroup]
2023-01-11T21:42:36.4373109Z FINISHED PRINTING LOG FILE of test_per_overload_api (/var/lib/jenkins/workspace/test/test-reports/test_per_overload_api_ksrmkocj)
2023-01-11T21:42:36.4373405Z 
2023-01-11T21:42:38.4491052Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:38.5137500Z Ignoring disabled issues:  []
2023-01-11T21:42:38.5300675Z Running test_pruning_op ... [2023-01-11 21:42:38.529760]
2023-01-11T21:42:38.5302715Z Executing ['/opt/conda/bin/python', '-bb', 'test_pruning_op.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:38.530035]
2023-01-11T21:42:40.3589369Z 
2023-01-11T21:42:40.3590021Z Expand the folded group to see the log file of test_pruning_op
2023-01-11T21:42:40.3590988Z ##[group]PRINTING LOG FILE of test_pruning_op (/var/lib/jenkins/workspace/test/test-reports/test_pruning_op_djq56_pm)
2023-01-11T21:42:40.3591279Z 
2023-01-11T21:42:40.3591688Z ##[endgroup]
2023-01-11T21:42:40.3592461Z FINISHED PRINTING LOG FILE of test_pruning_op (/var/lib/jenkins/workspace/test/test-reports/test_pruning_op_djq56_pm)
2023-01-11T21:42:40.3592746Z 
2023-01-11T21:42:42.3542678Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:42.4188544Z Ignoring disabled issues:  []
2023-01-11T21:42:42.4352848Z Running test_pytree ... [2023-01-11 21:42:42.435029]
2023-01-11T21:42:42.4355449Z Executing ['/opt/conda/bin/python', '-bb', 'test_pytree.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:42.435287]
2023-01-11T21:42:44.3042512Z 
2023-01-11T21:42:44.3043091Z Expand the folded group to see the log file of test_pytree
2023-01-11T21:42:44.3044279Z ##[group]PRINTING LOG FILE of test_pytree (/var/lib/jenkins/workspace/test/test-reports/test_pytree_cg04g9nu)
2023-01-11T21:42:44.3044695Z 
2023-01-11T21:42:44.3044831Z Running tests...
2023-01-11T21:42:44.3045426Z ----------------------------------------------------------------------
2023-01-11T21:42:44.3046076Z Test results will be stored in test-reports/python-unittest/test_pytree
2023-01-11T21:42:44.3046424Z   test_broadcast_to_and_flatten (__main__.TestPytree) ... ok (0.222s)
2023-01-11T21:42:44.3046718Z   test_flatten_unflatten_dict (__main__.TestPytree) ... ok (0.003s)
2023-01-11T21:42:44.3046992Z   test_flatten_unflatten_leaf (__main__.TestPytree) ... ok (0.002s)
2023-01-11T21:42:44.3047253Z   test_flatten_unflatten_list (__main__.TestPytree) ... ok (0.002s)
2023-01-11T21:42:44.3047536Z   test_flatten_unflatten_namedtuple (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3047821Z   test_flatten_unflatten_nested (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3048090Z   test_flatten_unflatten_odict (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3048379Z   test_flatten_unflatten_return_type_max (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3048674Z   test_flatten_unflatten_return_type_min (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3048963Z   test_flatten_unflatten_tuple (__main__.TestPytree) ... ok (0.002s)
2023-01-11T21:42:44.3049452Z   test_tree_all_any (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3049707Z   test_tree_only (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3049952Z   test_treemap (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3050195Z   test_treespec_equality (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3050451Z   test_treespec_repr (__main__.TestPytree) ... ok (0.001s)
2023-01-11T21:42:44.3050596Z 
2023-01-11T21:42:44.3050814Z ----------------------------------------------------------------------
2023-01-11T21:42:44.3051054Z Ran 15 tests in 0.240s
2023-01-11T21:42:44.3051154Z 
2023-01-11T21:42:44.3051213Z OK
2023-01-11T21:42:44.3051304Z 
2023-01-11T21:42:44.3051387Z Generating XML reports...
2023-01-11T21:42:44.3051790Z Generated XML report: test-reports/python-unittest/test_pytree/TEST-TestPytree-20230111214243.xml
2023-01-11T21:42:44.3052008Z 
2023-01-11T21:42:44.3052257Z ##[endgroup]
2023-01-11T21:42:44.3052633Z FINISHED PRINTING LOG FILE of test_pytree (/var/lib/jenkins/workspace/test/test-reports/test_pytree_cg04g9nu)
2023-01-11T21:42:44.3052841Z 
2023-01-11T21:42:46.1140337Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:42:46.1784757Z Ignoring disabled issues:  []
2023-01-11T21:42:46.1946613Z Running test_quantization ... [2023-01-11 21:42:46.194427]
2023-01-11T21:42:46.1949350Z Executing ['/opt/conda/bin/python', '-bb', 'test_quantization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:42:46.194685]
2023-01-11T21:43:49.4435672Z 
2023-01-11T21:43:49.4439934Z Expand the folded group to see the log file of test_optim
2023-01-11T21:43:49.4444678Z ##[group]PRINTING LOG FILE of test_optim (/var/lib/jenkins/workspace/test/test-reports/test_optim_kd7_d0t4)
2023-01-11T21:43:49.4448810Z 
2023-01-11T21:43:49.4449406Z Running tests...
2023-01-11T21:43:49.4490235Z ----------------------------------------------------------------------
2023-01-11T21:43:49.4490804Z Test results will be stored in test-reports/python-unittest/test_optim
2023-01-11T21:43:49.4491233Z   test_adadelta (__main__.TestDifferentiableOptimizer) ... ok (0.252s)
2023-01-11T21:43:49.4491883Z   test_adagrad (__main__.TestDifferentiableOptimizer) ... ok (0.006s)
2023-01-11T21:43:49.4492297Z   test_adam (__main__.TestDifferentiableOptimizer) ... ok (0.014s)
2023-01-11T21:43:49.4492811Z   test_adamax (__main__.TestDifferentiableOptimizer) ... ok (0.010s)
2023-01-11T21:43:49.4493261Z   test_adamw (__main__.TestDifferentiableOptimizer) ... ok (0.014s)
2023-01-11T21:43:49.4493714Z   test_asgd (__main__.TestDifferentiableOptimizer) ... ok (0.005s)
2023-01-11T21:43:49.4494170Z   test_nadam (__main__.TestDifferentiableOptimizer) ... ok (0.013s)
2023-01-11T21:43:49.4494611Z   test_radam (__main__.TestDifferentiableOptimizer) ... ok (0.009s)
2023-01-11T21:43:49.4495073Z   test_rmsprop (__main__.TestDifferentiableOptimizer) ... ok (0.013s)
2023-01-11T21:43:49.4495534Z   test_rprop (__main__.TestDifferentiableOptimizer) ... ok (0.010s)
2023-01-11T21:43:49.4496074Z   test_sgd (__main__.TestDifferentiableOptimizer) ... ok (0.004s)
2023-01-11T21:43:49.4496559Z   test_CosineAnnealingWarmRestarts_lr1_T_mult_1 (__main__.TestLRScheduler) ... ok (0.007s)
2023-01-11T21:43:49.4497098Z   test_CosineAnnealingWarmRestarts_lr1_T_mult_2 (__main__.TestLRScheduler) ... ok (0.007s)
2023-01-11T21:43:49.4497625Z   test_CosineAnnealingWarmRestarts_lr1_T_mult_4 (__main__.TestLRScheduler) ... ok (0.007s)
2023-01-11T21:43:49.4498116Z   test_CosineAnnealingWarmRestarts_lr2 (__main__.TestLRScheduler) ... ok (0.057s)
2023-01-11T21:43:49.4498757Z   test_CosineAnnealingWarmRestarts_lr3 (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:43:49.4499270Z   test_CosineAnnealingWarmRestarts_lr_state_dict (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4500950Z   test_chained_lr1 (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4501959Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4502288Z ok (0.002s)
2023-01-11T21:43:49.4502582Z   test_chained_lr2 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4502952Z   test_chained_lr2_get_last_lr_before_step (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4503395Z   test_chained_lr3 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4503749Z   test_chained_lr4 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4504083Z   test_chained_lr5 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4504454Z   test_closed_form_constantlr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:43:49.4504842Z   test_closed_form_cos_anneal_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:43:49.4505237Z   test_closed_form_exp_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:43:49.4505599Z   test_closed_form_linearlr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:43:49.4505985Z   test_closed_form_multi_step_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:43:49.4506367Z   test_closed_form_poly_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:43:49.4506726Z   test_closed_form_step_lr (__main__.TestLRScheduler) ... ok (0.006s)
2023-01-11T21:43:49.4508226Z   test_compound_cosanneal_and_exp_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4509283Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4509615Z ok (0.002s)
2023-01-11T21:43:49.4509944Z   test_compound_cosanneal_and_linearlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4510351Z   test_compound_cosanneal_and_multistep_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4510776Z   test_compound_cosanneal_and_step_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4511182Z   test_compound_exp_and_linearlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4511577Z   test_compound_exp_and_multistep_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4511996Z   test_compound_linearlr_and_multistep_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4512454Z   test_compound_reduce_lr_on_plateau1 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4512865Z   test_compound_reduce_lr_on_plateau2 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4513250Z   test_compound_reduce_lr_on_plateau3 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4513646Z   test_compound_reduce_lr_on_plateau4 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4514054Z   test_compound_reduce_lr_on_plateau5 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4514448Z   test_compound_step_and_constantlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4514842Z   test_compound_step_and_exp_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4515241Z   test_compound_step_and_multistep_lr (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4515617Z   test_constantlr (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4516000Z   test_constantlr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4516410Z   test_constantlr_with_epoch (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4517859Z   test_cos_anneal_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4518824Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4519132Z ok (0.001s)
2023-01-11T21:43:49.4519432Z   test_cos_anneal_lr_continue (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4519808Z   test_cosine_lr_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4520170Z   test_cosine_then_cyclic (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4520604Z   test_cycle_lr_cycle_momentum_fail_with_momentumless_optimizer (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4521032Z   test_cycle_lr_exp_range_mode (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4521424Z   test_cycle_lr_exp_range_mode_one_lr (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4521824Z   test_cycle_lr_exp_range_mode_step_size_up_down (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4522221Z   test_cycle_lr_invalid_mode (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4522616Z   test_cycle_lr_removed_after_out_of_scope (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4523027Z   test_cycle_lr_scale_fn_restored_from_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4523439Z   test_cycle_lr_state_dict_picklable (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4523833Z   test_cycle_lr_triangular2_mode (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:43:49.4524231Z   test_cycle_lr_triangular2_mode_one_lr (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:43:49.4524679Z   test_cycle_lr_triangular2_mode_step_size_up_down (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:43:49.4525095Z   test_cycle_lr_triangular_mode (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4525489Z   test_cycle_lr_triangular_mode_one_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4525909Z   test_cycle_lr_triangular_mode_one_lr_no_momentum (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4526328Z   test_cycle_lr_triangular_mode_step_size_up_down (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4526727Z   test_cycle_lr_with_adam (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4527122Z   test_cycle_lr_with_momentumless_optimizer (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4527558Z   test_error_when_getlr_has_epoch (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4527920Z   test_exp_lr (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4528278Z   test_exp_step_lr_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4528679Z   test_exponential_lr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4529212Z   test_get_last_lr_constantlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4529600Z   test_get_last_lr_linearlr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4529980Z   test_get_last_lr_multi_step_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4531538Z   test_get_last_lr_sequentiallr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:152: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
2023-01-11T21:43:49.4532539Z   warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
2023-01-11T21:43:49.4532826Z ok (0.002s)
2023-01-11T21:43:49.4533239Z   test_get_last_lr_step_lr (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4534912Z   test_lambda_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4536020Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4536392Z ok (0.002s)
2023-01-11T21:43:49.4536743Z   test_lambda_lr_state_dict_fn (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4537207Z   test_lambda_lr_state_dict_obj (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4537677Z   test_linear_linearlr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4539394Z   test_linearlr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4540516Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4540994Z ok (0.002s)
2023-01-11T21:43:49.4541404Z   test_linearlr_start_factor_limits1 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4541804Z   test_linearlr_start_factor_limits2 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4542196Z   test_linearlr_with_epoch (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4543788Z   test_multi_step_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4544817Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4545129Z ok (0.001s)
2023-01-11T21:43:49.4545436Z   test_multi_step_lr_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4545821Z   test_multi_step_lr_with_epoch (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4547267Z   test_multiplicative_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4548219Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4548554Z ok (0.002s)
2023-01-11T21:43:49.4548862Z   test_new_pattern_no_warning (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4549266Z   test_new_pattern_no_warning_with_arg (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4549690Z   test_new_pattern_no_warning_with_overridden_optim_step (__main__.TestLRScheduler) ... ok (0.005s)
2023-01-11T21:43:49.4550115Z   test_no_cyclic_references (__main__.TestLRScheduler) ... ok (0.107s)
2023-01-11T21:43:49.4550515Z   test_no_cyclic_references_in_step (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4550893Z   test_old_pattern_warning (__main__.TestLRScheduler) ... ok (0.004s)
2023-01-11T21:43:49.4551284Z   test_old_pattern_warning_resuming (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4551703Z   test_old_pattern_warning_resuming_with_arg (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4552121Z   test_old_pattern_warning_with_arg (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4552535Z   test_old_pattern_warning_with_overridden_optim_step (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4552987Z   test_onecycle_lr_cannot_calculate_total_steps (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4553411Z   test_onecycle_lr_cosine_annealing (__main__.TestLRScheduler) ... ok (0.003s)
2023-01-11T21:43:49.4553810Z   test_onecycle_lr_invalid_anneal_strategy (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4554223Z   test_onecycle_lr_invalid_pct_start (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4554626Z   test_onecycle_lr_linear_annealing (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4555060Z   test_onecycle_lr_linear_annealing_three_phases (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4556508Z   test_poly_lr (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4557508Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4557835Z ok (0.001s)
2023-01-11T21:43:49.4558177Z   test_polynomial_lr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4558575Z   test_reduce_lr_on_plateau1 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4558953Z   test_reduce_lr_on_plateau2 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4559331Z   test_reduce_lr_on_plateau3 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4559709Z   test_reduce_lr_on_plateau4 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4560113Z   test_reduce_lr_on_plateau5 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4560493Z   test_reduce_lr_on_plateau6 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4560872Z   test_reduce_lr_on_plateau7 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4561230Z   test_reduce_lr_on_plateau8 (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4561624Z   test_reduce_lr_on_plateau_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4563075Z   test_sequentiallr1 (__main__.TestLRScheduler) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4564042Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4565495Z /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:152: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
2023-01-11T21:43:49.4566420Z   warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
2023-01-11T21:43:49.4566700Z ok (0.002s)
2023-01-11T21:43:49.4566998Z   test_sequentiallr2 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4567365Z   test_sequentiallr3 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4567711Z   test_sequentiallr4 (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4568060Z   test_step_lr (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4568453Z   test_step_lr_is_constant_for_constant_epoch (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4568842Z   test_step_lr_state_dict (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4569315Z   test_swa_lr_state_dict (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4569721Z   test_swalr_cosine_anneal_after_multiplicative (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4570123Z   test_swalr_hypers (__main__.TestLRScheduler) ... ok (0.001s)
2023-01-11T21:43:49.4570505Z   test_swalr_linear_anneal_after_multiplicative (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4570901Z   test_swalr_no_anneal (__main__.TestLRScheduler) ... ok (0.002s)
2023-01-11T21:43:49.4572316Z   test_adadelta (__main__.TestOptim) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4573343Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4573801Z ok (2.787s)
2023-01-11T21:43:49.4574135Z   test_adadelta_complex (__main__.TestOptim) ... ok (0.005s)
2023-01-11T21:43:49.4574518Z   test_adagrad (__main__.TestOptim) ... ok (3.882s)
2023-01-11T21:43:49.4574886Z   test_adagrad_complex (__main__.TestOptim) ... ok (0.007s)
2023-01-11T21:43:49.4575288Z   test_adagrad_sparse (__main__.TestOptim) ... ok (11.745s)
2023-01-11T21:43:49.4575661Z   test_adam (__main__.TestOptim) ... ok (10.661s)
2023-01-11T21:43:49.4576023Z   test_adamax (__main__.TestOptim) ... ok (3.445s)
2023-01-11T21:43:49.4576440Z   test_adamw (__main__.TestOptim) ... ok (3.120s)
2023-01-11T21:43:49.4576794Z   test_asgd (__main__.TestOptim) ... ok (4.054s)
2023-01-11T21:43:49.4577208Z   test_duplicate_params_in_param_group (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:43:49.4577613Z   test_empty_grad (__main__.TestOptim) ... ok (0.010s)
2023-01-11T21:43:49.4578075Z   test_functional_fused_adam_with_foundinf (__main__.TestOptim) ... skip: CUDA is required. (0.001s)
2023-01-11T21:43:49.4578542Z   test_fused_optimizers (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:43:49.4578931Z   test_invalid_param_type (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:43:49.4579310Z   test_lbfgs (__main__.TestOptim) ... ok (0.409s)
2023-01-11T21:43:49.4579688Z   test_lbfgs_return_type (__main__.TestOptim) ... ok (0.002s)
2023-01-11T21:43:49.4580105Z   test_multi_tensor_optimizers (__main__.TestOptim) ... ok (0.002s)
2023-01-11T21:43:49.4581639Z   test_nadam (__main__.TestOptim) ... /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
2023-01-11T21:43:49.4582602Z   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
2023-01-11T21:43:49.4582934Z ok (1.516s)
2023-01-11T21:43:49.4583272Z   test_no_grad_for_all_params (__main__.TestOptim) ... ok (0.002s)
2023-01-11T21:43:49.4583605Z   test_post_hook (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:43:49.4583940Z   test_pre_and_post_hook (__main__.TestOptim) ... ok (0.002s)
2023-01-11T21:43:49.4584280Z   test_pre_hook (__main__.TestOptim) ... ok (0.001s)
2023-01-11T21:43:49.4584579Z   test_radam (__main__.TestOptim) ... ok (1.730s)
2023-01-11T21:43:49.4584894Z   test_rmsprop (__main__.TestOptim) ... ok (7.933s)
2023-01-11T21:43:49.4585209Z   test_rprop (__main__.TestOptim) ... ok (3.329s)
2023-01-11T21:43:49.4585509Z   test_sgd (__main__.TestOptim) ... ok (8.703s)
2023-01-11T21:43:49.4585827Z   test_sgd_complex (__main__.TestOptim) ... ok (0.019s)
2023-01-11T21:43:49.4586158Z   test_sgd_sparse (__main__.TestOptim) ... ok (18.576s)
2023-01-11T21:43:49.4586491Z   test_sparse_adam (__main__.TestOptim) ... ok (2.881s)
2023-01-11T21:43:49.4586833Z   test_averaged_model_all_devices (__main__.TestSWAUtils) ... ok (0.012s)
2023-01-11T21:43:49.4587239Z   test_averaged_model_exponential (__main__.TestSWAUtils) ... ok (0.010s)
2023-01-11T21:43:49.4587649Z   test_averaged_model_exponential_buffers (__main__.TestSWAUtils) ... ok (0.006s)
2023-01-11T21:43:49.4588038Z   test_averaged_model_mixed_device (__main__.TestSWAUtils) ... ok (0.001s)
2023-01-11T21:43:49.4588447Z   test_averaged_model_state_dict (__main__.TestSWAUtils) ... ok (0.005s)
2023-01-11T21:43:49.4588876Z   test_bn_update_eval_momentum (__main__.TestSWAUtils) ... ok (0.142s)
2023-01-11T21:43:49.4589239Z   test_update_bn_cnn (__main__.TestSWAUtils) ... ok (0.652s)
2023-01-11T21:43:49.4589645Z   test_update_bn_dnn (__main__.TestSWAUtils) ... ok (0.023s)
2023-01-11T21:43:49.4589844Z 
2023-01-11T21:43:49.4590124Z ----------------------------------------------------------------------
2023-01-11T21:43:49.4590449Z Ran 166 tests in 86.466s
2023-01-11T21:43:49.4590605Z 
2023-01-11T21:43:49.4590688Z OK (skipped=1)
2023-01-11T21:43:49.4590831Z 
2023-01-11T21:43:49.4590944Z Generating XML reports...
2023-01-11T21:43:49.4591530Z Generated XML report: test-reports/python-unittest/test_optim/TEST-TestDifferentiableOptimizer-20230111214222.xml
2023-01-11T21:43:49.4592232Z Generated XML report: test-reports/python-unittest/test_optim/TEST-TestLRScheduler-20230111214222.xml
2023-01-11T21:43:49.4592850Z Generated XML report: test-reports/python-unittest/test_optim/TEST-TestOptim-20230111214222.xml
2023-01-11T21:43:49.4593516Z Generated XML report: test-reports/python-unittest/test_optim/TEST-TestSWAUtils-20230111214222.xml
2023-01-11T21:43:49.4593802Z 
2023-01-11T21:43:49.4594188Z ##[endgroup]
2023-01-11T21:43:49.4594672Z FINISHED PRINTING LOG FILE of test_optim (/var/lib/jenkins/workspace/test/test-reports/test_optim_kd7_d0t4)
2023-01-11T21:43:49.4594943Z 
2023-01-11T21:43:51.4473549Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:43:51.5126749Z Ignoring disabled issues:  []
2023-01-11T21:43:51.5295461Z Running test_schema_check ... [2023-01-11 21:43:51.529159]
2023-01-11T21:43:51.5296449Z Executing ['/opt/conda/bin/python', '-bb', 'test_schema_check.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:43:51.529437]
2023-01-11T21:43:54.6754241Z 
2023-01-11T21:43:54.6754743Z Expand the folded group to see the log file of test_schema_check
2023-01-11T21:43:54.6755788Z ##[group]PRINTING LOG FILE of test_schema_check (/var/lib/jenkins/workspace/test/test-reports/test_schema_check_wlot4cyh)
2023-01-11T21:43:54.6756167Z 
2023-01-11T21:43:54.6756290Z Running tests...
2023-01-11T21:43:54.6756766Z ----------------------------------------------------------------------
2023-01-11T21:43:54.6757362Z Test results will be stored in test-reports/python-unittest/test_schema_check
2023-01-11T21:43:54.6757718Z   test_alias_check_fail_multiple_operators (__main__.TestSchemaCheck) ... ok (0.004s)
2023-01-11T21:43:54.6758215Z   test_alias_check_fail_multiple_operators_centered (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6758634Z   test_alias_check_fail_outputs_unexpectedly_aliasing (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6758966Z   test_alias_check_fail_simple (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6759277Z   test_is_alias_of_basic (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6759605Z   test_is_alias_of_empty_container (__main__.TestSchemaCheck) ... ok (0.004s)
2023-01-11T21:43:54.6759892Z   test_mutation_check_fail (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6760261Z   test_mutation_check_fail_multiple_operators (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6760565Z   test_overlaps_basic (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6810448Z   test_overlaps_empty_container (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6811149Z   test_schema_check_mode_empty_list_input (__main__.TestSchemaCheck) ... ok (0.009s)
2023-01-11T21:43:54.6811732Z   test_schema_check_mode_functionality (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6812287Z   test_schema_check_mode_functionality_aliasing_inputs (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6812998Z   test_schema_check_mode_functionality_default_replaced (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6813747Z   test_schema_check_mode_functionality_device_input (__main__.TestSchemaCheck) ... ok (0.005s)
2023-01-11T21:43:54.6814485Z   test_schema_check_mode_functionality_kwarg_tensor (__main__.TestSchemaCheck) ... ok (0.005s)
2023-01-11T21:43:54.6815205Z   test_schema_check_mode_functionality_list_input (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6816162Z   test_schema_check_mode_functionality_mutable_inputs (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6816916Z   test_schema_check_mode_functionality_nested_training_op (__main__.TestSchemaCheck) ... ok (0.011s)
2023-01-11T21:43:54.6817650Z   test_schema_check_mode_functionality_training_op (__main__.TestSchemaCheck) ... ok (0.011s)
2023-01-11T21:43:54.6818380Z   test_schema_check_mode_functionality_wildcard_after (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6819123Z   test_schema_check_mode_functionality_with_multiple_outputs (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6821098Z   test_schema_check_mode_functionality_with_multiple_outputs_aliasing (__main__.TestSchemaCheck) ... /var/lib/jenkins/workspace/test/test_schema_check.py:278: UserWarning: An output with one or more elements was resized since it had shape [1, 1, 3], which does not match the required output shape [1, 3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:43:54.6822834Z   torch.aminmax(x, dim=0, out=[actual, actual])
2023-01-11T21:43:54.6823318Z ok (0.002s)
2023-01-11T21:43:54.6823882Z   test_schema_check_mode_mutated_aliasing_aliasing_inputs (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6851083Z   test_schema_check_mode_mutated_aliasing_aliasing_outputs (__main__.TestSchemaCheck) ... /var/lib/jenkins/workspace/test/test_schema_check.py:180: UserWarning: An output with one or more elements was resized since it had shape [1, 1, 3], which does not match the required output shape [1, 3]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:43:54.6852281Z   torch.aminmax(x, dim=0, out=[actual, actual])
2023-01-11T21:43:54.6852589Z ok (0.002s)
2023-01-11T21:43:54.6852992Z   test_schema_check_mode_mutated_aliasing_as_strided (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6853504Z   test_schema_check_mode_mutated_aliasing_multiple_outputs (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6854015Z   test_schema_check_mode_mutated_aliasing_mutation (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6854497Z   test_schema_check_mode_mutated_aliasing_none (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6854977Z   test_schema_check_mode_mutated_aliasing_resize_ (__main__.TestSchemaCheck) ... ok (0.001s)
2023-01-11T21:43:54.6855453Z   test_schema_check_mode_operator_order (__main__.TestSchemaCheck) ... ok (0.005s)
2023-01-11T21:43:54.6855948Z   test_schema_check_mode_operator_order_without_grad (__main__.TestSchemaCheck) ... ok (0.005s)
2023-01-11T21:43:54.6856387Z   test_schema_info_bind_basic (__main__.TestSchemaCheck) ... ok (0.002s)
2023-01-11T21:43:54.6856624Z 
2023-01-11T21:43:54.6857010Z ----------------------------------------------------------------------
2023-01-11T21:43:54.6857349Z Ran 33 tests in 0.095s
2023-01-11T21:43:54.6857516Z 
2023-01-11T21:43:54.6857597Z OK
2023-01-11T21:43:54.6857714Z 
2023-01-11T21:43:54.6857841Z Generating XML reports...
2023-01-11T21:43:54.6858493Z Generated XML report: test-reports/python-unittest/test_schema_check/TEST-TestSchemaCheck-20230111214354.xml
2023-01-11T21:43:54.6858830Z 
2023-01-11T21:43:54.6859242Z ##[endgroup]
2023-01-11T21:43:54.6859794Z FINISHED PRINTING LOG FILE of test_schema_check (/var/lib/jenkins/workspace/test/test-reports/test_schema_check_wlot4cyh)
2023-01-11T21:43:54.6860095Z 
2023-01-11T21:43:56.5235054Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:43:56.5879930Z Ignoring disabled issues:  []
2023-01-11T21:43:56.6045217Z Running test_serialization ... [2023-01-11 21:43:56.604262]
2023-01-11T21:43:56.6047900Z Executing ['/opt/conda/bin/python', '-bb', 'test_serialization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:43:56.604516]
2023-01-11T21:44:29.0644349Z 
2023-01-11T21:44:29.0644885Z Expand the folded group to see the log file of test_serialization
2023-01-11T21:44:29.0645888Z ##[group]PRINTING LOG FILE of test_serialization (/var/lib/jenkins/workspace/test/test-reports/test_serialization_a68we09r)
2023-01-11T21:44:29.0646272Z 
2023-01-11T21:44:29.0649487Z Running tests...
2023-01-11T21:44:29.0650108Z ----------------------------------------------------------------------
2023-01-11T21:44:29.0651028Z Test results will be stored in test-reports/python-unittest/test_serialization
2023-01-11T21:44:29.0651546Z   test_load_error_msg (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0651855Z   test_load_nonexistent_device (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0654634Z   test_load_python2_unicode_module (__main__.TestOldSerialization) ... ok (0.082s)
2023-01-11T21:44:29.0654972Z   test_load_unicode_error_msg (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0655650Z   test_save_different_dtype_error (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:719: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0656275Z   torch.save([a.storage(), a.imag], f)
2023-01-11T21:44:29.0656822Z /var/lib/jenkins/workspace/test/test_serialization.py:722: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0657346Z   torch.save([a, a.imag.storage()], f)
2023-01-11T21:44:29.0657871Z /var/lib/jenkins/workspace/test/test_serialization.py:725: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0665264Z   torch.save([a.storage(), a.imag.storage()], f)
2023-01-11T21:44:29.0665869Z /var/lib/jenkins/workspace/test/test_serialization.py:729: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0666449Z   wrap_storage=a.storage().untyped(),
2023-01-11T21:44:29.0760996Z /var/lib/jenkins/workspace/test/test_serialization.py:728: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0761845Z   s_bytes = torch.TypedStorage(
2023-01-11T21:44:29.0762683Z /var/lib/jenkins/workspace/test/test_serialization.py:736: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0763460Z   torch.save([a.storage(), s_bytes], f)
2023-01-11T21:44:29.0763739Z ok (0.002s)
2023-01-11T21:44:29.0764834Z   test_save_different_dtype_unallocated (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:697: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0766042Z   wrap_storage=a.storage().untyped(),
2023-01-11T21:44:29.0766857Z /var/lib/jenkins/workspace/test/test_serialization.py:696: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0767749Z   s = torch.TypedStorage(
2023-01-11T21:44:29.0769268Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0770107Z   device=typed_storage.device,
2023-01-11T21:44:29.0772066Z /var/lib/jenkins/workspace/test/test_serialization.py:700: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0772860Z   save_load_check(a.storage(), s)
2023-01-11T21:44:29.0773136Z ok (0.179s)
2023-01-11T21:44:29.0773960Z   test_serialization (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0774780Z   b += [a[0].storage()]  # 4
2023-01-11T21:44:29.0775568Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0776466Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:44:29.0777259Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0778207Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0780657Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0781659Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0782516Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0783520Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:44:29.0784319Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0785223Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:44:29.0786175Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0787012Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:44:29.0787826Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0788607Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:44:29.0790952Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0791789Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:44:29.0792086Z ok (0.007s)
2023-01-11T21:44:29.0792882Z   test_serialization_backwards_compat (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:403: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0793726Z   b += [a[0].storage()]
2023-01-11T21:44:29.0795614Z /var/lib/jenkins/workspace/test/test_serialization.py:404: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0796547Z   b += [a[0].reshape(-1)[1:4].clone().storage()]
2023-01-11T21:44:29.0797377Z /var/lib/jenkins/workspace/test/test_serialization.py:416: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0798194Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:44:29.0799054Z /var/lib/jenkins/workspace/test/test_serialization.py:426: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0800012Z   return (self.new_tensor.storage(),
2023-01-11T21:44:29.0800801Z /var/lib/jenkins/workspace/test/test_serialization.py:446: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0801724Z   self.assertEqual(x.storage(), load_x.storage())
2023-01-11T21:44:29.0802063Z ok (0.017s)
2023-01-11T21:44:29.0803384Z   test_serialization_backwards_compat_safe (__main__.TestOldSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0804264Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:44:29.0804538Z ok (0.005s)
2023-01-11T21:44:29.0804972Z   test_serialization_container (__main__.TestOldSerialization) ... ok (0.009s)
2023-01-11T21:44:29.0805447Z   test_serialization_container_filelike (__main__.TestOldSerialization) ... ok (0.008s)
2023-01-11T21:44:29.0805985Z   test_serialization_dill (__main__.TestOldSerialization) ... skip: "dill" not found or not correct version (0.001s)
2023-01-11T21:44:29.0806565Z   test_serialization_dill_version_not_supported (__main__.TestOldSerialization) ... skip: "dill" not found or is correct version (0.000s)
2023-01-11T21:44:29.0807095Z   test_serialization_fake_zip (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0808093Z   test_serialization_filelike (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0808900Z   b += [a[0].storage()]  # 4
2023-01-11T21:44:29.0809862Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0810763Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:44:29.0811540Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0812470Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0813334Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0814316Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0815150Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0815924Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:44:29.0816832Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0818011Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:44:29.0819173Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0820037Z   device=typed_storage.device,
2023-01-11T21:44:29.0821010Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0821895Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:44:29.0822754Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0823626Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:44:29.0824441Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0825237Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:44:29.0825567Z ok (0.003s)
2023-01-11T21:44:29.0825970Z   test_serialization_filelike_api_requirements (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0826997Z   test_serialization_filelike_exceptions (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:616: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0827867Z   s = torch.CharStorage(s_data)
2023-01-11T21:44:29.0828167Z ok (0.001s)
2023-01-11T21:44:29.0828522Z   test_serialization_filelike_missing_attrs (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0828971Z   test_serialization_filelike_stress (__main__.TestOldSerialization) ... ok (0.338s)
2023-01-11T21:44:29.0829464Z   test_serialization_filelike_uses_readinto (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0829972Z   test_serialization_gzip (__main__.TestOldSerialization) ... ok (0.004s)
2023-01-11T21:44:29.0830437Z   test_serialization_map_location (__main__.TestOldSerialization) ... ok (0.016s)
2023-01-11T21:44:29.0830895Z   test_serialization_offset (__main__.TestOldSerialization) ... ok (7.430s)
2023-01-11T21:44:29.0831410Z   test_serialization_offset_filelike_weights_only_False (__main__.TestOldSerialization) ... ok (6.931s)
2023-01-11T21:44:29.0832940Z   test_serialization_offset_filelike_weights_only_True (__main__.TestOldSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0833866Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:44:29.0834270Z ok (6.921s)
2023-01-11T21:44:29.0834639Z   test_serialization_offset_gzip (__main__.TestOldSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0835101Z   test_serialization_safe (__main__.TestOldSerialization) ... ok (0.008s)
2023-01-11T21:44:29.0835541Z   test_serialization_save_warnings (__main__.TestOldSerialization) ... ok (0.003s)
2023-01-11T21:44:29.0836517Z   test_serialization_sparse (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0837482Z   b += [a[0].storage()]  # 4
2023-01-11T21:44:29.0838269Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0839128Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:44:29.0839888Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0840853Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0841739Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0842704Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0843582Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0844362Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:44:29.0845160Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0846029Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:44:29.0847201Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0848014Z   device=typed_storage.device,
2023-01-11T21:44:29.0848843Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0849839Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:44:29.0850860Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0851667Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:44:29.0852481Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0853365Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:44:29.0853704Z ok (0.006s)
2023-01-11T21:44:29.0854654Z   test_serialization_sparse_bsc_invalid (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:391: UserWarning: Sparse BSC tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:44:29.0855651Z   lambda x: x.to_sparse_bsc(1, 1), torch.Tensor.ccol_indices, torch.Tensor.row_indices)
2023-01-11T21:44:29.0856158Z /opt/conda/lib/python3.10/unittest/case.py:172: FutureWarning: Possible nested set at position 14
2023-01-11T21:44:29.0856609Z   expected_regex = re.compile(expected_regex)
2023-01-11T21:44:29.0856919Z ok (0.008s)
2023-01-11T21:44:29.0857321Z   test_serialization_sparse_bsr_invalid (__main__.TestOldSerialization) ... ok (0.007s)
2023-01-11T21:44:29.0857813Z   test_serialization_sparse_csc_invalid (__main__.TestOldSerialization) ... ok (0.006s)
2023-01-11T21:44:29.0858314Z   test_serialization_sparse_csr_invalid (__main__.TestOldSerialization) ... ok (0.006s)
2023-01-11T21:44:29.0858808Z   test_serialization_sparse_invalid (__main__.TestOldSerialization) ... ok (0.004s)
2023-01-11T21:44:29.0860201Z   test_serialization_sparse_safe (__main__.TestOldSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0861092Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:44:29.0861460Z ok (0.008s)
2023-01-11T21:44:29.0862341Z   test_serialization_storage_slice (__main__.TestOldSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:651: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0863325Z   self.assertEqual(s1[0], 0)
2023-01-11T21:44:29.0864074Z /var/lib/jenkins/workspace/test/test_serialization.py:652: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0864825Z   self.assertEqual(s2[0], 0)
2023-01-11T21:44:29.0865620Z /var/lib/jenkins/workspace/test/test_serialization.py:653: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0866527Z   self.assertEqual(s1.data_ptr() + 4, s2.data_ptr())
2023-01-11T21:44:29.0866843Z ok (0.001s)
2023-01-11T21:44:29.0867195Z   test_serialization_zipfile_utils (__main__.TestOldSerialization) ... ok (0.004s)
2023-01-11T21:44:29.0867656Z   test_serialize_device (__main__.TestOldSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0868085Z   test_load_error_msg (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0868492Z   test_load_nonexistent_device (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0868938Z   test_load_python2_unicode_module (__main__.TestSerialization) ... ok (0.008s)
2023-01-11T21:44:29.0869381Z   test_load_unicode_error_msg (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0869932Z   test_meta_serialization_weights_only_False (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0870445Z   test_meta_serialization_weights_only_True (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0870942Z   test_pathlike_serialization_weights_only_False (__main__.TestSerialization) ... ok (0.008s)
2023-01-11T21:44:29.0872413Z   test_pathlike_serialization_weights_only_True (__main__.TestSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0873299Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:44:29.0873592Z ok (0.007s)
2023-01-11T21:44:29.0874447Z   test_save_different_dtype_error (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:719: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0875293Z   torch.save([a.storage(), a.imag], f)
2023-01-11T21:44:29.0876092Z /var/lib/jenkins/workspace/test/test_serialization.py:722: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0876838Z   torch.save([a, a.imag.storage()], f)
2023-01-11T21:44:29.0877621Z /var/lib/jenkins/workspace/test/test_serialization.py:725: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0878393Z   torch.save([a.storage(), a.imag.storage()], f)
2023-01-11T21:44:29.0879198Z /var/lib/jenkins/workspace/test/test_serialization.py:729: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0879961Z   wrap_storage=a.storage().untyped(),
2023-01-11T21:44:29.0880681Z /var/lib/jenkins/workspace/test/test_serialization.py:728: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0881463Z   s_bytes = torch.TypedStorage(
2023-01-11T21:44:29.0882251Z /var/lib/jenkins/workspace/test/test_serialization.py:736: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0883129Z   torch.save([a.storage(), s_bytes], f)
2023-01-11T21:44:29.0883415Z ok (0.002s)
2023-01-11T21:44:29.0884268Z   test_save_different_dtype_unallocated (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:697: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0885190Z   wrap_storage=a.storage().untyped(),
2023-01-11T21:44:29.0886003Z /var/lib/jenkins/workspace/test/test_serialization.py:696: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0886761Z   s = torch.TypedStorage(
2023-01-11T21:44:29.0887927Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0888683Z   device=typed_storage.device,
2023-01-11T21:44:29.0889651Z /var/lib/jenkins/workspace/test/test_serialization.py:700: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0890409Z   save_load_check(a.storage(), s)
2023-01-11T21:44:29.0890673Z ok (0.216s)
2023-01-11T21:44:29.0891483Z   test_serialization (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0892258Z   b += [a[0].storage()]  # 4
2023-01-11T21:44:29.0893048Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0893923Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:44:29.0894735Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0895651Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0896518Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0897579Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0898428Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0899197Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:44:29.0900086Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0900980Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:44:29.0901756Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0902589Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:44:29.0903513Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0904303Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:44:29.0905165Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0905960Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:44:29.0906285Z ok (0.008s)
2023-01-11T21:44:29.0906623Z   test_serialization_2gb_file (__main__.TestSerialization) ... ok (7.625s)
2023-01-11T21:44:29.0907630Z   test_serialization_backwards_compat (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:403: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0908480Z   b += [a[0].storage()]
2023-01-11T21:44:29.0909259Z /var/lib/jenkins/workspace/test/test_serialization.py:404: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0910181Z   b += [a[0].reshape(-1)[1:4].clone().storage()]
2023-01-11T21:44:29.0911030Z /var/lib/jenkins/workspace/test/test_serialization.py:416: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0911907Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:44:29.0912835Z /var/lib/jenkins/workspace/test/test_serialization.py:426: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0913752Z   return (self.new_tensor.storage(),
2023-01-11T21:44:29.0914585Z /var/lib/jenkins/workspace/test/test_serialization.py:446: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0915370Z   self.assertEqual(x.storage(), load_x.storage())
2023-01-11T21:44:29.0915692Z ok (0.008s)
2023-01-11T21:44:29.0916172Z   test_serialization_backwards_compat_safe (__main__.TestSerialization) ... ok (0.006s)
2023-01-11T21:44:29.0916736Z   test_serialization_dill (__main__.TestSerialization) ... skip: "dill" not found or not correct version (0.001s)
2023-01-11T21:44:29.0917344Z   test_serialization_dill_version_not_supported (__main__.TestSerialization) ... skip: "dill" not found or is correct version (0.000s)
2023-01-11T21:44:29.0917899Z   test_serialization_efficient_zerotensor_weights_only_False (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0918457Z   test_serialization_efficient_zerotensor_weights_only_True (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0918996Z   test_serialization_fake_zip (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0919452Z   test_serialization_filelike (__main__.TestSerialization) ... ok (0.003s)
2023-01-11T21:44:29.0919921Z   test_serialization_filelike_api_requirements (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0920854Z   test_serialization_filelike_exceptions (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:616: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0921716Z   s = torch.CharStorage(s_data)
2023-01-11T21:44:29.0921981Z ok (0.003s)
2023-01-11T21:44:29.0922341Z   test_serialization_filelike_missing_attrs (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0922818Z   test_serialization_filelike_stress (__main__.TestSerialization) ... ok (0.465s)
2023-01-11T21:44:29.0923279Z   test_serialization_filelike_uses_readinto (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0923739Z   test_serialization_gzip (__main__.TestSerialization) ... ok (0.005s)
2023-01-11T21:44:29.0924179Z   test_serialization_map_location (__main__.TestSerialization) ... ok (0.004s)
2023-01-11T21:44:29.0924664Z   test_serialization_math_bits_weights_only_False (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0925151Z   test_serialization_math_bits_weights_only_True (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0925626Z   test_serialization_offset_gzip (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0926074Z   test_serialization_python_attr (__main__.TestSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0926497Z   test_serialization_safe (__main__.TestSerialization) ... ok (0.008s)
2023-01-11T21:44:29.0926934Z   test_serialization_save_warnings (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0927896Z   test_serialization_sparse (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:85: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0928716Z   b += [a[0].storage()]  # 4
2023-01-11T21:44:29.0929755Z /var/lib/jenkins/workspace/test/test_serialization.py:86: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0930610Z   b += [a[0].reshape(-1)[1:4].storage()]  # 5
2023-01-11T21:44:29.0931398Z /var/lib/jenkins/workspace/test/test_serialization.py:88: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0932485Z   t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0933360Z /var/lib/jenkins/workspace/test/test_serialization.py:89: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0934321Z   t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
2023-01-11T21:44:29.0935176Z /var/lib/jenkins/workspace/test/test_serialization.py:90: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0935981Z   b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
2023-01-11T21:44:29.0936795Z /var/lib/jenkins/workspace/test/test_serialization.py:91: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0937682Z   b += [a[0].reshape(-1)[0:2].storage()]  # 8
2023-01-11T21:44:29.0938841Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0939597Z   device=typed_storage.device,
2023-01-11T21:44:29.0940393Z /var/lib/jenkins/workspace/test/test_serialization.py:104: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0941250Z   self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), atol=0, rtol=0)
2023-01-11T21:44:29.0942109Z /var/lib/jenkins/workspace/test/test_serialization.py:110: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0942876Z   self.assertEqual(c[4][i + 1], c[5][i])
2023-01-11T21:44:29.0943793Z /var/lib/jenkins/workspace/test/test_serialization.py:120: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0944651Z   self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
2023-01-11T21:44:29.0945110Z ok (0.007s)
2023-01-11T21:44:29.0945489Z   test_serialization_sparse_bsc_invalid (__main__.TestSerialization) ... ok (0.006s)
2023-01-11T21:44:29.0945965Z   test_serialization_sparse_bsr_invalid (__main__.TestSerialization) ... ok (0.006s)
2023-01-11T21:44:29.0946451Z   test_serialization_sparse_csc_invalid (__main__.TestSerialization) ... ok (0.006s)
2023-01-11T21:44:29.0946928Z   test_serialization_sparse_csr_invalid (__main__.TestSerialization) ... ok (0.006s)
2023-01-11T21:44:29.0947388Z   test_serialization_sparse_invalid (__main__.TestSerialization) ... ok (0.004s)
2023-01-11T21:44:29.0948899Z   test_serialization_sparse_safe (__main__.TestSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0949712Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:44:29.0949981Z ok (0.008s)
2023-01-11T21:44:29.0950862Z   test_serialization_storage_slice (__main__.TestSerialization) ... /var/lib/jenkins/workspace/test/test_serialization.py:651: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0951672Z   self.assertEqual(s1[0], 0)
2023-01-11T21:44:29.0952512Z /var/lib/jenkins/workspace/test/test_serialization.py:652: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0953273Z   self.assertEqual(s2[0], 0)
2023-01-11T21:44:29.0954079Z /var/lib/jenkins/workspace/test/test_serialization.py:653: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:44:29.0954872Z   self.assertEqual(s1.data_ptr() + 4, s2.data_ptr())
2023-01-11T21:44:29.0955173Z ok (0.001s)
2023-01-11T21:44:29.0956255Z   test_serialization_zipfile_actually_jit (__main__.TestSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/serialization.py:801: UserWarning: 'torch.load' received a zip file that looks like a TorchScript archive dispatching to 'torch.jit.load' (call 'torch.jit.load' directly to silence this warning)
2023-01-11T21:44:29.0957184Z   warnings.warn("'torch.load' received a zip file that looks like a TorchScript archive"
2023-01-11T21:44:29.0957579Z ok (0.013s)
2023-01-11T21:44:29.0957914Z   test_serialization_zipfile_utils (__main__.TestSerialization) ... ok (0.004s)
2023-01-11T21:44:29.0958400Z   test_serialization_zipfile_weights_only_False (__main__.TestSerialization) ... ok (0.007s)
2023-01-11T21:44:29.0958906Z   test_serialization_zipfile_weights_only_True (__main__.TestSerialization) ... ok (0.010s)
2023-01-11T21:44:29.0959340Z   test_serialize_device (__main__.TestSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0959766Z   test_weights_only_assert (__main__.TestSerialization) ... Hello World!
2023-01-11T21:44:29.0960104Z ok (0.001s)
2023-01-11T21:44:29.0960474Z   test_cloned_deepcopy_requires_grad_False (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0961013Z   test_cloned_deepcopy_requires_grad_True (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0961507Z   test_empty_class_serialization (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0962083Z   test_tensor_subclass_deepcopy (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0962595Z   test_tensor_subclass_getstate_overwrite (__main__.TestSubclassSerialization) ... ok (0.002s)
2023-01-11T21:44:29.0963130Z   test_tensor_subclass_wrapper_serialization (__main__.TestSubclassSerialization) ... ok (0.001s)
2023-01-11T21:44:29.0963429Z 
2023-01-11T21:44:29.0963761Z ----------------------------------------------------------------------
2023-01-11T21:44:29.0964117Z Ran 91 tests in 30.520s
2023-01-11T21:44:29.0964269Z 
2023-01-11T21:44:29.0964371Z OK (skipped=4)
2023-01-11T21:44:29.0964527Z 
2023-01-11T21:44:29.0964647Z Generating XML reports...
2023-01-11T21:44:29.0965299Z Generated XML report: test-reports/python-unittest/test_serialization/TEST-TestOldSerialization-20230111214358.xml
2023-01-11T21:44:29.0966149Z Generated XML report: test-reports/python-unittest/test_serialization/TEST-TestSerialization-20230111214358.xml
2023-01-11T21:44:29.0966990Z Generated XML report: test-reports/python-unittest/test_serialization/TEST-TestSubclassSerialization-20230111214358.xml
2023-01-11T21:44:29.0967370Z 
2023-01-11T21:44:29.0967843Z ##[endgroup]
2023-01-11T21:44:29.0968407Z FINISHED PRINTING LOG FILE of test_serialization (/var/lib/jenkins/workspace/test/test-reports/test_serialization_a68we09r)
2023-01-11T21:44:29.0968735Z 
2023-01-11T21:44:30.9573997Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:44:31.0298549Z Ignoring disabled issues:  []
2023-01-11T21:44:31.2345929Z Running test_set_default_mobile_cpu_allocator ... [2023-01-11 21:44:31.234180]
2023-01-11T21:44:31.2347101Z Executing ['/opt/conda/bin/python', '-bb', 'test_set_default_mobile_cpu_allocator.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:44:31.234462]
2023-01-11T21:44:33.1579143Z 
2023-01-11T21:44:33.1579749Z Expand the folded group to see the log file of test_set_default_mobile_cpu_allocator
2023-01-11T21:44:33.1580926Z ##[group]PRINTING LOG FILE of test_set_default_mobile_cpu_allocator (/var/lib/jenkins/workspace/test/test-reports/test_set_default_mobile_cpu_allocator_2wfbbpqw)
2023-01-11T21:44:33.1581222Z 
2023-01-11T21:44:33.1581297Z Running tests...
2023-01-11T21:44:33.1581697Z ----------------------------------------------------------------------
2023-01-11T21:44:33.1582117Z Test results will be stored in test-reports/python-unittest/test_set_default_mobile_cpu_allocator
2023-01-11T21:44:33.1582477Z   test_exception (__main__.TestSetDefaultMobileCPUAllocator) ... ok (0.232s)
2023-01-11T21:44:33.1582820Z   test_no_exception (__main__.TestSetDefaultMobileCPUAllocator) ... ok (0.001s)
2023-01-11T21:44:33.1583067Z 
2023-01-11T21:44:33.1583251Z ----------------------------------------------------------------------
2023-01-11T21:44:33.1583494Z Ran 2 tests in 0.233s
2023-01-11T21:44:33.1583612Z 
2023-01-11T21:44:33.1583677Z OK
2023-01-11T21:44:33.1583769Z 
2023-01-11T21:44:33.1583840Z Generating XML reports...
2023-01-11T21:44:33.1584354Z Generated XML report: test-reports/python-unittest/test_set_default_mobile_cpu_allocator/TEST-TestSetDefaultMobileCPUAllocator-20230111214432.xml
2023-01-11T21:44:33.1584654Z 
2023-01-11T21:44:33.1584873Z ##[endgroup]
2023-01-11T21:44:33.1585320Z FINISHED PRINTING LOG FILE of test_set_default_mobile_cpu_allocator (/var/lib/jenkins/workspace/test/test-reports/test_set_default_mobile_cpu_allocator_2wfbbpqw)
2023-01-11T21:44:33.1585564Z 
2023-01-11T21:44:34.9877150Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:44:35.0524684Z Ignoring disabled issues:  []
2023-01-11T21:44:35.0691371Z Running test_shape_ops ... [2023-01-11 21:44:35.068853]
2023-01-11T21:44:35.0693290Z Executing ['/opt/conda/bin/python', '-bb', 'test_shape_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:44:35.069118]
2023-01-11T21:44:36.9180970Z 
2023-01-11T21:44:36.9181520Z Expand the folded group to see the log file of test_shape_ops
2023-01-11T21:44:36.9182576Z ##[group]PRINTING LOG FILE of test_shape_ops (/var/lib/jenkins/workspace/test/test-reports/test_shape_ops_5rtyveps)
2023-01-11T21:44:36.9183338Z 
2023-01-11T21:44:36.9183471Z Running tests...
2023-01-11T21:44:36.9183979Z ----------------------------------------------------------------------
2023-01-11T21:44:36.9184152Z 
2023-01-11T21:44:36.9184399Z ----------------------------------------------------------------------
2023-01-11T21:44:36.9184830Z Ran 0 tests in 0.000s
2023-01-11T21:44:36.9185041Z 
2023-01-11T21:44:36.9185146Z OK
2023-01-11T21:44:36.9185232Z 
2023-01-11T21:44:36.9185317Z Generating XML reports...
2023-01-11T21:44:36.9185649Z Test results will be stored in test-reports/python-unittest/test_shape_ops
2023-01-11T21:44:36.9185827Z 
2023-01-11T21:44:36.9186048Z ##[endgroup]
2023-01-11T21:44:36.9186502Z FINISHED PRINTING LOG FILE of test_shape_ops (/var/lib/jenkins/workspace/test/test-reports/test_shape_ops_5rtyveps)
2023-01-11T21:44:36.9186717Z 
2023-01-11T21:44:38.7518554Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:44:38.8162545Z Ignoring disabled issues:  []
2023-01-11T21:44:38.8329743Z Running test_subclass ... [2023-01-11 21:44:38.832686]
2023-01-11T21:44:38.8331786Z Executing ['/opt/conda/bin/python', '-bb', 'test_subclass.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:44:38.832934]
2023-01-11T21:44:40.8166468Z 
2023-01-11T21:44:40.8166993Z Expand the folded group to see the log file of test_subclass
2023-01-11T21:44:40.8168072Z ##[group]PRINTING LOG FILE of test_subclass (/var/lib/jenkins/workspace/test/test-reports/test_subclass_ip78gh3i)
2023-01-11T21:44:40.8168428Z 
2023-01-11T21:44:40.8168541Z Running tests...
2023-01-11T21:44:40.8169342Z ----------------------------------------------------------------------
2023-01-11T21:44:40.8170052Z Test results will be stored in test-reports/python-unittest/test_subclass
2023-01-11T21:44:40.8170810Z   test_deepcopy_base_tensor_as_param_False (__main__.TestSubclass) ... ok (0.222s)
2023-01-11T21:44:40.8171544Z   test_deepcopy_base_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8172382Z   test_deepcopy_diag_tensor_below_as_param_False (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:44:40.8173148Z   test_deepcopy_diag_tensor_below_as_param_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8173923Z   test_deepcopy_logging_tensor_as_param_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8174671Z   test_deepcopy_logging_tensor_as_param_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8175442Z   test_deepcopy_non_wrapper_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8176241Z   test_deepcopy_non_wrapper_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8177027Z   test_deepcopy_sparse_tensor_as_param_False (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:44:40.8177782Z   test_deepcopy_sparse_tensor_as_param_True (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:44:40.8178551Z   test_lazy_module_base_tensor (__main__.TestSubclass) ... expected failure (0.001s)
2023-01-11T21:44:40.8180278Z   test_lazy_module_diag_tensor_below (__main__.TestSubclass) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:44:40.8181634Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:44:40.8182235Z expected failure (0.002s)
2023-01-11T21:44:40.8182827Z   test_lazy_module_logging_tensor (__main__.TestSubclass) ... expected failure (0.001s)
2023-01-11T21:44:40.8183721Z   test_lazy_module_non_wrapper_tensor (__main__.TestSubclass) ... expected failure (0.001s)
2023-01-11T21:44:40.8184512Z   test_lazy_module_sparse_tensor (__main__.TestSubclass) ... expected failure (0.002s)
2023-01-11T21:44:40.8185257Z   test_module_optimization_base_tensor (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:44:40.8186259Z   test_module_optimization_diag_tensor_below (__main__.TestSubclass) ... ok (0.006s)
2023-01-11T21:44:40.8187029Z   test_module_optimization_logging_tensor (__main__.TestSubclass) ... ok (0.004s)
2023-01-11T21:44:40.8187764Z   test_module_optimization_non_wrapper_tensor (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8188512Z   test_module_optimization_sparse_tensor (__main__.TestSubclass) ... ok (0.007s)
2023-01-11T21:44:40.8189354Z   test_non_rewrapping_torch_dispatch_subclass_as_parameter_throws_for_detach (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8190256Z   test_param_invariants_base_tensor_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8191092Z   test_param_invariants_base_tensor_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8192106Z   test_param_invariants_diag_tensor_below_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8192996Z   test_param_invariants_diag_tensor_below_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8193889Z   test_param_invariants_logging_tensor_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8194737Z   test_param_invariants_logging_tensor_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8195572Z   test_param_invariants_non_wrapper_tensor_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8196494Z   test_param_invariants_non_wrapper_tensor_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8197378Z   test_param_invariants_sparse_tensor_tensor_requires_grad_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8198222Z   test_param_invariants_sparse_tensor_tensor_requires_grad_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8199105Z   test_parametrization_base_tensor_leave_parametrized_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8199978Z   test_parametrization_base_tensor_leave_parametrized_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8200853Z   test_parametrization_diag_tensor_below_leave_parametrized_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8201729Z   test_parametrization_diag_tensor_below_leave_parametrized_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8202629Z   test_parametrization_logging_tensor_leave_parametrized_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8203539Z   test_parametrization_logging_tensor_leave_parametrized_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8204440Z   test_parametrization_non_wrapper_tensor_leave_parametrized_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8205346Z   test_parametrization_non_wrapper_tensor_leave_parametrized_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8206196Z   test_parametrization_sparse_tensor_leave_parametrized_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8207066Z   test_parametrization_sparse_tensor_leave_parametrized_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8207862Z   test_repr_base_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8208576Z   test_repr_base_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8209507Z   test_repr_diag_tensor_below_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8210251Z   test_repr_diag_tensor_below_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8210990Z   test_repr_logging_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8211708Z   test_repr_logging_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8212441Z   test_repr_non_wrapper_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8213210Z   test_repr_non_wrapper_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8214069Z   test_repr_sparse_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8214786Z   test_repr_sparse_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8215534Z   test_serialization_base_tensor_as_param_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8216321Z   test_serialization_base_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8217119Z   test_serialization_diag_tensor_below_as_param_False (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:44:40.8217938Z   test_serialization_diag_tensor_below_as_param_True (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:44:40.8218737Z   test_serialization_logging_tensor_as_param_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8219639Z   test_serialization_logging_tensor_as_param_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8220452Z   test_serialization_non_wrapper_tensor_as_param_False (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8221260Z   test_serialization_non_wrapper_tensor_as_param_True (__main__.TestSubclass) ... ok (0.002s)
2023-01-11T21:44:40.8222032Z   test_serialization_sparse_tensor_as_param_False (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:44:40.8222831Z   test_serialization_sparse_tensor_as_param_True (__main__.TestSubclass) ... ok (0.003s)
2023-01-11T21:44:40.8223720Z   test_type_propagation_base_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8224497Z   test_type_propagation_base_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8225297Z   test_type_propagation_diag_tensor_below_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8226116Z   test_type_propagation_diag_tensor_below_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8226938Z   test_type_propagation_logging_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8227726Z   test_type_propagation_logging_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8228559Z   test_type_propagation_non_wrapper_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8229399Z   test_type_propagation_non_wrapper_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8230205Z   test_type_propagation_sparse_tensor_as_param_False (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8231011Z   test_type_propagation_sparse_tensor_as_param_True (__main__.TestSubclass) ... ok (0.001s)
2023-01-11T21:44:40.8231463Z 
2023-01-11T21:44:40.8231952Z ----------------------------------------------------------------------
2023-01-11T21:44:40.8232509Z Ran 71 tests in 0.337s
2023-01-11T21:44:40.8232772Z 
2023-01-11T21:44:40.8232950Z OK (expected failures=5)
2023-01-11T21:44:40.8233223Z 
2023-01-11T21:44:40.8233577Z Generating XML reports...
2023-01-11T21:44:40.8234534Z Generated XML report: test-reports/python-unittest/test_subclass/TEST-TestSubclass-20230111214440.xml
2023-01-11T21:44:40.8235095Z 
2023-01-11T21:44:40.8235654Z ##[endgroup]
2023-01-11T21:44:40.8236558Z FINISHED PRINTING LOG FILE of test_subclass (/var/lib/jenkins/workspace/test/test-reports/test_subclass_ip78gh3i)
2023-01-11T21:44:40.8237078Z 
2023-01-11T21:44:42.6635648Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:44:42.7275649Z Ignoring disabled issues:  []
2023-01-11T21:44:42.7440270Z Running test_tensorboard ... [2023-01-11 21:44:42.743789]
2023-01-11T21:44:42.7442469Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensorboard.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:44:42.744029]
2023-01-11T21:45:08.3922665Z 
2023-01-11T21:45:08.3923221Z Expand the folded group to see the log file of test_tensorboard
2023-01-11T21:45:08.3924239Z ##[group]PRINTING LOG FILE of test_tensorboard (/var/lib/jenkins/workspace/test/test-reports/test_tensorboard_sdo47tmx)
2023-01-11T21:45:08.3924892Z 
2023-01-11T21:45:08.3925732Z Running tests...
2023-01-11T21:45:08.3926322Z ----------------------------------------------------------------------
2023-01-11T21:45:08.3926780Z Test results will be stored in test-reports/python-unittest/test_tensorboard
2023-01-11T21:45:08.3927249Z   test_embedding (__main__.TestTensorBoardEmbedding) ... warning: Embedding dir exists, did you set global_step for add_embedding()?
2023-01-11T21:45:08.3927600Z ok (0.037s)
2023-01-11T21:45:08.3927922Z   test_embedding_64 (__main__.TestTensorBoardEmbedding) ... warning: Embedding dir exists, did you set global_step for add_embedding()?
2023-01-11T21:45:08.3928259Z ok (0.017s)
2023-01-11T21:45:08.3928504Z   test_figure (__main__.TestTensorBoardFigure) ... skip: no matplotlib (0.001s)
2023-01-11T21:45:08.3929124Z   test_figure_list (__main__.TestTensorBoardFigure) ... skip: no matplotlib (0.001s)
2023-01-11T21:45:08.3929497Z   test_caffe2_np (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.000s)
2023-01-11T21:45:08.3929869Z   test_caffe2_np_expect_fail (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.000s)
2023-01-11T21:45:08.3930210Z   test_caffe2_simple_cnnmodel (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.001s)
2023-01-11T21:45:08.3930600Z   test_caffe2_simple_model (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.001s)
2023-01-11T21:45:08.3930918Z   test_pytorch_np_expect_fail (__main__.TestTensorBoardNumpy) ... ok (0.000s)
2023-01-11T21:45:08.3931281Z   test_scalar (__main__.TestTensorBoardNumpy) ... ok (0.001s)
2023-01-11T21:45:08.3931576Z   test_pytorch_autograd_np (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.000s)
2023-01-11T21:45:08.3931981Z   test_pytorch_histogram (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.007s)
2023-01-11T21:45:08.3932325Z   test_pytorch_histogram_raw (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.009s)
2023-01-11T21:45:08.3932716Z   test_pytorch_np (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.001s)
2023-01-11T21:45:08.3933028Z   test_pytorch_write (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.005s)
2023-01-11T21:45:08.3933357Z   test_mlp_graph (__main__.TestTensorBoardPytorchGraph) ... ok (0.078s)
2023-01-11T21:45:08.3933731Z   test_nested_nn_squential (__main__.TestTensorBoardPytorchGraph) ... ok (0.072s)
2023-01-11T21:45:08.3934043Z   test_pytorch_graph (__main__.TestTensorBoardPytorchGraph) ... ok (0.024s)
2023-01-11T21:45:08.3935216Z   test_pytorch_graph_dict_input (__main__.TestTensorBoardPytorchGraph) ... Encountering a dict at the output of the tracer might cause the trace to be incorrect, this is only valid if the container structure does not change based on the module's inputs. Consider using a constant container instead (e.g. for `list`, use a `tuple` instead. for `dict`, use a `NamedTuple` instead). If you absolutely need this and know the side effects, pass strict=False to trace() to allow this behavior.
2023-01-11T21:45:08.3935999Z Error occurs, No graph saved
2023-01-11T21:45:08.3936186Z ok (0.034s)
2023-01-11T21:45:08.3936452Z   test_torchvision_smoke (__main__.TestTensorBoardPytorchGraph) ... ok (22.881s)
2023-01-11T21:45:08.3936870Z   test_wrong_input_size (__main__.TestTensorBoardPytorchGraph) ... mat1 and mat2 shapes cannot be multiplied (1x9 and 3x5)
2023-01-11T21:45:08.3937173Z Error occurs, No graph saved
2023-01-11T21:45:08.3937422Z ok (0.016s)
2023-01-11T21:45:08.3937681Z   test_audio (__main__.TestTensorBoardSummary) ... warning: audio amplitude out of range, auto clipped.
2023-01-11T21:45:08.3937938Z ok (0.015s)
2023-01-11T21:45:08.3938239Z   test_custom_scalars (__main__.TestTensorBoardSummary) ... ok (0.000s)
2023-01-11T21:45:08.3938539Z   test_empty_input (__main__.TestTensorBoardSummary) ... ok (0.000s)
2023-01-11T21:45:08.3938854Z   test_float32_image (__main__.TestTensorBoardSummary)
2023-01-11T21:45:08.3939170Z Tests that float32 image (pixel values in [0, 1]) are scaled correctly ... ok (0.001s)
2023-01-11T21:45:08.3939472Z   test_histogram_auto (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:45:08.3939914Z   test_histogram_doane (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:45:08.3940216Z   test_histogram_fd (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:45:08.3940573Z   test_hparams_bool (__main__.TestTensorBoardSummary) ... ok (0.003s)
2023-01-11T21:45:08.3940880Z   test_hparams_domain_discrete (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:45:08.3941238Z   test_hparams_number (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:45:08.3941546Z   test_hparams_smoke (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:45:08.3941840Z   test_hparams_string (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:45:08.3942273Z   test_hparams_wrong_parameter (__main__.TestTensorBoardSummary) ... parameter: hparam_dict should be a dictionary, nothing logged.
2023-01-11T21:45:08.3942687Z parameter: metric_dict should be a dictionary, nothing logged.
2023-01-11T21:45:08.3942958Z ok (0.009s)
2023-01-11T21:45:08.3943275Z   test_image_with_3_channel_batched (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:45:08.3943660Z   test_image_with_boxes (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:45:08.3943968Z   test_image_with_one_channel (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:45:08.3944353Z   test_image_with_one_channel_batched (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:45:08.3944665Z   test_image_without_channel (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:45:08.3944964Z   test_list_input (__main__.TestTensorBoardSummary) ... ok (0.000s)
2023-01-11T21:45:08.3945321Z   test_mesh (__main__.TestTensorBoardSummary) ... ok (0.003s)
2023-01-11T21:45:08.3945594Z   test_scalar_new_style (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T21:45:08.3945948Z   test_text (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T21:45:08.3946220Z   test_uint8_image (__main__.TestTensorBoardSummary)
2023-01-11T21:45:08.3946507Z Tests that uint8 image (pixel values in [0, 255]) is not changed ... ok (0.000s)
2023-01-11T21:45:08.3946852Z   test_video (__main__.TestTensorBoardSummary) ... ok (0.000s)
2023-01-11T21:45:08.3947143Z   test_pathlib (__main__.TestTensorBoardSummaryWriter) ... ok (0.002s)
2023-01-11T21:45:08.3947536Z   test_summary_writer_close (__main__.TestTensorBoardSummaryWriter) ... ok (0.005s)
2023-01-11T21:45:08.3947863Z   test_summary_writer_ctx (__main__.TestTensorBoardSummaryWriter) ... ok (0.004s)
2023-01-11T21:45:08.3948253Z   test_convert_to_HWC_dtype_remains_same (__main__.TestTensorBoardUtils) ... ok (0.001s)
2023-01-11T21:45:08.3948569Z   test_numpy_vid_uint8 (__main__.TestTensorBoardUtils) ... ok (0.015s)
2023-01-11T21:45:08.3948860Z   test_prepare_video (__main__.TestTensorBoardUtils) ... ok (0.087s)
2023-01-11T21:45:08.3949200Z   test_to_HWC (__main__.TestTensorBoardUtils) ... ok (0.001s)
2023-01-11T21:45:08.3949498Z   test_writer (__main__.TestTensorBoardWriter) ... add_video needs package moviepy
2023-01-11T21:45:08.3949802Z ok (0.056s)
2023-01-11T21:45:08.3949897Z 
2023-01-11T21:45:08.3950128Z ----------------------------------------------------------------------
2023-01-11T21:45:08.3950367Z Ran 53 tests in 23.414s
2023-01-11T21:45:08.3950537Z 
2023-01-11T21:45:08.3950621Z OK (skipped=6)
2023-01-11T21:45:08.3950730Z 
2023-01-11T21:45:08.3950803Z Generating XML reports...
2023-01-11T21:45:08.3951302Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardEmbedding-20230111214444.xml
2023-01-11T21:45:08.3951876Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardNumpy-20230111214444.xml
2023-01-11T21:45:08.3952516Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardPyTorchNumpy-20230111214444.xml
2023-01-11T21:45:08.3953159Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardPytorchGraph-20230111214444.xml
2023-01-11T21:45:08.3953790Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardSummary-20230111214444.xml
2023-01-11T21:45:08.3954482Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardSummaryWriter-20230111214444.xml
2023-01-11T21:45:08.3955043Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardUtils-20230111214444.xml
2023-01-11T21:45:08.3955648Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardWriter-20230111214444.xml
2023-01-11T21:45:08.3956254Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardFigure-20230111214444.xml
2023-01-11T21:45:08.3956496Z 
2023-01-11T21:45:08.3956884Z ##[endgroup]
2023-01-11T21:45:08.3957313Z FINISHED PRINTING LOG FILE of test_tensorboard (/var/lib/jenkins/workspace/test/test-reports/test_tensorboard_sdo47tmx)
2023-01-11T21:45:08.3957606Z 
2023-01-11T21:45:10.2157119Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:10.2794548Z Ignoring disabled issues:  []
2023-01-11T21:45:10.2960743Z Running test_tensorexpr_pybind ... [2023-01-11 21:45:10.295803]
2023-01-11T21:45:10.2962621Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensorexpr_pybind.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:10.296048]
2023-01-11T21:45:12.6475948Z 
2023-01-11T21:45:12.6476541Z Expand the folded group to see the log file of test_tensorexpr_pybind
2023-01-11T21:45:12.6477257Z ##[group]PRINTING LOG FILE of test_tensorexpr_pybind (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_pybind_mox3utv2)
2023-01-11T21:45:12.6477498Z 
2023-01-11T21:45:12.6477628Z Running tests...
2023-01-11T21:45:12.6478043Z ----------------------------------------------------------------------
2023-01-11T21:45:12.6478494Z Test results will be stored in test-reports/python-unittest/test_tensorexpr_pybind
2023-01-11T21:45:12.6478815Z   test_unary_ops (__main__.TestExprHandlePyBind) ... ok (0.227s)
2023-01-11T21:45:12.6479147Z   test_alloc_in_loop (__main__.TestTensorExprPyBind) ... ok (0.025s)
2023-01-11T21:45:12.6479434Z   test_call_raw (__main__.TestTensorExprPyBind) ... ok (0.001s)
2023-01-11T21:45:12.6479717Z   test_dtype_error (__main__.TestTensorExprPyBind) ... ok (0.001s)
2023-01-11T21:45:12.6480051Z   test_dynamic_shape (__main__.TestTensorExprPyBind) ... ok (0.002s)
2023-01-11T21:45:12.6480350Z   test_dynamic_shape_2d (__main__.TestTensorExprPyBind) ... ok (0.002s)
2023-01-11T21:45:12.6480663Z   test_external_calls (__main__.TestTensorExprPyBind) ... ok (0.001s)
2023-01-11T21:45:12.6480976Z   test_kernel_shape_prop (__main__.TestTensorExprPyBind) ... ok (0.162s)
2023-01-11T21:45:12.6481283Z   test_kernel_shape_prop_module (__main__.TestTensorExprPyBind) ... ok (0.040s)
2023-01-11T21:45:12.6481640Z   test_kernel_with_custom_lowering (__main__.TestTensorExprPyBind) ... ok (0.026s)
2023-01-11T21:45:12.6481953Z   test_kernel_with_expand (__main__.TestTensorExprPyBind) ... ok (0.026s)
2023-01-11T21:45:12.6482302Z   test_kernel_with_permute (__main__.TestTensorExprPyBind) ... ok (0.060s)
2023-01-11T21:45:12.6482616Z   test_kernel_with_scalar_inputs (__main__.TestTensorExprPyBind) ... ok (0.022s)
2023-01-11T21:45:12.6482926Z   test_kernel_with_t (__main__.TestTensorExprPyBind) ... ok (0.028s)
2023-01-11T21:45:12.6483249Z   test_kernel_with_tensor_inputs (__main__.TestTensorExprPyBind) ... ok (0.026s)
2023-01-11T21:45:12.6483557Z   test_kernel_with_transpose (__main__.TestTensorExprPyBind) ... ok (0.028s)
2023-01-11T21:45:12.6483890Z   test_simple_sum (__main__.TestTensorExprPyBind) ... ok (0.001s)
2023-01-11T21:45:12.6484049Z 
2023-01-11T21:45:12.6484253Z ----------------------------------------------------------------------
2023-01-11T21:45:12.6484542Z Ran 17 tests in 0.678s
2023-01-11T21:45:12.6484657Z 
2023-01-11T21:45:12.6484718Z OK
2023-01-11T21:45:12.6484796Z 
2023-01-11T21:45:12.6484883Z Generating XML reports...
2023-01-11T21:45:12.6485387Z Generated XML report: test-reports/python-unittest/test_tensorexpr_pybind/TEST-TestExprHandlePyBind-20230111214511.xml
2023-01-11T21:45:12.6486198Z Generated XML report: test-reports/python-unittest/test_tensorexpr_pybind/TEST-TestTensorExprPyBind-20230111214511.xml
2023-01-11T21:45:12.6486451Z 
2023-01-11T21:45:12.6486690Z ##[endgroup]
2023-01-11T21:45:12.6487131Z FINISHED PRINTING LOG FILE of test_tensorexpr_pybind (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_pybind_mox3utv2)
2023-01-11T21:45:12.6487365Z 
2023-01-11T21:45:14.4219441Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:14.4860360Z Ignoring disabled issues:  []
2023-01-11T21:45:14.5027497Z Running test_type_hints ... [2023-01-11 21:45:14.502506]
2023-01-11T21:45:14.5029853Z Executing ['/opt/conda/bin/python', '-bb', 'test_type_hints.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:14.502739]
2023-01-11T21:45:16.1352400Z 
2023-01-11T21:45:16.1352890Z Expand the folded group to see the log file of test_type_hints
2023-01-11T21:45:16.1353680Z ##[group]PRINTING LOG FILE of test_type_hints (/var/lib/jenkins/workspace/test/test-reports/test_type_hints_kktf7y8g)
2023-01-11T21:45:16.1354212Z 
2023-01-11T21:45:16.1354528Z Running tests...
2023-01-11T21:45:16.1355391Z ----------------------------------------------------------------------
2023-01-11T21:45:16.1356275Z Test results will be stored in test-reports/python-unittest/test_type_hints
2023-01-11T21:45:16.1356950Z   test_doc_examples (__main__.TestTypeHints)
2023-01-11T21:45:16.1357735Z Run documentation examples through mypy. ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:45:16.1358279Z 
2023-01-11T21:45:16.1371357Z ----------------------------------------------------------------------
2023-01-11T21:45:16.1371834Z Ran 1 test in 0.001s
2023-01-11T21:45:16.1372074Z 
2023-01-11T21:45:16.1372190Z OK (skipped=1)
2023-01-11T21:45:16.1372361Z 
2023-01-11T21:45:16.1372451Z Generating XML reports...
2023-01-11T21:45:16.1373044Z Generated XML report: test-reports/python-unittest/test_type_hints/TEST-TestTypeHints-20230111214515.xml
2023-01-11T21:45:16.1373430Z 
2023-01-11T21:45:16.1374031Z ##[endgroup]
2023-01-11T21:45:16.1374454Z FINISHED PRINTING LOG FILE of test_type_hints (/var/lib/jenkins/workspace/test/test-reports/test_type_hints_kktf7y8g)
2023-01-11T21:45:16.1374673Z 
2023-01-11T21:45:17.9657285Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:18.0298108Z Ignoring disabled issues:  []
2023-01-11T21:45:18.0463406Z Running test_type_info ... [2023-01-11 21:45:18.046104]
2023-01-11T21:45:18.0465625Z Executing ['/opt/conda/bin/python', '-bb', 'test_type_info.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:18.046352]
2023-01-11T21:45:19.8896833Z 
2023-01-11T21:45:19.8897338Z Expand the folded group to see the log file of test_type_info
2023-01-11T21:45:19.8898327Z ##[group]PRINTING LOG FILE of test_type_info (/var/lib/jenkins/workspace/test/test-reports/test_type_info_f162gzqz)
2023-01-11T21:45:19.8898772Z 
2023-01-11T21:45:19.8898886Z Running tests...
2023-01-11T21:45:19.8899447Z ----------------------------------------------------------------------
2023-01-11T21:45:19.8899993Z Test results will be stored in test-reports/python-unittest/test_type_info
2023-01-11T21:45:19.8900272Z   test_finfo (__main__.TestDTypeInfo) ... ok (0.223s)
2023-01-11T21:45:19.8900519Z   test_iinfo (__main__.TestDTypeInfo) ... ok (0.002s)
2023-01-11T21:45:19.8900812Z   test_invalid_input (__main__.TestDTypeInfo) ... ok (0.001s)
2023-01-11T21:45:19.8901100Z 
2023-01-11T21:45:19.8901427Z ----------------------------------------------------------------------
2023-01-11T21:45:19.8901783Z Ran 3 tests in 0.225s
2023-01-11T21:45:19.8901916Z 
2023-01-11T21:45:19.8901995Z OK
2023-01-11T21:45:19.8902093Z 
2023-01-11T21:45:19.8902178Z Generating XML reports...
2023-01-11T21:45:19.8902592Z Generated XML report: test-reports/python-unittest/test_type_info/TEST-TestDTypeInfo-20230111214519.xml
2023-01-11T21:45:19.8902868Z 
2023-01-11T21:45:19.8903381Z ##[endgroup]
2023-01-11T21:45:19.8903799Z FINISHED PRINTING LOG FILE of test_type_info (/var/lib/jenkins/workspace/test/test-reports/test_type_info_f162gzqz)
2023-01-11T21:45:19.8904009Z 
2023-01-11T21:45:21.6926127Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:21.7566355Z Ignoring disabled issues:  []
2023-01-11T21:45:21.7735222Z Running test_type_promotion ... [2023-01-11 21:45:21.773304]
2023-01-11T21:45:21.7737657Z Executing ['/opt/conda/bin/python', '-bb', 'test_type_promotion.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:21.773534]
2023-01-11T21:45:23.5779915Z 
2023-01-11T21:45:23.5780443Z Expand the folded group to see the log file of test_type_promotion
2023-01-11T21:45:23.5781771Z ##[group]PRINTING LOG FILE of test_type_promotion (/var/lib/jenkins/workspace/test/test-reports/test_type_promotion_60hwuhrv)
2023-01-11T21:45:23.5782187Z 
2023-01-11T21:45:23.5782311Z Running tests...
2023-01-11T21:45:23.5782908Z ----------------------------------------------------------------------
2023-01-11T21:45:23.5783272Z 
2023-01-11T21:45:23.5783608Z ----------------------------------------------------------------------
2023-01-11T21:45:23.5784001Z Ran 0 tests in 0.000s
2023-01-11T21:45:23.5784186Z 
2023-01-11T21:45:23.5784281Z OK
2023-01-11T21:45:23.5784417Z 
2023-01-11T21:45:23.5784559Z Generating XML reports...
2023-01-11T21:45:23.5785130Z Test results will be stored in test-reports/python-unittest/test_type_promotion
2023-01-11T21:45:23.5785444Z 
2023-01-11T21:45:23.5785821Z ##[endgroup]
2023-01-11T21:45:23.5786494Z FINISHED PRINTING LOG FILE of test_type_promotion (/var/lib/jenkins/workspace/test/test-reports/test_type_promotion_60hwuhrv)
2023-01-11T21:45:23.5786879Z 
2023-01-11T21:45:25.4286636Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:25.4925797Z Ignoring disabled issues:  []
2023-01-11T21:45:25.5092542Z Running test_unary_ufuncs ... [2023-01-11 21:45:25.508987]
2023-01-11T21:45:25.5094680Z Executing ['/opt/conda/bin/python', '-bb', 'test_unary_ufuncs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:25.509221]
2023-01-11T21:45:27.9800250Z 
2023-01-11T21:45:27.9803555Z Expand the folded group to see the log file of test_unary_ufuncs
2023-01-11T21:45:27.9804221Z ##[group]PRINTING LOG FILE of test_unary_ufuncs (/var/lib/jenkins/workspace/test/test-reports/test_unary_ufuncs_zxvoawu1)
2023-01-11T21:45:27.9804462Z 
2023-01-11T21:45:27.9804537Z Running tests...
2023-01-11T21:45:27.9804916Z ----------------------------------------------------------------------
2023-01-11T21:45:27.9805087Z 
2023-01-11T21:45:27.9805284Z ----------------------------------------------------------------------
2023-01-11T21:45:27.9805527Z Ran 0 tests in 0.000s
2023-01-11T21:45:27.9805642Z 
2023-01-11T21:45:27.9805703Z OK
2023-01-11T21:45:27.9805782Z 
2023-01-11T21:45:27.9805880Z Generating XML reports...
2023-01-11T21:45:27.9806213Z Test results will be stored in test-reports/python-unittest/test_unary_ufuncs
2023-01-11T21:45:27.9806408Z 
2023-01-11T21:45:27.9806621Z ##[endgroup]
2023-01-11T21:45:27.9806998Z FINISHED PRINTING LOG FILE of test_unary_ufuncs (/var/lib/jenkins/workspace/test/test-reports/test_unary_ufuncs_zxvoawu1)
2023-01-11T21:45:27.9807215Z 
2023-01-11T21:45:29.8324881Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:29.8970790Z Ignoring disabled issues:  []
2023-01-11T21:45:29.9140541Z Running test_view_ops ... [2023-01-11 21:45:29.913828]
2023-01-11T21:45:29.9143309Z Executing ['/opt/conda/bin/python', '-bb', 'test_view_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:29.914065]
2023-01-11T21:45:31.8467769Z 
2023-01-11T21:45:31.8468486Z Expand the folded group to see the log file of test_view_ops
2023-01-11T21:45:31.8469758Z ##[group]PRINTING LOG FILE of test_view_ops (/var/lib/jenkins/workspace/test/test-reports/test_view_ops_17g8ds2_)
2023-01-11T21:45:31.8470179Z 
2023-01-11T21:45:31.8470319Z Running tests...
2023-01-11T21:45:31.8471254Z ----------------------------------------------------------------------
2023-01-11T21:45:31.8471518Z 
2023-01-11T21:45:31.8471725Z ----------------------------------------------------------------------
2023-01-11T21:45:31.8471967Z Ran 0 tests in 0.000s
2023-01-11T21:45:31.8472081Z 
2023-01-11T21:45:31.8472129Z OK
2023-01-11T21:45:31.8472223Z 
2023-01-11T21:45:31.8472308Z Generating XML reports...
2023-01-11T21:45:31.8472635Z Test results will be stored in test-reports/python-unittest/test_view_ops
2023-01-11T21:45:31.8472819Z 
2023-01-11T21:45:31.8473040Z ##[endgroup]
2023-01-11T21:45:31.8473430Z FINISHED PRINTING LOG FILE of test_view_ops (/var/lib/jenkins/workspace/test/test-reports/test_view_ops_17g8ds2_)
2023-01-11T21:45:31.8473639Z 
2023-01-11T21:45:33.6930012Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:33.7778960Z Ignoring disabled issues:  []
2023-01-11T21:45:33.8021284Z Running test_vulkan ... [2023-01-11 21:45:33.801740]
2023-01-11T21:45:33.8022575Z Executing ['/opt/conda/bin/python', '-bb', 'test_vulkan.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:33.801995]
2023-01-11T21:45:35.5121640Z 
2023-01-11T21:45:35.5122159Z Expand the folded group to see the log file of test_vulkan
2023-01-11T21:45:35.5123197Z ##[group]PRINTING LOG FILE of test_vulkan (/var/lib/jenkins/workspace/test/test-reports/test_vulkan_i4ux6pic)
2023-01-11T21:45:35.5123586Z 
2023-01-11T21:45:35.5123954Z Running tests...
2023-01-11T21:45:35.5124852Z ----------------------------------------------------------------------
2023-01-11T21:45:35.5125751Z   test_conv (__main__.TestVulkanRewritePass) ... Test results will be stored in test-reports/python-unittest/test_vulkan
2023-01-11T21:45:35.5126229Z skip: Vulkan backend must be available for these tests. (0.003s)
2023-01-11T21:45:35.5126438Z 
2023-01-11T21:45:35.5126770Z ----------------------------------------------------------------------
2023-01-11T21:45:35.5127184Z Ran 1 test in 0.003s
2023-01-11T21:45:35.5127389Z 
2023-01-11T21:45:35.5127503Z OK (skipped=1)
2023-01-11T21:45:35.5127689Z 
2023-01-11T21:45:35.5127835Z Generating XML reports...
2023-01-11T21:45:35.5128600Z Generated XML report: test-reports/python-unittest/test_vulkan/TEST-TestVulkanRewritePass-20230111214535.xml
2023-01-11T21:45:35.5129175Z 
2023-01-11T21:45:35.5129482Z ##[endgroup]
2023-01-11T21:45:35.5129852Z FINISHED PRINTING LOG FILE of test_vulkan (/var/lib/jenkins/workspace/test/test-reports/test_vulkan_i4ux6pic)
2023-01-11T21:45:35.5130104Z 
2023-01-11T21:45:37.4348036Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:37.5168118Z Ignoring disabled issues:  []
2023-01-11T21:45:37.5335624Z Running test_weak ... [2023-01-11 21:45:37.533368]
2023-01-11T21:45:37.5338807Z Executing ['/opt/conda/bin/python', '-bb', 'test_weak.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:37.533612]
2023-01-11T21:45:43.0566974Z 
2023-01-11T21:45:43.0567483Z Expand the folded group to see the log file of test_weak
2023-01-11T21:45:43.0568415Z ##[group]PRINTING LOG FILE of test_weak (/var/lib/jenkins/workspace/test/test-reports/test_weak_2e50y3ph)
2023-01-11T21:45:43.0568615Z 
2023-01-11T21:45:43.0568717Z Running tests...
2023-01-11T21:45:43.0569284Z ----------------------------------------------------------------------
2023-01-11T21:45:43.0569662Z Test results will be stored in test-reports/python-unittest/test_weak
2023-01-11T21:45:43.0569965Z   test_bool (__main__.WeakKeyDictionaryTestCase) ... ok (0.225s)
2023-01-11T21:45:43.0570254Z   test_constructor (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:45:43.0570552Z   test_get (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:45:43.0570841Z   test_getitem (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:45:43.0571126Z   test_items (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:45:43.0571409Z   test_keys (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:45:43.0571864Z   test_len (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:45:43.0572134Z   test_pop (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:45:43.0572423Z   test_popitem (__main__.WeakKeyDictionaryTestCase) ... ok (0.000s)
2023-01-11T21:45:43.0572713Z   test_read (__main__.WeakKeyDictionaryTestCase) ... ok (0.002s)
2023-01-11T21:45:43.0573006Z   test_setdefault (__main__.WeakKeyDictionaryTestCase) ... ok (0.000s)
2023-01-11T21:45:43.0573291Z   test_update (__main__.WeakKeyDictionaryTestCase) ... ok (0.004s)
2023-01-11T21:45:43.0573580Z   test_values (__main__.WeakKeyDictionaryTestCase) ... ok (0.001s)
2023-01-11T21:45:43.0573870Z   test_write (__main__.WeakKeyDictionaryTestCase) ... ok (0.006s)
2023-01-11T21:45:43.0574141Z   test_make_weak_keyed_dict_from_dict (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:45:43.0574491Z   test_make_weak_keyed_dict_from_weak_keyed_dict (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:45:43.0574785Z   test_make_weak_keyed_dict_repr (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:45:43.0575051Z   test_threaded_weak_key_dict_copy (__main__.WeakTest) ... ok (1.412s)
2023-01-11T21:45:43.0575336Z   test_threaded_weak_key_dict_deepcopy (__main__.WeakTest) ... ok (2.086s)
2023-01-11T21:45:43.0575616Z   test_weak_keyed_bad_delitem (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:45:43.0575875Z   test_weak_keyed_delitem (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:45:43.0576122Z   test_weak_keyed_dict_popitem (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:45:43.0576402Z   test_weak_keyed_dict_setdefault (__main__.WeakTest) ... ok (0.000s)
2023-01-11T21:45:43.0576670Z   test_weak_keyed_dict_update (__main__.WeakTest) ... ok (0.001s)
2023-01-11T21:45:43.0576926Z   test_weak_keyed_union_operators (__main__.WeakTest) ... ok (0.002s)
2023-01-11T21:45:43.0577082Z 
2023-01-11T21:45:43.0577286Z ----------------------------------------------------------------------
2023-01-11T21:45:43.0577525Z Ran 25 tests in 3.750s
2023-01-11T21:45:43.0577638Z 
2023-01-11T21:45:43.0577699Z OK
2023-01-11T21:45:43.0577778Z 
2023-01-11T21:45:43.0577861Z Generating XML reports...
2023-01-11T21:45:43.0578302Z Generated XML report: test-reports/python-unittest/test_weak/TEST-WeakKeyDictionaryTestCase-20230111214538.xml
2023-01-11T21:45:43.0578798Z Generated XML report: test-reports/python-unittest/test_weak/TEST-WeakTest-20230111214538.xml
2023-01-11T21:45:43.0579003Z 
2023-01-11T21:45:43.0579240Z ##[endgroup]
2023-01-11T21:45:43.0579601Z FINISHED PRINTING LOG FILE of test_weak (/var/lib/jenkins/workspace/test/test-reports/test_weak_2e50y3ph)
2023-01-11T21:45:43.0579801Z 
2023-01-11T21:45:45.3062060Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:45.3900125Z Ignoring disabled issues:  []
2023-01-11T21:45:45.4069700Z Running test_xnnpack_integration ... [2023-01-11 21:45:45.406656]
2023-01-11T21:45:45.4071299Z Executing ['/opt/conda/bin/python', '-bb', 'test_xnnpack_integration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:45:45.406910]
2023-01-11T21:45:57.4192590Z 
2023-01-11T21:45:57.4193286Z Expand the folded group to see the log file of test_xnnpack_integration
2023-01-11T21:45:57.4194632Z ##[group]PRINTING LOG FILE of test_xnnpack_integration (/var/lib/jenkins/workspace/test/test-reports/test_xnnpack_integration_nydseh_3)
2023-01-11T21:45:57.4195232Z 
2023-01-11T21:45:57.4195356Z Running tests...
2023-01-11T21:45:57.4196057Z ----------------------------------------------------------------------
2023-01-11T21:45:57.4196708Z Test results will be stored in test-reports/python-unittest/test_xnnpack_integration
2023-01-11T21:45:57.4197364Z   test_conv1d_basic (__main__.TestXNNPACKConv1dTransformPass) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.003s)
2023-01-11T21:45:57.4198126Z   test_conv1d_with_relu_fc (__main__.TestXNNPACKConv1dTransformPass) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.003s)
2023-01-11T21:45:57.4198675Z   test_conv2d (__main__.TestXNNPACKOps) ... ok (1.367s)
2023-01-11T21:45:57.4199357Z   test_conv2d_transpose (__main__.TestXNNPACKOps) ... ok (1.722s)
2023-01-11T21:45:57.4199943Z   test_linear (__main__.TestXNNPACKOps) ... skip: Fails on some platforms, see https://github.com/pytorch/pytorch/issues/73488 (0.001s)
2023-01-11T21:45:57.4200496Z   test_linear_1d_input (__main__.TestXNNPACKOps) ... ok (0.256s)
2023-01-11T21:45:57.4200972Z   test_decomposed_linear (__main__.TestXNNPACKRewritePass) ... ok (0.149s)
2023-01-11T21:45:57.4201442Z   test_linear (__main__.TestXNNPACKRewritePass) ... ok (0.995s)
2023-01-11T21:45:57.4202047Z   test_combined_model (__main__.TestXNNPACKSerDes) ... skip: Fails on some platforms, see https://github.com/pytorch/pytorch/issues/73488 (0.004s)
2023-01-11T21:45:57.4202597Z   test_conv2d (__main__.TestXNNPACKSerDes) ... ok (2.370s)
2023-01-11T21:45:57.4203176Z   test_conv2d_transpose (__main__.TestXNNPACKSerDes) ... ok (2.691s)
2023-01-11T21:45:57.4203786Z   test_linear (__main__.TestXNNPACKSerDes) ... skip: Fails on some platforms, see https://github.com/pytorch/pytorch/issues/73488 (0.001s)
2023-01-11T21:45:57.4204170Z 
2023-01-11T21:45:57.4204533Z ----------------------------------------------------------------------
2023-01-11T21:45:57.4204900Z Ran 12 tests in 9.563s
2023-01-11T21:45:57.4205077Z 
2023-01-11T21:45:57.4205184Z OK (skipped=5)
2023-01-11T21:45:57.4205347Z 
2023-01-11T21:45:57.4205478Z Generating XML reports...
2023-01-11T21:45:57.4206166Z Generated XML report: test-reports/python-unittest/test_xnnpack_integration/TEST-TestXNNPACKOps-20230111214547.xml
2023-01-11T21:45:57.4207069Z Generated XML report: test-reports/python-unittest/test_xnnpack_integration/TEST-TestXNNPACKRewritePass-20230111214547.xml
2023-01-11T21:45:57.4207980Z Generated XML report: test-reports/python-unittest/test_xnnpack_integration/TEST-TestXNNPACKSerDes-20230111214547.xml
2023-01-11T21:45:57.4208923Z Generated XML report: test-reports/python-unittest/test_xnnpack_integration/TEST-TestXNNPACKConv1dTransformPass-20230111214547.xml
2023-01-11T21:45:57.4209601Z 
2023-01-11T21:45:57.4210033Z ##[endgroup]
2023-01-11T21:45:57.4210732Z FINISHED PRINTING LOG FILE of test_xnnpack_integration (/var/lib/jenkins/workspace/test/test-reports/test_xnnpack_integration_nydseh_3)
2023-01-11T21:45:57.4211108Z 
2023-01-11T21:45:59.7113091Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:45:59.7986078Z Ignoring disabled issues:  []
2023-01-11T21:56:51.6099039Z 
2023-01-11T21:56:51.6101183Z Expand the folded group to see the log file of test_quantization
2023-01-11T21:56:51.6102263Z ##[group]PRINTING LOG FILE of test_quantization (/var/lib/jenkins/workspace/test/test-reports/test_quantization_i5v0je0n)
2023-01-11T21:56:51.6106431Z /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
2023-01-11T21:56:51.6108027Z   warnings.warn('Lazy modules are a new feature under heavy development '
2023-01-11T21:56:51.6108328Z 
2023-01-11T21:56:51.6108439Z Running tests...
2023-01-11T21:56:51.6109046Z ----------------------------------------------------------------------
2023-01-11T21:56:51.6109661Z Test results will be stored in test-reports/python-unittest/test_quantization
2023-01-11T21:56:51.6110317Z   test_modules_import_nn_intrinsic (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.009s)
2023-01-11T21:56:51.6111153Z   test_modules_import_nn_intrinsic_qat (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.001s)
2023-01-11T21:56:51.6111646Z   test_modules_import_nn_intrinsic_quantized (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.001s)
2023-01-11T21:56:51.6112307Z   test_modules_intrinsic_qat_conv_fused (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.001s)
2023-01-11T21:56:51.6112894Z   test_modules_intrinsic_qat_linear_fused (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:51.6113753Z   test_modules_intrinsic_qat_linear_relu (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:51.6114278Z   test_modules_intrinsic_quantized_bn_relu (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:51.6114900Z   test_modules_intrinsic_quantized_conv_relu (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:51.6115571Z   test_modules_intrinsic_quantized_linear_relu (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:51.6116161Z   test_modules_nn_intrinsic_fused (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.001s)
2023-01-11T21:56:51.6116850Z   test_package_import_nn_intrinsic (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic) ... ok (0.000s)
2023-01-11T21:56:51.6117339Z   test_package_import_nn_intrinsic_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic)
2023-01-11T21:56:51.6117732Z Tests the migration of the torch.nn.intrinsic.modules ... ok (0.001s)
2023-01-11T21:56:51.6118112Z   test_package_import_nn_intrinsic_qat (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic)
2023-01-11T21:56:51.6118469Z Tests the migration of the torch.nn.intrinsic.modules ... ok (0.001s)
2023-01-11T21:56:51.6118849Z   test_package_import_nn_intrinsic_quantized (quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic)
2023-01-11T21:56:51.6119230Z Tests the migration of the torch.nn.intrinsic.quantized ... ok (0.001s)
2023-01-11T21:56:51.6119593Z   test_functional_import (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:51.6119945Z Tests the migration of the torch.nn.quantized.functional ... ok (0.001s)
2023-01-11T21:56:51.6120322Z   test_import_nn_qat_conv (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:51.6120765Z   test_import_nn_qat_dynamic_linear (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:51.6121321Z   test_import_nn_qat_embedding_ops (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:51.6121763Z   test_import_nn_qat_linear (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:51.6122214Z   test_import_nn_quantizable_activation (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6122665Z   test_import_nn_quantizable_rnn (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6123108Z   test_import_nn_quantized_dynamic_import (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6123554Z   test_modules_activation (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6123986Z   test_modules_batchnorm (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6124407Z   test_modules_conv (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6124815Z   test_modules_dropout (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6125250Z   test_modules_embedding_ops (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6125695Z   test_modules_functional_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6126128Z   test_modules_import (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6126532Z   test_modules_linear (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6127007Z   test_modules_normalization (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6127432Z   test_modules_utils (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:51.6127853Z   test_package_import_nn_qat (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:51.6128273Z   test_package_import_nn_qat_dynamic (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:51.6128629Z Tests the migration of the torch.nn.qat.modules ... ok (0.001s)
2023-01-11T21:56:51.6128991Z   test_package_import_nn_qat_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:51.6129660Z Tests the migration of the torch.nn.qat.modules ... ok (0.001s)
2023-01-11T21:56:51.6130187Z   test_package_import_nn_quantizable (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:51.6130809Z   test_package_import_nn_quantizable_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:51.6131330Z Tests the migration of the torch.nn.quantizable.modules ... ok (0.001s)
2023-01-11T21:56:51.6131878Z   test_package_import_nn_quantized (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.001s)
2023-01-11T21:56:51.6132643Z   test_package_import_nn_quantized_dynamic (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized) ... ok (0.000s)
2023-01-11T21:56:51.6133416Z   test_package_import_nn_quantized_dynamic_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:51.6134039Z Tests the migration of the torch.nn.quantized.modules ... ok (0.001s)
2023-01-11T21:56:51.6134629Z   test_package_import_nn_quantized_modules (quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized)
2023-01-11T21:56:51.6135243Z Tests the migration of the torch.nn.quantized.modules ... ok (0.001s)
2023-01-11T21:56:51.6135876Z   test_function_import_fake_quantize (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6136670Z   test_function_import_fuse_modules (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6137496Z   test_function_import_fuser_method_mappings (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6138116Z   test_function_import_observer (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6138820Z   test_function_import_qconfig (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6139515Z   test_function_import_quant_type (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6140229Z   test_function_import_quantization_mappings (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6140943Z   test_function_import_quantize (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6141643Z   test_function_import_quantize_jit (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6142375Z   test_function_import_stubs (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6142954Z   test_function_import_utils (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.001s)
2023-01-11T21:56:51.6143613Z   test_package_import_fake_quantize (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6144323Z   test_package_import_fuse_modules (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6145166Z   test_package_import_fuser_method_mappings (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6145961Z   test_package_import_observer (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6146462Z   test_package_import_qconfig (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6147231Z   test_package_import_quant_type (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6148082Z   test_package_import_quantization_mappings (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6148769Z   test_package_import_quantize (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6149388Z   test_package_import_quantize_jit (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6149942Z   test_package_import_stubs (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6150446Z   test_package_import_utils (quantization.ao_migration.test_quantization.TestAOMigrationQuantization) ... ok (0.000s)
2023-01-11T21:56:51.6150949Z   test_function_import_fx (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6151576Z   test_function_import_fx_convert (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6152063Z   test_function_import_fx_equalize (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6152568Z   test_function_import_fx_fuse (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6153087Z   test_function_import_fx_fusion_patterns (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6153603Z   test_function_import_fx_graph_module (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6154102Z   test_function_import_fx_match_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6154621Z   test_function_import_fx_pattern_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6155133Z   test_function_import_fx_prepare (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6155672Z   test_function_import_fx_quantization_patterns (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6156154Z   test_function_import_fx_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6168232Z   test_function_import_quantize_fx (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.001s)
2023-01-11T21:56:51.6168685Z   test_package_import_fx (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6169347Z   test_package_import_fx_convert (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6169807Z   test_package_import_fx_equalize (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6170262Z   test_package_import_fx_fuse (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6170700Z   test_package_import_fx_fusion_patterns (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6171277Z   test_package_import_fx_graph_module (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6171727Z   test_package_import_fx_match_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6172177Z   test_package_import_fx_pattern_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6172616Z   test_package_import_fx_prepare (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6173130Z   test_package_import_fx_quantization_patterns (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6173581Z   test_package_import_fx_utils (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6174026Z   test_package_import_quantize_fx (quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx) ... ok (0.000s)
2023-01-11T21:56:51.6174430Z   test_backend_config_from_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.004s)
2023-01-11T21:56:51.6174841Z   test_backend_config_set_backend_pattern_config (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6175244Z   test_backend_config_set_name (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6175631Z   test_backend_config_to_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.002s)
2023-01-11T21:56:51.6176019Z   test_backend_op_config_add_dtype_config (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6176422Z   test_backend_op_config_from_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.002s)
2023-01-11T21:56:51.6176832Z   test_backend_op_config_set_extra_inputs_getter (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6177243Z   test_backend_op_config_set_fused_module (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6177632Z   test_backend_op_config_set_fuser_method (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6178051Z   test_backend_op_config_set_input_output_observed (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6178471Z   test_backend_op_config_set_input_type_to_index (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6178909Z   test_backend_op_config_set_num_tensor_args_to_observation_type (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6179330Z   test_backend_op_config_set_observation_type (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6179740Z   test_backend_op_config_set_qat_module (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6180160Z   test_backend_op_config_set_reference_quantized_module (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6180578Z   test_backend_op_config_set_root_module (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6180972Z   test_backend_op_config_set_root_node_getter (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6181371Z   test_backend_op_config_to_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.002s)
2023-01-11T21:56:51.6181807Z   test_dtype_config_from_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6182259Z   test_dtype_config_to_dict (quantization.core.test_backend_config.TestBackendConfig) ... ok (0.001s)
2023-01-11T21:56:51.6182727Z   test_conv_chain (quantization.eager.test_bias_correction_eager.TestBiasCorrectionEager) ... ok (1.533s)
2023-01-11T21:56:51.6183203Z   test_linear_chain (quantization.eager.test_bias_correction_eager.TestBiasCorrectionEager) ... ok (0.762s)
2023-01-11T21:56:51.6183607Z   test_compare_tensor_scalar (quantization.core.test_quantized_op.TestComparatorOps) ... ok (0.598s)
2023-01-11T21:56:51.6184069Z   test_compare_tensor_tensor (quantization.core.test_quantized_op.TestComparatorOps) ... ok (0.237s)
2023-01-11T21:56:51.6184480Z   test_erase_class_tensor_shapes (quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized) ... ok (0.017s)
2023-01-11T21:56:51.6184913Z   test_quantization_modules (quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized) ... ok (0.098s)
2023-01-11T21:56:51.6186658Z   test_rnn_cell_quantized (quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized) ... /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:510: UserWarning: quantize_rnn_cell_modules function has been deprecated. Please use torch.ao.quantization.quantize_dynamic API instead.
2023-01-11T21:56:51.6187235Z   warnings.warn("quantize_rnn_cell_modules function has been deprecated. "
2023-01-11T21:56:51.6187895Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:100: UserWarning: torch.jit.QuantizedRNNCellBase is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.RNNCell instead.
2023-01-11T21:56:51.6188333Z   warnings.warn(
2023-01-11T21:56:51.6188935Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:213: UserWarning: torch.jit.QuantizedLSTMCell is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.LSTMCell instead.
2023-01-11T21:56:51.6189360Z   warnings.warn(
2023-01-11T21:56:51.6189934Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:236: UserWarning: torch.jit.QuantizedGRUCell is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.GRUCell instead.
2023-01-11T21:56:51.6190356Z   warnings.warn(
2023-01-11T21:56:51.6190945Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:178: UserWarning: torch.jit.QuantizedRNNCell is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.RNNCell instead.
2023-01-11T21:56:51.6191357Z   warnings.warn(
2023-01-11T21:56:51.6191511Z ok (0.114s)
2023-01-11T21:56:51.6192186Z   test_rnn_quantized (quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized) ... /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:556: UserWarning: quantize_rnn_modules function has been deprecated. Please use torch.ao.quantization.quantize_dynamic API instead.
2023-01-11T21:56:51.6192739Z   warnings.warn("quantize_rnn_modules function has been deprecated. "
2023-01-11T21:56:51.6193366Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:264: UserWarning: torch.jit.QuantizedRNNBase is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic instead.
2023-01-11T21:56:51.6193758Z   warnings.warn(
2023-01-11T21:56:51.6194336Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:369: UserWarning: torch.jit.QuantizedLSTM is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.LSTM instead.
2023-01-11T21:56:51.6194738Z   warnings.warn(
2023-01-11T21:56:51.6195306Z /opt/conda/lib/python3.10/site-packages/torch/jit/quantized.py:449: UserWarning: torch.jit.QuantizedGRU is deprecated and will be removed in an upcoming PyTorch release. Please use the torch.ao.nn.quantized.dynamic.GRU instead.
2023-01-11T21:56:51.6195694Z   warnings.warn(
2023-01-11T21:56:51.6195863Z ok (0.099s)
2023-01-11T21:56:51.6196255Z   test_device_affinity (quantization.core.test_workflow_module.TestDistributed) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:56:51.6196693Z   test_fake_quant_preserves_buffers (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:51.6197034Z Tests that fake quant only modifies buffers in place. Note: this is important ... ok (0.003s)
2023-01-11T21:56:51.6197383Z   test_observers_preserve_buffers (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:51.6197728Z Tests that observers only modify buffers in place. Note: this is important ... ok (0.009s)
2023-01-11T21:56:51.6198070Z   test_qat_convbn_fused_syncbn_replacement (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:51.6198858Z Tests that SyncBatchNorm replacement works for fused ConvBN. ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
2023-01-11T21:56:51.6199303Z   warnings.warn(
2023-01-11T21:56:51.6199470Z ok (0.006s)
2023-01-11T21:56:51.6199718Z   test_qat_data_parallel (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:51.6200145Z Tests that doing QAT in nn.DataParallel does not crash. ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:56:51.6200508Z   test_syncbn_preserves_qconfig (quantization.core.test_workflow_module.TestDistributed)
2023-01-11T21:56:51.6200835Z Makes sure that if a BatchNorm is not fused and a qconfig exists, ... ok (0.001s)
2023-01-11T21:56:51.6201337Z   test_cell_api (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... [W qlinear_dynamic.cpp:247] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function operator())
2023-01-11T21:56:51.6202321Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:309: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6202830Z   device=storage.device,
2023-01-11T21:56:51.6203009Z ok (1.021s)
2023-01-11T21:56:51.6203718Z   test_dynamic_conv1d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:59: UserWarning: The current implementation of the DynamicQuantizedConv1d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:51.6204218Z   warnings.warn(
2023-01-11T21:56:51.6204559Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6205398Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:471: UserWarning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/quantized/cpu/qconv_dynamic.cpp:82.)
2023-01-11T21:56:51.6205882Z   return callable(*args, **kwargs)
2023-01-11T21:56:51.6206223Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6206701Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6207170Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6207636Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6208126Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6208598Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6209210Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6209683Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6210186Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6210654Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6211130Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6211595Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6212061Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6212514Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6212976Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6213289Z ok (1.539s)
2023-01-11T21:56:51.6214026Z   test_dynamic_conv2d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:124: UserWarning: The current implementation of the DynamicQuantizedConv2d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:51.6214519Z   warnings.warn(
2023-01-11T21:56:51.6214858Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6215337Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6215805Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6216272Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6216723Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6217292Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6217760Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6218227Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6218719Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6219178Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6219651Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6220142Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6220606Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6221058Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6221527Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6221991Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6222294Z ok (1.296s)
2023-01-11T21:56:51.6223086Z   test_dynamic_conv3d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:189: UserWarning: The current implementation of the DynamicQuantizedConv3d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:51.6223593Z   warnings.warn(
2023-01-11T21:56:51.6223763Z ok (0.432s)
2023-01-11T21:56:51.6224589Z   test_dynamic_convtranspose1d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:259: UserWarning: The current implementation of the DynamicQuantizedConvTranpose1d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:51.6225107Z   warnings.warn(
2023-01-11T21:56:51.6225446Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6225926Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6226395Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6226849Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6227313Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6227786Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6228257Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6228725Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6229215Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6229688Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6230150Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6230654Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6231110Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6231574Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6232042Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6232509Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6232813Z ok (1.217s)
2023-01-11T21:56:51.6233570Z   test_dynamic_convtranspose2d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:320: UserWarning: The current implementation of the DynamicQuantizedConvTranpose2d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:51.6234105Z   warnings.warn(
2023-01-11T21:56:51.6234441Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6234920Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6235376Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6235843Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6236317Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6236782Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6237244Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6237694Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6238162Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6238628Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6239123Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6239575Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6240034Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6240531Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6240996Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6241463Z [W qconv_dynamic.cpp:82] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function apply_dynamic)
2023-01-11T21:56:51.6241753Z ok (1.209s)
2023-01-11T21:56:51.6242518Z   test_dynamic_convtranspose3d (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py:381: UserWarning: The current implementation of the DynamicQuantizedConvTranpose3d module has poor numerical accuracy and its use is not recommended
2023-01-11T21:56:51.6243048Z   warnings.warn(
2023-01-11T21:56:51.6243216Z ok (0.504s)
2023-01-11T21:56:51.6243496Z   test_gru_api (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... ok (0.335s)
2023-01-11T21:56:51.6243901Z   test_linear_api (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... ok (23.224s)
2023-01-11T21:56:51.6244306Z   test_lstm_api (quantization.core.test_quantized_module.TestDynamicQuantizedModule) ... ok (3.824s)
2023-01-11T21:56:51.6244682Z   test_dynamic_conv1d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.045s)
2023-01-11T21:56:51.6245064Z   test_dynamic_conv2d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.043s)
2023-01-11T21:56:51.6245442Z   test_dynamic_conv3d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.051s)
2023-01-11T21:56:51.6245834Z   test_dynamic_convtranspose1d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.047s)
2023-01-11T21:56:51.6246233Z   test_dynamic_convtranspose2d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.049s)
2023-01-11T21:56:51.6246640Z   test_dynamic_convtranspose3d (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.015s)
2023-01-11T21:56:51.6247141Z   test_linear_prepack_fp16_numerics (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6247609Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6247945Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6248288Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6248625Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6248950Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6249431Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6249772Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6250174Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6250496Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6250836Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6251173Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6251508Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6251829Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6252174Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6252548Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6252871Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6253211Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6253547Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6253791Z ok (0.141s)
2023-01-11T21:56:51.6254050Z   test_qlinear (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (2.384s)
2023-01-11T21:56:51.6254440Z   test_qlinear_dynamic_fp16 (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.130s)
2023-01-11T21:56:51.6254830Z   test_qlinear_legacy (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (0.237s)
2023-01-11T21:56:51.6255193Z   test_qlstmGRU (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (9.472s)
2023-01-11T21:56:51.6255565Z   test_qrnncell (quantization.core.test_quantized_op.TestDynamicQuantizedOps) ... ok (11.554s)
2023-01-11T21:56:51.6255922Z   test_converged (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:51.6256231Z Sanity checks on _equalize.converged working ... ok (0.002s)
2023-01-11T21:56:51.6256539Z   test_cross_layer_equalization (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:51.6256886Z applies _equalize.cross_layer_equalization on two modules and checks ... ok (0.002s)
2023-01-11T21:56:51.6257215Z   test_equalize (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:51.6257523Z First checks to see if _equalize.equalize can handle multiple ... ok (0.014s)
2023-01-11T21:56:51.6257859Z   test_equalize_fused_convrelu (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:51.6258186Z Checks to see if eager mode equalization supports fused ... ok (0.028s)
2023-01-11T21:56:51.6258521Z   test_equalize_fused_linearrelu (quantization.eager.test_equalize_eager.TestEqualizeEager)
2023-01-11T21:56:51.6258835Z Checks to see if eager mode equalization supports fused ... ok (0.022s)
2023-01-11T21:56:51.6259163Z   test_input_weight_eq_observer (quantization.fx.test_equalize_fx.TestEqualizeFx) ... ok (0.233s)
2023-01-11T21:56:51.6259536Z   test_input_weight_equalization_activation_values (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6260313Z After applying the equalization functions check if the input ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:1435: UserWarning: Passing a QConfig dictionary to prepare is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.
2023-01-11T21:56:51.6260777Z   warnings.warn(
2023-01-11T21:56:51.6261380Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:1441: UserWarning: Passing a QConfig dictionary to prepare for equalization is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.
2023-01-11T21:56:51.6261793Z   warnings.warn(
2023-01-11T21:56:51.6261948Z ok (0.073s)
2023-01-11T21:56:51.6262252Z   test_input_weight_equalization_branching (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6262947Z Tests that graphs containing branches are prepared correctly. ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:712: UserWarning: Cannot equalize linear1 because it is part of a branch.
2023-01-11T21:56:51.6263322Z   warnings.warn(
2023-01-11T21:56:51.6263739Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:712: UserWarning: Cannot equalize linear2 because it is part of a branch.
2023-01-11T21:56:51.6264048Z   warnings.warn(
2023-01-11T21:56:51.6264216Z ok (0.022s)
2023-01-11T21:56:51.6264470Z   test_input_weight_equalization_convert (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6265316Z Tests that the modified model for equalization (before quantization) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node linear1_packed_weight_0 target linear1_packed_weight_0 linear1_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6265935Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6266616Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node linear2_packed_weight_0 target linear2_packed_weight_0 linear2_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6267167Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6267826Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node linear_packed_weight_0 target linear_packed_weight_0 linear_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6268378Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6269045Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node conv1_packed_weight_0 target conv1_packed_weight_0 conv1_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6269589Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6270257Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node conv2_packed_weight_0 target conv2_packed_weight_0 conv2_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6270784Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6271443Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node conv_packed_weight_0 target conv_packed_weight_0 conv_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6271986Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6272219Z ok (1.972s)
2023-01-11T21:56:51.6272493Z   test_input_weight_equalization_equalization_scales (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6272853Z After applying the equalization functions, check if the equalization ... ok (0.065s)
2023-01-11T21:56:51.6273194Z   test_input_weight_equalization_graphs (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6273833Z Tests that the modified model for equalization has the same graph ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:302: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:56:51.6274223Z   warnings.warn(
2023-01-11T21:56:51.6274750Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/_equalize.py:190: UserWarning: Must run observer before calling calculate_equalization_scale. Returning default equalization scale torch.tensor(1).
2023-01-11T21:56:51.6275156Z   warnings.warn(
2023-01-11T21:56:51.6275683Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/_equalize.py:102: UserWarning: Must call calculate_equalization_scale before calling calculate_scaled_minmax. Will not scale the next quantization observer.
2023-01-11T21:56:51.6276060Z   warnings.warn(
2023-01-11T21:56:51.6276510Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:56:51.6276832Z   warnings.warn(
2023-01-11T21:56:51.6276985Z ok (0.494s)
2023-01-11T21:56:51.6277284Z   test_input_weight_equalization_prepare (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6277617Z Tests that graphs created after prepare_fx is as expected ... ok (0.213s)
2023-01-11T21:56:51.6277938Z   test_input_weight_equalization_results (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6278267Z Tests that for small models, the results of quantized models that ... ok (0.237s)
2023-01-11T21:56:51.6278605Z   test_input_weight_equalization_weights_bias (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6278947Z After applying the equalization functions check if the weights and ... ok (0.069s)
2023-01-11T21:56:51.6279259Z   test_selective_equalization (quantization.fx.test_equalize_fx.TestEqualizeFx)
2023-01-11T21:56:51.6279576Z Tests that we are able to run numeric suite on the equalized model ... ok (0.234s)
2023-01-11T21:56:51.6279917Z   test_dict_return_type (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.079s)
2023-01-11T21:56:51.6280288Z   test_matching_failure_node_count (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.045s)
2023-01-11T21:56:51.6280682Z   test_matching_failure_node_type (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.035s)
2023-01-11T21:56:51.6281038Z   test_methods (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher)
2023-01-11T21:56:51.6281335Z Verify that graph matching works on methods ... ok (0.049s)
2023-01-11T21:56:51.6281642Z   test_nodes_before_cat (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.076s)
2023-01-11T21:56:51.6282424Z   test_nodes_with_equal_types_get_matched (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:1204: UserWarning: must run observer before calling calculate_qparams.                                    Returning default scale and zero point 
2023-01-11T21:56:51.6282884Z   warnings.warn(
2023-01-11T21:56:51.6283053Z ok (0.115s)
2023-01-11T21:56:51.6283313Z   test_op_relationship_mapping (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher)
2023-01-11T21:56:51.6283643Z Tests that the mapping of op relationships is complete. ... ok (0.007s)
2023-01-11T21:56:51.6283976Z   test_results_order (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.172s)
2023-01-11T21:56:51.6284781Z   test_simple_fun (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_0 target _packed_weight_0 _packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6285388Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6285622Z ok (0.079s)
2023-01-11T21:56:51.6285886Z   test_simple_fusion (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.078s)
2023-01-11T21:56:51.6286231Z   test_simple_mod (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.058s)
2023-01-11T21:56:51.6286592Z   test_simple_mod_multi (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.066s)
2023-01-11T21:56:51.6286985Z   test_simple_tensor_ops (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher) ... ok (0.175s)
2023-01-11T21:56:51.6287345Z   test_user_defined_function (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher)
2023-01-11T21:56:51.6287661Z Verify that graph matching works on user defined functions ... ok (0.043s)
2023-01-11T21:56:51.6288411Z   test_mobilenet_v2 (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcherModels) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
2023-01-11T21:56:51.6288877Z   warnings.warn(
2023-01-11T21:56:51.6289716Z /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
2023-01-11T21:56:51.6290131Z   warnings.warn(msg)
2023-01-11T21:56:51.6290306Z ok (3.117s)
2023-01-11T21:56:51.6290587Z   test_mobilenet_v2_qat (quantization.fx.test_numeric_suite_fx.TestFXGraphMatcherModels) ... ok (3.823s)
2023-01-11T21:56:51.6291007Z   test_add_loggers_cuda (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:56:51.6291956Z   test_add_mul_inputs_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T21:56:51.6292624Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T21:56:51.6292843Z ok (2.718s)
2023-01-11T21:56:51.6293158Z   test_add_shadow_loggers_cuda (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:56:51.6294048Z   test_add_shadow_loggers_fun_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_1 target _packed_weight_1 _packed_weight_1 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6294694Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6294929Z ok (0.791s)
2023-01-11T21:56:51.6295224Z   test_add_shadow_loggers_fun_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.327s)
2023-01-11T21:56:51.6295625Z   test_add_shadow_loggers_meth_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6296023Z Verify that add_loggers works on methods ... skipping shadow loggers for node_b: str, start_node_a: str, unknown dtype cast
2023-01-11T21:56:51.6296475Z skipping shadow loggers for node_b: torch.ao.quantization.observer.FixedQParamsObserver, start_node_a: str, unknown dtype cast
2023-01-11T21:56:51.6296876Z skipping shadow loggers for node_b: str, start_node_a: str, unknown dtype cast
2023-01-11T21:56:51.6297093Z ok (0.121s)
2023-01-11T21:56:51.6297390Z   test_add_shadow_loggers_mod_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.742s)
2023-01-11T21:56:51.6297806Z   test_add_shadow_loggers_mod_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.449s)
2023-01-11T21:56:51.6298222Z   test_extend_logger_results_with_comparison (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.073s)
2023-01-11T21:56:51.6299109Z   test_extract_weights_conv_fun_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_2 target _packed_weight_2 _packed_weight_2 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6299807Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6300449Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_3 target _packed_weight_3 _packed_weight_3 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6300977Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6301637Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_4 target _packed_weight_4 _packed_weight_4 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6302164Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6302877Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_5 target _packed_weight_5 _packed_weight_5 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6303414Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6303637Z ok (0.693s)
2023-01-11T21:56:51.6303936Z   test_extract_weights_conv_fun_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.806s)
2023-01-11T21:56:51.6304378Z   test_extract_weights_cuda (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:56:51.6304811Z   test_extract_weights_dynamic (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.133s)
2023-01-11T21:56:51.6305210Z   test_extract_weights_fqn (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.076s)
2023-01-11T21:56:51.6305627Z   test_extract_weights_linear_fun_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.379s)
2023-01-11T21:56:51.6306047Z   test_extract_weights_linear_fun_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.389s)
2023-01-11T21:56:51.6306466Z   test_extract_weights_mod_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.805s)
2023-01-11T21:56:51.6306861Z   test_extract_weights_mod_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.959s)
2023-01-11T21:56:51.6307264Z   test_fp16_shadows_fp32 (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.053s)
2023-01-11T21:56:51.6307884Z   test_int8_shadows_fp32_coverage (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skipping shadow loggers for node_b: torch.nn.modules.pooling.AdaptiveAvgPool2d, start_node_a: torch.nn.modules.pooling.AdaptiveAvgPool2d, unknown dtype cast
2023-01-11T21:56:51.6308597Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch.nn.modules.pooling.AdaptiveAvgPool2d, unknown dtype cast
2023-01-11T21:56:51.6309125Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.mul, start_node_a: torch._ops.quantized.PyCapsule.mul, unsupported
2023-01-11T21:56:51.6309637Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._ops.quantized.PyCapsule.mul, unknown dtype cast
2023-01-11T21:56:51.6310148Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._ops.quantized.PyCapsule.add_relu, unsupported
2023-01-11T21:56:51.6310665Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._ops.quantized.PyCapsule.add_relu, unknown dtype cast
2023-01-11T21:56:51.6311005Z ok (0.156s)
2023-01-11T21:56:51.6311299Z   test_int8_shadows_fp32_simple (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.191s)
2023-01-11T21:56:51.6311749Z   test_int8_shadows_int8_fun (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.104s)
2023-01-11T21:56:51.6312150Z   test_int8_shadows_int8_mod (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.081s)
2023-01-11T21:56:51.6312529Z   test_layer_names (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.332s)
2023-01-11T21:56:51.6312930Z   test_linear_fp16_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.301s)
2023-01-11T21:56:51.6313347Z   test_linear_fp16_shadow_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.393s)
2023-01-11T21:56:51.6313846Z   test_linear_fp16_vs_linear_fp16_shadow_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.051s)
2023-01-11T21:56:51.6314260Z   test_linear_fp16_weights (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.160s)
2023-01-11T21:56:51.6314665Z   test_linear_kwargs_shadow (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.045s)
2023-01-11T21:56:51.6315080Z   test_loggers_preserve_qat_numerics (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.184s)
2023-01-11T21:56:51.6315477Z   test_logging_inputs (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6315939Z Verifies that logging inputs works correctly ... skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6316457Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6316764Z ok (0.589s)
2023-01-11T21:56:51.6317044Z   test_match_activations_fqn (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.079s)
2023-01-11T21:56:51.6317459Z   test_match_activations_fun_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.611s)
2023-01-11T21:56:51.6317868Z   test_match_activations_fun_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.384s)
2023-01-11T21:56:51.6318275Z   test_match_activations_meth_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6318587Z Verify that add_loggers works on methods ... ok (0.144s)
2023-01-11T21:56:51.6318923Z   test_match_activations_mod_ptq (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (1.015s)
2023-01-11T21:56:51.6319334Z   test_match_activations_mod_qat (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.278s)
2023-01-11T21:56:51.6319843Z   test_mul_add_cat_stack_skips_shadowing (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skipping shadow loggers for node_b: _operator.mul, start_node_a: _operator.mul, unsupported
2023-01-11T21:56:51.6320374Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: _operator.mul, unsupported
2023-01-11T21:56:51.6320855Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.mul, start_node_a: torch._VariableFunctionsClass.mul, unsupported
2023-01-11T21:56:51.6321362Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch._VariableFunctionsClass.mul, unsupported
2023-01-11T21:56:51.6321804Z skipping shadow loggers for node_b: _operator.add, start_node_a: _operator.add, unsupported
2023-01-11T21:56:51.6322210Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: _operator.add, unsupported
2023-01-11T21:56:51.6322689Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6323191Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6323732Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6324193Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.stack, start_node_a: torch._VariableFunctionsClass.stack, unsupported
2023-01-11T21:56:51.6324638Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.mul, start_node_a: _operator.mul, unsupported
2023-01-11T21:56:51.6325079Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.mul, start_node_a: torch._VariableFunctionsClass.mul, unsupported
2023-01-11T21:56:51.6325516Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: _operator.add, unsupported
2023-01-11T21:56:51.6325969Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6326435Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6326904Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.stack, start_node_a: torch._VariableFunctionsClass.stack, unsupported
2023-01-11T21:56:51.6327215Z ok (1.165s)
2023-01-11T21:56:51.6327487Z   test_op_io_dtype_coverage (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6327840Z Tests that all the ops quantization cares about have input and output ... ok (0.009s)
2023-01-11T21:56:51.6328208Z   test_op_with_either_fp32_or_int8_input (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6328713Z Verify that shadowing works with ops which accept either fp32 or ... skipping shadow loggers for node_b: torch.nn.modules.activation.ReLU, start_node_a: torch.nn.modules.activation.ReLU, unknown dtype cast
2023-01-11T21:56:51.6329440Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.modules.activation.ReLU, unknown dtype cast
2023-01-11T21:56:51.6329941Z skipping shadow loggers for node_b: torch.nn.functional.relu, start_node_a: torch.nn.functional.relu, unknown dtype cast
2023-01-11T21:56:51.6330422Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.functional.relu, unknown dtype cast
2023-01-11T21:56:51.6330931Z skipping shadow loggers for node_b: torch.nn.modules.activation.ReLU, start_node_a: torch.nn.modules.activation.ReLU, unknown dtype cast
2023-01-11T21:56:51.6331374Z skipping shadow loggers for node_b: torch.nn.functional.relu, start_node_a: torch.nn.functional.relu, unknown dtype cast
2023-01-11T21:56:51.6331662Z ok (0.499s)
2023-01-11T21:56:51.6332120Z   test_op_with_only_kwargs_skips_shadowing (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6332723Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.stack, start_node_a: torch._VariableFunctionsClass.stack, unsupported
2023-01-11T21:56:51.6333181Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6333649Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.stack, start_node_a: torch._VariableFunctionsClass.stack, unsupported
2023-01-11T21:56:51.6333957Z ok (0.255s)
2023-01-11T21:56:51.6334240Z   test_ops_with_same_fp32_and_int8_signature (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6334601Z Verifies that we can match pairs of ops which have the same aten ... ok (2.805s)
2023-01-11T21:56:51.6334966Z   test_shadow_activations_fqn (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.176s)
2023-01-11T21:56:51.6335445Z   test_shadow_loggers_preserve_qat_numerics (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.091s)
2023-01-11T21:56:51.6335867Z   test_unsupported_op_copy_skips_shadowing (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6336383Z Copying a `call_function` node is not implemented, test that this ... skipping shadow loggers for node_b: torch.nn.functional.layer_norm, start_node_a: torch.nn.functional.layer_norm, unhandled logic in subgraph copy
2023-01-11T21:56:51.6336967Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.functional.layer_norm, unhandled logic in subgraph copy
2023-01-11T21:56:51.6337542Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.layer_norm, start_node_a: torch.nn.functional.layer_norm, unhandled logic in subgraph copy
2023-01-11T21:56:51.6337861Z ok (0.406s)
2023-01-11T21:56:51.6338141Z   test_user_defined_function (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6338619Z Verify that NS APIs work on user defined functions ... skipping shadow loggers for node_b: torch._C._nn.linear, start_node_a: quantization.fx.test_numeric_suite_fx._wrapped_linear, unknown dtype cast
2023-01-11T21:56:51.6339182Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: quantization.fx.test_numeric_suite_fx._wrapped_linear, unknown dtype cast
2023-01-11T21:56:51.6339531Z ok (0.296s)
2023-01-11T21:56:51.6339801Z   test_user_module (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs)
2023-01-11T21:56:51.6340558Z For user defined modules, ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py:148: UserWarning: Passing a prepare_custom_config_dict to prepare is deprecated and will not be supported in a future version. Please pass in a PrepareCustomConfig instead.
2023-01-11T21:56:51.6340990Z   warnings.warn(
2023-01-11T21:56:51.6341147Z ok (0.171s)
2023-01-11T21:56:51.6341446Z   test_user_module_scriptable (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs) ... ok (0.194s)
2023-01-11T21:56:51.6341878Z   test_compare_activations_conv (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (1.102s)
2023-01-11T21:56:51.6342313Z   test_compare_activations_linear (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.485s)
2023-01-11T21:56:51.6343372Z   test_compare_activations_lstm_dynamic (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py:320: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:56:51.6344018Z   torch.tensor(weight_qparams["scale"], dtype=torch.float, device=device))
2023-01-11T21:56:51.6344730Z /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py:323: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:56:51.6345246Z   torch.tensor(weight_qparams["zero_point"], dtype=torch.int, device=device))
2023-01-11T21:56:51.6345469Z ok (0.223s)
2023-01-11T21:56:51.6345784Z   test_compare_shadow_activations_conv (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (1.160s)
2023-01-11T21:56:51.6346248Z   test_compare_shadow_activations_linear (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.594s)
2023-01-11T21:56:51.6346717Z   test_compare_shadow_activations_lstm_dynamic (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.130s)
2023-01-11T21:56:51.6347157Z   test_compare_weights_conv (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.661s)
2023-01-11T21:56:51.6347627Z   test_compare_weights_linear (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.399s)
2023-01-11T21:56:51.6348069Z   test_compare_weights_lstm_dynamic (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (0.219s)
2023-01-11T21:56:51.6348658Z   test_mobilenet_v2 (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6349267Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6349795Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6350298Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6350797Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6351282Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6351780Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6352273Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6352770Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6353255Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6353748Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6354250Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6354745Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6355239Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6355717Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6356213Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6356706Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6357203Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6357689Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6358180Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6358708Z skipping shadow loggers for node_b: torch.nn.functional.adaptive_avg_pool2d, start_node_a: torch.nn.functional.adaptive_avg_pool2d, unhandled logic in subgraph copy
2023-01-11T21:56:51.6359259Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch.nn.functional.adaptive_avg_pool2d, unhandled logic in subgraph copy
2023-01-11T21:56:51.6359802Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6360270Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6360737Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6361201Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6361650Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6362130Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6362591Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6363053Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6363503Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6363964Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6364457Z skipping shadow loggers for node_b: torch.nn.functional.adaptive_avg_pool2d, start_node_a: torch.nn.functional.adaptive_avg_pool2d, unhandled logic in subgraph copy
2023-01-11T21:56:51.6364793Z ok (10.020s)
2023-01-11T21:56:51.6365228Z   test_resnet18 (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6365844Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6366344Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6366844Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6367326Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6367827Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6368327Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6368827Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6369470Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6369964Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6370459Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6370956Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6371433Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6371987Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6372482Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.add, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6372976Z skipping shadow loggers for node_b: torch.ao.quantization.observer.MinMaxObserver, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6373464Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6373973Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6374443Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6374915Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6375373Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6375846Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6376312Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6376776Z skipping shadow loggers for node_b: torch._ops.quantized.PyCapsule.add_relu, start_node_a: torch._VariableFunctionsClass.add, unsupported
2023-01-11T21:56:51.6377310Z skipping shadow loggers for node_b: torch.nn.modules.pooling.AdaptiveAvgPool2d, start_node_a: torch.nn.modules.pooling.AdaptiveAvgPool2d, unknown dtype cast
2023-01-11T21:56:51.6377848Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.flatten, start_node_a: torch._VariableFunctionsClass.flatten, unknown dtype cast
2023-01-11T21:56:51.6378175Z ok (4.234s)
2023-01-11T21:56:51.6378496Z   test_sparsenn_compare_activations (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... ok (6.290s)
2023-01-11T21:56:51.6379109Z   test_sparsenn_shadow (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels) ... skipping shadow loggers for node_b: torch.nn.modules.sparse.EmbeddingBag, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:51.6379794Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:51.6380324Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6380821Z skipping shadow loggers for node_b: torch.nn.modules.sparse.EmbeddingBag, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:51.6381314Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6381797Z skipping shadow loggers for node_b: torch.nn.modules.sparse.EmbeddingBag, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:51.6382354Z skipping shadow loggers for node_b: torch.ao.quantization.observer.HistogramObserver, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:51.6382963Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6383460Z skipping shadow loggers for node_b: torch.nn.modules.sparse.EmbeddingBag, start_node_a: torch.nn.modules.sparse.EmbeddingBag, unknown dtype cast
2023-01-11T21:56:51.6383985Z skipping shadow loggers for node_b: torch._VariableFunctionsClass.cat, start_node_a: torch._VariableFunctionsClass.cat, unsupported
2023-01-11T21:56:51.6384291Z ok (7.001s)
2023-01-11T21:56:51.6384707Z   test_conv_bn_relu_mod (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6385095Z ok (0.068s)
2023-01-11T21:56:51.6385377Z   test_custom_functions_and_tracer (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... working
2023-01-11T21:56:51.6385653Z working
2023-01-11T21:56:51.6385813Z GraphModule(
2023-01-11T21:56:51.6386016Z   (fc1): Linear(in_features=2, out_features=2, bias=True)
2023-01-11T21:56:51.6386292Z   (fc2): Linear(in_features=2, out_features=2, bias=True)
2023-01-11T21:56:51.6386565Z   (shadow_0_0): OutputLogger(ref_name=model, model_name=subgraph_0_0,
2023-01-11T21:56:51.6386808Z   prev_node_name=fc1, ref_node_name=fc1,
2023-01-11T21:56:51.6387063Z   ref_node_target_type=torch.nn.modules.linear.Linear
2023-01-11T21:56:51.6387324Z   results_type=node_output, index_within_arg=0,
2023-01-11T21:56:51.6387522Z   index_of_arg=0, fqn=fc1)
2023-01-11T21:56:51.6387723Z   (shadow_wrapper_0_1): GraphModule(
2023-01-11T21:56:51.6388041Z     (mod_0): QuantizedLinear(in_features=2, out_features=2, scale=0.019699348136782646, zero_point=58, qscheme=torch.per_tensor_affine)
2023-01-11T21:56:51.6388348Z     (shadow_0_1): OutputComparisonLogger
2023-01-11T21:56:51.6388524Z   )
2023-01-11T21:56:51.6388745Z   (shadow_1_0): OutputLogger(ref_name=model, model_name=subgraph_1_0,
2023-01-11T21:56:51.6388997Z   prev_node_name=fc2, ref_node_name=fc2,
2023-01-11T21:56:51.6389236Z   ref_node_target_type=torch.nn.modules.linear.Linear
2023-01-11T21:56:51.6389499Z   results_type=node_output, index_within_arg=0,
2023-01-11T21:56:51.6389710Z   index_of_arg=0, fqn=fc2)
2023-01-11T21:56:51.6389902Z   (shadow_wrapper_1_1): GraphModule(
2023-01-11T21:56:51.6390216Z     (mod_0): QuantizedLinear(in_features=2, out_features=2, scale=0.01274071168154478, zero_point=96, qscheme=torch.per_tensor_affine)
2023-01-11T21:56:51.6390519Z     (shadow_1_1): OutputComparisonLogger
2023-01-11T21:56:51.6390696Z   )
2023-01-11T21:56:51.6390840Z )
2023-01-11T21:56:51.6390930Z 
2023-01-11T21:56:51.6390936Z 
2023-01-11T21:56:51.6390940Z 
2023-01-11T21:56:51.6391016Z def forward(self, x):
2023-01-11T21:56:51.6391195Z     fc1 = self.fc1(x)
2023-01-11T21:56:51.6391412Z     shadow_wrapper_0_1 = self.shadow_wrapper_0_1(fc1, x);  x = None
2023-01-11T21:56:51.6391649Z     shadow_0_0 = self.shadow_0_0(fc1)
2023-01-11T21:56:51.6391841Z     fc2 = self.fc2(fc1)
2023-01-11T21:56:51.6392059Z     shadow_wrapper_1_1 = self.shadow_wrapper_1_1(fc2, fc1);  fc1 = None
2023-01-11T21:56:51.6392297Z     shadow_1_0 = self.shadow_1_0(fc2)
2023-01-11T21:56:51.6392484Z     return fc2
2023-01-11T21:56:51.6392628Z     
2023-01-11T21:56:51.6392849Z # To see more debug info, please use `graph_module.print_readable()`
2023-01-11T21:56:51.6393217Z `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6393505Z ok (0.065s)
2023-01-11T21:56:51.6394672Z   test_functions (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}).
2023-01-11T21:56:51.6395619Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:51.6396018Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:51.6396295Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:51.6396617Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:51.6397028Z `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6397320Z ok (0.234s)
2023-01-11T21:56:51.6397731Z   test_linear_mod (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6398106Z ok (0.030s)
2023-01-11T21:56:51.6398557Z   test_linear_relu_mod (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6398949Z ok (0.094s)
2023-01-11T21:56:51.6399262Z   test_logger_enabled_and_save_activations_flags (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.043s)
2023-01-11T21:56:51.6399788Z   test_mobilenet_v2 (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6400176Z ok (3.743s)
2023-01-11T21:56:51.6400574Z   test_partial_qconfig_mapping (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... unable to find at least one qconfig for node %fc : [#users=1] = call_module[target=fc](args = (%x,), kwargs = {}), skipping
2023-01-11T21:56:51.6401091Z unable to find at least one qconfig for node %add : [#users=1] = call_function[target=operator.add](args = (%relu, %relu), kwargs = {}), skipping
2023-01-11T21:56:51.6401510Z `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6401797Z ok (0.174s)
2023-01-11T21:56:51.6402114Z   test_qconfig_multi_mapping_deduplication (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.001s)
2023-01-11T21:56:51.6402676Z   test_qconfig_multi_mapping_end_to_end (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6403070Z ok (0.071s)
2023-01-11T21:56:51.6403497Z   test_qconfig_multi_mapping_from_list (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6403902Z ok (0.070s)
2023-01-11T21:56:51.6404210Z   test_qconfig_multi_mapping_insert_padding (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.001s)
2023-01-11T21:56:51.6404760Z   test_qconfig_multi_mapping_ordering (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... `print_tabular` relies on the library `tabulate`, which could not be found on this machine. Run `pip install tabulate` to install the library.
2023-01-11T21:56:51.6405164Z ok (0.092s)
2023-01-11T21:56:51.6405459Z   test_qconfig_multi_mapping_repr (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.001s)
2023-01-11T21:56:51.6405900Z   test_qconfig_multi_mapping_retroactive_padding (quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows) ... ok (0.001s)
2023-01-11T21:56:51.6406309Z   test_fq_module_per_channel (quantization.core.test_workflow_module.TestFakeQuantize) ... ok (0.192s)
2023-01-11T21:56:51.6406703Z   test_fq_serializable_per_channel (quantization.core.test_workflow_module.TestFakeQuantize) ... ok (0.003s)
2023-01-11T21:56:51.6407129Z   test_quant_min_max_override (quantization.core.test_workflow_module.TestFakeQuantize) ... ok (0.001s)
2023-01-11T21:56:51.6407489Z   test_backward_per_channel (quantization.core.test_workflow_ops.TestFakeQuantizeOps)
2023-01-11T21:56:51.6407892Z Tests the backward method. ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.100s)
2023-01-11T21:56:51.6408314Z   test_backward_per_channel_cachemask_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.018s)
2023-01-11T21:56:51.6408763Z   test_backward_per_channel_cachemask_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.000s)
2023-01-11T21:56:51.6409297Z   test_backward_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps)
2023-01-11T21:56:51.6409682Z Tests the backward method. ... skip: temporarily disable the test (0.004s)
2023-01-11T21:56:51.6410045Z   test_backward_per_tensor_cachemask_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.013s)
2023-01-11T21:56:51.6410493Z   test_backward_per_tensor_cachemask_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.000s)
2023-01-11T21:56:51.6410900Z   test_fake_quant_control (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.009s)
2023-01-11T21:56:51.6411302Z   test_fake_quant_per_channel_qparam_range (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.375s)
2023-01-11T21:56:51.6411736Z   test_fake_quant_preserves_qparam_shapes_for_activations (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.005s)
2023-01-11T21:56:51.6412154Z   test_fixed_qparams_fq_module (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.109s)
2023-01-11T21:56:51.6412882Z   test_forward_backward_per_tensor_with_amp (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... /opt/conda/lib/python3.10/site-packages/torch/amp/autocast_mode.py:204: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
2023-01-11T21:56:51.6413482Z   warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
2023-01-11T21:56:51.6413742Z ok (0.007s)
2023-01-11T21:56:51.6414012Z   test_forward_per_channel (quantization.core.test_workflow_ops.TestFakeQuantizeOps)
2023-01-11T21:56:51.6414352Z Tests the forward path of the FakeQuantizePerTensorAffine op. ... ok (0.133s)
2023-01-11T21:56:51.6414731Z   test_forward_per_channel_cachemask_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.017s)
2023-01-11T21:56:51.6415175Z   test_forward_per_channel_cachemask_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.000s)
2023-01-11T21:56:51.6415630Z   test_forward_per_channel_half_precision_numerics (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.016s)
2023-01-11T21:56:51.6416009Z   test_forward_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps)
2023-01-11T21:56:51.6416361Z Tests the forward path of the FakeQuantizePerTensorAffine op. ... ok (0.101s)
2023-01-11T21:56:51.6416738Z   test_forward_per_tensor_cachemask_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.014s)
2023-01-11T21:56:51.6417164Z   test_forward_per_tensor_cachemask_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.000s)
2023-01-11T21:56:51.6417607Z   test_forward_per_tensor_half_precision_numerics (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.006s)
2023-01-11T21:56:51.6418014Z   test_fq_module_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.152s)
2023-01-11T21:56:51.6418408Z   test_fq_serializable_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.008s)
2023-01-11T21:56:51.6418897Z   test_learnable_backward_per_channel_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.004s)
2023-01-11T21:56:51.6419480Z   test_learnable_backward_per_channel_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.003s)
2023-01-11T21:56:51.6419925Z   test_learnable_backward_per_tensor_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.357s)
2023-01-11T21:56:51.6420373Z   test_learnable_backward_per_tensor_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.003s)
2023-01-11T21:56:51.6420793Z   test_learnable_forward_per_channel_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.157s)
2023-01-11T21:56:51.6421262Z   test_learnable_forward_per_channel_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.003s)
2023-01-11T21:56:51.6421803Z   test_learnable_forward_per_tensor_cpu (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.003s)
2023-01-11T21:56:51.6422337Z   test_learnable_forward_per_tensor_cuda (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... skip: No gpu is not available. (0.003s)
2023-01-11T21:56:51.6422822Z   test_numerical_consistency_per_channel (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.094s)
2023-01-11T21:56:51.6423223Z   test_numerical_consistency_per_tensor (quantization.core.test_workflow_ops.TestFakeQuantizeOps) ... ok (0.040s)
2023-01-11T21:56:51.6423604Z   test_forward_hooks_preserved (quantization.eager.test_fuse_eager.TestFuseEager)
2023-01-11T21:56:51.6423946Z Test case that checks whether forward pre hooks of the first module and ... ok (0.019s)
2023-01-11T21:56:51.6424291Z   test_fuse_function_customization (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.002s)
2023-01-11T21:56:51.6424657Z   test_fuse_module_eval (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.083s)
2023-01-11T21:56:51.6425016Z   test_fuse_module_train (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.563s)
2023-01-11T21:56:51.6425374Z   test_fusion_conv_with_bias (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.067s)
2023-01-11T21:56:51.6425732Z   test_fusion_convtranspose_bn_eval (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.006s)
2023-01-11T21:56:51.6426098Z   test_fusion_linear_bn_eval (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.003s)
2023-01-11T21:56:51.6426470Z   test_fusion_sequential_model_eval (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (1.624s)
2023-01-11T21:56:51.6426848Z   test_fusion_sequential_model_train (quantization.eager.test_fuse_eager.TestFuseEager) ... ok (0.365s)
2023-01-11T21:56:51.6427305Z   test_fuse_addtional_fuser_method (quantization.fx.test_quantize_fx.TestFuseFx) ... skip: Temporarily skipping the test case, will enable after the simplepattern format is supported (0.002s)
2023-01-11T21:56:51.6427745Z   test_fuse_conv_bn_relu (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.054s)
2023-01-11T21:56:51.6428086Z   test_fuse_convtranspose_bn_eval (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.016s)
2023-01-11T21:56:51.6428428Z   test_fuse_custom_pattern (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.017s)
2023-01-11T21:56:51.6428749Z   test_fuse_linear_bn_eval (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.012s)
2023-01-11T21:56:51.6429092Z   test_fuse_linear_bn_leaky_relu_onednn (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.020s)
2023-01-11T21:56:51.6429455Z   test_fuse_linear_tanh_for_onednn_backend (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.010s)
2023-01-11T21:56:51.6429786Z   test_fuse_module_relu (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.015s)
2023-01-11T21:56:51.6430119Z   test_fusion_pattern_with_matchallnode (quantization.fx.test_quantize_fx.TestFuseFx)
2023-01-11T21:56:51.6430463Z This test tests that the node matched by MatchAllNode will be regared as an input ... ok (0.005s)
2023-01-11T21:56:51.6430843Z   test_fusion_pattern_with_multiple_inputs (quantization.fx.test_quantize_fx.TestFuseFx)
2023-01-11T21:56:51.6431153Z This test tests two keys in backend_config: root_node_getter and ... ok (0.005s)
2023-01-11T21:56:51.6431498Z   test_linear_bn_leaky_relu_not_fused_by_default (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.010s)
2023-01-11T21:56:51.6431864Z   test_linear_tanh_not_fused_by_default (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.004s)
2023-01-11T21:56:51.6432204Z   test_problematic_fuse_example (quantization.fx.test_quantize_fx.TestFuseFx) ... ok (0.013s)
2023-01-11T21:56:51.6432532Z   test_qconfig_fused_module (quantization.fx.test_quantize_fx.TestFuseFx)
2023-01-11T21:56:51.6432834Z TODO: add test for all fused modules ... ok (0.036s)
2023-01-11T21:56:51.6433175Z   test_fused_backward_op_fake_quant_off (quantization.core.test_workflow_ops.TestFusedObsFakeQuant) ... ok (0.005s)
2023-01-11T21:56:51.6433578Z   test_fused_obs_fake_quant_backward_op (quantization.core.test_workflow_ops.TestFusedObsFakeQuant) ... ok (0.005s)
2023-01-11T21:56:51.6433977Z   test_fused_obs_fake_quant_moving_avg (quantization.core.test_workflow_ops.TestFusedObsFakeQuant)
2023-01-11T21:56:51.6434331Z Tests the case where we call the fused_obs_fake_quant op multiple times ... ok (0.011s)
2023-01-11T21:56:51.6434697Z   test_fused_obs_fake_quant_moving_avg_per_channel (quantization.core.test_workflow_ops.TestFusedObsFakeQuant)
2023-01-11T21:56:51.6435048Z Tests the case where we call the fused_obs_fake_quant op multiple times ... ok (0.038s)
2023-01-11T21:56:51.6435436Z   test_compare_fused_obs_fq_oss_module (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.009s)
2023-01-11T21:56:51.6435885Z   test_default_fused_qat_config (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.015s)
2023-01-11T21:56:51.6436306Z   test_embedding_bag_qat_config (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.018s)
2023-01-11T21:56:51.6436743Z   test_embedding_qat_config (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.048s)
2023-01-11T21:56:51.6437171Z   test_fused_mod_per_channel (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.025s)
2023-01-11T21:56:51.6437597Z   test_fused_mod_reduce_range (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.001s)
2023-01-11T21:56:51.6438011Z   test_fused_obs_fq_module (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.005s)
2023-01-11T21:56:51.6438440Z   test_fused_obs_fq_moving_avg_module (quantization.core.test_workflow_module.TestFusedObsFakeQuantModule) ... ok (0.007s)
2023-01-11T21:56:51.6438853Z   test_quantized_add_relu_fusion (quantization.jit.test_fusion_passes.TestFusionPasses) ... ok (0.019s)
2023-01-11T21:56:51.6439299Z   test_input_weight_equalization_determine_points (quantization.fx.test_model_report_fx.TestFxDetectInputWeightEqualization) ... ok (0.054s)
2023-01-11T21:56:51.6439782Z   test_input_weight_equalization_report_gen (quantization.fx.test_model_report_fx.TestFxDetectInputWeightEqualization) ... ok (0.048s)
2023-01-11T21:56:51.6440266Z   test_input_weight_equalization_report_gen_empty (quantization.fx.test_model_report_fx.TestFxDetectInputWeightEqualization) ... ok (0.012s)
2023-01-11T21:56:51.6440706Z   test_all_outlier_report_gen (quantization.fx.test_model_report_fx.TestFxDetectOutliers) ... ok (0.022s)
2023-01-11T21:56:51.6441122Z   test_multiple_run_consistent_spike_outlier_report_gen (quantization.fx.test_model_report_fx.TestFxDetectOutliers) ... ok (0.105s)
2023-01-11T21:56:51.6441516Z   test_no_outlier_report_gen (quantization.fx.test_model_report_fx.TestFxDetectOutliers) ... ok (0.025s)
2023-01-11T21:56:51.6441913Z   test_outlier_detection_determine_points (quantization.fx.test_model_report_fx.TestFxDetectOutliers) ... ok (0.160s)
2023-01-11T21:56:51.6442331Z   test_constructor (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:51.6442649Z Tests the constructor of the ModelReport class. ... ok (0.015s)
2023-01-11T21:56:51.6442980Z   test_equalization_mapping_generation (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:51.6443322Z Tests for generation of qconfigs by ModelReport API ... ok (0.053s)
2023-01-11T21:56:51.6443647Z   test_generate_report (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:51.6444273Z Tests model_report.generate_model_report to ensure report generation ... /var/lib/jenkins/workspace/test/quantization/fx/test_model_report_fx.py:1061: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:56:51.6444840Z   example_input = torch.tensor(torch.randint(100, (1, 3, 3, 3)), dtype=torch.float)
2023-01-11T21:56:51.6445077Z ok (0.147s)
2023-01-11T21:56:51.6445347Z   test_generate_visualizer (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:51.6445713Z Tests that the ModelReport class can properly create the ModelReportVisualizer instance ... ok (0.033s)
2023-01-11T21:56:51.6446099Z   test_prepare_model_callibration (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:51.6446483Z Tests model_report.prepare_detailed_calibration that prepares the model for callibration ... ok (0.025s)
2023-01-11T21:56:51.6446865Z   test_qconfig_mapping_generation (quantization.fx.test_model_report_fx.TestFxModelReportClass)
2023-01-11T21:56:51.6447185Z Tests for generation of qconfigs by ModelReport API ... ok (0.058s)
2023-01-11T21:56:51.6447567Z   test_nested_detection_case (quantization.fx.test_model_report_fx.TestFxModelReportDetectDynamicStatic) ... ok (0.048s)
2023-01-11T21:56:51.6448005Z   test_conv_sub_class_considered (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.023s)
2023-01-11T21:56:51.6448430Z   test_fusion_layer_in_sequential (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.019s)
2023-01-11T21:56:51.6448842Z   test_multi_linear_model_without_per_channel (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.016s)
2023-01-11T21:56:51.6449372Z   test_multiple_q_config_options (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.019s)
2023-01-11T21:56:51.6449788Z   test_qat_aware_model_example (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.011s)
2023-01-11T21:56:51.6450188Z   test_sequential_model_format (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.021s)
2023-01-11T21:56:51.6450582Z   test_simple_conv (quantization.fx.test_model_report_fx.TestFxModelReportDetector) ... ok (0.012s)
2023-01-11T21:56:51.6450976Z   test_observer_after_relu (quantization.fx.test_model_report_fx.TestFxModelReportObserver) ... ok (0.019s)
2023-01-11T21:56:51.6451388Z   test_random_epochs_and_batches (quantization.fx.test_model_report_fx.TestFxModelReportObserver) ... ok (0.114s)
2023-01-11T21:56:51.6451781Z   test_single_batch_of_ones (quantization.fx.test_model_report_fx.TestFxModelReportObserver) ... ok (0.003s)
2023-01-11T21:56:51.6452179Z   test_zero_tensor_errors (quantization.fx.test_model_report_fx.TestFxModelReportObserver) ... ok (0.013s)
2023-01-11T21:56:51.6452586Z   test_generate_tables_match_with_report (quantization.fx.test_model_report_fx.TestFxModelReportVisualizer)
2023-01-11T21:56:51.6452911Z Tests the generate_table_view() ... ok (0.031s)
2023-01-11T21:56:51.6453219Z   test_generate_tables_no_match (quantization.fx.test_model_report_fx.TestFxModelReportVisualizer)
2023-01-11T21:56:51.6453528Z Tests the generate_table_view() ... ok (0.029s)
2023-01-11T21:56:51.6453863Z   test_generate_tables_single_feat_match (quantization.fx.test_model_report_fx.TestFxModelReportVisualizer)
2023-01-11T21:56:51.6454169Z Tests the generate_table_view() ... ok (0.028s)
2023-01-11T21:56:51.6454536Z   test_get_modules_and_features (quantization.fx.test_model_report_fx.TestFxModelReportVisualizer)
2023-01-11T21:56:51.6454901Z Tests the get_all_unique_module_fqns and get_all_unique_feature_names methods of ... ok (0.029s)
2023-01-11T21:56:51.6455277Z   test_histogram_observer (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (21.038s)
2023-01-11T21:56:51.6455686Z   test_histogram_observer_against_reference (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (4.482s)
2023-01-11T21:56:51.6456123Z   test_histogram_observer_correct_numel (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (0.003s)
2023-01-11T21:56:51.6456541Z   test_histogram_observer_extreme_inputs (quantization.core.test_workflow_module.TestHistogramObserver)
2023-01-11T21:56:51.6456937Z Ensures that the HistogramObserver is able to work correctly in ... ok (0.001s)
2023-01-11T21:56:51.6457304Z   test_histogram_observer_one_sided (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (0.210s)
2023-01-11T21:56:51.6457725Z   test_histogram_observer_same_inputs (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (0.666s)
2023-01-11T21:56:51.6458132Z   test_observer_scriptable (quantization.core.test_workflow_module.TestHistogramObserver) ... ok (0.458s)
2023-01-11T21:56:51.6458538Z   test_fake_quant_true_quant_compare (quantization.eager.test_model_numerics.TestModelNumericsEager) ... ok (0.166s)
2023-01-11T21:56:51.6458970Z   test_float_quant_compare_per_channel (quantization.eager.test_model_numerics.TestModelNumericsEager) ... ok (0.027s)
2023-01-11T21:56:51.6459401Z   test_float_quant_compare_per_tensor (quantization.eager.test_model_numerics.TestModelNumericsEager) ... ok (0.137s)
2023-01-11T21:56:51.6459845Z   test_weight_only_activation_only_fakequant (quantization.eager.test_model_numerics.TestModelNumericsEager) ... ok (0.202s)
2023-01-11T21:56:51.6460275Z   test_compare_model_outputs_conv_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.170s)
2023-01-11T21:56:51.6460724Z   test_compare_model_outputs_functional_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.176s)
2023-01-11T21:56:51.6461174Z   test_compare_model_outputs_linear_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.027s)
2023-01-11T21:56:51.6461615Z   test_compare_model_outputs_linear_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.770s)
2023-01-11T21:56:51.6462037Z   test_compare_model_outputs_lstm_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.068s)
2023-01-11T21:56:51.6462464Z   test_compare_model_stub_conv_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.220s)
2023-01-11T21:56:51.6462994Z   test_compare_model_stub_functional_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.177s)
2023-01-11T21:56:51.6463445Z   test_compare_model_stub_linear_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.028s)
2023-01-11T21:56:51.6463865Z   test_compare_model_stub_linear_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.727s)
2023-01-11T21:56:51.6464296Z   test_compare_model_stub_lstm_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.068s)
2023-01-11T21:56:51.6464723Z   test_compare_model_stub_partial (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.715s)
2023-01-11T21:56:51.6465154Z   test_compare_model_stub_submodule_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.042s)
2023-01-11T21:56:51.6465572Z   test_compare_weights_conv_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.115s)
2023-01-11T21:56:51.6465999Z   test_compare_weights_linear_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.012s)
2023-01-11T21:56:51.6466466Z   test_compare_weights_linear_static (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.709s)
2023-01-11T21:56:51.6466893Z   test_compare_weights_lstm_dynamic (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.066s)
2023-01-11T21:56:51.6468022Z   test_mobilenet_v2 (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
2023-01-11T21:56:51.6468711Z   warnings.warn(msg)
2023-01-11T21:56:51.6469196Z Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
2023-01-11T21:56:51.6469460Z 
2023-01-11T21:56:51.6469548Z   0%|          | 0.00/13.6M [00:00<?, ?B/s]
2023-01-11T21:56:51.6469752Z 100%|##########| 13.6M/13.6M [00:00<00:00, 262MB/s]
2023-01-11T21:56:51.6469924Z ok (0.828s)
2023-01-11T21:56:51.6470927Z   test_mobilenet_v3 (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.
2023-01-11T21:56:51.6471586Z   warnings.warn(msg)
2023-01-11T21:56:51.6472068Z Downloading: "https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/mobilenet_v3_large-8738ca79.pth
2023-01-11T21:56:51.6472343Z 
2023-01-11T21:56:51.6472416Z   0%|          | 0.00/21.1M [00:00<?, ?B/s]
2023-01-11T21:56:51.6472623Z 100%|##########| 21.1M/21.1M [00:00<00:00, 334MB/s]
2023-01-11T21:56:51.6472810Z ok (1.106s)
2023-01-11T21:56:51.6473091Z   test_output_logger (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.002s)
2023-01-11T21:56:51.6473489Z   test_shadow_logger (quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager) ... ok (0.001s)
2023-01-11T21:56:51.6473889Z   test_histogram_observer_consistent_buffer_shape (quantization.core.test_workflow_module.TestObserver)
2023-01-11T21:56:51.6474248Z Ensures that the buffer shapes do not change from uninitialized to ... ok (0.003s)
2023-01-11T21:56:51.6474592Z   test_histogram_observer_save_load_state_dict (quantization.core.test_workflow_module.TestObserver)
2023-01-11T21:56:51.6474908Z Smoke test on saving/loading state_dict ... ok (0.001s)
2023-01-11T21:56:51.6475234Z   test_memoryless_minmaxobserver (quantization.core.test_workflow_module.TestObserver) ... ok (0.020s)
2023-01-11T21:56:51.6475645Z   test_memoryless_perchannelminmaxobserver (quantization.core.test_workflow_module.TestObserver) ... ok (0.021s)
2023-01-11T21:56:51.6476040Z   test_observer_qparams_respects_device_affinity (quantization.core.test_workflow_module.TestObserver)
2023-01-11T21:56:51.6476498Z Ensure that the scale and zero_point returned by the observer ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:56:51.6476867Z   test_observer_scriptable (quantization.core.test_workflow_module.TestObserver) ... ok (0.070s)
2023-01-11T21:56:51.6477223Z   test_per_channel_observers (quantization.core.test_workflow_module.TestObserver) ... ok (0.298s)
2023-01-11T21:56:51.6477590Z   test_per_tensor_observers (quantization.core.test_workflow_module.TestObserver) ... ok (0.070s)
2023-01-11T21:56:51.6477950Z   test_save_load_state_dict_script (quantization.core.test_workflow_module.TestObserver)
2023-01-11T21:56:51.6478330Z Tests that we can save and load state_dict for observers that are scripted ... ok (0.173s)
2023-01-11T21:56:51.6478819Z   test_state_dict_respects_device_affinity (quantization.core.test_workflow_module.TestObserver) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:56:51.6479218Z   test_zero_numel (quantization.core.test_workflow_module.TestObserver) ... ok (0.002s)
2023-01-11T21:56:51.6479794Z   test_constant_padNd (quantization.core.test_quantized_op.TestPadding) ... /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:767: UserWarning: non-inplace resize is deprecated
2023-01-11T21:56:51.6480235Z   warnings.warn("non-inplace resize is deprecated")
2023-01-11T21:56:51.6480434Z ok (6.514s)
2023-01-11T21:56:51.6480697Z   test_reflection_pad1d (quantization.core.test_quantized_op.TestPadding) ... ok (0.159s)
2023-01-11T21:56:51.6481083Z   test_reflection_pad2d (quantization.core.test_quantized_op.TestPadding) ... ok (19.682s)
2023-01-11T21:56:51.6481430Z   test_adaptive_avg_pool2d (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.195s)
2023-01-11T21:56:51.6481786Z   test_avg_pool2d (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.748s)
2023-01-11T21:56:51.6482128Z   test_hardtanh (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.065s)
2023-01-11T21:56:51.6482465Z   test_mean (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.451s)
2023-01-11T21:56:51.6482788Z   test_qnnpack_add (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.332s)
2023-01-11T21:56:51.6483526Z   test_qnnpack_add_broadcast (quantization.core.test_quantized_op.TestQNNPackOps) ... [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 1, 1], which does not match the required output shape [1, 3, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:51.6484532Z [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 1, 1, 1], which does not match the required output shape [1, 3, 4, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:51.6485446Z [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 1, 1], which does not match the required output shape [1, 3, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:51.6486350Z [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 1, 1, 1], which does not match the required output shape [1, 3, 4, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:51.6486871Z ok (0.004s)
2023-01-11T21:56:51.6487141Z   test_qnnpack_maxpool2d (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.830s)
2023-01-11T21:56:51.6487483Z   test_qnnpack_mul (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.141s)
2023-01-11T21:56:51.6487829Z   test_qnnpack_relu (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.103s)
2023-01-11T21:56:51.6488184Z   test_qnnpack_sigmoid (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.003s)
2023-01-11T21:56:51.6488537Z   test_qnnpack_sigmoid_sweep (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.002s)
2023-01-11T21:56:51.6488923Z   test_qnnpack_tanh (quantization.core.test_quantized_op.TestQNNPackOps) ... ok (0.003s)
2023-01-11T21:56:51.6489861Z   test_quantization_doc_custom (quantization.core.test_docs.TestQuantizationDocs) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py:540: UserWarning: Passing a convert_custom_config_dict to convert is deprecated and will not be supported in a future version. Please pass in a ConvertCustomConfig instead.
2023-01-11T21:56:51.6490370Z   warnings.warn(
2023-01-11T21:56:51.6490532Z ok (0.024s)
2023-01-11T21:56:51.6490806Z   test_quantization_doc_fx (quantization.core.test_docs.TestQuantizationDocs) ... ok (0.047s)
2023-01-11T21:56:51.6491177Z   test_quantization_doc_ptdq (quantization.core.test_docs.TestQuantizationDocs) ... ok (0.003s)
2023-01-11T21:56:51.6491613Z   test_quantization_doc_ptsq (quantization.core.test_docs.TestQuantizationDocs) ... ok (0.079s)
2023-01-11T21:56:51.6491976Z   test_quantization_doc_qat (quantization.core.test_docs.TestQuantizationDocs) ... ok (0.014s)
2023-01-11T21:56:51.6493340Z   test_embedding_bag (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitOps) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:818: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:56:51.6494173Z   if param.grad is not None:
2023-01-11T21:56:51.6494361Z ok (0.066s)
2023-01-11T21:56:51.6494668Z   test_embedding_bag_padding_idx_error (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitOps) ... ok (0.070s)
2023-01-11T21:56:51.6495055Z   test_linear (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitOps) ... ok (1.121s)
2023-01-11T21:56:51.6495456Z   test_convert_dynamic_fp16 (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.013s)
2023-01-11T21:56:51.6496225Z   test_dynamic_multi_op (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... [W insert_quant_dequant.cpp:1359] Warning: debug option for add_scalar and mul_scalar is not supported, please don't use debug option for models that uses these ops. (function operator())
2023-01-11T21:56:51.6496661Z ok (0.428s)
2023-01-11T21:56:51.6496947Z   test_dynamic_quant_multi_uses (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.426s)
2023-01-11T21:56:51.6497371Z   test_dynamic_shared_weights (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.707s)
2023-01-11T21:56:51.6497798Z   test_dynamic_weight_observer (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.122s)
2023-01-11T21:56:51.6498205Z   test_dynamic_with_if (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (1.340s)
2023-01-11T21:56:51.6498628Z   test_insert_quant_dequant_linear_dynamic (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.128s)
2023-01-11T21:56:51.6499050Z   test_prepare_dynamic (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.028s)
2023-01-11T21:56:51.6499470Z   test_prepare_dynamic_child_qconfig (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.028s)
2023-01-11T21:56:51.6499881Z   test_quantize_dynamic_fp16 (quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses) ... ok (0.015s)
2023-01-11T21:56:51.6500278Z   test_conv_1d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.034s)
2023-01-11T21:56:51.6500654Z   test_conv_2d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.033s)
2023-01-11T21:56:51.6501028Z   test_conv_3d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.037s)
2023-01-11T21:56:51.6501477Z   test_conv_transpose_1d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.033s)
2023-01-11T21:56:51.6501875Z   test_conv_transpose_2d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.033s)
2023-01-11T21:56:51.6502270Z   test_conv_transpose_3d (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.036s)
2023-01-11T21:56:51.6502660Z   test_functional_module (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (1.121s)
2023-01-11T21:56:51.6503104Z   test_int16_reference_module (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.023s)
2023-01-11T21:56:51.6503499Z   test_leaky_relu (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.003s)
2023-01-11T21:56:51.6503913Z   test_linear (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.031s)
2023-01-11T21:56:51.6504274Z   test_relu (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps) ... ok (0.003s)
2023-01-11T21:56:51.6504683Z   test_embedding_ops_dynamic (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic) ... ok (0.012s)
2023-01-11T21:56:51.6505115Z   test_forward_hooks_preserved (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6505542Z Test post-training dynamic quantization on preserving ... ok (0.003s)
2023-01-11T21:56:51.6505903Z   test_linear_relu_fusion (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic) ... ok (0.108s)
2023-01-11T21:56:51.6506306Z   test_nested1 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6506706Z Test quantization for nested model, top level 'fc3' and ... ok (0.798s)
2023-01-11T21:56:51.6507045Z   test_nested2 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6507377Z Another test case for quantized, we will quantize all submodules ... ok (0.997s)
2023-01-11T21:56:51.6507722Z   test_nested3 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6508066Z More complicated nested test case with child qconfig overrides ... ok (0.987s)
2023-01-11T21:56:51.6508421Z   test_per_channel_linear_quantize (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6508775Z Test quantization for per_channel dynamic quantization ... ok (0.529s)
2023-01-11T21:56:51.6509123Z   test_quantized_rnn (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6509526Z Test dynamic quantization, scriptability and serialization for dynamic quantized lstm modules on int8 and fp16 ... ok (1.246s)
2023-01-11T21:56:51.6509928Z   test_quantized_rnn_cell (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6510338Z Test dynamic quantization, scriptability and serialization for dynamic quantized rnn cell modules on int8 and fp16 ... ok (0.866s)
2023-01-11T21:56:51.6510742Z   test_single_layer (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6511122Z Dynamic Quantize SingleLayerLinearDynamicModel which has one Linear module, ... ok (0.440s)
2023-01-11T21:56:51.6511486Z   test_two_layers (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6511853Z TwoLayerLinearModel has two Linear modules but we only quantize the second one ... ok (0.385s)
2023-01-11T21:56:51.6512226Z   test_type_match_rule (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic)
2023-01-11T21:56:51.6512623Z Test quantization for nested model, top level 'fc3' and ... ok (0.667s)
2023-01-11T21:56:51.6512963Z   test_activations (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6513277Z Test quantization of activations ... ok (0.904s)
2023-01-11T21:56:51.6513619Z   test_activations_in_non_leaf_module_list (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6514023Z Ensure activations like `nn.Sigmoid` and `nn.Tanh` are properly handled in ... ok (0.003s)
2023-01-11T21:56:51.6514413Z   test_convtranspose_per_channel_fails_early (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6514878Z Verifies that attempting to quantize a ConvTranspose module with per-Channel ... ok (0.002s)
2023-01-11T21:56:51.6515264Z   test_convtranspose_per_channel_qconfig_none (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6515639Z Verifies that having qconfig==None for conv transpose does not crash ... ok (0.001s)
2023-01-11T21:56:51.6516018Z   test_custom_module_class (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.020s)
2023-01-11T21:56:51.6516471Z   test_dequant_stub (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.006s)
2023-01-11T21:56:51.6516887Z   test_forward_hooks_preserved (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (1.076s)
2023-01-11T21:56:51.6517291Z   test_manual (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6517621Z User inserts QuantStub and DeQuantStub in model code ... ok (0.673s)
2023-01-11T21:56:51.6517955Z   test_nested1 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6518338Z Test quantization for nested model, top level 'fc3' and ... ok (1.996s)
2023-01-11T21:56:51.6518685Z   test_nested2 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.341s)
2023-01-11T21:56:51.6519070Z   test_nested3 (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6519404Z More complicated nested test case with child qconfig overrides ... ok (2.798s)
2023-01-11T21:56:51.6519754Z   test_normalization (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6520543Z Test quantization of normalization layers ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize.py:310: UserWarning: None of the submodule got qconfig applied. Make sure you passed correct configuration through `qconfig_dict` or by assigning the `.qconfig` attribute directly on submodules
2023-01-11T21:56:51.6521077Z   warnings.warn("None of the submodule got qconfig applied. Make sure you "
2023-01-11T21:56:51.6521300Z ok (3.425s)
2023-01-11T21:56:51.6521593Z   test_quantized_embedding (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6522035Z Test the post-training quantization flow, serialization and scripting ... ok (0.112s)
2023-01-11T21:56:51.6522414Z   test_quantized_embedding_bag (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6522844Z Test the post-training quantization flow, serialization and scripting ... ok (0.098s)
2023-01-11T21:56:51.6523257Z   test_quantwrapper_attaches_qconfig_to_dequant (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic) ... ok (0.004s)
2023-01-11T21:56:51.6523686Z   test_resnet_base (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6524025Z Test quantization for bottleneck topology used in resnet/resnext ... ok (0.355s)
2023-01-11T21:56:51.6524389Z   test_save_load_state_dict (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6524776Z Test PTQ flow of creating a model and quantizing it and saving the quantized state_dict ... ok (1.070s)
2023-01-11T21:56:51.6525147Z   test_single_layer (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6525514Z Quantize SingleLayerLinearModel which has one Linear module, make sure it is swapped ... ok (3.326s)
2023-01-11T21:56:51.6525893Z   test_skip_quant (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6526267Z The case when we want to skip quantizing some layers ... ok (4.216s)
2023-01-11T21:56:51.6526599Z   test_two_layers (quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic)
2023-01-11T21:56:51.6526952Z TwoLayerLinearModel has two Linear modules but we only quantize the second one ... ok (0.607s)
2023-01-11T21:56:51.6527342Z   test_add_scalar_uses_input_qparams (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.005s)
2023-01-11T21:56:51.6527743Z   test_conv_linear (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (1.858s)
2023-01-11T21:56:51.6528101Z   test_conv_linear_symm (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT)
2023-01-11T21:56:51.6528430Z Same as test_conv_linear but with Symmetric quantization. ... ok (0.717s)
2023-01-11T21:56:51.6528824Z   test_defused_embedding_bag_linear (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.628s)
2023-01-11T21:56:51.6529331Z   test_dropout (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.691s)
2023-01-11T21:56:51.6529708Z   test_dynamic_qat_linear (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.689s)
2023-01-11T21:56:51.6530109Z   test_embedding_bag_linear (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.637s)
2023-01-11T21:56:51.6530521Z   test_embedding_qat_qconfig_equal (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.007s)
2023-01-11T21:56:51.6530917Z   test_eval_only_fake_quant (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT)
2023-01-11T21:56:51.6531217Z Using FakeQuant in evaluation only mode, ... ok (0.035s)
2023-01-11T21:56:51.6531554Z   test_forward_hooks_preserved (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.028s)
2023-01-11T21:56:51.6531947Z   test_manual (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (1.348s)
2023-01-11T21:56:51.6532328Z   test_mul_scalar_uses_input_qparams (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.004s)
2023-01-11T21:56:51.6532742Z   test_qat_embedding_bag_errors (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT) ... ok (0.004s)
2023-01-11T21:56:51.6533133Z   test_train_save_load_eval (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT)
2023-01-11T21:56:51.6533494Z Test QAT flow of creating a model, doing QAT and saving the quantized state_dict ... ok (0.307s)
2023-01-11T21:56:51.6533876Z   test_conv_bn_folded_vs_unfolded (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.896s)
2023-01-11T21:56:51.6534311Z   test_conv_bn_relu (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.342s)
2023-01-11T21:56:51.6534732Z   test_fixed_qparam_ops (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.012s)
2023-01-11T21:56:51.6535149Z   test_leaky_relu (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.006s)
2023-01-11T21:56:51.6535567Z   test_linear_bn_numerics (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.008s)
2023-01-11T21:56:51.6536000Z   test_linear_bn_symm_numerics (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.003s)
2023-01-11T21:56:51.6536434Z   test_linear_bn_workflow (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.038s)
2023-01-11T21:56:51.6536846Z   test_relu (quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics) ... ok (0.002s)
2023-01-11T21:56:51.6537225Z   test__convert_to_reference_decomposed_fx (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.025s)
2023-01-11T21:56:51.6537630Z   test__convert_to_reference_decomposed_fx_dynamic_quant (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.019s)
2023-01-11T21:56:51.6538047Z   test__convert_to_reference_decomposed_fx_per_channel_quant (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.024s)
2023-01-11T21:56:51.6538471Z   test_assert_on_size_after_quant_layer (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6538797Z Verifies that calculating a size of a quantized tensor works ... ok (0.022s)
2023-01-11T21:56:51.6539117Z   test_attention (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.128s)
2023-01-11T21:56:51.6539472Z   test_backend_config_check_for_weight_and_bias (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6539800Z Test to make sure the backend_config check for weight and bias ... ok (0.007s)
2023-01-11T21:56:51.6540132Z   test_backend_config_quantization_range (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6540857Z Check that quantization ranges specified through the BackendConfig are reflected in ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:783: UserWarning: QConfig activation quantization range must fall within the backend's:
2023-01-11T21:56:51.6542272Z QConfig range = (0, 63), BackendConfig range = (0, 31), ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=63, dtype=torch.quint8){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2cf800b80>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2cf800b80>})
2023-01-11T21:56:51.6543269Z   warnings.warn(("QConfig %s quantization range must fall within the backend's:\n"
2023-01-11T21:56:51.6543789Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:783: UserWarning: QConfig activation quantization range must fall within the backend's:
2023-01-11T21:56:51.6545131Z QConfig range = (0, 255), BackendConfig range = (0, 31), ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.quint8){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2cf800b80>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2cf800b80>})
2023-01-11T21:56:51.6546039Z   warnings.warn(("QConfig %s quantization range must fall within the backend's:\n"
2023-01-11T21:56:51.6546862Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:779: UserWarning: QConfig activation must specify 'quant_min' and 'quant_max', ignoring QConfig(activation=<class 'torch.ao.quantization.observer.ReuseInputObserver'>, weight=<class 'torch.ao.quantization.observer.ReuseInputObserver'>)
2023-01-11T21:56:51.6547540Z   warnings.warn("QConfig %s must specify 'quant_min' and 'quant_max', ignoring %s" %
2023-01-11T21:56:51.6547788Z ok (0.033s)
2023-01-11T21:56:51.6548033Z   test_backend_config_scale_min (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6549646Z Test QConfig eps validation against the BackendConfig's min scale value. ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:794: UserWarning: QConfig activation eps (tensor([6.1035e-05])) must be greater than or equal to the backend's min scale value (0.000244140625), ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.quint8, eps=6.103515625e-05){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2ddba2830>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2ddba2830>})
2023-01-11T21:56:51.6550712Z   warnings.warn(("QConfig %s eps (%s) must be greater than or equal to "
2023-01-11T21:56:51.6552214Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:794: UserWarning: QConfig activation eps (tensor([1.1921e-07])) must be greater than or equal to the backend's min scale value (0.000244140625), ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.quint8){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2ddba3eb0>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, eps=6.103515625e-05){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2ddba3eb0>})
2023-01-11T21:56:51.6553180Z   warnings.warn(("QConfig %s eps (%s) must be greater than or equal to "
2023-01-11T21:56:51.6554116Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:791: UserWarning: QConfig activation must specify 'eps', ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=1.0, zero_point=0){}, weight=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=1.0, zero_point=0){})
2023-01-11T21:56:51.6554869Z   warnings.warn("QConfig %s must specify 'eps', ignoring %s" % (debug_string, qconfig))
2023-01-11T21:56:51.6555109Z ok (0.044s)
2023-01-11T21:56:51.6555376Z   test_change_backend_config_for_fixed_qparam_ops (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6555733Z Making sure we can skip validation of qconfigs for fixedqparam ops based ... ok (0.007s)
2023-01-11T21:56:51.6556083Z   test_channel_shuffle_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.072s)
2023-01-11T21:56:51.6556405Z   test_conv_bn_relu (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6556784Z Tests fusion and quantization for "Conv - Bn" and "Conv - Bn - ReLU" ... ok (1.253s)
2023-01-11T21:56:51.6557109Z   test_conv_linear_not_reference (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6557394Z Test quantizing conv and linear ... ok (1.832s)
2023-01-11T21:56:51.6557664Z   test_conv_linear_reference (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6557983Z Test quantizing functional conv and linear with reference option ... ok (1.628s)
2023-01-11T21:56:51.6558310Z   test_conv_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.070s)
2023-01-11T21:56:51.6558650Z   test_convert_custom_config_from_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:51.6559054Z   test_convert_custom_config_set_observed_to_quantized_mapping (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6559474Z   test_convert_custom_config_set_preserved_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6559864Z   test_convert_custom_config_to_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6560648Z   test_convert_qconfig_mapping (quantization.fx.test_quantize_fx.TestQuantizeFx) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/convert.py:886: UserWarning: Passing a QConfig dictionary to convert is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.
2023-01-11T21:56:51.6561130Z   warnings.warn(
2023-01-11T21:56:51.6561746Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node mods1_1_packed_weight_0 target mods1_1_packed_weight_0 mods1_1_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6562296Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6562998Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node mods1_0_packed_weight_0 target mods1_0_packed_weight_0 mods1_0_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6563533Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6563770Z ok (0.100s)
2023-01-11T21:56:51.6564044Z   test_convtranspose_per_channel_fails_early (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6564460Z Verifies that attempting to quantize a ConvTranspose module with per-Channel ... ok (0.011s)
2023-01-11T21:56:51.6564813Z   test_copy_node_has_shared_actpp_instance (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6565160Z Test the output of CopyNode to have the same ... ok (0.041s)
2023-01-11T21:56:51.6565470Z   test_custom_module_class (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.087s)
2023-01-11T21:56:51.6565820Z   test_custom_module_class_input_has_multiple_users (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6566159Z Tests that the flow still works when the input of custom module ... ok (0.025s)
2023-01-11T21:56:51.6566504Z   test_deepcopy_preserve_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.014s)
2023-01-11T21:56:51.6566878Z   test_default_qconfig_mapping_override_global (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.012s)
2023-01-11T21:56:51.6567249Z   test_default_quant_after_none_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6567564Z Make sure default quant is inserted properly ... ok (0.019s)
2023-01-11T21:56:51.6567851Z   test_dequantize (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6568138Z Test to make sure dequantize node are placed before ... ok (0.226s)
2023-01-11T21:56:51.6568432Z   test_dict_output (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6568740Z Make sure quantization runs for models with dictionary output ... ok (0.019s)
2023-01-11T21:56:51.6569180Z   test_dynamic_linear_input_multiple_use (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6569505Z Tests input for dynamic linear being used by multiple ops ... ok (0.042s)
2023-01-11T21:56:51.6569830Z   test_dynamic_quant_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.055s)
2023-01-11T21:56:51.6570179Z   test_dynamic_quant_weight_observer (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6570480Z Test that weight observer is run in convert step ... ok (0.015s)
2023-01-11T21:56:51.6570781Z   test_dynamic_with_fusion (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6571576Z Tests that dynamic quantization APIs work with Linear + Relu fusion ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node mods2_packed_weight_0 target mods2_packed_weight_0 mods2_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6572199Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6572423Z ok (0.055s)
2023-01-11T21:56:51.6572687Z   test_dynamic_with_fusion_multiple_uses (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6573026Z Tests that dynamic quantization APIs work with Linear + Relu fusion ... ok (0.041s)
2023-01-11T21:56:51.6573176Z   test_fold_quant_dequant (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6573353Z Test that the sequence of quant-dequant nodes in the ... ok (0.022s)
2023-01-11T21:56:51.6573523Z   test_fp32_input_fp32_output (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:51.6573702Z   test_fp32_input_quantized_output (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.020s)
2023-01-11T21:56:51.6573840Z   test_fp32_sum (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6574101Z Verifies that fp32 sum works correctly if it's before or after ... ok (0.050s)
2023-01-11T21:56:51.6574276Z   test_fuse_custom_config_from_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6574468Z   test_fuse_custom_config_set_preserved_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6574639Z   test_fuse_custom_config_to_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6574805Z   test_fused_module_qat_swap (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:51.6574954Z   test_fusion_pattern_unquantized (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6575137Z Ensure that leaving a possible fusion pattern of multiple nodes ... ok (0.142s)
2023-01-11T21:56:51.6575310Z   test_get_default_qconfig_valid_backend (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6575487Z Checks that AssertionError is raised when non expected backend input is specified ... ok (0.001s)
2023-01-11T21:56:51.6575667Z   test_get_executorch_backend_config (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6575829Z   test_getattr_with_nontensor_result (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6575970Z Verifies that binary ops get quantized correctly if some ... ok (0.051s)
2023-01-11T21:56:51.6576126Z   test_linear_bn (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.748s)
2023-01-11T21:56:51.6576271Z   test_linear_leaky_relu_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6576460Z Test fusion and lowering of Linear - (bn -) LeakyReLU ... ok (0.046s)
2023-01-11T21:56:51.6576617Z   test_linear_qint8_activation (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6576752Z Test support for qint8 activation in reference pattern ... ok (0.015s)
2023-01-11T21:56:51.6576903Z   test_linear_tanh_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6577072Z Test fusion and lowering of Linear - Tanh ... ok (0.023s)
2023-01-11T21:56:51.6577260Z   test_masked_fill_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:51.6577425Z   test_match_pattern_with_multiple_args (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6577554Z Test that we can match a pattern that has multiple arguments ... ok (0.008s)
2023-01-11T21:56:51.6577717Z   test_mul_add_fp16_config (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.028s)
2023-01-11T21:56:51.6577893Z   test_no_obs_between_unmatched_node_and_copy_node (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6578036Z Verifies that an observer is not inserted between an unmatched ... ok (0.018s)
2023-01-11T21:56:51.6578201Z   test_non_traceable_module (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.013s)
2023-01-11T21:56:51.6578339Z   test_not_used (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6578446Z Test quantizing a not used value ... ok (0.014s)
2023-01-11T21:56:51.6578588Z   test_observer_fqn (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6578749Z Test to make sure the observer FQN is based on the quantizable op/module that it is observing ... ok (0.023s)
2023-01-11T21:56:51.6578901Z   test_output_lists_and_dicts (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6579049Z Verify that specifying complicated output types does not crash. ... ok (0.017s)
2023-01-11T21:56:51.6579217Z   test_packed_weight_fused_op (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.031s)
2023-01-11T21:56:51.6579359Z   test_pattern_match (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6579457Z test MatchAllNode with ... ok (0.006s)
2023-01-11T21:56:51.6579624Z   test_pattern_match_constant (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.005s)
2023-01-11T21:56:51.6579840Z   test_permute_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:51.6580021Z   test_prepare_custom_config_from_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:51.6580211Z   test_prepare_custom_config_set_float_to_observed_mapping (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6580409Z   test_prepare_custom_config_set_input_quantized_indexes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6580611Z   test_prepare_custom_config_set_non_traceable_module_classes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6580836Z   test_prepare_custom_config_set_non_traceable_module_names (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6581035Z   test_prepare_custom_config_set_output_quantized_indexes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6581235Z   test_prepare_custom_config_set_preserved_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6581431Z   test_prepare_custom_config_set_standalone_module_class (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6581626Z   test_prepare_custom_config_set_standalone_module_name (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6581801Z   test_prepare_custom_config_to_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:51.6581945Z   test_prepare_mode (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.052s)
2023-01-11T21:56:51.6582104Z   test_prepared_model_deepcopy (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6582253Z Ensures that copy.deepcopy works correctly on a prepared model. ... ok (0.019s)
2023-01-11T21:56:51.6582419Z   test_preserve_attributes (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.016s)
2023-01-11T21:56:51.6582568Z   test_preserve_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6582727Z Test to make sure the temporary config option to preserve qconfig attributes ... ok (0.021s)
2023-01-11T21:56:51.6582941Z   test_preserve_tuple (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6583058Z Test tuple input type is preserved ... ok (0.013s)
2023-01-11T21:56:51.6583237Z   test_propagate_dtypes_for_known_nodes_dict_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:51.6583440Z   test_propagate_dtypes_for_known_nodes_dict_split_tuple_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:51.6583644Z   test_propagate_dtypes_for_known_nodes_dict_tuple_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:51.6583838Z   test_propagate_dtypes_for_known_nodes_list_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:51.6584034Z   test_propagate_dtypes_for_known_nodes_split_list_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:51.6584231Z   test_propagate_dtypes_for_known_nodes_split_tuple_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:51.6584422Z   test_propagate_dtypes_for_known_nodes_tuple_args (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:51.6584582Z   test_qat_and_script (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.208s)
2023-01-11T21:56:51.6584884Z   test_qat_prepare_device_affinity (quantization.fx.test_quantize_fx.TestQuantizeFx) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:56:51.6585049Z   test_qat_skip_untraced (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.027s)
2023-01-11T21:56:51.6585202Z   test_qconfig_dict_setup (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.280s)
2023-01-11T21:56:51.6585411Z   test_qconfig_dict_with_fused_modules (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.112s)
2023-01-11T21:56:51.6585574Z   test_qconfig_for_call_func (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.031s)
2023-01-11T21:56:51.6585741Z   test_qconfig_for_call_method (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.057s)
2023-01-11T21:56:51.6585901Z   test_qconfig_function (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.017s)
2023-01-11T21:56:51.6586075Z   test_qconfig_mapping_from_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:51.6586239Z   test_qconfig_mapping_repr (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6586414Z   test_qconfig_mapping_set_global (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.001s)
2023-01-11T21:56:51.6586619Z   test_qconfig_mapping_set_module_name (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:51.6586806Z   test_qconfig_mapping_set_module_name_object_type_order (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.003s)
2023-01-11T21:56:51.6586990Z   test_qconfig_mapping_set_module_name_regex (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:51.6587168Z   test_qconfig_mapping_set_object_type (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:51.6587336Z   test_qconfig_mapping_to_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.002s)
2023-01-11T21:56:51.6587518Z   test_qconfig_module_name_object_type_order (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.072s)
2023-01-11T21:56:51.6587688Z   test_qconfig_module_name_regex (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:51.6587853Z   test_qconfig_module_type (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.024s)
2023-01-11T21:56:51.6588009Z   test_qconfig_none (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.019s)
2023-01-11T21:56:51.6588176Z   test_qconfig_precedence (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.018s)
2023-01-11T21:56:51.6588331Z   test_qconfig_qat_module_type (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.031s)
2023-01-11T21:56:51.6588488Z   test_qnnpack_backend_config (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6588664Z Test whether default QNNPACK QConfigs are compatible with the QNNPACK BackendConfig. ... ok (1.056s)
2023-01-11T21:56:51.6588823Z   test_qparams_buffers (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.050s)
2023-01-11T21:56:51.6588969Z   test_qparams_fqn (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6589096Z Test that the FQN of input_scale/zero_point is set ... ok (0.029s)
2023-01-11T21:56:51.6589260Z   test_quant_output_always_observed (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6589396Z If the output is hardcoded to be quantized, ensure that ... ok (0.095s)
2023-01-11T21:56:51.6589560Z   test_quantized_input_fp32_output (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.020s)
2023-01-11T21:56:51.6589740Z   test_quantized_input_quantized_output (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.019s)
2023-01-11T21:56:51.6589892Z   test_quantized_model_type (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6590042Z Test state_dict and deepcopy works properly in the quantized model ... ok (0.042s)
2023-01-11T21:56:51.6590186Z   test_ref_conv_module (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6590316Z Make sure the numerics for models with ref conv module ... ok (0.142s)
2023-01-11T21:56:51.6590465Z   test_ref_linear_module (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6590599Z Make sure the numerics for models with ref linear module ... ok (0.047s)
2023-01-11T21:56:51.6590751Z   test_register_patterns (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.003s)
2023-01-11T21:56:51.6590939Z   test_relu_lowering (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.019s)
2023-01-11T21:56:51.6591098Z   test_remove_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.015s)
2023-01-11T21:56:51.6591282Z   test_repeat_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:51.6591446Z   test_reroute_tuple_getitem_patterns (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6591613Z The following graph should redirect the output to `b`. After the transformation, ... ok (0.002s)
2023-01-11T21:56:51.6591796Z   test_reshape_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.020s)
2023-01-11T21:56:51.6591949Z   test_return_none (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.012s)
2023-01-11T21:56:51.6592144Z   test_reuse_input_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.237s)
2023-01-11T21:56:51.6592302Z   test_save_observer_state_dict (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.584s)
2023-01-11T21:56:51.6592462Z   test_sequential (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.327s)
2023-01-11T21:56:51.6592624Z   test_shape_followed_by_quantized_op (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6592744Z Make sure that shape does not dequantize ... ok (0.026s)
2023-01-11T21:56:51.6592920Z   test_size_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.018s)
2023-01-11T21:56:51.6593095Z   test_stack_trace_preserved_linear (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.023s)
2023-01-11T21:56:51.6593276Z   test_standalone_module_float_interface (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.116s)
2023-01-11T21:56:51.6593467Z   test_standalone_module_quantized_interface (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.114s)
2023-01-11T21:56:51.6593596Z   test_state_dict (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6593725Z Make sure packed params appear in state_dict ... ok (0.083s)
2023-01-11T21:56:51.6593869Z   test_static_lstm (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6594040Z Test statically quantized custom module LSTM followed by ops that consume individual ... ok (0.261s)
2023-01-11T21:56:51.6594198Z   test_static_lstm_consume_tuple (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6594364Z Test statically quantized custom module LSTM followed by a module that consumes the ... ok (0.243s)
2023-01-11T21:56:51.6594533Z   test_static_lstm_with_custom_fixed_qparams (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6594698Z Test statically quantized LSTM with custom fixed qparams assigned to each of the ... ok (0.069s)
2023-01-11T21:56:51.6594842Z   test_sub_scalar (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.018s)
2023-01-11T21:56:51.6595011Z   test_symmetric_qnnpack_qconfig_mapping (quantization.fx.test_quantize_fx.TestQuantizeFx)
2023-01-11T21:56:51.6595207Z Test whether `torch.ao.quantization.qconfig_mapping._get_symmetric_qnnpack_qconfig_mapping` ... ok (0.502s)
2023-01-11T21:56:51.6595400Z   test_torch_transpose_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.024s)
2023-01-11T21:56:51.6595591Z   test_torch_unsqueeze_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:51.6595763Z   test_trace_quantize_per_tensor (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.017s)
2023-01-11T21:56:51.6595950Z   test_transpose_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.022s)
2023-01-11T21:56:51.6596142Z   test_unsqueeze__nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.021s)
2023-01-11T21:56:51.6596328Z   test_unsqueeze_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.141s)
2023-01-11T21:56:51.6596524Z   test_view_nontensor_args_not_observed (quantization.fx.test_quantize_fx.TestQuantizeFx) ... ok (0.033s)
2023-01-11T21:56:51.6596694Z   test_model_dropout (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (1.622s)
2023-01-11T21:56:51.6596923Z   test_prepare_serialize_switch_device_convert (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: gpu is not available. (0.001s)
2023-01-11T21:56:51.6597104Z   test_qat_embedding_linear (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (0.608s)
2023-01-11T21:56:51.6597287Z   test_qat_embeddingbag_linear (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (0.646s)
2023-01-11T21:56:51.6597466Z   test_qat_functional_linear (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (0.299s)
2023-01-11T21:56:51.6597886Z   test_resnet18_ddp (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: TODO: Test is always failing - https://github.com/pytorch/pytorch/issues/54979 (0.001s)
2023-01-11T21:56:51.6598059Z   test_resnet_base (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... ok (1.284s)
2023-01-11T21:56:51.6598267Z   test_static_gpu_convert_basic (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: gpu is not available. (0.001s)
2023-01-11T21:56:51.6598482Z   test_switch_device_prepare_convert (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: gpu is not available. (0.001s)
2023-01-11T21:56:51.6598671Z   test_torchvision (quantization.fx.test_quantize_fx.TestQuantizeFxModels) ... skip: skip for now since tbb failed (0.001s)
2023-01-11T21:56:51.6598827Z   test_add (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (7.739s)
2023-01-11T21:56:51.6598986Z   test_add_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (7.989s)
2023-01-11T21:56:51.6599169Z   test_add_relu_multiple_uses_of_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.037s)
2023-01-11T21:56:51.6599332Z   test_ave_pool_with_custom_cfg (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6599467Z A test that checks correct patterns are produced for ... ok (0.016s)
2023-01-11T21:56:51.6599695Z   test_bmm (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:51.6599853Z   test_bmm_int_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6600057Z int8 is not supported for bmm so we won't produce reference ... ok (0.013s)
2023-01-11T21:56:51.6600197Z   test_boolean_tensor (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6600388Z Make sure we don't insert observer for boolean Tensors ... ok (0.015s)
2023-01-11T21:56:51.6600529Z   test_cat (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6600663Z quantization of the output of cat will depend on the ... ok (1.251s)
2023-01-11T21:56:51.6600818Z   test_chunk (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.020s)
2023-01-11T21:56:51.6600976Z   test_clamp (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (1.880s)
2023-01-11T21:56:51.6601138Z   test_conv_module (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.582s)
2023-01-11T21:56:51.6601307Z   test_conv_transpose_1d (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (1.092s)
2023-01-11T21:56:51.6601460Z   test_conv_transpose_2d (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.340s)
2023-01-11T21:56:51.6601618Z   test_copy_node_fp32_input (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6601761Z CopyNode works for both fp32 and int8 inputs, this is a test to make ... ok (0.017s)
2023-01-11T21:56:51.6601983Z   test_div (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.000s)
2023-01-11T21:56:51.6602136Z   test_elu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.897s)
2023-01-11T21:56:51.6602297Z   test_embedding (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.083s)
2023-01-11T21:56:51.6602494Z   test_embedding_bag (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.075s)
2023-01-11T21:56:51.6602663Z   test_fixed_qparams_ops (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.473s)
2023-01-11T21:56:51.6602823Z   test_fixed_qparams_ops_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.033s)
2023-01-11T21:56:51.6602995Z   test_fixed_qparams_ops_qint8 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.044s)
2023-01-11T21:56:51.6603166Z   test_fixed_qparams_ops_wrong_qconfig (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6604608Z Test that wrong qconfigs for fixed qparams ops results in the ops not being quantized. ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2dc06a710>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2dc06a710>}).
2023-01-11T21:56:51.6604839Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:51.6604963Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:51.6605092Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:51.6605280Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:51.6606547Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2dc06beb0>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2dc06beb0>}).
2023-01-11T21:56:51.6606772Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:51.6606897Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:51.6607027Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:51.6607214Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:51.6608467Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2dc06a560>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2dc06a560>}).
2023-01-11T21:56:51.6608691Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:51.6608844Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:51.6608971Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:51.6609288Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:51.6610611Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py:813: UserWarning: QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize for fixed qparams ops, ignoring QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, quant_min=0, quant_max=127){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2ddaca290>}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){'factory_kwargs': <function _add_module_to_qconfig_obs_ctr.<locals>.get_factory_kwargs_based_on_module_device at 0x7fe2ddaca290>}).
2023-01-11T21:56:51.6610858Z Please use torch.ao.quantization.get_default_qconfig_mapping or torch.ao.quantization.get_default_qat_qconfig_mapping. Example:
2023-01-11T21:56:51.6610971Z     qconfig_mapping = get_default_qconfig_mapping("fbgemm")
2023-01-11T21:56:51.6611103Z     model = prepare_fx(model, qconfig_mapping, example_inputs)
2023-01-11T21:56:51.6611289Z   warnings.warn(("QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
2023-01-11T21:56:51.6611357Z ok (0.027s)
2023-01-11T21:56:51.6611527Z   test_float_functional (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.092s)
2023-01-11T21:56:51.6611698Z   test_functional_conv (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (14.528s)
2023-01-11T21:56:51.6611871Z   test_functional_linear (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (18.847s)
2023-01-11T21:56:51.6612079Z   test_gelu_normal (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: TODO: reenable with backend_config api (0.000s)
2023-01-11T21:56:51.6612307Z   test_gelu_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:51.6612467Z   test_general_shape_ops (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6612597Z A test that checks dequantize will be swapped for ... ok (0.215s)
2023-01-11T21:56:51.6612753Z   test_general_value_ops (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6612887Z A test that checks correct patterns are produced for ... ok (0.064s)
2023-01-11T21:56:51.6613032Z   test_getitem (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6613190Z Make sure we only insert observer for getitem if the following node is matched ... ok (0.045s)
2023-01-11T21:56:51.6613353Z   test_hardswish (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.883s)
2023-01-11T21:56:51.6613501Z   test_instance_norm (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (3.643s)
2023-01-11T21:56:51.6613669Z   test_int8_input_no_unnecessary_fq (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6613804Z If the inputs to the graph are quantized and the only node ... ok (0.014s)
2023-01-11T21:56:51.6613963Z   test_layer_norm (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.692s)
2023-01-11T21:56:51.6614122Z   test_leaky_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.763s)
2023-01-11T21:56:51.6614292Z   test_linear_dynamic_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.401s)
2023-01-11T21:56:51.6614453Z   test_linear_module (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (5.721s)
2023-01-11T21:56:51.6614623Z   test_linear_static_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.510s)
2023-01-11T21:56:51.6614860Z   test_mish_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:51.6615038Z   test_mul (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (7.747s)
2023-01-11T21:56:51.6615199Z   test_mul_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (7.986s)
2023-01-11T21:56:51.6615371Z   test_multiple_qconfigs_for_single_value (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6615490Z Test multiple qconfigs for a single value ... ok (0.019s)
2023-01-11T21:56:51.6616132Z   test_norm_weight_bias (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node mods1_packed_weight_0 target mods1_packed_weight_0 mods1_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6616376Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6616445Z ok (1.585s)
2023-01-11T21:56:51.6616602Z   test_prelu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.461s)
2023-01-11T21:56:51.6616763Z   test_qbatch_norm (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.446s)
2023-01-11T21:56:51.6616915Z   test_qbatch_norm_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (1.836s)
2023-01-11T21:56:51.6617072Z   test_qmatmul (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (2.194s)
2023-01-11T21:56:51.6617241Z   test_quantized_add_qat (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.027s)
2023-01-11T21:56:51.6617401Z   test_quantized_conv_relu (quantization.fx.test_quantize_fx.TestQuantizeFxOps)
2023-01-11T21:56:51.6617525Z tests for conv1d_relu/conv2d_relu/conv3d_relu ... ok (2.392s)
2023-01-11T21:56:51.6617695Z   test_quantized_mul_qat (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.027s)
2023-01-11T21:56:51.6617868Z   test_ref_pattern_multi_use (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.025s)
2023-01-11T21:56:51.6618033Z   test_reshape_fp16 (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (0.028s)
2023-01-11T21:56:51.6618169Z   test_rnn (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (5.088s)
2023-01-11T21:56:51.6618326Z   test_rnn_cell (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... ok (3.021s)
2023-01-11T21:56:51.6618560Z   test_silu_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:51.6618768Z   test_softmax_normal (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: TODO: reenable with backend_config api (0.000s)
2023-01-11T21:56:51.6619011Z   test_softmax_reference (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.001s)
2023-01-11T21:56:51.6619235Z   test_sub (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.000s)
2023-01-11T21:56:51.6619452Z   test_sum (quantization.fx.test_quantize_fx.TestQuantizeFxOps) ... skip: This is no longer needed right now, can enable later with new api (0.000s)
2023-01-11T21:56:51.6619606Z   test_conv (quantization.jit.test_quantize_jit.TestQuantizeJit) ... ok (2.174s)
2023-01-11T21:56:51.6619763Z   test_conv_bn (quantization.jit.test_quantize_jit.TestQuantizeJit) ... ok (0.366s)
2023-01-11T21:56:51.6620113Z   test_conv_transpose (quantization.jit.test_quantize_jit.TestQuantizeJit) ... [W insert_observers.cpp:1580] Warning: prim::Loop is not yet supported in quantization, please make sure nothing needs to be quantized in the loop (function operator())
2023-01-11T21:56:51.6620181Z ok (0.575s)
2023-01-11T21:56:51.6620431Z   test_linear_dynamic_fp16 (quantization.jit.test_quantize_jit.TestQuantizeJit) ... [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6620602Z [W QuantUtils.h:215] Warning: FOUND weight out of range  (function HandleWeightsSaturation)
2023-01-11T21:56:51.6620706Z ok (0.032s)
2023-01-11T21:56:51.6621824Z   test_nested (quantization.jit.test_quantize_jit.TestQuantizeJit) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:818: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:56:51.6621912Z   if param.grad is not None:
2023-01-11T21:56:51.6622006Z ok (1.164s)
2023-01-11T21:56:51.6622184Z   test_observer_with_ignored_function (quantization.jit.test_quantize_jit.TestQuantizeJit)
2023-01-11T21:56:51.6622326Z Test observers with ignored function and make sure it works in ... ok (1.330s)
2023-01-11T21:56:51.6623005Z   test_single_linear (quantization.jit.test_quantize_jit.TestQuantizeJit) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
2023-01-11T21:56:51.6623083Z   warnings.warn(
2023-01-11T21:56:51.6623137Z ok (4.618s)
2023-01-11T21:56:51.6623314Z   test_single_linear_dynamic (quantization.jit.test_quantize_jit.TestQuantizeJit) ... ok (0.133s)
2023-01-11T21:56:51.6623476Z   test_skip_quant (quantization.jit.test_quantize_jit.TestQuantizeJit) ... ok (5.371s)
2023-01-11T21:56:51.6623647Z   test_cat_linear (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (0.089s)
2023-01-11T21:56:51.6623811Z   test_clamp (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (3.902s)
2023-01-11T21:56:51.6623997Z   test_conv_with_benchmark_flag (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (0.296s)
2023-01-11T21:56:51.6624164Z   test_dequantize_tuple (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:51.6624295Z Make sure dequantize can support Tuple of tensor ... ok (1.417s)
2023-01-11T21:56:51.6624455Z   test_elu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (15.697s)
2023-01-11T21:56:51.6624608Z   test_general_shape_ops (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:51.6624738Z A test that checks dequantize will be swapped for ... ok (0.119s)
2023-01-11T21:56:51.6624900Z   test_general_value_ops (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:51.6625035Z A test that checks correct patterns are produced for ... ok (0.328s)
2023-01-11T21:56:51.6625204Z   test_group_norm (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (12.738s)
2023-01-11T21:56:51.6625374Z   test_hardswish (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (12.174s)
2023-01-11T21:56:51.6625681Z   test_instance_norm (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6625899Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6626106Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6626299Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6626514Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6626754Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6626956Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6627168Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6627375Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6627634Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6627848Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6628055Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6628261Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6628467Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6628672Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6628860Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6629066Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6629270Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6629471Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6629678Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6629880Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6630083Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6630289Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6630491Z [W observer.py:1204] Warning: must run observer before calling calculate_qparams.                                Returning default scale and zero point  (function )
2023-01-11T21:56:51.6630560Z ok (13.129s)
2023-01-11T21:56:51.6630732Z   test_layer_norm (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (13.058s)
2023-01-11T21:56:51.6630885Z   test_linear (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (19.277s)
2023-01-11T21:56:51.6631088Z   test_qbatch_norm (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (8.989s)
2023-01-11T21:56:51.6631284Z   test_qbatch_norm_relu_BNFuncInplaceRelu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (5.922s)
2023-01-11T21:56:51.6631473Z   test_qbatch_norm_relu_BNFuncRelu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (5.932s)
2023-01-11T21:56:51.6631655Z   test_qbatch_norm_relu_BNRelu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (11.952s)
2023-01-11T21:56:51.6631825Z   test_quantized_add (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (5.574s)
2023-01-11T21:56:51.6631992Z   test_quantized_add_alpha (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:51.6632125Z Test quant fusion for multiple aten::add using same ... ok (2.894s)
2023-01-11T21:56:51.6632327Z   test_quantized_add_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (9.961s)
2023-01-11T21:56:51.6632487Z   test_quantized_add_relu_alpha (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:51.6632624Z Test quant fusion for multiple aten::add using same ... ok (21.133s)
2023-01-11T21:56:51.6632801Z   test_quantized_add_scalar (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (2.892s)
2023-01-11T21:56:51.6632984Z   test_quantized_add_scalar_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (6.358s)
2023-01-11T21:56:51.6633144Z   test_quantized_cat (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:51.6633280Z quantization of the output of cat will be depend on the ... ok (2.297s)
2023-01-11T21:56:51.6633450Z   test_quantized_conv (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (3.519s)
2023-01-11T21:56:51.6633613Z   test_quantized_conv_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps)
2023-01-11T21:56:51.6633730Z tests for conv1d_relu/conv2d_relu/conv3d_relu ... ok (14.079s)
2023-01-11T21:56:51.6633900Z   test_quantized_mul (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (1.965s)
2023-01-11T21:56:51.6634074Z   test_quantized_mul_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (9.980s)
2023-01-11T21:56:51.6634253Z   test_quantized_mul_scalar (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (1.801s)
2023-01-11T21:56:51.6634435Z   test_quantized_mul_scalar_relu (quantization.jit.test_quantize_jit.TestQuantizeJitOps) ... ok (6.411s)
2023-01-11T21:56:51.6634606Z   test_conv_trace (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.051s)
2023-01-11T21:56:51.6634793Z   test_convtranspose_trace (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.051s)
2023-01-11T21:56:51.6634972Z   test_dedup_module_uses (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.006s)
2023-01-11T21:56:51.6635138Z   test_finalize_debug (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.100s)
2023-01-11T21:56:51.6635322Z   test_finalize_for_linear (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.074s)
2023-01-11T21:56:51.6635505Z   test_foldbn_complex_cases (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.765s)
2023-01-11T21:56:51.6635684Z   test_foldbn_in_submodule (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.065s)
2023-01-11T21:56:51.6635852Z   test_foldbn_no_fusion (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:51.6636073Z Test that we don't fuse the cases when module type does not match ... ok (0.007s)
2023-01-11T21:56:51.6636262Z   test_foldbn_shared_classtype (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.583s)
2023-01-11T21:56:51.6636436Z   test_foldbn_trivial (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.054s)
2023-01-11T21:56:51.6636622Z   test_foldbn_trivial_nobias (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.054s)
2023-01-11T21:56:51.6636781Z   test_fuse_linear (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.040s)
2023-01-11T21:56:51.6636988Z   test_inplace_option (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.169s)
2023-01-11T21:56:51.6637166Z   test_insert_observers (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.029s)
2023-01-11T21:56:51.6637360Z   test_insert_observers_child_qconfig (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.036s)
2023-01-11T21:56:51.6637545Z   test_insert_observers_for_general_ops (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:51.6637741Z Make sure we skip observers for ops that doesn't require ... ok (0.029s)
2023-01-11T21:56:51.6637927Z   test_insert_observers_for_if (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.101s)
2023-01-11T21:56:51.6638157Z   test_insert_observers_for_if_consistent_observation (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:51.6638281Z check quantization for if works as long as ... ok (0.266s)
2023-01-11T21:56:51.6638459Z   test_insert_observers_for_nested_if (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.102s)
2023-01-11T21:56:51.6638661Z   test_insert_observers_for_reused_weight (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.025s)
2023-01-11T21:56:51.6638851Z   test_insert_observers_interface (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.070s)
2023-01-11T21:56:51.6639056Z   test_insert_observers_interface_unshare_type (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.255s)
2023-01-11T21:56:51.6639249Z   test_insert_observers_propagate_observed (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:51.6639394Z Make sure we propagate observed property through general ops ... ok (0.035s)
2023-01-11T21:56:51.6639613Z   test_insert_observers_propagate_observed_for_function (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.042s)
2023-01-11T21:56:51.6639817Z   test_insert_observers_propagate_observed_in_submodule (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:51.6639963Z Make sure we propagate observed property through general ops ... ok (0.038s)
2023-01-11T21:56:51.6640149Z   test_insert_observers_shared_class_type (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.034s)
2023-01-11T21:56:51.6640337Z   test_insert_observers_skip_values (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.121s)
2023-01-11T21:56:51.6640529Z   test_insert_observers_weight_dtype (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.029s)
2023-01-11T21:56:51.6640710Z   test_insert_quant_dequant (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.175s)
2023-01-11T21:56:51.6640913Z   test_insert_quant_dequant_shared_class_type (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.174s)
2023-01-11T21:56:51.6641096Z   test_interface_with_fork (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.079s)
2023-01-11T21:56:51.6641270Z   test_module_list (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.087s)
2023-01-11T21:56:51.6641443Z   test_quantize_fork_wait (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:51.6641699Z Tests the case where fork and wait calls are in different subgraphs ... [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:51.6641754Z ok (0.071s)
2023-01-11T21:56:51.6641947Z   test_replicate_dequant_same_value (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.074s)
2023-01-11T21:56:51.6642132Z   test_replicate_dequantize (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.008s)
2023-01-11T21:56:51.6642329Z   test_replicate_dequantize_in_block (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.008s)
2023-01-11T21:56:51.6642506Z   test_replicate_quantize_for_if (quantization.jit.test_quantize_jit.TestQuantizeJitPasses)
2023-01-11T21:56:51.6642776Z We want to move quantize nodes for output of prim::If ... [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:51.6642960Z [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:51.6643136Z [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:51.6643308Z [W utils.py:310] Warning: must run observer before calling calculate_qparams. Returning default values. (function )
2023-01-11T21:56:51.6643361Z ok (0.085s)
2023-01-11T21:56:51.6643555Z   test_skip_dequant_constant_prop (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.082s)
2023-01-11T21:56:51.6643784Z   test_swap_functional_linear (quantization.jit.test_quantize_jit.TestQuantizeJitPasses) ... ok (0.007s)
2023-01-11T21:56:51.6644594Z   test_resnet18 (quantization.fx.test_quantize_pt2e.TestQuantizePT2EModels) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/_pt2e/utils.py:101: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
2023-01-11T21:56:51.6644708Z   get_bias_node = m.graph.get_attr(bias_attr_name)
2023-01-11T21:56:51.6644776Z ok (3.785s)
2023-01-11T21:56:51.6645010Z   test_benchmark (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: used for local benchmarking, comment when we want to run it (0.002s)
2023-01-11T21:56:51.6645181Z   test_conv_reorder_issue_onednn (quantization.core.test_quantized_op.TestQuantizedConv)
2023-01-11T21:56:51.6645327Z Ensure reorder failure issue in conv is fixed for onednn backend. ... ok (0.007s)
2023-01-11T21:56:51.6645492Z   test_qconv1d (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (1.749s)
2023-01-11T21:56:51.6645677Z   test_qconv1d_cudnn (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: cudnn is not enabled. (0.005s)
2023-01-11T21:56:51.6645846Z   test_qconv1d_unpack (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (0.853s)
2023-01-11T21:56:51.6646008Z   test_qconv2d (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (5.975s)
2023-01-11T21:56:51.6646208Z   test_qconv2d_cudnn (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: cudnn is not enabled. (0.005s)
2023-01-11T21:56:51.6646378Z   test_qconv2d_unpack (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (1.003s)
2023-01-11T21:56:51.6646541Z   test_qconv3d (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (1.240s)
2023-01-11T21:56:51.6646716Z   test_qconv3d_unpack (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (0.661s)
2023-01-11T21:56:51.6646891Z   test_qconv_transpose1d (quantization.core.test_quantized_op.TestQuantizedConv) ... ok (1.544s)
2023-01-11T21:56:51.6647167Z   test_qconv_transpose2d (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.004s)
2023-01-11T21:56:51.6647429Z   test_qconv_transpose3d (quantization.core.test_quantized_op.TestQuantizedConv) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.003s)
2023-01-11T21:56:51.6647619Z   test_embedding (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... ok (0.267s)
2023-01-11T21:56:51.6647805Z   test_embedding_2d_indices (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps)
2023-01-11T21:56:51.6647946Z Tests the case where 2D indices are passed into the operator ... ok (0.003s)
2023-01-11T21:56:51.6648143Z   test_embedding_bag_2bit (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... ok (0.122s)
2023-01-11T21:56:51.6648369Z   test_embedding_bag_2bit_unpack (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... skip: Test needs Caffe2 (0.000s)
2023-01-11T21:56:51.6648590Z   test_embedding_bag_2d_indices (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps)
2023-01-11T21:56:51.6648730Z Tests the case where 2D indices are passed into the operator ... ok (0.004s)
2023-01-11T21:56:51.6648921Z   test_embedding_bag_4bit (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... ok (0.192s)
2023-01-11T21:56:51.6649238Z   test_embedding_bag_4bit_unpack (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... skip: Test needs Caffe2 (0.000s)
2023-01-11T21:56:51.6649427Z   test_embedding_bag_byte (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... ok (0.195s)
2023-01-11T21:56:51.6649706Z   test_embedding_bag_byte_unpack (quantization.core.test_quantized_op.TestQuantizedEmbeddingOps) ... skip: Test needs Caffe2 (0.000s)
2023-01-11T21:56:51.6649916Z   test_conv1d_api (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... ok (0.312s)
2023-01-11T21:56:51.6650124Z   test_conv2d_api (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... ok (0.258s)
2023-01-11T21:56:51.6650330Z   test_conv3d_api (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... ok (0.206s)
2023-01-11T21:56:51.6651052Z   test_grid_sample (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4235: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
2023-01-11T21:56:51.6651130Z   warnings.warn(
2023-01-11T21:56:51.6651198Z ok (0.057s)
2023-01-11T21:56:51.6651403Z   test_relu_api (quantization.core.test_quantized_functional.TestQuantizedFunctionalOps) ... ok (0.001s)
2023-01-11T21:56:51.6651558Z   test_qlinear (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (1.730s)
2023-01-11T21:56:51.6651766Z   test_qlinear_cudnn (quantization.core.test_quantized_op.TestQuantizedLinear) ... skip: cudnn is not enabled. (0.006s)
2023-01-11T21:56:51.6651945Z   test_qlinear_leaky_relu (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (1.029s)
2023-01-11T21:56:51.6652117Z   test_qlinear_relu (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (1.697s)
2023-01-11T21:56:51.6652289Z   test_qlinear_tanh (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (0.529s)
2023-01-11T21:56:51.6652466Z   test_qlinear_unpack (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (0.413s)
2023-01-11T21:56:51.6652679Z   test_qlinear_with_input_q_dq_qweight_dq_output_fp32 (quantization.core.test_quantized_op.TestQuantizedLinear) ... ok (0.284s)
2023-01-11T21:56:51.6652855Z   test_adaptive_avg_pool (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.629s)
2023-01-11T21:56:51.6653039Z   test_adaptive_avg_pool2d_nhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.076s)
2023-01-11T21:56:51.6653211Z   test_adaptive_avg_pool3d_ndhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.072s)
2023-01-11T21:56:51.6653381Z   test_add_scalar_relu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.193s)
2023-01-11T21:56:51.6653544Z   test_advanced_indexing (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:51.6653686Z Verifies that the x[:, [0], :, :] syntax works for quantized tensors. ... ok (0.006s)
2023-01-11T21:56:51.6653842Z   test_avg_pool2d (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:51.6654023Z Note: we currently cannot test the divisor_override, because quantized op will clamp the result ... ok (1.171s)
2023-01-11T21:56:51.6654180Z   test_avg_pool2d_nhwc (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:51.6654360Z Note: 1) we currently cannot test the divisor_override, because quantized op will clamp the result ... ok (1.116s)
2023-01-11T21:56:51.6654536Z   test_avg_pool3d (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:51.6654715Z Note: we currently cannot test the divisor_override, because quantized op will clamp the result ... ok (4.682s)
2023-01-11T21:56:51.6654868Z   test_avg_pool3d_nhwc (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:51.6655048Z Note: 1) we currently cannot test the divisor_override, because quantized op will clamp the result ... ok (6.672s)
2023-01-11T21:56:51.6655216Z   test_batch_norm (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.591s)
2023-01-11T21:56:51.6655385Z   test_batch_norm_relu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.654s)
2023-01-11T21:56:51.6655571Z   test_cat (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.361s)
2023-01-11T21:56:51.6655737Z   test_cat_nhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.275s)
2023-01-11T21:56:51.6655910Z   test_channel_shuffle (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (1.709s)
2023-01-11T21:56:51.6656503Z   test_custom_module_lstm (quantization.core.test_quantized_op.TestQuantizedOps) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
2023-01-11T21:56:51.6656580Z   warnings.warn(
2023-01-11T21:56:51.6656648Z ok (41.148s)
2023-01-11T21:56:51.6657230Z   test_custom_module_multi_head_attention (quantization.core.test_quantized_op.TestQuantizedOps) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py:1204: UserWarning: must run observer before calling calculate_qparams.                                    Returning default scale and zero point 
2023-01-11T21:56:51.6657304Z   warnings.warn(
2023-01-11T21:56:51.6657373Z ok (10.949s)
2023-01-11T21:56:51.6657879Z   test_empty_batch (quantization.core.test_quantized_op.TestQuantizedOps) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4022: UserWarning: nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.
2023-01-11T21:56:51.6658074Z   warnings.warn("nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.")
2023-01-11T21:56:51.6658141Z ok (0.025s)
2023-01-11T21:56:51.6658290Z   test_equal (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.182s)
2023-01-11T21:56:51.6658455Z   test_group_norm (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (6.184s)
2023-01-11T21:56:51.6658619Z   test_hardswish (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.542s)
2023-01-11T21:56:51.6658786Z   test_hardtanh (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.498s)
2023-01-11T21:56:51.6658954Z   test_instance_norm (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (1.557s)
2023-01-11T21:56:51.6659112Z   test_interpolate (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:51.6659257Z This test cover upsample_nearest2d and upsample_bilinear2d ... ok (3.808s)
2023-01-11T21:56:51.6659414Z   test_interpolate3d (quantization.core.test_quantized_op.TestQuantizedOps)
2023-01-11T21:56:51.6659525Z This test cover upsample_nearest3d ... ok (18.528s)
2023-01-11T21:56:51.6659676Z   test_leaky_relu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.023s)
2023-01-11T21:56:51.6659857Z   test_leaky_relu_observed_output (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.588s)
2023-01-11T21:56:51.6660029Z   test_linear_bias_unpack (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.003s)
2023-01-11T21:56:51.6660192Z   test_max_pool1d (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.241s)
2023-01-11T21:56:51.6660356Z   test_max_pool2d (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.566s)
2023-01-11T21:56:51.6660585Z   test_max_pool2d_cudnn (quantization.core.test_quantized_op.TestQuantizedOps) ... skip: cudnn is not enabled. (0.005s)
2023-01-11T21:56:51.6660753Z   test_max_pool2d_nhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.530s)
2023-01-11T21:56:51.6660908Z   test_mean (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.414s)
2023-01-11T21:56:51.6661061Z   test_mul_scalar_relu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.417s)
2023-01-11T21:56:51.6661638Z   test_qadd_broadcast (quantization.core.test_quantized_op.TestQuantizedOps) ... [W Resize.cpp:33] Warning: An output with one or more elements was resized since it had shape [1, 1, 4, 4], which does not match the required output shape [2, 1, 4, 4]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (function _resize_output_check)
2023-01-11T21:56:51.6661709Z ok (0.002s)
2023-01-11T21:56:51.6661906Z   test_qadd_relu_cudnn (quantization.core.test_quantized_op.TestQuantizedOps) ... skip: cudnn is not enabled. (0.001s)
2023-01-11T21:56:51.6662108Z   test_qadd_relu_cudnn_nhwc (quantization.core.test_quantized_op.TestQuantizedOps) ... skip: cudnn is not enabled. (0.001s)
2023-01-11T21:56:51.6662293Z   test_qadd_relu_different_qparams (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.005s)
2023-01-11T21:56:51.6662470Z   test_qadd_relu_same_qparams (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.005s)
2023-01-11T21:56:51.6662628Z   test_qcelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.143s)
2023-01-11T21:56:51.6662850Z   test_qclamp (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.650s)
2023-01-11T21:56:51.6663013Z   test_qelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.183s)
2023-01-11T21:56:51.6663157Z   test_qgelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.052s)
2023-01-11T21:56:51.6663328Z   test_qhardsigmoid (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.440s)
2023-01-11T21:56:51.6663493Z   test_qlayer_norm (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.104s)
2023-01-11T21:56:51.6663654Z   test_qmatmul (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.186s)
2023-01-11T21:56:51.6663823Z   test_qmul_broadcast (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.002s)
2023-01-11T21:56:51.6664009Z   test_qmul_relu_different_qparams (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.005s)
2023-01-11T21:56:51.6664185Z   test_qmul_relu_same_qparams (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.452s)
2023-01-11T21:56:51.6664348Z   test_qprelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.053s)
2023-01-11T21:56:51.6664507Z   test_qrelu (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (2.164s)
2023-01-11T21:56:51.6664653Z   test_qrelu6 (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.278s)
2023-01-11T21:56:51.6664817Z   test_qsoftmax (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.160s)
2023-01-11T21:56:51.6664987Z   test_qsoftmax_qnnpack (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.192s)
2023-01-11T21:56:51.6665248Z   test_qtanh (quantization.core.test_quantized_op.TestQuantizedOps) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.005s)
2023-01-11T21:56:51.6665414Z   test_qthreshold (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.155s)
2023-01-11T21:56:51.6665850Z   test_qtopk (quantization.core.test_quantized_op.TestQuantizedOps) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_op.py:1999: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:56:51.6665978Z   indices = torch.tensor(X).long()
2023-01-11T21:56:51.6666046Z ok (0.161s)
2023-01-11T21:56:51.6666226Z   test_quantized_mean_qnnpack (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.017s)
2023-01-11T21:56:51.6666376Z   test_sigmoid (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.187s)
2023-01-11T21:56:51.6666552Z   test_sigmoid_non_observed (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (0.805s)
2023-01-11T21:56:51.6666706Z   test_std (quantization.core.test_quantized_op.TestQuantizedOps) ... ok (1.541s)
2023-01-11T21:56:51.6666890Z   test_bfp16_quantize (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:51.6667206Z   test_choose_qparams (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: this is broken without changes to any relevant code, we need to remove hypothesis testing in CI (0.004s)
2023-01-11T21:56:51.6667406Z   test_choose_qparams_optimized (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.012s)
2023-01-11T21:56:51.6667580Z   test_clone (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6667816Z   test_compare_per_channel_device_numerics (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: CUDA is not available (0.001s)
2023-01-11T21:56:51.6668047Z   test_compare_per_tensor_device_numerics (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: CUDA is not available (0.001s)
2023-01-11T21:56:51.6668278Z   test_cuda_quantization_does_not_pin_memory (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: CUDA is not available (0.000s)
2023-01-11T21:56:51.6668477Z   test_decomposed_dequantize_per_channel (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6668685Z   test_decomposed_dequantize_per_tensor (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6668890Z   test_decomposed_dynamic_quant_pattern (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6669093Z   test_decomposed_quantize_per_channel (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:51.6669293Z   test_decomposed_quantize_per_tensor (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6669480Z   test_dequantize_fp16_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:51.6669694Z   test_dequantize_fp16_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:51.6670103Z   test_fp16_saturate_op (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:1390: UserWarning: FOUND weight out of range  (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/quantized/cpu/QuantUtils.h:215.)
2023-01-11T21:56:51.6670202Z   y = torch._saturate_weight_to_fp16(x)
2023-01-11T21:56:51.6670268Z ok (0.001s)
2023-01-11T21:56:51.6670444Z   test_jit_serialization (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.008s)
2023-01-11T21:56:51.6670644Z   test_per_channel_qtensor_creation_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6670874Z   test_per_channel_qtensor_creation_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:51.6671080Z   test_per_channel_qtensor_to_memory_format (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6671297Z   test_per_channel_to_device (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.001s)
2023-01-11T21:56:51.6671499Z   test_per_tensor_qtensor_to_memory_format (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6671742Z   test_per_tensor_to_device (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.001s)
2023-01-11T21:56:51.6672556Z   test_pickle_checkpoint_qtensor (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:309: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6672641Z   device=storage.device,
2023-01-11T21:56:51.6673295Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:330: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6673382Z   device=storage.device,
2023-01-11T21:56:51.6673437Z ok (0.008s)
2023-01-11T21:56:51.6673625Z   test_qscheme_pickle (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.003s)
2023-01-11T21:56:51.6673826Z   test_qtensor_channel_float_assignment (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.079s)
2023-01-11T21:56:51.6674005Z   test_qtensor_copy (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.016s)
2023-01-11T21:56:51.6674187Z   test_qtensor_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.007s)
2023-01-11T21:56:51.6674370Z   test_qtensor_creation (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.004s)
2023-01-11T21:56:51.6674577Z   test_qtensor_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:51.6674757Z   test_qtensor_dtypes (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:51.6674949Z   test_qtensor_fill_per_channel (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.005s)
2023-01-11T21:56:51.6675134Z   test_qtensor_fill_per_channel_nhwc (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.009s)
2023-01-11T21:56:51.6675324Z   test_qtensor_fill_per_tensor (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.005s)
2023-01-11T21:56:51.6675514Z   test_qtensor_fill_per_tensor_nhwc (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.010s)
2023-01-11T21:56:51.6675704Z   test_qtensor_float_assignment (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.003s)
2023-01-11T21:56:51.6675894Z   test_qtensor_index_put_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.011s)
2023-01-11T21:56:51.6676112Z   test_qtensor_index_put_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:51.6676305Z   test_qtensor_index_select_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:51.6676527Z   test_qtensor_index_select_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:51.6676712Z   test_qtensor_int_repr (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:51.6677268Z   test_qtensor_legacy_new_failure (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:410: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6677404Z   self.assertRaises(RuntimeError, lambda: qr.new(r.storage()))
2023-01-11T21:56:51.6677491Z ok (0.006s)
2023-01-11T21:56:51.6678031Z   test_qtensor_load_save (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6678173Z   self.assertEqual(qr2.storage().data_ptr(), qrv2.storage().data_ptr())
2023-01-11T21:56:51.6678241Z ok (0.009s)
2023-01-11T21:56:51.6678434Z   test_qtensor_masked_fill_cpu (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.011s)
2023-01-11T21:56:51.6678680Z   test_qtensor_masked_fill_cuda (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: No gpu is available. (0.000s)
2023-01-11T21:56:51.6678882Z   test_qtensor_per_channel_load_save (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.007s)
2023-01-11T21:56:51.6679081Z   test_qtensor_per_channel_permute (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.009s)
2023-01-11T21:56:51.6679263Z   test_qtensor_permute (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.008s)
2023-01-11T21:56:51.6679441Z   test_qtensor_quant_dequant (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.002s)
2023-01-11T21:56:51.6679640Z   test_qtensor_quantize_per_channel (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (9.190s)
2023-01-11T21:56:51.6679821Z   test_qtensor_reshape (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.003s)
2023-01-11T21:56:51.6680250Z   test_qtensor_resize (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:767: UserWarning: non-inplace resize is deprecated
2023-01-11T21:56:51.6680417Z   warnings.warn("non-inplace resize is deprecated")
2023-01-11T21:56:51.6680483Z ok (0.003s)
2023-01-11T21:56:51.6681033Z   test_qtensor_sub_byte_aligned_cols (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:290: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6681222Z   self.assertEqual(qr.storage().size(), rows * math.ceil(cols / elements_per_byte), f"with {dtype}, {elements_per_byte}")
2023-01-11T21:56:51.6681659Z /var/lib/jenkins/workspace/test/quantization/core/test_quantized_tensor.py:299: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6681853Z   self.assertEqual(q.storage().size(), math.ceil(num_elements / elements_per_byte), f"with {dtype}, {elements_per_byte}")
2023-01-11T21:56:51.6681922Z ok (0.006s)
2023-01-11T21:56:51.6682125Z   test_qtensor_sub_byte_not_aligned_cols (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.004s)
2023-01-11T21:56:51.6682300Z   test_qtensor_unsqueeze (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.013s)
2023-01-11T21:56:51.6682479Z   test_qtensor_view (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.008s)
2023-01-11T21:56:51.6682691Z   test_quant_pin_memory (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... skip: CUDA is not available (0.000s)
2023-01-11T21:56:51.6682896Z   test_quantize_per_channel_float_qparams (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (9.176s)
2023-01-11T21:56:51.6683118Z   test_quantize_per_channel_sub_byte (quantization.core.test_quantized_tensor.TestQuantizedTensor)
2023-01-11T21:56:51.6683336Z Tests the per channel quantization scheme for 4-bit qtensors. ... ok (0.308s)
2023-01-11T21:56:51.6683510Z   test_repeat (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:51.6683702Z   test_torch_qtensor_deepcopy (quantization.core.test_quantized_tensor.TestQuantizedTensor) ... ok (0.001s)
2023-01-11T21:56:51.6683911Z   test_observer_scriptable (quantization.core.test_workflow_module.TestRecordHistogramObserver) ... ok (0.014s)
2023-01-11T21:56:51.6684098Z   test_record_observer (quantization.core.test_workflow_module.TestRecordHistogramObserver) ... ok (0.008s)
2023-01-11T21:56:51.6684301Z   test_rnn (quantization.core.test_quantized_module.TestReferenceQuantizedModule)
2023-01-11T21:56:51.6684448Z Checks the rnn reference quantized modules has correct numerics ... ok (0.011s)
2023-01-11T21:56:51.6684632Z   test_rnn_cell (quantization.core.test_quantized_module.TestReferenceQuantizedModule)
2023-01-11T21:56:51.6684784Z Checks the rnn cell reference quantized modules has correct numerics ... ok (0.007s)
2023-01-11T21:56:51.6684963Z   test_sparse (quantization.core.test_quantized_module.TestReferenceQuantizedModule)
2023-01-11T21:56:51.6685065Z Embedding and EmbeddingBag ... ok (0.004s)
2023-01-11T21:56:51.6685243Z   test_conv2d (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.059s)
2023-01-11T21:56:51.6685418Z   test_conv2d_graph (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.021s)
2023-01-11T21:56:51.6685607Z   test_conv2d_graph_v2 (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.019s)
2023-01-11T21:56:51.6685796Z   test_conv2d_graph_v3 (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.020s)
2023-01-11T21:56:51.6685984Z   test_conv2d_nobias (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.048s)
2023-01-11T21:56:51.6686179Z   test_conv2d_nobias_graph (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.021s)
2023-01-11T21:56:51.6686376Z   test_conv2d_nobias_graph_v2 (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.019s)
2023-01-11T21:56:51.6686569Z   test_conv2d_nobias_graph_v3 (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.018s)
2023-01-11T21:56:51.6686751Z   test_conv2d_relu (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.049s)
2023-01-11T21:56:51.6686927Z   test_conv3d (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.015s)
2023-01-11T21:56:51.6687100Z   test_conv3d_relu (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.016s)
2023-01-11T21:56:51.6687294Z   test_default_qat_qconfig (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.010s)
2023-01-11T21:56:51.6687474Z   test_linear (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.043s)
2023-01-11T21:56:51.6687666Z   test_linear_dynamic (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.060s)
2023-01-11T21:56:51.6687851Z   test_linear_relu (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.043s)
2023-01-11T21:56:51.6688688Z   test_linear_relu_package_quantization_transforms (quantization.bc.test_backward_compatibility.TestSerialization) ... /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6688797Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:56:51.6689388Z /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py:1435: UserWarning: Passing a QConfig dictionary to prepare is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.
2023-01-11T21:56:51.6689515Z   warnings.warn(
2023-01-11T21:56:51.6690022Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node child_packed_weight_0 target child_packed_weight_0 child_packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6690233Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6690698Z /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:1346: UserWarning: Node _packed_weight_0 target _packed_weight_0 _packed_weight_0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
2023-01-11T21:56:51.6690959Z   warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
2023-01-11T21:56:51.6691030Z ok (0.377s)
2023-01-11T21:56:51.6691217Z   test_lstm (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.037s)
2023-01-11T21:56:51.6691415Z   test_per_channel_observer (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.004s)
2023-01-11T21:56:51.6691610Z   test_per_tensor_observer (quantization.bc.test_backward_compatibility.TestSerialization) ... ok (0.002s)
2023-01-11T21:56:51.6691792Z   test_batch_norm2d (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6691922Z Tests the correctness of the batchnorm2d module. ... ok (0.002s)
2023-01-11T21:56:51.6692466Z   test_batch_norm2d_serialization (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.
2023-01-11T21:56:51.6692530Z   warnings.warn(
2023-01-11T21:56:51.6692594Z ok (0.008s)
2023-01-11T21:56:51.6692777Z   test_batch_norm3d (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6692905Z Tests the correctness of the batchnorm3d module. ... ok (0.002s)
2023-01-11T21:56:51.6693115Z   test_batch_norm3d_serialization (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.008s)
2023-01-11T21:56:51.6693298Z   test_channel_shuffle (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6693431Z Tests the correctness of the ChannelShuffle module. ... ok (0.018s)
2023-01-11T21:56:51.6693623Z   test_conv1d_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (5.650s)
2023-01-11T21:56:51.6693796Z   test_conv2d_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (4.237s)
2023-01-11T21:56:51.6693980Z   test_conv3d_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.539s)
2023-01-11T21:56:51.6694157Z   test_dropout (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6694279Z Tests the correctness of the dropout module. ... ok (0.001s)
2023-01-11T21:56:51.6694490Z   test_dropout_serialization (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.005s)
2023-01-11T21:56:51.6694660Z   test_elu (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6694775Z Tests the correctness of the ELU module. ... ok (0.002s)
2023-01-11T21:56:51.6694972Z   test_embedding_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (6.041s)
2023-01-11T21:56:51.6695159Z   test_embedding_bag_api (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6695317Z Test execution and serialization for dynamic quantized embedding_bag modules on int8 ... ok (6.314s)
2023-01-11T21:56:51.6695494Z   test_group_norm (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6695618Z Tests the correctness of the groupnorm module. ... ok (0.003s)
2023-01-11T21:56:51.6695826Z   test_instance_norm (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6695961Z Tests the correctness of the instancenorm{n}d modules. ... ok (0.007s)
2023-01-11T21:56:51.6696138Z   test_layer_norm (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6696259Z Tests the correctness of the layernorm module. ... ok (0.003s)
2023-01-11T21:56:51.6696455Z   test_leaky_relu (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.002s)
2023-01-11T21:56:51.6697290Z   test_linear (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... /opt/conda/lib/python3.10/site-packages/torch/package/package_exporter.py:900: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6697406Z   storage_type_str = obj.pickle_storage_type()
2023-01-11T21:56:51.6698067Z /opt/conda/lib/python3.10/site-packages/torch/package/package_exporter.py:903: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:56:51.6698153Z   storage_numel = obj.size()
2023-01-11T21:56:51.6698221Z ok (22.240s)
2023-01-11T21:56:51.6698409Z   test_linear_leaky_relu (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6698574Z test API functionality for nn.intrinsic.quantized.linear_leaky_relu ... ok (10.941s)
2023-01-11T21:56:51.6698772Z   test_linear_relu (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (21.256s)
2023-01-11T21:56:51.6698952Z   test_linear_tanh (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6699109Z test API functionality for nn.intrinsic.quantized.linear_tanh ... ok (5.394s)
2023-01-11T21:56:51.6699273Z   test_pool_api (quantization.core.test_quantized_module.TestStaticQuantizedModule)
2023-01-11T21:56:51.6699392Z Tests the correctness of the pool module. ... ok (0.013s)
2023-01-11T21:56:51.6699579Z   test_prelu (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.002s)
2023-01-11T21:56:51.6699780Z   test_quant_dequant_api (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.002s)
2023-01-11T21:56:51.6699964Z   test_relu (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.001s)
2023-01-11T21:56:51.6700153Z   test_sigmoid (quantization.core.test_quantized_module.TestStaticQuantizedModule) ... ok (0.002s)
2023-01-11T21:56:51.6700360Z   test_subgraph_rewriter_annotations_int (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (5.033s)
2023-01-11T21:56:51.6700586Z   test_subgraph_rewriter_correct_output_replacement (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.400s)
2023-01-11T21:56:51.6700797Z   test_subgraph_rewriter_graph_argument_order (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.050s)
2023-01-11T21:56:51.6701036Z   test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.036s)
2023-01-11T21:56:51.6701248Z   test_subgraph_rewriter_multiple_pattern_match (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.045s)
2023-01-11T21:56:51.6701460Z   test_subgraph_rewriter_pattern_is_entire_graph (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.044s)
2023-01-11T21:56:51.6701712Z   test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.044s)
2023-01-11T21:56:51.6701948Z   test_subgraph_rewriter_placeholder_matching (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter)
2023-01-11T21:56:51.6702092Z This tests that a placeholder Node can be matched to a Node with ... ok (0.044s)
2023-01-11T21:56:51.6702299Z   test_subgraph_rewriter_preserves_logic (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.044s)
2023-01-11T21:56:51.6702522Z   test_subgraph_rewriter_replaces_referenced_submodules (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.035s)
2023-01-11T21:56:51.6702868Z   test_subgraph_rewriter_single_pattern_match (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.044s)
2023-01-11T21:56:51.6703113Z   test_subgraph_rewriter_traced_as_callable (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.045s)
2023-01-11T21:56:51.6703343Z   test_subgraph_rewriter_with_oneliner_pattern (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.043s)
2023-01-11T21:56:51.6703630Z   test_subgraph_writer_replace_consecutive_submodules (quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.044s)
2023-01-11T21:56:51.6703833Z   test_get_fqn_to_example_inputs_complex_args (quantization.core.test_utils.TestUtils)
2023-01-11T21:56:51.6704024Z Test that we can record complex example inputs such as lists and dicts ... ok (0.006s)
2023-01-11T21:56:51.6704219Z   test_get_fqn_to_example_inputs_default_kwargs (quantization.core.test_utils.TestUtils)
2023-01-11T21:56:51.6704414Z Test that we can get example inputs for functions with default keyword arguments ... ok (0.006s)
2023-01-11T21:56:51.6704594Z   test_get_fqn_to_example_inputs_simple (quantization.core.test_utils.TestUtils) ... ok (0.006s)
2023-01-11T21:56:51.6704916Z   test_quantize_weight_clamping_per_channel (quantization.core.test_utils.TestUtils)
2023-01-11T21:56:51.6705116Z Test quant_{min, max} from per channel observer is honored by `_quantize_weight` method ... ok (0.002s)
2023-01-11T21:56:51.6705266Z   test_quantize_weight_clamping_per_tensor (quantization.core.test_utils.TestUtils)
2023-01-11T21:56:51.6705461Z Test quant_{min, max} from per tensor observer is honored by `_quantize_weight` method ... ok (0.002s)
2023-01-11T21:56:51.6705473Z 
2023-01-11T21:56:51.6705715Z ----------------------------------------------------------------------
2023-01-11T21:56:51.6705833Z Ran 1001 tests in 842.182s
2023-01-11T21:56:51.6705838Z 
2023-01-11T21:56:51.6705938Z OK (skipped=62)
2023-01-11T21:56:51.6705943Z 
2023-01-11T21:56:51.6706056Z Generating XML reports...
2023-01-11T21:56:51.6706502Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.ao_migration.test_ao_migration.TestAOMigrationNNIntrinsic-20230111214248.xml
2023-01-11T21:56:51.6706966Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.ao_migration.test_ao_migration.TestAOMigrationNNQuantized-20230111214248.xml
2023-01-11T21:56:51.6707409Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.ao_migration.test_quantization.TestAOMigrationQuantization-20230111214248.xml
2023-01-11T21:56:51.6707875Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.ao_migration.test_quantization_fx.TestAOMigrationQuantizationFx-20230111214248.xml
2023-01-11T21:56:51.6708230Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_backend_config.TestBackendConfig-20230111214248.xml
2023-01-11T21:56:51.6708655Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_bias_correction_eager.TestBiasCorrectionEager-20230111214248.xml
2023-01-11T21:56:51.6709047Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestComparatorOps-20230111214248.xml
2023-01-11T21:56:51.6709476Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_deprecated_jit_quant.TestDeprecatedJitQuantized-20230111214248.xml
2023-01-11T21:56:51.6709900Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestDistributed-20230111214248.xml
2023-01-11T21:56:51.6710329Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_module.TestDynamicQuantizedModule-20230111214248.xml
2023-01-11T21:56:51.6710778Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestDynamicQuantizedOps-20230111214248.xml
2023-01-11T21:56:51.6711180Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_equalize_eager.TestEqualizeEager-20230111214248.xml
2023-01-11T21:56:51.6711580Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_equalize_fx.TestEqualizeFx-20230111214248.xml
2023-01-11T21:56:51.6711970Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXGraphMatcher-20230111214248.xml
2023-01-11T21:56:51.6712380Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXGraphMatcherModels-20230111214248.xml
2023-01-11T21:56:51.6712750Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIs-20230111214248.xml
2023-01-11T21:56:51.6713185Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteCoreAPIsModels-20230111214248.xml
2023-01-11T21:56:51.6713592Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_numeric_suite_fx.TestFXNumericSuiteNShadows-20230111214248.xml
2023-01-11T21:56:51.6713985Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestFakeQuantize-20230111214248.xml
2023-01-11T21:56:51.6714444Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_ops.TestFakeQuantizeOps-20230111214248.xml
2023-01-11T21:56:51.6714820Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_fuse_eager.TestFuseEager-20230111214248.xml
2023-01-11T21:56:51.6715175Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_fx.TestFuseFx-20230111214248.xml
2023-01-11T21:56:51.6715572Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_ops.TestFusedObsFakeQuant-20230111214248.xml
2023-01-11T21:56:51.6715997Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestFusedObsFakeQuantModule-20230111214248.xml
2023-01-11T21:56:51.6716383Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_fusion_passes.TestFusionPasses-20230111214248.xml
2023-01-11T21:56:51.6716826Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxDetectInputWeightEqualization-20230111214248.xml
2023-01-11T21:56:51.6717224Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxDetectOutliers-20230111214248.xml
2023-01-11T21:56:51.6717582Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportClass-20230111214248.xml
2023-01-11T21:56:51.6718045Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportDetectDynamicStatic-20230111214248.xml
2023-01-11T21:56:51.6718457Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportDetector-20230111214248.xml
2023-01-11T21:56:51.6718859Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportObserver-20230111214248.xml
2023-01-11T21:56:51.6719299Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_model_report_fx.TestFxModelReportVisualizer-20230111214248.xml
2023-01-11T21:56:51.6719705Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestHistogramObserver-20230111214248.xml
2023-01-11T21:56:51.6720133Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_model_numerics.TestModelNumericsEager-20230111214248.xml
2023-01-11T21:56:51.6720545Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_numeric_suite_eager.TestNumericSuiteEager-20230111214248.xml
2023-01-11T21:56:51.6720979Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestObserver-20230111214248.xml
2023-01-11T21:56:51.6721374Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestPadding-20230111214248.xml
2023-01-11T21:56:51.6721750Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQNNPackOps-20230111214248.xml
2023-01-11T21:56:51.6722090Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_docs.TestQuantizationDocs-20230111214248.xml
2023-01-11T21:56:51.6722497Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeDynamicJitOps-20230111214248.xml
2023-01-11T21:56:51.6722912Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeDynamicJitPasses-20230111214248.xml
2023-01-11T21:56:51.6723328Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerOps-20230111214248.xml
2023-01-11T21:56:51.6723760Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQDynamic-20230111214248.xml
2023-01-11T21:56:51.6724179Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_ptq.TestQuantizeEagerPTQStatic-20230111214248.xml
2023-01-11T21:56:51.6724580Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQAT-20230111214248.xml
2023-01-11T21:56:51.6725031Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.eager.test_quantize_eager_qat.TestQuantizeEagerQATNumerics-20230111214248.xml
2023-01-11T21:56:51.6725398Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_fx.TestQuantizeFx-20230111214248.xml
2023-01-11T21:56:51.6725784Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_fx.TestQuantizeFxModels-20230111214248.xml
2023-01-11T21:56:51.6726166Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_fx.TestQuantizeFxOps-20230111214248.xml
2023-01-11T21:56:51.6726544Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeJit-20230111214248.xml
2023-01-11T21:56:51.6726888Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeJitOps-20230111214248.xml
2023-01-11T21:56:51.6727278Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.jit.test_quantize_jit.TestQuantizeJitPasses-20230111214248.xml
2023-01-11T21:56:51.6727705Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_quantize_pt2e.TestQuantizePT2EModels-20230111214248.xml
2023-01-11T21:56:51.6728116Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQuantizedConv-20230111214248.xml
2023-01-11T21:56:51.6728542Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQuantizedEmbeddingOps-20230111214248.xml
2023-01-11T21:56:51.6728989Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_functional.TestQuantizedFunctionalOps-20230111214248.xml
2023-01-11T21:56:51.6729496Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQuantizedLinear-20230111214248.xml
2023-01-11T21:56:51.6737704Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_op.TestQuantizedOps-20230111214248.xml
2023-01-11T21:56:51.6738200Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_tensor.TestQuantizedTensor-20230111214248.xml
2023-01-11T21:56:51.6738618Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_workflow_module.TestRecordHistogramObserver-20230111214248.xml
2023-01-11T21:56:51.6739023Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_module.TestReferenceQuantizedModule-20230111214248.xml
2023-01-11T21:56:51.6739411Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.bc.test_backward_compatibility.TestSerialization-20230111214248.xml
2023-01-11T21:56:51.6739803Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_quantized_module.TestStaticQuantizedModule-20230111214248.xml
2023-01-11T21:56:51.6740173Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.fx.test_subgraph_rewriter.TestSubgraphRewriter-20230111214248.xml
2023-01-11T21:56:51.6740501Z Generated XML report: test-reports/python-unittest/test_quantization/TEST-quantization.core.test_utils.TestUtils-20230111214248.xml
2023-01-11T21:56:51.6740509Z 
2023-01-11T21:56:51.6740836Z ##[endgroup]
2023-01-11T21:56:51.6741130Z FINISHED PRINTING LOG FILE of test_quantization (/var/lib/jenkins/workspace/test/test-reports/test_quantization_i5v0je0n)
2023-01-11T21:56:51.6741136Z 
2023-01-11T21:56:51.9597950Z Running inductor/test_torchinductor_opinfo ... [2023-01-11 21:56:51.959426]
2023-01-11T21:56:53.5492081Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:56:53.5632112Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:56:53.6177195Z Ignoring disabled issues:  []
2023-01-11T21:56:53.6276301Z Ignoring disabled issues:  []
2023-01-11T21:56:53.6600973Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:56:53.659757]
2023-01-11T21:56:53.6602670Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:56:53.659777]
2023-01-11T21:57:02.9615427Z 
2023-01-11T21:57:02.9616025Z Expand the folded group to see the log file of inductor/test_torchinductor_opinfo
2023-01-11T21:57:02.9617191Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_2ge6id92)
2023-01-11T21:57:02.9618051Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:57:02.9618956Z Test results will be stored in test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-43da9767f75f5d75.xml
2023-01-11T21:57:02.9619828Z ============================= test session starts ==============================
2023-01-11T21:57:02.9620457Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:57:02.9620897Z cachedir: .pytest_cache
2023-01-11T21:57:02.9621609Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:57:02.9622223Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:57:02.9623070Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:57:02.9624416Z collecting ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor_opinfo.py:0: PytestCollectionWarning: cannot collect test class 'TestExpect' because it has a __new__ constructor (from: test/inductor/test_torchinductor_opinfo.py)
2023-01-11T21:57:02.9625146Z collected 0 items
2023-01-11T21:57:02.9625452Z Running 0 items in this shard: 
2023-01-11T21:57:02.9625672Z 
2023-01-11T21:57:02.9625858Z =============================== warnings summary ===============================
2023-01-11T21:57:02.9626474Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:57:02.9627395Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:57:02.9628018Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:57:02.9628251Z 
2023-01-11T21:57:02.9628644Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:57:02.9629650Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-43da9767f75f5d75.xml -
2023-01-11T21:57:02.9630311Z ============================== 1 warning in 0.25s ==============================
2023-01-11T21:57:02.9630828Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:57:02.9631176Z 
2023-01-11T21:57:02.9631597Z ##[endgroup]
2023-01-11T21:57:02.9632404Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_2ge6id92)
2023-01-11T21:57:02.9632860Z 
2023-01-11T21:57:08.2005621Z 
2023-01-11T21:57:08.2006223Z Expand the folded group to see the log file of inductor/test_torchinductor_opinfo
2023-01-11T21:57:08.2007443Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_y22owck4)
2023-01-11T21:57:08.2008201Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:57:08.2008728Z Test results will be stored in test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-c75b30b3898389b7.xml
2023-01-11T21:57:08.2009237Z ============================= test session starts ==============================
2023-01-11T21:57:08.2009608Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:57:08.2009861Z cachedir: .pytest_cache
2023-01-11T21:57:08.2010275Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:57:08.2010614Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:57:08.2011035Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:57:08.2011715Z collecting ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor_opinfo.py:0: PytestCollectionWarning: cannot collect test class 'TestExpect' because it has a __new__ constructor (from: test/inductor/test_torchinductor_opinfo.py)
2023-01-11T21:57:08.2012130Z collected 0 items
2023-01-11T21:57:08.2012307Z Running 0 items in this shard: 
2023-01-11T21:57:08.2012429Z 
2023-01-11T21:57:08.2012717Z =============================== warnings summary ===============================
2023-01-11T21:57:08.2013078Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:57:08.2013597Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:57:08.2013973Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:57:08.2014103Z 
2023-01-11T21:57:08.2014328Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:57:08.2014896Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-c75b30b3898389b7.xml -
2023-01-11T21:57:08.2015341Z ============================== 1 warning in 0.03s ==============================
2023-01-11T21:57:08.2015633Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:57:08.2015829Z 
2023-01-11T21:57:08.2016073Z ##[endgroup]
2023-01-11T21:57:08.2016523Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_y22owck4)
2023-01-11T21:57:08.2016765Z 
2023-01-11T21:57:08.5378194Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:57:08.537288]
2023-01-11T21:57:17.4885640Z 
2023-01-11T21:57:17.4886192Z Expand the folded group to see the log file of inductor/test_torchinductor_opinfo
2023-01-11T21:57:17.4887301Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_tylrswaw)
2023-01-11T21:57:17.4888228Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:57:17.4889336Z Test results will be stored in test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-775c9b995ca41897.xml
2023-01-11T21:57:17.4889898Z ============================= test session starts ==============================
2023-01-11T21:57:17.4890264Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:57:17.4890506Z cachedir: .pytest_cache
2023-01-11T21:57:17.4890918Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:57:17.4891271Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:57:17.4891726Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:57:17.4892884Z collecting ... /var/lib/jenkins/workspace/test/inductor/test_torchinductor_opinfo.py:0: PytestCollectionWarning: cannot collect test class 'TestExpect' because it has a __new__ constructor (from: test/inductor/test_torchinductor_opinfo.py)
2023-01-11T21:57:17.4893388Z collected 0 items
2023-01-11T21:57:17.4893681Z Running 0 items in this shard: 
2023-01-11T21:57:17.4893845Z 
2023-01-11T21:57:17.4894018Z =============================== warnings summary ===============================
2023-01-11T21:57:17.4894650Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:57:17.4895218Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:57:17.4895573Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:57:17.4895705Z 
2023-01-11T21:57:17.4895938Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:57:17.4896499Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-775c9b995ca41897.xml -
2023-01-11T21:57:17.4897074Z ============================== 1 warning in 0.03s ==============================
2023-01-11T21:57:17.4897376Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:57:17.4897570Z 
2023-01-11T21:57:17.4897823Z ##[endgroup]
2023-01-11T21:57:17.4898275Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor_opinfo (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_opinfo_tylrswaw)
2023-01-11T21:57:17.4898530Z 
2023-01-11T21:57:17.4898697Z Running test_autograd ... [2023-01-11 21:57:17.489032]
2023-01-11T21:57:17.4899158Z Executing ['/opt/conda/bin/python', '-bb', 'test_autograd.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:57:17.489246]
2023-01-11T21:57:36.5694419Z 
2023-01-11T21:57:36.5695144Z Expand the folded group to see the log file of test_autograd
2023-01-11T21:57:36.5696103Z ##[group]PRINTING LOG FILE of test_autograd (/var/lib/jenkins/workspace/test/test-reports/test_autograd_uadvh1se)
2023-01-11T21:57:36.5696460Z 
2023-01-11T21:57:36.5696566Z Running tests...
2023-01-11T21:57:36.5697137Z ----------------------------------------------------------------------
2023-01-11T21:57:36.5697714Z Test results will be stored in test-reports/python-unittest/test_autograd
2023-01-11T21:57:36.5698260Z   test_backward_out_of_context (__main__.TestAllowMutationOnSaved) ... ok (0.003s)
2023-01-11T21:57:36.5698760Z   test_basic (__main__.TestAllowMutationOnSaved) ... ok (0.007s)
2023-01-11T21:57:36.5699231Z   test_disallow_nesting (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:36.5699725Z   test_double_backward (__main__.TestAllowMutationOnSaved) ... ok (0.002s)
2023-01-11T21:57:36.5700229Z   test_save_base_and_modify_view (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:36.5700778Z   test_save_view_modify_base (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:36.5701257Z   test_saved_but_not_anymore (__main__.TestAllowMutationOnSaved) ... ok (0.002s)
2023-01-11T21:57:36.5701808Z   test_saved_same_tensor_different_versions (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:36.5702335Z   test_saved_same_tensor_many_times (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:36.5702866Z   test_views (__main__.TestAllowMutationOnSaved) ... ok (0.007s)
2023-01-11T21:57:36.5703295Z   test_with_math_views (__main__.TestAllowMutationOnSaved) ... ok (0.002s)
2023-01-11T21:57:36.5703737Z   test_with_out_variant (__main__.TestAllowMutationOnSaved) ... ok (0.001s)
2023-01-11T21:57:36.5704209Z   test_access_saved_tensor_twice_without_recomputation_works (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5706322Z   test_accumulate_grad (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:197: UserWarning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/autograd/engine.cpp:1134.)
2023-01-11T21:57:36.5707713Z   Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
2023-01-11T21:57:36.5708085Z ok (0.002s)
2023-01-11T21:57:36.5708400Z   test_accumulate_grad_tensor_reference (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5708859Z   test_accumulate_grad_with_zero_numel_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5709605Z   test_anomaly_assign_parent_cleanup (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:4090: UserWarning: Anomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.
2023-01-11T21:57:36.5710221Z   with detect_anomaly():
2023-01-11T21:57:36.5710645Z ok (0.001s)
2023-01-11T21:57:36.5710956Z   test_anomaly_detect_nan (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5711364Z   test_anomaly_grad_warnings (__main__.TestAutograd) ... ok (0.013s)
2023-01-11T21:57:36.5711777Z   test_anomaly_mode_no_check_nan (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5712225Z   test_attribute_deletion (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5712634Z   test_autograd_function_extension_enabled_by_default (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5713127Z   test_autograd_function_extension_feature_flag (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5713603Z   test_autograd_inplace_view_of_view (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5714082Z   test_autograd_inplace_views_creation_meta (__main__.TestAutograd) ... ok (0.220s)
2023-01-11T21:57:36.5714670Z   test_autograd_inplace_views_cross_dtype (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5715162Z   test_autograd_multiple_views_python (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5715671Z   test_autograd_python_custom_function_inplace (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:36.5716247Z   test_autograd_simple_views_python (__main__.TestAutograd) ... ok (0.069s)
2023-01-11T21:57:36.5716801Z   test_autograd_views_codegen (__main__.TestAutograd) ... ok (0.017s)
2023-01-11T21:57:36.5717284Z   test_backward (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5717744Z   test_backward_badcalls (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5718199Z   test_backward_copy (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5718660Z   test_backward_create_graph_warns (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5719157Z   test_backward_no_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5719668Z   test_backward_twice_retained_graph_with_saved_values (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5720221Z   test_backward_twice_retained_graph_without_saved_values (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5720796Z   test_backward_twice_with_saved_values (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5721327Z   test_backward_twice_without_saved_values (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5721838Z   test_backward_with_inputs (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5722298Z   test_backward_with_nonleaf_inputs (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5723656Z   test_calculate_shape_util (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/nested/__init__.py:58: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:57:36.5724613Z   return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)
2023-01-11T21:57:36.5725025Z ok (0.001s)
2023-01-11T21:57:36.5725407Z   test_callback_adds_callback (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5725895Z   test_cant_create_saved_tensors (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5726383Z   test_checkpoint_valid_reset_on_error (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5727018Z   test_checkpointing (__main__.TestAutograd) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:57:36.5727614Z   test_checkpointing_non_reentrant_autocast_cpu (__main__.TestAutograd)
2023-01-11T21:57:36.5728353Z Test that autocast args such as the dtype are preserved during non-reentrant ... ok (0.002s)
2023-01-11T21:57:36.5728916Z   test_checkpointing_non_reentrant_autocast_gpu (__main__.TestAutograd)
2023-01-11T21:57:36.5729724Z Test that autocast args/kwargs such as the dtype are preserved during ... skip: Test requires CUDA bf16 support (0.000s)
2023-01-11T21:57:36.5730374Z   test_checkpointing_without_reentrant_arbitrary_input_output (__main__.TestAutograd)
2023-01-11T21:57:36.5731127Z Ensures checkpointing without reentrant autograd works with functions ... ok (0.002s)
2023-01-11T21:57:36.5731694Z   test_checkpointing_without_reentrant_correct_grad (__main__.TestAutograd)
2023-01-11T21:57:36.5732225Z Verifies that correct gradients are calculated for checkpoint ... ok (0.002s)
2023-01-11T21:57:36.5732840Z   test_checkpointing_without_reentrant_custom_function_works (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5733491Z   test_checkpointing_without_reentrant_dataparallel (__main__.TestAutograd)
2023-01-11T21:57:36.5734082Z Verifies gradient correctness when checkpoint without reentrant autograd ... ok (0.002s)
2023-01-11T21:57:36.5734720Z   test_checkpointing_without_reentrant_detached_tensor_use_reentrant_False (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5735514Z   test_checkpointing_without_reentrant_detached_tensor_use_reentrant_True (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5736157Z   test_checkpointing_without_reentrant_input_requires_grad_False (__main__.TestAutograd)
2023-01-11T21:57:36.5736842Z Basic test for checkpoint without reentrant autograd. ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:57:36.5737488Z   test_checkpointing_without_reentrant_input_requires_grad_True (__main__.TestAutograd)
2023-01-11T21:57:36.5738165Z Basic test for checkpoint without reentrant autograd. ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:57:36.5738873Z   test_checkpointing_without_reentrant_memory_savings (__main__.TestAutograd) ... skip: Test requires CUDA (0.001s)
2023-01-11T21:57:36.5739490Z   test_checkpointing_without_reentrant_parameter_used_in_an_out (__main__.TestAutograd)
2023-01-11T21:57:36.5740059Z Ensures that gradient hooks are only called once per tensor. ... ok (0.001s)
2023-01-11T21:57:36.5740593Z   test_copy_slices_graph_task_updates (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5741169Z   test_create_graph_and_full_backward_hook_cycle (__main__.TestAutograd) ... ok (0.403s)
2023-01-11T21:57:36.5741745Z   test_current_graph_task_execution_order (__main__.TestAutograd) ... ok (0.010s)
2023-01-11T21:57:36.5742248Z   test_current_graph_task_id (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5742790Z   test_current_node (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5743278Z   test_custom_autograd_no_early_free (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:36.5743811Z   test_custom_autograd_repeated_grad_grad (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5744320Z   test_custom_function_cycle (__main__.TestAutograd) ... ok (0.127s)
2023-01-11T21:57:36.5744804Z   test_custom_function_error (__main__.TestAutograd) ... ok (0.066s)
2023-01-11T21:57:36.5745291Z   test_custom_function_exception (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5745840Z   test_custom_function_forward_mode_forward_is_no_op (__main__.TestAutograd) ... ok (0.021s)
2023-01-11T21:57:36.5746392Z   test_custom_function_forward_mode_inplace_checks (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5746970Z   test_custom_function_forward_mode_non_differentiable (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5747547Z   test_custom_function_forward_mode_non_tensor_before_tensor_args (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5748135Z   test_custom_function_forward_mode_view_checks (__main__.TestAutograd) ... ok (0.013s)
2023-01-11T21:57:36.5748700Z   test_custom_function_forward_mode_wrong_formula (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5749242Z   test_custom_function_local_inplace (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5749793Z   test_custom_function_mark_dirty_not_differentiable (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5750339Z   test_custom_function_no_tensors (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5750886Z   test_custom_function_non_tensor_inputs_outputs (__main__.TestAutograd) ... ok (0.009s)
2023-01-11T21:57:36.5751511Z   test_custom_function_return_view_in_nograd (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5752047Z   test_custom_function_save_for_forward (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5752567Z   test_custom_function_saved_tensors (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:36.5753112Z   test_custom_function_setup_context_multi_input (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5753667Z   test_custom_function_setup_context_multi_output (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5754234Z   test_custom_function_setup_context_simple (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5754746Z   test_deep_reentrant (__main__.TestAutograd) ... ok (0.209s)
2023-01-11T21:57:36.5755334Z   test_default_saved_variable_hooks_double_backward (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5755827Z   test_dep_nograd (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5756294Z   test_dependent_backward (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5756735Z   test_detach (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5757132Z   test_detach_base (__main__.TestAutograd)
2023-01-11T21:57:36.5757550Z detaching base does not detach view ... ok (0.001s)
2023-01-11T21:57:36.5758063Z   test_detach_then_inplace_raises_in_autograd (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5759013Z   test_diagonal_expanded_v (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:2510: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
2023-01-11T21:57:36.5759910Z   v_expanded = torch.tensor(value).expand(10)
2023-01-11T21:57:36.5760274Z ok (0.001s)
2023-01-11T21:57:36.5760621Z   test_dir (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5761052Z   test_disabling_saved_tensor_hooks (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5761607Z   test_disabling_saved_tensor_hooks_nested (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5762123Z   test_dont_materialize_grads (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5762603Z   test_duplicate_backward_root (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5763067Z   test_first_grad_fn_access_in_no_grad_mode (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5763549Z   test_free_deep_graph (__main__.TestAutograd) ... ok (1.754s)
2023-01-11T21:57:36.5764019Z   test_free_deep_graph_complicated (__main__.TestAutograd) ... ok (1.197s)
2023-01-11T21:57:36.5764515Z   test_free_deep_graph_pyfunction (__main__.TestAutograd) ... ok (1.645s)
2023-01-11T21:57:36.5765045Z   test_full_backward_hook_double_backward (__main__.TestAutograd) ... ok (0.065s)
2023-01-11T21:57:36.5765492Z   test_function (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5765912Z   test_function_returns_input (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5766368Z   test_function_returns_undefined_tensor (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5766823Z   test_gc_in_destructor (__main__.TestAutograd)
2023-01-11T21:57:36.5767336Z Previously, if a Function destructor triggered a garbage collection, ... ok (0.621s)
2023-01-11T21:57:36.5767843Z   test_grad (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5768661Z   test_grad_badcalls (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5769251Z   test_grad_batched_grad (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5769695Z   test_grad_empty_inputs (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5770162Z   test_grad_fn_attr_bindings (__main__.TestAutograd) ... ok (0.017s)
2023-01-11T21:57:36.5770642Z   test_grad_fn_badcalls (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5771093Z   test_grad_fn_prehooks (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5771717Z   test_grad_fn_prehooks_multiple_outputs (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5772254Z   test_grad_fn_prehooks_remove_hooks (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5772765Z   test_grad_mode_class_decoration (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5773277Z   test_grad_mode_restored_reentrant (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5775671Z   test_grad_nonleaf (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:776: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:57:36.5777074Z   self.assertIsNone(x.grad)
2023-01-11T21:57:36.5777396Z ok (0.003s)
2023-01-11T21:57:36.5777761Z   test_grad_nonleaf_many_outputs (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5780020Z   test_grad_nonleaf_register_hook (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:828: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:57:36.5781448Z   self.assertIsNone(x_list[0].grad)
2023-01-11T21:57:36.5783569Z /var/lib/jenkins/workspace/test/test_autograd.py:835: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /var/lib/jenkins/workspace/build/aten/src/ATen/core/TensorBody.h:485.)
2023-01-11T21:57:36.5784916Z   self.assertIsNone(x_list[i].grad)
2023-01-11T21:57:36.5785246Z ok (0.002s)
2023-01-11T21:57:36.5785614Z   test_grad_unreachable (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5786188Z   test_grad_unreachable_discovery (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5786743Z   test_gradcheck_backward_mul_by_grad_output (__main__.TestAutograd) ... ok (0.022s)
2023-01-11T21:57:36.5787270Z   test_gradcheck_check_batched_grad (__main__.TestAutograd) ... ok (0.050s)
2023-01-11T21:57:36.5787799Z   test_gradcheck_check_forward_or_backward_only (__main__.TestAutograd)
2023-01-11T21:57:36.5788359Z Depending on settings for check_forward_ad and check_backward_ad, the ... ok (0.019s)
2023-01-11T21:57:36.5789889Z   test_gradcheck_check_no_differentiable_outputs (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:36.5790776Z   warnings.warn(
2023-01-11T21:57:36.5791068Z ok (0.003s)
2023-01-11T21:57:36.5791500Z   test_gradcheck_complex_non_complex_outputs (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:36.5792948Z   test_gradcheck_custom_error (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:36.5793891Z   warnings.warn(
2023-01-11T21:57:36.5794186Z ok (0.014s)
2023-01-11T21:57:36.5794618Z   test_gradcheck_dense_and_sparse_inputs (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:36.5795127Z   test_gradcheck_forward_ad (__main__.TestAutograd) ... ok (0.107s)
2023-01-11T21:57:36.5795625Z   test_gradcheck_forward_ad_batched_grad (__main__.TestAutograd) ... ok (0.009s)
2023-01-11T21:57:36.5796203Z   test_gradcheck_forward_ad_respects_requires_grad (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5796774Z   test_gradcheck_forward_ad_runs_with_no_requires_grad (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5798762Z   test_gradcheck_get_analytical_jacobian (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:616: UserWarning: get_analytical_jacobian was part of PyTorch's private API and not meant to be exposed. We are deprecating it and it will be removed in a future version of PyTorch. If you have a specific use for this or feature request for this to be a stable API, please file us an issue at https://github.com/pytorch/pytorch/issues/new
2023-01-11T21:57:36.5800202Z   warnings.warn("get_analytical_jacobian was part of PyTorch's private API and not "
2023-01-11T21:57:36.5800636Z ok (0.010s)
2023-01-11T21:57:36.5802302Z   test_gradcheck_get_numerical_jacobian (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:209: UserWarning: get_numerical_jacobian was part of PyTorch's private API and not meant to be exposed. We are deprecating it and it will be removed in a future version of PyTorch. If you have a specific use for this or feature request for this to be a stable API, please file us an issue at https://github.com/pytorch/pytorch/issues/new
2023-01-11T21:57:36.5803655Z   warnings.warn("get_numerical_jacobian was part of PyTorch's private API and not "
2023-01-11T21:57:36.5804063Z ok (0.008s)
2023-01-11T21:57:36.5805348Z   test_gradcheck_jacobian_mismatch (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:36.5806216Z   warnings.warn(
2023-01-11T21:57:36.5806512Z ok (0.037s)
2023-01-11T21:57:36.5807811Z   test_gradcheck_multiple_mkldnn_inputs (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #1 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:36.5808697Z   warnings.warn(
2023-01-11T21:57:36.5808987Z ok (0.017s)
2023-01-11T21:57:36.5809552Z   test_gradcheck_nondeterministic (__main__.TestAutograd) ... ok (0.074s)
2023-01-11T21:57:36.5810100Z   test_gradcheck_output_shape_or_dtype_depend_on_values (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5810613Z   test_gradcheck_single_input (__main__.TestAutograd) ... ok (0.011s)
2023-01-11T21:57:36.5811780Z   test_gradcheck_sparse_bsc_input (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:4389: UserWarning: Sparse BSC tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:57:36.5812964Z   gradcheck(fn, torch.rand(4, 8, dtype=torch.double).to_sparse_bsc((2, 2)).requires_grad_(True),
2023-01-11T21:57:36.5813395Z ok (0.025s)
2023-01-11T21:57:36.5813812Z   test_gradcheck_sparse_bsr_input (__main__.TestAutograd) ... ok (0.019s)
2023-01-11T21:57:36.5814482Z   test_gradcheck_sparse_csc_input (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5814985Z   test_gradcheck_sparse_csr_input (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5815500Z   test_gradcheck_sparse_input (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5816944Z   test_gradcheck_test_outputs (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:57:36.5817816Z   warnings.warn(
2023-01-11T21:57:36.5818104Z ok (0.002s)
2023-01-11T21:57:36.5818494Z   test_gradcheck_undefined_grad (__main__.TestAutograd) ... ok (0.010s)
2023-01-11T21:57:36.5819142Z   test_gradcheck_validates_input_mkldnn (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5819658Z   test_gradcheck_validates_inputs (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5820132Z   test_graph_save_on_cpu (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:36.5820656Z   test_graph_save_on_cpu_cuda (__main__.TestAutograd) ... skip: test requires CUDA (0.001s)
2023-01-11T21:57:36.5821165Z   test_hessian_vector (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5821587Z   test_hook_none (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5822032Z   test_hook_with_no_name (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5822464Z   test_hooks (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5822961Z   test_hooks_cpp (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5823444Z   test_index_backward_does_not_save_tensor (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5823934Z   test_indexing (__main__.TestAutograd) ... ok (0.017s)
2023-01-11T21:57:36.5824413Z   test_indexing_duplicates (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5824857Z   test_inplace (__main__.TestAutograd) ... ok (0.022s)
2023-01-11T21:57:36.5825322Z   test_inplace_not_requires_grad (__main__.TestAutograd) ... ok (0.009s)
2023-01-11T21:57:36.5825816Z   test_inplace_on_view_backward (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5826291Z   test_inplace_on_view_leaf_errors (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5826795Z   test_inplace_on_view_saved_output (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5827294Z   test_inplace_on_view_weak_grad_fn (__main__.TestAutograd) ... ok (0.083s)
2023-01-11T21:57:36.5827758Z   test_input_buffer_accum (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5828225Z   test_integer_outputs (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5828687Z   test_invalid_gradients (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:36.5829160Z   test_isolated_node (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5829612Z   test_leaf_assignment (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5830098Z   test_legacy_function_deprecation_exception (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5831532Z   test_lobpcg (__main__.TestAutograd) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/80338 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.002s)
2023-01-11T21:57:36.5832419Z   test_mark_non_differentiable (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5832935Z   test_mark_non_differentiable_mixed (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5833453Z   test_mark_non_differentiable_none (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5833903Z   test_materialize_grads (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5834322Z   test_multi_backward (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5834744Z   test_multi_backward_no_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5835323Z   test_multi_grad_hooks (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5836912Z   test_named_tensor_for_complex_views (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:1114: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1816.)
2023-01-11T21:57:36.5837917Z   return super(Tensor, self).refine_names(names)
2023-01-11T21:57:36.5838215Z ok (0.001s)
2023-01-11T21:57:36.5838632Z   test_naughty_anomaly_access (__main__.TestAutograd) ... expected failure (0.002s)
2023-01-11T21:57:36.5839284Z   test_naughty_autograd_function_attribute_access (__main__.TestAutograd) ... ok (0.009s)
2023-01-11T21:57:36.5839838Z   test_naughty_autograd_function_stashing_ctx (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5840363Z   test_nested_anomaly_detect_nan (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5840900Z   test_nested_anomaly_printstack_cleanup (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5841376Z   test_next_functions (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5841800Z   test_no_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5842231Z   test_no_grad_assignment (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5842674Z   test_no_grad_copy (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5843994Z   test_no_grad_copy_sparse (__main__.TestAutograd) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2325: UserWarning: Argument order of nn.functional.embedding_bag was changed. Usage `embedding_bag(weight, input, ...)` is deprecated, and should now be `embedding_bag(input, weight, ...)`.
2023-01-11T21:57:36.5844794Z   warnings.warn(
2023-01-11T21:57:36.5845090Z ok (0.005s)
2023-01-11T21:57:36.5845446Z   test_no_grad_input (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5845909Z   test_no_grad_modifies_version (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5846388Z   test_no_grad_python_function (__main__.TestAutograd)
2023-01-11T21:57:36.5846837Z Python Functions should respect grad mode. ... ok (0.001s)
2023-01-11T21:57:36.5847308Z   test_no_requires_grad_inplace (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5847788Z   test_no_unnecessary_save (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5848274Z   test_no_unnecessary_unwrapping (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5848760Z   test_not_implemented_fwad (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5849382Z   test_not_implemented_grad (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5849861Z   test_numpy_requires_grad (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5850321Z   test_once_differentiable (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5850845Z   test_out_variant_raises_when_inputs_require_grad (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5851430Z   test_pack_hook_with_inplace_modification_should_fail (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5851932Z   test_pickle (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:36.5852372Z   test_pow_zero_tensor_gradient (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5852862Z   test_power_function (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5853734Z   test_profiler (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:27 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5854577Z STAGE:2023-01-11 21:57:27 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5855392Z STAGE:2023-01-11 21:57:27 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5855855Z ok (0.002s)
2023-01-11T21:57:36.5856393Z   test_profiler_aggregation_fake (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5857270Z   test_profiler_aggregation_lstm (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:27 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5858152Z STAGE:2023-01-11 21:57:27 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5858932Z STAGE:2023-01-11 21:57:27 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5859301Z 
2023-01-11T21:57:36.5859501Z ===================================================================================================================================================================
2023-01-11T21:57:36.5859815Z TEST
2023-01-11T21:57:36.5860745Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5861539Z                          Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                      Input Shapes  
2023-01-11T21:57:36.5862504Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5863282Z                    aten::lstm         0.98%     208.000us         5.63%       1.197ms       1.197ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5863785Z                    aten::lstm         0.94%     199.000us         4.93%       1.048ms       1.048ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5864275Z                    aten::lstm         0.90%     192.000us         4.82%       1.024ms       1.024ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5864763Z                    aten::lstm         0.89%     189.000us         5.01%       1.064ms       1.064ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5865238Z                    aten::lstm         0.88%     187.000us         4.92%       1.045ms       1.045ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5865697Z                    aten::lstm         0.88%     187.000us         4.79%       1.019ms       1.019ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5866178Z                    aten::lstm         0.87%     184.000us         4.76%       1.011ms       1.011ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5866675Z                    aten::lstm         0.87%     184.000us         4.85%       1.030ms       1.030ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5867166Z                    aten::lstm         0.86%     182.000us         4.84%       1.029ms       1.029ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5867661Z                    aten::lstm         0.85%     180.000us         4.80%       1.021ms       1.021ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5868598Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5869327Z Self CPU time total: 21.254ms
2023-01-11T21:57:36.5869537Z 
2023-01-11T21:57:36.5870187Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5870956Z                          Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                      Input Shapes  
2023-01-11T21:57:36.5871983Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5872827Z                    aten::lstm        17.10%       3.634ms        98.15%      20.860ms       1.043ms            20      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5873340Z                   aten::addmm        10.63%       2.259ms        12.86%       2.734ms      13.670us           200                 [[80], [3, 20], [20, 80], [], []]  
2023-01-11T21:57:36.5873864Z                     aten::mul         9.29%       1.974ms         9.29%       1.974ms       3.290us           600                                [[3, 20], [3, 20]]  
2023-01-11T21:57:36.5874427Z                aten::sigmoid_         8.57%       1.822ms         8.57%       1.822ms       3.037us           600                                         [[3, 20]]  
2023-01-11T21:57:36.5874938Z            aten::unsafe_split         6.54%       1.391ms        16.35%       3.475ms      17.375us           200                                 [[3, 80], [], []]  
2023-01-11T21:57:36.5875433Z                   aten::slice         4.92%       1.046ms         5.00%       1.063ms       1.329us           800                         [[3, 80], [], [], [], []]  
2023-01-11T21:57:36.5875912Z                  aten::narrow         4.80%       1.020ms         9.39%       1.995ms       2.494us           800                             [[3, 80], [], [], []]  
2023-01-11T21:57:36.5876394Z                   aten::tanh_         3.74%     794.000us         3.74%     794.000us       3.970us           200                                         [[3, 20]]  
2023-01-11T21:57:36.5876869Z                    aten::tanh         3.23%     687.000us         3.23%     687.000us       3.435us           200                                         [[3, 20]]  
2023-01-11T21:57:36.5877360Z                  aten::linear         3.06%     651.000us        19.15%       4.070ms      20.350us           200                         [[3, 20], [80, 20], [80]]  
2023-01-11T21:57:36.5878300Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5878901Z Self CPU time total: 21.254ms
2023-01-11T21:57:36.5879120Z 
2023-01-11T21:57:36.5879328Z ===================================================================================================================================================================
2023-01-11T21:57:36.5879653Z TEST
2023-01-11T21:57:36.5879987Z ===================================================================================================================================================================
2023-01-11T21:57:36.5880516Z This report only display top-level ops statistics
2023-01-11T21:57:36.5881453Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5882232Z                          Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                      Input Shapes  
2023-01-11T21:57:36.5883191Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5883882Z                    aten::lstm         0.98%     208.000us         5.63%       1.197ms       1.197ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5884386Z                    aten::lstm         0.94%     199.000us         4.93%       1.048ms       1.048ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5884879Z                    aten::lstm         0.90%     192.000us         4.82%       1.024ms       1.024ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5885359Z                    aten::lstm         0.89%     189.000us         5.01%       1.064ms       1.064ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5885966Z                    aten::lstm         0.88%     187.000us         4.92%       1.045ms       1.045ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5886440Z                    aten::lstm         0.88%     187.000us         4.79%       1.019ms       1.019ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5886915Z                    aten::lstm         0.87%     184.000us         4.76%       1.011ms       1.011ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5887464Z                    aten::lstm         0.87%     184.000us         4.85%       1.030ms       1.030ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5887933Z                    aten::lstm         0.86%     182.000us         4.84%       1.029ms       1.029ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5888398Z                    aten::lstm         0.85%     180.000us         4.80%       1.021ms       1.021ms             1      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5889456Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5890070Z Self CPU time total: 21.254ms
2023-01-11T21:57:36.5890290Z 
2023-01-11T21:57:36.5890773Z ERROR:2023-01-11 21:57:28 16138:16138 CudaDeviceProperties.cpp:27] cudaGetDeviceCount failed with code 35
2023-01-11T21:57:36.5891325Z ===================================================================================================================================================================
2023-01-11T21:57:36.5891845Z This report only display top-level ops statistics
2023-01-11T21:57:36.5892776Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5893580Z                          Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                      Input Shapes  
2023-01-11T21:57:36.5894550Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5895248Z                    aten::lstm        17.10%       3.634ms        98.15%      20.860ms       1.043ms            20      [[5, 3, 10], [], [], [], [], [], [], [], []]  
2023-01-11T21:57:36.5895747Z                   aten::randn         0.92%     196.000us         1.81%     385.000us       6.417us            60                              [[], [], [], [], []]  
2023-01-11T21:57:36.5896652Z -----------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------------------------------------------  
2023-01-11T21:57:36.5897253Z Self CPU time total: 21.254ms
2023-01-11T21:57:36.5897459Z 
2023-01-11T21:57:36.5897661Z Total time based on python measurements:  23.352ms
2023-01-11T21:57:36.5898103Z CPU time measurement python side overhead: 9.87%
2023-01-11T21:57:36.5898463Z ok (1.131s)
2023-01-11T21:57:36.5898830Z   test_profiler_aggregation_table (__main__.TestAutograd)
2023-01-11T21:57:36.5899712Z Test if the profiling result is aggregated for `str(prof)` ... STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5900587Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5901412Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5901879Z ok (0.002s)
2023-01-11T21:57:36.5902267Z   test_profiler_function_event_avg (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5903397Z   test_profiler_propagation (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5904253Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5905059Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5905539Z ok (0.046s)
2023-01-11T21:57:36.5906248Z   test_profiler_seq_nr (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5906851Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5907386Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5907917Z -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  
2023-01-11T21:57:36.5908333Z                                                    Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls  
2023-01-11T21:57:36.5908828Z -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  
2023-01-11T21:57:36.5909203Z                                               aten::sum        17.24%      15.000us        18.39%      16.000us      16.000us             1  
2023-01-11T21:57:36.5909480Z                                               aten::add        16.09%      14.000us        16.09%      14.000us      14.000us             1  
2023-01-11T21:57:36.5909759Z                                             aten::randn         9.20%       8.000us        17.24%      15.000us       7.500us             2  
2023-01-11T21:57:36.5910055Z                         torch::autograd::AccumulateGrad         8.05%       7.000us        20.69%      18.000us       9.000us             2  
2023-01-11T21:57:36.5910343Z                                             aten::copy_         8.05%       7.000us         8.05%       7.000us       3.500us             2  
2023-01-11T21:57:36.5910987Z                                            aten::expand         5.75%       5.000us         6.90%       6.000us       6.000us             1  
2023-01-11T21:57:36.5911267Z                                           aten::normal_         4.60%       4.000us         4.60%       4.000us       2.000us             2  
2023-01-11T21:57:36.5911579Z       autograd::engine::evaluate_function: SumBackward1         4.60%       4.000us        14.94%      13.000us      13.000us             1  
2023-01-11T21:57:36.5911889Z                                             aten::empty         3.45%       3.000us         3.45%       3.000us       1.500us             2  
2023-01-11T21:57:36.5912159Z                                        aten::empty_like         3.45%       3.000us         4.60%       4.000us       4.000us             1  
2023-01-11T21:57:36.5912436Z                                            SumBackward1         3.45%       3.000us        10.34%       9.000us       9.000us             1  
2023-01-11T21:57:36.5912746Z autograd::engine::evaluate_function: torch::autograd...         3.45%       3.000us        24.14%      21.000us      10.500us             2  
2023-01-11T21:57:36.5913064Z                                 aten::new_empty_strided         3.45%       3.000us         4.60%       4.000us       2.000us             2  
2023-01-11T21:57:36.5913351Z                                        aten::as_strided         2.30%       2.000us         2.30%       2.000us       1.000us             2  
2023-01-11T21:57:36.5913617Z                                         aten::ones_like         2.30%       2.000us         6.90%       6.000us       6.000us             1  
2023-01-11T21:57:36.5913900Z                                     aten::empty_strided         2.30%       2.000us         2.30%       2.000us       0.667us             3  
2023-01-11T21:57:36.5914277Z       autograd::engine::evaluate_function: AddBackward0         1.15%       1.000us         2.30%       2.000us       2.000us             1  
2023-01-11T21:57:36.5914585Z                                            AddBackward0         1.15%       1.000us         1.15%       1.000us       1.000us             1  
2023-01-11T21:57:36.5914848Z                                             aten::fill_         0.00%       0.000us         0.00%       0.000us       0.000us             2  
2023-01-11T21:57:36.5915325Z -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  
2023-01-11T21:57:36.5915649Z Self CPU time total: 87.000us
2023-01-11T21:57:36.5915770Z 
2023-01-11T21:57:36.5915871Z ok (0.004s)
2023-01-11T21:57:36.5916286Z   test_profiler_shapes (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5916769Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5917215Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5917416Z 
2023-01-11T21:57:36.5917752Z ----------------------  ------------  ------------  ------------  ------------  ------------  ------------  ---------------------------------------  
2023-01-11T21:57:36.5918171Z                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                             Input Shapes  
2023-01-11T21:57:36.5918912Z ----------------------  ------------  ------------  ------------  ------------  ------------  ------------  ---------------------------------------  
2023-01-11T21:57:36.5919544Z           aten::linear         3.80%       6.000us        51.90%      82.000us      82.000us             1              [[128, 20], [30, 20], [30]]  
2023-01-11T21:57:36.5920011Z                aten::t         7.59%      12.000us        12.03%      19.000us      19.000us             1                               [[30, 20]]  
2023-01-11T21:57:36.5920493Z        aten::transpose         3.16%       5.000us         4.43%       7.000us       7.000us             1                       [[30, 20], [], []]  
2023-01-11T21:57:36.5920986Z       aten::as_strided         1.27%       2.000us         1.27%       2.000us       2.000us             1                   [[30, 20], [], [], []]  
2023-01-11T21:57:36.5921483Z            aten::addmm        30.38%      48.000us        36.08%      57.000us      57.000us             1      [[30], [128, 20], [20, 30], [], []]  
2023-01-11T21:57:36.5921992Z           aten::expand         1.27%       2.000us         1.27%       2.000us       2.000us             1                           [[30], [], []]  
2023-01-11T21:57:36.5922498Z       aten::as_strided         0.00%       0.000us         0.00%       0.000us       0.000us             1                       [[30], [], [], []]  
2023-01-11T21:57:36.5923006Z            aten::copy_         4.43%       7.000us         4.43%       7.000us       7.000us             1               [[128, 30], [128, 30], []]  
2023-01-11T21:57:36.5923493Z     aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                              [[128, 30]]  
2023-01-11T21:57:36.5923997Z     aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                              [[128, 20]]  
2023-01-11T21:57:36.5924510Z           aten::linear         1.90%       3.000us        48.10%      76.000us      76.000us             1              [[128, 30], [40, 30], [40]]  
2023-01-11T21:57:36.5925008Z                aten::t         1.90%       3.000us         3.16%       5.000us       5.000us             1                               [[40, 30]]  
2023-01-11T21:57:36.5925604Z        aten::transpose         1.27%       2.000us         1.27%       2.000us       2.000us             1                       [[40, 30], [], []]  
2023-01-11T21:57:36.5926090Z       aten::as_strided         0.00%       0.000us         0.00%       0.000us       0.000us             1                   [[40, 30], [], [], []]  
2023-01-11T21:57:36.5926577Z            aten::addmm        39.87%      63.000us        43.04%      68.000us      68.000us             1      [[40], [128, 30], [30, 40], [], []]  
2023-01-11T21:57:36.5927067Z           aten::expand         0.63%       1.000us         0.63%       1.000us       1.000us             1                           [[40], [], []]  
2023-01-11T21:57:36.5927632Z       aten::as_strided         0.00%       0.000us         0.00%       0.000us       0.000us             1                       [[40], [], [], []]  
2023-01-11T21:57:36.5928127Z            aten::copy_         2.53%       4.000us         2.53%       4.000us       4.000us             1               [[128, 40], [128, 40], []]  
2023-01-11T21:57:36.5928620Z     aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                              [[128, 40]]  
2023-01-11T21:57:36.5929224Z     aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                              [[128, 30]]  
2023-01-11T21:57:36.5930072Z ----------------------  ------------  ------------  ------------  ------------  ------------  ------------  ---------------------------------------  
2023-01-11T21:57:36.5930641Z Self CPU time total: 158.000us
2023-01-11T21:57:36.5930855Z 
2023-01-11T21:57:36.5930954Z ok (0.003s)
2023-01-11T21:57:36.5931757Z   test_profiler_unboxed_only (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5932640Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5933453Z STAGE:2023-01-11 21:57:28 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5933902Z ok (0.001s)
2023-01-11T21:57:36.5934325Z   test_pynode_destruction_deadlock (__main__.TestAutograd) ... ok (1.346s)
2023-01-11T21:57:36.5935228Z   test_record_function (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5936087Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5936922Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5937712Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5938524Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5939312Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5939781Z ok (0.003s)
2023-01-11T21:57:36.5940548Z   test_record_function_callbacks (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5941432Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5942241Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5942798Z ok (0.002s)
2023-01-11T21:57:36.5943606Z   test_record_function_legacy (__main__.TestAutograd) ... STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:57:36.5944495Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:57:36.5945303Z STAGE:2023-01-11 21:57:29 16138:16138 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:57:36.5945931Z ok (0.002s)
2023-01-11T21:57:36.5946360Z   test_record_function_multithreaded (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5946878Z   test_reentrant_child_error (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5947382Z   test_reentrant_priority (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5947904Z   test_reentrant_with_callbacks_both_depths (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5948445Z   test_reentrant_with_callbacks_depth_0 (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5948995Z   test_reentrant_with_callbacks_depth_1 (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5949545Z   test_reentrant_with_leaf_variable_hook (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5950187Z   test_reentrant_with_non_leaf_variable_hook (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5950670Z   test_requires_grad (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5951141Z   test_requires_grad_ (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5951627Z   test_requires_grad_inplace (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5952089Z   test_retain_grad (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5952546Z   test_retain_grad_cycle (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5953019Z   test_retain_grad_inplace (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5953501Z   test_retain_grad_inplace_over_view (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5954001Z   test_return_duplicate (__main__.TestAutograd) ... ok (0.005s)
2023-01-11T21:57:36.5954501Z   test_return_duplicate_inplace (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5954984Z   test_return_leaf (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5955450Z   test_return_leaf_inplace (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5955945Z   test_save_none_for_backward (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5956464Z   test_save_on_cpu_and_checkpoint (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5956943Z   test_save_output_nr (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5957530Z   test_saved_variable_packing_unpacking_did_not_save_original_with_default_hooks (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5958232Z   test_saved_variable_packing_unpacking_did_not_save_original_with_hooks (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5958883Z   test_saved_variable_packing_unpacking_saved_original_with_default_hooks (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5959516Z   test_saved_variable_packing_unpacking_saved_original_with_hooks (__main__.TestAutograd) ... ok (0.012s)
2023-01-11T21:57:36.5960118Z   test_saved_variable_saved_original_inplace_detach (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5960688Z   test_saved_variable_version_counter (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5961211Z   test_saved_variables_deprecated (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5961735Z   test_saving_variable_to_disk (__main__.TestAutograd) ... ok (0.008s)
2023-01-11T21:57:36.5962222Z   test_select_expanded_v (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5962688Z   test_select_sum (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5963126Z   test_set_data_preserve_pyobj (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5963638Z   test_set_data_self_requires_grad (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5964157Z   test_set_data_tensorimpl_type (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5964649Z   test_set_grad_coroutines (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5965191Z   test_set_grad_coroutines_benign_exceptions (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5965776Z   test_set_grad_coroutines_critical_exceptions (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5966413Z   test_set_grad_coroutines_exit (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5966889Z   test_set_grad_enabled (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5967384Z   test_set_grad_generator_functions (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5967903Z   test_set_grad_generator_functions_recursive (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5968391Z   test_setitem (__main__.TestAutograd) ... ok (0.013s)
2023-01-11T21:57:36.5968829Z   test_setitem_mask (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5969507Z   test_setting_default_saved_variable_hooks_twice_should_not_fail (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5970075Z   test_setting_default_saved_variable_hooks_twice_should_use_inner (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5970662Z   test_shape (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5971088Z   test_sharded_grad (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5971548Z   test_simple_reentrant (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5971986Z   test_slice_expanded_v (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5972464Z   test_sparse_gather_both_scalar (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5972965Z   test_sparse_gather_dim0 (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5973431Z   test_sparse_gather_dim1 (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5973899Z   test_sparse_gather_dim_neg (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5974385Z   test_sparse_gather_ind_scalar (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5974885Z   test_sparse_gather_x_scalar (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5975355Z   test_sparse_mm_backward (__main__.TestAutograd) ... ok (0.006s)
2023-01-11T21:57:36.5976178Z   test_symeig_no_eigenvectors (__main__.TestAutograd) ... /var/lib/jenkins/workspace/test/test_autograd.py:4200: UserWarning: torch.symeig is deprecated in favor of torch.linalg.eigh and will be removed in a future PyTorch release.
2023-01-11T21:57:36.5977147Z The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.
2023-01-11T21:57:36.5977722Z L, _ = torch.symeig(A, upper=upper)
2023-01-11T21:57:36.5978066Z should be replaced with
2023-01-11T21:57:36.5978601Z L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')
2023-01-11T21:57:36.5978979Z and
2023-01-11T21:57:36.5979298Z L, V = torch.symeig(A, eigenvectors=True)
2023-01-11T21:57:36.5979665Z should be replaced with
2023-01-11T21:57:36.5980518Z L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2910.)
2023-01-11T21:57:36.5981162Z   w, v = torch.symeig(A, eigenvectors=False)
2023-01-11T21:57:36.5981507Z ok (0.009s)
2023-01-11T21:57:36.5981889Z   test_tensor_grad_warnings (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5982362Z   test_thread_shutdown (__main__.TestAutograd) ... ok (1.370s)
2023-01-11T21:57:36.5982923Z   test_to_sparse_backward (__main__.TestAutograd) ... ok (0.046s)
2023-01-11T21:57:36.5983394Z   test_too_many_grads (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5983858Z   test_type_conversions (__main__.TestAutograd) ... ok (0.003s)
2023-01-11T21:57:36.5984313Z   test_unrelated_inputs (__main__.TestAutograd) ... ok (0.004s)
2023-01-11T21:57:36.5984769Z   test_unused_output (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5985255Z   test_var_mean_differentiable (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5985734Z   test_variable_traverse (__main__.TestAutograd) ... ok (0.106s)
2023-01-11T21:57:36.5986219Z   test_version_counter (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5986695Z   test_view_func_replay (__main__.TestAutograd) ... ok (0.002s)
2023-01-11T21:57:36.5987130Z   test_volatile_deprecated (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5987743Z   test_will_engine_execute_node (__main__.TestAutograd) ... ok (0.007s)
2023-01-11T21:57:36.5988237Z   test_wrapped_number_saved_variable_hooks (__main__.TestAutograd) ... ok (0.001s)
2023-01-11T21:57:36.5988845Z   test_view_func_for_complex_views (autograd.test_complex.TestAutogradComplex) ... ok (0.003s)
2023-01-11T21:57:36.5989455Z   test_view_with_multi_output (autograd.test_complex.TestAutogradComplex) ... ok (0.007s)
2023-01-11T21:57:36.5990756Z   test_advanced_packing_unpacking (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:8092: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:36.5991908Z   self.assertEqual(dual.storage().data_ptr(), foo.storage().data_ptr())
2023-01-11T21:57:36.5992948Z /var/lib/jenkins/workspace/test/test_autograd.py:8101: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:36.5993985Z   self.assertEqual(dual_primal.storage().data_ptr(), foo.storage().data_ptr())
2023-01-11T21:57:36.5995028Z /var/lib/jenkins/workspace/test/test_autograd.py:8102: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:36.5996076Z   self.assertEqual(dual_tangent.storage().data_ptr(), bar.storage().data_ptr())
2023-01-11T21:57:36.5996512Z ok (0.004s)
2023-01-11T21:57:36.5996967Z   test_backward_graph_destruction (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.5997548Z   test_basic_packing_unpacking (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.5998157Z   test_codegen_ignores_undefined_outputs (__main__.TestAutogradForwardMode) ... ok (0.007s)
2023-01-11T21:57:36.5999382Z   test_create_new_zeros_with_same_meta (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:8351: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:36.6000550Z   self.assertEqual(len(result.storage()), len(target.storage()) * prod_of_t_bdims)
2023-01-11T21:57:36.6000963Z ok (0.006s)
2023-01-11T21:57:36.6001386Z   test_default_level (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6001956Z   test_detach_view_tracking (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6002538Z   test_forward_level_cleanup (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6003092Z   test_fwd_grad_enabled (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6003634Z   test_grad_cleanup (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6004208Z   test_make_dual_forbid_integral_dtype (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6004808Z   test_make_dual_inference_tensor_in_inference_mode (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6005416Z   test_make_dual_torch_dispatch (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:36.6006002Z   test_metadata_check_check_conj (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:36.6006613Z   test_metadata_check_checks_ignores_size_zero (__main__.TestAutogradForwardMode) ... ok (0.003s)
2023-01-11T21:57:36.6007940Z   test_metadata_check_checks_storage_numel (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:7790: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:36.6009185Z   self.assertEqual(len(primal.storage()), 5)
2023-01-11T21:57:36.6009552Z ok (0.001s)
2023-01-11T21:57:36.6010056Z   test_metadata_check_ignore_storage_offset_for_zero_numel_tensor (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6011423Z   test_metadata_check_when_primal_has_conj_bit (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:7826: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:36.6012558Z   self.assertEqual(len(a.storage()), len(b.storage()))
2023-01-11T21:57:36.6012925Z ok (0.001s)
2023-01-11T21:57:36.6014021Z   test_metadata_check_when_primal_has_neg_bit (__main__.TestAutogradForwardMode) ... /var/lib/jenkins/workspace/test/test_autograd.py:7841: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:36.6015153Z   self.assertEqual(len(a.storage()), len(b.storage()))
2023-01-11T21:57:36.6015498Z ok (0.001s)
2023-01-11T21:57:36.6015904Z   test_nested_level (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:36.6016444Z   test_non_differentiable (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6016995Z   test_out_variant (__main__.TestAutogradForwardMode) ... ok (0.004s)
2023-01-11T21:57:36.6017503Z   test_print (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:36.6018087Z   test_set_fw_grad_having_own_fw_grad_at_same_level (__main__.TestAutogradForwardMode) ... ok (0.004s)
2023-01-11T21:57:36.6018704Z   test_set_fwd_grad_enabled (__main__.TestAutogradForwardMode) ... ok (0.001s)
2023-01-11T21:57:36.6019233Z   test_size_check (__main__.TestAutogradForwardMode) ... ok (0.004s)
2023-01-11T21:57:36.6019788Z   test_view_inplace_always_creates_a_view (__main__.TestAutogradForwardMode) ... ok (0.003s)
2023-01-11T21:57:36.6020386Z   test_view_inplace_differentiable_views (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:36.6020999Z   test_view_inplace_non_differentiable_views (__main__.TestAutogradForwardMode) ... ok (0.002s)
2023-01-11T21:57:36.6021645Z   test_inplace_on_view_not_same_layout (__main__.TestAutogradForwardModeBatchedGrad) ... ok (0.001s)
2023-01-11T21:57:36.6022341Z   test_inplace_on_view_same_layout (__main__.TestAutogradForwardModeBatchedGrad) ... ok (0.001s)
2023-01-11T21:57:36.6023738Z   test_metadata_check_for_storage_numel_skipped (__main__.TestAutogradForwardModeBatchedGrad) ... /var/lib/jenkins/workspace/test/test_autograd.py:7716: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:57:36.6024900Z   self.assertEqual(len(primal.storage()), 5)
2023-01-11T21:57:36.6025205Z ok (0.004s)
2023-01-11T21:57:36.6025666Z   test_out_of_place_basic (__main__.TestAutogradForwardModeBatchedGrad) ... ok (0.006s)
2023-01-11T21:57:36.6026375Z   test_out_of_place_not_same_layout (__main__.TestAutogradForwardModeBatchedGrad) ... ok (0.001s)
2023-01-11T21:57:36.6027029Z   test_construct_standard_basis_for_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6027933Z   test_construct_standard_basis_for_cuda_base_tensor (autograd.test_functional.TestAutogradFunctional) ... skip: test requires CUDA (0.000s)
2023-01-11T21:57:36.6028779Z   test_construct_standard_basis_for_cuda_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... skip: test requires CUDA (0.000s)
2023-01-11T21:57:36.6029570Z   test_construct_standard_basis_for_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.011s)
2023-01-11T21:57:36.6030316Z   test_hessian_create_graph_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.134s)
2023-01-11T21:57:36.6031214Z   test_hessian_create_graph_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.808s)
2023-01-11T21:57:36.6032005Z   test_hessian_create_graph_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.070s)
2023-01-11T21:57:36.6032796Z   test_hessian_create_graph_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.361s)
2023-01-11T21:57:36.6033537Z   test_hessian_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6034259Z   test_hessian_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.008s)
2023-01-11T21:57:36.6034982Z   test_hessian_err_check_strict_vectorize_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6035745Z   test_hessian_err_check_strict_vectorize_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6036508Z   test_hessian_err_check_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6037270Z   test_hessian_err_check_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.014s)
2023-01-11T21:57:36.6038042Z   test_hessian_err_check_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6038787Z   test_hessian_err_check_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.007s)
2023-01-11T21:57:36.6039521Z   test_hessian_match_vhp_hvp_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6040222Z   test_hessian_match_vhp_hvp_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.009s)
2023-01-11T21:57:36.6040928Z   test_hessian_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6041618Z   test_hessian_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.009s)
2023-01-11T21:57:36.6042347Z   test_hessian_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6043030Z   test_hessian_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.012s)
2023-01-11T21:57:36.6043719Z   test_hessian_output_vectorized_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6044439Z   test_hessian_output_vectorized_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:36.6045188Z   test_hessian_scalar_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6045932Z   test_hessian_scalar_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.008s)
2023-01-11T21:57:36.6046680Z   test_hessian_scalar_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6047432Z   test_hessian_scalar_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6048203Z   test_hessian_vectorize_correctness_multi_input_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.014s)
2023-01-11T21:57:36.6049488Z   test_hessian_vectorize_correctness_multi_input_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.107s)
2023-01-11T21:57:36.6050079Z   test_hessian_vectorize_correctness_simple_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:36.6050519Z   test_hessian_vectorize_correctness_simple_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.030s)
2023-01-11T21:57:36.6050977Z   test_hessian_vectorize_correctness_unrelated_outputs_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6051597Z   test_hessian_vectorize_correctness_unrelated_outputs_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.028s)
2023-01-11T21:57:36.6052051Z   test_hessian_vectorize_raises_no_warnings_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6052480Z   test_hessian_vectorize_raises_no_warnings_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6052895Z   test_hvp_create_graph_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.085s)
2023-01-11T21:57:36.6053297Z   test_hvp_create_graph_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.448s)
2023-01-11T21:57:36.6053680Z   test_hvp_err_check_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6054065Z   test_hvp_err_check_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.008s)
2023-01-11T21:57:36.6054455Z   test_hvp_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6054857Z   test_hvp_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6055238Z   test_hvp_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6055620Z   test_hvp_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:36.6056003Z   test_hvp_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6056387Z   test_hvp_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6056753Z   test_hvp_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6057132Z   test_hvp_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:36.6057546Z   test_jacobian_create_graph_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.058s)
2023-01-11T21:57:36.6057987Z   test_jacobian_create_graph_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.322s)
2023-01-11T21:57:36.6058413Z   test_jacobian_create_graph_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.046s)
2023-01-11T21:57:36.6058846Z   test_jacobian_create_graph_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.228s)
2023-01-11T21:57:36.6059267Z   test_jacobian_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6059663Z   test_jacobian_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.007s)
2023-01-11T21:57:36.6060084Z   test_jacobian_err_check_strict_vectorize_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6060516Z   test_jacobian_err_check_strict_vectorize_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6060944Z   test_jacobian_err_check_vectorize_False_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6061421Z   test_jacobian_err_check_vectorize_False_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.007s)
2023-01-11T21:57:36.6061848Z   test_jacobian_err_check_vectorize_True_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6062271Z   test_jacobian_err_check_vectorize_True_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6062763Z   test_jacobian_match_vjp_jvp_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6063158Z   test_jacobian_match_vjp_jvp_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.007s)
2023-01-11T21:57:36.6063555Z   test_jacobian_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6063984Z   test_jacobian_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:36.6064379Z   test_jacobian_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6064761Z   test_jacobian_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.009s)
2023-01-11T21:57:36.6065162Z   test_jacobian_output_vectorized_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6065579Z   test_jacobian_output_vectorized_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:36.6065980Z   test_jacobian_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6066355Z   test_jacobian_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6066760Z   test_jacobian_scalar_vectorized_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6067174Z   test_jacobian_scalar_vectorized_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6067641Z   test_jacobian_vectorize_correctness_different_devices_base_tensor (autograd.test_functional.TestAutogradFunctional) ... skip: test requires CUDA (0.000s)
2023-01-11T21:57:36.6068138Z   test_jacobian_vectorize_correctness_different_devices_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... skip: test requires CUDA (0.000s)
2023-01-11T21:57:36.6068618Z   test_jacobian_vectorize_correctness_different_dtype_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6069076Z   test_jacobian_vectorize_correctness_different_dtype_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.009s)
2023-01-11T21:57:36.6069527Z   test_jacobian_vectorize_correctness_multi_input_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6069956Z   test_jacobian_vectorize_correctness_multi_input_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.018s)
2023-01-11T21:57:36.6070413Z   test_jacobian_vectorize_correctness_multi_input_multi_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.008s)
2023-01-11T21:57:36.6070880Z   test_jacobian_vectorize_correctness_multi_input_multi_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.059s)
2023-01-11T21:57:36.6071330Z   test_jacobian_vectorize_correctness_simple_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6071755Z   test_jacobian_vectorize_correctness_simple_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.019s)
2023-01-11T21:57:36.6072204Z   test_jacobian_vectorize_correctness_unrelated_outputs_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6072668Z   test_jacobian_vectorize_correctness_unrelated_outputs_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.021s)
2023-01-11T21:57:36.6073150Z   test_jacobian_vectorize_correctness_zero_dim_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:36.6073577Z   test_jacobian_vectorize_correctness_zero_dim_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.033s)
2023-01-11T21:57:36.6074013Z   test_jacobian_vectorize_raises_no_warnings_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6074444Z   test_jacobian_vectorize_raises_no_warnings_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6074859Z   test_jvp_create_graph_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.060s)
2023-01-11T21:57:36.6075240Z   test_jvp_create_graph_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.315s)
2023-01-11T21:57:36.6075660Z   test_jvp_err_check_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6076051Z   test_jvp_err_check_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6076428Z   test_jvp_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6076824Z   test_jvp_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6077214Z   test_jvp_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6077593Z   test_jvp_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6077958Z   test_jvp_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6078336Z   test_jvp_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6078714Z   test_jvp_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6079096Z   test_jvp_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6079470Z   test_vhp_create_graph_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.076s)
2023-01-11T21:57:36.6079863Z   test_vhp_create_graph_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.411s)
2023-01-11T21:57:36.6080250Z   test_vhp_err_check_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6080621Z   test_vhp_err_check_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6081010Z   test_vhp_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6081408Z   test_vhp_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6081795Z   test_vhp_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6082164Z   test_vhp_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.006s)
2023-01-11T21:57:36.6082543Z   test_vhp_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6082922Z   test_vhp_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6083298Z   test_vhp_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6083667Z   test_vhp_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6084054Z   test_vjp_create_graph_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.053s)
2023-01-11T21:57:36.6084445Z   test_vjp_create_graph_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.279s)
2023-01-11T21:57:36.6084818Z   test_vjp_err_check_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6085236Z   test_vjp_err_check_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6085625Z   test_vjp_err_check_strict_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6086025Z   test_vjp_err_check_strict_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6086404Z   test_vjp_no_grad_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6086782Z   test_vjp_no_grad_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.005s)
2023-01-11T21:57:36.6087161Z   test_vjp_output_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.002s)
2023-01-11T21:57:36.6087565Z   test_vjp_output_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.004s)
2023-01-11T21:57:36.6087937Z   test_vjp_scalar_base_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.001s)
2023-01-11T21:57:36.6088319Z   test_vjp_scalar_logging_tensor (autograd.test_functional.TestAutogradFunctional) ... ok (0.003s)
2023-01-11T21:57:36.6088683Z   test_inference_mode_context_manager (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6089011Z   test_inference_mode_decorator (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6089591Z   test_inference_mode_existing_autograd_session (__main__.TestAutogradInferenceMode) ... ok (0.006s)
2023-01-11T21:57:36.6089965Z   test_inference_mode_handle_direct_view_on_rebase (__main__.TestAutogradInferenceMode) ... ok (0.007s)
2023-01-11T21:57:36.6090337Z   test_inference_mode_handle_indirect_view_on_rebase (__main__.TestAutogradInferenceMode) ... ok (0.005s)
2023-01-11T21:57:36.6090709Z   test_inference_mode_inf_tensor_in_inf_mode_functional_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6091091Z   test_inference_mode_inf_tensor_in_inf_mode_inplace_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6091468Z   test_inference_mode_inf_tensor_in_inf_mode_view_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6091845Z   test_inference_mode_inf_tensor_in_normal_mode_functional_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6092222Z   test_inference_mode_inf_tensor_in_normal_mode_inplace_op (__main__.TestAutogradInferenceMode) ... ok (0.007s)
2023-01-11T21:57:36.6092601Z   test_inference_mode_inf_tensor_in_normal_mode_view_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6092957Z   test_inference_mode_tensor_creation (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6093319Z   test_mix_inference_and_normal_tensor_functional_op (__main__.TestAutogradInferenceMode) ... ok (0.004s)
2023-01-11T21:57:36.6093678Z   test_mix_inference_and_normal_tensor_inplace_op (__main__.TestAutogradInferenceMode) ... ok (0.009s)
2023-01-11T21:57:36.6094043Z   test_mix_inference_and_normal_tensor_view_op (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6094413Z   test_normal_tensor_inplace_output_in_inference_mode (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6094774Z   test_normal_tensor_inplace_output_in_normal_mode (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6095142Z   test_normal_tensor_view_output_in_inference_mode (__main__.TestAutogradInferenceMode) ... ok (0.001s)
2023-01-11T21:57:36.6095502Z   test_normal_tensor_view_output_in_normal_mode (__main__.TestAutogradInferenceMode) ... ok (0.004s)
2023-01-11T21:57:36.6095831Z   test_cat_stack_r_to_c (__main__.TestMultithreadAutograd) ... ok (0.106s)
2023-01-11T21:57:36.6096143Z   test_dataparallel_saved_tensors_hooks (__main__.TestMultithreadAutograd) ... ok (0.001s)
2023-01-11T21:57:36.6096468Z   test_fork_join_in_middle (__main__.TestMultithreadAutograd) ... ok (0.017s)
2023-01-11T21:57:36.6096774Z   test_multi_grad_hooks (__main__.TestMultithreadAutograd) ... ok (0.005s)
2023-01-11T21:57:36.6097167Z   test_multithreaded_exception_propagation (__main__.TestMultithreadAutograd) ... ok (0.001s)
2023-01-11T21:57:36.6097504Z   test_preserve_backtrace (__main__.TestMultithreadAutograd) ... ok (0.001s)
2023-01-11T21:57:36.6097821Z   test_python_thread_in_middle (__main__.TestMultithreadAutograd) ... ok (0.021s)
2023-01-11T21:57:36.6098129Z   test_simple_backward (__main__.TestMultithreadAutograd) ... ok (0.006s)
2023-01-11T21:57:36.6098436Z   test_simple_backward_same_input (__main__.TestMultithreadAutograd) ... ok (0.009s)
2023-01-11T21:57:36.6098621Z 
2023-01-11T21:57:36.6098863Z ----------------------------------------------------------------------
2023-01-11T21:57:36.6099109Z Ran 478 tests in 16.478s
2023-01-11T21:57:36.6099224Z 
2023-01-11T21:57:36.6099362Z OK (skipped=11, expected failures=1)
2023-01-11T21:57:36.6099484Z 
2023-01-11T21:57:36.6099569Z Generating XML reports...
2023-01-11T21:57:36.6100011Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAllowMutationOnSaved-20230111215719.xml
2023-01-11T21:57:36.6100529Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAutograd-20230111215719.xml
2023-01-11T21:57:36.6101072Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-autograd.test_complex.TestAutogradComplex-20230111215719.xml
2023-01-11T21:57:36.6101640Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAutogradForwardMode-20230111215719.xml
2023-01-11T21:57:36.6102223Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAutogradForwardModeBatchedGrad-20230111215719.xml
2023-01-11T21:57:36.6102950Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-autograd.test_functional.TestAutogradFunctional-20230111215719.xml
2023-01-11T21:57:36.6103531Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestAutogradInferenceMode-20230111215719.xml
2023-01-11T21:57:36.6104076Z Generated XML report: test-reports/python-unittest/test_autograd/TEST-TestMultithreadAutograd-20230111215719.xml
2023-01-11T21:57:36.6104320Z 
2023-01-11T21:57:36.6104640Z ##[endgroup]
2023-01-11T21:57:36.6105005Z FINISHED PRINTING LOG FILE of test_autograd (/var/lib/jenkins/workspace/test/test-reports/test_autograd_uadvh1se)
2023-01-11T21:57:36.6105219Z 
2023-01-11T21:57:36.6105398Z Running test_cpp_extensions_jit ... [2023-01-11 21:57:36.570751]
2023-01-11T21:57:36.6105886Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_jit.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:57:36.571009]
2023-01-11T21:58:10.5982284Z 
2023-01-11T21:58:10.5983055Z Expand the folded group to see the log file of test_cpp_extensions_jit
2023-01-11T21:58:10.5984091Z ##[group]PRINTING LOG FILE of test_cpp_extensions_jit (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_jit_6smzcg8i)
2023-01-11T21:58:10.5984589Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:58:10.5986436Z 
2023-01-11T21:58:10.5986608Z Running tests...
2023-01-11T21:58:10.5987152Z ----------------------------------------------------------------------
2023-01-11T21:58:10.5987796Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_jit
2023-01-11T21:58:10.5988127Z   test_autograd_from_cpp (__main__.TestCppExtensionJIT) ... ok (1.454s)
2023-01-11T21:58:10.5990547Z   test_compilation_error_formatting (__main__.TestCppExtensionJIT) ... ok (13.283s)
2023-01-11T21:58:10.5991366Z   test_cpp_frontend_module_has_same_output_as_python (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.5992203Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/cpp_frontend_extension...
2023-01-11T21:58:10.5992811Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/cpp_frontend_extension/build.ninja...
2023-01-11T21:58:10.5993133Z Building extension module cpp_frontend_extension...
2023-01-11T21:58:10.5993545Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.5995967Z [1/2] c++ -MMD -MF cpp_frontend_extension.o.d -DTORCH_EXTENSION_NAME=cpp_frontend_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/workspace/test/cpp_extensions/cpp_frontend_extension.cpp -o cpp_frontend_extension.o 
2023-01-11T21:58:10.5998288Z [2/2] c++ cpp_frontend_extension.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o cpp_frontend_extension.so
2023-01-11T21:58:10.5998883Z Loading extension module cpp_frontend_extension...
2023-01-11T21:58:10.5999211Z ok (1.269s)
2023-01-11T21:58:10.5999825Z   test_cpp_frontend_module_has_up_to_date_attributes (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6000792Z No modifications detected for re-loaded extension module cpp_frontend_extension, skipping build step...
2023-01-11T21:58:10.6001277Z Loading extension module cpp_frontend_extension...
2023-01-11T21:58:10.6001487Z ok (0.002s)
2023-01-11T21:58:10.6001821Z   test_cpp_frontend_module_python_inter_op (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6002358Z No modifications detected for re-loaded extension module cpp_frontend_extension, skipping build step...
2023-01-11T21:58:10.6002895Z Loading extension module cpp_frontend_extension...
2023-01-11T21:58:10.6003285Z ok (0.008s)
2023-01-11T21:58:10.6003783Z   test_cpp_frontend_module_python_inter_op_with_cuda (__main__.TestCppExtensionJIT) ... skip: CUDA not found (0.001s)
2023-01-11T21:58:10.6004525Z   test_custom_compound_op_autograd (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6004937Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module...
2023-01-11T21:58:10.6005304Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module/build.ninja...
2023-01-11T21:58:10.6005596Z Building extension module is_python_module...
2023-01-11T21:58:10.6005850Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6008004Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=is_python_module -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module/main.cpp -o main.o 
2023-01-11T21:58:10.6009700Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o is_python_module.so
2023-01-11T21:58:10.6010040Z Loading extension module is_python_module...
2023-01-11T21:58:10.6010679Z /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:58:10.6011202Z   warnings.warn(
2023-01-11T21:58:10.6011813Z /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #1 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:58:10.6012230Z   warnings.warn(
2023-01-11T21:58:10.6012831Z /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #0 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:58:10.6013238Z   warnings.warn(
2023-01-11T21:58:10.6013888Z /opt/conda/lib/python3.10/site-packages/torch/autograd/gradcheck.py:688: UserWarning: Input #1 requires gradient and is not a double precision floating point or complex. This check will likely fail if all the inputs are not of double precision floating point or complex. 
2023-01-11T21:58:10.6014310Z   warnings.warn(
2023-01-11T21:58:10.6014477Z ok (1.193s)
2023-01-11T21:58:10.6014679Z   test_half_support (__main__.TestCppExtensionJIT)
2023-01-11T21:58:10.6015000Z Checks for an issue with operator< ambiguity for half when certain ... skip: Temporarily disabled (0.001s)
2023-01-11T21:58:10.6015379Z   test_inline_jit_compile_custom_op_cuda (__main__.TestCppExtensionJIT) ... skip: Temporarily disabled (0.001s)
2023-01-11T21:58:10.6015747Z   test_inline_jit_compile_extension_cuda (__main__.TestCppExtensionJIT) ... skip: Temporarily disabled (0.001s)
2023-01-11T21:58:10.6016203Z   test_inline_jit_compile_extension_multiple_sources_and_no_functions (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6016653Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension...
2023-01-11T21:58:10.6017026Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension/build.ninja...
2023-01-11T21:58:10.6017325Z Building extension module inline_jit_extension...
2023-01-11T21:58:10.6017577Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6018870Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=inline_jit_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension/main.cpp -o main.o 
2023-01-11T21:58:10.6019918Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o inline_jit_extension.so
2023-01-11T21:58:10.6020264Z Loading extension module inline_jit_extension...
2023-01-11T21:58:10.6020460Z ok (1.173s)
2023-01-11T21:58:10.6020737Z   test_inline_jit_compile_extension_throws_when_functions_is_bad (__main__.TestCppExtensionJIT) ... ok (0.002s)
2023-01-11T21:58:10.6021183Z   test_inline_jit_compile_extension_with_functions_as_dict (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6021638Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_dict...
2023-01-11T21:58:10.6022072Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_dict/build.ninja...
2023-01-11T21:58:10.6022414Z Building extension module inline_jit_extension_with_functions_dict...
2023-01-11T21:58:10.6022761Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6024155Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=inline_jit_extension_with_functions_dict -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_dict/main.cpp -o main.o 
2023-01-11T21:58:10.6025309Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o inline_jit_extension_with_functions_dict.so
2023-01-11T21:58:10.6025701Z Loading extension module inline_jit_extension_with_functions_dict...
2023-01-11T21:58:10.6025918Z ok (1.166s)
2023-01-11T21:58:10.6026263Z   test_inline_jit_compile_extension_with_functions_as_list (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6026721Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_list...
2023-01-11T21:58:10.6027128Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_list/build.ninja...
2023-01-11T21:58:10.6027470Z Building extension module inline_jit_extension_with_functions_list...
2023-01-11T21:58:10.6027742Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6029098Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=inline_jit_extension_with_functions_list -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/inline_jit_extension_with_functions_list/main.cpp -o main.o 
2023-01-11T21:58:10.6030216Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o inline_jit_extension_with_functions_list.so
2023-01-11T21:58:10.6030604Z Loading extension module inline_jit_extension_with_functions_list...
2023-01-11T21:58:10.6030823Z ok (1.231s)
2023-01-11T21:58:10.6031143Z   test_jit_compile_extension (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6031550Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/jit_extension...
2023-01-11T21:58:10.6031901Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/jit_extension/build.ninja...
2023-01-11T21:58:10.6032195Z Building extension module jit_extension...
2023-01-11T21:58:10.6032439Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6033796Z [1/3] c++ -MMD -MF jit_extension.o.d -DTORCH_EXTENSION_NAME=jit_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/var/lib/jenkins/workspace/test/cpp_extensions -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -c /var/lib/jenkins/workspace/test/cpp_extensions/jit_extension.cpp -o jit_extension.o 
2023-01-11T21:58:10.6035826Z [2/3] c++ -MMD -MF jit_extension2.o.d -DTORCH_EXTENSION_NAME=jit_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/var/lib/jenkins/workspace/test/cpp_extensions -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -c /var/lib/jenkins/workspace/test/cpp_extensions/jit_extension2.cpp -o jit_extension2.o 
2023-01-11T21:58:10.6036995Z [3/3] c++ jit_extension.o jit_extension2.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o jit_extension.so
2023-01-11T21:58:10.6037348Z Loading extension module jit_extension...
2023-01-11T21:58:10.6037536Z ok (1.641s)
2023-01-11T21:58:10.6037792Z   test_jit_cuda_archflags (__main__.TestCppExtensionJIT) ... skip: CUDA not found (0.001s)
2023-01-11T21:58:10.6038130Z   test_jit_cuda_extension (__main__.TestCppExtensionJIT) ... skip: CUDA not found (0.000s)
2023-01-11T21:58:10.6038457Z   test_jit_cudnn_extension (__main__.TestCppExtensionJIT) ... skip: CuDNN not found (0.001s)
2023-01-11T21:58:10.6038857Z   test_lenient_flag_handling_in_jit_extensions (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6039295Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/lenient_flag_handling_extension...
2023-01-11T21:58:10.6039696Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/lenient_flag_handling_extension/build.ninja...
2023-01-11T21:58:10.6040020Z Building extension module lenient_flag_handling_extension...
2023-01-11T21:58:10.6040285Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6041720Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=lenient_flag_handling_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/var/lib/jenkins/workspace/test/cpp_extensions -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -O0 -Wall -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/lenient_flag_handling_extension/main.cpp -o main.o 
2023-01-11T21:58:10.6042872Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o lenient_flag_handling_extension.so
2023-01-11T21:58:10.6043244Z Loading extension module lenient_flag_handling_extension...
2023-01-11T21:58:10.6043460Z ok (1.422s)
2023-01-11T21:58:10.6043763Z   test_reload_jit_extension (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6044173Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension...
2023-01-11T21:58:10.6044554Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/build.ninja...
2023-01-11T21:58:10.6044859Z Building extension module reloaded_jit_extension...
2023-01-11T21:58:10.6045115Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6046418Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=reloaded_jit_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/main.cpp -o main.o 
2023-01-11T21:58:10.6047497Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o reloaded_jit_extension.so
2023-01-11T21:58:10.6047846Z Loading extension module reloaded_jit_extension...
2023-01-11T21:58:10.6048162Z Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6048676Z The input conditions for extension module reloaded_jit_extension have changed. Bumping to version 1 and re-building as reloaded_jit_extension_v1...
2023-01-11T21:58:10.6049216Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/build.ninja...
2023-01-11T21:58:10.6050878Z Building extension module reloaded_jit_extension_v1...
2023-01-11T21:58:10.6051324Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6053132Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=reloaded_jit_extension_v1 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/main.cpp -o main.o 
2023-01-11T21:58:10.6054547Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o reloaded_jit_extension_v1.so
2023-01-11T21:58:10.6055002Z Loading extension module reloaded_jit_extension_v1...
2023-01-11T21:58:10.6055306Z Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6056033Z No modifications detected for re-loaded extension module reloaded_jit_extension_v1, skipping build step...
2023-01-11T21:58:10.6056453Z Loading extension module reloaded_jit_extension_v1...
2023-01-11T21:58:10.6056752Z Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6057265Z The input conditions for extension module reloaded_jit_extension have changed. Bumping to version 2 and re-building as reloaded_jit_extension_v2...
2023-01-11T21:58:10.6057680Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/build.ninja...
2023-01-11T21:58:10.6058009Z Building extension module reloaded_jit_extension_v2...
2023-01-11T21:58:10.6058269Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6059581Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=reloaded_jit_extension_v2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/reloaded_jit_extension/main.cpp -o main.o 
2023-01-11T21:58:10.6060649Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o reloaded_jit_extension_v2.so
2023-01-11T21:58:10.6061133Z Loading extension module reloaded_jit_extension_v2...
2023-01-11T21:58:10.6061347Z ok (3.528s)
2023-01-11T21:58:10.6061705Z   test_returns_shared_library_path_when_is_python_module_is_true (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6062283Z The input conditions for extension module is_python_module have changed. Bumping to version 1 and re-building as is_python_module_v1...
2023-01-11T21:58:10.6062753Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module/build.ninja...
2023-01-11T21:58:10.6063069Z Building extension module is_python_module_v1...
2023-01-11T21:58:10.6063386Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6064673Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=is_python_module_v1 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/is_python_module/main.cpp -o main.o 
2023-01-11T21:58:10.6065723Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o is_python_module_v1.so
2023-01-11T21:58:10.6066055Z Loading extension module is_python_module_v1...
2023-01-11T21:58:10.6066265Z ok (1.187s)
2023-01-11T21:58:10.6066615Z   test_set_default_type_also_changes_aten_default_type (__main__.TestCppExtensionJIT) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T21:58:10.6067056Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/test_set_default_type...
2023-01-11T21:58:10.6067421Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/test_set_default_type/build.ninja...
2023-01-11T21:58:10.6067735Z Building extension module test_set_default_type...
2023-01-11T21:58:10.6067993Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:10.6069300Z [1/2] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=test_set_default_type -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -c /var/lib/jenkins/.cache/torch_extensions/py310_cu117/test_set_default_type/main.cpp -o main.o 
2023-01-11T21:58:10.6070373Z [2/2] c++ main.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o test_set_default_type.so
2023-01-11T21:58:10.6070768Z Loading extension module test_set_default_type...
2023-01-11T21:58:10.6071044Z ok (1.154s)
2023-01-11T21:58:10.6071398Z   test_warning (__main__.TestCppExtensionJIT) ... [W main.cpp:12] Warning: Error with CPUDoubleType (function foo)
2023-01-11T21:58:10.6071839Z [W main.cpp:12] Warning: Error with CPUDoubleType (function foo)
2023-01-11T21:58:10.6072199Z [W main.cpp:12] Warning: Error with CPUDoubleType (function foo)
2023-01-11T21:58:10.6082905Z [W main.cpp:12] Warning: Error with CPUDoubleType (function foo)
2023-01-11T21:58:10.6083401Z UserWarning: Error with torch.DoubleTensor (Triggered internally at /var/lib/jenkins/.cache/torch_extensions/py310_cu117/warn_mod/main.cpp:12.)
2023-01-11T21:58:10.6083832Z ok (2.328s)
2023-01-11T21:58:10.6083941Z 
2023-01-11T21:58:10.6084189Z ----------------------------------------------------------------------
2023-01-11T21:58:10.6084446Z Ran 23 tests in 32.120s
2023-01-11T21:58:10.6084570Z 
2023-01-11T21:58:10.6084632Z OK (skipped=7)
2023-01-11T21:58:10.6084749Z 
2023-01-11T21:58:10.6084837Z Generating XML reports...
2023-01-11T21:58:10.6085298Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_jit/TEST-TestCppExtensionJIT-20230111215738.xml
2023-01-11T21:58:10.6085571Z 
2023-01-11T21:58:10.6085906Z ##[endgroup]
2023-01-11T21:58:10.6086325Z FINISHED PRINTING LOG FILE of test_cpp_extensions_jit (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_jit_6smzcg8i)
2023-01-11T21:58:10.6086576Z 
2023-01-11T21:58:10.6086782Z Running test_cuda ... [2023-01-11 21:58:10.598557]
2023-01-11T21:58:10.6087261Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:10.598803]
2023-01-11T21:58:12.2039878Z 
2023-01-11T21:58:12.2040402Z Expand the folded group to see the log file of test_cuda
2023-01-11T21:58:12.2042296Z ##[group]PRINTING LOG FILE of test_cuda (/var/lib/jenkins/workspace/test/test-reports/test_cuda_028r8olj)
2023-01-11T21:58:12.2042862Z CUDA not available, skipping tests
2023-01-11T21:58:12.2043094Z 
2023-01-11T21:58:12.2043224Z Running tests...
2023-01-11T21:58:12.2043903Z ----------------------------------------------------------------------
2023-01-11T21:58:12.2044193Z 
2023-01-11T21:58:12.2044541Z ----------------------------------------------------------------------
2023-01-11T21:58:12.2044830Z Ran 0 tests in 0.000s
2023-01-11T21:58:12.2044941Z 
2023-01-11T21:58:12.2045002Z OK
2023-01-11T21:58:12.2045094Z 
2023-01-11T21:58:12.2045178Z Generating XML reports...
2023-01-11T21:58:12.2045544Z Test results will be stored in test-reports/python-unittest/test_cuda
2023-01-11T21:58:12.2045726Z 
2023-01-11T21:58:12.2045946Z ##[endgroup]
2023-01-11T21:58:12.2046302Z FINISHED PRINTING LOG FILE of test_cuda (/var/lib/jenkins/workspace/test/test-reports/test_cuda_028r8olj)
2023-01-11T21:58:12.2046500Z 
2023-01-11T21:58:12.2046671Z Running test_fake_tensor ... [2023-01-11 21:58:12.204079]
2023-01-11T21:58:12.2047134Z Executing ['/opt/conda/bin/python', '-bb', 'test_fake_tensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:12.204337]
2023-01-11T21:58:14.6129501Z 
2023-01-11T21:58:14.6130007Z Expand the folded group to see the log file of test_fake_tensor
2023-01-11T21:58:14.6130958Z ##[group]PRINTING LOG FILE of test_fake_tensor (/var/lib/jenkins/workspace/test/test-reports/test_fake_tensor_wpqhlsjf)
2023-01-11T21:58:14.6131231Z 
2023-01-11T21:58:14.6131365Z Running tests...
2023-01-11T21:58:14.6132017Z ----------------------------------------------------------------------
2023-01-11T21:58:14.6133138Z Test results will be stored in test-reports/python-unittest/test_fake_tensor
2023-01-11T21:58:14.6133575Z   test_aliased_const_write (__main__.FakeTensorConstHandling) ... ok (0.221s)
2023-01-11T21:58:14.6133958Z   test_constant_invalidation (__main__.FakeTensorConstHandling) ... ok (0.005s)
2023-01-11T21:58:14.6134277Z   test_fake_tensor_batch_norm_cpu (__main__.FakeTensorConstHandling) ... ok (0.078s)
2023-01-11T21:58:14.6134712Z   test_fake_tensor_in_intlist_repro (__main__.FakeTensorConstHandling) ... ok (0.008s)
2023-01-11T21:58:14.6135171Z   test_inplace_add (__main__.FakeTensorConstHandling) ... ok (0.001s)
2023-01-11T21:58:14.6135574Z   test_inplace_view_invalidation (__main__.FakeTensorConstHandling) ... ok (0.001s)
2023-01-11T21:58:14.6136113Z   test_shared_storage_invalidation (__main__.FakeTensorConstHandling) ... ok (0.005s)
2023-01-11T21:58:14.6137283Z   test_shared_storages (__main__.FakeTensorConstHandling) ... /var/lib/jenkins/workspace/test/test_fake_tensor.py:513: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:14.6138200Z   self.assertEqual(x.storage()._cdata, y.storage()._cdata)
2023-01-11T21:58:14.6138801Z /var/lib/jenkins/workspace/test/test_fake_tensor.py:514: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:14.6139414Z   self.assertEqual(x.constant.storage()._cdata, y.constant.storage()._cdata)
2023-01-11T21:58:14.6139643Z ok (0.001s)
2023-01-11T21:58:14.6139972Z   test_simple (__main__.FakeTensorConstHandling) ... ok (0.001s)
2023-01-11T21:58:14.6140280Z   test_dead_key (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:58:14.6140579Z   test_dead_weak_ref (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:58:14.6140937Z   test_memoized_conversion_from_meta (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:58:14.6141274Z   test_memoized_conversion_to_meta (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:58:14.6141632Z   test_no_active_mode (__main__.FakeTensorConverterTest) ... ok (0.008s)
2023-01-11T21:58:14.6141920Z   test_no_ref_cycle (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:58:14.6142257Z   test_separate_mode_error (__main__.FakeTensorConverterTest) ... ok (0.007s)
2023-01-11T21:58:14.6143016Z   test_separate_tensor_storages_non_view (__main__.FakeTensorConverterTest) ... /var/lib/jenkins/workspace/test/test_fake_tensor.py:602: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:14.6143596Z   y.set_(x.storage())
2023-01-11T21:58:14.6143804Z ok (0.001s)
2023-01-11T21:58:14.6144061Z   test_separate_tensor_storages_view (__main__.FakeTensorConverterTest) ... ok (0.001s)
2023-01-11T21:58:14.6144385Z   test_like_ops (__main__.FakeTensorOperatorInvariants) ... ok (0.014s)
2023-01-11T21:58:14.6144762Z   test_non_kwarg_only_device (__main__.FakeTensorOperatorInvariants) ... ok (0.054s)
2023-01-11T21:58:14.6145093Z   test_sparse_new (__main__.FakeTensorOperatorInvariants) ... expected failure (0.002s)
2023-01-11T21:58:14.6145506Z   test_tensor_constructors_all_have_kwarg_device (__main__.FakeTensorOperatorInvariants) ... ok (0.104s)
2023-01-11T21:58:14.6146416Z   test_fake_tensor_prop_on_nn_module (__main__.FakeTensorPropTest) ... /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py:564: UserWarning: Was not able to add assertion to guarantee correct input value to specialized function. It is up to the user to make sure that your inputs match the inputs you specialized the function with.
2023-01-11T21:58:14.6146952Z   warnings.warn(
2023-01-11T21:58:14.6147112Z ok (0.023s)
2023-01-11T21:58:14.6147320Z   test_basic (__main__.FakeTensorTest) ... ok (0.002s)
2023-01-11T21:58:14.6147643Z   test_binary_op_type_promotion (__main__.FakeTensorTest) ... ok (0.008s)
2023-01-11T21:58:14.6147909Z   test_constructor (__main__.FakeTensorTest) ... ok (0.004s)
2023-01-11T21:58:14.6148193Z   test_cpu_fallback (__main__.FakeTensorTest) ... skip: requires cuda (0.001s)
2023-01-11T21:58:14.6148545Z   test_cuda_lstm (__main__.FakeTensorTest) ... skip: requires cuda (0.001s)
2023-01-11T21:58:14.6148854Z   test_cudnn_rnn_with_fallback (__main__.FakeTensorTest) ... skip: requires cuda (0.002s)
2023-01-11T21:58:14.6149218Z   test_cudnn_rnn_without_fallback (__main__.FakeTensorTest) ... skip: requires cuda (0.002s)
2023-01-11T21:58:14.6149533Z   test_data_dependent_operator (__main__.FakeTensorTest) ... ok (0.004s)
2023-01-11T21:58:14.6150240Z   test_deepcopy (__main__.FakeTensorTest) ... /var/lib/jenkins/workspace/test/test_fake_tensor.py:466: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:14.6150904Z   self.assertEqual(mod_copied.b.storage()._cdata, mod_copied.a.storage()._cdata)
2023-01-11T21:58:14.6151133Z ok (0.005s)
2023-01-11T21:58:14.6151400Z   test_fake_dispatch_keys (__main__.FakeTensorTest) ... ok (0.008s)
2023-01-11T21:58:14.6151677Z   test_fake_grad_copy (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:58:14.6151930Z   test_fake_mode_error (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:58:14.6152308Z   test_fallback_memory_prop (__main__.FakeTensorTest) ... skip: requires cuda (0.001s)
2023-01-11T21:58:14.6152602Z   test_from_numpy (__main__.FakeTensorTest) ... ok (0.010s)
2023-01-11T21:58:14.6152944Z   test_index_cuda_with_cpu (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6170321Z   test_like_constructor (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6170907Z   test_mode (__main__.FakeTensorTest) ... ok (0.004s)
2023-01-11T21:58:14.6171373Z   test_nan_to_num (__main__.FakeTensorTest) ... ok (0.013s)
2023-01-11T21:58:14.6171813Z   test_new (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6172129Z   test_non_kwarg_device (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6172439Z   test_non_overlapping_stride_zero (__main__.FakeTensorTest) ... ok (0.014s)
2023-01-11T21:58:14.6172746Z   test_non_parameter_grad (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:58:14.6173042Z   test_normalize_device (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6173345Z   test_parameter_instantiation (__main__.FakeTensorTest) ... ok (0.004s)
2023-01-11T21:58:14.6173635Z   test_print_in_fake_mode (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:58:14.6173903Z   test_randperm (__main__.FakeTensorTest) ... ok (0.007s)
2023-01-11T21:58:14.6174162Z   test_recursive_invocation (__main__.FakeTensorTest) ... ok (0.001s)
2023-01-11T21:58:14.6174438Z   test_scalar_inputs (__main__.FakeTensorTest) ... ok (0.005s)
2023-01-11T21:58:14.6174717Z   test_setitem (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6175012Z   test_shape_take_not_device (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6175311Z   test_throw (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6175597Z   test_type_as (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6175917Z   test_upsample_bilinear_small_channels (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6176223Z   test_zero_dim (__main__.FakeTensorTest) ... skip: requires cuda (0.000s)
2023-01-11T21:58:14.6176390Z 
2023-01-11T21:58:14.6176643Z ----------------------------------------------------------------------
2023-01-11T21:58:14.6176885Z Ran 57 tests in 0.644s
2023-01-11T21:58:14.6176999Z 
2023-01-11T21:58:14.6177082Z OK (skipped=16, expected failures=1)
2023-01-11T21:58:14.6177214Z 
2023-01-11T21:58:14.6177298Z Generating XML reports...
2023-01-11T21:58:14.6177740Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorConstHandling-20230111215813.xml
2023-01-11T21:58:14.6178300Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorConverterTest-20230111215813.xml
2023-01-11T21:58:14.6178857Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorOperatorInvariants-20230111215813.xml
2023-01-11T21:58:14.6179406Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorPropTest-20230111215813.xml
2023-01-11T21:58:14.6179909Z Generated XML report: test-reports/python-unittest/test_fake_tensor/TEST-FakeTensorTest-20230111215813.xml
2023-01-11T21:58:14.6180277Z 
2023-01-11T21:58:14.6180644Z ##[endgroup]
2023-01-11T21:58:14.6181032Z FINISHED PRINTING LOG FILE of test_fake_tensor (/var/lib/jenkins/workspace/test/test-reports/test_fake_tensor_wpqhlsjf)
2023-01-11T21:58:14.6181246Z 
2023-01-11T21:58:14.6181394Z Running test_nn ... [2023-01-11 21:58:14.613162]
2023-01-11T21:58:14.6181837Z Executing ['/opt/conda/bin/python', '-bb', 'test_nn.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:14.613403]
2023-01-11T21:58:48.6424693Z 
2023-01-11T21:58:48.6425156Z Expand the folded group to see the log file of test_nn
2023-01-11T21:58:48.6428909Z ##[group]PRINTING LOG FILE of test_nn (/var/lib/jenkins/workspace/test/test-reports/test_nn_7jcit4ac)
2023-01-11T21:58:48.6429331Z 
2023-01-11T21:58:48.6429708Z Running tests...
2023-01-11T21:58:48.6430317Z ----------------------------------------------------------------------
2023-01-11T21:58:48.6430962Z Test results will be stored in test-reports/python-unittest/test_nn
2023-01-11T21:58:48.6431469Z   test_add_relu (__main__.TestAddRelu) ... ok (0.002s)
2023-01-11T21:58:48.6431912Z   test_add_relu_broadcasting (__main__.TestAddRelu) ... ok (0.001s)
2023-01-11T21:58:48.6432256Z   test_constant_pad_nd (__main__.TestConstantPadNd) ... ok (0.001s)
2023-01-11T21:58:48.6432728Z   test_preserves_memory_format (__main__.TestConstantPadNd) ... ok (0.001s)
2023-01-11T21:58:48.6433248Z   test_pickle_softsign (__main__.TestFunctionalPickle) ... ok (0.000s)
2023-01-11T21:58:48.6433693Z   test_fuse_module_eval_numerics (__main__.TestFusionEval) ... ok (0.255s)
2023-01-11T21:58:48.6434191Z   test_fuse_conv_bn_requires_grad (__main__.TestFusionUtils) ... ok (0.002s)
2023-01-11T21:58:48.6434666Z   test_fuse_linear_bn_requires_grad (__main__.TestFusionUtils) ... ok (0.001s)
2023-01-11T21:58:48.6435122Z   test_AdaptiveAvgPool1d (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6435567Z   test_AdaptiveAvgPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6435962Z   test_AdaptiveAvgPool1d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6436455Z   test_AdaptiveAvgPool1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6436935Z   test_AdaptiveAvgPool1d_one_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6437449Z   test_AdaptiveAvgPool1d_one_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6437904Z   test_AdaptiveAvgPool2d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6438344Z   test_AdaptiveAvgPool2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6438787Z   test_AdaptiveAvgPool2d_single (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6439200Z   test_AdaptiveAvgPool2d_single_1x1output (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6439731Z   test_AdaptiveAvgPool2d_single_1x1output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6440192Z   test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6440697Z   test_AdaptiveAvgPool2d_tuple (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6441174Z   test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6441664Z   test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6442186Z   test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6442678Z   test_AdaptiveAvgPool3d_last_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6443199Z   test_AdaptiveAvgPool3d_last_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6443647Z   test_AdaptiveAvgPool3d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6444110Z   test_AdaptiveAvgPool3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6444763Z   test_AdaptiveAvgPool3d_single (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6445261Z   test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6445756Z   test_AdaptiveAvgPool3d_tuple (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6446230Z   test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6446672Z   test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6447069Z   test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6447527Z   test_AdaptiveLogSoftmax (__main__.TestNN) ... ok (0.074s)
2023-01-11T21:58:48.6448130Z   test_AdaptiveLogSoftmax_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6448653Z   test_AdaptiveMaxPool1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6449340Z   test_AdaptiveMaxPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6449777Z   test_AdaptiveMaxPool1d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6450355Z   test_AdaptiveMaxPool1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6450935Z   test_AdaptiveMaxPool2d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6451479Z   test_AdaptiveMaxPool2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6452060Z   test_AdaptiveMaxPool2d_single (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6452521Z   test_AdaptiveMaxPool2d_single_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6452961Z   test_AdaptiveMaxPool2d_tuple (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6453467Z   test_AdaptiveMaxPool2d_tuple_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6454029Z   test_AdaptiveMaxPool2d_tuple_none (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6454626Z   test_AdaptiveMaxPool2d_tuple_none_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6455079Z   test_AdaptiveMaxPool3d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6455592Z   test_AdaptiveMaxPool3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6456124Z   test_AdaptiveMaxPool3d_single (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6456685Z   test_AdaptiveMaxPool3d_single_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6457296Z   test_AdaptiveMaxPool3d_single_nonatomic (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6457779Z   test_AdaptiveMaxPool3d_single_nonatomic_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6458308Z   test_AdaptiveMaxPool3d_tuple (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6458842Z   test_AdaptiveMaxPool3d_tuple_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6459422Z   test_AdaptiveMaxPool3d_tuple_nonatomic (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6459959Z   test_AdaptiveMaxPool3d_tuple_nonatomic_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6460278Z   test_AdaptiveMaxPool3d_tuple_none (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6460601Z   test_AdaptiveMaxPool3d_tuple_none_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6460891Z   test_AvgPool1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6461170Z   test_AvgPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6461446Z   test_AvgPool1d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6461746Z   test_AvgPool1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6462156Z   test_AvgPool1d_stride (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6462434Z   test_AvgPool1d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6462901Z   test_AvgPool1d_stride_pad (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6463309Z   test_AvgPool1d_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6463597Z   test_AvgPool2d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6463861Z   test_AvgPool2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6464141Z   test_AvgPool2d_divisor (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6464429Z   test_AvgPool2d_divisor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6464884Z   test_AvgPool2d_divisor_stride (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6465211Z   test_AvgPool2d_divisor_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6465521Z   test_AvgPool2d_divisor_stride_pad (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6465837Z   test_AvgPool2d_divisor_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6467000Z   test_AvgPool2d_divisor_stride_pad_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6467347Z   test_AvgPool2d_divisor_stride_pad_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6467686Z   test_AvgPool2d_divisor_stride_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6468023Z   test_AvgPool2d_divisor_stride_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6468341Z   test_AvgPool2d_divisor_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6468665Z   test_AvgPool2d_divisor_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6468972Z   test_AvgPool2d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6469255Z   test_AvgPool2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6469546Z   test_AvgPool2d_stride (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6469828Z   test_AvgPool2d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6470117Z   test_AvgPool2d_stride_pad (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6470399Z   test_AvgPool2d_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6470674Z   test_AvgPool3d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6470947Z   test_AvgPool3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6471215Z   test_AvgPool3d_divisor (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6471502Z   test_AvgPool3d_divisor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6471796Z   test_AvgPool3d_divisor_stride (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6472069Z   test_AvgPool3d_divisor_stride1_pad0_gpu_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6472405Z   test_AvgPool3d_divisor_stride1_pad0_gpu_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6472755Z   test_AvgPool3d_divisor_stride1_pad0_gpu_input_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6473123Z   test_AvgPool3d_divisor_stride1_pad0_gpu_input_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6473476Z   test_AvgPool3d_divisor_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6473782Z   test_AvgPool3d_divisor_stride_pad (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6474098Z   test_AvgPool3d_divisor_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6474489Z   test_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6474834Z   test_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6475202Z   test_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6475586Z   test_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6475948Z   test_AvgPool3d_divisor_stride_pad_gpu_general_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6476291Z   test_AvgPool3d_divisor_stride_pad_gpu_general_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6476695Z   test_AvgPool3d_divisor_stride_pad_gpu_general_output_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6477084Z   test_AvgPool3d_divisor_stride_pad_gpu_general_output_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6477447Z   test_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6477792Z   test_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6478159Z   test_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6478543Z   test_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6478890Z   test_AvgPool3d_divisor_stride_pad_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6479237Z   test_AvgPool3d_divisor_stride_pad_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6479570Z   test_AvgPool3d_divisor_stride_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6479906Z   test_AvgPool3d_divisor_stride_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6480217Z   test_AvgPool3d_divisor_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6480542Z   test_AvgPool3d_divisor_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6480848Z   test_AvgPool3d_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6481147Z   test_AvgPool3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6481423Z   test_AvgPool3d_stride (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6481691Z   test_AvgPool3d_stride1_pad0_gpu_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6482015Z   test_AvgPool3d_stride1_pad0_gpu_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6482338Z   test_AvgPool3d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6482625Z   test_AvgPool3d_stride_pad (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6482917Z   test_AvgPool3d_stride_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6483231Z   test_AvgPool3d_stride_pad_gpu_fixedkw_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6483557Z   test_AvgPool3d_stride_pad_gpu_fixedkw_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6483893Z   test_AvgPool3d_stride_pad_gpu_general_output (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6484229Z   test_AvgPool3d_stride_pad_gpu_general_output_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6484552Z   test_AvgPool3d_stride_pad_gpu_input_nooverlap (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6484892Z   test_AvgPool3d_stride_pad_gpu_input_nooverlap_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6485224Z   test_BCELoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6485501Z   test_BCELoss_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6485806Z   test_BCELoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6486121Z   test_BCELoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6486431Z   test_BCELoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6486704Z   test_BCELoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6487010Z   test_BCELoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6487387Z   test_BCELoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6487731Z   test_BCELoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6488022Z   test_BCELoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6488326Z   test_BCELoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6488670Z   test_BCELoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6489012Z   test_BCELoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6489566Z   test_BCELoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6489873Z   test_BCELoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6490220Z   test_BCELoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6490549Z   test_BCELoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6490847Z   test_BCELoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6491133Z   test_BCELoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6491421Z   test_BCELoss_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6491711Z   test_BCELoss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6492008Z   test_BCELoss_scalar_weights (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6492317Z   test_BCELoss_scalar_weights_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6492657Z   test_BCELoss_scalar_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6493001Z   test_BCELoss_scalar_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6493346Z   test_BCELoss_scalar_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6493642Z   test_BCELoss_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6493923Z   test_BCELoss_weights_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6494254Z   test_BCELoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6494585Z   test_BCELoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6494914Z   test_BCELoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6495198Z   test_BCELoss_weights_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6495509Z   test_BCELoss_weights_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6495820Z   test_BCELoss_weights_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6496206Z   test_BCELoss_weights_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6496513Z   test_BCEWithLogitsLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6496822Z   test_BCEWithLogitsLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6497163Z   test_BCEWithLogitsLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6497495Z   test_BCEWithLogitsLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6498197Z   test_BCEWithLogitsLoss_legacy_enum (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:58:48.6498645Z   warnings.warn(warning.format(ret))
2023-01-11T21:58:48.6499138Z /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T21:58:48.6499480Z   warnings.warn(warning.format(ret))
2023-01-11T21:58:48.6499674Z ok (0.006s)
2023-01-11T21:58:48.6499941Z   test_BCEWithLogitsLoss_legacy_enum_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6500255Z   test_BCEWithLogitsLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6500595Z   test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6500968Z   test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6501340Z   test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6501660Z   test_BCEWithLogitsLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6501999Z   test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6502378Z   test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6502936Z   test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6503267Z   test_BCEWithLogitsLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6503603Z   test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6503973Z   test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6504330Z   test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6504651Z   test_BCEWithLogitsLoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6504972Z   test_BCEWithLogitsLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6505293Z   test_BCEWithLogitsLoss_no_reduce_scalar (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6505615Z   test_BCEWithLogitsLoss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6505939Z   test_BCEWithLogitsLoss_scalar_weights (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6506275Z   test_BCEWithLogitsLoss_scalar_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6506637Z   test_BCEWithLogitsLoss_scalar_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6507010Z   test_BCEWithLogitsLoss_scalar_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6507331Z   test_BCEWithLogitsLoss_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6507653Z   test_BCEWithLogitsLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6508047Z   test_BCEWithLogitsLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6508407Z   test_BCEWithLogitsLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6508715Z   test_BatchNorm1d_3d_input (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6509014Z   test_BatchNorm1d_3d_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6509297Z   test_BatchNorm1d_3d_input_eval (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6509602Z   test_BatchNorm1d_3d_input_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6509943Z   test_BatchNorm1d_3d_input_not_affine (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6510251Z   test_BatchNorm1d_3d_input_not_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6510573Z   test_BatchNorm1d_3d_input_not_affine_eval (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6510901Z   test_BatchNorm1d_3d_input_not_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6511204Z   test_BatchNorm1d_affine (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6511484Z   test_BatchNorm1d_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6511779Z   test_BatchNorm1d_affine_eval (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6512079Z   test_BatchNorm1d_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6512379Z   test_BatchNorm1d_affine_simple_average (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6512708Z   test_BatchNorm1d_affine_simple_average_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6513033Z   test_BatchNorm1d_affine_simple_average_eval (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6513372Z   test_BatchNorm1d_affine_simple_average_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6513674Z   test_BatchNorm1d_not_affine (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6513975Z   test_BatchNorm1d_not_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6514276Z   test_BatchNorm1d_not_affine_eval (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6514573Z   test_BatchNorm1d_not_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6514884Z   test_BatchNorm1d_not_tracking_stats (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6515205Z   test_BatchNorm1d_not_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6515523Z   test_BatchNorm1d_not_tracking_stats_eval (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6515837Z   test_BatchNorm1d_not_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6516148Z   test_BatchNorm1d_zero_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6516459Z   test_BatchNorm1d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6516749Z   test_BatchNorm1d_zero_batch_eval (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6517059Z   test_BatchNorm1d_zero_batch_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6517345Z   test_BatchNorm2d (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6517610Z   test_BatchNorm2d_2d_simple_average (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6517917Z   test_BatchNorm2d_2d_simple_average_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6518236Z   test_BatchNorm2d_2d_simple_average_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6518562Z   test_BatchNorm2d_2d_simple_average_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6518937Z   test_BatchNorm2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6519218Z   test_BatchNorm2d_eval (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6519505Z   test_BatchNorm2d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6519793Z   test_BatchNorm2d_momentum (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6520075Z   test_BatchNorm2d_momentum_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6520374Z   test_BatchNorm2d_momentum_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6520680Z   test_BatchNorm2d_momentum_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6520999Z   test_BatchNorm2d_not_affine (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6521301Z   test_BatchNorm2d_not_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6521600Z   test_BatchNorm2d_not_affine_eval (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6521911Z   test_BatchNorm2d_not_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6522207Z   test_BatchNorm2d_not_tracking_stats (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:48.6522525Z   test_BatchNorm2d_not_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6522844Z   test_BatchNorm2d_not_tracking_stats_eval (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:48.6523158Z   test_BatchNorm2d_not_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6523465Z   test_BatchNorm2d_zero_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6523767Z   test_BatchNorm2d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6524068Z   test_BatchNorm2d_zero_batch_eval (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6524369Z   test_BatchNorm2d_zero_batch_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6524656Z   test_BatchNorm3d (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6524917Z   test_BatchNorm3d_3d_simple_average (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6525223Z   test_BatchNorm3d_3d_simple_average_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6525538Z   test_BatchNorm3d_3d_simple_average_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6525865Z   test_BatchNorm3d_3d_simple_average_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6526196Z   test_BatchNorm3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6526468Z   test_BatchNorm3d_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6526757Z   test_BatchNorm3d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6527049Z   test_BatchNorm3d_momentum (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6527332Z   test_BatchNorm3d_momentum_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6527626Z   test_BatchNorm3d_momentum_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6527932Z   test_BatchNorm3d_momentum_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6528230Z   test_BatchNorm3d_not_affine (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6528516Z   test_BatchNorm3d_not_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6528817Z   test_BatchNorm3d_not_affine_eval (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6529248Z   test_BatchNorm3d_not_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6529567Z   test_BatchNorm3d_not_tracking_stats (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6529870Z   test_BatchNorm3d_not_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6530260Z   test_BatchNorm3d_not_tracking_stats_eval (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6530587Z   test_BatchNorm3d_not_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6530884Z   test_BatchNorm3d_zero_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6531185Z   test_BatchNorm3d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6531492Z   test_BatchNorm3d_zero_batch_eval (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6531805Z   test_BatchNorm3d_zero_batch_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6532070Z   test_CELU (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6532374Z   test_CELU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6532647Z   test_CELU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6532921Z   test_CELU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6533197Z   test_CELU_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6533471Z   test_CELU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6533763Z   test_CTCLoss_2d_int_target_lengths_intlists (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6534101Z   test_CTCLoss_2d_int_target_lengths_intlists_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6534471Z   test_CTCLoss_2d_int_target_lengths_intlists_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6534814Z   test_CTCLoss_2d_int_target_lengths_intlists_sum_reduction (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6535169Z   test_CTCLoss_2d_int_target_lengths_intlists_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6535573Z   test_CTCLoss_2d_int_target_lengths_intlists_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6535914Z   test_CTCLoss_2d_int_target_lengths_tensors (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6536248Z   test_CTCLoss_2d_int_target_lengths_tensors_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6536606Z   test_CTCLoss_2d_int_target_lengths_tensors_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6536949Z   test_CTCLoss_2d_int_target_lengths_tensors_sum_reduction (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6537308Z   test_CTCLoss_2d_int_target_lengths_tensors_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6537705Z   test_CTCLoss_2d_int_target_lengths_tensors_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6538024Z   test_CTCLoss_2d_lengths_tensors (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6538340Z   test_CTCLoss_2d_lengths_tensors_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6538689Z   test_CTCLoss_2d_lengths_tensors_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6538997Z   test_CTCLoss_2d_lengths_tensors_sum_reduction (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6539337Z   test_CTCLoss_2d_lengths_tensors_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6539711Z   test_CTCLoss_2d_lengths_tensors_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6540059Z   test_CTCLoss_critical_target_len (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:48.6540339Z   test_CTCLoss_lengthchecks_cpu (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6540672Z   test_CTCLoss_lengthchecks_cuda (__main__.TestNN) ... skip: CUDA not available (0.000s)
2023-01-11T21:58:48.6540962Z   test_CTCLoss_lengths_intlists (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6541271Z   test_CTCLoss_lengths_intlists_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6541604Z   test_CTCLoss_lengths_intlists_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6541926Z   test_CTCLoss_lengths_intlists_sum_reduction (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6542266Z   test_CTCLoss_lengths_intlists_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6542698Z   test_CTCLoss_lengths_intlists_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6543054Z   test_CTCLoss_lengths_tensors (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6543368Z   test_CTCLoss_lengths_tensors_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6543716Z   test_CTCLoss_lengths_tensors_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6544024Z   test_CTCLoss_lengths_tensors_sum_reduction (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6544361Z   test_CTCLoss_lengths_tensors_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6544732Z   test_CTCLoss_lengths_tensors_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6545068Z   test_CTCLoss_long_targets (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:48.6545339Z   test_CTCLoss_typechecks (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6545627Z   test_CTCLoss_zero_infinity (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:48.6545903Z   test_ConstantPad1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6546150Z   test_ConstantPad1d_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6546449Z   test_ConstantPad1d_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6546747Z   test_ConstantPad1d_complex (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6547050Z   test_ConstantPad1d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6547362Z   test_ConstantPad1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6547645Z   test_ConstantPad2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6547902Z   test_ConstantPad2d_complex (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6548192Z   test_ConstantPad2d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6548519Z   test_ConstantPad2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6548815Z   test_ConstantPad2d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6549130Z   test_ConstantPad2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6549411Z   test_ConstantPad3d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6549671Z   test_ConstantPad3d_complex (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6549972Z   test_ConstantPad3d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6550286Z   test_ConstantPad3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6550578Z   test_ConstantPad3d_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6550891Z   test_ConstantPad3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6551158Z   test_Conv1d (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6551415Z   test_Conv1d_circular_stride2_pad2 (__main__.TestNN) ... ok (0.025s)
2023-01-11T21:58:48.6551730Z   test_Conv1d_circular_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6552084Z   test_Conv1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6552344Z   test_Conv1d_dilated (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6552624Z   test_Conv1d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6552899Z   test_Conv1d_groups (__main__.TestNN) ... ok (0.058s)
2023-01-11T21:58:48.6553165Z   test_Conv1d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6553440Z   test_Conv1d_pad1 (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6553713Z   test_Conv1d_pad1_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6553988Z   test_Conv1d_pad1size1 (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6554293Z   test_Conv1d_pad1size1_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6554574Z   test_Conv1d_pad2 (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6554850Z   test_Conv1d_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6555115Z   test_Conv1d_pad2size1 (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6555398Z   test_Conv1d_pad2size1_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6555681Z   test_Conv1d_pad_same (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6556497Z   test_Conv1d_pad_same2 (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py:309: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T21:58:48.6557041Z   return F.conv1d(input, weight, bias, self.stride,
2023-01-11T21:58:48.6557238Z ok (0.021s)
2023-01-11T21:58:48.6557484Z   test_Conv1d_pad_same2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6557804Z   test_Conv1d_pad_same_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6558082Z   test_Conv1d_pad_same_dilated (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6558384Z   test_Conv1d_pad_same_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6558676Z   test_Conv1d_pad_valid (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6558946Z   test_Conv1d_pad_valid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6559240Z   test_Conv1d_reflect_stride2_pad2 (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6559549Z   test_Conv1d_reflect_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6559855Z   test_Conv1d_replicate_stride2_pad2 (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:48.6560157Z   test_Conv1d_replicate_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6560453Z   test_Conv1d_stride (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6560733Z   test_Conv1d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6561000Z   test_Conv1d_zero_batch (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6561284Z   test_Conv1d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6561575Z   test_Conv1d_zeros_stride2_pad2 (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:48.6561874Z   test_Conv1d_zeros_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6562137Z   test_Conv2d (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6562388Z   test_Conv2d_circular_stride2_pad2 (__main__.TestNN) ... ok (0.032s)
2023-01-11T21:58:48.6562698Z   test_Conv2d_circular_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6563000Z   test_Conv2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6563313Z   test_Conv2d_depthwise (__main__.TestNN) ... ok (0.024s)
2023-01-11T21:58:48.6563599Z   test_Conv2d_depthwise_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6563891Z   test_Conv2d_depthwise_dilated (__main__.TestNN) ... ok (0.024s)
2023-01-11T21:58:48.6564184Z   test_Conv2d_depthwise_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6564481Z   test_Conv2d_depthwise_padded (__main__.TestNN) ... ok (0.026s)
2023-01-11T21:58:48.6564784Z   test_Conv2d_depthwise_padded_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6565071Z   test_Conv2d_depthwise_strided (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:48.6565401Z   test_Conv2d_depthwise_strided_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6565713Z   test_Conv2d_depthwise_with_multiplier (__main__.TestNN) ... ok (0.026s)
2023-01-11T21:58:48.6566041Z   test_Conv2d_depthwise_with_multiplier_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6566323Z   test_Conv2d_dilated (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6566605Z   test_Conv2d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6566902Z   test_Conv2d_dilated_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6567206Z   test_Conv2d_dilated_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6567493Z   test_Conv2d_groups (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:48.6567773Z   test_Conv2d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6568058Z   test_Conv2d_groups_thnn (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:48.6568336Z   test_Conv2d_groups_thnn_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6568639Z   test_Conv2d_groups_thnn_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6568960Z   test_Conv2d_groups_thnn_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6569413Z   test_Conv2d_groups_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6569727Z   test_Conv2d_groups_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6570013Z   test_Conv2d_no_bias (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6570293Z   test_Conv2d_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6570573Z   test_Conv2d_no_bias_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6570888Z   test_Conv2d_no_bias_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6571178Z   test_Conv2d_pad_same (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:48.6571448Z   test_Conv2d_pad_same_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6571737Z   test_Conv2d_pad_same_dilated (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6572036Z   test_Conv2d_pad_same_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6572328Z   test_Conv2d_pad_valid (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6572598Z   test_Conv2d_pad_valid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6572876Z   test_Conv2d_padding (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6573160Z   test_Conv2d_padding_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6573446Z   test_Conv2d_padding_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6573765Z   test_Conv2d_padding_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6574073Z   test_Conv2d_reflect_stride2_pad2 (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6574447Z   test_Conv2d_reflect_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6574743Z   test_Conv2d_replicate_stride2_pad2 (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6575058Z   test_Conv2d_replicate_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6575351Z   test_Conv2d_strided (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6575618Z   test_Conv2d_strided_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6575910Z   test_Conv2d_strided_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6576227Z   test_Conv2d_strided_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6576566Z   test_Conv2d_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6576855Z   test_Conv2d_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6577140Z   test_Conv2d_zero_batch (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6577425Z   test_Conv2d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6577714Z   test_Conv2d_zero_batch_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6578037Z   test_Conv2d_zero_batch_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6578345Z   test_Conv2d_zeros_stride2_pad2 (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6578647Z   test_Conv2d_zeros_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6578911Z   test_Conv3d (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6579150Z   test_Conv3d_1x1x1_no_bias (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6579438Z   test_Conv3d_1x1x1_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6579724Z   test_Conv3d_1x1x1_no_bias_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6580042Z   test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6580351Z   test_Conv3d_circular_stride2_pad2 (__main__.TestNN) ... ok (0.042s)
2023-01-11T21:58:48.6580662Z   test_Conv3d_circular_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6580965Z   test_Conv3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6581237Z   test_Conv3d_dilated (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:48.6581514Z   test_Conv3d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6581787Z   test_Conv3d_dilated_strided (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:48.6582087Z   test_Conv3d_dilated_strided_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6582370Z   test_Conv3d_groups (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6582738Z   test_Conv3d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6583020Z   test_Conv3d_groups_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6583337Z   test_Conv3d_groups_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6583625Z   test_Conv3d_no_bias (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6583888Z   test_Conv3d_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6584179Z   test_Conv3d_no_bias_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6584492Z   test_Conv3d_no_bias_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6584784Z   test_Conv3d_pad_same (__main__.TestNN) ... ok (0.028s)
2023-01-11T21:58:48.6585055Z   test_Conv3d_pad_same_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6585383Z   test_Conv3d_pad_same_dilated (__main__.TestNN) ... ok (0.028s)
2023-01-11T21:58:48.6585682Z   test_Conv3d_pad_same_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6585957Z   test_Conv3d_pad_valid (__main__.TestNN) ... ok (0.024s)
2023-01-11T21:58:48.6586239Z   test_Conv3d_pad_valid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6586537Z   test_Conv3d_replicate_stride2_pad2 (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:48.6586852Z   test_Conv3d_replicate_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6587132Z   test_Conv3d_stride (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6587412Z   test_Conv3d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6587731Z   test_Conv3d_stride_padding (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6588017Z   test_Conv3d_stride_padding_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6588331Z   test_Conv3d_stride_padding_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6588667Z   test_Conv3d_stride_padding_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6588985Z   test_Conv3d_stride_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6589287Z   test_Conv3d_stride_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6589588Z   test_Conv3d_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6589887Z   test_Conv3d_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6590162Z   test_Conv3d_zero_batch (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6590449Z   test_Conv3d_zero_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6590750Z   test_Conv3d_zero_batch_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6591075Z   test_Conv3d_zero_batch_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6591370Z   test_Conv3d_zeros_stride2_pad2 (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:48.6591674Z   test_Conv3d_zeros_stride2_pad2_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6591970Z   test_ConvTranspose1d (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:48.6592248Z   test_ConvTranspose1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6592541Z   test_ConvTranspose1d_dilated (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6592849Z   test_ConvTranspose1d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6593155Z   test_ConvTranspose1d_groups (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:48.6593451Z   test_ConvTranspose1d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6593756Z   test_ConvTranspose1d_no_bias (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6594059Z   test_ConvTranspose1d_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6594339Z   test_ConvTranspose2d (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6594625Z   test_ConvTranspose2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6594918Z   test_ConvTranspose2d_dilated (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6595221Z   test_ConvTranspose2d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6595529Z   test_ConvTranspose2d_dilated_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6595876Z   test_ConvTranspose2d_dilated_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6596195Z   test_ConvTranspose2d_groups (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6596538Z   test_ConvTranspose2d_groups_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6596859Z   test_ConvTranspose2d_groups_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6597196Z   test_ConvTranspose2d_groups_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6597512Z   test_ConvTranspose2d_no_bias (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6597804Z   test_ConvTranspose2d_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6598119Z   test_ConvTranspose2d_no_bias_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6598458Z   test_ConvTranspose2d_no_bias_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6598799Z   test_ConvTranspose2d_with_long_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6599125Z   test_ConvTranspose2d_with_long_tensor_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6599428Z   test_ConvTranspose3d (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6599715Z   test_ConvTranspose3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6599997Z   test_ConvTranspose3d_dilated (__main__.TestNN) ... ok (0.021s)
2023-01-11T21:58:48.6600302Z   test_ConvTranspose3d_dilated_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6600602Z   test_CosineEmbeddingLoss (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6600904Z   test_CosineEmbeddingLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6601256Z   test_CosineEmbeddingLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6601604Z   test_CosineEmbeddingLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6601921Z   test_CosineEmbeddingLoss_margin (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6602235Z   test_CosineEmbeddingLoss_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6602598Z   test_CosineEmbeddingLoss_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6602960Z   test_CosineEmbeddingLoss_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6603295Z   test_CosineEmbeddingLoss_margin_sum_reduction (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6603638Z   test_CosineEmbeddingLoss_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6604035Z   test_CosineEmbeddingLoss_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6604429Z   test_CosineEmbeddingLoss_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6604774Z   test_CosineEmbeddingLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6605110Z   test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6605494Z   test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6605872Z   test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6606196Z   test_CosineEmbeddingLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6606538Z   test_CosineEmbeddingLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6606919Z   test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6607293Z   test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6607660Z   test_CosineEmbeddingLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6607999Z   test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6608375Z   test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6608749Z   test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6609219Z   test_CosineEmbeddingLoss_sum_reduction (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6609735Z   test_CosineEmbeddingLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6610391Z   test_CosineEmbeddingLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6610966Z   test_CosineEmbeddingLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6611415Z   test_CrossEntropyLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6611782Z   test_CrossEntropyLoss_2d (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6612136Z   test_CrossEntropyLoss_2d_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6612470Z   test_CrossEntropyLoss_2d_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6612810Z   test_CrossEntropyLoss_2d_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6613122Z   test_CrossEntropyLoss_2d_ignore_index (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6613454Z   test_CrossEntropyLoss_2d_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6613810Z   test_CrossEntropyLoss_2d_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6614177Z   test_CrossEntropyLoss_2d_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6614518Z   test_CrossEntropyLoss_2d_indices_target_smoothing (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6614867Z   test_CrossEntropyLoss_2d_indices_target_smoothing_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6615261Z   test_CrossEntropyLoss_2d_indices_target_smoothing_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6615651Z   test_CrossEntropyLoss_2d_indices_target_smoothing_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6616014Z   test_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6616387Z   test_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6616809Z   test_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6617223Z   test_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6617599Z   test_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6617973Z   test_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6618390Z   test_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6618810Z   test_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6619243Z   test_CrossEntropyLoss_2d_indices_target_smoothing_weight (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6619606Z   test_CrossEntropyLoss_2d_indices_target_smoothing_weight_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6620014Z   test_CrossEntropyLoss_2d_indices_target_smoothing_weight_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6620417Z   test_CrossEntropyLoss_2d_indices_target_smoothing_weight_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6620757Z   test_CrossEntropyLoss_2d_prob_target (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6621079Z   test_CrossEntropyLoss_2d_prob_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6621471Z   test_CrossEntropyLoss_2d_prob_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6621840Z   test_CrossEntropyLoss_2d_prob_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6622176Z   test_CrossEntropyLoss_2d_prob_target_smoothing (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6622584Z   test_CrossEntropyLoss_2d_prob_target_smoothing_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6622979Z   test_CrossEntropyLoss_2d_prob_target_smoothing_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6623359Z   test_CrossEntropyLoss_2d_prob_target_smoothing_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6623721Z   test_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6624089Z   test_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6624502Z   test_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6624916Z   test_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6625278Z   test_CrossEntropyLoss_2d_prob_target_smoothing_weight (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6625635Z   test_CrossEntropyLoss_2d_prob_target_smoothing_weight_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6626035Z   test_CrossEntropyLoss_2d_prob_target_smoothing_weight_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6626430Z   test_CrossEntropyLoss_2d_prob_target_smoothing_weight_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6626774Z   test_CrossEntropyLoss_2d_prob_target_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6627134Z   test_CrossEntropyLoss_2d_prob_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6627645Z   test_CrossEntropyLoss_2d_prob_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6628037Z   test_CrossEntropyLoss_2d_prob_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6628371Z   test_CrossEntropyLoss_2d_prob_target_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6628723Z   test_CrossEntropyLoss_2d_prob_target_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6629109Z   test_CrossEntropyLoss_2d_prob_target_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6629493Z   test_CrossEntropyLoss_2d_prob_target_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6629838Z   test_CrossEntropyLoss_2d_prob_target_weights_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6630250Z   test_CrossEntropyLoss_2d_prob_target_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6630656Z   test_CrossEntropyLoss_2d_prob_target_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6631065Z   test_CrossEntropyLoss_2d_prob_target_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6631397Z   test_CrossEntropyLoss_2d_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6631735Z   test_CrossEntropyLoss_2d_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6632134Z   test_CrossEntropyLoss_2d_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6632501Z   test_CrossEntropyLoss_2d_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6632813Z   test_CrossEntropyLoss_2d_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6633139Z   test_CrossEntropyLoss_2d_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6633496Z   test_CrossEntropyLoss_2d_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6633840Z   test_CrossEntropyLoss_2d_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6634171Z   test_CrossEntropyLoss_3d_indices_target_smoothing (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6634530Z   test_CrossEntropyLoss_3d_indices_target_smoothing_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6634922Z   test_CrossEntropyLoss_3d_indices_target_smoothing_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6635304Z   test_CrossEntropyLoss_3d_indices_target_smoothing_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6635669Z   test_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6636052Z   test_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6636470Z   test_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6636867Z   test_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6637249Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6637632Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6638053Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6638454Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6638841Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6639249Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6639692Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6640114Z   test_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6640512Z   test_CrossEntropyLoss_3d_prob_target (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6640843Z   test_CrossEntropyLoss_3d_prob_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6641206Z   test_CrossEntropyLoss_3d_prob_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6641558Z   test_CrossEntropyLoss_3d_prob_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6641893Z   test_CrossEntropyLoss_3d_prob_target_smoothing (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6642244Z   test_CrossEntropyLoss_3d_prob_target_smoothing_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6642659Z   test_CrossEntropyLoss_3d_prob_target_smoothing_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6643032Z   test_CrossEntropyLoss_3d_prob_target_smoothing_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6643394Z   test_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6643781Z   test_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6644197Z   test_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6644595Z   test_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6644957Z   test_CrossEntropyLoss_3d_prob_target_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6645319Z   test_CrossEntropyLoss_3d_prob_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6645712Z   test_CrossEntropyLoss_3d_prob_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6646089Z   test_CrossEntropyLoss_3d_prob_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6646432Z   test_CrossEntropyLoss_3d_prob_target_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6646781Z   test_CrossEntropyLoss_3d_prob_target_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6647151Z   test_CrossEntropyLoss_3d_prob_target_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6647533Z   test_CrossEntropyLoss_3d_prob_target_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6647893Z   test_CrossEntropyLoss_3d_prob_target_weights_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6648263Z   test_CrossEntropyLoss_3d_prob_target_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6648658Z   test_CrossEntropyLoss_3d_prob_target_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6649201Z   test_CrossEntropyLoss_3d_prob_target_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6649554Z   test_CrossEntropyLoss_4d_prob_target (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6649886Z   test_CrossEntropyLoss_4d_prob_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6650238Z   test_CrossEntropyLoss_4d_prob_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6650600Z   test_CrossEntropyLoss_4d_prob_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6650940Z   test_CrossEntropyLoss_4d_prob_target_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6651361Z   test_CrossEntropyLoss_4d_prob_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6651737Z   test_CrossEntropyLoss_4d_prob_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6652124Z   test_CrossEntropyLoss_4d_prob_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6652468Z   test_CrossEntropyLoss_4d_prob_target_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6652816Z   test_CrossEntropyLoss_4d_prob_target_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6653223Z   test_CrossEntropyLoss_4d_prob_target_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6653609Z   test_CrossEntropyLoss_4d_prob_target_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6653967Z   test_CrossEntropyLoss_4d_prob_target_weights_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6654328Z   test_CrossEntropyLoss_4d_prob_target_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6654736Z   test_CrossEntropyLoss_4d_prob_target_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6655147Z   test_CrossEntropyLoss_4d_prob_target_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6655518Z   test_CrossEntropyLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6655847Z   test_CrossEntropyLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6656179Z   test_CrossEntropyLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6656481Z   test_CrossEntropyLoss_dim_is_3 (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6656802Z   test_CrossEntropyLoss_dim_is_3_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6657141Z   test_CrossEntropyLoss_dim_is_3_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6657490Z   test_CrossEntropyLoss_dim_is_3_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6657814Z   test_CrossEntropyLoss_dim_is_3_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6658158Z   test_CrossEntropyLoss_dim_is_3_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6658525Z   test_CrossEntropyLoss_dim_is_3_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6658903Z   test_CrossEntropyLoss_dim_is_3_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6659231Z   test_CrossEntropyLoss_higher_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6659548Z   test_CrossEntropyLoss_higher_dim_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6659911Z   test_CrossEntropyLoss_higher_dim_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6660270Z   test_CrossEntropyLoss_higher_dim_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6660604Z   test_CrossEntropyLoss_higher_dim_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6660945Z   test_CrossEntropyLoss_higher_dim_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6661333Z   test_CrossEntropyLoss_higher_dim_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6661717Z   test_CrossEntropyLoss_higher_dim_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6662078Z   test_CrossEntropyLoss_weights (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6662386Z   test_CrossEntropyLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6662838Z   test_CrossEntropyLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6663194Z   test_CrossEntropyLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6663479Z   test_CrossMapLRN2d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6663766Z   test_CrossMapLRN2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6664034Z   test_ELU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6664331Z   test_ELU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6664589Z   test_ELU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6664872Z   test_ELU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6665144Z   test_ELU_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6665407Z   test_ELU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6665676Z   test_Embedding (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6665935Z   test_EmbeddingBag_discontiguous (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6666253Z   test_EmbeddingBag_discontiguous_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6666540Z   test_EmbeddingBag_max (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6666828Z   test_EmbeddingBag_max_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6667128Z   test_EmbeddingBag_max_padding_idx (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6667434Z   test_EmbeddingBag_max_padding_idx_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6667739Z   test_EmbeddingBag_mean (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6668029Z   test_EmbeddingBag_mean_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6668331Z   test_EmbeddingBag_mean_padding_idx (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6668638Z   test_EmbeddingBag_mean_padding_idx_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6668943Z   test_EmbeddingBag_sparse (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6669244Z   test_EmbeddingBag_sparse_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6669522Z   test_EmbeddingBag_sum (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6669813Z   test_EmbeddingBag_sum_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6670114Z   test_EmbeddingBag_sum_padding_idx (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6670436Z   test_EmbeddingBag_sum_padding_idx_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6670754Z   test_Embedding_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6671042Z   test_Embedding_discontiguous (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6671349Z   test_Embedding_discontiguous_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6671629Z   test_Embedding_sparse (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6671915Z   test_Embedding_sparse_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6672189Z   test_Flatten (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6672455Z   test_Flatten_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6672723Z   test_Flatten_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6673012Z   test_Flatten_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6673339Z   test_Fold (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6673588Z   test_Fold_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6673858Z   test_Fold_int_input (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6674139Z   test_Fold_int_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6674412Z   test_Fold_no_batch_dim_input (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6674711Z   test_Fold_no_batch_dim_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6675012Z   test_Fold_no_batch_dim_int_input (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6675353Z   test_Fold_no_batch_dim_int_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6675651Z   test_FractionalMaxPool2d_ratio (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6675975Z   test_FractionalMaxPool2d_ratio_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6676305Z   test_FractionalMaxPool2d_ratio_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6676637Z   test_FractionalMaxPool2d_ratio_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6676992Z   test_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6677369Z   test_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6677726Z   test_FractionalMaxPool2d_ratio_return_indices (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6678068Z   test_FractionalMaxPool2d_ratio_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6678393Z   test_FractionalMaxPool2d_size (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6678707Z   test_FractionalMaxPool2d_size_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6679036Z   test_FractionalMaxPool2d_size_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6679365Z   test_FractionalMaxPool2d_size_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6679719Z   test_FractionalMaxPool2d_size_no_batch_dim_no_random_samples (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6680093Z   test_FractionalMaxPool2d_size_no_batch_dim_no_random_samples_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6680423Z   test_FractionalMaxPool3d_asymsize (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6680747Z   test_FractionalMaxPool3d_asymsize_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6681058Z   test_FractionalMaxPool3d_ratio (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6681375Z   test_FractionalMaxPool3d_ratio_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6681692Z   test_FractionalMaxPool3d_ratio_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6682033Z   test_FractionalMaxPool3d_ratio_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6682386Z   test_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6682762Z   test_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6683112Z   test_FractionalMaxPool3d_ratio_return_indices (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6683462Z   test_FractionalMaxPool3d_ratio_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6683789Z   test_FractionalMaxPool3d_size (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6684096Z   test_FractionalMaxPool3d_size_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6684459Z   test_FractionalMaxPool3d_size_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6684803Z   test_FractionalMaxPool3d_size_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6685158Z   test_FractionalMaxPool3d_size_no_batch_dim_no_random_samples (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6685522Z   test_FractionalMaxPool3d_size_no_batch_dim_no_random_samples_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6685827Z   test_GELU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6686084Z   test_GELU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6686343Z   test_GELU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6686659Z   test_GELU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6686938Z   test_GELU_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6687215Z   test_GELU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6687462Z   test_GLU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6687719Z   test_GLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6687974Z   test_GLU_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6688223Z   test_GLU_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6688492Z   test_GLU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6688774Z   test_GLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6689218Z   test_GroupNorm_1d_affine (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:48.6689481Z   test_GroupNorm_1d_affine_GN (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6689781Z   test_GroupNorm_1d_affine_GN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6690083Z   test_GroupNorm_1d_affine_GN_eval (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6690378Z   test_GroupNorm_1d_affine_GN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6690707Z   test_GroupNorm_1d_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6690997Z   test_GroupNorm_1d_affine_eval (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:48.6691300Z   test_GroupNorm_1d_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6691631Z   test_GroupNorm_1d_affine_large_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6691986Z   test_GroupNorm_1d_affine_large_batch_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6692295Z   test_GroupNorm_1d_no_affine_IN (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6692585Z   test_GroupNorm_1d_no_affine_IN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6692891Z   test_GroupNorm_1d_no_affine_IN_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6693202Z   test_GroupNorm_1d_no_affine_IN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6693505Z   test_GroupNorm_1d_no_affine_LN (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6693795Z   test_GroupNorm_1d_no_affine_LN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6694100Z   test_GroupNorm_1d_no_affine_LN_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6694414Z   test_GroupNorm_1d_no_affine_LN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6694698Z   test_GroupNorm_2d_affine (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:48.6694992Z   test_GroupNorm_2d_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6695282Z   test_GroupNorm_2d_affine_eval (__main__.TestNN) ... ok (0.022s)
2023-01-11T21:58:48.6695675Z   test_GroupNorm_2d_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6696008Z   test_GroupNorm_2d_affine_large_feature_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6696364Z   test_GroupNorm_2d_affine_large_feature_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6696677Z   test_GroupNorm_2d_no_affine_IN (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6696982Z   test_GroupNorm_2d_no_affine_IN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6697273Z   test_GroupNorm_2d_no_affine_IN_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6697625Z   test_GroupNorm_2d_no_affine_IN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6697931Z   test_GroupNorm_2d_no_affine_LN (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6698221Z   test_GroupNorm_2d_no_affine_LN_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6698522Z   test_GroupNorm_2d_no_affine_LN_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6698833Z   test_GroupNorm_2d_no_affine_LN_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6699187Z   test_GroupNorm_2d_no_affine_large_feature_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6699537Z   test_GroupNorm_2d_no_affine_large_feature_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6699836Z   test_Hardshrink (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6700112Z   test_Hardshrink_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6700388Z   test_Hardshrink_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6700689Z   test_Hardshrink_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6700983Z   test_Hardshrink_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6701273Z   test_Hardshrink_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6701555Z   test_Hardsigmoid_no_batch_dim (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6701863Z   test_Hardsigmoid_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6702157Z   test_Hardswish_no_batch_dim (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6702442Z   test_Hardswish_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6702807Z   test_Hardtanh (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6703078Z   test_Hardtanh_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6703361Z   test_Hardtanh_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6703642Z   test_Hardtanh_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6703926Z   test_Hardtanh_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6704210Z   test_Hardtanh_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6704488Z   test_HingeEmbeddingLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6704803Z   test_HingeEmbeddingLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6705154Z   test_HingeEmbeddingLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6705498Z   test_HingeEmbeddingLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6705797Z   test_HingeEmbeddingLoss_margin (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6706125Z   test_HingeEmbeddingLoss_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6706485Z   test_HingeEmbeddingLoss_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6706869Z   test_HingeEmbeddingLoss_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6707196Z   test_HingeEmbeddingLoss_margin_no_reduce (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6707535Z   test_HingeEmbeddingLoss_margin_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6707874Z   test_HingeEmbeddingLoss_margin_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6708213Z   test_HingeEmbeddingLoss_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6708605Z   test_HingeEmbeddingLoss_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6709022Z   test_HingeEmbeddingLoss_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6709367Z   test_HingeEmbeddingLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6709701Z   test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6710078Z   test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6710457Z   test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6710780Z   test_HingeEmbeddingLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6711123Z   test_HingeEmbeddingLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6711502Z   test_HingeEmbeddingLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6711877Z   test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6712202Z   test_HingeEmbeddingLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6712543Z   test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6712917Z   test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6713290Z   test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6713602Z   test_HingeEmbeddingLoss_no_reduce (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6713923Z   test_HingeEmbeddingLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6714244Z   test_HingeEmbeddingLoss_scalar_margin (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6714568Z   test_HingeEmbeddingLoss_scalar_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6714946Z   test_HingeEmbeddingLoss_scalar_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6715316Z   test_HingeEmbeddingLoss_scalar_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6715665Z   test_HingeEmbeddingLoss_scalar_margin_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6716019Z   test_HingeEmbeddingLoss_scalar_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6716419Z   test_HingeEmbeddingLoss_scalar_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6716816Z   test_HingeEmbeddingLoss_scalar_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6717158Z   test_HingeEmbeddingLoss_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6717518Z   test_HingeEmbeddingLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6717895Z   test_HingeEmbeddingLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6718270Z   test_HingeEmbeddingLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6718569Z   test_HuberLoss (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6718839Z   test_HuberLoss_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6719158Z   test_HuberLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6719473Z   test_HuberLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6719799Z   test_HuberLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6720081Z   test_HuberLoss_delta (__main__.TestNN) ... ok (0.072s)
2023-01-11T21:58:48.6720366Z   test_HuberLoss_delta_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6720657Z   test_HuberLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6720964Z   test_HuberLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6721317Z   test_HuberLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6721664Z   test_HuberLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6721954Z   test_HuberLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6722268Z   test_HuberLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6722617Z   test_HuberLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6722967Z   test_HuberLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6723258Z   test_HuberLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6723567Z   test_HuberLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6723913Z   test_HuberLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6724255Z   test_HuberLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6724543Z   test_HuberLoss_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6724857Z   test_HuberLoss_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6725206Z   test_HuberLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6725543Z   test_HuberLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6725888Z   test_HuberLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6726181Z   test_InstanceNorm1d (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6726466Z   test_InstanceNorm1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6726740Z   test_InstanceNorm1d_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6727034Z   test_InstanceNorm1d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6727335Z   test_InstanceNorm1d_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6727639Z   test_InstanceNorm1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6727950Z   test_InstanceNorm1d_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6728310Z   test_InstanceNorm1d_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6728626Z   test_InstanceNorm1d_tracking_stats (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6728936Z   test_InstanceNorm1d_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6729403Z   test_InstanceNorm1d_tracking_stats_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6729735Z   test_InstanceNorm1d_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6730072Z   test_InstanceNorm1d_tracking_stats_no_batch_dim (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6730405Z   test_InstanceNorm1d_tracking_stats_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6730803Z   test_InstanceNorm1d_tracking_stats_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6731157Z   test_InstanceNorm1d_tracking_stats_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6731460Z   test_InstanceNorm2d (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6731744Z   test_InstanceNorm2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6732036Z   test_InstanceNorm2d_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6732331Z   test_InstanceNorm2d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6732621Z   test_InstanceNorm2d_no_batch_dim (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6732932Z   test_InstanceNorm2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6733244Z   test_InstanceNorm2d_no_batch_dim_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6733556Z   test_InstanceNorm2d_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6733869Z   test_InstanceNorm2d_tracking_stats (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6734192Z   test_InstanceNorm2d_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6734512Z   test_InstanceNorm2d_tracking_stats_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6734828Z   test_InstanceNorm2d_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6735157Z   test_InstanceNorm2d_tracking_stats_no_batch_dim (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6735498Z   test_InstanceNorm2d_tracking_stats_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6735827Z   test_InstanceNorm2d_tracking_stats_no_batch_dim_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6736180Z   test_InstanceNorm2d_tracking_stats_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6736488Z   test_InstanceNorm3d (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6736774Z   test_InstanceNorm3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6737051Z   test_InstanceNorm3d_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6737343Z   test_InstanceNorm3d_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6737642Z   test_InstanceNorm3d_no_batch_dim (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6737942Z   test_InstanceNorm3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6738256Z   test_InstanceNorm3d_no_batch_dim_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6738578Z   test_InstanceNorm3d_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6738898Z   test_InstanceNorm3d_tracking_stats (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6739204Z   test_InstanceNorm3d_tracking_stats_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6739567Z   test_InstanceNorm3d_tracking_stats_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6739894Z   test_InstanceNorm3d_tracking_stats_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6740227Z   test_InstanceNorm3d_tracking_stats_no_batch_dim (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6740556Z   test_InstanceNorm3d_tracking_stats_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6740898Z   test_InstanceNorm3d_tracking_stats_no_batch_dim_eval (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6741246Z   test_InstanceNorm3d_tracking_stats_no_batch_dim_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6742200Z   test_KLDivLoss (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6742794Z   warnings.warn(
2023-01-11T21:58:48.6743513Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6743982Z   warnings.warn(
2023-01-11T21:58:48.6744674Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6745124Z   warnings.warn(
2023-01-11T21:58:48.6745294Z ok (0.005s)
2023-01-11T21:58:48.6745509Z   test_KLDivLoss_batch_mean (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6745780Z   test_KLDivLoss_batch_mean_log_target (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6746077Z   test_KLDivLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6746402Z   test_KLDivLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6746722Z   test_KLDivLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6747560Z   test_KLDivLoss_log_target (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6748081Z   warnings.warn(
2023-01-11T21:58:48.6748779Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6749250Z   warnings.warn(
2023-01-11T21:58:48.6749418Z ok (0.005s)
2023-01-11T21:58:48.6749673Z   test_KLDivLoss_log_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6750022Z   test_KLDivLoss_log_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6750367Z   test_KLDivLoss_log_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6750669Z   test_KLDivLoss_log_target_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6751064Z   test_KLDivLoss_log_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6751440Z   test_KLDivLoss_log_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6751810Z   test_KLDivLoss_log_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6752111Z   test_KLDivLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6752433Z   test_KLDivLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6752790Z   test_KLDivLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6753174Z   test_KLDivLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6753472Z   test_KLDivLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6753792Z   test_KLDivLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6754146Z   test_KLDivLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6754486Z   test_KLDivLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6754790Z   test_KLDivLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6755103Z   test_KLDivLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6755455Z   test_KLDivLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6755794Z   test_KLDivLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6756092Z   test_KLDivLoss_no_reduce (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6756386Z   test_KLDivLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6756675Z   test_KLDivLoss_no_reduce_log_target (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6756993Z   test_KLDivLoss_no_reduce_log_target_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6757299Z   test_KLDivLoss_no_reduce_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6757607Z   test_KLDivLoss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6757915Z   test_KLDivLoss_no_reduce_scalar_log_target (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6758249Z   test_KLDivLoss_no_reduce_scalar_log_target_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6759118Z   test_KLDivLoss_scalar (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6759627Z   warnings.warn(
2023-01-11T21:58:48.6760317Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6760789Z   warnings.warn(
2023-01-11T21:58:48.6761489Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6761984Z   warnings.warn(
2023-01-11T21:58:48.6762154Z ok (0.004s)
2023-01-11T21:58:48.6762397Z   test_KLDivLoss_scalar_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6762736Z   test_KLDivLoss_scalar_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6763068Z   test_KLDivLoss_scalar_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6763948Z   test_KLDivLoss_scalar_log_target (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6764461Z   warnings.warn(
2023-01-11T21:58:48.6765159Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6765625Z   warnings.warn(
2023-01-11T21:58:48.6765796Z ok (0.004s)
2023-01-11T21:58:48.6766052Z   test_KLDivLoss_scalar_log_target_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6766417Z   test_KLDivLoss_scalar_log_target_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6766771Z   test_KLDivLoss_scalar_log_target_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6767090Z   test_KLDivLoss_scalar_log_target_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6767439Z   test_KLDivLoss_scalar_log_target_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6767822Z   test_KLDivLoss_scalar_log_target_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6768202Z   test_KLDivLoss_scalar_log_target_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6768521Z   test_KLDivLoss_scalar_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6768849Z   test_KLDivLoss_scalar_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6769376Z   test_KLDivLoss_scalar_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6769745Z   test_KLDivLoss_scalar_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6770047Z   test_KLDivLoss_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6770360Z   test_KLDivLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6770710Z   test_KLDivLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6771044Z   test_KLDivLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6771360Z   test_KLDivLoss_with_log_target_no_reduce (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6771688Z   test_KLDivLoss_with_log_target_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6772004Z   test_KLDivLoss_with_target_no_reduce (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6772311Z   test_KLDivLoss_with_target_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6772601Z   test_L1Loss (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6772878Z   test_L1Loss_cuda_cdouble (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6773246Z   test_L1Loss_cuda_cfloat (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6773557Z   test_L1Loss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6773868Z   test_L1Loss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6774175Z   test_L1Loss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6774444Z   test_L1Loss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6774749Z   test_L1Loss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6775094Z   test_L1Loss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6775477Z   test_L1Loss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6775768Z   test_L1Loss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6776079Z   test_L1Loss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6776418Z   test_L1Loss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6776745Z   test_L1Loss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6777040Z   test_L1Loss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6777341Z   test_L1Loss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6777681Z   test_L1Loss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6778005Z   test_L1Loss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6778294Z   test_L1Loss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6778552Z   test_L1Loss_no_reduce_complex (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6778843Z   test_L1Loss_no_reduce_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6779168Z   test_L1Loss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6779457Z   test_L1Loss_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6779753Z   test_L1Loss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6780025Z   test_L1Loss_scalar (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6780313Z   test_L1Loss_scalar_cuda_cdouble (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6780636Z   test_L1Loss_scalar_cuda_cfloat (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6780792Z   test_L1Loss_scalar_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6780942Z   test_L1Loss_scalar_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6781082Z   test_L1Loss_scalar_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6781186Z   test_LPPool1d (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6781329Z   test_LPPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6781450Z   test_LPPool1d_no_batch_dim (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6781604Z   test_LPPool1d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6781714Z   test_LPPool1d_norm (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6781860Z   test_LPPool1d_norm_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6781964Z   test_LPPool2d (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6782093Z   test_LPPool2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6782239Z   test_LPPool2d_norm (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6782385Z   test_LPPool2d_norm_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6782543Z   test_LSTM_cell (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6782680Z   test_LSTM_cell_forward_hidden_size (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6782806Z   test_LSTM_cell_forward_input_size (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6782939Z   test_LayerNorm_1d_elementwise_affine (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6783109Z   test_LayerNorm_1d_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6783235Z   test_LayerNorm_1d_elementwise_affine_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6783445Z   test_LayerNorm_1d_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6783585Z   test_LayerNorm_1d_empty_elementwise_affine (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6783762Z   test_LayerNorm_1d_empty_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6783905Z   test_LayerNorm_1d_empty_elementwise_affine_eval (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6784086Z   test_LayerNorm_1d_empty_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6784220Z   test_LayerNorm_1d_no_elementwise_affine (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6784391Z   test_LayerNorm_1d_no_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6784517Z   test_LayerNorm_1d_no_elementwise_affine_eval (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6784693Z   test_LayerNorm_1d_no_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6784825Z   test_LayerNorm_3d_elementwise_affine (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6784995Z   test_LayerNorm_3d_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6785136Z   test_LayerNorm_3d_elementwise_affine_eval (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6785310Z   test_LayerNorm_3d_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6785444Z   test_LayerNorm_3d_no_affine_large_feature (__main__.TestNN) ... ok (0.755s)
2023-01-11T21:58:48.6785614Z   test_LayerNorm_3d_no_affine_large_feature_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6785755Z   test_LayerNorm_3d_no_affine_large_feature_eval (__main__.TestNN) ... ok (0.688s)
2023-01-11T21:58:48.6785921Z   test_LayerNorm_3d_no_affine_large_feature_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6786054Z   test_LayerNorm_3d_no_elementwise_affine (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6786227Z   test_LayerNorm_3d_no_elementwise_affine_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6786364Z   test_LayerNorm_3d_no_elementwise_affine_eval (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6786538Z   test_LayerNorm_3d_no_elementwise_affine_eval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6786643Z   test_LeakyReLU (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6786788Z   test_LeakyReLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6786906Z   test_LeakyReLU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6787052Z   test_LeakyReLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6787171Z   test_LeakyReLU_with_negval (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6787331Z   test_LeakyReLU_with_negval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6787497Z   test_LeakyReLU_with_negval_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6787663Z   test_LeakyReLU_with_negval_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6787788Z   test_LeakyReLU_with_zero_negval (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6787950Z   test_LeakyReLU_with_zero_negval_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6788051Z   test_Linear (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6788179Z   test_Linear_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6788294Z   test_Linear_no_batch_dim (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6788444Z   test_Linear_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6788586Z   test_Linear_no_bias (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6788735Z   test_Linear_no_bias_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6788862Z   test_LocalResponseNorm_1d (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6789022Z   test_LocalResponseNorm_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6789157Z   test_LocalResponseNorm_2d_uneven_pad (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6789315Z   test_LocalResponseNorm_2d_uneven_pad_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6789451Z   test_LocalResponseNorm_3d_custom_params (__main__.TestNN) ... ok (0.018s)
2023-01-11T21:58:48.6789625Z   test_LocalResponseNorm_3d_custom_params_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6789733Z   test_LogSigmoid (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6789880Z   test_LogSigmoid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6790000Z   test_LogSigmoid_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6790161Z   test_LogSigmoid_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6790278Z   test_LogSigmoid_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6790419Z   test_LogSigmoid_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6790525Z   test_LogSoftmax (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6790669Z   test_LogSoftmax_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6790793Z   test_LogSoftmax_multiparam (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6790953Z   test_LogSoftmax_multiparam_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6791083Z   test_LogSoftmax_multiparam_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6791252Z   test_LogSoftmax_multiparam_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6791373Z   test_LogSoftmax_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6791518Z   test_LogSoftmax_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6791620Z   test_MSELoss (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6791766Z   test_MSELoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6791913Z   test_MSELoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6792056Z   test_MSELoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6792178Z   test_MSELoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6792345Z   test_MSELoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6792510Z   test_MSELoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6792673Z   test_MSELoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6792811Z   test_MSELoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6792974Z   test_MSELoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6793136Z   test_MSELoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6793295Z   test_MSELoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6793415Z   test_MSELoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6793575Z   test_MSELoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6793765Z   test_MSELoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6793931Z   test_MSELoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6794034Z   test_MSELoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6794185Z   test_MSELoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6794306Z   test_MSELoss_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6794464Z   test_MSELoss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6794573Z   test_MSELoss_prec (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6794728Z   test_MSELoss_prec_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6794880Z   test_MSELoss_prec_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6795032Z   test_MSELoss_prec_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6795181Z   test_MSELoss_prec_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6795281Z   test_MSELoss_scalar (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6795438Z   test_MSELoss_scalar_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6795592Z   test_MSELoss_scalar_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6795746Z   test_MSELoss_scalar_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6795896Z   test_MSELoss_scalar_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6796024Z   test_MSELoss_scalar_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6796199Z   test_MSELoss_scalar_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6796369Z   test_MSELoss_scalar_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6796529Z   test_MSELoss_scalar_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6796700Z   test_MSELoss_scalar_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6796818Z   test_MSELoss_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6796981Z   test_MSELoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6797143Z   test_MSELoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6797305Z   test_MSELoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6797426Z   test_MarginRankingLoss (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6797593Z   test_MarginRankingLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6797755Z   test_MarginRankingLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6797938Z   test_MarginRankingLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6798065Z   test_MarginRankingLoss_margin (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6798235Z   test_MarginRankingLoss_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6798403Z   test_MarginRankingLoss_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6798571Z   test_MarginRankingLoss_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6798714Z   test_MarginRankingLoss_margin_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6798929Z   test_MarginRankingLoss_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6799116Z   test_MarginRankingLoss_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6799301Z   test_MarginRankingLoss_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6799428Z   test_MarginRankingLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6799611Z   test_MarginRankingLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6799789Z   test_MarginRankingLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6799969Z   test_MarginRankingLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6800107Z   test_MarginRankingLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6800286Z   test_MarginRankingLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6800469Z   test_MarginRankingLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6800647Z   test_MarginRankingLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6800784Z   test_MarginRankingLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6800949Z   test_MarginRankingLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6801125Z   test_MarginRankingLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6801300Z   test_MarginRankingLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6801438Z   test_MarginRankingLoss_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6801619Z   test_MarginRankingLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6801797Z   test_MarginRankingLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6801972Z   test_MarginRankingLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6802079Z   test_MaxPool1d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6802210Z   test_MaxPool1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6802334Z   test_MaxPool1d_return_indices (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6802495Z   test_MaxPool1d_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6802608Z   test_MaxPool1d_stride (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6802760Z   test_MaxPool1d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6802950Z   test_MaxPool2d_3d_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6803133Z   test_MaxPool2d_3d_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6803312Z   test_MaxPool2d_4d_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6803452Z   test_MaxPool2d_4d_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6803740Z   test_MaxPool2d_return_indices (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6803932Z   test_MaxPool2d_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6804065Z   test_MaxPool3d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6804196Z   test_MaxPool3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6804354Z   test_MaxPool3d_return_indices (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6804577Z   test_MaxPool3d_return_indices_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6804720Z   test_MaxPool3d_stride (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6804901Z   test_MaxPool3d_stride_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6805053Z   test_MaxPool3d_stride_padding (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6805268Z   test_MaxPool3d_stride_padding_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6805419Z   test_MaxUnpool1d_net (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6805556Z   test_MaxUnpool1d_net_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6805711Z   test_MaxUnpool1d_net_no_batch_dim (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6805902Z   test_MaxUnpool1d_net_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6806043Z   test_MaxUnpool2d_net (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6806221Z   test_MaxUnpool2d_net_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6806409Z   test_MaxUnpool2d_net_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6806600Z   test_MaxUnpool2d_net_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6806769Z   test_MaxUnpool3d_net (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6806906Z   test_MaxUnpool3d_net_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6807060Z   test_MaxUnpool3d_net_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6807250Z   test_MaxUnpool3d_net_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6807376Z   test_Mish (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6807538Z   test_Mish_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6822125Z   test_Mish_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6822384Z   test_Mish_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6822581Z   test_Mish_scalar (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6822724Z   test_Mish_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6822836Z   test_ModuleDict (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6822938Z   test_ModuleList (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6823062Z   test_MultiLabelMarginLoss (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6823200Z   test_MultiLabelMarginLoss_0d_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6823385Z   test_MultiLabelMarginLoss_0d_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6823510Z   test_MultiLabelMarginLoss_1d (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6823691Z   test_MultiLabelMarginLoss_1d_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6823854Z   test_MultiLabelMarginLoss_1d_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6824159Z   test_MultiLabelMarginLoss_1d_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6824327Z   test_MultiLabelMarginLoss_1d_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6824465Z   test_MultiLabelMarginLoss_1d_no_reduce (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6824640Z   test_MultiLabelMarginLoss_1d_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6824782Z   test_MultiLabelMarginLoss_1d_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6824972Z   test_MultiLabelMarginLoss_1d_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6825200Z   test_MultiLabelMarginLoss_1d_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6825392Z   test_MultiLabelMarginLoss_1d_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6825568Z   test_MultiLabelMarginLoss_1d_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6825738Z   test_MultiLabelMarginLoss_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6825905Z   test_MultiLabelMarginLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6826071Z   test_MultiLabelMarginLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6826235Z   test_MultiLabelMarginLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6826372Z   test_MultiLabelMarginLoss_index_neg (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6826547Z   test_MultiLabelMarginLoss_index_neg_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6826690Z   test_MultiLabelMarginLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6826883Z   test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6827055Z   test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6827238Z   test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6827382Z   test_MultiLabelMarginLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6827569Z   test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6827753Z   test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6827930Z   test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6828073Z   test_MultiLabelMarginLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6828256Z   test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6828437Z   test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6828605Z   test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6828740Z   test_MultiLabelMarginLoss_no_reduce (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:48.6828910Z   test_MultiLabelMarginLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6829051Z   test_MultiLabelMarginLoss_sum_reduction (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6829232Z   test_MultiLabelMarginLoss_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6829449Z   test_MultiLabelMarginLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6829629Z   test_MultiLabelMarginLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6829808Z   test_MultiLabelMarginLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6829942Z   test_MultiLabelSoftMarginLoss (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6830109Z   test_MultiLabelSoftMarginLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6830284Z   test_MultiLabelSoftMarginLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6830492Z   test_MultiLabelSoftMarginLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6830646Z   test_MultiLabelSoftMarginLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6830844Z   test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6831039Z   test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6831232Z   test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6831381Z   test_MultiLabelSoftMarginLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6831573Z   test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6831753Z   test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6831939Z   test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6832093Z   test_MultiLabelSoftMarginLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6832281Z   test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6832469Z   test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6832658Z   test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6832801Z   test_MultiLabelSoftMarginLoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6832982Z   test_MultiLabelSoftMarginLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6833128Z   test_MultiLabelSoftMarginLoss_weights (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6833303Z   test_MultiLabelSoftMarginLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6833490Z   test_MultiLabelSoftMarginLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6833673Z   test_MultiLabelSoftMarginLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6833828Z   test_MultiLabelSoftMarginLoss_weights_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6834016Z   test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6834174Z   test_MultiLabelSoftMarginLoss_weights_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6834376Z   test_MultiLabelSoftMarginLoss_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6834576Z   test_MultiLabelSoftMarginLoss_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6834827Z   test_MultiLabelSoftMarginLoss_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6834929Z   test_MultiMarginLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6835047Z   test_MultiMarginLoss_1d (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6835208Z   test_MultiMarginLoss_1d_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6835366Z   test_MultiMarginLoss_1d_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6835524Z   test_MultiMarginLoss_1d_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6835653Z   test_MultiMarginLoss_1d_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6835847Z   test_MultiMarginLoss_1d_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6835982Z   test_MultiMarginLoss_1d_sum_reduction (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6836148Z   test_MultiMarginLoss_1d_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6836322Z   test_MultiMarginLoss_1d_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6836498Z   test_MultiMarginLoss_1d_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6836658Z   test_MultiMarginLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6836816Z   test_MultiMarginLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6836971Z   test_MultiMarginLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6837094Z   test_MultiMarginLoss_margin (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6837262Z   test_MultiMarginLoss_margin_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6837430Z   test_MultiMarginLoss_margin_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6837583Z   test_MultiMarginLoss_margin_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6837717Z   test_MultiMarginLoss_margin_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6837886Z   test_MultiMarginLoss_margin_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6838024Z   test_MultiMarginLoss_margin_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6838206Z   test_MultiMarginLoss_margin_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6838389Z   test_MultiMarginLoss_margin_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6838566Z   test_MultiMarginLoss_margin_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6838693Z   test_MultiMarginLoss_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6838854Z   test_MultiMarginLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6838958Z   test_MultiMarginLoss_p (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6839119Z   test_MultiMarginLoss_p_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6839277Z   test_MultiMarginLoss_p_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6839436Z   test_MultiMarginLoss_p_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6839565Z   test_MultiMarginLoss_p_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6839731Z   test_MultiMarginLoss_p_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6839898Z   test_MultiMarginLoss_p_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6840074Z   test_MultiMarginLoss_p_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6840237Z   test_MultiMarginLoss_p_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6840409Z   test_MultiMarginLoss_p_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6840542Z   test_MultiMarginLoss_sum_reduction (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6840712Z   test_MultiMarginLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6840888Z   test_MultiMarginLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6841092Z   test_MultiMarginLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6841220Z   test_MultiMarginLoss_weights (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6841387Z   test_MultiMarginLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6841551Z   test_MultiMarginLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6841705Z   test_MultiMarginLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6841840Z   test_MultiMarginLoss_weights_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6842011Z   test_MultiMarginLoss_weights_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6842153Z   test_MultiMarginLoss_weights_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6842336Z   test_MultiMarginLoss_weights_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6842519Z   test_MultiMarginLoss_weights_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6842700Z   test_MultiMarginLoss_weights_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6842802Z   test_NLLLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6842919Z   test_NLLLoss2d_no_reduce (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6843061Z   test_NLLLoss2d_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6843192Z   test_NLLLoss2d_no_reduce_ignore_index (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6843358Z   test_NLLLoss2d_no_reduce_ignore_index_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6843485Z   test_NLLLoss2d_no_reduce_weights (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6843648Z   test_NLLLoss2d_no_reduce_weights_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6843766Z   test_NLLLossNd_no_reduce (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6843917Z   test_NLLLossNd_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6844047Z   test_NLLLossNd_no_reduce_ignore_index (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6844201Z   test_NLLLossNd_no_reduce_ignore_index_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6844328Z   test_NLLLossNd_no_reduce_weights (__main__.TestNN) ... ok (0.019s)
2023-01-11T21:58:48.6844489Z   test_NLLLossNd_no_reduce_weights_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6844593Z   test_NLLLoss_2d (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6844743Z   test_NLLLoss_2d_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6844893Z   test_NLLLoss_2d_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6845074Z   test_NLLLoss_2d_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6845218Z   test_NLLLoss_2d_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6845326Z   test_NLLLoss_2d_ignore_index (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6845491Z   test_NLLLoss_2d_ignore_index_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6845657Z   test_NLLLoss_2d_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6845817Z   test_NLLLoss_2d_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6845976Z   test_NLLLoss_2d_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6846129Z   test_NLLLoss_2d_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6846300Z   test_NLLLoss_2d_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6846467Z   test_NLLLoss_2d_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6846630Z   test_NLLLoss_2d_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6846779Z   test_NLLLoss_2d_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6846894Z   test_NLLLoss_2d_weights (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6847052Z   test_NLLLoss_2d_weights_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6847212Z   test_NLLLoss_2d_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6847372Z   test_NLLLoss_2d_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6847526Z   test_NLLLoss_2d_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6847677Z   test_NLLLoss_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6847823Z   test_NLLLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6847970Z   test_NLLLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6848101Z   test_NLLLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6848211Z   test_NLLLoss_dim_is_3 (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6848368Z   test_NLLLoss_dim_is_3_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6848521Z   test_NLLLoss_dim_is_3_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6848681Z   test_NLLLoss_dim_is_3_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6848833Z   test_NLLLoss_dim_is_3_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6848964Z   test_NLLLoss_dim_is_3_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6849351Z   test_NLLLoss_dim_is_3_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6849517Z   test_NLLLoss_dim_is_3_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6849687Z   test_NLLLoss_dim_is_3_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6849853Z   test_NLLLoss_dim_is_3_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6849969Z   test_NLLLoss_higher_dim (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6850133Z   test_NLLLoss_higher_dim_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6850292Z   test_NLLLoss_higher_dim_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6850504Z   test_NLLLoss_higher_dim_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6850660Z   test_NLLLoss_higher_dim_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6850792Z   test_NLLLoss_higher_dim_sum_reduction (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6850957Z   test_NLLLoss_higher_dim_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6851132Z   test_NLLLoss_higher_dim_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6851304Z   test_NLLLoss_higher_dim_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6851513Z   test_NLLLoss_higher_dim_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6851633Z   test_NLLLoss_ignore_index (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6851800Z   test_NLLLoss_ignore_index_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6851962Z   test_NLLLoss_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6852123Z   test_NLLLoss_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6852280Z   test_NLLLoss_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6852390Z   test_NLLLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6852556Z   test_NLLLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6852722Z   test_NLLLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6852880Z   test_NLLLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6853001Z   test_NLLLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6853166Z   test_NLLLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6853328Z   test_NLLLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6853489Z   test_NLLLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6853595Z   test_NLLLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6853756Z   test_NLLLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6853918Z   test_NLLLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6854080Z   test_NLLLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6854193Z   test_NLLLoss_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6854341Z   test_NLLLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6854469Z   test_NLLLoss_no_reduce_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6854633Z   test_NLLLoss_no_reduce_ignore_index_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6854753Z   test_NLLLoss_no_reduce_weights (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6854903Z   test_NLLLoss_no_reduce_weights_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6855041Z   test_NLLLoss_no_reduce_weights_ignore_index (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6855217Z   test_NLLLoss_no_reduce_weights_ignore_index_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6855359Z   test_NLLLoss_no_reduce_weights_ignore_index_neg (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6855567Z   test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6855686Z   test_NLLLoss_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6855851Z   test_NLLLoss_sum_reduction_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6856010Z   test_NLLLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6856158Z   test_NLLLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6856318Z   test_NLLLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6856464Z   test_NLLLoss_weights (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6856627Z   test_NLLLoss_weights_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6856785Z   test_NLLLoss_weights_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6856939Z   test_NLLLoss_weights_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6857093Z   test_NLLLoss_weights_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6857221Z   test_NLLLoss_weights_ignore_index (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6857394Z   test_NLLLoss_weights_ignore_index_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6857552Z   test_NLLLoss_weights_ignore_index_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6857721Z   test_NLLLoss_weights_ignore_index_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6857891Z   test_NLLLoss_weights_ignore_index_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6858022Z   test_NLLLoss_weights_ignore_index_neg (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6858197Z   test_NLLLoss_weights_ignore_index_neg_cuda_bfloat16 (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6858369Z   test_NLLLoss_weights_ignore_index_neg_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6858538Z   test_NLLLoss_weights_ignore_index_neg_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6858711Z   test_NLLLoss_weights_ignore_index_neg_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6858812Z   test_PReLU_1d (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6858942Z   test_PReLU_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6859062Z   test_PReLU_1d_multiparam (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6859216Z   test_PReLU_1d_multiparam_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6859318Z   test_PReLU_2d (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6859454Z   test_PReLU_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6859569Z   test_PReLU_2d_multiparam (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6859720Z   test_PReLU_2d_multiparam_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6859817Z   test_PReLU_3d (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6859946Z   test_PReLU_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6860060Z   test_PReLU_3d_multiparam (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6860213Z   test_PReLU_3d_multiparam_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6860346Z   test_PReLU_backward_requires_grad_false (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6860455Z   test_PReLU_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6860632Z   test_PReLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6860731Z   test_PReLU_scalar (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6860871Z   test_PReLU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6860980Z   test_Padding122112_3dcircular (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6861135Z   test_Padding122112_3dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6861250Z   test_Padding1221_2dcircular (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6861400Z   test_Padding1221_2dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6861508Z   test_Padding12_1dcircular (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6861687Z   test_Padding12_1dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6861805Z   test_Padding2322_2dcircular (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6861953Z   test_Padding2322_2dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6862059Z   test_Padding31_1dcircular (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6862204Z   test_Padding31_1dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6862319Z   test_Padding322112_3dcircular (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6862545Z   test_Padding322112_3dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6862666Z   test_Padding332122_3dcircular (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6862820Z   test_Padding332122_3dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6862935Z   test_Padding3331_2dcircular (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6863083Z   test_Padding3331_2dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6863191Z   test_Padding33_1dcircular (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6863341Z   test_Padding33_1dcircular_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6863456Z   test_PairwiseDistance (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6863589Z   test_PairwiseDistance_broadcast_lhs (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6863760Z   test_PairwiseDistance_broadcast_lhs_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6863891Z   test_PairwiseDistance_broadcast_rhs (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6864057Z   test_PairwiseDistance_broadcast_rhs_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6864208Z   test_PairwiseDistance_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6864328Z   test_PairwiseDistance_no_batch_dim (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6864490Z   test_PairwiseDistance_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6864624Z   test_PairwiseDistance_with_non_default_args (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6864795Z   test_PairwiseDistance_with_non_default_args_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6864898Z   test_ParameterDict (__main__.TestNN) ... ok (0.025s)
2023-01-11T21:58:48.6865019Z   test_ParameterDict_replication (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6865124Z   test_ParameterList (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6865234Z   test_ParameterList_meta (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6865348Z   test_ParameterList_replication (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6865453Z   test_PixelShuffle (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6865594Z   test_PixelShuffle_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6865736Z   test_PixelUnshuffle (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6865880Z   test_PixelUnshuffle_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6865997Z   test_PoissonNLLLoss_full_loss (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6866158Z   test_PoissonNLLLoss_full_loss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6866319Z   test_PoissonNLLLoss_full_loss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6866470Z   test_PoissonNLLLoss_full_loss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6866603Z   test_PoissonNLLLoss_full_loss_no_log_input (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6866842Z   test_PoissonNLLLoss_full_loss_no_log_input_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6867016Z   test_PoissonNLLLoss_full_loss_no_log_input_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6867190Z   test_PoissonNLLLoss_full_loss_no_log_input_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6867314Z   test_PoissonNLLLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6867482Z   test_PoissonNLLLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6867649Z   test_PoissonNLLLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6867819Z   test_PoissonNLLLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6867941Z   test_PoissonNLLLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6868112Z   test_PoissonNLLLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6868288Z   test_PoissonNLLLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6868457Z   test_PoissonNLLLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6868587Z   test_PoissonNLLLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6868756Z   test_PoissonNLLLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6868924Z   test_PoissonNLLLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6869092Z   test_PoissonNLLLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6869215Z   test_PoissonNLLLoss_no_full_loss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6869371Z   test_PoissonNLLLoss_no_full_loss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6869534Z   test_PoissonNLLLoss_no_full_loss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6869694Z   test_PoissonNLLLoss_no_full_loss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6869835Z   test_PoissonNLLLoss_no_full_loss_no_log_input (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6870016Z   test_PoissonNLLLoss_no_full_loss_no_log_input_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6870191Z   test_PoissonNLLLoss_no_full_loss_no_log_input_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6870364Z   test_PoissonNLLLoss_no_full_loss_no_log_input_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6870482Z   test_PoissonNLLLoss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6870634Z   test_PoissonNLLLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6870782Z   test_RNN_cell (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6870903Z   test_RNN_cell_forward_hidden_size (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6871022Z   test_RNN_cell_forward_input_size (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6871143Z   test_RNN_cell_forward_zero_hidden_size (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6871258Z   test_RNN_cell_no_broadcasting (__main__.TestNN) ... ok (0.033s)
2023-01-11T21:58:48.6871390Z   test_RNN_change_dropout (__main__.TestNN) ... skip: needs cudnn >= 5.1 (0.001s)
2023-01-11T21:58:48.6871527Z   test_RNN_cpu_vs_cudnn_no_dropout (__main__.TestNN) ... skip: needs cudnn (0.000s)
2023-01-11T21:58:48.6871670Z   test_RNN_cpu_vs_cudnn_with_dropout (__main__.TestNN) ... skip: needs cudnn >= 5.1 (0.000s)
2023-01-11T21:58:48.6871822Z   test_RNN_cudnn_weight_norm (__main__.TestNN) ... skip: needs cudnn (0.001s)
2023-01-11T21:58:48.6871945Z   test_RNN_dropout (__main__.TestNN) ... skip: needs cudnn >= 5.1 (0.001s)
2023-01-11T21:58:48.6872075Z   test_RNN_dropout_state (__main__.TestNN) ... skip: needs cudnn >= 5.1 (0.001s)
2023-01-11T21:58:48.6872183Z   test_RNN_input_size_zero (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6872288Z   test_RNN_nonlinearity (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6872381Z   test_RReLU (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6872512Z   test_RReLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6872617Z   test_RReLU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6872757Z   test_RReLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6872867Z   test_RReLU_with_up_down (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6873014Z   test_RReLU_with_up_down_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6873134Z   test_RReLU_with_up_down_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6873295Z   test_RReLU_with_up_down_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6873389Z   test_ReLU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6873485Z   test_ReLU6 (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6873610Z   test_ReLU6_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6873717Z   test_ReLU6_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6873856Z   test_ReLU6_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6873957Z   test_ReLU6_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6874093Z   test_ReLU6_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6874222Z   test_ReLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6874326Z   test_ReLU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6874470Z   test_ReLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6874564Z   test_ReLU_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6874701Z   test_ReLU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6874813Z   test_ReflectionPad1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6874932Z   test_ReflectionPad1d_batch (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6875084Z   test_ReflectionPad1d_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6875202Z   test_ReflectionPad1d_complex (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6875356Z   test_ReflectionPad1d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6875504Z   test_ReflectionPad1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6875608Z   test_ReflectionPad2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6875759Z   test_ReflectionPad2d_complex (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6875919Z   test_ReflectionPad2d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6876068Z   test_ReflectionPad2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6876196Z   test_ReflectionPad2d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6876359Z   test_ReflectionPad2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6876465Z   test_ReflectionPad3d (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6876580Z   test_ReflectionPad3d_complex (__main__.TestNN) ... ok (0.023s)
2023-01-11T21:58:48.6876755Z   test_ReflectionPad3d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6876901Z   test_ReflectionPad3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6877025Z   test_ReflectionPad3d_no_batch_dim (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6877182Z   test_ReflectionPad3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6877293Z   test_ReplicationPad1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6877410Z   test_ReplicationPad1d_batch (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6877565Z   test_ReplicationPad1d_batch_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6877684Z   test_ReplicationPad1d_complex (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6877843Z   test_ReplicationPad1d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6877984Z   test_ReplicationPad1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6878096Z   test_ReplicationPad2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6878212Z   test_ReplicationPad2d_complex (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6878372Z   test_ReplicationPad2d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6878519Z   test_ReplicationPad2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6878644Z   test_ReplicationPad2d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6878808Z   test_ReplicationPad2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6878923Z   test_ReplicationPad3d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6879035Z   test_ReplicationPad3d_complex (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6879195Z   test_ReplicationPad3d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6879342Z   test_ReplicationPad3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6879466Z   test_ReplicationPad3d_no_batch_dim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6879630Z   test_ReplicationPad3d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6879721Z   test_SELU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6879849Z   test_SELU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6879958Z   test_SELU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6880092Z   test_SELU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6880191Z   test_SELU_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6880328Z   test_SELU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6880438Z   test_Sequential_add (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6880552Z   test_Sequential_append (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6880666Z   test_Sequential_delitem (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6880799Z   test_Sequential_extend (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6880907Z   test_Sequential_getitem (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6881006Z   test_Sequential_iadd (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6881110Z   test_Sequential_imul (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6881222Z   test_Sequential_insert (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6881341Z   test_Sequential_insert_fail_case (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6881445Z   test_Sequential_mul (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6881550Z   test_Sequential_pop (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6881655Z   test_Sequential_rmul (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6881756Z   test_Sequential_setitem (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6881896Z   test_Sequential_setitem_named (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6881991Z   test_SiLU (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6882128Z   test_SiLU_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6882238Z   test_SiLU_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6882384Z   test_SiLU_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6882487Z   test_SiLU_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6882627Z   test_SiLU_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6882717Z   test_Sigmoid (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6882851Z   test_Sigmoid_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6882963Z   test_Sigmoid_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6883108Z   test_Sigmoid_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6883209Z   test_Sigmoid_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6883352Z   test_Sigmoid_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6883458Z   test_SmoothL1Loss (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6883559Z   test_SmoothL1Loss_beta (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6883703Z   test_SmoothL1Loss_beta_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6883853Z   test_SmoothL1Loss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6884001Z   test_SmoothL1Loss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6884147Z   test_SmoothL1Loss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6884271Z   test_SmoothL1Loss_no_batch_dim_mean (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6884443Z   test_SmoothL1Loss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6884607Z   test_SmoothL1Loss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6884765Z   test_SmoothL1Loss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6884879Z   test_SmoothL1Loss_no_batch_dim_none (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6885043Z   test_SmoothL1Loss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6885203Z   test_SmoothL1Loss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6885358Z   test_SmoothL1Loss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6885483Z   test_SmoothL1Loss_no_batch_dim_sum (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6885649Z   test_SmoothL1Loss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6885845Z   test_SmoothL1Loss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6886011Z   test_SmoothL1Loss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6886129Z   test_SmoothL1Loss_no_reduce (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6886273Z   test_SmoothL1Loss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6886399Z   test_SmoothL1Loss_no_reduce_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6886561Z   test_SmoothL1Loss_no_reduce_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6886673Z   test_SmoothL1Loss_scalar (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6886856Z   test_SmoothL1Loss_scalar_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6887015Z   test_SmoothL1Loss_scalar_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6887169Z   test_SmoothL1Loss_scalar_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6887296Z   test_SmoothL1Loss_scalar_sum_reduction (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6887459Z   test_SmoothL1Loss_scalar_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6887625Z   test_SmoothL1Loss_scalar_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6887791Z   test_SmoothL1Loss_scalar_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6887912Z   test_SmoothL1Loss_sum_reduction (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6888075Z   test_SmoothL1Loss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6888240Z   test_SmoothL1Loss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6888405Z   test_SmoothL1Loss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6888521Z   test_SmoothL1Loss_zero_beta (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6888672Z   test_SmoothL1Loss_zero_beta_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6888774Z   test_SoftMarginLoss (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6888929Z   test_SoftMarginLoss_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6889245Z   test_SoftMarginLoss_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6889434Z   test_SoftMarginLoss_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6889574Z   test_SoftMarginLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6889748Z   test_SoftMarginLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6889923Z   test_SoftMarginLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6890093Z   test_SoftMarginLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6890224Z   test_SoftMarginLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6890387Z   test_SoftMarginLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6890558Z   test_SoftMarginLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6890730Z   test_SoftMarginLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6890860Z   test_SoftMarginLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6891088Z   test_SoftMarginLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6891255Z   test_SoftMarginLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6891423Z   test_SoftMarginLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6891544Z   test_SoftMarginLoss_no_reduce (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6891692Z   test_SoftMarginLoss_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6891820Z   test_SoftMarginLoss_sum_reduction (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6891990Z   test_SoftMarginLoss_sum_reduction_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6892197Z   test_SoftMarginLoss_sum_reduction_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6892368Z   test_SoftMarginLoss_sum_reduction_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6892467Z   test_Softmax (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6892569Z   test_Softmax2d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6892708Z   test_Softmax2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6892825Z   test_Softmax2d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6892969Z   test_Softmax2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6893109Z   test_Softmax_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6893222Z   test_Softmax_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6893374Z   test_Softmax_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6893482Z   test_Softmax_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6893628Z   test_Softmax_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6893728Z   test_Softmin (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6893865Z   test_Softmin_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6893965Z   test_Softmin_multidim (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6894114Z   test_Softmin_multidim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6894225Z   test_Softmin_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6894373Z   test_Softmin_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6894481Z   test_Softmin_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6894626Z   test_Softmin_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6894724Z   test_Softplus (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6894823Z   test_Softplus_beta (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6894961Z   test_Softplus_beta_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6895073Z   test_Softplus_beta_threshold (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6895222Z   test_Softplus_beta_threshold_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6895346Z   test_Softplus_beta_threshold_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6895511Z   test_Softplus_beta_threshold_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6895651Z   test_Softplus_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6895766Z   test_Softplus_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6895921Z   test_Softplus_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6896015Z   test_Softshrink (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6896188Z   test_Softshrink_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6896301Z   test_Softshrink_lambda (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6896449Z   test_Softshrink_lambda_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6896569Z   test_Softshrink_lambda_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6896725Z   test_Softshrink_lambda_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6896842Z   test_Softshrink_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6896996Z   test_Softshrink_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6897086Z   test_Softsign (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6897253Z   test_Softsign_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6897370Z   test_Softsign_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6897522Z   test_Softsign_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6897633Z   test_Softsign_scalar (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6897778Z   test_Softsign_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6897874Z   test_Tanh (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6897996Z   test_Tanh_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6898106Z   test_Tanh_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6898252Z   test_Tanh_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6898359Z   test_Tanh_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6898501Z   test_Tanh_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6898605Z   test_Tanhshrink (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6898749Z   test_Tanhshrink_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6898868Z   test_Tanhshrink_no_batch_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6899010Z   test_Tanhshrink_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6899123Z   test_Tanhshrink_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6899273Z   test_Tanhshrink_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6899390Z   test_Threshold_large_value (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6899543Z   test_Threshold_large_value_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6899659Z   test_Threshold_no_batch_dim (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6899815Z   test_Threshold_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6899938Z   test_Threshold_threshold_value (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6900085Z   test_Threshold_threshold_value_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6900215Z   test_Threshold_threshold_value_scalar (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6900382Z   test_Threshold_threshold_value_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6900532Z   test_TransformerDecoderLayer_gelu_activation (__main__.TestNN) ... ok (0.081s)
2023-01-11T21:58:48.6900721Z   test_TransformerDecoderLayer_gelu_activation_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6900867Z   test_TransformerDecoderLayer_relu_activation (__main__.TestNN) ... ok (0.080s)
2023-01-11T21:58:48.6901052Z   test_TransformerDecoderLayer_relu_activation_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6901198Z   test_TransformerEncoderLayer_gelu_activation (__main__.TestNN) ... ok (0.051s)
2023-01-11T21:58:48.6901414Z   test_TransformerEncoderLayer_gelu_activation_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6901550Z   test_TransformerEncoderLayer_relu_activation (__main__.TestNN) ... ok (0.040s)
2023-01-11T21:58:48.6901734Z   test_TransformerEncoderLayer_relu_activation_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6901848Z   test_Transformer_cell (__main__.TestNN) ... ok (0.593s)
2023-01-11T21:58:48.6901975Z   test_Transformer_multilayer_coder (__main__.TestNN) ... ok (0.469s)
2023-01-11T21:58:48.6902140Z   test_Transformer_multilayer_coder_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6902279Z   test_TripletMarginLoss_no_batch_dim_mean (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6902558Z   test_TripletMarginLoss_no_batch_dim_mean_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6902764Z   test_TripletMarginLoss_no_batch_dim_mean_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6902939Z   test_TripletMarginLoss_no_batch_dim_mean_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6903066Z   test_TripletMarginLoss_no_batch_dim_none (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6903246Z   test_TripletMarginLoss_no_batch_dim_none_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6903418Z   test_TripletMarginLoss_no_batch_dim_none_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6903590Z   test_TripletMarginLoss_no_batch_dim_none_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6903729Z   test_TripletMarginLoss_no_batch_dim_sum (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6903907Z   test_TripletMarginLoss_no_batch_dim_sum_cuda_double (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6904087Z   test_TripletMarginLoss_no_batch_dim_sum_cuda_float (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6904261Z   test_TripletMarginLoss_no_batch_dim_sum_cuda_half (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6904380Z   test_Unflatten_no_batch_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6904524Z   test_Unflatten_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6904625Z   test_Unfold (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6904764Z   test_Unfold_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6904877Z   test_Unfold_int_input (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6905025Z   test_Unfold_int_input_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6905130Z   test_ZeroPad2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6905247Z   test_ZeroPad2d_complex (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6905389Z   test_ZeroPad2d_complex_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6905532Z   test_ZeroPad2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6905654Z   test_ZeroPad2d_negative_dims (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6905812Z   test_ZeroPad2d_negative_dims_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6905932Z   test_ZeroPad2d_no_batch_dim (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6906088Z   test_ZeroPad2d_no_batch_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6906204Z   test_adaptive_log_softmax (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6906307Z   test_add_module (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6906431Z   test_add_module_raises_error_if_attr_exists (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6906586Z   test_affine_grid (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6906694Z   test_affine_grid_3d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6906814Z   test_affine_grid_error_checking (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6906917Z   test_assignment (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6907067Z   test_batchnorm_buffer_update_when_stats_are_not_tracked (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6907208Z   test_batchnorm_cudnn_half (__main__.TestNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:58:48.6907347Z   test_batchnorm_cudnn_nhwc (__main__.TestNN) ... skip: CUDA unavailable (0.002s)
2023-01-11T21:58:48.6907450Z   test_batchnorm_nhwc_cpu (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6907618Z   test_batchnorm_nhwc_cuda (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:48.6907949Z   test_batchnorm_non_contig_cpu_bn_module_<class 'torch_nn_modules_batchnorm_BatchNorm2d'> (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6908230Z   test_batchnorm_non_contig_cpu_bn_module_<class 'torch_nn_modules_batchnorm_SyncBatchNorm'> (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6908389Z   test_batchnorm_nonaffine_cuda_half_input (__main__.TestNN) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:58:48.6908541Z   test_batchnorm_raises_error_if_bias_is_not_same_size_as_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6908694Z   test_batchnorm_raises_error_if_less_than_one_value_per_channel (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6908855Z   test_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6909018Z   test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6909178Z   test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6909334Z   test_batchnorm_raises_error_if_weight_is_not_same_size_as_input (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6909462Z   test_bce_loss_always_nonnegative (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6909585Z   test_bce_loss_broadcasts_weights (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6909698Z   test_bce_loss_input_range (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6909816Z   test_bce_loss_size_mismatch (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6909955Z   test_bce_with_logits_broadcasts_pos_weights (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6910086Z   test_bce_with_logits_broadcasts_weights (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6910229Z   test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6910410Z   test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad (__main__.TestNN) ... ok (0.032s)
2023-01-11T21:58:48.6910548Z   test_bce_with_logits_has_correct_forward_grad (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6910683Z   test_bce_with_logits_has_correct_grad_at_zero (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6910834Z   test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6910996Z   test_bce_with_logits_raises_if_target_and_input_are_different_size (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6911114Z   test_bce_with_logits_stability (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6911267Z   test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6911368Z   test_bilinear (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6911477Z   test_bilinear_broadcasting (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6911587Z   test_bilinear_no_bias (__main__.TestNN) ... ok (0.025s)
2023-01-11T21:58:48.6911704Z   test_bilinear_non_contiguous (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6911987Z   test_broadcast_double_backwards_gpu (__main__.TestNN) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:58:48.6912208Z   test_broadcast_no_grad (__main__.TestNN) ... skip: multi-GPU not supported (0.000s)
2023-01-11T21:58:48.6912445Z   test_broadcast_not_requiring_grad (__main__.TestNN) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:58:48.6912563Z   test_buffer_not_persistent (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6912686Z   test_buffer_not_persistent_assign (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6912796Z   test_buffer_not_persistent_del (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6912918Z   test_buffer_not_persistent_load (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6913046Z   test_buffer_not_persistent_overwrite (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6913196Z   test_buffers_and_named_buffers (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6913945Z   test_call_supports_python_dict_output (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1296: UserWarning: Using non-full backward hooks on a Module that does not return a single Tensor or a tuple of Tensors is deprecated and will be removed in future versions. This hook will be missing some of the grad_output. Please use register_full_backward_hook to get the documented behavior.
2023-01-11T21:58:48.6914166Z   warnings.warn("Using non-full backward hooks on a Module that does not return a "
2023-01-11T21:58:48.6914232Z ok (0.002s)
2023-01-11T21:58:48.6914342Z   test_channel_shuffle (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6914466Z   test_channel_shuffle_return_self (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6914556Z   test_children (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6914665Z   test_clip_grad_norm (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6914776Z   test_clip_grad_value (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6914884Z   test_container_copy (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6915028Z   test_convert_sync_batchnorm (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:48.6915161Z   test_cosine_embedding_loss_invalid_shape (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6915295Z   test_cosine_embedding_loss_margin_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6915412Z   test_cosine_embedding_loss_no_reduce (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6915545Z   test_cosine_embedding_loss_with_diff_type (__main__.TestNN) ... ok (0.035s)
2023-01-11T21:58:48.6915656Z   test_cosine_similarity (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6915770Z   test_cross_entropy_loss (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6915895Z   test_cross_entropy_loss_precision (__main__.TestNN) ... ok (3.451s)
2023-01-11T21:58:48.6916019Z   test_cross_entropy_loss_zero_div (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6916280Z   test_cudnn_rnn_dropout_states_device (__main__.TestNN) ... skip: CUDNN or multi-gpu not available (0.000s)
2023-01-11T21:58:48.6916427Z   test_cudnn_weight_format (__main__.TestNN) ... skip: CUDNN not available (0.001s)
2023-01-11T21:58:48.6916558Z   test_cudnn_weight_tying (__main__.TestNN) ... skip: CUDNN not available (0.001s)
2023-01-11T21:58:48.6916652Z   test_dir (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6916754Z   test_dir_digit (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6916869Z   test_elu_inplace_gradgrad (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6916982Z   test_elu_inplace_on_view (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6917096Z   test_error_RNN_seq_len_zero (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6917200Z   test_extra_state (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6917331Z   test_extra_state_missing_get_extra_state (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6917452Z   test_extra_state_missing_set_extra_state (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6917567Z   test_extra_state_non_dict (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6917967Z   test_fb_fc_packed (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:2184: UserWarning: fbgemm_pack_gemm_matrix_fp16 is deprecated and will be removed in a future PyTorch release. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/QuantizedLinear.cpp:382.)
2023-01-11T21:58:48.6918093Z   packed_w_tensor = torch.fbgemm_pack_gemm_matrix_fp16(w_tensor)
2023-01-11T21:58:48.6918433Z /var/lib/jenkins/workspace/test/test_nn.py:2185: UserWarning: fbgemm_linear_fp16_weight_fp32_activation is deprecated and will be removed in a future PyTorch release. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/QuantizedLinear.cpp:417.)
2023-01-11T21:58:48.6918587Z   actual_output = torch.fbgemm_linear_fp16_weight(x_tensor, packed_w_tensor, b_tensor)
2023-01-11T21:58:48.6918686Z ok (0.002s)
2023-01-11T21:58:48.6918789Z   test_flatten (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6918901Z   test_fold_invalid_arg (__main__.TestNN) ... ok (0.028s)
2023-01-11T21:58:48.6919010Z   test_gaussian_nll_loss_args (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6919138Z   test_gaussian_nll_loss_broadcasting (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6919240Z   test_get_buffer (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6919363Z   test_get_buffer_from_submodules (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6919499Z   test_getattr_with_property (__main__.TestNN) ... expected failure (0.001s)
2023-01-11T21:58:48.6919605Z   test_grid_sample (__main__.TestNN) ... ok (0.950s)
2023-01-11T21:58:48.6919712Z   test_grid_sample_3d (__main__.TestNN) ... ok (0.090s)
2023-01-11T21:58:48.6920345Z   test_grid_sample_error_checking (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4235: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
2023-01-11T21:58:48.6920412Z   warnings.warn(
2023-01-11T21:58:48.6920477Z ok (0.034s)
2023-01-11T21:58:48.6920590Z   test_hardtanh_backward (__main__.TestNN) ... ok (0.117s)
2023-01-11T21:58:48.6920714Z   test_hardtanh_inplace_gradgrad (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6920833Z   test_huber_loss_invalid_delta (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6920940Z   test_inplace_thnn (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6921044Z   test_interpolate (__main__.TestNN) ... ok (0.162s)
2023-01-11T21:58:48.6921161Z   test_interpolate_bicubic_2d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6921308Z   test_interpolate_bicubic_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6921438Z   test_interpolate_bicubic_2d_zero_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6921603Z   test_interpolate_bicubic_2d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6921730Z   test_interpolate_bicubic_scale_2d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6921893Z   test_interpolate_bicubic_scale_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6922035Z   test_interpolate_bicubic_scale_tuple_shared_2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6922213Z   test_interpolate_bicubic_scale_tuple_shared_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6922354Z   test_interpolate_bicubic_scale_tuple_skewed_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6922497Z   test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6922693Z   test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6922869Z   test_interpolate_bicubic_scale_tuple_skewed_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6923030Z   test_interpolate_bicubic_tuple_2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6923174Z   test_interpolate_bicubic_tuple_2d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6923351Z   test_interpolate_bicubic_tuple_2d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6923513Z   test_interpolate_bicubic_tuple_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6923633Z   test_interpolate_bilinear_2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6923790Z   test_interpolate_bilinear_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6923909Z   test_interpolate_bilinear_2d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6924106Z   test_interpolate_bilinear_2d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6924232Z   test_interpolate_bilinear_scale_2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6924398Z   test_interpolate_bilinear_scale_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6924540Z   test_interpolate_bilinear_scale_tuple_shared_2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6924718Z   test_interpolate_bilinear_scale_tuple_shared_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6924857Z   test_interpolate_bilinear_scale_tuple_skewed_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6925012Z   test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6925206Z   test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6925374Z   test_interpolate_bilinear_scale_tuple_skewed_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6925502Z   test_interpolate_bilinear_tuple_2d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6925645Z   test_interpolate_bilinear_tuple_2d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6925821Z   test_interpolate_bilinear_tuple_2d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6925986Z   test_interpolate_bilinear_tuple_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T21:58:48.6926112Z   test_interpolate_buffer_overflow (__main__.TestNN) ... ok (3.839s)
2023-01-11T21:58:48.6926268Z   test_interpolate_illegal_memory_access (__main__.TestNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:58:48.6926384Z   test_interpolate_linear_1d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6926508Z   test_interpolate_linear_1d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6926677Z   test_interpolate_linear_1d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6926831Z   test_interpolate_linear_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6926958Z   test_interpolate_linear_1d_zero_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6927122Z   test_interpolate_linear_1d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6927245Z   test_interpolate_linear_scale_1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6927383Z   test_interpolate_linear_scale_1d_align_corners (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6927559Z   test_interpolate_linear_scale_1d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6927718Z   test_interpolate_linear_scale_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6927834Z   test_interpolate_linear_tuple_1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6927992Z   test_interpolate_linear_tuple_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6928147Z   test_interpolate_nearest_1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6928301Z   test_interpolate_nearest_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6928429Z   test_interpolate_nearest_1d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6928594Z   test_interpolate_nearest_1d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6928711Z   test_interpolate_nearest_2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6928867Z   test_interpolate_nearest_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6928993Z   test_interpolate_nearest_2d_launch_configs (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6929448Z   test_interpolate_nearest_2d_launch_configs_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6929588Z   test_interpolate_nearest_2d_zero_dim (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6929757Z   test_interpolate_nearest_2d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6929877Z   test_interpolate_nearest_3d (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6930032Z   test_interpolate_nearest_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6930159Z   test_interpolate_nearest_3d_zero_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6930326Z   test_interpolate_nearest_3d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6930441Z   test_interpolate_nearest_scale_1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6930606Z   test_interpolate_nearest_scale_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6930733Z   test_interpolate_nearest_scale_2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6930892Z   test_interpolate_nearest_scale_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6931016Z   test_interpolate_nearest_scale_3d (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6931173Z   test_interpolate_nearest_scale_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6931299Z   test_interpolate_nearest_tuple_1d (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6931460Z   test_interpolate_nearest_tuple_1d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6931584Z   test_interpolate_nearest_tuple_2d (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6931733Z   test_interpolate_nearest_tuple_2d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6931860Z   test_interpolate_nearest_tuple_3d (__main__.TestNN) ... ok (0.012s)
2023-01-11T21:58:48.6932020Z   test_interpolate_nearest_tuple_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6932146Z   test_interpolate_trilinear_3d (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6932305Z   test_interpolate_trilinear_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6932436Z   test_interpolate_trilinear_3d_zero_dim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6932605Z   test_interpolate_trilinear_3d_zero_dim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6932732Z   test_interpolate_trilinear_scale_3d (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6932865Z   test_interpolate_trilinear_scale_3d_align_corners (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6933043Z   test_interpolate_trilinear_scale_3d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6933208Z   test_interpolate_trilinear_scale_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6933339Z   test_interpolate_trilinear_tuple_3d (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6933523Z   test_interpolate_trilinear_tuple_3d_align_corners (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6933704Z   test_interpolate_trilinear_tuple_3d_align_corners_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6933872Z   test_interpolate_trilinear_tuple_3d_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6933989Z   test_kl_div_log_softmax_target (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6934710Z   test_kl_div_with_diff_type (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6934786Z   warnings.warn(
2023-01-11T21:58:48.6934842Z ok (0.002s)
2023-01-11T21:58:48.6934973Z   test_kl_div_with_diff_type_log_target (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6935086Z   test_l1_loss_correct (__main__.TestNN) ... ok (10.175s)
2023-01-11T21:58:48.6935224Z   test_layer_norm_grads_with_create_graph_flag (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6935367Z   test_linear_autograd_device_cpu_bias_weightCOO (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6935865Z   test_linear_autograd_device_cpu_bias_weightCSC (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:6609: UserWarning: Sparse CSC tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:58:48.6936003Z   module.weight = nn.Parameter(module.weight.to_sparse_csc())
2023-01-11T21:58:48.6936068Z ok (0.012s)
2023-01-11T21:58:48.6936196Z   test_linear_autograd_device_cpu_bias_weightCSR (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6936347Z   test_linear_autograd_device_cpu_bias_weightStrided (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6936490Z   test_linear_autograd_device_cpu_nobias_weightCOO (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6936631Z   test_linear_autograd_device_cpu_nobias_weightCSC (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6936766Z   test_linear_autograd_device_cpu_nobias_weightCSR (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6936915Z   test_linear_autograd_device_cpu_nobias_weightStrided (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6937033Z   test_linear_broadcasting (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6937142Z   test_load_state_dict (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6937248Z   test_load_state_dict_BC (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6937364Z   test_load_state_dict_child (__main__.TestNN) ... ok (0.181s)
2023-01-11T21:58:48.6937482Z   test_load_state_dict_custom (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6937601Z   test_load_state_dict_invalid (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6937720Z   test_load_state_dict_ref_cycle (__main__.TestNN) ... ok (0.115s)
2023-01-11T21:58:48.6937833Z   test_load_state_dict_type (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6937944Z   test_log_softmax_cpu (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6938055Z   test_log_softmax_dim0 (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6938193Z   test_log_softmax_dim0_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6938301Z   test_log_softmax_dim3 (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6938448Z   test_log_softmax_dim3_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6938563Z   test_log_softmax_lastdim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6938715Z   test_log_softmax_lastdim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6938861Z   test_log_softmax_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6939009Z   test_log_softmax_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6939120Z   test_log_softmax_spatial (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6939260Z   test_log_softmax_spatial_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6939383Z   test_log_softmax_spatial_special (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6939543Z   test_log_softmax_spatial_special_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6939942Z   test_loss_equal_input_target_shape (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:2799: UserWarning: Using a target size (torch.Size([5, 3])) that is different to the input size (torch.Size([3, 5])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6940097Z   'mse_loss': lambda x, y: F.mse_loss(x, y),
2023-01-11T21:58:48.6940411Z /var/lib/jenkins/workspace/test/test_nn.py:2800: UserWarning: Using a target size (torch.Size([5, 3])) that is different to the input size (torch.Size([3, 5])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6940547Z   'l1_loss': lambda x, y: F.l1_loss(x, y),
2023-01-11T21:58:48.6940859Z /var/lib/jenkins/workspace/test/test_nn.py:2801: UserWarning: Using a target size (torch.Size([5, 3])) that is different to the input size (torch.Size([3, 5])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6941019Z   'smooth_l1_loss': lambda x, y: F.smooth_l1_loss(x, y),
2023-01-11T21:58:48.6941328Z /var/lib/jenkins/workspace/test/test_nn.py:2802: UserWarning: Using a target size (torch.Size([5, 3])) that is different to the input size (torch.Size([3, 5])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6941479Z   'huber_loss': lambda x, y: F.huber_loss(x, y),
2023-01-11T21:58:48.6942068Z /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T21:58:48.6942145Z   warnings.warn(
2023-01-11T21:58:48.6942210Z ok (0.023s)
2023-01-11T21:58:48.6942344Z   test_margin_ranking_loss_margin_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6942543Z   test_margin_ranking_loss_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6942678Z   test_module_apply_inplace_op (__main__.TestNN) ... ok (0.011s)
2023-01-11T21:58:48.6942792Z   test_module_backcompat (__main__.TestNN) ... ok (0.029s)
2023-01-11T21:58:48.6942908Z   test_module_to_argparse (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6942997Z   test_modules (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6943109Z   test_mse_loss_size_warning (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6943253Z   test_multimarginloss_1d_input_0d_target_no_reduce (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6943435Z   test_multimarginloss_1d_input_0d_target_no_reduce_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6943544Z   test_named_children (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6943653Z   test_named_modules (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6943786Z   test_named_parameters_remove_duplicate (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6944154Z   test_nested_tensor_from_mask (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:2204: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:58:48.6944312Z   nt = torch._nested_tensor_from_mask(input, mask)
2023-01-11T21:58:48.6944366Z ok (0.002s)
2023-01-11T21:58:48.6944489Z   test_nested_tensor_from_mask_error (__main__.TestNN) ... ok (0.020s)
2023-01-11T21:58:48.6944588Z   test_no_grad (__main__.TestNN) ... ok (0.009s)
2023-01-11T21:58:48.6944704Z   test_non_leaf_parameters (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6944803Z   test_normalize (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6944938Z   test_overwrite_module_params_on_conversion (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6945091Z   test_pack_sequence_batch_sizes_throw (__main__.TestNN) ... skip: CUDA not available (0.000s)
2023-01-11T21:58:48.6945190Z   test_pad_scalar_error (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6945328Z   test_padding_list (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6945442Z   test_pairwise_distance (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6945561Z   test_parameter_assignment (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6945685Z   test_parameterlistdict_pickle (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6945824Z   test_parameterlistdict_setting_attributes (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6945953Z   test_parameters_and_named_parameters (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6946070Z   test_parameters_to_vector (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6946158Z   test_parse_to (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6946300Z   test_partial_flat_weights (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:48.6946400Z   test_pdist (__main__.TestNN) ... ok (0.015s)
2023-01-11T21:58:48.6946554Z   test_pdist_cpu_gradgrad_unimplemented (__main__.TestNN) ... expected failure (0.007s)
2023-01-11T21:58:48.6946707Z   test_pdist_cuda_gradgrad_unimplemented (__main__.TestNN) ... expected failure (0.010s)
2023-01-11T21:58:48.6946819Z   test_pdist_empty_col (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6946926Z   test_pdist_empty_row (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6947030Z   test_pdist_large (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6947107Z   test_pdist_zeros (__main__.TestNN)
2023-01-11T21:58:48.6947222Z Test that grad is still valid when dist is 0 ... ok (0.008s)
2023-01-11T21:58:48.6947342Z   test_pixel_shuffle_nhwc_cpu (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6947462Z   test_pixel_shuffle_unshuffle (__main__.TestNN) ... ok (0.463s)
2023-01-11T21:58:48.6947826Z   test_pointwise_loss_broadcast (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6948000Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:48.6948315Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6948484Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:48.6948795Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6948950Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:48.6949264Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6949491Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:48.6949797Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6949963Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:48.6950272Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6950467Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:48.6950780Z /var/lib/jenkins/workspace/test/test_nn.py:5402: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6950947Z   'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
2023-01-11T21:58:48.6951257Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6951422Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6951733Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6951885Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6952192Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6952353Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6952662Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6952825Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6953130Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6953292Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6953598Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6953755Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6954059Z /var/lib/jenkins/workspace/test/test_nn.py:5403: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6954215Z   'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6954518Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6954738Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6955030Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6955213Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6955551Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6955738Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6956045Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6956226Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6956528Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6956712Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6957016Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6957202Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6957506Z /var/lib/jenkins/workspace/test/test_nn.py:5404: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6957686Z   'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
2023-01-11T21:58:48.6957983Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6958161Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:48.6958468Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6958639Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:48.6958937Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6959104Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:48.6959407Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6959610Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:48.6959911Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6960082Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:48.6960385Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6960553Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:48.6960872Z /var/lib/jenkins/workspace/test/test_nn.py:5405: UserWarning: Using a target size (torch.Size([2, 10])) that is different to the input size (torch.Size([2, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2023-01-11T21:58:48.6961047Z   'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
2023-01-11T21:58:48.6961113Z ok (0.038s)
2023-01-11T21:58:48.6961257Z   test_pointwise_loss_target_grad_none_reduction (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6961387Z   test_projections_errors_on_gru_and_rnn (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6961513Z   test_projections_lstm_args_check (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6961749Z   test_projections_lstm_check_device (__main__.TestNN) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:58:48.6961887Z   test_projections_lstm_initial_hidden_state (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6962027Z   test_register_buffer_allows_overwriting_with_same_name (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6962169Z   test_register_buffer_raises_error_if_attr_exists (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6962319Z   test_register_buffer_raises_error_if_name_is_not_string (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6962455Z   test_register_buffer_raises_error_if_not_tensor (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6962608Z   test_register_parameter_allows_overwriting_with_same_name (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6962752Z   test_register_parameter_raises_error_if_attr_exists (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6962902Z   test_register_parameter_raises_error_if_name_is_not_string (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6963026Z   test_register_state_dict_pre_hook (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6963167Z   test_register_state_dict_pre_hook_backward_compat (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6963271Z   test_relu_inplace_on_view (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6963369Z   test_repr (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6963479Z   test_requires_grad_ (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6963582Z   test_rnn_args_check (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6963797Z   test_rnn_check_device (__main__.TestNN) ... skip: multi-GPU not supported (0.001s)
2023-01-11T21:58:48.6963915Z   test_rnn_initial_hidden_state (__main__.TestNN) ... ok (0.014s)
2023-01-11T21:58:48.6964024Z   test_rnn_weight_norm (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6964462Z   test_share_memory (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:181: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:48.6964571Z   self.assertFalse(p.storage().is_shared())
2023-01-11T21:58:48.6964960Z /var/lib/jenkins/workspace/test/test_nn.py:186: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:48.6965097Z   self.assertTrue(p.storage().is_shared())
2023-01-11T21:58:48.6965162Z ok (0.001s)
2023-01-11T21:58:48.6965289Z   test_smoothl1loss_intergral_target (__main__.TestNN) ... ok (0.006s)
2023-01-11T21:58:48.6965429Z   test_smoothl1loss_negative_beta_not_supported (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6965534Z   test_softmax_cpu (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6965653Z   test_softmax_functional_dim0 (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6965839Z   test_softmax_functional_dim0_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6965949Z   test_softmax_functional_dim3 (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6966105Z   test_softmax_functional_dim3_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6966230Z   test_softmax_functional_scalar (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6966388Z   test_softmax_functional_scalar_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6966496Z   test_softmax_lastdim (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6966645Z   test_softmax_lastdim_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6966761Z   test_softmax_lastdim_dtype (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6966913Z   test_softmax_lastdim_dtype_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6967010Z   test_softmax_spatial (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6967159Z   test_softmax_spatial_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6967275Z   test_softmax_spatial_dtype (__main__.TestNN) ... ok (0.007s)
2023-01-11T21:58:48.6967432Z   test_softmax_spatial_dtype_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6967550Z   test_softmax_spatial_special (__main__.TestNN) ... ok (0.008s)
2023-01-11T21:58:48.6967704Z   test_softmax_spatial_special_cuda (__main__.TestNN) ... skip: Excluded from CUDA tests (0.001s)
2023-01-11T21:58:48.6967802Z   test_softmin (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6968241Z   test_spectral_norm (__main__.TestNN) ... /var/lib/jenkins/workspace/test/test_nn.py:1899: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:48.6968376Z   self.assertEqual(m.weight_orig.storage(), m.weight.storage())
2023-01-11T21:58:48.6969193Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:48.6969335Z   device=typed_storage.device,
2023-01-11T21:58:48.6969419Z ok (0.024s)
2023-01-11T21:58:48.6969539Z   test_spectral_norm_dim (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6969656Z   test_spectral_norm_forward (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6969781Z   test_spectral_norm_load_state_dict (__main__.TestNN) ... ok (0.028s)
2023-01-11T21:58:48.6969896Z   test_spectral_norm_pickle (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6969998Z   test_state_dict (__main__.TestNN) ... ok (0.003s)
2023-01-11T21:58:48.6970150Z   test_sync_batchnorm_accuracy_cuda (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:48.6970291Z   test_sync_batchnorm_backward_elemt (__main__.TestNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:58:48.6970465Z   test_threshold_bfloat16 (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6970570Z   test_threshold_int (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6970667Z   test_to (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6970791Z   test_train_errors_for_invalid_mode (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6970910Z   test_transformer_args_check (__main__.TestNN) ... ok (0.170s)
2023-01-11T21:58:48.6971037Z   test_transformer_layer_args_check (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6971144Z   test_transformerdecoder (__main__.TestNN) ... ok (0.102s)
2023-01-11T21:58:48.6971273Z   test_transformerdecoderlayer (__main__.TestNN) ... ok (0.025s)
2023-01-11T21:58:48.6971440Z   test_transformerdecoderlayer_gelu (__main__.TestNN) ... ok (0.032s)
2023-01-11T21:58:48.6971558Z   test_triplet_margin_loss (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6971681Z   test_triplet_margin_loss_no_reduce (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6971805Z   test_triplet_margin_loss_swap (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6971936Z   test_triplet_margin_loss_swap_no_reduce (__main__.TestNN) ... ok (0.004s)
2023-01-11T21:58:48.6972032Z   test_type (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6972706Z   test_unflatten (__main__.TestNN) ... /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:1114: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1816.)
2023-01-11T21:58:48.6972806Z   return super(Tensor, self).refine_names(names)
2023-01-11T21:58:48.6972870Z ok (0.002s)
2023-01-11T21:58:48.6972987Z   test_unflatten_invalid_arg (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6973101Z   test_unfold_invalid_arg (__main__.TestNN) ... ok (0.013s)
2023-01-11T21:58:48.6973252Z   test_upsamplingBilinear2d_spatial_invariance (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6973371Z   test_upsamplingLinear1d (__main__.TestNN) ... ok (0.016s)
2023-01-11T21:58:48.6973515Z   test_upsamplingLinear1d_spatial_invariance (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6973638Z   test_upsamplingTrilinear3d (__main__.TestNN) ... ok (0.043s)
2023-01-11T21:58:48.6973775Z   test_upsamplingTrilinear3d_spatial_invariance (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6973889Z   test_upsampling_bfloat16 (__main__.TestNN) ... ok (0.017s)
2023-01-11T21:58:48.6974026Z   test_upsampling_not_recompute_scale_factor (__main__.TestNN) ... ok (0.010s)
2023-01-11T21:58:48.6974146Z   test_upsampling_small_scale (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6974263Z   test_vector_to_parameters (__main__.TestNN) ... ok (0.001s)
2023-01-11T21:58:48.6974369Z   test_weight_norm (__main__.TestNN) ... ok (0.005s)
2023-01-11T21:58:48.6974482Z   test_weight_norm_pickle (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6974574Z   test_zero_grad (__main__.TestNN) ... ok (0.002s)
2023-01-11T21:58:48.6974593Z 
2023-01-11T21:58:48.6974785Z ----------------------------------------------------------------------
2023-01-11T21:58:48.6974861Z Ran 2070 tests in 31.391s
2023-01-11T21:58:48.6974867Z 
2023-01-11T21:58:48.6974961Z OK (skipped=1111, expected failures=3)
2023-01-11T21:58:48.6974966Z 
2023-01-11T21:58:48.6975049Z Generating XML reports...
2023-01-11T21:58:48.6975310Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestAddRelu-20230111215816.xml
2023-01-11T21:58:48.6975589Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestConstantPadNd-20230111215816.xml
2023-01-11T21:58:48.6975877Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestFunctionalPickle-20230111215816.xml
2023-01-11T21:58:48.6976137Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestFusionEval-20230111215816.xml
2023-01-11T21:58:48.6976426Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestFusionUtils-20230111215816.xml
2023-01-11T21:58:48.6976666Z Generated XML report: test-reports/python-unittest/test_nn/TEST-TestNN-20230111215816.xml
2023-01-11T21:58:48.6976672Z 
2023-01-11T21:58:48.6977002Z ##[endgroup]
2023-01-11T21:58:48.6977258Z FINISHED PRINTING LOG FILE of test_nn (/var/lib/jenkins/workspace/test/test-reports/test_nn_7jcit4ac)
2023-01-11T21:58:48.6977263Z 
2023-01-11T21:58:48.6977422Z Running test_overrides ... [2023-01-11 21:58:48.646200]
2023-01-11T21:58:48.6977756Z Executing ['/opt/conda/bin/python', '-bb', 'test_overrides.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:48.646413]
2023-01-11T21:58:51.5922637Z 
2023-01-11T21:58:51.5923465Z Expand the folded group to see the log file of test_overrides
2023-01-11T21:58:51.5924633Z ##[group]PRINTING LOG FILE of test_overrides (/var/lib/jenkins/workspace/test/test-reports/test_overrides_srimhfgs)
2023-01-11T21:58:51.5925335Z 
2023-01-11T21:58:51.5925446Z Running tests...
2023-01-11T21:58:51.5926088Z ----------------------------------------------------------------------
2023-01-11T21:58:51.5926726Z Test results will be stored in test-reports/python-unittest/test_overrides
2023-01-11T21:58:51.5927293Z   test_broadcast_all (__main__.TestBroadcastAllOverride) ... ok (0.217s)
2023-01-11T21:58:51.5928372Z   test_parameter_does_not_prevent_dispatch (__main__.TestDisabledTorchFunction) ... ok (0.001s)
2023-01-11T21:58:51.5961057Z   test_wrapper (__main__.TestEinsumOverride) ... ok (0.002s)
2023-01-11T21:58:51.5961364Z   test_gradcheck (__main__.TestGradCheckOverride) ... ok (0.015s)
2023-01-11T21:58:51.5961640Z   test_newones (__main__.TestGradNewOnesOverride) ... ok (0.001s)
2023-01-11T21:58:51.5961911Z   test_getitem (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:51.5962174Z   test_getitem_subclass (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:51.5962419Z   test_setitem (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:51.5962682Z   test_setitem_subclass (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:51.5965544Z   test_setitem_val (__main__.TestIndexing) ... ok (0.001s)
2023-01-11T21:58:51.5966001Z   test_iterator (__main__.TestIterator) ... ok (0.001s)
2023-01-11T21:58:51.5966431Z   test_max (__main__.TestNamedTuple) ... ok (0.001s)
2023-01-11T21:58:51.5966854Z   test_pickle (__main__.TestPickle) ... ok (0.001s)
2023-01-11T21:58:51.5967203Z   test_rnn (__main__.TestRNN) ... ok (0.002s)
2023-01-11T21:58:51.5967586Z   test_resolve_name (__main__.TestResolveName) ... ok (0.082s)
2023-01-11T21:58:51.5968042Z   test_all_same_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5968500Z   test_basic (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5969202Z   test_disable_enable_subclass (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5969740Z   test_disable_subclass_not_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5970336Z   test_distributions_bernoulli (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5970858Z   test_error_using_class_method_on_mode (__main__.TestTorchFunctionMode) ... ok (0.002s)
2023-01-11T21:58:51.5971328Z   test_factory_override (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5971873Z   test_get_cur_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5972325Z   test_get_mode_stack (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5972758Z   test_mode_notimplemented_loop (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5973335Z   test_modes_handle_first (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5973696Z   test_modes_return_notimplemented (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5974040Z   test_nested_modes_with_python_has_torch_function (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5974354Z   test_nested_same_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5974779Z   test_nn_parse_to (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5975070Z   test_reentrant_mode_idiom (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5975368Z   test_restacking_with_ancestor (__main__.TestTorchFunctionMode) ... ok (0.000s)
2023-01-11T21:58:51.5975670Z   test_subclass_hash (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5975954Z   test_with_mode (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5976244Z   test_with_mode_created_separately (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5976555Z   test_with_nested_modes (__main__.TestTorchFunctionMode) ... ok (0.001s)
2023-01-11T21:58:51.5976855Z   test_Tensor___add__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5977208Z   test_Tensor___and__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5977501Z   test_Tensor___array__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5977817Z   test_Tensor___array_wrap__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5978124Z   test_Tensor___bool__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5978417Z   test_Tensor___complex__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5978731Z   test_Tensor___contains__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5979065Z   test_Tensor___cuda_array_interface_____get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5979394Z   test_Tensor___deepcopy__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5979687Z   test_Tensor___div__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5979994Z   test_Tensor___dlpack__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5980309Z   test_Tensor___dlpack_device__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5980608Z   test_Tensor___eq__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5980908Z   test_Tensor___float__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5981213Z   test_Tensor___floordiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5981519Z   test_Tensor___format__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5981807Z   test_Tensor___ge__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5982109Z   test_Tensor___getitem__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5982410Z   test_Tensor___gt__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5982772Z   test_Tensor___iadd__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5983075Z   test_Tensor___iand__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5983375Z   test_Tensor___idiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5985096Z   test_Tensor___ifloordiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5985640Z   test_Tensor___ilshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5986203Z   test_Tensor___imod__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5986806Z   test_Tensor___imul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5987359Z   test_Tensor___index__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5987987Z   test_Tensor___int__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5988549Z   test_Tensor___invert__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5988902Z   test_Tensor___ior__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5989198Z   test_Tensor___irshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5989606Z   test_Tensor___isub__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5990089Z   test_Tensor___ixor__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5990568Z   test_Tensor___le__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5991048Z   test_Tensor___len__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5991532Z   test_Tensor___long__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5992024Z   test_Tensor___lshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5992476Z   test_Tensor___lt__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5992964Z   test_Tensor___matmul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5993547Z   test_Tensor___mod__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5994050Z   test_Tensor___mul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5994518Z   test_Tensor___ne__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5994996Z   test_Tensor___nonzero__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5995482Z   test_Tensor___or__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5995941Z   test_Tensor___radd__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5996445Z   test_Tensor___rand__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5996949Z   test_Tensor___rdiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5997487Z   test_Tensor___reduce_ex__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5998051Z   test_Tensor___repr__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5998599Z   test_Tensor___reversed__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5999097Z   test_Tensor___rfloordiv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.5999603Z   test_Tensor___rlshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6000098Z   test_Tensor___rmatmul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6000589Z   test_Tensor___rmod__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6001069Z   test_Tensor___rmul__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6001533Z   test_Tensor___ror__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6002001Z   test_Tensor___rpow__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6002495Z   test_Tensor___rrshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6002989Z   test_Tensor___rshift__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6003471Z   test_Tensor___rsub__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6003955Z   test_Tensor___rxor__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6004512Z   test_Tensor___setitem__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6005063Z   test_Tensor___setstate__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6005620Z   test_Tensor___sub__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6006170Z   test_Tensor___truediv__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6006713Z   test_Tensor___xor__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6007027Z   test_Tensor__autocast_to_full_precision (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6007384Z   test_Tensor__autocast_to_reduced_precision (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6007723Z   test_Tensor__coalesced_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6008019Z   test_Tensor__dimI (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6008384Z   test_Tensor__dimV (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6008686Z   test_Tensor__indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6008990Z   test_Tensor__is_view (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6009482Z   test_Tensor__nested_tensor_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6009797Z   test_Tensor__nnz (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6010100Z   test_Tensor__to_dense (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6010399Z   test_Tensor__update_names (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6010784Z   test_Tensor__values (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6011086Z   test_Tensor_abs (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6011385Z   test_Tensor_abs_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6011674Z   test_Tensor_absolute (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6011985Z   test_Tensor_absolute_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6012800Z   test_Tensor_acos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6013088Z   test_Tensor_acos_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6013387Z   test_Tensor_acosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6013691Z   test_Tensor_acosh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6013989Z   test_Tensor_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6014276Z   test_Tensor_add_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6014575Z   test_Tensor_addbmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6014882Z   test_Tensor_addbmm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6015174Z   test_Tensor_addcdiv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6015482Z   test_Tensor_addcdiv_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6015789Z   test_Tensor_addcmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6016081Z   test_Tensor_addcmul_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6016383Z   test_Tensor_addmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6016681Z   test_Tensor_addmm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6016980Z   test_Tensor_addmv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6017267Z   test_Tensor_addmv_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6017567Z   test_Tensor_addr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6017868Z   test_Tensor_addr_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6018155Z   test_Tensor_adjoint (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6018460Z   test_Tensor_align_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6018764Z   test_Tensor_align_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6019062Z   test_Tensor_all (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6019350Z   test_Tensor_allclose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6019651Z   test_Tensor_amax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6019946Z   test_Tensor_amin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6020238Z   test_Tensor_aminmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6020540Z   test_Tensor_angle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6020902Z   test_Tensor_any (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6021202Z   test_Tensor_apply_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6021492Z   test_Tensor_arccos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6021791Z   test_Tensor_arccos_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6022093Z   test_Tensor_arccosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6022384Z   test_Tensor_arccosh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6022765Z   test_Tensor_arcsin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6023063Z   test_Tensor_arcsin_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6023398Z   test_Tensor_arcsinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6023688Z   test_Tensor_arcsinh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6023990Z   test_Tensor_arctan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6024289Z   test_Tensor_arctan2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6024578Z   test_Tensor_arctan2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6024875Z   test_Tensor_arctan_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6025177Z   test_Tensor_arctanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6025464Z   test_Tensor_arctanh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6025764Z   test_Tensor_argmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6026064Z   test_Tensor_argmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6026367Z   test_Tensor_argsort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6026656Z   test_Tensor_argwhere (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6026967Z   test_Tensor_as_strided (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6027280Z   test_Tensor_as_strided_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6027588Z   test_Tensor_as_strided_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6027900Z   test_Tensor_asin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6028197Z   test_Tensor_asin_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6028497Z   test_Tensor_asinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6028785Z   test_Tensor_asinh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6029083Z   test_Tensor_atan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6029383Z   test_Tensor_atan2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6029668Z   test_Tensor_atan2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6029966Z   test_Tensor_atan_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6030264Z   test_Tensor_atanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6030562Z   test_Tensor_atanh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6030852Z   test_Tensor_backward (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6031164Z   test_Tensor_baddbmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6031469Z   test_Tensor_baddbmm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6031766Z   test_Tensor_bernoulli (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6032076Z   test_Tensor_bernoulli_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6032385Z   test_Tensor_bfloat16 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6032687Z   test_Tensor_bincount (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6033022Z   test_Tensor_bitwise_and (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6033333Z   test_Tensor_bitwise_and_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6033654Z   test_Tensor_bitwise_left_shift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6033970Z   test_Tensor_bitwise_left_shift_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6034289Z   test_Tensor_bitwise_not (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6034599Z   test_Tensor_bitwise_not_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6034909Z   test_Tensor_bitwise_or (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6035235Z   test_Tensor_bitwise_or_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6035559Z   test_Tensor_bitwise_right_shift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6035892Z   test_Tensor_bitwise_right_shift_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6036202Z   test_Tensor_bitwise_xor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6036513Z   test_Tensor_bitwise_xor_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6036817Z   test_Tensor_bmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6037101Z   test_Tensor_bool (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6037407Z   test_Tensor_broadcast_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6037714Z   test_Tensor_byte (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6038017Z   test_Tensor_cauchy_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6038311Z   test_Tensor_ccol_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6038626Z   test_Tensor_cdouble (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6038924Z   test_Tensor_ceil (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6039211Z   test_Tensor_ceil_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6039513Z   test_Tensor_cfloat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6039812Z   test_Tensor_chalf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6040110Z   test_Tensor_char (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6040403Z   test_Tensor_cholesky (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6040722Z   test_Tensor_cholesky_inverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6041046Z   test_Tensor_cholesky_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6041342Z   test_Tensor_chunk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6041643Z   test_Tensor_clamp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6041943Z   test_Tensor_clamp_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6042248Z   test_Tensor_clamp_max (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6042546Z   test_Tensor_clamp_max_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6042856Z   test_Tensor_clamp_min (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6043163Z   test_Tensor_clamp_min_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6043453Z   test_Tensor_clip (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6043749Z   test_Tensor_clip_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6044049Z   test_Tensor_clone (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6044351Z   test_Tensor_coalesce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6044704Z   test_Tensor_col_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6045007Z   test_Tensor_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6045315Z   test_Tensor_conj_physical (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6045622Z   test_Tensor_conj_physical_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6045937Z   test_Tensor_contiguous (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6046242Z   test_Tensor_copy_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6046546Z   test_Tensor_copysign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6046844Z   test_Tensor_copysign_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6047183Z   test_Tensor_corrcoef (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6047488Z   test_Tensor_cos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6047775Z   test_Tensor_cos_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6048072Z   test_Tensor_cosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6048369Z   test_Tensor_cosh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6048663Z   test_Tensor_count_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6048970Z   test_Tensor_cov (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6049409Z   test_Tensor_cpu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6049712Z   test_Tensor_cross (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6050006Z   test_Tensor_crow_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6050314Z   test_Tensor_cuda (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6050613Z   test_Tensor_cummax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6050903Z   test_Tensor_cummin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6051207Z   test_Tensor_cumprod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6051513Z   test_Tensor_cumprod_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6051815Z   test_Tensor_cumsum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6052103Z   test_Tensor_cumsum_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6052405Z   test_Tensor_data_ptr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6052710Z   test_Tensor_deg2rad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6053000Z   test_Tensor_deg2rad_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6053307Z   test_Tensor_dense_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6053621Z   test_Tensor_dequantize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6053926Z   test_Tensor_det (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6054212Z   test_Tensor_detach (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6054513Z   test_Tensor_detach_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6054813Z   test_Tensor_diag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6055108Z   test_Tensor_diag_embed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6055416Z   test_Tensor_diagflat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6055723Z   test_Tensor_diagonal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6056029Z   test_Tensor_diagonal_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6056342Z   test_Tensor_diff (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6056707Z   test_Tensor_digamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6057010Z   test_Tensor_digamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6057297Z   test_Tensor_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6057591Z   test_Tensor_dist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6057885Z   test_Tensor_div (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6058166Z   test_Tensor_div_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6058467Z   test_Tensor_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6058772Z   test_Tensor_divide_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6059108Z   test_Tensor_dot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6059398Z   test_Tensor_double (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6059704Z   test_Tensor_dsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6060015Z   test_Tensor_element_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6060306Z   test_Tensor_eq (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6060597Z   test_Tensor_eq_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6060890Z   test_Tensor_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6061187Z   test_Tensor_erf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6061468Z   test_Tensor_erf_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6061762Z   test_Tensor_erfc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6062061Z   test_Tensor_erfc_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6062348Z   test_Tensor_erfinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6062712Z   test_Tensor_erfinv_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6063009Z   test_Tensor_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6063306Z   test_Tensor_exp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6063592Z   test_Tensor_exp2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6063887Z   test_Tensor_exp_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6064187Z   test_Tensor_expand (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6064482Z   test_Tensor_expand_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6064783Z   test_Tensor_expm1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6065082Z   test_Tensor_expm1_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6065377Z   test_Tensor_exponential_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6065685Z   test_Tensor_fill_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6065993Z   test_Tensor_fill_diagonal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6066303Z   test_Tensor_fix (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6066585Z   test_Tensor_fix_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6066884Z   test_Tensor_flatten (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6067185Z   test_Tensor_flip (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6067473Z   test_Tensor_fliplr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6067773Z   test_Tensor_flipud (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6068073Z   test_Tensor_float (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6068379Z   test_Tensor_float_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6068713Z   test_Tensor_float_power_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6069015Z   test_Tensor_floor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6069309Z   test_Tensor_floor_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6069601Z   test_Tensor_floor_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6069913Z   test_Tensor_floor_divide_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6070218Z   test_Tensor_fmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6070514Z   test_Tensor_fmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6070794Z   test_Tensor_fmod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6071120Z   test_Tensor_fmod_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6071416Z   test_Tensor_frac (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6071701Z   test_Tensor_frac_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6072000Z   test_Tensor_frexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6072301Z   test_Tensor_gather (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6072589Z   test_Tensor_gcd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6072887Z   test_Tensor_gcd_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6073181Z   test_Tensor_ge (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6073474Z   test_Tensor_ge_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6073765Z   test_Tensor_geometric_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6074075Z   test_Tensor_geqrf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6074375Z   test_Tensor_ger (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6074665Z   test_Tensor_get_device (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6074974Z   test_Tensor_greater (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6075279Z   test_Tensor_greater_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6075592Z   test_Tensor_greater_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6075898Z   test_Tensor_greater_equal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6076201Z   test_Tensor_gt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6076493Z   test_Tensor_gt_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6076777Z   test_Tensor_half (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6077082Z   test_Tensor_hardshrink (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6077390Z   test_Tensor_has_names (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6077701Z   test_Tensor_heaviside (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6077997Z   test_Tensor_heaviside_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6078302Z   test_Tensor_histc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6078606Z   test_Tensor_histogram (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6078896Z   test_Tensor_hsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6079198Z   test_Tensor_hypot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6079496Z   test_Tensor_hypot_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6079778Z   test_Tensor_i0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6080071Z   test_Tensor_i0_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6080369Z   test_Tensor_igamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6080703Z   test_Tensor_igamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6080996Z   test_Tensor_igammac (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6081302Z   test_Tensor_igammac_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6081609Z   test_Tensor_index_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6081904Z   test_Tensor_index_add_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6082212Z   test_Tensor_index_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6082523Z   test_Tensor_index_copy_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6082833Z   test_Tensor_index_fill (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6083180Z   test_Tensor_index_fill_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6083495Z   test_Tensor_index_put (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6083804Z   test_Tensor_index_put_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6084105Z   test_Tensor_index_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6084421Z   test_Tensor_index_reduce_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6084736Z   test_Tensor_index_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6085048Z   test_Tensor_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6085338Z   test_Tensor_inner (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6085634Z   test_Tensor_int (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6085934Z   test_Tensor_int_repr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6086227Z   test_Tensor_inverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6086526Z   test_Tensor_ipu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6086833Z   test_Tensor_is_coalesced (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6087148Z   test_Tensor_is_complex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6087440Z   test_Tensor_is_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6087754Z   test_Tensor_is_contiguous (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6088076Z   test_Tensor_is_distributed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6088387Z   test_Tensor_is_floating_point (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6088707Z   test_Tensor_is_inference (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6089015Z   test_Tensor_is_neg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6089485Z   test_Tensor_is_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6089786Z   test_Tensor_is_pinned (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6090097Z   test_Tensor_is_same_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6090407Z   test_Tensor_is_set_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6090699Z   test_Tensor_is_shared (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6091005Z   test_Tensor_is_signed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6091308Z   test_Tensor_isclose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6091615Z   test_Tensor_isfinite (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6091907Z   test_Tensor_isinf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6092205Z   test_Tensor_isnan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6092568Z   test_Tensor_isneginf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6092863Z   test_Tensor_isposinf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6093166Z   test_Tensor_isreal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6093465Z   test_Tensor_istft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6093752Z   test_Tensor_item (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6094049Z   test_Tensor_kron (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6094351Z   test_Tensor_kthvalue (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6094650Z   test_Tensor_lcm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6094978Z   test_Tensor_lcm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6095279Z   test_Tensor_ldexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6095580Z   test_Tensor_ldexp_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6095868Z   test_Tensor_le (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6096159Z   test_Tensor_le_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6096450Z   test_Tensor_lerp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6096750Z   test_Tensor_lerp_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6097032Z   test_Tensor_less (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6097327Z   test_Tensor_less_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6097630Z   test_Tensor_less_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6097930Z   test_Tensor_less_equal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6098236Z   test_Tensor_lgamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6098540Z   test_Tensor_lgamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6098842Z   test_Tensor_log (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6099124Z   test_Tensor_log10 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6099419Z   test_Tensor_log10_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6099716Z   test_Tensor_log1p (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6100000Z   test_Tensor_log1p_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6100297Z   test_Tensor_log2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6100593Z   test_Tensor_log2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6100877Z   test_Tensor_log_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6101180Z   test_Tensor_log_normal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6101493Z   test_Tensor_log_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6101804Z   test_Tensor_logaddexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6102105Z   test_Tensor_logaddexp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6102420Z   test_Tensor_logcumsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6102802Z   test_Tensor_logdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6103101Z   test_Tensor_logical_and (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6103416Z   test_Tensor_logical_and_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6103730Z   test_Tensor_logical_not (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6104042Z   test_Tensor_logical_not_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6104342Z   test_Tensor_logical_or (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6104685Z   test_Tensor_logical_or_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6104994Z   test_Tensor_logical_xor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6105293Z   test_Tensor_logical_xor_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6105597Z   test_Tensor_logit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6105896Z   test_Tensor_logit_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6106204Z   test_Tensor_logsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6106497Z   test_Tensor_long (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6106791Z   test_Tensor_lt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6107121Z   test_Tensor_lt_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6107402Z   test_Tensor_lu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6107700Z   test_Tensor_lu_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6108000Z   test_Tensor_map2_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6108297Z   test_Tensor_map_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6108590Z   test_Tensor_masked_fill (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6108900Z   test_Tensor_masked_fill_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6109215Z   test_Tensor_masked_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6109524Z   test_Tensor_masked_scatter_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6109845Z   test_Tensor_masked_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6110152Z   test_Tensor_matmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6110461Z   test_Tensor_matrix_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6110759Z   test_Tensor_matrix_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6111062Z   test_Tensor_max (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6111363Z   test_Tensor_maximum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6111651Z   test_Tensor_mean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6111948Z   test_Tensor_median (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6112246Z   test_Tensor_min (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6112531Z   test_Tensor_minimum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6112830Z   test_Tensor_mm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6113124Z   test_Tensor_mode (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6113426Z   test_Tensor_moveaxis (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6113721Z   test_Tensor_movedim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6114021Z   test_Tensor_msort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6114317Z   test_Tensor_mul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6114599Z   test_Tensor_mul_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6114904Z   test_Tensor_multinomial (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6115213Z   test_Tensor_multiply (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6115523Z   test_Tensor_multiply_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6115814Z   test_Tensor_mv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6116112Z   test_Tensor_mvlgamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6116452Z   test_Tensor_mvlgamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6116750Z   test_Tensor_nan_to_num (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6117056Z   test_Tensor_nan_to_num_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6117362Z   test_Tensor_nanmean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6117670Z   test_Tensor_nanmedian (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6117969Z   test_Tensor_nanquantile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6118277Z   test_Tensor_nansum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6118577Z   test_Tensor_narrow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6118898Z   test_Tensor_narrow_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6119212Z   test_Tensor_ndimension (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6119517Z   test_Tensor_ne (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6119808Z   test_Tensor_ne_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6120089Z   test_Tensor_neg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6120382Z   test_Tensor_neg_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6120683Z   test_Tensor_negative (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6120982Z   test_Tensor_negative_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6121289Z   test_Tensor_nelement (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6121594Z   test_Tensor_nextafter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6121893Z   test_Tensor_nextafter_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6122200Z   test_Tensor_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6122500Z   test_Tensor_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6122797Z   test_Tensor_normal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6123089Z   test_Tensor_not_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6123398Z   test_Tensor_not_equal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6123700Z   test_Tensor_numel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6123986Z   test_Tensor_numpy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6124284Z   test_Tensor_orgqr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6124579Z   test_Tensor_ormqr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6124876Z   test_Tensor_outer (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6125165Z   test_Tensor_permute (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6125478Z   test_Tensor_pin_memory (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6125789Z   test_Tensor_pinverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6126086Z   test_Tensor_polygamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6126396Z   test_Tensor_polygamma_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6126704Z   test_Tensor_positive (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6127008Z   test_Tensor_pow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6127295Z   test_Tensor_pow_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6127590Z   test_Tensor_prelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6127891Z   test_Tensor_prod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6128174Z   test_Tensor_put (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6128501Z   test_Tensor_put_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6128816Z   test_Tensor_q_per_channel_axis (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6129258Z   test_Tensor_q_per_channel_scales (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6129601Z   test_Tensor_q_per_channel_zero_points (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6129922Z   test_Tensor_q_scale (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6130233Z   test_Tensor_q_zero_point (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6130522Z   test_Tensor_qr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6130871Z   test_Tensor_qscheme (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6131177Z   test_Tensor_quantile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6131473Z   test_Tensor_rad2deg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6131775Z   test_Tensor_rad2deg_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6132077Z   test_Tensor_random_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6132375Z   test_Tensor_ravel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6132672Z   test_Tensor_reciprocal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6132985Z   test_Tensor_reciprocal_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6133305Z   test_Tensor_record_stream (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6133606Z   test_Tensor_refine_names (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6133923Z   test_Tensor_register_hook (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6134229Z   test_Tensor_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6134527Z   test_Tensor_relu_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6134818Z   test_Tensor_remainder (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6135126Z   test_Tensor_remainder_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6135433Z   test_Tensor_rename (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6135723Z   test_Tensor_rename_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6136024Z   test_Tensor_renorm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6136324Z   test_Tensor_renorm_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6136624Z   test_Tensor_repeat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6136929Z   test_Tensor_repeat_interleave (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6137255Z   test_Tensor_requires_grad_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6137570Z   test_Tensor_reshape (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6137867Z   test_Tensor_reshape_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6138172Z   test_Tensor_resize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6138473Z   test_Tensor_resize_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6138777Z   test_Tensor_resize_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6139070Z   test_Tensor_resize_as_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6139387Z   test_Tensor_resize_as_sparse_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6139706Z   test_Tensor_resolve_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6140005Z   test_Tensor_resolve_neg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6140362Z   test_Tensor_retain_grad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6140665Z   test_Tensor_roll (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6140962Z   test_Tensor_rot90 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6141246Z   test_Tensor_round (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6141547Z   test_Tensor_round_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6141853Z   test_Tensor_row_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6142145Z   test_Tensor_rsqrt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6142439Z   test_Tensor_rsqrt_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6142853Z   test_Tensor_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6143151Z   test_Tensor_scatter_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6143463Z   test_Tensor_scatter_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6143777Z   test_Tensor_scatter_add_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6144095Z   test_Tensor_scatter_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6144405Z   test_Tensor_scatter_reduce_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6144715Z   test_Tensor_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6145024Z   test_Tensor_select_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6145318Z   test_Tensor_set_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6145611Z   test_Tensor_sgn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6145905Z   test_Tensor_sgn_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6146212Z   test_Tensor_share_memory_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6146509Z   test_Tensor_short (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6146809Z   test_Tensor_sigmoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6147114Z   test_Tensor_sigmoid_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6147402Z   test_Tensor_sign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6147700Z   test_Tensor_sign_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6147999Z   test_Tensor_signbit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6148298Z   test_Tensor_sin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6148584Z   test_Tensor_sin_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6148880Z   test_Tensor_sinc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6149177Z   test_Tensor_sinc_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6149464Z   test_Tensor_sinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6149758Z   test_Tensor_sinh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6150051Z   test_Tensor_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6150360Z   test_Tensor_slice_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6150661Z   test_Tensor_slogdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6150960Z   test_Tensor_smm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6151259Z   test_Tensor_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6151548Z   test_Tensor_sort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6151854Z   test_Tensor_sparse_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6152198Z   test_Tensor_sparse_mask (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6152500Z   test_Tensor_sparse_resize_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6152832Z   test_Tensor_sparse_resize_and_clear_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6153153Z   test_Tensor_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6153465Z   test_Tensor_split_with_sizes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6153760Z   test_Tensor_sqrt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6154057Z   test_Tensor_sqrt_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6154355Z   test_Tensor_square (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6154674Z   test_Tensor_square_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6154978Z   test_Tensor_squeeze (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6155282Z   test_Tensor_squeeze_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6155590Z   test_Tensor_sspaddmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6155878Z   test_Tensor_std (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6156169Z   test_Tensor_stft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6156470Z   test_Tensor_storage (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6156770Z   test_Tensor_storage_offset (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6157087Z   test_Tensor_storage_type (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6157392Z   test_Tensor_sub (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6157687Z   test_Tensor_sub_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6157975Z   test_Tensor_subtract (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6158282Z   test_Tensor_subtract_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6158582Z   test_Tensor_sum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6158873Z   test_Tensor_sum_to_size (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6159171Z   test_Tensor_svd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6159471Z   test_Tensor_swapaxes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6159780Z   test_Tensor_swapaxes_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6160074Z   test_Tensor_swapdims (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6160382Z   test_Tensor_swapdims_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6160689Z   test_Tensor_symeig (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6160972Z   test_Tensor_t (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6161268Z   test_Tensor_t_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6161562Z   test_Tensor_take (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6161857Z   test_Tensor_take_along_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6162162Z   test_Tensor_tan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6162458Z   test_Tensor_tan_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6162751Z   test_Tensor_tanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6163035Z   test_Tensor_tanh_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6163340Z   test_Tensor_tensor_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6163646Z   test_Tensor_tile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6163926Z   test_Tensor_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6164256Z   test_Tensor_to_dense (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6164560Z   test_Tensor_to_mkldnn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6164865Z   test_Tensor_to_sparse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6165152Z   test_Tensor_tolist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6165450Z   test_Tensor_topk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6165748Z   test_Tensor_trace (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6166043Z   test_Tensor_transpose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6166398Z   test_Tensor_transpose_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6166822Z   test_Tensor_triangular_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6167248Z   test_Tensor_tril (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6167624Z   test_Tensor_tril_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6167945Z   test_Tensor_triu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6168299Z   test_Tensor_triu_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6168669Z   test_Tensor_true_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6169129Z   test_Tensor_true_divide_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6169515Z   test_Tensor_trunc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6169873Z   test_Tensor_trunc_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6170230Z   test_Tensor_type (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6170549Z   test_Tensor_type_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6170963Z   test_Tensor_unbind (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6171320Z   test_Tensor_unfold (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6171639Z   test_Tensor_uniform_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6171999Z   test_Tensor_unique (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6172395Z   test_Tensor_unique_consecutive (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6172777Z   test_Tensor_unsafe_chunk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6173106Z   test_Tensor_unsafe_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6173484Z   test_Tensor_unsafe_split_with_sizes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6173874Z   test_Tensor_unsqueeze (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6174238Z   test_Tensor_unsqueeze_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6174615Z   test_Tensor_untyped_storage (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6174983Z   test_Tensor_values (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6175347Z   test_Tensor_var (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6175660Z   test_Tensor_vdot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6176033Z   test_Tensor_view (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6176393Z   test_Tensor_view_as (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6176712Z   test_Tensor_vsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6177075Z   test_Tensor_where (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6177475Z   test_Tensor_xlogy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6177789Z   test_Tensor_xlogy_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6178201Z   test_Tensor_xpu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6178562Z   test_Tensor_zero_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6178928Z   test__TensorBase_H___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6179281Z   test__TensorBase_T___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6179671Z   test__TensorBase__backward_hooks___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6180072Z   test__TensorBase__base___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6180413Z   test__TensorBase__cdata___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6180858Z   test__TensorBase__grad___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6181276Z   test__TensorBase__grad_fn___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6195030Z   test__TensorBase__version___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6195445Z   test__TensorBase_data___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6195787Z   test__TensorBase_device___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6196124Z   test__TensorBase_dtype___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6196458Z   test__TensorBase_grad___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6196777Z   test__TensorBase_grad_fn___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6197105Z   test__TensorBase_imag___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6197438Z   test__TensorBase_is_cpu___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6197757Z   test__TensorBase_is_cuda___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6198091Z   test__TensorBase_is_ipu___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6198420Z   test__TensorBase_is_leaf___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6198746Z   test__TensorBase_is_meta___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6199061Z   test__TensorBase_is_mkldnn___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6199389Z   test__TensorBase_is_mps___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6199719Z   test__TensorBase_is_nested___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6200033Z   test__TensorBase_is_ort___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6200362Z   test__TensorBase_is_quantized___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6200697Z   test__TensorBase_is_sparse___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6201033Z   test__TensorBase_is_sparse_csr___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6201356Z   test__TensorBase_is_vulkan___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6201682Z   test__TensorBase_is_xpu___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6202007Z   test__TensorBase_layout___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6202330Z   test__TensorBase_mH___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6202634Z   test__TensorBase_mT___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6202956Z   test__TensorBase_name___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6203278Z   test__TensorBase_names___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6203677Z   test__TensorBase_ndim___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6204007Z   test__TensorBase_output_nr___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6204340Z   test__TensorBase_real___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6204672Z   test__TensorBase_requires_grad___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6204998Z   test__TensorBase_retains_grad___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6205332Z   test__TensorBase_shape___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6205663Z   test__TensorBase_volatile___get__ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6205995Z   test_base (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6206284Z   test_grad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6206601Z   test_has_torch_function_non_sequence (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6206917Z   test_mean_semantics (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:51.6207195Z Test that a function with one argument can be overrided ... ok (0.001s)
2023-01-11T21:58:51.6207480Z   test_mm_semantics (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:51.6207775Z Test that a function with multiple arguments can be overrided ... ok (0.002s)
2023-01-11T21:58:51.6208063Z   test_pow_rpow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6208363Z   test_precedence_semantics (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:51.6208660Z Test semantics for __torch_function__ for functions that take ... ok (0.002s)
2023-01-11T21:58:51.6208952Z   test_tensor_subclass_propagation (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:51.6209448Z this test exercises the functionality described in ... ok (0.001s)
2023-01-11T21:58:51.6209755Z   test_torch__C__fft_fft_fft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6210075Z   test_torch__C__fft_fft_fft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6210386Z   test_torch__C__fft_fft_fftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6210709Z   test_torch__C__fft_fft_fftshift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6211029Z   test_torch__C__fft_fft_hfft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6211333Z   test_torch__C__fft_fft_hfft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6211650Z   test_torch__C__fft_fft_hfftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6211963Z   test_torch__C__fft_fft_ifft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6212281Z   test_torch__C__fft_fft_ifft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6212579Z   test_torch__C__fft_fft_ifftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6212904Z   test_torch__C__fft_fft_ifftshift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6213224Z   test_torch__C__fft_fft_ihfft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6213529Z   test_torch__C__fft_fft_ihfft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6213847Z   test_torch__C__fft_fft_ihfftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6214163Z   test_torch__C__fft_fft_irfft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6214480Z   test_torch__C__fft_fft_irfft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6214782Z   test_torch__C__fft_fft_irfftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6215098Z   test_torch__C__fft_fft_rfft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6215409Z   test_torch__C__fft_fft_rfft2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6215776Z   test_torch__C__fft_fft_rfftn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6216109Z   test_torch__C__linalg_linalg_cholesky (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6216458Z   test_torch__C__linalg_linalg_cholesky_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6216800Z   test_torch__C__linalg_linalg_cond (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6217123Z   test_torch__C__linalg_linalg_cross (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6217456Z   test_torch__C__linalg_linalg_det (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6217840Z   test_torch__C__linalg_linalg_diagonal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6218176Z   test_torch__C__linalg_linalg_eig (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6218497Z   test_torch__C__linalg_linalg_eigh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6218832Z   test_torch__C__linalg_linalg_eigvals (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6219169Z   test_torch__C__linalg_linalg_eigvalsh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6219512Z   test_torch__C__linalg_linalg_householder_product (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6219862Z   test_torch__C__linalg_linalg_inv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6220199Z   test_torch__C__linalg_linalg_inv_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6220540Z   test_torch__C__linalg_linalg_ldl_factor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6220876Z   test_torch__C__linalg_linalg_ldl_factor_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6221223Z   test_torch__C__linalg_linalg_ldl_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6221559Z   test_torch__C__linalg_linalg_lstsq (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6221876Z   test_torch__C__linalg_linalg_lu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6222207Z   test_torch__C__linalg_linalg_lu_factor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6222626Z   test_torch__C__linalg_linalg_lu_factor_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6222974Z   test_torch__C__linalg_linalg_lu_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6223300Z   test_torch__C__linalg_linalg_matmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6223644Z   test_torch__C__linalg_linalg_matrix_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6223989Z   test_torch__C__linalg_linalg_matrix_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6224342Z   test_torch__C__linalg_linalg_matrix_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6224673Z   test_torch__C__linalg_linalg_matrix_rank (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6225018Z   test_torch__C__linalg_linalg_multi_dot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6225355Z   test_torch__C__linalg_linalg_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6225673Z   test_torch__C__linalg_linalg_pinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6225999Z   test_torch__C__linalg_linalg_qr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6226332Z   test_torch__C__linalg_linalg_slogdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6226668Z   test_torch__C__linalg_linalg_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6226989Z   test_torch__C__linalg_linalg_solve_ex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6227373Z   test_torch__C__linalg_linalg_solve_triangular (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6227716Z   test_torch__C__linalg_linalg_svd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6228036Z   test_torch__C__linalg_linalg_svdvals (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6228372Z   test_torch__C__linalg_linalg_tensorinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6228715Z   test_torch__C__linalg_linalg_tensorsolve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6229058Z   test_torch__C__linalg_linalg_vander (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6229413Z   test_torch__C__linalg_linalg_vecdot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6229754Z   test_torch__C__linalg_linalg_vector_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6230087Z   test_torch__C__nn_avg_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6230407Z   test_torch__C__nn_avg_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6230712Z   test_torch__C__nn_gelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6231022Z   test_torch__C__nn_linear (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6231339Z   test_torch__C__nn_log_sigmoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6231643Z   test_torch__C__nn_one_hot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6231958Z   test_torch__C__nn_softplus (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6232279Z   test_torch__C__nn_softshrink (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6232613Z   test_torch__C__special_special_airy_ai (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6232947Z   test_torch__C__special_special_bessel_j0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6233291Z   test_torch__C__special_special_bessel_j1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6233637Z   test_torch__C__special_special_bessel_y0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6233964Z   test_torch__C__special_special_bessel_y1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6234327Z   test_torch__C__special_special_chebyshev_polynomial_t (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6234709Z   test_torch__C__special_special_chebyshev_polynomial_u (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6235084Z   test_torch__C__special_special_chebyshev_polynomial_v (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6235438Z   test_torch__C__special_special_chebyshev_polynomial_w (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6235801Z   test_torch__C__special_special_digamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6236139Z   test_torch__C__special_special_entr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6236474Z   test_torch__C__special_special_erf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6236795Z   test_torch__C__special_special_erfc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6237130Z   test_torch__C__special_special_erfcx (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6237466Z   test_torch__C__special_special_erfinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6237792Z   test_torch__C__special_special_exp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6238128Z   test_torch__C__special_special_expit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6238503Z   test_torch__C__special_special_expm1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6238844Z   test_torch__C__special_special_gammainc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6239178Z   test_torch__C__special_special_gammaincc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6239525Z   test_torch__C__special_special_gammaln (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6239887Z   test_torch__C__special_special_hermite_polynomial_h (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6240260Z   test_torch__C__special_special_hermite_polynomial_he (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6240602Z   test_torch__C__special_special_i0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6240969Z   test_torch__C__special_special_i0e (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6241305Z   test_torch__C__special_special_i1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6241621Z   test_torch__C__special_special_i1e (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6241978Z   test_torch__C__special_special_laguerre_polynomial_l (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6242357Z   test_torch__C__special_special_legendre_polynomial_p (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6242711Z   test_torch__C__special_special_log1p (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6243041Z   test_torch__C__special_special_log_ndtr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6243390Z   test_torch__C__special_special_log_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6243733Z   test_torch__C__special_special_logit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6244068Z   test_torch__C__special_special_logsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6244434Z   test_torch__C__special_special_modified_bessel_i0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6244805Z   test_torch__C__special_special_modified_bessel_i1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6245168Z   test_torch__C__special_special_modified_bessel_k0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6245522Z   test_torch__C__special_special_modified_bessel_k1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6245879Z   test_torch__C__special_special_multigammaln (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6246229Z   test_torch__C__special_special_ndtr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6246566Z   test_torch__C__special_special_ndtri (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6246900Z   test_torch__C__special_special_polygamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6247241Z   test_torch__C__special_special_psi (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6247576Z   test_torch__C__special_special_round (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6247925Z   test_torch__C__special_special_scaled_modified_bessel_k0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6248307Z   test_torch__C__special_special_scaled_modified_bessel_k1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6248694Z   test_torch__C__special_special_shifted_chebyshev_polynomial_t (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6249244Z   test_torch__C__special_special_shifted_chebyshev_polynomial_u (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6249632Z   test_torch__C__special_special_shifted_chebyshev_polynomial_v (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6250073Z   test_torch__C__special_special_shifted_chebyshev_polynomial_w (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6250440Z   test_torch__C__special_special_sinc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6250786Z   test_torch__C__special_special_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6251134Z   test_torch__C__special_special_spherical_bessel_j0 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6251494Z   test_torch__C__special_special_xlog1py (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6251839Z   test_torch__C__special_special_xlogy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6252237Z   test_torch__C__special_special_zeta (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6252553Z   test_torch__assert_async (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6252872Z   test_torch__conj_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6253195Z   test_torch__fw_primal_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6253502Z   test_torch__indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6253826Z   test_torch__lobpcg_lobpcg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6254157Z   test_torch__lowrank_pca_lowrank (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6254495Z   test_torch__lowrank_svd_lowrank (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6254811Z   test_torch__make_dual_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6255142Z   test_torch__native_batch_norm_legit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6255474Z   test_torch__neg_view_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6255788Z   test_torch__reshape_alias_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6256113Z   test_torch__rowwise_prune (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6256446Z   test_torch__sparse_broadcast_to_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6256778Z   test_torch__values_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6257070Z   test_torch_abs (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6257373Z   test_torch_absolute (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6257680Z   test_torch_acos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6257967Z   test_torch_acosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6258291Z   test_torch_adaptive_avg_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6258624Z   test_torch_adaptive_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6258939Z   test_torch_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6259229Z   test_torch_addbmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6259534Z   test_torch_addcdiv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6259840Z   test_torch_addcmul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6260132Z   test_torch_addmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6260436Z   test_torch_addmv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6260739Z   test_torch_addr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6261028Z   test_torch_adjoint (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6261351Z   test_torch_affine_grid_generator (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6261679Z   test_torch_alias_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6262014Z   test_torch_all (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6262302Z   test_torch_allclose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6262679Z   test_torch_alpha_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6262989Z   test_torch_amax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6263273Z   test_torch_amin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6263575Z   test_torch_aminmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6263878Z   test_torch_angle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6264177Z   test_torch_any (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6264498Z   test_torch_arccos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6264803Z   test_torch_arccosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6265113Z   test_torch_arcsin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6265404Z   test_torch_arcsinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6265708Z   test_torch_arctan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6266011Z   test_torch_arctan2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6266311Z   test_torch_arctanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6266598Z   test_torch_argmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6266900Z   test_torch_argmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6267204Z   test_torch_argsort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6267499Z   test_torch_argwhere (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6267815Z   test_torch_as_strided_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6268143Z   test_torch_as_strided_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6268456Z   test_torch_asin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6268741Z   test_torch_asinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6269038Z   test_torch_atan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6269337Z   test_torch_atan2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6269620Z   test_torch_atanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6269922Z   test_torch_avg_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6270234Z   test_torch_baddbmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6270527Z   test_torch_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6270853Z   test_torch_batch_norm_backward_elemt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6271194Z   test_torch_batch_norm_backward_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6271524Z   test_torch_batch_norm_elemt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6271840Z   test_torch_batch_norm_gather_stats (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6272188Z   test_torch_batch_norm_gather_stats_with_counts (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6272525Z   test_torch_batch_norm_stats (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6272855Z   test_torch_batch_norm_update_stats (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6273164Z   test_torch_bernoulli (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6273472Z   test_torch_bilinear (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6273842Z   test_torch_binary_cross_entropy_with_logits (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6274158Z   test_torch_bincount (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6274464Z   test_torch_binomial (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6274772Z   test_torch_bitwise_and (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6275080Z   test_torch_bitwise_left_shift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6275397Z   test_torch_bitwise_not (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6275706Z   test_torch_bitwise_or (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6276027Z   test_torch_bitwise_right_shift (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6276365Z   test_torch_bitwise_xor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6276666Z   test_torch_bmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6276973Z   test_torch_broadcast_to (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6277273Z   test_torch_bucketize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6277574Z   test_torch_cat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6277883Z   test_torch_ccol_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6278194Z   test_torch_ceil (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6278476Z   test_torch_celu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6278786Z   test_torch_channel_shuffle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6279100Z   test_torch_cholesky (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6279401Z   test_torch_cholesky_inverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6279724Z   test_torch_cholesky_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6280055Z   test_torch_choose_qparams_optimized (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6280375Z   test_torch_chunk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6280659Z   test_torch_clamp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6280963Z   test_torch_clamp_max (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6281267Z   test_torch_clamp_min (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6281554Z   test_torch_clip (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6281854Z   test_torch_clone (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6282169Z   test_torch_col_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6282487Z   test_torch_column_stack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6282789Z   test_torch_combinations (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6283094Z   test_torch_complex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6283397Z   test_torch_concat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6283694Z   test_torch_concatenate (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6283997Z   test_torch_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6284306Z   test_torch_conj_physical (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6284626Z   test_torch_constant_pad_nd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6284926Z   test_torch_conv1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6285228Z   test_torch_conv2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6285530Z   test_torch_conv3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6285861Z   test_torch_conv_tbc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6286176Z   test_torch_conv_transpose1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6286498Z   test_torch_conv_transpose2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6286818Z   test_torch_conv_transpose3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6287121Z   test_torch_convolution (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6287432Z   test_torch_copysign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6287741Z   test_torch_corrcoef (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6288059Z   test_torch_cos (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6288356Z   test_torch_cosh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6288673Z   test_torch_cosine_embedding_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6289007Z   test_torch_cosine_similarity (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6289409Z   test_torch_count_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6289716Z   test_torch_cov (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6290013Z   test_torch_cross (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6290316Z   test_torch_crow_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6290634Z   test_torch_ctc_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6290937Z   test_torch_cummax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6291230Z   test_torch_cummin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6291536Z   test_torch_cumprod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6291844Z   test_torch_cumsum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6292163Z   test_torch_cumulative_trapezoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6292471Z   test_torch_deg2rad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6292779Z   test_torch_dequantize (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6293083Z   test_torch_det (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6293368Z   test_torch_detach (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6293678Z   test_torch_detach_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6293982Z   test_torch_diag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6294287Z   test_torch_diag_embed (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6294585Z   test_torch_diagflat (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6294893Z   test_torch_diagonal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6295205Z   test_torch_diagonal_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6295518Z   test_torch_diagonal_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6295829Z   test_torch_diff (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6296129Z   test_torch_digamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6296430Z   test_torch_dist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6296709Z   test_torch_div (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6297007Z   test_torch_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6297307Z   test_torch_dot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6297594Z   test_torch_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6297949Z   test_torch_dsmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6298252Z   test_torch_dsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6298546Z   test_torch_dstack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6298858Z   test_torch_embedding (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6299177Z   test_torch_embedding_bag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6299495Z   test_torch_empty_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6299784Z   test_torch_eq (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6300085Z   test_torch_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6300420Z   test_torch_erf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6300704Z   test_torch_erfc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6301004Z   test_torch_erfinv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6301302Z   test_torch_exp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6301602Z   test_torch_exp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6301897Z   test_torch_expand_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6302208Z   test_torch_expm1 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6302607Z   test_torch_fake_quantize_per_channel_affine (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6302959Z   test_torch_fake_quantize_per_tensor_affine (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6303315Z   test_torch_fbgemm_linear_fp16_weight (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6303680Z   test_torch_fbgemm_linear_fp16_weight_fp32_activation (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6304039Z   test_torch_fbgemm_linear_int8_weight (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6304383Z   test_torch_fbgemm_linear_int8_weight_fp32_activation (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6304746Z   test_torch_fbgemm_linear_quantize_weight (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6305097Z   test_torch_fbgemm_pack_gemm_matrix_fp16 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6305450Z   test_torch_fbgemm_pack_quantized_matrix (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6305783Z   test_torch_feature_alpha_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6306116Z   test_torch_feature_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6306427Z   test_torch_fix (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6306717Z   test_torch_flatten (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6307018Z   test_torch_flip (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6307322Z   test_torch_fliplr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6307627Z   test_torch_flipud (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6307921Z   test_torch_float_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6308226Z   test_torch_floor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6308537Z   test_torch_floor_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6308830Z   test_torch_fmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6309122Z   test_torch_fmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6309416Z   test_torch_fmod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6309735Z   test_torch_frac (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6310033Z   test_torch_frexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6310344Z   test_torch_frobenius_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6310487Z   test_torch_full_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6310645Z   test_torch_functional_atleast_1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6310801Z   test_torch_functional_atleast_2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6310943Z   test_torch_functional_atleast_3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6311100Z   test_torch_functional_block_diag (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6311297Z   test_torch_functional_broadcast_tensors (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6311461Z   test_torch_functional_cartesian_prod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6311618Z   test_torch_functional_cdist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6311777Z   test_torch_functional_chain_matmul (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6311930Z   test_torch_functional_einsum (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6312078Z   test_torch_functional_lu (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6312222Z   test_torch_functional_meshgrid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6312372Z   test_torch_functional_norm (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6312525Z   test_torch_functional_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6312679Z   test_torch_functional_stft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6312836Z   test_torch_functional_tensordot (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6312992Z   test_torch_functional_unique (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6313159Z   test_torch_functional_unique_consecutive (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6313326Z   test_torch_fused_moving_avg_obs_fake_quant (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6313455Z   test_torch_gather (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6313593Z   test_torch_gcd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6313727Z   test_torch_ge (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6313864Z   test_torch_geqrf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6314000Z   test_torch_ger (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6314142Z   test_torch_gradient (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6314286Z   test_torch_greater (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6314432Z   test_torch_greater_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6314568Z   test_torch_grid_sampler (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6314717Z   test_torch_grid_sampler_2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6314864Z   test_torch_grid_sampler_3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6315009Z   test_torch_group_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6315142Z   test_torch_gru (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6315284Z   test_torch_gru_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6315423Z   test_torch_gt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6315567Z   test_torch_hardshrink (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6315758Z   test_torch_heaviside (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6315914Z   test_torch_hinge_embedding_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6316053Z   test_torch_histc (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6316197Z   test_torch_histogram (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6316347Z   test_torch_histogramdd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6316486Z   test_torch_hsmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6316628Z   test_torch_hsplit (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6316798Z   test_torch_hstack (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6316925Z   test_torch_hypot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6317063Z   test_torch_i0 (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6317206Z   test_torch_igamma (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6317348Z   test_torch_igammac (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6317486Z   test_torch_imag (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6317632Z   test_torch_index_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6317779Z   test_torch_index_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6317924Z   test_torch_index_fill (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6318052Z   test_torch_index_put (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6318200Z   test_torch_index_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6318346Z   test_torch_index_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6318493Z   test_torch_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6318630Z   test_torch_inner (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6318776Z   test_torch_instance_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6318916Z   test_torch_int_repr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6319055Z   test_torch_inverse (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6319186Z   test_torch_is_complex (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6319323Z   test_torch_is_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6319472Z   test_torch_is_distributed (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6319626Z   test_torch_is_floating_point (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6319769Z   test_torch_is_inference (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6319908Z   test_torch_is_neg (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6320051Z   test_torch_is_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6320193Z   test_torch_is_same_size (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6320321Z   test_torch_is_signed (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6320461Z   test_torch_isclose (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6320602Z   test_torch_isfinite (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6320737Z   test_torch_isin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6320874Z   test_torch_isinf (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6321014Z   test_torch_isnan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6321155Z   test_torch_isneginf (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6321328Z   test_torch_isposinf (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6321459Z   test_torch_isreal (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6321595Z   test_torch_istft (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6321731Z   test_torch_kl_div (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6321867Z   test_torch_kron (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6322006Z   test_torch_kthvalue (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6322149Z   test_torch_layer_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6322285Z   test_torch_lcm (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6322464Z   test_torch_ldexp (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6322588Z   test_torch_le (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6322727Z   test_torch_lerp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6322863Z   test_torch_less (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6323004Z   test_torch_less_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6323143Z   test_torch_lgamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6323277Z   test_torch_log (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6323411Z   test_torch_log10 (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6323546Z   test_torch_log1p (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6323669Z   test_torch_log2 (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6323816Z   test_torch_log_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6323961Z   test_torch_logaddexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6324111Z   test_torch_logaddexp2 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6324258Z   test_torch_logcumsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6324398Z   test_torch_logdet (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6324543Z   test_torch_logical_and (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6324686Z   test_torch_logical_not (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6324816Z   test_torch_logical_or (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6324957Z   test_torch_logical_xor (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6325094Z   test_torch_logit (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6325238Z   test_torch_logsumexp (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6325373Z   test_torch_lstm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6325515Z   test_torch_lstm_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6325650Z   test_torch_lt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6325789Z   test_torch_lu_solve (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6325918Z   test_torch_lu_unpack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6326072Z   test_torch_margin_ranking_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6326215Z   test_torch_masked_fill (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6326363Z   test_torch_masked_scatter (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6326511Z   test_torch_masked_select (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6326650Z   test_torch_matmul (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6326822Z   test_torch_matrix_exp (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6326968Z   test_torch_matrix_power (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6327091Z   test_torch_max (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6327235Z   test_torch_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6327393Z   test_torch_max_pool1d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6327535Z   test_torch_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6327675Z   test_torch_max_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6327815Z   test_torch_maximum (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6327982Z   test_torch_mean (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6328122Z   test_torch_median (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6328248Z   test_torch_min (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6328385Z   test_torch_minimum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6328535Z   test_torch_miopen_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6328690Z   test_torch_miopen_convolution (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6328860Z   test_torch_miopen_convolution_add_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6329112Z   test_torch_miopen_convolution_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6329284Z   test_torch_miopen_convolution_transpose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6329458Z   test_torch_miopen_depthwise_convolution (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6329591Z   test_torch_miopen_rnn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6329731Z   test_torch_mode (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6329875Z   test_torch_moveaxis (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6330016Z   test_torch_movedim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6330158Z   test_torch_msort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6330294Z   test_torch_mul (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6330441Z   test_torch_multinomial (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6330584Z   test_torch_multiply (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6330707Z   test_torch_mv (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6330852Z   test_torch_mvlgamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6330993Z   test_torch_nan_to_num (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6331135Z   test_torch_nanmean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6331280Z   test_torch_nanmedian (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6331426Z   test_torch_nanquantile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6331571Z   test_torch_nansum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6331712Z   test_torch_narrow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6331845Z   test_torch_narrow_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6331999Z   test_torch_native_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6332158Z   test_torch_native_channel_shuffle (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6332310Z   test_torch_native_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6332464Z   test_torch_native_group_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6332667Z   test_torch_native_layer_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6332810Z   test_torch_native_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6332945Z   test_torch_ne (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6333067Z   test_torch_neg (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6333211Z   test_torch_negative (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6333354Z   test_torch_nextafter (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6333515Z   test_torch_nn_functional__threshold (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6333729Z   test_torch_nn_functional_adaptive_avg_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6333909Z   test_torch_nn_functional_adaptive_avg_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6334082Z   test_torch_nn_functional_adaptive_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6334272Z   test_torch_nn_functional_adaptive_max_pool1d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6334443Z   test_torch_nn_functional_adaptive_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6334620Z   test_torch_nn_functional_adaptive_max_pool2d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6334790Z   test_torch_nn_functional_adaptive_max_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6334976Z   test_torch_nn_functional_adaptive_max_pool3d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6335139Z   test_torch_nn_functional_affine_grid (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6335303Z   test_torch_nn_functional_alpha_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6335464Z   test_torch_nn_functional_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6335635Z   test_torch_nn_functional_binary_cross_entropy (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6335816Z   test_torch_nn_functional_binary_cross_entropy_with_logits (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6335968Z   test_torch_nn_functional_celu (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6336130Z   test_torch_nn_functional_cosine_embedding_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6336293Z   test_torch_nn_functional_cross_entropy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6336449Z   test_torch_nn_functional_ctc_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6336606Z   test_torch_nn_functional_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6336768Z   test_torch_nn_functional_dropout1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6336925Z   test_torch_nn_functional_dropout2d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6337079Z   test_torch_nn_functional_dropout3d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6337233Z   test_torch_nn_functional_elu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6337378Z   test_torch_nn_functional_embedding (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6337541Z   test_torch_nn_functional_embedding_bag (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6337716Z   test_torch_nn_functional_feature_alpha_dropout (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6337870Z   test_torch_nn_functional_fold (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6338073Z   test_torch_nn_functional_fractional_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6338260Z   test_torch_nn_functional_fractional_max_pool2d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6338430Z   test_torch_nn_functional_fractional_max_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6338613Z   test_torch_nn_functional_fractional_max_pool3d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6338782Z   test_torch_nn_functional_gaussian_nll_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6338921Z   test_torch_nn_functional_glu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6339112Z   test_torch_nn_functional_grid_sample (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6339272Z   test_torch_nn_functional_group_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6339437Z   test_torch_nn_functional_gumbel_softmax (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6339596Z   test_torch_nn_functional_hardtanh (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6339769Z   test_torch_nn_functional_hinge_embedding_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6339927Z   test_torch_nn_functional_huber_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6340089Z   test_torch_nn_functional_instance_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6340240Z   test_torch_nn_functional_interpolate (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6340394Z   test_torch_nn_functional_kl_div (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6340556Z   test_torch_nn_functional_l1_loss (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6340715Z   test_torch_nn_functional_layer_norm (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6340877Z   test_torch_nn_functional_leaky_relu (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6341047Z   test_torch_nn_functional_local_response_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6341207Z   test_torch_nn_functional_log_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6341363Z   test_torch_nn_functional_lp_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6341521Z   test_torch_nn_functional_lp_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6341677Z   test_torch_nn_functional_margin_ranking_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6341837Z   test_torch_nn_functional_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6342009Z   test_torch_nn_functional_max_pool1d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6342169Z   test_torch_nn_functional_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6342344Z   test_torch_nn_functional_max_pool2d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6342573Z   test_torch_nn_functional_max_pool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6342751Z   test_torch_nn_functional_max_pool3d_with_indices (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6342914Z   test_torch_nn_functional_max_unpool1d (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6343078Z   test_torch_nn_functional_max_unpool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6343226Z   test_torch_nn_functional_max_unpool3d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6343381Z   test_torch_nn_functional_mish (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6343579Z   test_torch_nn_functional_mse_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6343758Z   test_torch_nn_functional_multi_head_attention_forward (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6343927Z   test_torch_nn_functional_multi_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6344103Z   test_torch_nn_functional_multilabel_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6344285Z   test_torch_nn_functional_multilabel_soft_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6344441Z   test_torch_nn_functional_nll_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6344587Z   test_torch_nn_functional_normalize (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6344772Z   test_torch_nn_functional_pad (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6344942Z   test_torch_nn_functional_poisson_nll_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6345097Z   test_torch_nn_functional_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6345256Z   test_torch_nn_functional_relu6 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6345411Z   test_torch_nn_functional_rrelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6345565Z   test_torch_nn_functional_selu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6345714Z   test_torch_nn_functional_silu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6345879Z   test_torch_nn_functional_smooth_l1_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6346035Z   test_torch_nn_functional_soft_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6346193Z   test_torch_nn_functional_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6346353Z   test_torch_nn_functional_softmin (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6346513Z   test_torch_nn_functional_softsign (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6346673Z   test_torch_nn_functional_tanhshrink (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6346845Z   test_torch_nn_functional_triplet_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6347031Z   test_torch_nn_functional_triplet_margin_with_distance_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6347190Z   test_torch_nn_functional_unfold (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6347329Z   test_torch_nn_init_constant_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6347488Z   test_torch_nn_init_kaiming_uniform_ (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6347637Z   test_torch_nn_init_normal_ (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6347789Z   test_torch_nn_init_uniform_ (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6347932Z   test_torch_nonzero (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6348082Z   test_torch_norm_except_dim (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6348227Z   test_torch_not_equal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6348376Z   test_torch_nuclear_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6348502Z   test_torch_numel (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6348642Z   test_torch_ones_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6348781Z   test_torch_orgqr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6348919Z   test_torch_ormqr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6349091Z   test_torch_outer (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6349245Z   test_torch_pairwise_distance (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6349385Z   test_torch_pdist (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6349527Z   test_torch_permute (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6349663Z   test_torch_permute_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6349807Z   test_torch_pinverse (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6349956Z   test_torch_pixel_shuffle (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6350107Z   test_torch_pixel_unshuffle (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6350278Z   test_torch_poisson (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6350433Z   test_torch_poisson_nll_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6350574Z   test_torch_polar (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6350719Z   test_torch_polygamma (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6350849Z   test_torch_positive (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6350985Z   test_torch_pow (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6351122Z   test_torch_prelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6351260Z   test_torch_prod (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6351396Z   test_torch_put (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6351551Z   test_torch_q_per_channel_axis (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6351709Z   test_torch_q_per_channel_scales (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6351869Z   test_torch_q_per_channel_zero_points (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6351998Z   test_torch_q_scale (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6352144Z   test_torch_q_zero_point (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6352283Z   test_torch_qr (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6352423Z   test_torch_quantile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6352578Z   test_torch_quantize_per_channel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6352733Z   test_torch_quantize_per_tensor (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6352894Z   test_torch_quantize_per_tensor_dynamic (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6353053Z   test_torch_quantized_batch_norm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6353208Z   test_torch_quantized_gru_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6353350Z   test_torch_quantized_lstm_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6353504Z   test_torch_quantized_max_pool1d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6353658Z   test_torch_quantized_max_pool2d (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6353817Z   test_torch_quantized_rnn_relu_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6353973Z   test_torch_quantized_rnn_tanh_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6354114Z   test_torch_rad2deg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6354257Z   test_torch_rand_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6354406Z   test_torch_randint_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6354538Z   test_torch_randn_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6354705Z   test_torch_ravel (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6354844Z   test_torch_real (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6354994Z   test_torch_reciprocal (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6355129Z   test_torch_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6355273Z   test_torch_remainder (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6355413Z   test_torch_renorm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6355566Z   test_torch_repeat_interleave (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6355695Z   test_torch_reshape (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6355872Z   test_torch_resolve_conj (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6356020Z   test_torch_resolve_neg (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6356162Z   test_torch_rnn_relu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6356308Z   test_torch_rnn_relu_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6356445Z   test_torch_rnn_tanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6356588Z   test_torch_rnn_tanh_cell (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6356725Z   test_torch_roll (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6356849Z   test_torch_rot90 (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6356985Z   test_torch_round (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6357136Z   test_torch_row_indices_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6357279Z   test_torch_row_stack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6357414Z   test_torch_rrelu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6357549Z   test_torch_rsqrt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6357687Z   test_torch_rsub (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6357828Z   test_torch_saddmm (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6357955Z   test_torch_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6358100Z   test_torch_scatter_add (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6358249Z   test_torch_scatter_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6358401Z   test_torch_searchsorted (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6358554Z   test_torch_segment_reduce (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6358694Z   test_torch_select (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6358841Z   test_torch_select_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6358994Z   test_torch_select_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6359119Z   test_torch_selu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6359255Z   test_torch_sgn (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6359395Z   test_torch_sigmoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6359530Z   test_torch_sign (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6359672Z   test_torch_signbit (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6359808Z   test_torch_sin (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6359946Z   test_torch_sinc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6360079Z   test_torch_sinh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6360246Z   test_torch_slice_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6360391Z   test_torch_slice_scatter (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6360533Z   test_torch_slogdet (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6360667Z   test_torch_smm (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6360804Z   test_torch_softmax (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6360942Z   test_torch_sort (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6361087Z   test_torch_split_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6361236Z   test_torch_split_with_sizes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6361420Z   test_torch_split_with_sizes_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6361559Z   test_torch_sqrt (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6361700Z   test_torch_square (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6361836Z   test_torch_squeeze (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6361981Z   test_torch_squeeze_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6362119Z   test_torch_stack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6362254Z   test_torch_std (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6362395Z   test_torch_std_mean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6362516Z   test_torch_sub (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6362658Z   test_torch_subtract (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6362795Z   test_torch_sum (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6362929Z   test_torch_svd (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6363073Z   test_torch_swapaxes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6363214Z   test_torch_swapdims (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6363351Z   test_torch_symeig (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6363484Z   test_torch_t (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6363613Z   test_torch_t_copy (__main__.TestTorchFunctionOverride) ... ok (0.000s)
2023-01-11T21:58:51.6363749Z   test_torch_take (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6363900Z   test_torch_take_along_dim (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6364034Z   test_torch_tan (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6364173Z   test_torch_tanh (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6364320Z   test_torch_tensor_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6364470Z   test_torch_threshold (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6364607Z   test_torch_tile (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6364729Z   test_torch_topk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6364868Z   test_torch_trace (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6365010Z   test_torch_transpose (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6365159Z   test_torch_transpose_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6365302Z   test_torch_trapezoid (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6365438Z   test_torch_trapz (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6365594Z   test_torch_triangular_solve (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6365728Z   test_torch_tril (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6365906Z   test_torch_triplet_margin_loss (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6366043Z   test_torch_triu (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6366187Z   test_torch_true_divide (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6366323Z   test_torch_trunc (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6366462Z   test_torch_unbind (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6366607Z   test_torch_unbind_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6366749Z   test_torch_unflatten (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6366892Z   test_torch_unfold_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6367062Z   test_torch_unsafe_chunk (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6367211Z   test_torch_unsafe_split (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6367369Z   test_torch_unsafe_split_with_sizes (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6367511Z   test_torch_unsqueeze (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6367660Z   test_torch_unsqueeze_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6367804Z   test_torch_values_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6367941Z   test_torch_var (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6368081Z   test_torch_var_mean (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6368204Z   test_torch_vdot (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6368359Z   test_torch_view_as_complex (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6368513Z   test_torch_view_as_complex_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6368659Z   test_torch_view_as_real (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6368810Z   test_torch_view_as_real_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6368950Z   test_torch_view_copy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6369184Z   test_torch_vsplit (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6369324Z   test_torch_vstack (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6369449Z   test_torch_where (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6369586Z   test_torch_xlogy (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6369735Z   test_torch_zeros_like (__main__.TestTorchFunctionOverride) ... ok (0.001s)
2023-01-11T21:58:51.6369886Z   test_user_implementation_raises (__main__.TestTorchFunctionOverride)
2023-01-11T21:58:51.6370045Z Test that errors raised in user implementations propagate correctly ... ok (0.001s)
2023-01-11T21:58:51.6370208Z   test_warn_on_invalid_torch_function (__main__.TestTorchFunctionWarning) ... ok (0.009s)
2023-01-11T21:58:51.6370349Z   test_wrap_torch_function (__main__.TestWrapTorchFunction) ... ok (0.001s)
2023-01-11T21:58:51.6370357Z 
2023-01-11T21:58:51.6370653Z ----------------------------------------------------------------------
2023-01-11T21:58:51.6370719Z Ran 1416 tests in 1.160s
2023-01-11T21:58:51.6370723Z 
2023-01-11T21:58:51.6370785Z OK
2023-01-11T21:58:51.6370790Z 
2023-01-11T21:58:51.6370877Z Generating XML reports...
2023-01-11T21:58:51.6371219Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestBroadcastAllOverride-20230111215849.xml
2023-01-11T21:58:51.6371540Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestDisabledTorchFunction-20230111215849.xml
2023-01-11T21:58:51.6371831Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestEinsumOverride-20230111215849.xml
2023-01-11T21:58:51.6372192Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestGradCheckOverride-20230111215849.xml
2023-01-11T21:58:51.6372502Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestGradNewOnesOverride-20230111215849.xml
2023-01-11T21:58:51.6372775Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestIndexing-20230111215849.xml
2023-01-11T21:58:51.6373033Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestIterator-20230111215849.xml
2023-01-11T21:58:51.6373311Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestNamedTuple-20230111215849.xml
2023-01-11T21:58:51.6373576Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestPickle-20230111215849.xml
2023-01-11T21:58:51.6373865Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestRNN-20230111215849.xml
2023-01-11T21:58:51.6374142Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestResolveName-20230111215849.xml
2023-01-11T21:58:51.6374443Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionMode-20230111215849.xml
2023-01-11T21:58:51.6374751Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionOverride-20230111215849.xml
2023-01-11T21:58:51.6375056Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionWarning-20230111215849.xml
2023-01-11T21:58:51.6375354Z Generated XML report: test-reports/python-unittest/test_overrides/TEST-TestWrapTorchFunction-20230111215849.xml
2023-01-11T21:58:51.6375360Z 
2023-01-11T21:58:51.6375774Z ##[endgroup]
2023-01-11T21:58:51.6376054Z FINISHED PRINTING LOG FILE of test_overrides (/var/lib/jenkins/workspace/test/test-reports/test_overrides_srimhfgs)
2023-01-11T21:58:51.6376059Z 
2023-01-11T21:58:51.6376224Z Running test_sparse_csr ... [2023-01-11 21:58:51.595423]
2023-01-11T21:58:51.6376559Z Executing ['/opt/conda/bin/python', '-bb', 'test_sparse_csr.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:51.595729]
2023-01-11T21:58:54.4917922Z 
2023-01-11T21:58:54.4918509Z Expand the folded group to see the log file of test_sparse_csr
2023-01-11T21:58:54.4920765Z ##[group]PRINTING LOG FILE of test_sparse_csr (/var/lib/jenkins/workspace/test/test-reports/test_sparse_csr_a9xhvaav)
2023-01-11T21:58:54.4921189Z 
2023-01-11T21:58:54.4921328Z Running tests...
2023-01-11T21:58:54.4921950Z ----------------------------------------------------------------------
2023-01-11T21:58:54.4922372Z Test results will be stored in test-reports/python-unittest/test_sparse_csr
2023-01-11T21:58:54.4922727Z   test_make_crow_indices (__main__.TestSparseCSRSampler) ... ok (0.385s)
2023-01-11T21:58:54.4922898Z 
2023-01-11T21:58:54.4923111Z ----------------------------------------------------------------------
2023-01-11T21:58:54.4923352Z Ran 1 test in 0.385s
2023-01-11T21:58:54.4923463Z 
2023-01-11T21:58:54.4923526Z OK
2023-01-11T21:58:54.4923610Z 
2023-01-11T21:58:54.4923693Z Generating XML reports...
2023-01-11T21:58:54.4924127Z Generated XML report: test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCSRSampler-20230111215853.xml
2023-01-11T21:58:54.4924369Z 
2023-01-11T21:58:54.4924593Z ##[endgroup]
2023-01-11T21:58:54.4924959Z FINISHED PRINTING LOG FILE of test_sparse_csr (/var/lib/jenkins/workspace/test/test-reports/test_sparse_csr_a9xhvaav)
2023-01-11T21:58:54.4925168Z 
2023-01-11T21:58:54.4925321Z Running test_torch ... [2023-01-11 21:58:54.491946]
2023-01-11T21:58:54.4925959Z Executing ['/opt/conda/bin/python', '-bb', 'test_torch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:54.492189]
2023-01-11T21:58:58.2341821Z 
2023-01-11T21:58:58.2348353Z Expand the folded group to see the log file of test_torch
2023-01-11T21:58:58.2358936Z ##[group]PRINTING LOG FILE of test_torch (/var/lib/jenkins/workspace/test/test-reports/test_torch_h_p19gpb)
2023-01-11T21:58:58.2359283Z 
2023-01-11T21:58:58.2359537Z Running tests...
2023-01-11T21:58:58.2360179Z ----------------------------------------------------------------------
2023-01-11T21:58:58.2360556Z Test results will be stored in test-reports/python-unittest/test_torch
2023-01-11T21:58:58.2360858Z   test_basic_vitals (__main__.TestBasicVitalSigns) ... ok (0.001s)
2023-01-11T21:58:58.2361143Z   test_basic_vitals_read_write (__main__.TestBasicVitalSigns) ... ok (0.001s)
2023-01-11T21:58:58.2361447Z   test_dataloader_vitals (__main__.TestBasicVitalSigns) ... ok (0.001s)
2023-01-11T21:58:58.2361716Z   test_RNGState (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2361971Z   test_RNGStateAliasing (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2362219Z   test_RNG_after_pickle (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2362456Z   test_Size (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2362750Z   test_Size_iter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2362980Z   test_Size_scalar (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2363229Z   test_add_meta_scalar (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2363493Z   test_allow_tensor_metadata_change (__main__.TestTorch) ... ok (0.000s)
2023-01-11T21:58:58.2363738Z   test_apply (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2363980Z   test_as_subclass (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:58.2364225Z   test_assert_async (__main__.TestTorch) ... ok (0.018s)
2023-01-11T21:58:58.2364489Z   test_backward_hooks_traverse (__main__.TestTorch) ... ok (0.054s)
2023-01-11T21:58:58.2364751Z   test_batch_norm_cpu_inference (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:58.2365507Z   test_bmm_multithreaded (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2366194Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2366855Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2367479Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2368135Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2368754Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2369950Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 23, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2371010Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2372103Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2373223Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2374598Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2375707Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2376844Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2377937Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2379108Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2380170Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2381285Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2382309Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2383565Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2384682Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2385867Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 23, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2387140Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2388361Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2389509Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2390825Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2391968Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2393193Z /var/lib/jenkins/workspace/test/test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:58:58.2394275Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:58:58.2394575Z ok (0.764s)
2023-01-11T21:58:58.2394952Z   test_boxMullerState (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2395482Z   test_c10_layer_norm (__main__.TestTorch) ... skip: Pytorch is compiled without Caffe2 (0.001s)
2023-01-11T21:58:58.2395967Z   test_cat_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2396393Z   test_chunk_neg_dim (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:58:58.2396831Z   test_conj_neg_tolist (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2397258Z   test_contains (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2397697Z   test_copy_broadcast (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:58:58.2398142Z   test_copy_dtypes (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2398556Z   test_copy_float16 (__main__.TestTorch) ... ok (0.088s)
2023-01-11T21:58:58.2398982Z   test_copy_many_to_one (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:58:58.2400278Z   test_copy_transpose (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:7754: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:58:58.2401165Z   x = torch.arange(100 * 100).reshape(100, 100).to(dtype=torch.complex32).t()
2023-01-11T21:58:58.2401547Z ok (0.003s)
2023-01-11T21:58:58.2402167Z   test_cuda_not_built (__main__.TestTorch) ... skip: CUDA is built, can't test CUDA not built error (0.001s)
2023-01-11T21:58:58.2402654Z   test_cummax_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2403092Z   test_cummin_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2403518Z   test_cumprod_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2403956Z   test_cumsum_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2404381Z   test_cxx_flags (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2405822Z   test_dead_weak_ref (__main__.TestTorch) ... [W python_variable.cpp:319] Warning: Deallocating Tensor that still has live PyObject references.  This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it.  Subsequent accesses to this tensor via the PyObject will now fail. (function decref)
2023-01-11T21:58:58.2406834Z ok (0.004s)
2023-01-11T21:58:58.2407228Z   test_deepcopy_gradient (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2407681Z   test_deepcopy_parameter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2408121Z   test_deterministic_flag (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:58.2408545Z   test_device (__main__.TestTorch) ... ok (0.018s)
2023-01-11T21:58:58.2408953Z   test_dir (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2409523Z   test_doc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2409901Z   test_doc_template (__main__.TestTorch)
2023-01-11T21:58:58.2410525Z Test that all public API doc strings use the same standard template for ... ok (0.019s)
2023-01-11T21:58:58.2411018Z   test_dot_data_use (__main__.TestTorch) ... ok (0.012s)
2023-01-11T21:58:58.2411473Z   test_dtype_is_signed (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:58.2412477Z   test_element_size (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:5939: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2413473Z   byte = torch.ByteStorage().element_size()
2023-01-11T21:58:58.2414490Z /var/lib/jenkins/workspace/test/test_torch.py:5940: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2415369Z   char = torch.CharStorage().element_size()
2023-01-11T21:58:58.2416376Z /var/lib/jenkins/workspace/test/test_torch.py:5941: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2417299Z   short = torch.ShortStorage().element_size()
2023-01-11T21:58:58.2418275Z /var/lib/jenkins/workspace/test/test_torch.py:5942: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2419173Z   int = torch.IntStorage().element_size()
2023-01-11T21:58:58.2420097Z /var/lib/jenkins/workspace/test/test_torch.py:5943: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2421012Z   long = torch.LongStorage().element_size()
2023-01-11T21:58:58.2421929Z /var/lib/jenkins/workspace/test/test_torch.py:5944: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2422829Z   float = torch.FloatStorage().element_size()
2023-01-11T21:58:58.2423818Z /var/lib/jenkins/workspace/test/test_torch.py:5945: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2424878Z   double = torch.DoubleStorage().element_size()
2023-01-11T21:58:58.2425831Z /var/lib/jenkins/workspace/test/test_torch.py:5946: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2426738Z   bool = torch.BoolStorage().element_size()
2023-01-11T21:58:58.2427787Z /var/lib/jenkins/workspace/test/test_torch.py:5947: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2428732Z   bfloat16 = torch.BFloat16Storage().element_size()
2023-01-11T21:58:58.2429721Z /var/lib/jenkins/workspace/test/test_torch.py:5948: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2430690Z   complexfloat = torch.ComplexFloatStorage().element_size()
2023-01-11T21:58:58.2431718Z /var/lib/jenkins/workspace/test/test_torch.py:5949: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2432746Z   complexdouble = torch.ComplexDoubleStorage().element_size()
2023-01-11T21:58:58.2433127Z ok (0.002s)
2023-01-11T21:58:58.2433471Z   test_empty_meta (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:58:58.2433892Z   test_empty_storage_view (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2434276Z   test_equal (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2434725Z   test_error_msg_type_translation (__main__.TestTorch) ... ok (0.011s)
2023-01-11T21:58:58.2435203Z   test_fill_diagonal (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:58.2435643Z   test_fix_weakref_no_leak (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2436076Z   test_format_scalar_meta (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2437145Z   test_from_buffer (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6322: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2438230Z   self.assertEqual(torch.ByteStorage.from_buffer(a).tolist(), [1, 2, 3, 4])
2023-01-11T21:58:58.2439757Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:675: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2440666Z   return list(self)
2023-01-11T21:58:58.2442027Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:649: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2443108Z   return iter(map(lambda i: self[i], range(self.size())))
2023-01-11T21:58:58.2444106Z /var/lib/jenkins/workspace/test/test_torch.py:6323: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2445302Z   shorts = torch.ShortStorage.from_buffer(a, 'big')
2023-01-11T21:58:58.2446283Z /var/lib/jenkins/workspace/test/test_torch.py:6324: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2447197Z   self.assertEqual(shorts.size(), 2)
2023-01-11T21:58:58.2448259Z /var/lib/jenkins/workspace/test/test_torch.py:6325: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2449365Z   self.assertEqual(shorts.tolist(), [258, 772])
2023-01-11T21:58:58.2450331Z /var/lib/jenkins/workspace/test/test_torch.py:6326: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2451373Z   ints = torch.IntStorage.from_buffer(a, 'little')
2023-01-11T21:58:58.2452357Z /var/lib/jenkins/workspace/test/test_torch.py:6327: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2453266Z   self.assertEqual(ints.size(), 1)
2023-01-11T21:58:58.2454216Z /var/lib/jenkins/workspace/test/test_torch.py:6328: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2455119Z   self.assertEqual(ints[0], 67305985)
2023-01-11T21:58:58.2456080Z /var/lib/jenkins/workspace/test/test_torch.py:6330: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2457150Z   floats = torch.FloatStorage.from_buffer(f, 'big')
2023-01-11T21:58:58.2458166Z /var/lib/jenkins/workspace/test/test_torch.py:6331: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2459011Z   self.assertEqual(floats.size(), 1)
2023-01-11T21:58:58.2459991Z /var/lib/jenkins/workspace/test/test_torch.py:6332: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2460873Z   self.assertEqual(floats[0], 2.25)
2023-01-11T21:58:58.2461834Z /var/lib/jenkins/workspace/test/test_torch.py:6335: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2463127Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:58:58.2464105Z /var/lib/jenkins/workspace/test/test_torch.py:6336: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2465032Z   self.assertEqual(bools.size(), 8)
2023-01-11T21:58:58.2466127Z /var/lib/jenkins/workspace/test/test_torch.py:6337: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2467112Z   self.assertEqual(bools.tolist(), [False, True, True, True, True, True, True, True])
2023-01-11T21:58:58.2468190Z /var/lib/jenkins/workspace/test/test_torch.py:6338: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2469299Z   self.assertEqual(bools.type(), 'torch.BoolStorage')
2023-01-11T21:58:58.2470779Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2471835Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:58:58.2473284Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2474397Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:58:58.2475825Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:978: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2476866Z   return (cls_device == instance.device.type) and (cls.dtype == instance.dtype)
2023-01-11T21:58:58.2478340Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2479297Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:58:58.2480308Z /var/lib/jenkins/workspace/test/test_torch.py:6342: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2481355Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:58:58.2482343Z /var/lib/jenkins/workspace/test/test_torch.py:6343: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2483355Z   self.assertEqual(bools.size(), 19)
2023-01-11T21:58:58.2484317Z /var/lib/jenkins/workspace/test/test_torch.py:6346: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2485369Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:58:58.2486407Z /var/lib/jenkins/workspace/test/test_torch.py:6347: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2487236Z   self.assertEqual(bools.size(), 4)
2023-01-11T21:58:58.2488151Z /var/lib/jenkins/workspace/test/test_torch.py:6348: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2489266Z   self.assertEqual(bools.tolist(), [False, True, True, True])
2023-01-11T21:58:58.2490196Z /var/lib/jenkins/workspace/test/test_torch.py:6349: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2491094Z   bytes = torch.ByteStorage.from_buffer(a)
2023-01-11T21:58:58.2492040Z /var/lib/jenkins/workspace/test/test_torch.py:6350: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2492968Z   self.assertEqual(bytes.nbytes(), 4)
2023-01-11T21:58:58.2493856Z /var/lib/jenkins/workspace/test/test_torch.py:6351: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2494802Z   self.assertEqual(bytes.tolist(), [1, 2, 3, 4])
2023-01-11T21:58:58.2495149Z ok (0.004s)
2023-01-11T21:58:58.2496071Z   test_from_file (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6663: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2497101Z   s1 = torch.FloatStorage.from_file(filename, True, size)
2023-01-11T21:58:58.2498583Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:899: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2499521Z   storage = cls(wrap_storage=untyped_storage)
2023-01-11T21:58:58.2500478Z /var/lib/jenkins/workspace/test/test_torch.py:6665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2501568Z   self.assertEqual(s1.data_ptr(), torch.FloatTensor(s1).data_ptr())
2023-01-11T21:58:58.2502665Z /var/lib/jenkins/workspace/test/test_torch.py:6668: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2503616Z   s2 = torch.FloatStorage.from_file(filename, True, size)
2023-01-11T21:58:58.2503987Z ok (0.005s)
2023-01-11T21:58:58.2504338Z   test_gather_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2504889Z   test_generator_cpu (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2505339Z   test_has_internal_overlap (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2506407Z   test_has_storage (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:7615: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2507394Z   self.assertIsNotNone(torch.tensor([]).storage())
2023-01-11T21:58:58.2508394Z /var/lib/jenkins/workspace/test/test_torch.py:7616: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2509340Z   self.assertIsNotNone(torch.empty(0).storage())
2023-01-11T21:58:58.2510349Z /var/lib/jenkins/workspace/test/test_torch.py:7617: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2511300Z   self.assertIsNotNone(torch.tensor([]).clone().storage())
2023-01-11T21:58:58.2512301Z /var/lib/jenkins/workspace/test/test_torch.py:7618: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2513278Z   self.assertIsNotNone(torch.tensor([0, 0, 0]).nonzero().storage())
2023-01-11T21:58:58.2514264Z /var/lib/jenkins/workspace/test/test_torch.py:7619: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2515254Z   self.assertIsNotNone(torch.tensor([]).new().storage())
2023-01-11T21:58:58.2515617Z ok (0.001s)
2023-01-11T21:58:58.2515974Z   test_index_add (__main__.TestTorch) ... ok (0.017s)
2023-01-11T21:58:58.2516427Z   test_index_add_all_dtypes (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:58.2517985Z   test_index_add_correctness (__main__.TestTorch) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91184 for platform(s) linux, rocm, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.002s)
2023-01-11T21:58:58.2518998Z   test_index_add_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2519440Z   test_index_copy_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2519976Z   test_index_fill_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2520432Z   test_index_select_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2520889Z   test_invalid_generator_raises (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2521350Z   test_is_nonzero (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:58.2522243Z   test_is_same_size (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:5821: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:58:58.2523185Z   nt1 = torch.nested.nested_tensor([torch.ones(2, 4), torch.ones(3, 4), torch.ones(5, 4)])
2023-01-11T21:58:58.2523630Z ok (0.006s)
2023-01-11T21:58:58.2524151Z   test_iter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2524578Z   test_kthvalue_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2525021Z   test_logcumsumexp_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2525465Z   test_manual_seed (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:58.2525858Z   test_map (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2526256Z   test_map2 (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2526671Z   test_max_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2527103Z   test_mean_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2527535Z   test_median_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2527983Z   test_memory_format (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2528582Z   test_memory_format_contiguous_returns_same_tensor_if_already_satisfies (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2529294Z   test_memory_format_empty (__main__.TestTorch) ... ok (0.011s)
2023-01-11T21:58:58.2529705Z   test_min_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2530128Z   test_mode_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2530716Z   test_multinomial_invalid_probs (__main__.TestTorch) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:58:58.2531259Z   test_nanmedian_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2531697Z   test_narrow_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2532111Z   test_ndim (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2533139Z   test_new (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:7016: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2534153Z   self.assertEqual(x.new(y.storage()).data_ptr(), y.data_ptr())
2023-01-11T21:58:58.2535176Z /var/lib/jenkins/workspace/test/test_torch.py:7022: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2536177Z   self.assertRaises(RuntimeError, lambda: x.new(z.storage()))
2023-01-11T21:58:58.2536558Z ok (0.005s)
2023-01-11T21:58:58.2536937Z   test_newaxis_numpy_comparison (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:58.2537385Z   test_newindex (__main__.TestTorch) ... ok (0.010s)
2023-01-11T21:58:58.2538080Z   test_no_cuda_monkeypatch (__main__.TestTorch) ... skip: Skipped for cuda-enabled build (0.000s)
2023-01-11T21:58:58.2538564Z   test_norm_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2538988Z   test_normal_shape (__main__.TestTorch) ... ok (0.026s)
2023-01-11T21:58:58.2539408Z   test_numel (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2539950Z   test_parallel_info (__main__.TestTorch) ... ok (0.000s)
2023-01-11T21:58:58.2540361Z   test_parsing_double (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2540816Z   test_parsing_int64 (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2541776Z   test_parsing_intlist (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6302: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
2023-01-11T21:58:58.2543156Z Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
2023-01-11T21:58:58.2543909Z   self.assertRaises(TypeError, lambda: torch.ones((np.float(3.), torch.tensor(4))))
2023-01-11T21:58:58.2544339Z ok (0.014s)
2023-01-11T21:58:58.2544682Z   test_permute (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2545084Z   test_pickle (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2545526Z   test_pickle_dtype (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2545978Z   test_pickle_function (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2546439Z   test_pickle_parameter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2546925Z   test_pickle_parameter_no_requires_grad (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2547404Z   test_pickle_size (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2547829Z   test_pin_memory (__main__.TestTorch) ... ok (0.012s)
2023-01-11T21:58:58.2548221Z   test_print (__main__.TestTorch) ... ok (0.040s)
2023-01-11T21:58:58.2548637Z   test_prod_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2549065Z   test_pyobj_preserved (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2549462Z   test_qengine (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2549879Z   test_renorm_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2550337Z   test_resurrected_weak_ref (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2550777Z   test_reversed (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2551195Z   test_scatter_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2551612Z   test_select_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2552102Z   test_set_flush_denormal (__main__.TestTorch) ... skip: flush_denormal not supported (0.001s)
2023-01-11T21:58:58.2552633Z   test_setting_real_imag_to_a_number (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2553086Z   test_show_config (__main__.TestTorch) ... ok (0.000s)
2023-01-11T21:58:58.2553499Z   test_size_neg_dim (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2554544Z   test_sizeof (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6980: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2555510Z   sizeof_empty = torch.randn(0).storage().__sizeof__()
2023-01-11T21:58:58.2556971Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2557971Z   return super(TypedStorage, self).__sizeof__() + self.nbytes()
2023-01-11T21:58:58.2558991Z /var/lib/jenkins/workspace/test/test_torch.py:6981: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2560053Z   sizeof_10 = torch.randn(10).storage().__sizeof__()
2023-01-11T21:58:58.2560742Z /var/lib/jenkins/workspace/test/test_torch.py:6982: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2561262Z   sizeof_100 = torch.randn(100).storage().__sizeof__()
2023-01-11T21:58:58.2561860Z /var/lib/jenkins/workspace/test/test_torch.py:6986: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2562404Z   sizeof_empty = torch.randn(0).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:58:58.2562962Z /var/lib/jenkins/workspace/test/test_torch.py:6987: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2563481Z   sizeof_10 = torch.randn(10).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:58:58.2564030Z /var/lib/jenkins/workspace/test/test_torch.py:6988: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2564564Z   sizeof_100 = torch.randn(100).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:58:58.2564780Z ok (0.001s)
2023-01-11T21:58:58.2564971Z   test_slice (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2565279Z   test_slow_test (__main__.TestTorch) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.000s)
2023-01-11T21:58:58.2565592Z   test_sobolengine_bounds (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:58.2565856Z   test_sobolengine_bounds_scrambled (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:58.2566135Z   test_sobolengine_continuing (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2566420Z   test_sobolengine_continuing_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2566710Z   test_sobolengine_distribution (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:58.2566994Z   test_sobolengine_distribution_scrambled (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:58:58.2567278Z   test_sobolengine_draw (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2567541Z   test_sobolengine_draw_base2 (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2567809Z   test_sobolengine_draw_base2_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2568096Z   test_sobolengine_draw_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2568376Z   test_sobolengine_fast_forward (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2568664Z   test_sobolengine_fast_forward_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2568960Z   test_sobolengine_first_point (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2569410Z   test_sobolengine_high_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:58.2569676Z   test_sobolengine_raise (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2569923Z   test_sobolengine_reset (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2570269Z   test_sobolengine_reset_scrambled (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2570660Z   test_sort_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2570899Z   test_split_neg_dim (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:58.2571232Z   test_squeeze_neg_dim (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:58.2571479Z   test_std_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2572065Z   test_storage_casts (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6470: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2572682Z   storage = torch.IntStorage([-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2573267Z /var/lib/jenkins/workspace/test/test_torch.py:6471: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2573802Z   self.assertEqual(storage.size(), 6)
2023-01-11T21:58:58.2574453Z /var/lib/jenkins/workspace/test/test_torch.py:6472: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2575027Z   self.assertEqual(storage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2575800Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:675: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2576296Z   return list(self)
2023-01-11T21:58:58.2577024Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:649: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2577562Z   return iter(map(lambda i: self[i], range(self.size())))
2023-01-11T21:58:58.2578104Z /var/lib/jenkins/workspace/test/test_torch.py:6473: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2578662Z   self.assertEqual(storage.type(), 'torch.IntStorage')
2023-01-11T21:58:58.2579437Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2580007Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:58:58.2580756Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2581346Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:58:58.2582161Z /var/lib/jenkins/workspace/test/test_torch.py:6476: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2583144Z   floatStorage = storage.float()
2023-01-11T21:58:58.2583966Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:808: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2584563Z   storage = torch.tensor([], dtype=self.dtype, device=self.device).set_(self).to(dtype)._typed_storage()
2023-01-11T21:58:58.2585466Z /opt/conda/lib/python3.10/site-packages/torch/storage.py:809: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2585976Z   if storage.data_ptr() == self.data_ptr():
2023-01-11T21:58:58.2586504Z /var/lib/jenkins/workspace/test/test_torch.py:6477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2587020Z   self.assertEqual(floatStorage.size(), 6)
2023-01-11T21:58:58.2587558Z /var/lib/jenkins/workspace/test/test_torch.py:6478: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2588146Z   self.assertEqual(floatStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2588678Z /var/lib/jenkins/workspace/test/test_torch.py:6479: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2589271Z   self.assertEqual(floatStorage.type(), 'torch.FloatStorage')
2023-01-11T21:58:58.2589830Z /var/lib/jenkins/workspace/test/test_torch.py:6480: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2590412Z   self.assertEqual(floatStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2590963Z /var/lib/jenkins/workspace/test/test_torch.py:6483: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2591444Z   halfStorage = storage.half()
2023-01-11T21:58:58.2591956Z /var/lib/jenkins/workspace/test/test_torch.py:6484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2592459Z   self.assertEqual(halfStorage.size(), 6)
2023-01-11T21:58:58.2592997Z /var/lib/jenkins/workspace/test/test_torch.py:6485: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2593606Z   self.assertEqual(halfStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2594139Z /var/lib/jenkins/workspace/test/test_torch.py:6486: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2594725Z   self.assertEqual(halfStorage.type(), 'torch.HalfStorage')
2023-01-11T21:58:58.2595307Z /var/lib/jenkins/workspace/test/test_torch.py:6487: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2595889Z   self.assertEqual(halfStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2596615Z /var/lib/jenkins/workspace/test/test_torch.py:6490: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2597508Z   bfloat16Storage = storage.bfloat16()
2023-01-11T21:58:58.2598481Z /var/lib/jenkins/workspace/test/test_torch.py:6491: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2599419Z   self.assertEqual(bfloat16Storage.size(), 6)
2023-01-11T21:58:58.2600417Z /var/lib/jenkins/workspace/test/test_torch.py:6492: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2601507Z   self.assertEqual(bfloat16Storage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2602506Z /var/lib/jenkins/workspace/test/test_torch.py:6493: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2603620Z   self.assertEqual(bfloat16Storage.type(), 'torch.BFloat16Storage')
2023-01-11T21:58:58.2604681Z /var/lib/jenkins/workspace/test/test_torch.py:6494: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2605781Z   self.assertEqual(bfloat16Storage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2606810Z /var/lib/jenkins/workspace/test/test_torch.py:6497: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2607695Z   longStorage = storage.long()
2023-01-11T21:58:58.2608651Z /var/lib/jenkins/workspace/test/test_torch.py:6498: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2610025Z   self.assertEqual(longStorage.size(), 6)
2023-01-11T21:58:58.2610996Z /var/lib/jenkins/workspace/test/test_torch.py:6499: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2612079Z   self.assertEqual(longStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2613180Z /var/lib/jenkins/workspace/test/test_torch.py:6500: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2614271Z   self.assertEqual(longStorage.type(), 'torch.LongStorage')
2023-01-11T21:58:58.2615260Z /var/lib/jenkins/workspace/test/test_torch.py:6501: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2616354Z   self.assertEqual(longStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2617379Z /var/lib/jenkins/workspace/test/test_torch.py:6504: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2618286Z   shortStorage = storage.short()
2023-01-11T21:58:58.2619213Z /var/lib/jenkins/workspace/test/test_torch.py:6505: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2620180Z   self.assertEqual(shortStorage.size(), 6)
2023-01-11T21:58:58.2621187Z /var/lib/jenkins/workspace/test/test_torch.py:6506: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2622234Z   self.assertEqual(shortStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2623311Z /var/lib/jenkins/workspace/test/test_torch.py:6507: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2624404Z   self.assertEqual(shortStorage.type(), 'torch.ShortStorage')
2023-01-11T21:58:58.2625412Z /var/lib/jenkins/workspace/test/test_torch.py:6508: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2626518Z   self.assertEqual(shortStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2627513Z /var/lib/jenkins/workspace/test/test_torch.py:6511: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2628529Z   doubleStorage = storage.double()
2023-01-11T21:58:58.2629510Z /var/lib/jenkins/workspace/test/test_torch.py:6512: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2630475Z   self.assertEqual(doubleStorage.size(), 6)
2023-01-11T21:58:58.2631472Z /var/lib/jenkins/workspace/test/test_torch.py:6513: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2632650Z   self.assertEqual(doubleStorage.tolist(), [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
2023-01-11T21:58:58.2633608Z /var/lib/jenkins/workspace/test/test_torch.py:6514: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2634657Z   self.assertEqual(doubleStorage.type(), 'torch.DoubleStorage')
2023-01-11T21:58:58.2635716Z /var/lib/jenkins/workspace/test/test_torch.py:6515: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2636822Z   self.assertEqual(doubleStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2637831Z /var/lib/jenkins/workspace/test/test_torch.py:6518: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2638661Z   charStorage = storage.char()
2023-01-11T21:58:58.2639603Z /var/lib/jenkins/workspace/test/test_torch.py:6519: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2640548Z   self.assertEqual(charStorage.size(), 6)
2023-01-11T21:58:58.2641524Z /var/lib/jenkins/workspace/test/test_torch.py:6520: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2642635Z   self.assertEqual(charStorage.tolist(), [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
2023-01-11T21:58:58.2643596Z /var/lib/jenkins/workspace/test/test_torch.py:6521: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2644692Z   self.assertEqual(charStorage.type(), 'torch.CharStorage')
2023-01-11T21:58:58.2645737Z /var/lib/jenkins/workspace/test/test_torch.py:6522: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2646824Z   self.assertEqual(charStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2647934Z /var/lib/jenkins/workspace/test/test_torch.py:6525: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2648823Z   byteStorage = storage.byte()
2023-01-11T21:58:58.2649907Z /var/lib/jenkins/workspace/test/test_torch.py:6526: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2650966Z   self.assertEqual(byteStorage.size(), 6)
2023-01-11T21:58:58.2651942Z /var/lib/jenkins/workspace/test/test_torch.py:6527: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2652901Z   self.assertEqual(byteStorage.tolist(), [255, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2653890Z /var/lib/jenkins/workspace/test/test_torch.py:6528: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2654981Z   self.assertEqual(byteStorage.type(), 'torch.ByteStorage')
2023-01-11T21:58:58.2655924Z /var/lib/jenkins/workspace/test/test_torch.py:6529: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2656909Z   self.assertEqual(byteStorage.int().tolist(), [255, 0, 1, 2, 3, 4])
2023-01-11T21:58:58.2657923Z /var/lib/jenkins/workspace/test/test_torch.py:6532: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2658822Z   boolStorage = storage.bool()
2023-01-11T21:58:58.2659753Z /var/lib/jenkins/workspace/test/test_torch.py:6533: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2660690Z   self.assertEqual(boolStorage.size(), 6)
2023-01-11T21:58:58.2661679Z /var/lib/jenkins/workspace/test/test_torch.py:6534: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2662700Z   self.assertEqual(boolStorage.tolist(), [True, False, True, True, True, True])
2023-01-11T21:58:58.2663781Z /var/lib/jenkins/workspace/test/test_torch.py:6535: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2664905Z   self.assertEqual(boolStorage.type(), 'torch.BoolStorage')
2023-01-11T21:58:58.2666073Z /var/lib/jenkins/workspace/test/test_torch.py:6536: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2667056Z   self.assertEqual(boolStorage.int().tolist(), [1, 0, 1, 1, 1, 1])
2023-01-11T21:58:58.2668025Z /var/lib/jenkins/workspace/test/test_torch.py:6539: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2669267Z   complexfloat_storage = torch.ComplexFloatStorage([-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:58:58.2670357Z /var/lib/jenkins/workspace/test/test_torch.py:6540: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2671354Z   self.assertEqual(complexfloat_storage.size(), 6)
2023-01-11T21:58:58.2672344Z /var/lib/jenkins/workspace/test/test_torch.py:6541: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2673444Z   self.assertEqual(complexfloat_storage.tolist(), [-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:58:58.2674422Z /var/lib/jenkins/workspace/test/test_torch.py:6542: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2675532Z   self.assertEqual(complexfloat_storage.type(), 'torch.ComplexFloatStorage')
2023-01-11T21:58:58.2676637Z /var/lib/jenkins/workspace/test/test_torch.py:6545: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2677644Z   complexdouble_storage = complexfloat_storage.complex_double()
2023-01-11T21:58:58.2678652Z /var/lib/jenkins/workspace/test/test_torch.py:6546: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2679636Z   self.assertEqual(complexdouble_storage.size(), 6)
2023-01-11T21:58:58.2680682Z /var/lib/jenkins/workspace/test/test_torch.py:6547: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2681853Z   self.assertEqual(complexdouble_storage.tolist(), [-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:58:58.2682925Z /var/lib/jenkins/workspace/test/test_torch.py:6548: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2684193Z   self.assertEqual(complexdouble_storage.type(), 'torch.ComplexDoubleStorage')
2023-01-11T21:58:58.2684642Z ok (0.011s)
2023-01-11T21:58:58.2685614Z   test_storage_error (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6364: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2686605Z   torch.storage._LegacyStorage()
2023-01-11T21:58:58.2688094Z /opt/conda/lib/python3.10/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2689161Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:58:58.2690157Z /var/lib/jenkins/workspace/test/test_torch.py:6378: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2691134Z   storage_class(device='cpu')
2023-01-11T21:58:58.2692097Z /var/lib/jenkins/workspace/test/test_torch.py:6381: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2693013Z   storage_class(dtype=torch.float)
2023-01-11T21:58:58.2693980Z /var/lib/jenkins/workspace/test/test_torch.py:6387: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2694875Z   storage_class(0, 0)
2023-01-11T21:58:58.2695829Z /var/lib/jenkins/workspace/test/test_torch.py:6390: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2696790Z   storage_class('string')
2023-01-11T21:58:58.2697754Z /var/lib/jenkins/workspace/test/test_torch.py:6393: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2698659Z   storage_class(torch.tensor([]))
2023-01-11T21:58:58.2699618Z /var/lib/jenkins/workspace/test/test_torch.py:6395: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2700504Z   s = storage_class()
2023-01-11T21:58:58.2701410Z /var/lib/jenkins/workspace/test/test_torch.py:6398: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2702358Z   storage_class(0, wrap_storage=s.untyped())
2023-01-11T21:58:58.2703416Z /var/lib/jenkins/workspace/test/test_torch.py:6401: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2704447Z   storage_class(wrap_storage=s)
2023-01-11T21:58:58.2705410Z /var/lib/jenkins/workspace/test/test_torch.py:6420: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2706394Z   torch.TypedStorage(0, wrap_storage=s.untyped(), dtype=dtype)
2023-01-11T21:58:58.2707511Z /var/lib/jenkins/workspace/test/test_torch.py:6423: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2708484Z   torch.TypedStorage(wrap_storage=s.untyped())
2023-01-11T21:58:58.2709475Z /var/lib/jenkins/workspace/test/test_torch.py:6426: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2710446Z   torch.TypedStorage(wrap_storage=s.untyped(), dtype=0)
2023-01-11T21:58:58.2711472Z /var/lib/jenkins/workspace/test/test_torch.py:6429: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2712484Z   torch.TypedStorage(wrap_storage=s.untyped(), dtype=dtype, device=device)
2023-01-11T21:58:58.2713541Z /var/lib/jenkins/workspace/test/test_torch.py:6432: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2714495Z   torch.TypedStorage(wrap_storage=s, dtype=dtype)
2023-01-11T21:58:58.2715496Z /var/lib/jenkins/workspace/test/test_torch.py:6435: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2716567Z   torch.TypedStorage(dtype=dtype, device='xla')
2023-01-11T21:58:58.2717561Z /var/lib/jenkins/workspace/test/test_torch.py:6443: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2718554Z   torch.TypedStorage(torch.tensor([]), dtype=dtype, device=device)
2023-01-11T21:58:58.2719579Z /var/lib/jenkins/workspace/test/test_torch.py:6446: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2720531Z   torch.TypedStorage(0, 0, dtype=dtype, device=device)
2023-01-11T21:58:58.2721547Z /var/lib/jenkins/workspace/test/test_torch.py:6449: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2722621Z   s_other = torch.TypedStorage([1, 2, 3, 4], device=device, dtype=dtype)
2023-01-11T21:58:58.2723642Z /var/lib/jenkins/workspace/test/test_torch.py:6452: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2724510Z   s.fill_(s_other)
2023-01-11T21:58:58.2724803Z ok (0.041s)
2023-01-11T21:58:58.2725884Z   test_storage_error_no_attribute (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6461: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2726885Z   storage_class.from_buffer()
2023-01-11T21:58:58.2727214Z ok (0.001s)
2023-01-11T21:58:58.2727583Z   test_structseq_repr (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2728038Z   test_subclass_preserved (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2728518Z   test_subclass_tensors (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2728963Z   test_sum_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2729495Z   test_t_not_2d_error (__main__.TestTorch) ... ok (0.009s)
2023-01-11T21:58:58.2729939Z   test_tensor_base_init (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2730404Z   test_tensor_base_new (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2730857Z   test_tensor_ctor_scalar (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2731298Z   test_tensor_cycle_via_dict (__main__.TestTorch) ... ok (0.105s)
2023-01-11T21:58:58.2731762Z   test_tensor_cycle_via_slots (__main__.TestTorch) ... ok (0.043s)
2023-01-11T21:58:58.2732239Z   test_tensor_dict_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2732708Z   test_tensor_finalizer_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2733819Z   test_tensor_set (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:5839: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2734856Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:58:58.2735898Z /var/lib/jenkins/workspace/test/test_torch.py:5841: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2736805Z   t1.set_(t2.storage(), 0, size)
2023-01-11T21:58:58.2737740Z /var/lib/jenkins/workspace/test/test_torch.py:5843: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2738645Z   t1.set_(t2.storage(), 0, tuple(size))
2023-01-11T21:58:58.2739611Z /var/lib/jenkins/workspace/test/test_torch.py:5847: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2740663Z   t1.set_(t2.storage(), 0, size, stride)
2023-01-11T21:58:58.2741647Z /var/lib/jenkins/workspace/test/test_torch.py:5849: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2742650Z   t1.set_(t2.storage(), 0, size=size, stride=stride)
2023-01-11T21:58:58.2743730Z /var/lib/jenkins/workspace/test/test_torch.py:5857: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2744699Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:58:58.2745677Z /var/lib/jenkins/workspace/test/test_torch.py:5859: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2746529Z   t1.set_(source=t2.storage())
2023-01-11T21:58:58.2747399Z /var/lib/jenkins/workspace/test/test_torch.py:5860: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2748341Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:58:58.2749288Z /var/lib/jenkins/workspace/test/test_torch.py:5862: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2750204Z   t1.set_(source=t2.storage(), storage_offset=0, size=size, stride=stride)
2023-01-11T21:58:58.2751153Z /var/lib/jenkins/workspace/test/test_torch.py:5869: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2752104Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:58:58.2752479Z ok (0.003s)
2023-01-11T21:58:58.2753490Z   test_tensor_set_errors (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:5876: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2754571Z   self.assertRaises(RuntimeError, lambda: f_cpu.set_(d_cpu.storage()))
2023-01-11T21:58:58.2755609Z /var/lib/jenkins/workspace/test/test_torch.py:5878: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2756596Z   lambda: f_cpu.set_(d_cpu.storage(), 0, d_cpu.size(), d_cpu.stride()))
2023-01-11T21:58:58.2756988Z ok (0.004s)
2023-01-11T21:58:58.2757379Z   test_tensor_slot_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2757834Z   test_tensor_weakref_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2758419Z   test_tensoriterator_output_setup (__main__.TestTorch) ... ok (0.114s)
2023-01-11T21:58:58.2759563Z   test_to (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:7842: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:58:58.2760570Z   a = torch.tensor([[0, 1, 2], [2, 0, 3]]).to_sparse_csr()
2023-01-11T21:58:58.2760897Z ok (0.003s)
2023-01-11T21:58:58.2761297Z   test_to_with_tensor (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2761733Z   test_topk_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2762226Z   test_torch_from_file (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:58.2762701Z   test_transpose_neg_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:58:58.2763139Z   test_type (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2763571Z   test_type_alias (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2764037Z   test_type_conversion_via_dtype_name (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2765187Z   test_typed_storage_deprecation_warning (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6624: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2766191Z   s0 = torch.FloatStorage(10)
2023-01-11T21:58:58.2766519Z ok (0.002s)
2023-01-11T21:58:58.2767544Z   test_typed_storage_internal_no_warning (__main__.TestTorch) ... /var/lib/jenkins/workspace/test/test_torch.py:6554: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2768567Z   s0 = torch.FloatStorage(10)
2023-01-11T21:58:58.2769666Z /var/lib/jenkins/workspace/test/test_torch.py:6555: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:58:58.2770561Z   s0_untyped = s0.untyped()
2023-01-11T21:58:58.2770869Z ok (0.002s)
2023-01-11T21:58:58.2771244Z   test_unbind_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2771695Z   test_unflatten (__main__.TestTorch) ... ok (0.021s)
2023-01-11T21:58:58.2772130Z   test_unfold_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2772590Z   test_unsqueeze_neg_dim (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:58:58.2773061Z   test_upsample_nearest1d_meta (__main__.TestTorch) ... ok (0.011s)
2023-01-11T21:58:58.2773550Z   test_upsample_nearest2d_meta (__main__.TestTorch) ... ok (0.013s)
2023-01-11T21:58:58.2773997Z   test_var_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:58:58.2774430Z   test_warn_types (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2774875Z   test_wildcard_import (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:58:58.2775133Z 
2023-01-11T21:58:58.2775507Z ----------------------------------------------------------------------
2023-01-11T21:58:58.2775931Z Ran 181 tests in 1.786s
2023-01-11T21:58:58.2776132Z 
2023-01-11T21:58:58.2776256Z OK (skipped=7)
2023-01-11T21:58:58.2776447Z 
2023-01-11T21:58:58.2776599Z Generating XML reports...
2023-01-11T21:58:58.2777322Z Generated XML report: test-reports/python-unittest/test_torch/TEST-TestBasicVitalSigns-20230111215856.xml
2023-01-11T21:58:58.2778223Z Generated XML report: test-reports/python-unittest/test_torch/TEST-TestTorch-20230111215856.xml
2023-01-11T21:58:58.2778910Z [TORCH_VITAL] Dataloader.enabled		 True
2023-01-11T21:58:58.2779354Z [TORCH_VITAL] Dataloader.basic_unit_test		 TEST_VALUE_STRING
2023-01-11T21:58:58.2779784Z [TORCH_VITAL] CUDA.used		 False
2023-01-11T21:58:58.2780011Z 
2023-01-11T21:58:58.2780476Z ##[endgroup]
2023-01-11T21:58:58.2781109Z FINISHED PRINTING LOG FILE of test_torch (/var/lib/jenkins/workspace/test/test-reports/test_torch_h_p19gpb)
2023-01-11T21:58:58.2781482Z 
2023-01-11T21:58:58.2781835Z Running distributions/test_distributions ... [2023-01-11 21:58:58.235137]
2023-01-11T21:58:58.2782852Z Executing ['/opt/conda/bin/python', '-bb', 'distributions/test_distributions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:58.235531]
2023-01-11T21:59:39.9136079Z 
2023-01-11T21:59:39.9136941Z Expand the folded group to see the log file of distributions/test_distributions
2023-01-11T21:59:39.9138011Z ##[group]PRINTING LOG FILE of distributions/test_distributions (/var/lib/jenkins/workspace/test/test-reports/distributions-test_distributions_n99q6cs3)
2023-01-11T21:59:39.9140149Z 
2023-01-11T21:59:39.9140501Z Running tests...
2023-01-11T21:59:39.9141216Z ----------------------------------------------------------------------
2023-01-11T21:59:39.9142010Z Test results will be stored in test-reports/python-unittest/distributions.test_distributions
2023-01-11T21:59:39.9142934Z   test_cdf (__main__.TestAgainstScipy) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:107: UserWarning: Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.
2023-01-11T21:59:39.9143932Z   warnings.warn("Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.")
2023-01-11T21:59:39.9144862Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9145214Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9145498Z ok (0.046s)
2023-01-11T21:59:39.9145771Z   test_icdf (__main__.TestAgainstScipy) ... ok (0.023s)
2023-01-11T21:59:39.9148167Z   test_mean (__main__.TestAgainstScipy) ... ok (1.179s)
2023-01-11T21:59:39.9149657Z   test_variance_stddev (__main__.TestAgainstScipy) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_comparison.py:679: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:210.)
2023-01-11T21:59:39.9150518Z   return torch.as_tensor(tensor_like)
2023-01-11T21:59:39.9150784Z ok (0.034s)
2023-01-11T21:59:39.9151106Z   test_params_constraints (__main__.TestConstraints) ... ok (0.054s)
2023-01-11T21:59:39.9151739Z   test_support_constraints (__main__.TestConstraints) ... ok (0.072s)
2023-01-11T21:59:39.9152305Z   test_bernoulli_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9152690Z   test_bernoulli_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9153079Z   test_beta_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9153443Z   test_beta_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9163631Z   test_binomial_shape (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9163970Z   test_binomial_shape_vectorized_n (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9164301Z   test_categorical_shape (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:59:39.9164622Z   test_cauchy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.001s)
2023-01-11T21:59:39.9164956Z   test_cauchy_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9165439Z   test_chi2_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9165754Z   test_chi2_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9166096Z   test_continuous_bernoulli_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9166454Z   test_continuous_bernoulli_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:59:39.9166790Z   test_dirichlet_shape (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9167081Z   test_entropy_shape (__main__.TestDistributionShapes) ... ok (0.021s)
2023-01-11T21:59:39.9167404Z   test_exponential_shape_scalar_param (__main__.TestDistributionShapes) ... ok (0.001s)
2023-01-11T21:59:39.9167785Z   test_exponential_shape_tensor_param (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9168112Z   test_gamma_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9168441Z   test_gamma_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9178910Z   test_geometric_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9179628Z   test_geometric_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9180012Z   test_gumbel_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9180356Z   test_halfcauchy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9180701Z   test_halfcauchy_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9186001Z   test_kumaraswamy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9186732Z   test_laplace_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9187446Z   test_laplace_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9188166Z   test_mixture_same_family_shape (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9188825Z   test_multinomial_shape (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9189509Z   test_normal_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9190215Z   test_normal_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9190900Z   test_one_hot_categorical_shape (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:59:39.9191596Z   test_pareto_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9192312Z   test_studentT_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9193034Z   test_studentT_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9193725Z   test_uniform_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.001s)
2023-01-11T21:59:39.9194443Z   test_uniform_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9195144Z   test_vonmises_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9195831Z   test_vonmises_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9196537Z   test_weibull_scale_scalar_params (__main__.TestDistributionShapes) ... ok (0.002s)
2023-01-11T21:59:39.9197250Z   test_wishart_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:59:39.9197955Z   test_wishart_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.007s)
2023-01-11T21:59:39.9198625Z   test_argmax_relaxed_categorical (__main__.TestDistributions) ... ok (0.019s)
2023-01-11T21:59:39.9199247Z   test_bernoulli (__main__.TestDistributions) ... ok (0.071s)
2023-01-11T21:59:39.9199826Z   test_bernoulli_3d (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:39.9200628Z   test_bernoulli_enumerate_support (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9201247Z   test_beta_log_prob (__main__.TestDistributions) ... ok (0.063s)
2023-01-11T21:59:39.9201832Z   test_beta_sample (__main__.TestDistributions) ... ok (0.279s)
2023-01-11T21:59:39.9202408Z   test_beta_shape (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:39.9202969Z   test_beta_underflow (__main__.TestDistributions) ... ok (0.034s)
2023-01-11T21:59:39.9203620Z   test_beta_underflow_gpu (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:59:39.9204253Z   test_binomial (__main__.TestDistributions) ... ok (0.024s)
2023-01-11T21:59:39.9204842Z   test_binomial_enumerate_support (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9205567Z   test_binomial_extreme_vals (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:39.9206225Z   test_binomial_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.056s)
2023-01-11T21:59:39.9206897Z   test_binomial_log_prob_vectorized_count (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:39.9207554Z   test_binomial_sample (__main__.TestDistributions) ... ok (0.054s)
2023-01-11T21:59:39.9208169Z   test_binomial_stable (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9208826Z   test_binomial_vectorized_count (__main__.TestDistributions) ... ok (0.076s)
2023-01-11T21:59:39.9209539Z   test_categorical_1d (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:39.9210054Z   test_categorical_2d (__main__.TestDistributions) ... ok (0.008s)
2023-01-11T21:59:39.9210429Z   test_categorical_enumerate_support (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:39.9210707Z   test_cauchy (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:39.9211290Z   test_cdf_icdf_inverse (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9211666Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9211876Z ok (0.194s)
2023-01-11T21:59:39.9212086Z   test_cdf_log_prob (__main__.TestDistributions) ... ok (0.086s)
2023-01-11T21:59:39.9212362Z   test_chi2_sample (__main__.TestDistributions) ... ok (0.091s)
2023-01-11T21:59:39.9212634Z   test_chi2_shape (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:59:39.9212901Z   test_continuous_bernoulli (__main__.TestDistributions) ... ok (0.013s)
2023-01-11T21:59:39.9213197Z   test_continuous_bernoulli_3d (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9213495Z   test_dirichlet_log_prob (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9213767Z   test_dirichlet_mode (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9214049Z   test_dirichlet_sample (__main__.TestDistributions) ... ok (0.036s)
2023-01-11T21:59:39.9214330Z   test_dirichlet_shape (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9214855Z   test_distribution_expand (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9215216Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9215416Z ok (0.551s)
2023-01-11T21:59:39.9215654Z   test_distribution_subclass_expand (__main__.TestDistributions) ... ok (0.141s)
2023-01-11T21:59:39.9215945Z   test_enumerate_support_type (__main__.TestDistributions) ... ok (0.026s)
2023-01-11T21:59:39.9216229Z   test_exponential (__main__.TestDistributions) ... ok (0.030s)
2023-01-11T21:59:39.9216509Z   test_exponential_sample (__main__.TestDistributions) ... ok (0.086s)
2023-01-11T21:59:39.9216792Z   test_fishersnedecor (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:39.9217067Z   test_fishersnedecor_sample (__main__.TestDistributions) ... ok (0.854s)
2023-01-11T21:59:39.9217379Z   test_gamma_gpu_sample (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:59:39.9217776Z   test_gamma_gpu_shape (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:59:39.9218076Z   test_gamma_log_prob_at_boundary (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:59:39.9218364Z   test_gamma_sample (__main__.TestDistributions) ... ok (0.262s)
2023-01-11T21:59:39.9218639Z   test_gamma_shape (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:59:39.9218908Z   test_geometric (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:39.9219187Z   test_geometric_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.011s)
2023-01-11T21:59:39.9219488Z   test_geometric_sample (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:39.9219764Z   test_gumbel (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:39.9220020Z   test_gumbel_sample (__main__.TestDistributions) ... ok (0.503s)
2023-01-11T21:59:39.9220333Z   test_halfcauchy (__main__.TestDistributions) ... ok (0.009s)
2023-01-11T21:59:39.9220600Z   test_halfnormal (__main__.TestDistributions) ... ok (0.009s)
2023-01-11T21:59:39.9220870Z   test_halfnormal_logprob (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:39.9221158Z   test_halfnormal_sample (__main__.TestDistributions) ... ok (0.085s)
2023-01-11T21:59:39.9221437Z   test_has_examples (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:39.9221959Z   test_independent_expand (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9222320Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9222597Z ok (0.662s)
2023-01-11T21:59:39.9222831Z   test_independent_shape (__main__.TestDistributions) ... ok (0.263s)
2023-01-11T21:59:39.9223127Z   test_invalid_parameter_broadcasting (__main__.TestDistributions) ... ok (0.032s)
2023-01-11T21:59:39.9223442Z   test_kumaraswamy_mean_variance (__main__.TestDistributions) ... ok (0.029s)
2023-01-11T21:59:39.9223740Z   test_kumaraswamy_shape (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:39.9224020Z   test_laplace (__main__.TestDistributions) ... ok (0.018s)
2023-01-11T21:59:39.9224360Z   test_laplace_sample (__main__.TestDistributions) ... ok (0.257s)
2023-01-11T21:59:39.9224649Z   test_lazy_property_grad (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9224926Z   test_lkj_cholesky_log_prob (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:39.9225216Z   test_logisticnormal (__main__.TestDistributions) ... ok (0.022s)
2023-01-11T21:59:39.9225512Z   test_logisticnormal_logprob (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:39.9225800Z   test_logisticnormal_sample (__main__.TestDistributions) ... ok (0.286s)
2023-01-11T21:59:39.9226192Z   test_lognormal (__main__.TestDistributions) ... ok (0.017s)
2023-01-11T21:59:39.9226474Z   test_lognormal_logprob (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:39.9226751Z   test_lognormal_sample (__main__.TestDistributions) ... ok (0.256s)
2023-01-11T21:59:39.9227066Z   test_lowrank_multivariate_normal_log_prob (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:39.9227393Z   test_lowrank_multivariate_normal_moments (__main__.TestDistributions) ... ok (0.036s)
2023-01-11T21:59:39.9227729Z   test_lowrank_multivariate_normal_properties (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:39.9228049Z   test_lowrank_multivariate_normal_sample (__main__.TestDistributions) ... ok (0.037s)
2023-01-11T21:59:39.9228378Z   test_lowrank_multivariate_normal_shape (__main__.TestDistributions) ... ok (0.046s)
2023-01-11T21:59:39.9228693Z   test_mixture_same_family_log_prob (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:39.9228985Z   test_mixture_same_family_sample (__main__.TestDistributions) ... ok (0.044s)
2023-01-11T21:59:39.9229287Z   test_mixture_same_family_shape (__main__.TestDistributions) ... ok (0.008s)
2023-01-11T21:59:39.9229807Z   test_mode (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9230209Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9230398Z ok (0.112s)
2023-01-11T21:59:39.9230624Z   test_multinomial_1d (__main__.TestDistributions) ... ok (0.011s)
2023-01-11T21:59:39.9230927Z   test_multinomial_1d_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:59:39.9231215Z   test_multinomial_2d (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:39.9231508Z   test_multivariate_normal_log_prob (__main__.TestDistributions) ... ok (0.008s)
2023-01-11T21:59:39.9231817Z   test_multivariate_normal_moments (__main__.TestDistributions) ... ok (0.025s)
2023-01-11T21:59:39.9232134Z   test_multivariate_normal_properties (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9232469Z   test_multivariate_normal_sample (__main__.TestDistributions) ... ok (0.097s)
2023-01-11T21:59:39.9232774Z   test_multivariate_normal_shape (__main__.TestDistributions) ... ok (0.067s)
2023-01-11T21:59:39.9233107Z   test_multivariate_normal_stable_with_precision_matrix (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:39.9233413Z   test_negative_binomial (__main__.TestDistributions) ... ok (0.018s)
2023-01-11T21:59:39.9233710Z   test_negative_binomial_log_prob (__main__.TestDistributions) ... ok (0.052s)
2023-01-11T21:59:39.9234032Z   test_negative_binomial_log_prob_vectorized_count (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:59:39.9234335Z   test_normal (__main__.TestDistributions) ... ok (0.016s)
2023-01-11T21:59:39.9234590Z   test_normal_sample (__main__.TestDistributions) ... ok (0.254s)
2023-01-11T21:59:39.9234876Z   test_one_hot_categorical_1d (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:39.9235170Z   test_one_hot_categorical_2d (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:39.9235469Z   test_one_hot_categorical_enumerate_support (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9235765Z   test_pareto (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:59:39.9236041Z   test_pareto_sample (__main__.TestDistributions) ... ok (0.253s)
2023-01-11T21:59:39.9236324Z   test_poisson_forward_ad (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:39.9236619Z   test_poisson_gpu_sample (__main__.TestDistributions) ... skip: CUDA not found (0.000s)
2023-01-11T21:59:39.9236926Z   test_poisson_log_prob (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:59:39.9237207Z   test_poisson_sample (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:39.9237471Z   test_poisson_shape (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:39.9237755Z   test_relaxed_bernoulli (__main__.TestDistributions) ... ok (0.015s)
2023-01-11T21:59:39.9238053Z   test_relaxed_one_hot_categorical_1d (__main__.TestDistributions) ... ok (0.009s)
2023-01-11T21:59:39.9238354Z   test_relaxed_one_hot_categorical_2d (__main__.TestDistributions) ... ok (0.015s)
2023-01-11T21:59:39.9238638Z   test_repr (__main__.TestDistributions) ... ok (0.019s)
2023-01-11T21:59:39.9238923Z   test_rounded_relaxed_bernoulli (__main__.TestDistributions) ... ok (0.018s)
2023-01-11T21:59:39.9239470Z   test_rsample_requires_grad (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9239829Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9240034Z ok (0.017s)
2023-01-11T21:59:39.9240262Z   test_sample_detached (__main__.TestDistributions) ... ok (0.018s)
2023-01-11T21:59:39.9240524Z   test_studentT (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:59:39.9240798Z   test_studentT_log_prob (__main__.TestDistributions) ... ok (0.092s)
2023-01-11T21:59:39.9241080Z   test_studentT_sample (__main__.TestDistributions) ... ok (1.073s)
2023-01-11T21:59:39.9241369Z   test_support_attributes (__main__.TestDistributions) ... ok (0.023s)
2023-01-11T21:59:39.9241632Z   test_uniform (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:59:39.9241982Z   test_valid_parameter_broadcasting (__main__.TestDistributions) ... ok (0.026s)
2023-01-11T21:59:39.9242281Z   test_vonmises_logprob (__main__.TestDistributions) ... ok (0.016s)
2023-01-11T21:59:39.9242546Z   test_vonmises_sample (__main__.TestDistributions) ... ok (6.361s)
2023-01-11T21:59:39.9243159Z   test_wishart_log_prob (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:107: UserWarning: Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.
2023-01-11T21:59:39.9243788Z   warnings.warn("Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.")
2023-01-11T21:59:39.9244264Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9244618Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9244811Z ok (0.075s)
2023-01-11T21:59:39.9245037Z   test_wishart_moments (__main__.TestDistributions) ... ok (1.490s)
2023-01-11T21:59:39.9245325Z   test_wishart_properties (__main__.TestDistributions) ... ok (0.002s)
2023-01-11T21:59:39.9245597Z   test_wishart_sample (__main__.TestDistributions) ... ok (0.278s)
2023-01-11T21:59:39.9246105Z   test_wishart_shape (__main__.TestDistributions) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9246463Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9246872Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9247173Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9247578Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9247888Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9248274Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9248586Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9248985Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9249480Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9249871Z /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9250180Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9250381Z ok (0.127s)
2023-01-11T21:59:39.9250619Z   test_wishart_stable_with_precision_matrix (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:59:39.9250953Z   test_zero_excluded_binomial (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:59:39.9251251Z   test_cat_event_dim (__main__.TestFunctors) ... ok (0.001s)
2023-01-11T21:59:39.9251495Z   test_cat_transform (__main__.TestFunctors) ... ok (0.003s)
2023-01-11T21:59:39.9251772Z   test_cat_transform_non_uniform (__main__.TestFunctors) ... ok (0.003s)
2023-01-11T21:59:39.9252045Z   test_stack_transform (__main__.TestFunctors) ... ok (0.003s)
2023-01-11T21:59:39.9252510Z   test_cdf (__main__.TestJit) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:59:39.9252838Z   warnings.warn("Singular sample detected.")
2023-01-11T21:59:39.9253036Z ok (2.465s)
2023-01-11T21:59:39.9253561Z   test_entropy (__main__.TestJit) ... /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py:107: UserWarning: Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.
2023-01-11T21:59:39.9254102Z   warnings.warn("Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.")
2023-01-11T21:59:39.9254365Z ok (3.285s)
2023-01-11T21:59:39.9254577Z   test_enumerate_support (__main__.TestJit) ... ok (0.227s)
2023-01-11T21:59:39.9254892Z   test_log_prob (__main__.TestJit) ... ok (5.580s)
2023-01-11T21:59:39.9255108Z   test_mean (__main__.TestJit) ... ok (1.512s)
2023-01-11T21:59:39.9255338Z   test_rsample (__main__.TestJit) ... ok (0.377s)
2023-01-11T21:59:39.9255568Z   test_sample (__main__.TestJit) ... ok (0.510s)
2023-01-11T21:59:39.9255787Z   test_variance (__main__.TestJit) ... ok (2.586s)
2023-01-11T21:59:39.9256048Z   test_entropy_exponential_family (__main__.TestKL) ... ok (0.038s)
2023-01-11T21:59:39.9256317Z   test_entropy_monte_carlo (__main__.TestKL) ... ok (2.581s)
2023-01-11T21:59:39.9256552Z   test_kl_edgecases (__main__.TestKL) ... ok (0.011s)
2023-01-11T21:59:39.9256802Z   test_kl_exponential_family (__main__.TestKL) ... ok (0.022s)
2023-01-11T21:59:39.9257047Z   test_kl_infinite (__main__.TestKL) ... ok (0.015s)
2023-01-11T21:59:39.9257369Z   test_kl_lowrank_multivariate_normal (__main__.TestKL) ... ok (0.021s)
2023-01-11T21:59:39.9257651Z   test_kl_lowrank_multivariate_normal_batched (__main__.TestKL) ... ok (0.016s)
2023-01-11T21:59:39.9257924Z   test_kl_monte_carlo (__main__.TestKL) ... ok (0.663s)
2023-01-11T21:59:39.9258177Z   test_kl_multivariate_normal (__main__.TestKL) ... ok (0.029s)
2023-01-11T21:59:39.9258438Z   test_kl_multivariate_normal_batched (__main__.TestKL) ... ok (0.014s)
2023-01-11T21:59:39.9258735Z   test_kl_multivariate_normal_batched_broadcasted (__main__.TestKL) ... ok (0.014s)
2023-01-11T21:59:39.9259005Z   test_kl_shape (__main__.TestKL) ... ok (0.046s)
2023-01-11T21:59:39.9259230Z   test_kl_transformed (__main__.TestKL) ... ok (0.011s)
2023-01-11T21:59:39.9259532Z   test_lazy_logits_initialization (__main__.TestLazyLogitsInitialization) ... ok (0.004s)
2023-01-11T21:59:39.9259886Z   test_lazy_probs_initialization (__main__.TestLazyLogitsInitialization) ... ok (0.002s)
2023-01-11T21:59:39.9260219Z   test_bernoulli_gradient (__main__.TestNumericalStability) ... ok (0.014s)
2023-01-11T21:59:39.9260525Z   test_bernoulli_with_logits_overflow (__main__.TestNumericalStability) ... ok (0.003s)
2023-01-11T21:59:39.9260857Z   test_bernoulli_with_logits_underflow (__main__.TestNumericalStability) ... ok (0.003s)
2023-01-11T21:59:39.9261179Z   test_categorical_log_prob (__main__.TestNumericalStability) ... ok (0.002s)
2023-01-11T21:59:39.9261490Z   test_categorical_log_prob_with_logits (__main__.TestNumericalStability) ... ok (0.002s)
2023-01-11T21:59:39.9261822Z   test_continuous_bernoulli_gradient (__main__.TestNumericalStability) ... ok (0.028s)
2023-01-11T21:59:39.9262165Z   test_continuous_bernoulli_with_logits_overflow (__main__.TestNumericalStability) ... ok (0.004s)
2023-01-11T21:59:39.9262603Z   test_continuous_bernoulli_with_logits_underflow (__main__.TestNumericalStability) ... ok (0.004s)
2023-01-11T21:59:39.9262926Z   test_multinomial_log_prob (__main__.TestNumericalStability) ... ok (0.002s)
2023-01-11T21:59:39.9263249Z   test_multinomial_log_prob_with_logits (__main__.TestNumericalStability) ... ok (0.002s)
2023-01-11T21:59:39.9263545Z   test_beta_wrt_alpha (__main__.TestRsample) ... ok (0.044s)
2023-01-11T21:59:39.9263794Z   test_beta_wrt_beta (__main__.TestRsample) ... ok (0.043s)
2023-01-11T21:59:39.9264037Z   test_chi2 (__main__.TestRsample) ... ok (0.024s)
2023-01-11T21:59:39.9264304Z   test_dirichlet_multivariate (__main__.TestRsample) ... ok (0.503s)
2023-01-11T21:59:39.9264588Z   test_dirichlet_on_diagonal (__main__.TestRsample) ... ok (0.046s)
2023-01-11T21:59:39.9264855Z   test_dirichlet_tangent_field (__main__.TestRsample) ... ok (0.083s)
2023-01-11T21:59:39.9265111Z   test_gamma (__main__.TestRsample) ... ok (0.023s)
2023-01-11T21:59:39.9265358Z   test_invalid (__main__.TestValidation) ... ok (0.019s)
2023-01-11T21:59:39.9265614Z   test_invalid_log_probs_arg (__main__.TestValidation) ... ok (0.279s)
2023-01-11T21:59:39.9265879Z   test_valid (__main__.TestValidation) ... ok (0.012s)
2023-01-11T21:59:39.9266162Z   test_warning_unimplemented_constraints (__main__.TestValidation) ... ok (0.007s)
2023-01-11T21:59:39.9266339Z 
2023-01-11T21:59:39.9266587Z ----------------------------------------------------------------------
2023-01-11T21:59:39.9266820Z Ran 219 tests in 39.189s
2023-01-11T21:59:39.9266935Z 
2023-01-11T21:59:39.9267006Z OK (skipped=5)
2023-01-11T21:59:39.9267111Z 
2023-01-11T21:59:39.9267195Z Generating XML reports...
2023-01-11T21:59:39.9267623Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestAgainstScipy-20230111215900.xml
2023-01-11T21:59:39.9268192Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestConstraints-20230111215900.xml
2023-01-11T21:59:39.9268774Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributionShapes-20230111215900.xml
2023-01-11T21:59:39.9269382Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributions-20230111215900.xml
2023-01-11T21:59:39.9269915Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestFunctors-20230111215900.xml
2023-01-11T21:59:39.9270443Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestJit-20230111215900.xml
2023-01-11T21:59:39.9270952Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestKL-20230111215900.xml
2023-01-11T21:59:39.9271533Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestLazyLogitsInitialization-20230111215900.xml
2023-01-11T21:59:39.9272130Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestNumericalStability-20230111215900.xml
2023-01-11T21:59:39.9272687Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestRsample-20230111215900.xml
2023-01-11T21:59:39.9273225Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestValidation-20230111215900.xml
2023-01-11T21:59:39.9273469Z 
2023-01-11T21:59:39.9273789Z ##[endgroup]
2023-01-11T21:59:39.9274243Z FINISHED PRINTING LOG FILE of distributions/test_distributions (/var/lib/jenkins/workspace/test/test-reports/distributions-test_distributions_n99q6cs3)
2023-01-11T21:59:39.9274501Z 
2023-01-11T21:59:39.9274670Z Running nn/test_convolution ... [2023-01-11 21:59:39.914082]
2023-01-11T21:59:39.9275145Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_convolution.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:59:39.914374]
2023-01-11T21:59:47.7655476Z 
2023-01-11T21:59:47.7656166Z Expand the folded group to see the log file of nn/test_convolution
2023-01-11T21:59:47.7657159Z ##[group]PRINTING LOG FILE of nn/test_convolution (/var/lib/jenkins/workspace/test/test-reports/nn-test_convolution_uccajnut)
2023-01-11T21:59:47.7657540Z 
2023-01-11T21:59:47.7657669Z Running tests...
2023-01-11T21:59:47.7658316Z ----------------------------------------------------------------------
2023-01-11T21:59:47.7658707Z Test results will be stored in test-reports/python-unittest/nn.test_convolution
2023-01-11T21:59:47.7659684Z   test_Conv1d_module_same_padding (__main__.TestConvolutionNN) ... /var/lib/jenkins/workspace/test/nn/test_convolution.py:152: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T21:59:47.7660755Z   expect = F.conv1d(x, module.weight, module.bias, padding='same')
2023-01-11T21:59:47.7661125Z ok (0.005s)
2023-01-11T21:59:47.7661520Z   test_Conv2d_1x1 (__main__.TestConvolutionNN) ... ok (0.005s)
2023-01-11T21:59:47.7661887Z   test_Conv2d_OneDNN (__main__.TestConvolutionNN) ... ok (0.018s)
2023-01-11T21:59:47.7662224Z   test_Conv2d_backward_twice (__main__.TestConvolutionNN) ... ok (0.007s)
2023-01-11T21:59:47.7662597Z   test_Conv2d_groups_nobias (__main__.TestConvolutionNN) ... ok (0.041s)
2023-01-11T21:59:47.7662922Z   test_Conv2d_groups_nobias_v2 (__main__.TestConvolutionNN) ... ok (0.005s)
2023-01-11T21:59:47.7663436Z   test_Conv2d_inconsistent_types (__main__.TestConvolutionNN) ... ok (0.008s)
2023-01-11T21:59:47.7663787Z   test_Conv2d_inconsistent_types_on_GPU_with_cudnn (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:59:47.7664177Z   test_Conv2d_inconsistent_types_on_GPU_without_cudnn (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:59:47.7664502Z   test_Conv2d_missing_argument (__main__.TestConvolutionNN) ... ok (0.002s)
2023-01-11T21:59:47.7664801Z   test_Conv2d_module_same_padding (__main__.TestConvolutionNN) ... ok (0.004s)
2023-01-11T21:59:47.7665101Z   test_Conv3d_groups_nobias (__main__.TestConvolutionNN) ... ok (0.005s)
2023-01-11T21:59:47.7665392Z   test_Conv3d_groups_wbias (__main__.TestConvolutionNN) ... ok (0.005s)
2023-01-11T21:59:47.7665728Z   test_Conv3d_module_same_padding (__main__.TestConvolutionNN) ... ok (0.004s)
2023-01-11T21:59:47.7666067Z   test_ConvTranspose2d_half_cublas_gemm (__main__.TestConvolutionNN) ... skip: CUDA not available (0.000s)
2023-01-11T21:59:47.7666408Z   test_ConvTranspose2d_output_size (__main__.TestConvolutionNN) ... ok (0.003s)
2023-01-11T21:59:47.7666732Z   test_ConvTranspose2d_output_size_downsample_upsample (__main__.TestConvolutionNN) ... ok (4.116s)
2023-01-11T21:59:47.7667071Z   test_ConvTranspose3d_correct_output_size (__main__.TestConvolutionNN) ... ok (0.001s)
2023-01-11T21:59:47.7667389Z   test_conv2d_discontiguous_weight (__main__.TestConvolutionNN) ... ok (0.045s)
2023-01-11T21:59:47.7667684Z   test_conv_backcompat (__main__.TestConvolutionNN) ... ok (0.010s)
2023-01-11T21:59:47.7667998Z   test_conv_cudnn_memory_layout_dominance (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:59:47.7668323Z   test_conv_invalid_groups (__main__.TestConvolutionNN) ... ok (0.001s)
2023-01-11T21:59:47.7668647Z   test_conv_modules_raise_error_on_incorrect_input_size (__main__.TestConvolutionNN) ... ok (0.095s)
2023-01-11T21:59:47.7668951Z   test_conv_padding_mode (__main__.TestConvolutionNN) ... ok (0.001s)
2023-01-11T21:59:47.7669958Z   test_conv_shapecheck (__main__.TestConvolutionNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1120: UserWarning: Complex modules are a new feature under active development whose design may change, and some modules might not work as expected when using complex tensors as parameters or buffers. Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml if a complex module does not work as expected.
2023-01-11T21:59:47.7670574Z   warnings.warn(
2023-01-11T21:59:47.7670746Z ok (0.173s)
2023-01-11T21:59:47.7670952Z   test_conv_tbc (__main__.TestConvolutionNN) ... ok (0.008s)
2023-01-11T21:59:47.7671273Z   test_cudnn_non_contiguous (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:59:47.7671618Z   test_cudnn_noncontiguous_weight (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.000s)
2023-01-11T21:59:47.7671937Z   test_functional_grad_conv (__main__.TestConvolutionNN) ... ok (0.004s)
2023-01-11T21:59:47.7672232Z   test_functional_grad_conv2d (__main__.TestConvolutionNN) ... ok (0.136s)
2023-01-11T21:59:47.7672509Z   test_grad_conv1d_input (__main__.TestConvolutionNN) ... ok (0.057s)
2023-01-11T21:59:47.7672793Z   test_grad_conv1d_weight (__main__.TestConvolutionNN) ... ok (0.044s)
2023-01-11T21:59:47.7673078Z   test_grad_conv2d_input (__main__.TestConvolutionNN) ... ok (0.066s)
2023-01-11T21:59:47.7673352Z   test_grad_conv2d_weight (__main__.TestConvolutionNN) ... ok (0.056s)
2023-01-11T21:59:47.7673631Z   test_grad_conv3d_input (__main__.TestConvolutionNN) ... ok (0.076s)
2023-01-11T21:59:47.7673915Z   test_grad_conv3d_weight (__main__.TestConvolutionNN) ... ok (0.070s)
2023-01-11T21:59:47.7674232Z   test_grouped_conv_cudnn_nhwc_support (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:59:47.7675239Z   test_invalid_conv1d (__main__.TestConvolutionNN) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1120: UserWarning: Complex modules are a new feature under active development whose design may change, and some modules might not work as expected when using complex tensors as parameters or buffers. Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml if a complex module does not work as expected.
2023-01-11T21:59:47.7675890Z   warnings.warn(
2023-01-11T21:59:47.7676059Z ok (0.077s)
2023-01-11T21:59:47.7676280Z   test_invalid_conv2d (__main__.TestConvolutionNN) ... ok (0.152s)
2023-01-11T21:59:47.7676543Z   test_invalid_conv3d (__main__.TestConvolutionNN) ... ok (0.075s)
2023-01-11T21:59:47.7676828Z   test_mismatch_shape_conv2d (__main__.TestConvolutionNN) ... ok (0.007s)
2023-01-11T21:59:47.7677110Z   test_nnpack_conv (__main__.TestConvolutionNN) ... ok (0.296s)
2023-01-11T21:59:47.7677456Z   test_thnn_conv_strided_padded_dilated (__main__.TestConvolutionNN) ... skip: CUDA not available (0.002s)
2023-01-11T21:59:47.7677654Z 
2023-01-11T21:59:47.7677858Z ----------------------------------------------------------------------
2023-01-11T21:59:47.7678097Z Ran 43 tests in 5.687s
2023-01-11T21:59:47.7678209Z 
2023-01-11T21:59:47.7678281Z OK (skipped=8)
2023-01-11T21:59:47.7678373Z 
2023-01-11T21:59:47.7678457Z Generating XML reports...
2023-01-11T21:59:47.7678874Z Generated XML report: test-reports/python-unittest/nn.test_convolution/TEST-TestConvolutionNN-20230111215941.xml
2023-01-11T21:59:47.7679110Z 
2023-01-11T21:59:47.7679374Z ##[endgroup]
2023-01-11T21:59:47.7679751Z FINISHED PRINTING LOG FILE of nn/test_convolution (/var/lib/jenkins/workspace/test/test-reports/nn-test_convolution_uccajnut)
2023-01-11T21:59:47.7679974Z 
2023-01-11T21:59:47.7680135Z Running nn/test_pooling ... [2023-01-11 21:59:47.765811]
2023-01-11T21:59:47.7680599Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_pooling.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:59:47.766078]
2023-01-11T21:59:50.0713993Z 
2023-01-11T21:59:50.0714544Z Expand the folded group to see the log file of nn/test_pooling
2023-01-11T21:59:50.0715651Z ##[group]PRINTING LOG FILE of nn/test_pooling (/var/lib/jenkins/workspace/test/test-reports/nn-test_pooling_dmmwoj91)
2023-01-11T21:59:50.0716151Z 
2023-01-11T21:59:50.0716236Z Running tests...
2023-01-11T21:59:50.0716735Z ----------------------------------------------------------------------
2023-01-11T21:59:50.0717227Z Test results will be stored in test-reports/python-unittest/nn.test_pooling
2023-01-11T21:59:50.0717588Z   test_avg_pool1d_ceil_mode (__main__.TestAvgPool) ... ok (0.002s)
2023-01-11T21:59:50.0717956Z   test_avg_pool2d_ceil_mode (__main__.TestAvgPool) ... ok (0.001s)
2023-01-11T21:59:50.0718426Z   test_avg_pool3d_ceil_mode (__main__.TestAvgPool) ... ok (0.001s)
2023-01-11T21:59:50.0718905Z   test_doubletensor_avg_pool2d (__main__.TestAvgPool) ... ok (0.007s)
2023-01-11T21:59:50.0719258Z   test_doubletensor_avg_pool2d_with_divisor (__main__.TestAvgPool) ... ok (0.005s)
2023-01-11T21:59:50.0719596Z   test_doubletensor_avg_pool3d (__main__.TestAvgPool) ... ok (0.046s)
2023-01-11T21:59:50.0719883Z   test_doubletensor_avg_pool3d_with_divisor (__main__.TestAvgPool) ... ok (0.143s)
2023-01-11T21:59:50.0720269Z   test_MaxUnpool2d_output_size (__main__.TestPoolingNN) ... ok (0.007s)
2023-01-11T21:59:50.0720560Z   test_adaptive_pooling_avg_nhwc (__main__.TestPoolingNN) ... ok (0.002s)
2023-01-11T21:59:50.0720894Z   test_adaptive_pooling_avg_nhwc_launch_config_backward (__main__.TestPoolingNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:59:50.0721278Z   test_adaptive_pooling_avg_nhwc_launch_config_forward (__main__.TestPoolingNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:59:50.0721633Z   test_adaptive_pooling_avg_nhwc_non_contiguous (__main__.TestPoolingNN) ... ok (0.002s)
2023-01-11T21:59:50.0721944Z   test_adaptive_pooling_bfloat16 (__main__.TestPoolingNN) ... ok (0.005s)
2023-01-11T21:59:50.0722224Z   test_adaptive_pooling_input_size (__main__.TestPoolingNN) ... ok (0.001s)
2023-01-11T21:59:50.0722729Z   test_adaptive_pooling_size_none (__main__.TestPoolingNN) ... ok (0.001s)
2023-01-11T21:59:50.0723025Z   test_adaptive_pooling_size_overflow (__main__.TestPoolingNN) ... ok (0.009s)
2023-01-11T21:59:50.0723291Z   test_max_unpool (__main__.TestPoolingNN) ... ok (0.222s)
2023-01-11T21:59:50.0723558Z   test_max_unpool2d_nhwc_cpu (__main__.TestPoolingNN) ... ok (0.002s)
2023-01-11T21:59:50.0723715Z 
2023-01-11T21:59:50.0723933Z ----------------------------------------------------------------------
2023-01-11T21:59:50.0724169Z Ran 18 tests in 0.459s
2023-01-11T21:59:50.0724283Z 
2023-01-11T21:59:50.0724353Z OK (skipped=2)
2023-01-11T21:59:50.0724458Z 
2023-01-11T21:59:50.0724541Z Generating XML reports...
2023-01-11T21:59:50.0724944Z Generated XML report: test-reports/python-unittest/nn.test_pooling/TEST-TestAvgPool-20230111215949.xml
2023-01-11T21:59:50.0725496Z Generated XML report: test-reports/python-unittest/nn.test_pooling/TEST-TestPoolingNN-20230111215949.xml
2023-01-11T21:59:50.0725721Z 
2023-01-11T21:59:50.0725986Z ##[endgroup]
2023-01-11T21:59:50.0726369Z FINISHED PRINTING LOG FILE of nn/test_pooling (/var/lib/jenkins/workspace/test/test-reports/nn-test_pooling_dmmwoj91)
2023-01-11T21:59:50.0726584Z 
2023-01-11T21:59:50.0726739Z Running test_cpp_api_parity ... [2023-01-11 21:59:50.071548]
2023-01-11T21:59:50.0727211Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_api_parity.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:59:50.071800]
2023-01-11T22:00:01.6412027Z 
2023-01-11T22:00:01.6412583Z Expand the folded group to see the log file of test_cpp_api_parity
2023-01-11T22:00:01.6413617Z ##[group]PRINTING LOG FILE of test_cpp_api_parity (/var/lib/jenkins/workspace/test/test-reports/test_cpp_api_parity_cteeoalo)
2023-01-11T22:00:01.6414532Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:01.6414799Z 
2023-01-11T22:00:01.6414939Z Running tests...
2023-01-11T22:00:01.6415452Z ----------------------------------------------------------------------
2023-01-11T22:00:01.6416126Z Test results will be stored in test-reports/python-unittest/test_cpp_api_parity
2023-01-11T22:00:01.6416647Z   test_torch_nn_AdaptiveAvgPool1d (__main__.TestCppApiParity) ... ok (0.029s)
2023-01-11T22:00:01.6417222Z   test_torch_nn_AdaptiveAvgPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6417815Z   test_torch_nn_AdaptiveAvgPool1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6418407Z   test_torch_nn_AdaptiveAvgPool1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6419041Z   test_torch_nn_AdaptiveAvgPool1d_one_output (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6419676Z   test_torch_nn_AdaptiveAvgPool1d_one_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6420215Z   test_torch_nn_AdaptiveAvgPool2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6420827Z   test_torch_nn_AdaptiveAvgPool2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6421403Z   test_torch_nn_AdaptiveAvgPool2d_single (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6422027Z   test_torch_nn_AdaptiveAvgPool2d_single_1x1output (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6422742Z   test_torch_nn_AdaptiveAvgPool2d_single_1x1output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6423405Z   test_torch_nn_AdaptiveAvgPool2d_single_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6423978Z   test_torch_nn_AdaptiveAvgPool2d_tuple (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6424599Z   test_torch_nn_AdaptiveAvgPool2d_tuple_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6425265Z   test_torch_nn_AdaptiveAvgPool2d_tuple_none (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6426209Z   test_torch_nn_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6426868Z   test_torch_nn_AdaptiveAvgPool3d_last_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6427470Z   test_torch_nn_AdaptiveAvgPool3d_last_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6428083Z   test_torch_nn_AdaptiveAvgPool3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6428766Z   test_torch_nn_AdaptiveAvgPool3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6429257Z   test_torch_nn_AdaptiveAvgPool3d_single (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6429862Z   test_torch_nn_AdaptiveAvgPool3d_single_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6430418Z   test_torch_nn_AdaptiveAvgPool3d_tuple (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6430977Z   test_torch_nn_AdaptiveAvgPool3d_tuple_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6431655Z   test_torch_nn_AdaptiveAvgPool3d_tuple_none (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6432275Z   test_torch_nn_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6432945Z   test_torch_nn_AdaptiveMaxPool1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6433337Z   test_torch_nn_AdaptiveMaxPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6434023Z   test_torch_nn_AdaptiveMaxPool1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6434424Z   test_torch_nn_AdaptiveMaxPool1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6434772Z   test_torch_nn_AdaptiveMaxPool2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6435138Z   test_torch_nn_AdaptiveMaxPool2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6435496Z   test_torch_nn_AdaptiveMaxPool2d_single (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6435851Z   test_torch_nn_AdaptiveMaxPool2d_single_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6436242Z   test_torch_nn_AdaptiveMaxPool2d_tuple (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6436619Z   test_torch_nn_AdaptiveMaxPool2d_tuple_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6437270Z   test_torch_nn_AdaptiveMaxPool2d_tuple_none (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6437752Z   test_torch_nn_AdaptiveMaxPool2d_tuple_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6438100Z   test_torch_nn_AdaptiveMaxPool3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6438472Z   test_torch_nn_AdaptiveMaxPool3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6438828Z   test_torch_nn_AdaptiveMaxPool3d_single (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6439171Z   test_torch_nn_AdaptiveMaxPool3d_single_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6439536Z   test_torch_nn_AdaptiveMaxPool3d_single_nonatomic (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6439909Z   test_torch_nn_AdaptiveMaxPool3d_single_nonatomic_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6440271Z   test_torch_nn_AdaptiveMaxPool3d_tuple (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6440616Z   test_torch_nn_AdaptiveMaxPool3d_tuple_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6440978Z   test_torch_nn_AdaptiveMaxPool3d_tuple_nonatomic (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6441411Z   test_torch_nn_AdaptiveMaxPool3d_tuple_nonatomic_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6441776Z   test_torch_nn_AdaptiveMaxPool3d_tuple_none (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T22:00:01.6442121Z   test_torch_nn_AdaptiveMaxPool3d_tuple_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6442451Z   test_torch_nn_AvgPool1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6442772Z   test_torch_nn_AvgPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6443085Z   test_torch_nn_AvgPool1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6443471Z   test_torch_nn_AvgPool1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6443810Z   test_torch_nn_AvgPool1d_stride (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6444146Z   test_torch_nn_AvgPool1d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6444465Z   test_torch_nn_AvgPool1d_stride_pad (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6444804Z   test_torch_nn_AvgPool1d_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6445129Z   test_torch_nn_AvgPool2d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6445432Z   test_torch_nn_AvgPool2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6445754Z   test_torch_nn_AvgPool2d_divisor (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6446089Z   test_torch_nn_AvgPool2d_divisor_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6446432Z   test_torch_nn_AvgPool2d_divisor_stride (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6446765Z   test_torch_nn_AvgPool2d_divisor_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6447115Z   test_torch_nn_AvgPool2d_divisor_stride_pad (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6447467Z   test_torch_nn_AvgPool2d_divisor_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6447808Z   test_torch_nn_AvgPool2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6448137Z   test_torch_nn_AvgPool2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6448470Z   test_torch_nn_AvgPool2d_stride (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6448803Z   test_torch_nn_AvgPool2d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6449320Z   test_torch_nn_AvgPool2d_stride_pad (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6449666Z   test_torch_nn_AvgPool2d_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6449992Z   test_torch_nn_AvgPool3d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6450308Z   test_torch_nn_AvgPool3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6450618Z   test_torch_nn_AvgPool3d_divisor (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6450951Z   test_torch_nn_AvgPool3d_divisor_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6451290Z   test_torch_nn_AvgPool3d_divisor_stride (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6451614Z   test_torch_nn_AvgPool3d_divisor_stride1_pad0_gpu_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6451993Z   test_torch_nn_AvgPool3d_divisor_stride1_pad0_gpu_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6452380Z   test_torch_nn_AvgPool3d_divisor_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6452804Z   test_torch_nn_AvgPool3d_divisor_stride_pad (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6453145Z   test_torch_nn_AvgPool3d_divisor_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6453521Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6453914Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_fixedkw_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6454305Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_general_output (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6454686Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_general_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6455121Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6455522Z   test_torch_nn_AvgPool3d_divisor_stride_pad_gpu_input_nooverlap_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6455886Z   test_torch_nn_AvgPool3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6456218Z   test_torch_nn_AvgPool3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6456549Z   test_torch_nn_AvgPool3d_stride (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6456869Z   test_torch_nn_AvgPool3d_stride1_pad0_gpu_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6457222Z   test_torch_nn_AvgPool3d_stride1_pad0_gpu_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6457590Z   test_torch_nn_AvgPool3d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6457925Z   test_torch_nn_AvgPool3d_stride_pad (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6458265Z   test_torch_nn_AvgPool3d_stride_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6458612Z   test_torch_nn_AvgPool3d_stride_pad_gpu_fixedkw_output (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6458992Z   test_torch_nn_AvgPool3d_stride_pad_gpu_fixedkw_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6459370Z   test_torch_nn_AvgPool3d_stride_pad_gpu_general_output (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6459751Z   test_torch_nn_AvgPool3d_stride_pad_gpu_general_output_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6460117Z   test_torch_nn_AvgPool3d_stride_pad_gpu_input_nooverlap (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6460496Z   test_torch_nn_AvgPool3d_stride_pad_gpu_input_nooverlap_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6460835Z   test_torch_nn_BCELoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6461147Z   test_torch_nn_BCELoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6461477Z   test_torch_nn_BCELoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T22:00:01.6461832Z   test_torch_nn_BCELoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6462191Z   test_torch_nn_BCELoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.007s)
2023-01-11T22:00:01.6462603Z   test_torch_nn_BCELoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6462963Z   test_torch_nn_BCELoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.007s)
2023-01-11T22:00:01.6463318Z   test_torch_nn_BCELoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6463662Z   test_torch_nn_BCELoss_scalar_weights (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6464037Z   test_torch_nn_BCELoss_scalar_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6464372Z   test_torch_nn_BCELoss_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6464705Z   test_torch_nn_BCELoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6465041Z   test_torch_nn_BCEWithLogitsLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6465371Z   test_torch_nn_BCEWithLogitsLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6465744Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T22:00:01.6466169Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6466545Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T22:00:01.6466934Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6467323Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T22:00:01.6467716Z   test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6468072Z   test_torch_nn_BCEWithLogitsLoss_scalar_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6468447Z   test_torch_nn_BCEWithLogitsLoss_scalar_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6468809Z   test_torch_nn_BCEWithLogitsLoss_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6469170Z   test_torch_nn_BCEWithLogitsLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6469506Z   test_torch_nn_BatchNorm1d_3d_input (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6469846Z   test_torch_nn_BatchNorm1d_3d_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6470195Z   test_torch_nn_BatchNorm1d_3d_input_not_affine (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6470543Z   test_torch_nn_BatchNorm1d_3d_input_not_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6470884Z   test_torch_nn_BatchNorm1d_affine (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6471224Z   test_torch_nn_BatchNorm1d_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6471574Z   test_torch_nn_BatchNorm1d_affine_simple_average (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6471936Z   test_torch_nn_BatchNorm1d_affine_simple_average_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6472291Z   test_torch_nn_BatchNorm1d_not_affine (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6472635Z   test_torch_nn_BatchNorm1d_not_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6472984Z   test_torch_nn_BatchNorm1d_not_tracking_stats (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6473767Z   test_torch_nn_BatchNorm1d_not_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6474109Z   test_torch_nn_BatchNorm1d_zero_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6474451Z   test_torch_nn_BatchNorm1d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6474777Z   test_torch_nn_BatchNorm2d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6475080Z   test_torch_nn_BatchNorm2d_2d_simple_average (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6475436Z   test_torch_nn_BatchNorm2d_2d_simple_average_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6475835Z   test_torch_nn_BatchNorm2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6476149Z   test_torch_nn_BatchNorm2d_momentum (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6476483Z   test_torch_nn_BatchNorm2d_momentum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6476815Z   test_torch_nn_BatchNorm2d_not_affine (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6477157Z   test_torch_nn_BatchNorm2d_not_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6477494Z   test_torch_nn_BatchNorm2d_not_tracking_stats (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6477895Z   test_torch_nn_BatchNorm2d_not_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6478236Z   test_torch_nn_BatchNorm2d_zero_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6478584Z   test_torch_nn_BatchNorm2d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6478894Z   test_torch_nn_BatchNorm3d (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6479205Z   test_torch_nn_BatchNorm3d_3d_simple_average (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6479560Z   test_torch_nn_BatchNorm3d_3d_simple_average_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6479908Z   test_torch_nn_BatchNorm3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6480233Z   test_torch_nn_BatchNorm3d_momentum (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6480577Z   test_torch_nn_BatchNorm3d_momentum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6480910Z   test_torch_nn_BatchNorm3d_not_affine (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6481238Z   test_torch_nn_BatchNorm3d_not_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6481580Z   test_torch_nn_BatchNorm3d_not_tracking_stats (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6481936Z   test_torch_nn_BatchNorm3d_not_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6482265Z   test_torch_nn_BatchNorm3d_zero_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6482601Z   test_torch_nn_BatchNorm3d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6482916Z   test_torch_nn_CELU (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6483223Z   test_torch_nn_CELU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6483519Z   test_torch_nn_CELU_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6483841Z   test_torch_nn_CELU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6484187Z   test_torch_nn_CTCLoss_2d_int_target_lengths_tensors (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6484553Z   test_torch_nn_CTCLoss_2d_int_target_lengths_tensors_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6484892Z   test_torch_nn_CTCLoss_2d_lengths_tensors (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6485236Z   test_torch_nn_CTCLoss_2d_lengths_tensors_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6485577Z   test_torch_nn_CTCLoss_lengths_tensors (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6485910Z   test_torch_nn_CTCLoss_lengths_tensors_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6486237Z   test_torch_nn_ConstantPad1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6486543Z   test_torch_nn_ConstantPad1d_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6486917Z   test_torch_nn_ConstantPad1d_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6487246Z   test_torch_nn_ConstantPad1d_complex (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6487590Z   test_torch_nn_ConstantPad1d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6487949Z   test_torch_nn_ConstantPad1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6488272Z   test_torch_nn_ConstantPad2d (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6488569Z   test_torch_nn_ConstantPad2d_complex (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6488945Z   test_torch_nn_ConstantPad2d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6489492Z   test_torch_nn_ConstantPad2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6489822Z   test_torch_nn_ConstantPad2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6490173Z   test_torch_nn_ConstantPad2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6490506Z   test_torch_nn_ConstantPad3d (__main__.TestCppApiParity) ... ok (0.034s)
2023-01-11T22:00:01.6490820Z   test_torch_nn_ConstantPad3d_complex (__main__.TestCppApiParity) ... ok (0.041s)
2023-01-11T22:00:01.6491155Z   test_torch_nn_ConstantPad3d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6491513Z   test_torch_nn_ConstantPad3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6491848Z   test_torch_nn_ConstantPad3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.021s)
2023-01-11T22:00:01.6492189Z   test_torch_nn_ConstantPad3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6492514Z   test_torch_nn_Conv1d (__main__.TestCppApiParity) ... ok (0.033s)
2023-01-11T22:00:01.6492819Z   test_torch_nn_Conv1d_circular_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6493175Z   test_torch_nn_Conv1d_circular_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6493514Z   test_torch_nn_Conv1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6493826Z   test_torch_nn_Conv1d_dilated (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6494150Z   test_torch_nn_Conv1d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6494470Z   test_torch_nn_Conv1d_groups (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6494784Z   test_torch_nn_Conv1d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6495098Z   test_torch_nn_Conv1d_pad1 (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6495423Z   test_torch_nn_Conv1d_pad1_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6495729Z   test_torch_nn_Conv1d_pad1size1 (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6496057Z   test_torch_nn_Conv1d_pad1size1_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6496374Z   test_torch_nn_Conv1d_pad2 (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6496695Z   test_torch_nn_Conv1d_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6496999Z   test_torch_nn_Conv1d_pad2size1 (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6497327Z   test_torch_nn_Conv1d_pad2size1_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6497648Z   test_torch_nn_Conv1d_pad_same (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6498563Z   test_torch_nn_Conv1d_pad_same2 (__main__.TestCppApiParity) ... /opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py:309: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T22:00:01.6499197Z   return F.conv1d(input, weight, bias, self.stride,
2023-01-11T22:00:01.6499402Z ok (0.012s)
2023-01-11T22:00:01.6499667Z   test_torch_nn_Conv1d_pad_same2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6500023Z   test_torch_nn_Conv1d_pad_same_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6500345Z   test_torch_nn_Conv1d_pad_same_dilated (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6500750Z   test_torch_nn_Conv1d_pad_same_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6501086Z   test_torch_nn_Conv1d_pad_valid (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6501406Z   test_torch_nn_Conv1d_pad_valid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6501743Z   test_torch_nn_Conv1d_reflect_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6502096Z   test_torch_nn_Conv1d_reflect_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6502516Z   test_torch_nn_Conv1d_replicate_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6502867Z   test_torch_nn_Conv1d_replicate_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6503201Z   test_torch_nn_Conv1d_stride (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6503527Z   test_torch_nn_Conv1d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6503839Z   test_torch_nn_Conv1d_zero_batch (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6504171Z   test_torch_nn_Conv1d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6504506Z   test_torch_nn_Conv1d_zeros_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6504852Z   test_torch_nn_Conv1d_zeros_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6505159Z   test_torch_nn_Conv2d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6505462Z   test_torch_nn_Conv2d_circular_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6505818Z   test_torch_nn_Conv2d_circular_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6506171Z   test_torch_nn_Conv2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6506477Z   test_torch_nn_Conv2d_depthwise (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6506808Z   test_torch_nn_Conv2d_depthwise_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6507147Z   test_torch_nn_Conv2d_depthwise_dilated (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6507481Z   test_torch_nn_Conv2d_depthwise_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6507819Z   test_torch_nn_Conv2d_depthwise_padded (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6508172Z   test_torch_nn_Conv2d_depthwise_padded_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6508520Z   test_torch_nn_Conv2d_depthwise_strided (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6508853Z   test_torch_nn_Conv2d_depthwise_strided_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6509204Z   test_torch_nn_Conv2d_depthwise_with_multiplier (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6509564Z   test_torch_nn_Conv2d_depthwise_with_multiplier_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6509947Z   test_torch_nn_Conv2d_dilated (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6510261Z   test_torch_nn_Conv2d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6510583Z   test_torch_nn_Conv2d_groups (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6510907Z   test_torch_nn_Conv2d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6511215Z   test_torch_nn_Conv2d_groups_thnn (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6511544Z   test_torch_nn_Conv2d_groups_thnn_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6511901Z   test_torch_nn_Conv2d_no_bias (__main__.TestCppApiParity) ... ok (0.017s)
2023-01-11T22:00:01.6512225Z   test_torch_nn_Conv2d_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6512535Z   test_torch_nn_Conv2d_pad_same (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6512863Z   test_torch_nn_Conv2d_pad_same_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6513194Z   test_torch_nn_Conv2d_pad_same_dilated (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6513524Z   test_torch_nn_Conv2d_pad_same_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6513853Z   test_torch_nn_Conv2d_pad_valid (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6514179Z   test_torch_nn_Conv2d_pad_valid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6514503Z   test_torch_nn_Conv2d_padding (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6514816Z   test_torch_nn_Conv2d_padding_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6515150Z   test_torch_nn_Conv2d_reflect_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6515499Z   test_torch_nn_Conv2d_reflect_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6515848Z   test_torch_nn_Conv2d_replicate_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6516188Z   test_torch_nn_Conv2d_replicate_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6516519Z   test_torch_nn_Conv2d_strided (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6516841Z   test_torch_nn_Conv2d_strided_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6517146Z   test_torch_nn_Conv2d_zero_batch (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6517477Z   test_torch_nn_Conv2d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6517807Z   test_torch_nn_Conv2d_zeros_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6518149Z   test_torch_nn_Conv2d_zeros_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6518451Z   test_torch_nn_Conv3d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6518738Z   test_torch_nn_Conv3d_1x1x1_no_bias (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6519062Z   test_torch_nn_Conv3d_1x1x1_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6519383Z   test_torch_nn_Conv3d_circular_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6519729Z   test_torch_nn_Conv3d_circular_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6520078Z   test_torch_nn_Conv3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6520391Z   test_torch_nn_Conv3d_dilated (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6520701Z   test_torch_nn_Conv3d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6521065Z   test_torch_nn_Conv3d_dilated_strided (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6521404Z   test_torch_nn_Conv3d_dilated_strided_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6521726Z   test_torch_nn_Conv3d_groups (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6522032Z   test_torch_nn_Conv3d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6522346Z   test_torch_nn_Conv3d_no_bias (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6522665Z   test_torch_nn_Conv3d_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6522999Z   test_torch_nn_Conv3d_pad_same (__main__.TestCppApiParity) ... ok (0.025s)
2023-01-11T22:00:01.6523322Z   test_torch_nn_Conv3d_pad_same_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6523652Z   test_torch_nn_Conv3d_pad_same_dilated (__main__.TestCppApiParity) ... ok (0.025s)
2023-01-11T22:00:01.6523992Z   test_torch_nn_Conv3d_pad_same_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6524309Z   test_torch_nn_Conv3d_pad_valid (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T22:00:01.6524632Z   test_torch_nn_Conv3d_pad_valid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6524968Z   test_torch_nn_Conv3d_replicate_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6525311Z   test_torch_nn_Conv3d_replicate_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6525641Z   test_torch_nn_Conv3d_stride (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6525961Z   test_torch_nn_Conv3d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6526295Z   test_torch_nn_Conv3d_stride_padding (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6526622Z   test_torch_nn_Conv3d_stride_padding_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6526949Z   test_torch_nn_Conv3d_zero_batch (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6527278Z   test_torch_nn_Conv3d_zero_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6527613Z   test_torch_nn_Conv3d_zeros_stride2_pad2 (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6527945Z   test_torch_nn_Conv3d_zeros_stride2_pad2_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6528274Z   test_torch_nn_ConvTranspose1d (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6528612Z   test_torch_nn_ConvTranspose1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6528937Z   test_torch_nn_ConvTranspose1d_dilated (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6529447Z   test_torch_nn_ConvTranspose1d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6529794Z   test_torch_nn_ConvTranspose1d_groups (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6530141Z   test_torch_nn_ConvTranspose1d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6530473Z   test_torch_nn_ConvTranspose1d_no_bias (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6530817Z   test_torch_nn_ConvTranspose1d_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6531151Z   test_torch_nn_ConvTranspose2d (__main__.TestCppApiParity) ... ok (0.021s)
2023-01-11T22:00:01.6531475Z   test_torch_nn_ConvTranspose2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6531811Z   test_torch_nn_ConvTranspose2d_dilated (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6532226Z   test_torch_nn_ConvTranspose2d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6532573Z   test_torch_nn_ConvTranspose2d_groups (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6532907Z   test_torch_nn_ConvTranspose2d_groups_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6533252Z   test_torch_nn_ConvTranspose2d_no_bias (__main__.TestCppApiParity) ... ok (0.019s)
2023-01-11T22:00:01.6533597Z   test_torch_nn_ConvTranspose2d_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6533930Z   test_torch_nn_ConvTranspose3d (__main__.TestCppApiParity) ... ok (0.017s)
2023-01-11T22:00:01.6534249Z   test_torch_nn_ConvTranspose3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6534625Z   test_torch_nn_ConvTranspose3d_dilated (__main__.TestCppApiParity) ... ok (0.021s)
2023-01-11T22:00:01.6534973Z   test_torch_nn_ConvTranspose3d_dilated_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6535312Z   test_torch_nn_CosineEmbeddingLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6535661Z   test_torch_nn_CosineEmbeddingLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6536012Z   test_torch_nn_CosineEmbeddingLoss_margin (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6536380Z   test_torch_nn_CosineEmbeddingLoss_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6536740Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6537121Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6537505Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6537893Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6538255Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6538632Z   test_torch_nn_CosineEmbeddingLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6538984Z   test_torch_nn_CrossEntropyLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6539284Z   test_torch_nn_CrossEntropyLoss_2d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6539632Z   test_torch_nn_CrossEntropyLoss_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6539984Z   test_torch_nn_CrossEntropyLoss_2d_ignore_index (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6540354Z   test_torch_nn_CrossEntropyLoss_2d_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6540720Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6541112Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6541511Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6541927Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6542328Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6542821Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_sum_reduction_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6543237Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_weight (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6543686Z   test_torch_nn_CrossEntropyLoss_2d_indices_target_smoothing_weight_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6544055Z   test_torch_nn_CrossEntropyLoss_2d_prob_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6544420Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6544793Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6545177Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6545592Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6546007Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_sum_reduction_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6546416Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_weight (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6546816Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_smoothing_weight_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6547192Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6547576Z   test_torch_nn_CrossEntropyLoss_2d_prob_target_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6547942Z   test_torch_nn_CrossEntropyLoss_2d_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6548291Z   test_torch_nn_CrossEntropyLoss_2d_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6548665Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6549056Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6549459Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6549867Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6550278Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6550691Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6551122Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6551554Z   test_torch_nn_CrossEntropyLoss_3d_indices_target_smoothing_sum_reduction_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6551952Z   test_torch_nn_CrossEntropyLoss_3d_prob_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6552315Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6552684Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_smoothing (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6553055Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_smoothing_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6553449Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6553861Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_smoothing_sum_reduction_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6554286Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6554649Z   test_torch_nn_CrossEntropyLoss_3d_prob_target_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6555014Z   test_torch_nn_CrossEntropyLoss_4d_prob_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6555374Z   test_torch_nn_CrossEntropyLoss_4d_prob_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6555743Z   test_torch_nn_CrossEntropyLoss_4d_prob_target_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6556109Z   test_torch_nn_CrossEntropyLoss_4d_prob_target_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6556523Z   test_torch_nn_CrossEntropyLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6556862Z   test_torch_nn_CrossEntropyLoss_dim_is_3 (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6557204Z   test_torch_nn_CrossEntropyLoss_dim_is_3_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6557552Z   test_torch_nn_CrossEntropyLoss_higher_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6557912Z   test_torch_nn_CrossEntropyLoss_higher_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6558265Z   test_torch_nn_CrossEntropyLoss_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6558608Z   test_torch_nn_CrossEntropyLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6558941Z   test_torch_nn_CrossMapLRN2d (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6559275Z   test_torch_nn_CrossMapLRN2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6559586Z   test_torch_nn_ELU (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6559876Z   test_torch_nn_ELU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6560182Z   test_torch_nn_ELU_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6560498Z   test_torch_nn_ELU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6560803Z   test_torch_nn_Embedding (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6561113Z   test_torch_nn_EmbeddingBag_discontiguous (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6561471Z   test_torch_nn_EmbeddingBag_discontiguous_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6561815Z   test_torch_nn_EmbeddingBag_max (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6562140Z   test_torch_nn_EmbeddingBag_max_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6562486Z   test_torch_nn_EmbeddingBag_max_padding_idx (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6562847Z   test_torch_nn_EmbeddingBag_max_padding_idx_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6563175Z   test_torch_nn_EmbeddingBag_mean (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6563509Z   test_torch_nn_EmbeddingBag_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6563853Z   test_torch_nn_EmbeddingBag_mean_padding_idx (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6564218Z   test_torch_nn_EmbeddingBag_mean_padding_idx_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6564551Z   test_torch_nn_EmbeddingBag_sparse (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6564900Z   test_torch_nn_EmbeddingBag_sparse_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6565233Z   test_torch_nn_EmbeddingBag_sum (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6565602Z   test_torch_nn_EmbeddingBag_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6565934Z   test_torch_nn_EmbeddingBag_sum_padding_idx (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6566291Z   test_torch_nn_EmbeddingBag_sum_padding_idx_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6566653Z   test_torch_nn_Embedding_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6566973Z   test_torch_nn_Embedding_discontiguous (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6567325Z   test_torch_nn_Embedding_discontiguous_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6567659Z   test_torch_nn_Embedding_sparse (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6568023Z   test_torch_nn_Embedding_sparse_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6568329Z   test_torch_nn_Flatten (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6568637Z   test_torch_nn_Flatten_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6568956Z   test_torch_nn_Flatten_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6569442Z   test_torch_nn_Flatten_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6569759Z   test_torch_nn_Fold (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6570063Z   test_torch_nn_Fold_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6570373Z   test_torch_nn_Fold_int_input (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6570688Z   test_torch_nn_Fold_int_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6571021Z   test_torch_nn_Fold_no_batch_dim_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6571363Z   test_torch_nn_Fold_no_batch_dim_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6571705Z   test_torch_nn_Fold_no_batch_dim_int_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6572038Z   test_torch_nn_Fold_no_batch_dim_int_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6572386Z   test_torch_nn_FractionalMaxPool2d_ratio (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6572744Z   test_torch_nn_FractionalMaxPool2d_ratio_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6573098Z   test_torch_nn_FractionalMaxPool2d_ratio_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6573481Z   test_torch_nn_FractionalMaxPool2d_ratio_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6573876Z   test_torch_nn_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6574296Z   test_torch_nn_FractionalMaxPool2d_ratio_no_batch_dim_no_random_samples_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6574669Z   test_torch_nn_FractionalMaxPool2d_size (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6575026Z   test_torch_nn_FractionalMaxPool2d_size_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6575391Z   test_torch_nn_FractionalMaxPool2d_size_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6575769Z   test_torch_nn_FractionalMaxPool2d_size_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6576152Z   test_torch_nn_FractionalMaxPool2d_size_no_batch_dim_no_random_samples (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6576573Z   test_torch_nn_FractionalMaxPool2d_size_no_batch_dim_no_random_samples_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6577036Z   test_torch_nn_FractionalMaxPool3d_asymsize (__main__.TestCppApiParity) ... ok (0.015s)
2023-01-11T22:00:01.6577402Z   test_torch_nn_FractionalMaxPool3d_asymsize_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6577749Z   test_torch_nn_FractionalMaxPool3d_ratio (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6578110Z   test_torch_nn_FractionalMaxPool3d_ratio_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6578481Z   test_torch_nn_FractionalMaxPool3d_ratio_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6578850Z   test_torch_nn_FractionalMaxPool3d_ratio_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6579299Z   test_torch_nn_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6579722Z   test_torch_nn_FractionalMaxPool3d_ratio_no_batch_dim_no_random_samples_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6580110Z   test_torch_nn_FractionalMaxPool3d_size (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6580456Z   test_torch_nn_FractionalMaxPool3d_size_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6580818Z   test_torch_nn_FractionalMaxPool3d_size_no_batch_dim (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6581203Z   test_torch_nn_FractionalMaxPool3d_size_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6581597Z   test_torch_nn_FractionalMaxPool3d_size_no_batch_dim_no_random_samples (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6582006Z   test_torch_nn_FractionalMaxPool3d_size_no_batch_dim_no_random_samples_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6582363Z   test_torch_nn_GELU (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6582741Z   test_torch_nn_GELU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6583049Z   test_torch_nn_GELU_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6583357Z   test_torch_nn_GELU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6583666Z   test_torch_nn_GLU (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6583970Z   test_torch_nn_GLU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6584262Z   test_torch_nn_GLU_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6584568Z   test_torch_nn_GLU_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6584892Z   test_torch_nn_GroupNorm_1d_affine (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6585202Z   test_torch_nn_GroupNorm_1d_affine_GN (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6585530Z   test_torch_nn_GroupNorm_1d_affine_GN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6585891Z   test_torch_nn_GroupNorm_1d_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6586236Z   test_torch_nn_GroupNorm_1d_affine_large_batch (__main__.TestCppApiParity) ... ok (0.019s)
2023-01-11T22:00:01.6586582Z   test_torch_nn_GroupNorm_1d_affine_large_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6586925Z   test_torch_nn_GroupNorm_1d_no_affine_IN (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6587269Z   test_torch_nn_GroupNorm_1d_no_affine_IN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6587614Z   test_torch_nn_GroupNorm_1d_no_affine_LN (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6587943Z   test_torch_nn_GroupNorm_1d_no_affine_LN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6588325Z   test_torch_nn_GroupNorm_2d_affine (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6588663Z   test_torch_nn_GroupNorm_2d_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6589010Z   test_torch_nn_GroupNorm_2d_affine_large_feature (__main__.TestCppApiParity) ... ok (0.026s)
2023-01-11T22:00:01.6589356Z   test_torch_nn_GroupNorm_2d_affine_large_feature_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6589704Z   test_torch_nn_GroupNorm_2d_no_affine_IN (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6590050Z   test_torch_nn_GroupNorm_2d_no_affine_IN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6590407Z   test_torch_nn_GroupNorm_2d_no_affine_LN (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6590750Z   test_torch_nn_GroupNorm_2d_no_affine_LN_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6591104Z   test_torch_nn_GroupNorm_2d_no_affine_large_feature (__main__.TestCppApiParity) ... ok (0.093s)
2023-01-11T22:00:01.6591472Z   test_torch_nn_GroupNorm_2d_no_affine_large_feature_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6591794Z   test_torch_nn_Hardshrink (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6592112Z   test_torch_nn_Hardshrink_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6592434Z   test_torch_nn_Hardshrink_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6592765Z   test_torch_nn_Hardshrink_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6593073Z   test_torch_nn_Hardtanh (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6593386Z   test_torch_nn_Hardtanh_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6593702Z   test_torch_nn_Hardtanh_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6594022Z   test_torch_nn_Hardtanh_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6594360Z   test_torch_nn_HingeEmbeddingLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6594706Z   test_torch_nn_HingeEmbeddingLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6595056Z   test_torch_nn_HingeEmbeddingLoss_margin (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6595403Z   test_torch_nn_HingeEmbeddingLoss_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6595784Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.007s)
2023-01-11T22:00:01.6596175Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6596568Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T22:00:01.6596945Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6597332Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.007s)
2023-01-11T22:00:01.6597720Z   test_torch_nn_HingeEmbeddingLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6598074Z   test_torch_nn_HingeEmbeddingLoss_scalar_margin (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6598451Z   test_torch_nn_HingeEmbeddingLoss_scalar_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6598792Z   test_torch_nn_HuberLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6599110Z   test_torch_nn_HuberLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6599452Z   test_torch_nn_InstanceNorm1d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6599781Z   test_torch_nn_InstanceNorm1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6600123Z   test_torch_nn_InstanceNorm1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6600479Z   test_torch_nn_InstanceNorm1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6600821Z   test_torch_nn_InstanceNorm1d_tracking_stats (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6601182Z   test_torch_nn_InstanceNorm1d_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6601587Z   test_torch_nn_InstanceNorm1d_tracking_stats_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6601960Z   test_torch_nn_InstanceNorm1d_tracking_stats_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6602311Z   test_torch_nn_InstanceNorm2d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6602642Z   test_torch_nn_InstanceNorm2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6602980Z   test_torch_nn_InstanceNorm2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6603320Z   test_torch_nn_InstanceNorm2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6603670Z   test_torch_nn_InstanceNorm2d_tracking_stats (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6604031Z   test_torch_nn_InstanceNorm2d_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6604399Z   test_torch_nn_InstanceNorm2d_tracking_stats_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6604769Z   test_torch_nn_InstanceNorm2d_tracking_stats_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6605122Z   test_torch_nn_InstanceNorm3d (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6605453Z   test_torch_nn_InstanceNorm3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6605780Z   test_torch_nn_InstanceNorm3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6606130Z   test_torch_nn_InstanceNorm3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6606485Z   test_torch_nn_InstanceNorm3d_tracking_stats (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6606844Z   test_torch_nn_InstanceNorm3d_tracking_stats_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6607202Z   test_torch_nn_InstanceNorm3d_tracking_stats_no_batch_dim (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6607583Z   test_torch_nn_InstanceNorm3d_tracking_stats_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6608582Z   test_torch_nn_KLDivLoss (__main__.TestCppApiParity) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2918: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.
2023-01-11T22:00:01.6609273Z   warnings.warn(
2023-01-11T22:00:01.6610178Z /var/lib/jenkins/workspace/test/cpp_api_parity/module_impl_check.py:149: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/api/include/torch/nn/functional/loss.h:57.)
2023-01-11T22:00:01.6610923Z   cpp_test_fn(arg_dict_file_path, module_file_path, forward_output_file_path, backward_grad_dict_file_path)
2023-01-11T22:00:01.6611187Z ok (0.008s)
2023-01-11T22:00:01.6611448Z   test_torch_nn_KLDivLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6611777Z   test_torch_nn_KLDivLoss_log_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6612110Z   test_torch_nn_KLDivLoss_log_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6612449Z   test_torch_nn_KLDivLoss_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6612781Z   test_torch_nn_KLDivLoss_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6613154Z   test_torch_nn_KLDivLoss_scalar_log_target (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6613511Z   test_torch_nn_KLDivLoss_scalar_log_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6613841Z   test_torch_nn_L1Loss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6614150Z   test_torch_nn_L1Loss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6614452Z   test_torch_nn_L1Loss_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6614771Z   test_torch_nn_L1Loss_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6615088Z   test_torch_nn_LPPool1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6615395Z   test_torch_nn_LPPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6615723Z   test_torch_nn_LPPool1d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6616064Z   test_torch_nn_LPPool1d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6616396Z   test_torch_nn_LPPool1d_norm (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6616713Z   test_torch_nn_LPPool1d_norm_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6617024Z   test_torch_nn_LPPool2d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6617341Z   test_torch_nn_LPPool2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6617657Z   test_torch_nn_LPPool2d_norm (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6617969Z   test_torch_nn_LPPool2d_norm_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6618309Z   test_torch_nn_LayerNorm_1d_elementwise_affine (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6618672Z   test_torch_nn_LayerNorm_1d_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6619028Z   test_torch_nn_LayerNorm_1d_empty_elementwise_affine (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6619402Z   test_torch_nn_LayerNorm_1d_empty_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6619769Z   test_torch_nn_LayerNorm_1d_no_elementwise_affine (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6620133Z   test_torch_nn_LayerNorm_1d_no_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6620479Z   test_torch_nn_LayerNorm_3d_elementwise_affine (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6620836Z   test_torch_nn_LayerNorm_3d_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6621196Z   test_torch_nn_LayerNorm_3d_no_affine_large_feature (__main__.TestCppApiParity) ... ok (0.052s)
2023-01-11T22:00:01.6621570Z   test_torch_nn_LayerNorm_3d_no_affine_large_feature_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6621920Z   test_torch_nn_LayerNorm_3d_no_elementwise_affine (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6622316Z   test_torch_nn_LayerNorm_3d_no_elementwise_affine_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6622731Z   test_torch_nn_LeakyReLU (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6623041Z   test_torch_nn_LeakyReLU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6623367Z   test_torch_nn_LeakyReLU_with_negval (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6623710Z   test_torch_nn_LeakyReLU_with_negval_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6624052Z   test_torch_nn_LeakyReLU_with_negval_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6624434Z   test_torch_nn_LeakyReLU_with_negval_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6624785Z   test_torch_nn_LeakyReLU_with_zero_negval (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6625136Z   test_torch_nn_LeakyReLU_with_zero_negval_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6625457Z   test_torch_nn_Linear (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6625751Z   test_torch_nn_Linear_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6626064Z   test_torch_nn_Linear_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6626396Z   test_torch_nn_Linear_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6626704Z   test_torch_nn_Linear_no_bias (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6627028Z   test_torch_nn_Linear_no_bias_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6627361Z   test_torch_nn_LocalResponseNorm_1d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6627705Z   test_torch_nn_LocalResponseNorm_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6628048Z   test_torch_nn_LocalResponseNorm_2d_uneven_pad (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6628410Z   test_torch_nn_LocalResponseNorm_2d_uneven_pad_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6628775Z   test_torch_nn_LocalResponseNorm_3d_custom_params (__main__.TestCppApiParity) ... ok (0.027s)
2023-01-11T22:00:01.6629135Z   test_torch_nn_LocalResponseNorm_3d_custom_params_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6629473Z   test_torch_nn_LogSigmoid (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6629791Z   test_torch_nn_LogSigmoid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6630115Z   test_torch_nn_LogSigmoid_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6630437Z   test_torch_nn_LogSigmoid_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6630759Z   test_torch_nn_LogSoftmax (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6631075Z   test_torch_nn_LogSoftmax_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6631403Z   test_torch_nn_LogSoftmax_multiparam (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6631736Z   test_torch_nn_LogSoftmax_multiparam_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6632086Z   test_torch_nn_LogSoftmax_multiparam_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6632447Z   test_torch_nn_LogSoftmax_multiparam_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6632787Z   test_torch_nn_LogSoftmax_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6633129Z   test_torch_nn_LogSoftmax_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6633490Z   test_torch_nn_MSELoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6633799Z   test_torch_nn_MSELoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6634095Z   test_torch_nn_MSELoss_prec (__main__.TestCppApiParity) ... ok (0.039s)
2023-01-11T22:00:01.6634414Z   test_torch_nn_MSELoss_prec_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6634731Z   test_torch_nn_MSELoss_scalar (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6635042Z   test_torch_nn_MSELoss_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6635371Z   test_torch_nn_MarginRankingLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6635745Z   test_torch_nn_MarginRankingLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6636097Z   test_torch_nn_MarginRankingLoss_margin (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6636444Z   test_torch_nn_MarginRankingLoss_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6636805Z   test_torch_nn_MarginRankingLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6637180Z   test_torch_nn_MarginRankingLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6637550Z   test_torch_nn_MarginRankingLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6637912Z   test_torch_nn_MarginRankingLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6638277Z   test_torch_nn_MarginRankingLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6638649Z   test_torch_nn_MarginRankingLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6638975Z   test_torch_nn_MaxPool1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6639290Z   test_torch_nn_MaxPool1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6639608Z   test_torch_nn_MaxPool1d_stride (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6639939Z   test_torch_nn_MaxPool1d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6640260Z   test_torch_nn_MaxPool2d_3d_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6640590Z   test_torch_nn_MaxPool2d_3d_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6640919Z   test_torch_nn_MaxPool2d_4d_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6641251Z   test_torch_nn_MaxPool2d_4d_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6641556Z   test_torch_nn_MaxPool3d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6641873Z   test_torch_nn_MaxPool3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6642195Z   test_torch_nn_MaxPool3d_stride (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6642515Z   test_torch_nn_MaxPool3d_stride_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6642849Z   test_torch_nn_MaxPool3d_stride_padding (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6643197Z   test_torch_nn_MaxPool3d_stride_padding_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6643520Z   test_torch_nn_Mish (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6643809Z   test_torch_nn_Mish_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6644120Z   test_torch_nn_Mish_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6644438Z   test_torch_nn_Mish_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6644794Z   test_torch_nn_MultiLabelMarginLoss (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6645123Z   test_torch_nn_MultiLabelMarginLoss_1d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6645482Z   test_torch_nn_MultiLabelMarginLoss_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6645862Z   test_torch_nn_MultiLabelMarginLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6646218Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6646600Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6647012Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6647397Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6647763Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6648146Z   test_torch_nn_MultiLabelMarginLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6648513Z   test_torch_nn_MultiLabelSoftMarginLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6648870Z   test_torch_nn_MultiLabelSoftMarginLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6649414Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6649820Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6650219Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6650607Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6651000Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6651391Z   test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6651773Z   test_torch_nn_MultiLabelSoftMarginLoss_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6652147Z   test_torch_nn_MultiLabelSoftMarginLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6652496Z   test_torch_nn_MultiMarginLoss (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6652806Z   test_torch_nn_MultiMarginLoss_1d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6653144Z   test_torch_nn_MultiMarginLoss_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6653496Z   test_torch_nn_MultiMarginLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6653834Z   test_torch_nn_MultiMarginLoss_margin (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6654187Z   test_torch_nn_MultiMarginLoss_margin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6654511Z   test_torch_nn_MultiMarginLoss_p (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6654846Z   test_torch_nn_MultiMarginLoss_p_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6655187Z   test_torch_nn_MultiMarginLoss_weights (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6655542Z   test_torch_nn_MultiMarginLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6655863Z   test_torch_nn_NLLLoss (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6656144Z   test_torch_nn_NLLLoss_2d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6656522Z   test_torch_nn_NLLLoss_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6656840Z   test_torch_nn_NLLLoss_2d_ignore_index (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6657184Z   test_torch_nn_NLLLoss_2d_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6657517Z   test_torch_nn_NLLLoss_2d_weights (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6657854Z   test_torch_nn_NLLLoss_2d_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6658186Z   test_torch_nn_NLLLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6658553Z   test_torch_nn_NLLLoss_dim_is_3 (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6658880Z   test_torch_nn_NLLLoss_dim_is_3_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6659211Z   test_torch_nn_NLLLoss_higher_dim (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6659531Z   test_torch_nn_NLLLoss_higher_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6659864Z   test_torch_nn_NLLLoss_ignore_index (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6660204Z   test_torch_nn_NLLLoss_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6660548Z   test_torch_nn_NLLLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.012s)
2023-01-11T22:00:01.6660907Z   test_torch_nn_NLLLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6661265Z   test_torch_nn_NLLLoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.010s)
2023-01-11T22:00:01.6661624Z   test_torch_nn_NLLLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6661971Z   test_torch_nn_NLLLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.010s)
2023-01-11T22:00:01.6662329Z   test_torch_nn_NLLLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6662738Z   test_torch_nn_NLLLoss_weights (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6663059Z   test_torch_nn_NLLLoss_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6663399Z   test_torch_nn_NLLLoss_weights_ignore_index (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6663756Z   test_torch_nn_NLLLoss_weights_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6664110Z   test_torch_nn_NLLLoss_weights_ignore_index_neg (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6664458Z   test_torch_nn_NLLLoss_weights_ignore_index_neg_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6664791Z   test_torch_nn_PReLU_1d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6665105Z   test_torch_nn_PReLU_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6665425Z   test_torch_nn_PReLU_1d_multiparam (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6665752Z   test_torch_nn_PReLU_1d_multiparam_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6666073Z   test_torch_nn_PReLU_2d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6666385Z   test_torch_nn_PReLU_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6666694Z   test_torch_nn_PReLU_2d_multiparam (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6667034Z   test_torch_nn_PReLU_2d_multiparam_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6667352Z   test_torch_nn_PReLU_3d (__main__.TestCppApiParity) ... ok (0.017s)
2023-01-11T22:00:01.6667702Z   test_torch_nn_PReLU_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6668012Z   test_torch_nn_PReLU_3d_multiparam (__main__.TestCppApiParity) ... ok (0.017s)
2023-01-11T22:00:01.6668350Z   test_torch_nn_PReLU_3d_multiparam_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6668676Z   test_torch_nn_PReLU_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6668986Z   test_torch_nn_PReLU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6669312Z   test_torch_nn_PairwiseDistance (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6669639Z   test_torch_nn_PairwiseDistance_broadcast_lhs (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6670052Z   test_torch_nn_PairwiseDistance_broadcast_lhs_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6670404Z   test_torch_nn_PairwiseDistance_broadcast_rhs (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6670770Z   test_torch_nn_PairwiseDistance_broadcast_rhs_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6671141Z   test_torch_nn_PairwiseDistance_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6671486Z   test_torch_nn_PairwiseDistance_no_batch_dim (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6671836Z   test_torch_nn_PairwiseDistance_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6672207Z   test_torch_nn_PairwiseDistance_with_non_default_args (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6672589Z   test_torch_nn_PairwiseDistance_with_non_default_args_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6672924Z   test_torch_nn_PixelShuffle (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6673248Z   test_torch_nn_PixelShuffle_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6673569Z   test_torch_nn_PixelUnshuffle (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6673898Z   test_torch_nn_PixelUnshuffle_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6674223Z   test_torch_nn_PoissonNLLLoss_full_loss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6674568Z   test_torch_nn_PoissonNLLLoss_full_loss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6674922Z   test_torch_nn_PoissonNLLLoss_full_loss_no_log_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6675290Z   test_torch_nn_PoissonNLLLoss_full_loss_no_log_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6675636Z   test_torch_nn_PoissonNLLLoss_no_full_loss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6675988Z   test_torch_nn_PoissonNLLLoss_no_full_loss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6676352Z   test_torch_nn_PoissonNLLLoss_no_full_loss_no_log_input (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6676714Z   test_torch_nn_PoissonNLLLoss_no_full_loss_no_log_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6677047Z   test_torch_nn_RReLU (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6677357Z   test_torch_nn_RReLU_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6677674Z   test_torch_nn_RReLU_with_up_down (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6677996Z   test_torch_nn_RReLU_with_up_down_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6678334Z   test_torch_nn_RReLU_with_up_down_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6678683Z   test_torch_nn_RReLU_with_up_down_scalar_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6679045Z   test_torch_nn_ReLU (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6679303Z   test_torch_nn_ReLU6 (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6679603Z   test_torch_nn_ReLU6_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6679906Z   test_torch_nn_ReLU6_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6680210Z   test_torch_nn_ReLU6_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6680545Z   test_torch_nn_ReLU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6680852Z   test_torch_nn_ReLU_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6681198Z   test_torch_nn_ReLU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6681512Z   test_torch_nn_ReflectionPad1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6681828Z   test_torch_nn_ReflectionPad1d_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6682172Z   test_torch_nn_ReflectionPad1d_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6682506Z   test_torch_nn_ReflectionPad1d_complex (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6682854Z   test_torch_nn_ReflectionPad1d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6683225Z   test_torch_nn_ReflectionPad1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6683552Z   test_torch_nn_ReflectionPad2d (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T22:00:01.6683855Z   test_torch_nn_ReflectionPad2d_complex (__main__.TestCppApiParity) ... ok (0.026s)
2023-01-11T22:00:01.6684206Z   test_torch_nn_ReflectionPad2d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6684578Z   test_torch_nn_ReflectionPad2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6684907Z   test_torch_nn_ReflectionPad2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6685264Z   test_torch_nn_ReflectionPad2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6685603Z   test_torch_nn_ReflectionPad3d (__main__.TestCppApiParity) ... ok (0.030s)
2023-01-11T22:00:01.6685918Z   test_torch_nn_ReflectionPad3d_complex (__main__.TestCppApiParity) ... ok (0.047s)
2023-01-11T22:00:01.6686260Z   test_torch_nn_ReflectionPad3d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6686627Z   test_torch_nn_ReflectionPad3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6686967Z   test_torch_nn_ReflectionPad3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.021s)
2023-01-11T22:00:01.6687326Z   test_torch_nn_ReflectionPad3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6687658Z   test_torch_nn_ReplicationPad1d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6687973Z   test_torch_nn_ReplicationPad1d_batch (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6688326Z   test_torch_nn_ReplicationPad1d_batch_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6688666Z   test_torch_nn_ReplicationPad1d_complex (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6689143Z   test_torch_nn_ReplicationPad1d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6689540Z   test_torch_nn_ReplicationPad1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6689880Z   test_torch_nn_ReplicationPad2d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6690185Z   test_torch_nn_ReplicationPad2d_complex (__main__.TestCppApiParity) ... ok (0.016s)
2023-01-11T22:00:01.6690600Z   test_torch_nn_ReplicationPad2d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6690971Z   test_torch_nn_ReplicationPad2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6691321Z   test_torch_nn_ReplicationPad2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6691672Z   test_torch_nn_ReplicationPad2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6692017Z   test_torch_nn_ReplicationPad3d (__main__.TestCppApiParity) ... ok (0.018s)
2023-01-11T22:00:01.6692338Z   test_torch_nn_ReplicationPad3d_complex (__main__.TestCppApiParity) ... ok (0.027s)
2023-01-11T22:00:01.6692716Z   test_torch_nn_ReplicationPad3d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6693092Z   test_torch_nn_ReplicationPad3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6693448Z   test_torch_nn_ReplicationPad3d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.014s)
2023-01-11T22:00:01.6693810Z   test_torch_nn_ReplicationPad3d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6694124Z   test_torch_nn_SELU (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6694422Z   test_torch_nn_SELU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6694732Z   test_torch_nn_SELU_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6695042Z   test_torch_nn_SELU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6695374Z   test_torch_nn_SampleModule_has_parity (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6695722Z   test_torch_nn_SampleModule_has_parity_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6696082Z   test_torch_nn_SampleModule_no_parity (__main__.TestCppApiParity) ... expected failure (0.011s)
2023-01-11T22:00:01.6696429Z   test_torch_nn_SampleModule_no_parity_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6696745Z   test_torch_nn_SiLU (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6697050Z   test_torch_nn_SiLU_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6697355Z   test_torch_nn_SiLU_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6697662Z   test_torch_nn_SiLU_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6697970Z   test_torch_nn_Sigmoid (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6698280Z   test_torch_nn_Sigmoid_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6698585Z   test_torch_nn_Sigmoid_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6698911Z   test_torch_nn_Sigmoid_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6699232Z   test_torch_nn_SmoothL1Loss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6699553Z   test_torch_nn_SmoothL1Loss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6699866Z   test_torch_nn_SmoothL1Loss_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6700203Z   test_torch_nn_SmoothL1Loss_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6700531Z   test_torch_nn_SoftMarginLoss (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6700849Z   test_torch_nn_SoftMarginLoss_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6701210Z   test_torch_nn_SoftMarginLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T22:00:01.6701588Z   test_torch_nn_SoftMarginLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6701994Z   test_torch_nn_SoftMarginLoss_no_batch_dim_none (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T22:00:01.6702351Z   test_torch_nn_SoftMarginLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6702805Z   test_torch_nn_SoftMarginLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... expected failure (0.008s)
2023-01-11T22:00:01.6703182Z   test_torch_nn_SoftMarginLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6703512Z   test_torch_nn_Softmax (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6703784Z   test_torch_nn_Softmax2d (__main__.TestCppApiParity) ... ok (0.013s)
2023-01-11T22:00:01.6704138Z   test_torch_nn_Softmax2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6704467Z   test_torch_nn_Softmax2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6704796Z   test_torch_nn_Softmax2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6705146Z   test_torch_nn_Softmax_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6705463Z   test_torch_nn_Softmax_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6705797Z   test_torch_nn_Softmax_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6706105Z   test_torch_nn_Softmax_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6706428Z   test_torch_nn_Softmax_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6706744Z   test_torch_nn_Softmin (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6707038Z   test_torch_nn_Softmin_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6707354Z   test_torch_nn_Softmin_multidim (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6707684Z   test_torch_nn_Softmin_multidim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6708012Z   test_torch_nn_Softmin_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6708332Z   test_torch_nn_Softmin_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6708652Z   test_torch_nn_Softmin_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6708975Z   test_torch_nn_Softmin_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6709277Z   test_torch_nn_Softplus (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6709567Z   test_torch_nn_Softplus_beta (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6709890Z   test_torch_nn_Softplus_beta_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6710219Z   test_torch_nn_Softplus_beta_threshold (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6710550Z   test_torch_nn_Softplus_beta_threshold_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6710896Z   test_torch_nn_Softplus_beta_threshold_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6711251Z   test_torch_nn_Softplus_beta_threshold_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6711608Z   test_torch_nn_Softplus_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6711904Z   test_torch_nn_Softshrink (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6712222Z   test_torch_nn_Softshrink_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6712540Z   test_torch_nn_Softshrink_lambda (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6712892Z   test_torch_nn_Softshrink_lambda_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6713229Z   test_torch_nn_Softshrink_lambda_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6713578Z   test_torch_nn_Softshrink_lambda_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6713903Z   test_torch_nn_Softsign (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6714203Z   test_torch_nn_Softsign_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6714517Z   test_torch_nn_Softsign_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6714846Z   test_torch_nn_Softsign_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6715181Z   test_torch_nn_Tanh (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6715488Z   test_torch_nn_Tanh_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6715800Z   test_torch_nn_Tanh_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6716116Z   test_torch_nn_Tanh_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6716417Z   test_torch_nn_Tanhshrink (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6716733Z   test_torch_nn_Tanhshrink_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6717053Z   test_torch_nn_Tanhshrink_scalar (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6717374Z   test_torch_nn_Tanhshrink_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6717706Z   test_torch_nn_Threshold_large_value (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6718051Z   test_torch_nn_Threshold_large_value_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6718389Z   test_torch_nn_Threshold_threshold_value (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6718726Z   test_torch_nn_Threshold_threshold_value_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6719075Z   test_torch_nn_Threshold_threshold_value_scalar (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6719438Z   test_torch_nn_Threshold_threshold_value_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6719818Z   test_torch_nn_TransformerDecoderLayer_gelu_activation (__main__.TestCppApiParity) ... ok (0.052s)
2023-01-11T22:00:01.6720204Z   test_torch_nn_TransformerDecoderLayer_gelu_activation_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6720593Z   test_torch_nn_TransformerDecoderLayer_relu_activation (__main__.TestCppApiParity) ... ok (0.051s)
2023-01-11T22:00:01.6741084Z   test_torch_nn_TransformerDecoderLayer_relu_activation_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6741881Z   test_torch_nn_TransformerEncoderLayer_gelu_activation (__main__.TestCppApiParity) ... ok (0.038s)
2023-01-11T22:00:01.6742677Z   test_torch_nn_TransformerEncoderLayer_gelu_activation_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6743420Z   test_torch_nn_TransformerEncoderLayer_relu_activation (__main__.TestCppApiParity) ... ok (0.038s)
2023-01-11T22:00:01.6744177Z   test_torch_nn_TransformerEncoderLayer_relu_activation_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6744885Z   test_torch_nn_Transformer_multilayer_coder (__main__.TestCppApiParity) ... ok (0.163s)
2023-01-11T22:00:01.6745547Z   test_torch_nn_Transformer_multilayer_coder_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6746212Z   test_torch_nn_TripletMarginLoss_no_batch_dim_mean (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6746931Z   test_torch_nn_TripletMarginLoss_no_batch_dim_mean_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6747784Z   test_torch_nn_TripletMarginLoss_no_batch_dim_none (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6748442Z   test_torch_nn_TripletMarginLoss_no_batch_dim_none_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6749104Z   test_torch_nn_TripletMarginLoss_no_batch_dim_sum (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6749776Z   test_torch_nn_TripletMarginLoss_no_batch_dim_sum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6750412Z   test_torch_nn_Unflatten_no_batch_dim (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6751030Z   test_torch_nn_Unflatten_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6751720Z   test_torch_nn_Unfold (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6752296Z   test_torch_nn_Unfold_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6752886Z   test_torch_nn_Unfold_int_input (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6753473Z   test_torch_nn_Unfold_int_input_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6754069Z   test_torch_nn_ZeroPad2d (__main__.TestCppApiParity) ... ok (0.010s)
2023-01-11T22:00:01.6754607Z   test_torch_nn_ZeroPad2d_complex (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6755207Z   test_torch_nn_ZeroPad2d_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6755854Z   test_torch_nn_ZeroPad2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6756459Z   test_torch_nn_ZeroPad2d_negative_dims (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6757082Z   test_torch_nn_ZeroPad2d_negative_dims_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6757702Z   test_torch_nn_ZeroPad2d_no_batch_dim (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6758314Z   test_torch_nn_ZeroPad2d_no_batch_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6758955Z   test_torch_nn_functional_BCELoss_no_reduce (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T22:00:01.6759594Z   test_torch_nn_functional_BCELoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6760265Z   test_torch_nn_functional_BCELoss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6760949Z   test_torch_nn_functional_BCELoss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6761598Z   test_torch_nn_functional_BCELoss_weights_no_reduce (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T22:00:01.6762262Z   test_torch_nn_functional_BCELoss_weights_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6762958Z   test_torch_nn_functional_BCELoss_weights_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6763681Z   test_torch_nn_functional_BCELoss_weights_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6765073Z   test_torch_nn_functional_BCEWithLogitsLoss_legacy_enum (__main__.TestCppApiParity) ... /opt/conda/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
2023-01-11T22:00:01.6765882Z   warnings.warn(warning.format(ret))
2023-01-11T22:00:01.6766231Z ok (0.006s)
2023-01-11T22:00:01.6766745Z   test_torch_nn_functional_BCEWithLogitsLoss_legacy_enum_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6767441Z   test_torch_nn_functional_BCEWithLogitsLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T22:00:01.6768156Z   test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6768960Z   test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6769720Z   test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6770305Z   test_torch_nn_functional_HingeEmbeddingLoss_margin_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6770901Z   test_torch_nn_functional_HingeEmbeddingLoss_margin_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6771528Z   test_torch_nn_functional_HingeEmbeddingLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6772392Z   test_torch_nn_functional_HingeEmbeddingLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6773052Z   test_torch_nn_functional_HuberLoss_delta (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6773672Z   test_torch_nn_functional_HuberLoss_delta_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6774319Z   test_torch_nn_functional_KLDivLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6774954Z   test_torch_nn_functional_KLDivLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6775554Z   test_torch_nn_functional_KLDivLoss_no_reduce_log_target (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6776239Z   test_torch_nn_functional_KLDivLoss_no_reduce_log_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6776889Z   test_torch_nn_functional_KLDivLoss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6777540Z   test_torch_nn_functional_KLDivLoss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6778198Z   test_torch_nn_functional_KLDivLoss_no_reduce_scalar_log_target (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6778841Z   test_torch_nn_functional_KLDivLoss_no_reduce_scalar_log_target_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6779577Z   test_torch_nn_functional_KLDivLoss_with_log_target_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6780286Z   test_torch_nn_functional_KLDivLoss_with_log_target_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6780985Z   test_torch_nn_functional_KLDivLoss_with_target_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6781673Z   test_torch_nn_functional_KLDivLoss_with_target_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6782302Z   test_torch_nn_functional_L1Loss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6782972Z   test_torch_nn_functional_L1Loss_no_reduce_complex (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6783645Z   test_torch_nn_functional_L1Loss_no_reduce_complex_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6784343Z   test_torch_nn_functional_L1Loss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6784991Z   test_torch_nn_functional_L1Loss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6785656Z   test_torch_nn_functional_L1Loss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6786316Z   test_torch_nn_functional_MSELoss_no_reduce (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T22:00:01.6786961Z   test_torch_nn_functional_MSELoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6787598Z   test_torch_nn_functional_MSELoss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6788431Z   test_torch_nn_functional_MSELoss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6789113Z   test_torch_nn_functional_MultiLabelMarginLoss_0d_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6789801Z   test_torch_nn_functional_MultiLabelMarginLoss_0d_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6790489Z   test_torch_nn_functional_MultiLabelMarginLoss_1d_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6791224Z   test_torch_nn_functional_MultiLabelMarginLoss_1d_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6792021Z   test_torch_nn_functional_MultiLabelMarginLoss_index_neg (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6792750Z   test_torch_nn_functional_MultiLabelMarginLoss_index_neg_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6793466Z   test_torch_nn_functional_MultiLabelMarginLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6794203Z   test_torch_nn_functional_MultiLabelMarginLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6794904Z   test_torch_nn_functional_MultiLabelSoftMarginLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6795639Z   test_torch_nn_functional_MultiLabelSoftMarginLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6796391Z   test_torch_nn_functional_MultiLabelSoftMarginLoss_weights_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6797170Z   test_torch_nn_functional_MultiLabelSoftMarginLoss_weights_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6797904Z   test_torch_nn_functional_MultiMarginLoss_1d_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6798684Z   test_torch_nn_functional_MultiMarginLoss_1d_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6799360Z   test_torch_nn_functional_MultiMarginLoss_margin_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6800081Z   test_torch_nn_functional_MultiMarginLoss_margin_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6800770Z   test_torch_nn_functional_MultiMarginLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6801443Z   test_torch_nn_functional_MultiMarginLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6802135Z   test_torch_nn_functional_MultiMarginLoss_p_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6802799Z   test_torch_nn_functional_MultiMarginLoss_p_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6803505Z   test_torch_nn_functional_MultiMarginLoss_weights_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6804219Z   test_torch_nn_functional_MultiMarginLoss_weights_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6804874Z   test_torch_nn_functional_NLLLoss2d_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6805517Z   test_torch_nn_functional_NLLLoss2d_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6806182Z   test_torch_nn_functional_NLLLoss2d_no_reduce_ignore_index (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6806859Z   test_torch_nn_functional_NLLLoss2d_no_reduce_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6807556Z   test_torch_nn_functional_NLLLoss2d_no_reduce_weights (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6808344Z   test_torch_nn_functional_NLLLoss2d_no_reduce_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6808991Z   test_torch_nn_functional_NLLLossNd_no_reduce (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6809769Z   test_torch_nn_functional_NLLLossNd_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6810460Z   test_torch_nn_functional_NLLLossNd_no_reduce_ignore_index (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6811182Z   test_torch_nn_functional_NLLLossNd_no_reduce_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6811847Z   test_torch_nn_functional_NLLLossNd_no_reduce_weights (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6812634Z   test_torch_nn_functional_NLLLossNd_no_reduce_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6813283Z   test_torch_nn_functional_NLLLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6813917Z   test_torch_nn_functional_NLLLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6814570Z   test_torch_nn_functional_NLLLoss_no_reduce_ignore_index (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6815243Z   test_torch_nn_functional_NLLLoss_no_reduce_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6815918Z   test_torch_nn_functional_NLLLoss_no_reduce_weights (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6816587Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6817284Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6818013Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6818756Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_neg (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6819440Z   test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_neg_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6820110Z   test_torch_nn_functional_Padding122112_3dcircular (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6820790Z   test_torch_nn_functional_Padding122112_3dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6821465Z   test_torch_nn_functional_Padding1221_2dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6822143Z   test_torch_nn_functional_Padding1221_2dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6822875Z   test_torch_nn_functional_Padding12_1dcircular (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6823559Z   test_torch_nn_functional_Padding12_1dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6824231Z   test_torch_nn_functional_Padding2322_2dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6824887Z   test_torch_nn_functional_Padding2322_2dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6825557Z   test_torch_nn_functional_Padding31_1dcircular (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6826202Z   test_torch_nn_functional_Padding31_1dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6826869Z   test_torch_nn_functional_Padding322112_3dcircular (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6827515Z   test_torch_nn_functional_Padding322112_3dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6828185Z   test_torch_nn_functional_Padding332122_3dcircular (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6828974Z   test_torch_nn_functional_Padding332122_3dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6829608Z   test_torch_nn_functional_Padding3331_2dcircular (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6830242Z   test_torch_nn_functional_Padding3331_2dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6830895Z   test_torch_nn_functional_Padding33_1dcircular (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6831557Z   test_torch_nn_functional_Padding33_1dcircular_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6832208Z   test_torch_nn_functional_PoissonNLLLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6833031Z   test_torch_nn_functional_PoissonNLLLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6833686Z   test_torch_nn_functional_SmoothL1Loss_beta (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6834323Z   test_torch_nn_functional_SmoothL1Loss_beta_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6834942Z   test_torch_nn_functional_SmoothL1Loss_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6835603Z   test_torch_nn_functional_SmoothL1Loss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6836272Z   test_torch_nn_functional_SmoothL1Loss_no_reduce_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6836953Z   test_torch_nn_functional_SmoothL1Loss_no_reduce_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6837609Z   test_torch_nn_functional_SmoothL1Loss_zero_beta (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6838230Z   test_torch_nn_functional_SmoothL1Loss_zero_beta_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6838863Z   test_torch_nn_functional_SoftMarginLoss_no_reduce (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6839489Z   test_torch_nn_functional_SoftMarginLoss_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6840067Z   test_torch_nn_functional_interpolate_bicubic_2d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6840753Z   test_torch_nn_functional_interpolate_bicubic_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6841410Z   test_torch_nn_functional_interpolate_bicubic_2d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6842027Z   test_torch_nn_functional_interpolate_bicubic_2d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6842656Z   test_torch_nn_functional_interpolate_bicubic_scale_2d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6843321Z   test_torch_nn_functional_interpolate_bicubic_scale_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6844008Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_shared_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6844749Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_shared_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6845455Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6846105Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6846909Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6847687Z   test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6848469Z   test_torch_nn_functional_interpolate_bicubic_tuple_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6849359Z   test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6850108Z   test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6850814Z   test_torch_nn_functional_interpolate_bicubic_tuple_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6851430Z   test_torch_nn_functional_interpolate_bilinear_2d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6852221Z   test_torch_nn_functional_interpolate_bilinear_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6852909Z   test_torch_nn_functional_interpolate_bilinear_2d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6853570Z   test_torch_nn_functional_interpolate_bilinear_2d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6854234Z   test_torch_nn_functional_interpolate_bilinear_scale_2d (__main__.TestCppApiParity) ... ok (0.009s)
2023-01-11T22:00:01.6854908Z   test_torch_nn_functional_interpolate_bilinear_scale_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6855619Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6856331Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6857049Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6857742Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6858524Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6858895Z   test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6859190Z   test_torch_nn_functional_interpolate_bilinear_tuple_2d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6859508Z   test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6859890Z   test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6860257Z   test_torch_nn_functional_interpolate_bilinear_tuple_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6860552Z   test_torch_nn_functional_interpolate_linear_1d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6860868Z   test_torch_nn_functional_interpolate_linear_1d_align_corners (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6861227Z   test_torch_nn_functional_interpolate_linear_1d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6861569Z   test_torch_nn_functional_interpolate_linear_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6861869Z   test_torch_nn_functional_interpolate_linear_1d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6862220Z   test_torch_nn_functional_interpolate_linear_1d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6862581Z   test_torch_nn_functional_interpolate_linear_scale_1d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6863047Z   test_torch_nn_functional_interpolate_linear_scale_1d_align_corners (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6863413Z   test_torch_nn_functional_interpolate_linear_scale_1d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6863769Z   test_torch_nn_functional_interpolate_linear_scale_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6864070Z   test_torch_nn_functional_interpolate_linear_tuple_1d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6864410Z   test_torch_nn_functional_interpolate_linear_tuple_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6864700Z   test_torch_nn_functional_interpolate_nearest_1d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6865119Z   test_torch_nn_functional_interpolate_nearest_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6865441Z   test_torch_nn_functional_interpolate_nearest_1d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6865774Z   test_torch_nn_functional_interpolate_nearest_1d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6866073Z   test_torch_nn_functional_interpolate_nearest_2d (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T22:00:01.6866404Z   test_torch_nn_functional_interpolate_nearest_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6866731Z   test_torch_nn_functional_interpolate_nearest_2d_launch_configs (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6867110Z   test_torch_nn_functional_interpolate_nearest_2d_launch_configs_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6867411Z   test_torch_nn_functional_interpolate_nearest_2d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6867771Z   test_torch_nn_functional_interpolate_nearest_2d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6868073Z   test_torch_nn_functional_interpolate_nearest_3d (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6868417Z   test_torch_nn_functional_interpolate_nearest_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6868703Z   test_torch_nn_functional_interpolate_nearest_3d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6869044Z   test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6869340Z   test_torch_nn_functional_interpolate_nearest_scale_1d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6869699Z   test_torch_nn_functional_interpolate_nearest_scale_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6869987Z   test_torch_nn_functional_interpolate_nearest_scale_2d (__main__.TestCppApiParity) ... ok (0.008s)
2023-01-11T22:00:01.6870337Z   test_torch_nn_functional_interpolate_nearest_scale_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6870647Z   test_torch_nn_functional_interpolate_nearest_scale_3d (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6871007Z   test_torch_nn_functional_interpolate_nearest_scale_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6871323Z   test_torch_nn_functional_interpolate_nearest_tuple_1d (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6871665Z   test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6871964Z   test_torch_nn_functional_interpolate_nearest_tuple_2d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6872318Z   test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6872725Z   test_torch_nn_functional_interpolate_nearest_tuple_3d (__main__.TestCppApiParity) ... ok (0.012s)
2023-01-11T22:00:01.6873084Z   test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6873382Z   test_torch_nn_functional_interpolate_trilinear_3d (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6873702Z   test_torch_nn_functional_interpolate_trilinear_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6874013Z   test_torch_nn_functional_interpolate_trilinear_3d_zero_dim (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6874353Z   test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6874721Z   test_torch_nn_functional_interpolate_trilinear_scale_3d (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6875055Z   test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners (__main__.TestCppApiParity) ... ok (0.011s)
2023-01-11T22:00:01.6875443Z   test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6875791Z   test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6876092Z   test_torch_nn_functional_interpolate_trilinear_tuple_3d (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6876421Z   test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners (__main__.TestCppApiParity) ... ok (0.007s)
2023-01-11T22:00:01.6876794Z   test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6877147Z   test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6877427Z   test_torch_nn_functional_log_softmax_dim0 (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6877756Z   test_torch_nn_functional_log_softmax_dim0_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6878011Z   test_torch_nn_functional_log_softmax_dim3 (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6878341Z   test_torch_nn_functional_log_softmax_dim3_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6878625Z   test_torch_nn_functional_log_softmax_lastdim (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T22:00:01.6878966Z   test_torch_nn_functional_log_softmax_lastdim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6879248Z   test_torch_nn_functional_log_softmax_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6879570Z   test_torch_nn_functional_log_softmax_scalar_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6879853Z   test_torch_nn_functional_log_softmax_spatial (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6880220Z   test_torch_nn_functional_log_softmax_spatial_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6880607Z   test_torch_nn_functional_log_softmax_spatial_special (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6880936Z   test_torch_nn_functional_log_softmax_spatial_special_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6881271Z   test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6881666Z   test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6881969Z   test_torch_nn_functional_sample_functional_has_parity (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6882438Z   test_torch_nn_functional_sample_functional_has_parity_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6882783Z   test_torch_nn_functional_sample_functional_no_parity (__main__.TestCppApiParity) ... expected failure (0.004s)
2023-01-11T22:00:01.6883143Z   test_torch_nn_functional_sample_functional_no_parity_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6883447Z   test_torch_nn_functional_softmax_functional_dim0 (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6883807Z   test_torch_nn_functional_softmax_functional_dim0_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6884098Z   test_torch_nn_functional_softmax_functional_dim3 (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6884539Z   test_torch_nn_functional_softmax_functional_dim3_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6884861Z   test_torch_nn_functional_softmax_functional_scalar (__main__.TestCppApiParity) ... ok (0.004s)
2023-01-11T22:00:01.6885254Z   test_torch_nn_functional_softmax_functional_scalar_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6885546Z   test_torch_nn_functional_softmax_lastdim (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T22:00:01.6885855Z   test_torch_nn_functional_softmax_lastdim_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6886124Z   test_torch_nn_functional_softmax_lastdim_dtype (__main__.TestCppApiParity) ... ok (0.006s)
2023-01-11T22:00:01.6886466Z   test_torch_nn_functional_softmax_lastdim_dtype_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6886740Z   test_torch_nn_functional_softmax_spatial (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6887071Z   test_torch_nn_functional_softmax_spatial_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6887338Z   test_torch_nn_functional_softmax_spatial_dtype (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6887682Z   test_torch_nn_functional_softmax_spatial_dtype_cuda (__main__.TestCppApiParity) ... skip: Excluded from CUDA tests (0.000s)
2023-01-11T22:00:01.6887965Z   test_torch_nn_functional_softmax_spatial_special (__main__.TestCppApiParity) ... ok (0.005s)
2023-01-11T22:00:01.6888296Z   test_torch_nn_functional_softmax_spatial_special_cuda (__main__.TestCppApiParity) ... skip: CUDA unavailable (0.000s)
2023-01-11T22:00:01.6888310Z 
2023-01-11T22:00:01.6888744Z ----------------------------------------------------------------------
2023-01-11T22:00:01.6888886Z Ran 1100 tests in 5.570s
2023-01-11T22:00:01.6888896Z 
2023-01-11T22:00:01.6889222Z OK (skipped=550, expected failures=17)
2023-01-11T22:00:01.6889232Z 
2023-01-11T22:00:01.6889381Z Generating XML reports...
2023-01-11T22:00:01.6889952Z Generated XML report: test-reports/python-unittest/test_cpp_api_parity/TEST-TestCppApiParity-20230111215955.xml
2023-01-11T22:00:01.6889968Z 
2023-01-11T22:00:01.6890464Z ##[endgroup]
2023-01-11T22:00:01.6891022Z FINISHED PRINTING LOG FILE of test_cpp_api_parity (/var/lib/jenkins/workspace/test/test-reports/test_cpp_api_parity_cteeoalo)
2023-01-11T22:00:01.6891032Z 
2023-01-11T22:00:01.6891378Z Running test_cpp_extensions_aot_ninja ... [2023-01-11 22:00:01.643541]
2023-01-11T22:00:03.1850608Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:03.2880590Z running install
2023-01-11T22:00:03.2881539Z /opt/conda/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T22:00:03.2881925Z   warnings.warn(
2023-01-11T22:00:03.2966649Z running build
2023-01-11T22:00:03.2966969Z running build_py
2023-01-11T22:00:03.3017900Z creating build
2023-01-11T22:00:03.3018499Z creating build/lib.linux-x86_64-cpython-310
2023-01-11T22:00:03.3019074Z creating build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension
2023-01-11T22:00:03.3021109Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension
2023-01-11T22:00:03.3021408Z running build_ext
2023-01-11T22:00:03.3318485Z building 'torch_test_cpp_extension.cpp' extension
2023-01-11T22:00:03.3318913Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310
2023-01-11T22:00:03.3600365Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T22:00:03.3600900Z Compiling objects...
2023-01-11T22:00:03.3601275Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:04.3451677Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/extension.o.d -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:04.3453368Z In file included from /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/Exceptions.h:14:0,
2023-01-11T22:00:04.3453867Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2023-01-11T22:00:04.3454331Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/torch/extension.h:6,
2023-01-11T22:00:04.3454695Z                  from /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp:1:
2023-01-11T22:00:04.3478163Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2023-01-11T22:00:04.3478638Z /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp:40:53:   required from here
2023-01-11T22:00:04.3479527Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than the type of its field ‘pybind11::class_<MatrixMultiplier>::<anonymous>’ [-Wattributes]
2023-01-11T22:00:04.3480035Z  class class_ : public detail::generic_type {
2023-01-11T22:00:04.3480238Z        ^~~~~~
2023-01-11T22:00:04.3480912Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2023-01-11T22:00:04.3545354Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/cpp.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:04.7251418Z building 'torch_test_cpp_extension.ort' extension
2023-01-11T22:00:04.7534050Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T22:00:04.7556846Z Compiling objects...
2023-01-11T22:00:04.7557090Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:05.6821518Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/ort_extension.o.d -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/ort_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/ort_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=ort -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:05.6866145Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/ort_extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/ort.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:06.0141173Z building 'torch_test_cpp_extension.rng' extension
2023-01-11T22:00:06.0426508Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T22:00:06.0426941Z Compiling objects...
2023-01-11T22:00:06.0427197Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:07.1596365Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/rng_extension.o.d -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:07.1598055Z In file included from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8:0,
2023-01-11T22:00:07.1598530Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2023-01-11T22:00:07.1598953Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2023-01-11T22:00:07.1599462Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:8,
2023-01-11T22:00:07.1599828Z                  from /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp:6:
2023-01-11T22:00:07.1600357Z /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1008:0: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2023-01-11T22:00:07.1600681Z  # pragma unroll
2023-01-11T22:00:07.1600875Z  
2023-01-11T22:00:07.1643929Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-310/rng_extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/rng.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:07.5359460Z running install_lib
2023-01-11T22:00:07.5399889Z creating install
2023-01-11T22:00:07.5400232Z creating install/opt
2023-01-11T22:00:07.5400609Z creating install/opt/conda
2023-01-11T22:00:07.5401011Z creating install/opt/conda/lib
2023-01-11T22:00:07.5401348Z creating install/opt/conda/lib/python3.10
2023-01-11T22:00:07.5401947Z creating install/opt/conda/lib/python3.10/site-packages
2023-01-11T22:00:07.5402543Z creating install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:07.5404103Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:07.5405243Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/cpp.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:07.5476089Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/ort.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:07.5548430Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/rng.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:07.5625042Z byte-compiling ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-310.pyc
2023-01-11T22:00:07.5625865Z running install_egg_info
2023-01-11T22:00:07.5721016Z running egg_info
2023-01-11T22:00:07.5721620Z creating torch_test_cpp_extension.egg-info
2023-01-11T22:00:07.5754917Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2023-01-11T22:00:07.5756438Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2023-01-11T22:00:07.5758341Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2023-01-11T22:00:07.5759522Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:07.5794630Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:07.5799968Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:07.5800665Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension-0.0.0-py3.10.egg-info
2023-01-11T22:00:07.5804114Z running install_scripts
2023-01-11T22:00:09.3961386Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:09.4153990Z running install
2023-01-11T22:00:09.4155276Z /opt/conda/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T22:00:09.4155658Z   warnings.warn(
2023-01-11T22:00:09.4239410Z running build
2023-01-11T22:00:09.4239702Z running build_ext
2023-01-11T22:00:09.4531055Z building 'no_python_abi_suffix_test' extension
2023-01-11T22:00:09.4531441Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build
2023-01-11T22:00:09.4531913Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310
2023-01-11T22:00:09.4804233Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T22:00:09.4804692Z Compiling objects...
2023-01-11T22:00:09.4804921Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:09.5391216Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/include/python3.10 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:09.5425177Z creating build/lib.linux-x86_64-cpython-310
2023-01-11T22:00:09.5427360Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/no_python_abi_suffix_test.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/no_python_abi_suffix_test.so
2023-01-11T22:00:09.5995637Z running install_lib
2023-01-11T22:00:09.6033311Z creating install
2023-01-11T22:00:09.6034534Z creating install/opt
2023-01-11T22:00:09.6034923Z creating install/opt/conda
2023-01-11T22:00:09.6035299Z creating install/opt/conda/lib
2023-01-11T22:00:09.6035674Z creating install/opt/conda/lib/python3.10
2023-01-11T22:00:09.6036301Z creating install/opt/conda/lib/python3.10/site-packages
2023-01-11T22:00:09.6036752Z copying build/lib.linux-x86_64-cpython-310/no_python_abi_suffix_test.so -> ./install/opt/conda/lib/python3.10/site-packages
2023-01-11T22:00:09.6039907Z running install_egg_info
2023-01-11T22:00:09.6131606Z running egg_info
2023-01-11T22:00:09.6131981Z creating no_python_abi_suffix_test.egg-info
2023-01-11T22:00:09.6163933Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2023-01-11T22:00:09.6165521Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2023-01-11T22:00:09.6167410Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2023-01-11T22:00:09.6168097Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:09.6201884Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:09.6206651Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:09.6207458Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/lib/python3.10/site-packages/no_python_abi_suffix_test-0.0.0-py3.10.egg-info
2023-01-11T22:00:09.6211108Z running install_scripts
2023-01-11T22:00:09.9027605Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:09.902393]
2023-01-11T22:00:12.0781169Z 
2023-01-11T22:00:12.0781720Z Expand the folded group to see the log file of test_cpp_extensions_aot_ninja
2023-01-11T22:00:12.0783119Z ##[group]PRINTING LOG FILE of test_cpp_extensions_aot_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_ninja_f88xphfw)
2023-01-11T22:00:12.0783906Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:12.0784095Z 
2023-01-11T22:00:12.0784204Z Running tests...
2023-01-11T22:00:12.0784640Z ----------------------------------------------------------------------
2023-01-11T22:00:12.0785648Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_aot_ninja
2023-01-11T22:00:12.0786005Z   test_backward (__main__.TestCppExtensionAOT) ... ok (0.011s)
2023-01-11T22:00:12.0786306Z   test_cublas_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:12.0786639Z   test_cuda_dlink_libs (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:12.0786969Z   test_cuda_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:12.0787300Z   test_cusolver_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:12.0787606Z   test_extension_function (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:12.0787907Z   test_extension_module (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:12.0788312Z   test_no_python_abi_suffix_sets_the_correct_library_name (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:12.0788619Z   test_optional (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:12.0788883Z   test_add (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:12.0789150Z   test_conv_backend_override (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:12.0789425Z   test_unregistered (__main__.TestORTTensor) ... ok (0.006s)
2023-01-11T22:00:12.0789666Z   test_zeros (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:12.0789945Z   test_pybind_return_types (__main__.TestPybindTypeCasters) ... ok (0.001s)
2023-01-11T22:00:12.0790230Z   test_rng (__main__.TestRNGExtension) ... ok (0.002s)
2023-01-11T22:00:12.0790500Z   test_torch_library (__main__.TestTorchLibrary) ... skip: CUDA not found (0.001s)
2023-01-11T22:00:12.0790676Z 
2023-01-11T22:00:12.0790885Z ----------------------------------------------------------------------
2023-01-11T22:00:12.0791231Z Ran 16 tests in 0.029s
2023-01-11T22:00:12.0791345Z 
2023-01-11T22:00:12.0791419Z OK (skipped=5)
2023-01-11T22:00:12.0791513Z 
2023-01-11T22:00:12.0791602Z Generating XML reports...
2023-01-11T22:00:12.0792063Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestCppExtensionAOT-20230111220011.xml
2023-01-11T22:00:12.0792618Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestORTTensor-20230111220011.xml
2023-01-11T22:00:12.0793232Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestPybindTypeCasters-20230111220011.xml
2023-01-11T22:00:12.0793790Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestRNGExtension-20230111220011.xml
2023-01-11T22:00:12.0794328Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestTorchLibrary-20230111220011.xml
2023-01-11T22:00:12.0794569Z 
2023-01-11T22:00:12.0794827Z ##[endgroup]
2023-01-11T22:00:12.0795239Z FINISHED PRINTING LOG FILE of test_cpp_extensions_aot_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_ninja_f88xphfw)
2023-01-11T22:00:12.0795481Z 
2023-01-11T22:00:12.0795684Z Running test_cpp_extensions_aot_no_ninja ... [2023-01-11 22:00:12.078338]
2023-01-11T22:00:13.5554071Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:13.6542917Z running install
2023-01-11T22:00:13.6543710Z /opt/conda/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T22:00:13.6544098Z   warnings.warn(
2023-01-11T22:00:13.6629428Z running build
2023-01-11T22:00:13.6629603Z running build_py
2023-01-11T22:00:13.6667049Z creating build
2023-01-11T22:00:13.6667540Z creating build/lib.linux-x86_64-cpython-310
2023-01-11T22:00:13.6668063Z creating build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension
2023-01-11T22:00:13.6668772Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension
2023-01-11T22:00:13.6670020Z running build_ext
2023-01-11T22:00:13.6681559Z building 'torch_test_cpp_extension.cpp' extension
2023-01-11T22:00:13.6682372Z creating build/temp.linux-x86_64-cpython-310
2023-01-11T22:00:13.6685559Z gcc -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c extension.cpp -o build/temp.linux-x86_64-cpython-310/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:14.6284933Z In file included from /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/Exceptions.h:14:0,
2023-01-11T22:00:14.6285452Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2023-01-11T22:00:14.6285893Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/torch/extension.h:6,
2023-01-11T22:00:14.6286178Z                  from extension.cpp:1:
2023-01-11T22:00:14.6286753Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2023-01-11T22:00:14.6287085Z extension.cpp:40:53:   required from here
2023-01-11T22:00:14.6287785Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than the type of its field ‘pybind11::class_<MatrixMultiplier>::<anonymous>’ [-Wattributes]
2023-01-11T22:00:14.6288239Z  class class_ : public detail::generic_type {
2023-01-11T22:00:14.6288445Z        ^~~~~~
2023-01-11T22:00:14.6289210Z /opt/conda/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2023-01-11T22:00:14.6291611Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib build/temp.linux-x86_64-cpython-310/extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/cpp.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:14.9749275Z building 'torch_test_cpp_extension.ort' extension
2023-01-11T22:00:14.9751183Z gcc -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c ort_extension.cpp -o build/temp.linux-x86_64-cpython-310/ort_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=ort -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:15.8496931Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib build/temp.linux-x86_64-cpython-310/ort_extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/ort.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:16.1639259Z building 'torch_test_cpp_extension.rng' extension
2023-01-11T22:00:16.1641197Z gcc -pthread -B /opt/conda/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/conda/include -fPIC -O2 -isystem /opt/conda/include -fPIC -I/opt/conda/lib/python3.10/site-packages/torch/include -I/opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/lib/python3.10/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/include/python3.10 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-310/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T22:00:17.1334498Z In file included from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8:0,
2023-01-11T22:00:17.1335255Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2023-01-11T22:00:17.1335912Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2023-01-11T22:00:17.1336397Z                  from /opt/conda/lib/python3.10/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:8,
2023-01-11T22:00:17.1336700Z                  from rng_extension.cpp:6:
2023-01-11T22:00:17.1337141Z /opt/conda/lib/python3.10/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1008:0: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2023-01-11T22:00:17.1337436Z  # pragma unroll
2023-01-11T22:00:17.1337601Z  
2023-01-11T22:00:17.1340899Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib build/temp.linux-x86_64-cpython-310/rng_extension.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/rng.cpython-310-x86_64-linux-gnu.so
2023-01-11T22:00:17.4897723Z running install_lib
2023-01-11T22:00:17.4937837Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/cpp.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:17.5022503Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/ort.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:17.5109253Z copying build/lib.linux-x86_64-cpython-310/torch_test_cpp_extension/rng.cpython-310-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension
2023-01-11T22:00:17.5198201Z running install_egg_info
2023-01-11T22:00:17.5292463Z running egg_info
2023-01-11T22:00:17.5323513Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2023-01-11T22:00:17.5332059Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2023-01-11T22:00:17.5338981Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2023-01-11T22:00:17.5378825Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:17.5384540Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T22:00:17.5390429Z removing './install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension-0.0.0-py3.10.egg-info' (and everything under it)
2023-01-11T22:00:17.5392166Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/lib/python3.10/site-packages/torch_test_cpp_extension-0.0.0-py3.10.egg-info
2023-01-11T22:00:17.5395505Z running install_scripts
2023-01-11T22:00:19.3135096Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:19.3326041Z running install
2023-01-11T22:00:19.3327365Z /opt/conda/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T22:00:19.3327799Z   warnings.warn(
2023-01-11T22:00:19.3428648Z running build
2023-01-11T22:00:19.3428948Z running build_ext
2023-01-11T22:00:19.3722266Z building 'no_python_abi_suffix_test' extension
2023-01-11T22:00:19.3996078Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/build.ninja...
2023-01-11T22:00:19.4002147Z Compiling objects...
2023-01-11T22:00:19.4002592Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:19.4244069Z ninja: no work to do.
2023-01-11T22:00:19.4280182Z g++ -pthread -B /opt/conda/compiler_compat -shared -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-310/no_python_abi_suffix_test.o -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-310/no_python_abi_suffix_test.so
2023-01-11T22:00:19.4848026Z running install_lib
2023-01-11T22:00:19.4885750Z copying build/lib.linux-x86_64-cpython-310/no_python_abi_suffix_test.so -> ./install/opt/conda/lib/python3.10/site-packages
2023-01-11T22:00:19.4890576Z running install_egg_info
2023-01-11T22:00:19.4981877Z running egg_info
2023-01-11T22:00:19.5013679Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2023-01-11T22:00:19.5039911Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2023-01-11T22:00:19.5048729Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2023-01-11T22:00:19.5088513Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:19.5095205Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T22:00:19.5104033Z removing './install/opt/conda/lib/python3.10/site-packages/no_python_abi_suffix_test-0.0.0-py3.10.egg-info' (and everything under it)
2023-01-11T22:00:19.5105824Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/lib/python3.10/site-packages/no_python_abi_suffix_test-0.0.0-py3.10.egg-info
2023-01-11T22:00:19.5110272Z running install_scripts
2023-01-11T22:00:19.7930897Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:19.792701]
2023-01-11T22:00:21.9528722Z 
2023-01-11T22:00:21.9529287Z Expand the folded group to see the log file of test_cpp_extensions_aot_no_ninja
2023-01-11T22:00:21.9530421Z ##[group]PRINTING LOG FILE of test_cpp_extensions_aot_no_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_no_ninja_wgarsge_)
2023-01-11T22:00:21.9531260Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:21.9531430Z 
2023-01-11T22:00:21.9531505Z Running tests...
2023-01-11T22:00:21.9531811Z ----------------------------------------------------------------------
2023-01-11T22:00:21.9532379Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_aot_no_ninja
2023-01-11T22:00:21.9532777Z   test_backward (__main__.TestCppExtensionAOT) ... ok (0.011s)
2023-01-11T22:00:21.9533344Z   test_cublas_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:21.9533955Z   test_cuda_dlink_libs (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:21.9534443Z   test_cuda_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:21.9534802Z   test_cusolver_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.000s)
2023-01-11T22:00:21.9535106Z   test_extension_function (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:21.9535593Z   test_extension_module (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:21.9535924Z   test_no_python_abi_suffix_sets_the_correct_library_name (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:21.9536244Z   test_optional (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T22:00:21.9536490Z   test_add (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:21.9536759Z   test_conv_backend_override (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:21.9538573Z   test_unregistered (__main__.TestORTTensor) ... ok (0.006s)
2023-01-11T22:00:21.9538936Z   test_zeros (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T22:00:21.9539410Z   test_pybind_return_types (__main__.TestPybindTypeCasters) ... ok (0.001s)
2023-01-11T22:00:21.9539905Z   test_rng (__main__.TestRNGExtension) ... ok (0.002s)
2023-01-11T22:00:21.9540577Z   test_torch_library (__main__.TestTorchLibrary) ... skip: CUDA not found (0.001s)
2023-01-11T22:00:21.9540915Z 
2023-01-11T22:00:21.9541346Z ----------------------------------------------------------------------
2023-01-11T22:00:21.9541758Z Ran 16 tests in 0.029s
2023-01-11T22:00:21.9541879Z 
2023-01-11T22:00:21.9541954Z OK (skipped=5)
2023-01-11T22:00:21.9542047Z 
2023-01-11T22:00:21.9542135Z Generating XML reports...
2023-01-11T22:00:21.9542678Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestCppExtensionAOT-20230111220021.xml
2023-01-11T22:00:21.9543245Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestORTTensor-20230111220021.xml
2023-01-11T22:00:21.9543811Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestPybindTypeCasters-20230111220021.xml
2023-01-11T22:00:21.9544365Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestRNGExtension-20230111220021.xml
2023-01-11T22:00:21.9544920Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestTorchLibrary-20230111220021.xml
2023-01-11T22:00:21.9545216Z 
2023-01-11T22:00:21.9545575Z ##[endgroup]
2023-01-11T22:00:21.9546003Z FINISHED PRINTING LOG FILE of test_cpp_extensions_aot_no_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_no_ninja_wgarsge_)
2023-01-11T22:00:21.9546253Z 
2023-01-11T22:00:21.9546472Z Running test_cpp_extensions_open_device_registration ... [2023-01-11 22:00:21.953010]
2023-01-11T22:00:21.9547019Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_open_device_registration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:21.953299]
2023-01-11T22:00:45.1698994Z 
2023-01-11T22:00:45.1700782Z Expand the folded group to see the log file of test_cpp_extensions_open_device_registration
2023-01-11T22:00:45.1701848Z ##[group]PRINTING LOG FILE of test_cpp_extensions_open_device_registration (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_open_device_registration_m4hq1ltr)
2023-01-11T22:00:45.1702549Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:00:45.1702714Z 
2023-01-11T22:00:45.1702792Z Running tests...
2023-01-11T22:00:45.1703107Z ----------------------------------------------------------------------
2023-01-11T22:00:45.1705443Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_open_device_registration
2023-01-11T22:00:45.1706264Z   test_open_device_registration (__main__.TestCppExtensionOpenRgistration) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T22:00:45.1706718Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/custom_device_extension...
2023-01-11T22:00:45.1707099Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/custom_device_extension/build.ninja...
2023-01-11T22:00:45.1707426Z Building extension module custom_device_extension...
2023-01-11T22:00:45.1707672Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:00:45.1709323Z [1/2] c++ -MMD -MF open_registration_extension.o.d -DTORCH_EXTENSION_NAME=custom_device_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/var/lib/jenkins/workspace/test/cpp_extensions -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension.cpp -o open_registration_extension.o 
2023-01-11T22:00:45.1710653Z [2/2] c++ open_registration_extension.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o custom_device_extension.so
2023-01-11T22:00:45.1711038Z Loading extension module custom_device_extension...
2023-01-11T22:00:45.1711254Z ok (21.330s)
2023-01-11T22:00:45.1711360Z 
2023-01-11T22:00:45.1711563Z ----------------------------------------------------------------------
2023-01-11T22:00:45.1711792Z Ran 1 test in 21.344s
2023-01-11T22:00:45.1711905Z 
2023-01-11T22:00:45.1711965Z OK
2023-01-11T22:00:45.1712057Z 
2023-01-11T22:00:45.1712143Z Generating XML reports...
2023-01-11T22:00:45.1712641Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_open_device_registration/TEST-TestCppExtensionOpenRgistration-20230111220023.xml
2023-01-11T22:00:45.1712949Z 
2023-01-11T22:00:45.1713239Z ##[endgroup]
2023-01-11T22:00:45.1713721Z FINISHED PRINTING LOG FILE of test_cpp_extensions_open_device_registration (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_open_device_registration_m4hq1ltr)
2023-01-11T22:00:45.1714001Z 
2023-01-11T22:00:45.1714184Z Running test_cuda_nvml_based_avail ... [2023-01-11 22:00:45.170032]
2023-01-11T22:00:45.1714694Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '-v', '--subprocess', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:45.170318]
2023-01-11T22:00:46.6963750Z 
2023-01-11T22:00:46.6964311Z Expand the folded group to see the log file of test_cuda_nvml_based_avail
2023-01-11T22:00:46.6965176Z ##[group]PRINTING LOG FILE of test_cuda_nvml_based_avail (/var/lib/jenkins/workspace/test/test-reports/test_cuda_nvml_based_avail_8xh_7anv)
2023-01-11T22:00:46.6965568Z CUDA not available, skipping tests
2023-01-11T22:00:46.6965701Z 
2023-01-11T22:00:46.6965904Z ##[endgroup]
2023-01-11T22:00:46.6966457Z FINISHED PRINTING LOG FILE of test_cuda_nvml_based_avail (/var/lib/jenkins/workspace/test/test-reports/test_cuda_nvml_based_avail_8xh_7anv)
2023-01-11T22:00:46.6966689Z 
2023-01-11T22:00:46.6966888Z Running test_cuda_primary_ctx ... [2023-01-11 22:00:46.696439]
2023-01-11T22:00:46.6968421Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_primary_ctx.py', '-v', '--subprocess', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:46.696668]
2023-01-11T22:00:48.2308752Z 
2023-01-11T22:00:48.2309492Z Expand the folded group to see the log file of test_cuda_primary_ctx
2023-01-11T22:00:48.2310482Z ##[group]PRINTING LOG FILE of test_cuda_primary_ctx (/var/lib/jenkins/workspace/test/test-reports/test_cuda_primary_ctx_65c_auj6)
2023-01-11T22:00:48.2310959Z CUDA not available, skipping tests
2023-01-11T22:00:48.2311157Z 
2023-01-11T22:00:48.2311492Z ##[endgroup]
2023-01-11T22:00:48.2312220Z FINISHED PRINTING LOG FILE of test_cuda_primary_ctx (/var/lib/jenkins/workspace/test/test-reports/test_cuda_primary_ctx_65c_auj6)
2023-01-11T22:00:48.2312562Z 
2023-01-11T22:00:48.2312842Z Running test_cuda_trace ... [2023-01-11 22:00:48.230966]
2023-01-11T22:00:48.2314581Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_trace.py', '-v', '--subprocess', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:48.231237]
2023-01-11T22:00:49.7609981Z 
2023-01-11T22:00:49.7610906Z Expand the folded group to see the log file of test_cuda_trace
2023-01-11T22:00:49.7611693Z ##[group]PRINTING LOG FILE of test_cuda_trace (/var/lib/jenkins/workspace/test/test-reports/test_cuda_trace_3vw5om71)
2023-01-11T22:00:49.7612197Z CUDA not available, skipping tests
2023-01-11T22:00:49.7612345Z 
2023-01-11T22:00:49.7612708Z ##[endgroup]
2023-01-11T22:00:49.7613546Z FINISHED PRINTING LOG FILE of test_cuda_trace (/var/lib/jenkins/workspace/test/test-reports/test_cuda_trace_3vw5om71)
2023-01-11T22:00:49.7613868Z 
2023-01-11T22:00:49.7614077Z Running test_dispatch ... [2023-01-11 22:00:49.761052]
2023-01-11T22:00:49.7615613Z Executing ['/opt/conda/bin/python', '-bb', 'test_dispatch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:00:49.761333]
2023-01-11T22:01:14.7158485Z 
2023-01-11T22:01:14.7159100Z Expand the folded group to see the log file of test_dispatch
2023-01-11T22:01:14.7159773Z ##[group]PRINTING LOG FILE of test_dispatch (/var/lib/jenkins/workspace/test/test-reports/test_dispatch_e4h4n955)
2023-01-11T22:01:14.7160418Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:01:14.7209768Z 
2023-01-11T22:01:14.7210028Z Running tests...
2023-01-11T22:01:14.7210661Z ----------------------------------------------------------------------
2023-01-11T22:01:14.7211363Z Test results will be stored in test-reports/python-unittest/test_dispatch
2023-01-11T22:01:14.7211663Z   test_all_invariants (__main__.TestDispatch) ... ok (0.223s)
2023-01-11T22:01:14.7211934Z   test_computed_table (__main__.TestDispatch) ... ok (7.065s)
2023-01-11T22:01:14.7212226Z   test_computed_table_with_ambiguous_autogradother (__main__.TestDispatch) ... ok (0.011s)
2023-01-11T22:01:14.7212545Z   test_computed_table_with_autograd (__main__.TestDispatch) ... ok (0.002s)
2023-01-11T22:01:14.7212879Z   test_computed_table_with_cpu_autograd_defaultbackend (__main__.TestDispatch) ... ok (0.198s)
2023-01-11T22:01:14.7213196Z   test_computed_table_with_cpu_autograd_math (__main__.TestDispatch) ... ok (0.210s)
2023-01-11T22:01:14.7213538Z   test_computed_table_with_cpu_autograd_math_defaultbackend (__main__.TestDispatch) ... ok (6.666s)
2023-01-11T22:01:14.7213876Z   test_computed_table_with_cpu_defaultbackend (__main__.TestDispatch) ... ok (0.010s)
2023-01-11T22:01:14.7214184Z   test_computed_table_with_cpu_math (__main__.TestDispatch) ... ok (0.011s)
2023-01-11T22:01:14.7214500Z   test_computed_table_with_cpu_math_autogradcpu_fallthrough (__main__.TestDispatch) ... ok (0.002s)
2023-01-11T22:01:14.7214821Z   test_computed_table_with_math (__main__.TestDispatch) ... ok (0.002s)
2023-01-11T22:01:14.7215082Z   test_def (__main__.TestDispatch) ... ok (6.754s)
2023-01-11T22:01:14.7215333Z   test_def_impl_schema_mismatch (__main__.TestDispatch) ... ok (0.011s)
2023-01-11T22:01:14.7215601Z   test_def_only (__main__.TestDispatch) ... ok (0.001s)
2023-01-11T22:01:14.7215867Z   test_def_with_explicit_alias (__main__.TestDispatch) ... ok (0.001s)
2023-01-11T22:01:14.7216130Z   test_def_with_inference (__main__.TestDispatch) ... ok (0.225s)
2023-01-11T22:01:14.7216449Z   test_dispatch_print_registrations_for_dispatch_key_invalid (__main__.TestDispatch) ... ok (0.002s)
2023-01-11T22:01:14.7216764Z   test_find_dangling_impls (__main__.TestDispatch) ... ok (0.001s)
2023-01-11T22:01:14.7217127Z   test_find_dangling_impls_ext (__main__.TestDispatch) ... Using /var/lib/jenkins/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...
2023-01-11T22:01:14.7217519Z Creating extension directory /var/lib/jenkins/.cache/torch_extensions/py310_cu117/dangling_impl_extension...
2023-01-11T22:01:14.7217903Z Emitting ninja build file /var/lib/jenkins/.cache/torch_extensions/py310_cu117/dangling_impl_extension/build.ninja...
2023-01-11T22:01:14.7218226Z Building extension module dangling_impl_extension...
2023-01-11T22:01:14.7218489Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T22:01:14.7219867Z [1/2] c++ -MMD -MF dangling_impl_extension.o.d -DTORCH_EXTENSION_NAME=dangling_impl_extension -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /opt/conda/lib/python3.10/site-packages/torch/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /opt/conda/lib/python3.10/site-packages/torch/include/TH -isystem /opt/conda/lib/python3.10/site-packages/torch/include/THC -isystem /opt/conda/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=1 -fPIC -std=c++17 -g -c /var/lib/jenkins/workspace/test/cpp_extensions/dangling_impl_extension.cpp -o dangling_impl_extension.o 
2023-01-11T22:01:14.7221244Z [2/2] c++ dangling_impl_extension.o -shared -L/opt/conda/lib/python3.10/site-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o dangling_impl_extension.so
2023-01-11T22:01:14.7221768Z Loading extension module dangling_impl_extension...
2023-01-11T22:01:14.7221975Z ok (1.349s)
2023-01-11T22:01:14.7222193Z   test_impl_only (__main__.TestDispatch) ... ok (0.214s)
2023-01-11T22:01:14.7222480Z   test_multiple_def_alias_defaulting (__main__.TestDispatch) ... ok (0.008s)
2023-01-11T22:01:14.7222829Z   test_multiple_def_alias_mismatch (__main__.TestDispatch) ... ok (0.008s)
2023-01-11T22:01:14.7223118Z   test_multiple_def_error (__main__.TestDispatch) ... ok (0.008s)
2023-01-11T22:01:14.7223391Z   test_multiple_fallback (__main__.TestDispatch) ... ok (0.009s)
2023-01-11T22:01:14.7223774Z   test_overwrite_math (__main__.TestDispatch) ... [W OperatorEntry.cpp:159] Warning: Overriding a previously registered kernel for the same operator and the same dispatch key
2023-01-11T22:01:14.7224134Z   operator: __test45643__::foo
2023-01-11T22:01:14.7224323Z     no debug info
2023-01-11T22:01:14.7224510Z   dispatch key: (catch all)
2023-01-11T22:01:14.7224690Z   previous kernel: fn1
2023-01-11T22:01:14.7224912Z        new kernel: fn2 (function registerKernel)
2023-01-11T22:01:14.7225251Z [W OperatorEntry.cpp:159] Warning: Overriding a previously registered kernel for the same operator and the same dispatch key
2023-01-11T22:01:14.7225543Z   operator: __test45644__::foo
2023-01-11T22:01:14.7225727Z     no debug info
2023-01-11T22:01:14.7225925Z   dispatch key: (catch all)
2023-01-11T22:01:14.7226103Z   previous kernel: fn1
2023-01-11T22:01:14.7226321Z        new kernel: fn2 (function registerKernel)
2023-01-11T22:01:14.7226520Z ok (0.001s)
2023-01-11T22:01:14.7226744Z   test_autogradother (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:14.7227028Z   test_basic (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:14.7227330Z   test_defaultbackend_autogradcpu (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:14.7227650Z   test_defaultbackend_math (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:14.7227954Z   test_duplicate_registrations (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:14.7228266Z   test_math_autogradcpu (__main__.TestPythonDispatcher) ... ok (0.001s)
2023-01-11T22:01:14.7228590Z   test_quantized_structured_not_implemented (__main__.TestPythonDispatcher) ... ok (0.024s)
2023-01-11T22:01:14.7228782Z 
2023-01-11T22:01:14.7228983Z ----------------------------------------------------------------------
2023-01-11T22:01:14.7229226Z Ran 32 tests in 23.022s
2023-01-11T22:01:14.7229339Z 
2023-01-11T22:01:14.7229401Z OK
2023-01-11T22:01:14.7229491Z 
2023-01-11T22:01:14.7229576Z Generating XML reports...
2023-01-11T22:01:14.7229962Z Generated XML report: test-reports/python-unittest/test_dispatch/TEST-TestDispatch-20230111220051.xml
2023-01-11T22:01:14.7230480Z Generated XML report: test-reports/python-unittest/test_dispatch/TEST-TestPythonDispatcher-20230111220051.xml
2023-01-11T22:01:14.7230715Z 
2023-01-11T22:01:14.7231008Z ##[endgroup]
2023-01-11T22:01:14.7231382Z FINISHED PRINTING LOG FILE of test_dispatch (/var/lib/jenkins/workspace/test/test-reports/test_dispatch_e4h4n955)
2023-01-11T22:01:14.7231595Z 
2023-01-11T22:01:14.7231746Z Running test_fx ... [2023-01-11 22:01:14.715980]
2023-01-11T22:01:14.7232245Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:01:14.716215]
2023-01-11T22:05:04.0041497Z 
2023-01-11T22:05:04.0041943Z Expand the folded group to see the log file of test_fx
2023-01-11T22:05:04.0044143Z ##[group]PRINTING LOG FILE of test_fx (/var/lib/jenkins/workspace/test/test-reports/test_fx_4tp8se8h)
2023-01-11T22:05:04.0044456Z 
2023-01-11T22:05:04.0044568Z Running tests...
2023-01-11T22:05:04.0049547Z ----------------------------------------------------------------------
2023-01-11T22:05:04.0050194Z Test results will be stored in test-reports/python-unittest/test_fx
2023-01-11T22:05:04.0050862Z   test_annotate (fx.test_gradual_type.AnnotationsTest) ... ok (0.011s)
2023-01-11T22:05:04.0051325Z   test_annotations (fx.test_gradual_type.AnnotationsTest)
2023-01-11T22:05:04.0052024Z Test type annotations in the forward function. ... ok (0.003s)
2023-01-11T22:05:04.0052513Z   test_broadcasting1 (fx.test_gradual_type.AnnotationsTest) ... ok (0.001s)
2023-01-11T22:05:04.0053017Z   test_broadcasting2 (fx.test_gradual_type.AnnotationsTest) ... ok (0.001s)
2023-01-11T22:05:04.0053531Z   test_broadcasting3 (fx.test_gradual_type.AnnotationsTest) ... ok (0.001s)
2023-01-11T22:05:04.0054017Z   test_consistency (fx.test_gradual_type.AnnotationsTest)
2023-01-11T22:05:04.0054464Z Test the consistency relation. ... ok (0.001s)
2023-01-11T22:05:04.0054907Z   test_precision (fx.test_gradual_type.AnnotationsTest)
2023-01-11T22:05:04.0055180Z Test the consistency relation. ... ok (0.001s)
2023-01-11T22:05:04.0055440Z   test_banned_list (fx.test_cse_pass.TestCSEPass) ... ok (0.057s)
2023-01-11T22:05:04.0055700Z   test_empty (fx.test_cse_pass.TestCSEPass) ... ok (0.006s)
2023-01-11T22:05:04.0055990Z   test_immutable_list_multiple_entries (fx.test_cse_pass.TestCSEPass) ... ok (0.055s)
2023-01-11T22:05:04.0056299Z   test_immutable_list_type (fx.test_cse_pass.TestCSEPass) ... ok (0.055s)
2023-01-11T22:05:04.0056564Z   test_kwarg (fx.test_cse_pass.TestCSEPass) ... ok (0.024s)
2023-01-11T22:05:04.0056856Z   test_nested_immutable_list_type (fx.test_cse_pass.TestCSEPass) ... ok (0.027s)
2023-01-11T22:05:04.0057144Z   test_nochange (fx.test_cse_pass.TestCSEPass) ... ok (0.035s)
2023-01-11T22:05:04.0057413Z   test_rand_like (fx.test_cse_pass.TestCSEPass) ... ok (0.027s)
2023-01-11T22:05:04.0057666Z   test_rand_n (fx.test_cse_pass.TestCSEPass) ... ok (0.025s)
2023-01-11T22:05:04.0057927Z   test_random (fx.test_cse_pass.TestCSEPass) ... ok (0.083s)
2023-01-11T22:05:04.0058188Z   test_simple (fx.test_cse_pass.TestCSEPass) ... ok (0.041s)
2023-01-11T22:05:04.0058436Z   test_simple_2 (fx.test_cse_pass.TestCSEPass) ... ok (0.054s)
2023-01-11T22:05:04.0058722Z   test_simple_multiple_same_ops (fx.test_cse_pass.TestCSEPass) ... ok (0.054s)
2023-01-11T22:05:04.0059005Z   test_two_args (fx.test_cse_pass.TestCSEPass) ... ok (0.055s)
2023-01-11T22:05:04.0059282Z   test_two_args_default (fx.test_cse_pass.TestCSEPass) ... ok (0.055s)
2023-01-11T22:05:04.0059603Z   test_correctness_CSEPass_MutationInput_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.027s)
2023-01-11T22:05:04.0059983Z   test_correctness_CSEPass_MutationMetadata_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.014s)
2023-01-11T22:05:04.0060375Z   test_correctness_CSEPass_MutationTorchTensorCall_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.025s)
2023-01-11T22:05:04.0060740Z   test_correctness_CSEPass_Mutation_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.027s)
2023-01-11T22:05:04.0061139Z   test_correctness_CSEPass_ReturnList_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.038s)
2023-01-11T22:05:04.0061493Z   test_correctness_CSEPass_TakeList_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.013s)
2023-01-11T22:05:04.0061929Z   test_correctness_factory_CSEPass_FactoryFunctionCall_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.018s)
2023-01-11T22:05:04.0062464Z   test_correctness_factory_CSEPass_MutationFactory_cpu (fx.test_common_passes.TestCommonPass) ... ok (0.025s)
2023-01-11T22:05:04.0063248Z   test_check_inline_non_const (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0064518Z Perform constant folding conversion and check that the non-const module is inlined ... /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/const_fold.py:248: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
2023-01-11T22:05:04.0065424Z   new_node = root_const_gm.graph.get_attr(in_node.target)
2023-01-11T22:05:04.0065634Z ok (0.007s)
2023-01-11T22:05:04.0065866Z   test_check_inline_non_const_mult_return (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0066335Z Perform constant folding conversion and check that the non-const module is inlined ... ok (0.006s)
2023-01-11T22:05:04.0066681Z   test_check_skip_folding_quant_dequant_pattern (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0067002Z Set up skip_folding_quant_dequant function to skip quant/dequant pattern. ... ok (0.024s)
2023-01-11T22:05:04.0067328Z   test_const_fold_basic_one_attr_name_collision (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0067667Z Perform constant folding conversion, from original mod to split constant folding ... ok (0.005s)
2023-01-11T22:05:04.0068004Z   test_const_fold_basic_one_attr_no_name_collision (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0068334Z Perform constant folding conversion, from original mod to split constant folding ... ok (0.005s)
2023-01-11T22:05:04.0068672Z   test_const_fold_basic_placeholder_reordered (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0068995Z Test code path where placeholder comes after normal op node in FX ... ok (0.003s)
2023-01-11T22:05:04.0069285Z   test_const_fold_basic_two_attr (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0069597Z Perform constant folding conversion, from original mod to split constant ... ok (0.005s)
2023-01-11T22:05:04.0069924Z   test_const_fold_basic_two_attr_three_input (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0070246Z Perform constant folding conversion, from original mod to split constant ... ok (0.005s)
2023-01-11T22:05:04.0070572Z   test_const_fold_has_inlined_call_module_node (fx.test_fx_const_fold.TestConstFold) ... ok (0.005s)
2023-01-11T22:05:04.0071127Z   test_const_fold_module_attr (fx.test_fx_const_fold.TestConstFold) ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:973: UserWarning: Failed to fetch module mod!
2023-01-11T22:05:04.0071506Z   warnings.warn(f"Failed to fetch module {module_path}!")
2023-01-11T22:05:04.0071714Z ok (0.095s)
2023-01-11T22:05:04.0071944Z   test_const_fold_multi_const_folded_attrs (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0072271Z Perform constant folding conversion, from original mod to split constant ... ok (0.007s)
2023-01-11T22:05:04.0072570Z   test_const_fold_noop (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0072856Z Check that a graph with no constant folding is handled correctly. ... ok (0.003s)
2023-01-11T22:05:04.0073162Z   test_const_fold_submod_hierarchy (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0073717Z Perform constant folding conversion, from original mod to split constant folding ... /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py:973: UserWarning: Failed to fetch module my_mod!
2023-01-11T22:05:04.0074110Z   warnings.warn(f"Failed to fetch module {module_path}!")
2023-01-11T22:05:04.0074306Z ok (0.005s)
2023-01-11T22:05:04.0074548Z   test_const_fold_tensor_meta (fx.test_fx_const_fold.TestConstFold) ... ok (0.010s)
2023-01-11T22:05:04.0074874Z   test_const_fold_unused_placeholder (fx.test_fx_const_fold.TestConstFold) ... ok (0.005s)
2023-01-11T22:05:04.0075176Z   test_dict_output (fx.test_fx_const_fold.TestConstFold) ... ok (0.004s)
2023-01-11T22:05:04.0075453Z   test_fold_module (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0075760Z Perform constant folding with a call_module node. ... ok (0.005s)
2023-01-11T22:05:04.0076023Z   test_retain_node_meta (fx.test_fx_const_fold.TestConstFold)
2023-01-11T22:05:04.0076330Z Perform constant folding conversion, and validate that node meta is retained. ... ok (0.005s)
2023-01-11T22:05:04.0076645Z   test_three_outputs (fx.test_fx_const_fold.TestConstFold) ... ok (0.005s)
2023-01-11T22:05:04.0076936Z   test_two_outputs (fx.test_fx_const_fold.TestConstFold) ... ok (0.004s)
2023-01-11T22:05:04.0077281Z   test_param_dim_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.006s)
2023-01-11T22:05:04.0077696Z   test_param_ndim_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:04.0078147Z   test_param_nelement_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:04.0078571Z   test_param_numel_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:04.0078975Z   test_param_shape_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:04.0079381Z   test_param_size_const (fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow) ... ok (0.005s)
2023-01-11T22:05:04.0079700Z   test_dead_chain (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:04.0080025Z Tests that a chain of two nodes in the graph are DCE'd correctly. ... graph():
2023-01-11T22:05:04.0080286Z     %x : [#users=2] = placeholder[target=x]
2023-01-11T22:05:04.0080555Z     %add : [#users=1] = call_function[target=operator.add](args = (%x, 1), kwargs = {})
2023-01-11T22:05:04.0080864Z     %mul : [#users=0] = call_function[target=operator.mul](args = (%add, 7), kwargs = {})
2023-01-11T22:05:04.0081116Z     %attr_1 : [#users=1] = get_attr[target=attr_1]
2023-01-11T22:05:04.0081391Z     %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, %attr_1), kwargs = {})
2023-01-11T22:05:04.0081632Z     return add_1
2023-01-11T22:05:04.0081784Z ok (0.003s)
2023-01-11T22:05:04.0081986Z   test_dead_getattr (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:04.0082301Z Tests that a getatrr in the graph is DCE'd correctly. ... graph():
2023-01-11T22:05:04.0082537Z     %x : [#users=2] = placeholder[target=x]
2023-01-11T22:05:04.0082802Z     %add : [#users=1] = call_function[target=operator.add](args = (%x, 1), kwargs = {})
2023-01-11T22:05:04.0083065Z     %attr_1 : [#users=1] = get_attr[target=attr_1]
2023-01-11T22:05:04.0083342Z     %mul : [#users=0] = call_function[target=operator.mul](args = (%add, %attr_1), kwargs = {})
2023-01-11T22:05:04.0083642Z     %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, 11), kwargs = {})
2023-01-11T22:05:04.0083880Z     return add_1
2023-01-11T22:05:04.0084042Z ok (0.003s)
2023-01-11T22:05:04.0084241Z   test_dead_placeholder (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:04.0084597Z Tests that a placeholder in the graph is not DCE'd, as that would change ... graph():
2023-01-11T22:05:04.0084869Z     %x : [#users=1] = placeholder[target=x]
2023-01-11T22:05:04.0085074Z     %y : [#users=0] = placeholder[target=y]
2023-01-11T22:05:04.0085336Z     %add : [#users=1] = call_function[target=operator.add](args = (%x, 7), kwargs = {})
2023-01-11T22:05:04.0085569Z     return add
2023-01-11T22:05:04.0085718Z ok (0.002s)
2023-01-11T22:05:04.0085939Z   test_dead_placeholder_with_user (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:04.0086303Z Tests that a placeholder in the graph is not DCE'd, as that would change ... graph():
2023-01-11T22:05:04.0086574Z     %x : [#users=1] = placeholder[target=x]
2023-01-11T22:05:04.0086779Z     %y : [#users=1] = placeholder[target=y]
2023-01-11T22:05:04.0087043Z     %add : [#users=0] = call_function[target=operator.add](args = (%y, 2), kwargs = {})
2023-01-11T22:05:04.0087349Z     %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, 7), kwargs = {})
2023-01-11T22:05:04.0087567Z     return add_1
2023-01-11T22:05:04.0087767Z ok (0.003s)
2023-01-11T22:05:04.0087988Z   test_keep_module_with_side_effects (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:04.0088351Z Test that DCE doesn't remove a module if it's specified as having side effects. ... graph():
2023-01-11T22:05:04.0088644Z     %a : torch.Tensor [#users=2] = placeholder[target=a]
2023-01-11T22:05:04.0088910Z     %relu : [#users=0] = call_module[target=relu](args = (%a,), kwargs = {})
2023-01-11T22:05:04.0089323Z     %mul : [#users=1] = call_function[target=operator.mul](args = (%a, 2), kwargs = {})
2023-01-11T22:05:04.0089541Z     return mul
2023-01-11T22:05:04.0089702Z ok (0.003s)
2023-01-11T22:05:04.0089909Z   test_keep_torch_assert (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:04.0090246Z Test that DCE doesn't remove torch._assert since it has side effects. ... graph():
2023-01-11T22:05:04.0090588Z     %a : torch.Tensor [#users=2] = placeholder[target=a]
2023-01-11T22:05:04.0090871Z     %equal : [#users=1] = call_function[target=torch.equal](args = (%a, %a), kwargs = {})
2023-01-11T22:05:04.0091192Z     %_assert : [#users=0] = call_function[target=torch._assert](args = (%equal, a must equal a), kwargs = {})
2023-01-11T22:05:04.0091517Z     %mul : [#users=1] = call_function[target=operator.mul](args = (%a, 2), kwargs = {})
2023-01-11T22:05:04.0091746Z     return mul
2023-01-11T22:05:04.0091897Z ok (0.003s)
2023-01-11T22:05:04.0092093Z   test_simple (fx.test_dce_pass.TestDCE)
2023-01-11T22:05:04.0092410Z Tests that a single node in the graph is DCE'd correctly. ... graph():
2023-01-11T22:05:04.0092666Z     %x : [#users=2] = placeholder[target=x]
2023-01-11T22:05:04.0092921Z     %add : [#users=0] = call_function[target=operator.add](args = (%x, 1), kwargs = {})
2023-01-11T22:05:04.0093187Z     %attr_1 : [#users=1] = get_attr[target=attr_1]
2023-01-11T22:05:04.0093467Z     %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, %attr_1), kwargs = {})
2023-01-11T22:05:04.0093693Z     return add_1
2023-01-11T22:05:04.0093859Z ok (0.003s)
2023-01-11T22:05:04.0094067Z   test_all_input_nodes (__main__.TestFX) ... ok (0.008s)
2023-01-11T22:05:04.0094310Z   test_annotation_with_future (__main__.TestFX) ... ok (0.007s)
2023-01-11T22:05:04.0095090Z   test_annotations_empty_tuple (__main__.TestFX) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T22:05:04.0095690Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T22:05:04.0095911Z ok (0.027s)
2023-01-11T22:05:04.0096131Z   test_annotations_with_forward_references (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0096428Z   test_annotations_with_no_forward_references (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0096762Z   test_annotations_with_non_torch_reference_and_internal_forward_references (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0097130Z   test_annotations_with_non_torch_reference_and_no_internal_forward_references (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0097417Z   test_args_kwargs (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0097664Z   test_args_kwargs_no_self (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0098006Z   test_assert (__main__.TestFX) ... skip: Does not work on Python-3.10 (0.000s)
2023-01-11T22:05:04.0098287Z   test_ast_rewriter_reassigns_submodules (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0098568Z   test_ast_rewriter_rewrites_assert (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0098854Z   test_ast_rewriter_rewrites_assert_with_message (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0099125Z   test_ast_rewriter_wrap (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0099378Z   test_ast_rewriter_wrap_fn_directly (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0099656Z   test_ast_rewriter_wrap_with_submodule (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0099988Z   test_ast_rewriter_wrapped_via_decorator (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0100284Z   test_ast_rewriter_wrapped_via_decorator_and_transformed (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0100574Z   test_autowrap_functions (__main__.TestFX) ... ok (0.063s)
2023-01-11T22:05:04.0100837Z   test_concrete_arg_none_assert (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0101084Z   test_construct_root_dict (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0101324Z   test_copy_it (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0101560Z   test_copy_no_remap (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0101792Z   test_ctx_mgr (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0102012Z   test_custom_codegen (__main__.TestFX) ... ok (0.019s)
2023-01-11T22:05:04.0102304Z   test_custom_codegen_with_transformer (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0102566Z   test_custom_import (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0102884Z   test_custom_proxy_dynamic_value (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0103174Z   test_custom_proxy_input_dependent_control_flow (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0103455Z   test_custom_proxy_type (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0103704Z   test_custom_proxy_type_literal (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0104013Z   test_custom_traceback_not_raised_when_exception_source_is_submodule (__main__.TestFX) ... ok (0.008s)
2023-01-11T22:05:04.0104362Z   test_custom_traceback_raised_when_exception_source_is_graphmodule (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0104677Z   test_deepcopy_graph_with_tracer_cls (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0104956Z   test_deepcopy_graphmodule_with_transform (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0105240Z   test_deepcopy_recursion_depth (__main__.TestFX) ... ok (0.058s)
2023-01-11T22:05:04.0105496Z   test_deepcopy_tracer (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0105751Z   test_deepcopy_with_submods_params (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0106026Z   test_delete_unused_submodules_leaf (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0106272Z   test_dict (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0106505Z   test_direct_param_use (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0106739Z   test_disallow_override (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0106973Z   test_ellipsis (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0107213Z   test_empty_graph_codegen (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0107448Z   test_erase_node_error (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0107695Z   test_example_shape_prop (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0107938Z   test_find_uses (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0108173Z   test_fn_type_annotation_empty (__main__.TestFX) ... ok (0.017s)
2023-01-11T22:05:04.0108433Z   test_fn_type_annotations (__main__.TestFX) ... ok (0.024s)
2023-01-11T22:05:04.0108672Z   test_fx_and_or (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0108888Z   test_fx_create_arg (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0109117Z   test_fx_shifts (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0109351Z   test_fx_stateless (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0109597Z   test_get_torch_func_signature (__main__.TestFX) ... ok (0.121s)
2023-01-11T22:05:04.0109877Z   test_getitem (__main__.TestFX) ... skip: Will be checked in test_getitem_subproc (0.000s)
2023-01-11T22:05:04.0110160Z   test_getitem_subproc (__main__.TestFX) ... ok (0.028s)
2023-01-11T22:05:04.0110412Z   test_graph_edit_with_proxy (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0110639Z   test_graph_fns (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0110875Z   test_graph_module (__main__.TestFX) ... ok (0.035s)
2023-01-11T22:05:04.0111152Z   test_graph_module_init_buffer_param_copied_dict_init (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0111500Z   test_graph_module_init_buffer_param_copied_mod_init (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0111783Z   test_graph_module_replicate_for_dp (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0112049Z   test_graph_unique_names (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0112306Z   test_graph_unique_names_manual (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0112561Z   test_immutable_dict_pytree_ops (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0112830Z   test_immutable_list_pytree_ops (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0113085Z   test_imul_code_print (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0113305Z   test_inf_nan (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0113534Z   test_inf_nan_kwds (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0113796Z   test_inline_graph (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0114037Z   test_insertion_point (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0114266Z   test_interpreter (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0114520Z   test_interpreter_default_args (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0114785Z   test_interpreter_gc_values (__main__.TestFX) ... ok (0.248s)
2023-01-11T22:05:04.0115035Z   test_interpreter_noop_resnet18 (__main__.TestFX) ... ok (0.307s)
2023-01-11T22:05:04.0115308Z   test_interpreter_not_enough_args (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0115578Z   test_interpreter_onthefly_swap (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0116019Z   test_interpreter_partial_eval (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0116294Z   test_interpreter_run_node_override (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0116560Z   test_interpreter_star_args (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0116823Z   test_interpreter_with_codegen (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0117053Z   test_layout (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0117285Z   test_leaf_module (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0117522Z   test_matmul_tracing (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0117766Z   test_module_deepcopy_edit_nodes (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0118017Z   test_move_before (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0118261Z   test_multi_insert_point (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0118501Z   test_multiple_default_args (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0118756Z   test_named_tuple_inlined (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0119018Z   test_namedtuple_return_qualname (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0119285Z   test_namedtuple_return_trace (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0119529Z   test_native_callable (__main__.TestFX) ... ok (0.024s)
2023-01-11T22:05:04.0119770Z   test_no_mutation (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0120008Z   test_node_tagging (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0120245Z   test_nonetype_annotation (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0120973Z   test_partial_trace (__main__.TestFX) ... /opt/conda/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py:564: UserWarning: Was not able to add assertion to guarantee correct input f to specialized function. It is up to the user to make sure that your inputs match the inputs you specialized the function with.
2023-01-11T22:05:04.0121436Z   warnings.warn(
2023-01-11T22:05:04.0121546Z 
2023-01-11T22:05:04.0121551Z 
2023-01-11T22:05:04.0121555Z 
2023-01-11T22:05:04.0121637Z def forward(self, x, y_1):
2023-01-11T22:05:04.0121818Z     eq = y_1 == True;  y_1 = None
2023-01-11T22:05:04.0122179Z     _assert = torch._assert(eq, 'y has been specialized to have value True but got another value');  eq = None
2023-01-11T22:05:04.0122443Z     mul = 2 * x;  x = None
2023-01-11T22:05:04.0122605Z     return mul
2023-01-11T22:05:04.0122762Z     
2023-01-11T22:05:04.0122948Z ok (0.007s)
2023-01-11T22:05:04.0123145Z   test_pickle_custom_import (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0123406Z   test_pickle_graphmodule (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0123671Z   test_pickle_nonetype_annotation (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:04.0123939Z   test_pickle_torch_custom_ops (__main__.TestFX) ... ok (0.006s)
2023-01-11T22:05:04.0124458Z   test_prepend_self (__main__.TestFX) ... /opt/conda/lib/python3.10/site-packages/torch/fx/node.py:244: UserWarning: Trying to prepend a node to itself. This behavior has no effect on the graph.
2023-01-11T22:05:04.0124893Z   warnings.warn("Trying to prepend a node to itself. This behavior has no effect on the graph.")
2023-01-11T22:05:04.0125142Z ok (0.001s)
2023-01-11T22:05:04.0125331Z   test_pretty_print (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0125684Z   test_pretty_print_graph (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0125939Z   test_pretty_print_node (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0126196Z   test_pretty_print_targets (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0126451Z   test_profiler_ranges_side_effect (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0126701Z   test_pytree (__main__.TestFX) ... ok (0.098s)
2023-01-11T22:05:04.0126938Z   test_pytree_concrete (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0127181Z   test_reassign_args_kwargs_uses (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0127444Z   test_regular_and_default_args (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0127691Z   test_remove_uses (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0127935Z   test_remove_uses_with_custom_filter (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0128191Z   test_replace_input (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0128433Z   test_replace_uses (__main__.TestFX) ... ok (0.135s)
2023-01-11T22:05:04.0128657Z   test_reserved_getattr (__main__.TestFX)
2023-01-11T22:05:04.0128919Z Ensure that we do not name any nodes with a reserved builtin like `getattr` ... ok (0.003s)
2023-01-11T22:05:04.0129342Z   test_return_tuple (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0129587Z   test_return_type_exists (__main__.TestFX) ... ok (0.017s)
2023-01-11T22:05:04.0129830Z   test_script_method_trace (__main__.TestFX) ... ok (0.008s)
2023-01-11T22:05:04.0130085Z   test_script_tensor_constant (__main__.TestFX) ... ok (0.017s)
2023-01-11T22:05:04.0130329Z   test_sequential (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0130570Z   test_shape_prop_aggregate (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0130824Z   test_shape_prop_layout (__main__.TestFX) ... ok (0.024s)
2023-01-11T22:05:04.0131077Z   test_shape_prop_layout_3d (__main__.TestFX) ... ok (0.511s)
2023-01-11T22:05:04.0131331Z   test_single_default_arg (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0131563Z   test_snake_case (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0131792Z   test_sqrt (__main__.TestFX) ... ok (0.008s)
2023-01-11T22:05:04.0132024Z   test_stack_traces (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0132274Z   test_stack_traces_with_transformer (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0132545Z   test_string_literal_return (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0132820Z   test_submodule_manipulation_API (__main__.TestFX) ... ok (0.018s)
2023-01-11T22:05:04.0133078Z   test_symbolic_trace_assert (__main__.TestFX) ... ok (0.009s)
2023-01-11T22:05:04.0133346Z   test_symbolic_trace_sequential (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0133607Z   test_tensor_attribute (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0133867Z   test_tensor_attribute_coalseced (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0134115Z   test_tensor_constant (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0134365Z   test_throw_out_variant (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0134613Z   test_torch_custom_ops (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0134913Z   test_torch_fx_getattr (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0135674Z   test_torch_fx_len (__main__.TestFX) ... /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
2023-01-11T22:05:04.0136300Z   warnings.warn("The TorchScript type system doesn't support "
2023-01-11T22:05:04.0136595Z ok (0.023s)
2023-01-11T22:05:04.0136794Z   test_torch_op_overloads (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0137063Z   test_torchbind_class_attribute_in_fx (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0137361Z   test_torchbind_class_attribute_in_fx_tensor_arg (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0137715Z   test_trace_buffer_slice (__main__.TestFX) ... skip: Hotfix for SEV remediation (0.001s)
2023-01-11T22:05:04.0138010Z   test_trace_dict_int_keys (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0138266Z   test_trace_dict_proxy_keys (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0138520Z   test_trace_fn_constant (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0138751Z   test_trace_function (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0139007Z   test_trace_multiple_funcs (__main__.TestFX) ... 2.0.0a0+git8419ddd
2023-01-11T22:05:04.0139222Z ok (0.006s)
2023-01-11T22:05:04.0139450Z   test_tracing_graphmodules_as_leaf_submodules (__main__.TestFX) ... ok (0.014s)
2023-01-11T22:05:04.0139739Z   test_transformer_multi_outputs (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0140000Z   test_transformer_noop (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0140242Z   test_transformer_op_swap (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0140501Z   test_tuple_no_subscript (__main__.TestFX) ... ok (0.005s)
2023-01-11T22:05:04.0140748Z   test_typename_print (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0140974Z   test_unpack (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0141223Z   test_unpack_dict_better_error (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0141486Z   test_unpack_list_better_error (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0141743Z   test_update_args_api (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0141993Z   test_update_args_kwargs_yells_at_you (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0142258Z   test_update_kwargs_api (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0142542Z   test_user_friendly_call_provenance_with_function (__main__.TestFX) ... ok (0.016s)
2023-01-11T22:05:04.0142937Z   test_user_friendly_call_provenance_with_module (__main__.TestFX) ... ok (0.016s)
2023-01-11T22:05:04.0143201Z   test_wrap (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0143452Z   test_wrap_decorated_function (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0143710Z   test_wrap_fn_directly (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0143951Z   test_wrap_with_submodule (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0144201Z   test_wrapped_method (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0144447Z   test_wrapped_retrace (__main__.TestFX) ... ok (0.004s)
2023-01-11T22:05:04.0144693Z   test_wrapped_via_decorator (__main__.TestFX) ... ok (0.002s)
2023-01-11T22:05:04.0144973Z   test_wrapped_via_decorator_and_transformed (__main__.TestFX) ... ok (0.003s)
2023-01-11T22:05:04.0145249Z   test_wrong_target_type (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0145474Z   test_wrong_topo (__main__.TestFX) ... ok (0.001s)
2023-01-11T22:05:04.0145761Z   test_class_member_back_compat (__main__.TestFXAPIBackwardCompatibility)
2023-01-11T22:05:04.0146079Z Test backward compatibility for members of classes with ... ok (0.002s)
2023-01-11T22:05:04.0146392Z   test_function_back_compat (__main__.TestFXAPIBackwardCompatibility)
2023-01-11T22:05:04.0146692Z Test backward compatibility for function signatures with ... ok (0.008s)
2023-01-11T22:05:04.0147129Z   test_public_api_surface (__main__.TestFXAPIBackwardCompatibility) ... ok (0.002s)
2023-01-11T22:05:04.0147479Z   test_nn_functional_adaptive_avg_pool1d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0147802Z   test_nn_functional_adaptive_avg_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0148126Z   test_nn_functional_adaptive_avg_pool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0148456Z   test_nn_functional_adaptive_max_pool1d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0148806Z   test_nn_functional_adaptive_max_pool1d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0149136Z   test_nn_functional_adaptive_max_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0149514Z   test_nn_functional_adaptive_max_pool2d_with_indices (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0149857Z   test_nn_functional_adaptive_max_pool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0150189Z   test_nn_functional_adaptive_max_pool3d_with_indices (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0150522Z   test_nn_functional_affine_grid (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0150838Z   test_nn_functional_alpha_dropout (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0151158Z   test_nn_functional_avg_pool1d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0151453Z   test_nn_functional_avg_pool2d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0151757Z   test_nn_functional_avg_pool3d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0152066Z   test_nn_functional_batch_norm (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0152366Z   test_nn_functional_bilinear (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0152686Z   test_nn_functional_binary_cross_entropy (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0153033Z   test_nn_functional_binary_cross_entropy_with_logits (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0153355Z   test_nn_functional_celu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0153643Z   test_nn_functional_celu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0153955Z   test_nn_functional_channel_shuffle (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0154267Z   test_nn_functional_conv1d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0154558Z   test_nn_functional_conv2d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0154858Z   test_nn_functional_conv3d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0155166Z   test_nn_functional_conv_tbc (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0155483Z   test_nn_functional_conv_transpose1d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0155799Z   test_nn_functional_conv_transpose2d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0156123Z   test_nn_functional_conv_transpose3d (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0156456Z   test_nn_functional_cosine_embedding_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0156777Z   test_nn_functional_cosine_similarity (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0157099Z   test_nn_functional_cross_entropy (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0157409Z   test_nn_functional_ctc_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0157815Z   test_nn_functional_dropout (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0158116Z   test_nn_functional_dropout1d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0158426Z   test_nn_functional_dropout2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0158768Z   test_nn_functional_dropout3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0159057Z   test_nn_functional_elu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0159353Z   test_nn_functional_elu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0159656Z   test_nn_functional_embedding (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0159972Z   test_nn_functional_embedding_bag (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0160287Z   test_nn_functional_feature_alpha_dropout (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0160604Z   test_nn_functional_fold (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0160950Z   test_nn_functional_fractional_max_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0161289Z   test_nn_functional_fractional_max_pool2d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0161640Z   test_nn_functional_fractional_max_pool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0161986Z   test_nn_functional_fractional_max_pool3d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0162328Z   test_nn_functional_gaussian_nll_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0162627Z   test_nn_functional_gelu (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0162923Z   test_nn_functional_glu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0163228Z   test_nn_functional_grid_sample (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0163540Z   test_nn_functional_group_norm (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0163848Z   test_nn_functional_gumbel_softmax (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0164164Z   test_nn_functional_hardshrink (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0164480Z   test_nn_functional_hardsigmoid (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0164780Z   test_nn_functional_hardswish (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0165088Z   test_nn_functional_hardtanh (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0165396Z   test_nn_functional_hardtanh_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0165718Z   test_nn_functional_hinge_embedding_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0166028Z   test_nn_functional_huber_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0166342Z   test_nn_functional_instance_norm (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0166663Z   test_nn_functional_interpolate (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0166961Z   test_nn_functional_kl_div (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0167264Z   test_nn_functional_l1_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0167571Z   test_nn_functional_layer_norm (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0167877Z   test_nn_functional_leaky_relu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0168172Z   test_nn_functional_leaky_relu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0168475Z   test_nn_functional_linear (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0168793Z   test_nn_functional_local_response_norm (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0169390Z   test_nn_functional_log_softmax (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0169713Z   test_nn_functional_logsigmoid (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0170027Z   test_nn_functional_lp_pool1d (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0170397Z   test_nn_functional_lp_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0170704Z   test_nn_functional_margin_ranking_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0171024Z   test_nn_functional_max_pool1d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0171348Z   test_nn_functional_max_pool1d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0171660Z   test_nn_functional_max_pool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0171981Z   test_nn_functional_max_pool2d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0172303Z   test_nn_functional_max_pool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0172662Z   test_nn_functional_max_pool3d_with_indices (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0172977Z   test_nn_functional_max_unpool1d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0173295Z   test_nn_functional_max_unpool2d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0173606Z   test_nn_functional_max_unpool3d (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0173898Z   test_nn_functional_mish (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0174199Z   test_nn_functional_mse_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0174529Z   test_nn_functional_multi_head_attention_forward (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0174865Z   test_nn_functional_multi_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0175183Z   test_nn_functional_multilabel_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0175527Z   test_nn_functional_multilabel_soft_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0175866Z   test_nn_functional_native_channel_shuffle (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0176174Z   test_nn_functional_nll_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0176486Z   test_nn_functional_normalize (__main__.TestFunctionalTracing) ... ok (0.003s)
2023-01-11T22:05:04.0176791Z   test_nn_functional_one_hot (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0177093Z   test_nn_functional_pad (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0177396Z   test_nn_functional_pairwise_distance (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0177709Z   test_nn_functional_pdist (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0178019Z   test_nn_functional_pixel_shuffle (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0178329Z   test_nn_functional_pixel_unshuffle (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0178653Z   test_nn_functional_poisson_nll_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0178965Z   test_nn_functional_prelu (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0179265Z   test_nn_functional_relu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0179550Z   test_nn_functional_relu6 (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0179849Z   test_nn_functional_relu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0180143Z   test_nn_functional_rrelu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0180430Z   test_nn_functional_rrelu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0180733Z   test_nn_functional_selu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0181030Z   test_nn_functional_selu_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0181330Z   test_nn_functional_silu (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0181624Z   test_nn_functional_smooth_l1_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0192503Z   test_nn_functional_soft_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0192831Z   test_nn_functional_softmax (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0193129Z   test_nn_functional_softmin (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0193445Z   test_nn_functional_softplus (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0193765Z   test_nn_functional_softshrink (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0194090Z   test_nn_functional_threshold (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0194395Z   test_nn_functional_threshold_ (__main__.TestFunctionalTracing) ... ok (0.001s)
2023-01-11T22:05:04.0194814Z   test_nn_functional_triplet_margin_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0195173Z   test_nn_functional_triplet_margin_with_distance_loss (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0195507Z   test_nn_functional_unfold (__main__.TestFunctionalTracing) ... ok (0.002s)
2023-01-11T22:05:04.0196193Z   test_nn_functional_upsample (__main__.TestFunctionalTracing) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:3736: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
2023-01-11T22:05:04.0196719Z   warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
2023-01-11T22:05:04.0196998Z ok (0.002s)
2023-01-11T22:05:04.0197578Z   test_nn_functional_upsample_bilinear (__main__.TestFunctionalTracing) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4078: UserWarning: nn.functional.upsample_bilinear is deprecated. Use nn.functional.interpolate instead.
2023-01-11T22:05:04.0198119Z   warnings.warn("nn.functional.upsample_bilinear is deprecated. Use nn.functional.interpolate instead.")
2023-01-11T22:05:04.0198401Z ok (0.002s)
2023-01-11T22:05:04.0198985Z   test_nn_functional_upsample_nearest (__main__.TestFunctionalTracing) ... /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:4022: UserWarning: nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.
2023-01-11T22:05:04.0199502Z   warnings.warn("nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.")
2023-01-11T22:05:04.0199781Z ok (0.002s)
2023-01-11T22:05:04.0200011Z   test_pass_manager (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:04.0200305Z Tests that the pass manager runs the passes correctly. ... ok (0.006s)
2023-01-11T22:05:04.0200591Z   test_pass_manager_bad_checks (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:04.0200914Z Checks that we error if we pass in a check function with the wrong parameters ... ok (0.001s)
2023-01-11T22:05:04.0201234Z   test_pass_manager_checks (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:04.0201512Z Tests that users can add in check functions correctly ... ok (0.002s)
2023-01-11T22:05:04.0201805Z   test_pass_manager_error (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:04.0202065Z Tests error catching + debug ... ok (0.004s)
2023-01-11T22:05:04.0202329Z   test_this_before_that_pass_constraint (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:04.0202619Z Tests the construction of constraints ... ok (0.001s)
2023-01-11T22:05:04.0202890Z   test_topological_sort (fx.test_pass_infra.TestPassManager)
2023-01-11T22:05:04.0203182Z Tests that passes are correctly ordered based on contraints. ... ok (0.002s)
2023-01-11T22:05:04.0203519Z   test_matching_pattern_with_list_type_arg (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.006s)
2023-01-11T22:05:04.0203907Z   test_replace_pattern_with_filters (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.014s)
2023-01-11T22:05:04.0204293Z   test_subgraph_rewriter_annotations_int (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.004s)
2023-01-11T22:05:04.0204677Z   test_subgraph_rewriter_call_method (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0205102Z   test_subgraph_rewriter_correct_output_replacement (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:04.0205509Z   test_subgraph_rewriter_graph_argument_order (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0205950Z   test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0206364Z   test_subgraph_rewriter_local_revert (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.010s)
2023-01-11T22:05:04.0206753Z   test_subgraph_rewriter_multiple_pattern_match (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.009s)
2023-01-11T22:05:04.0207199Z   test_subgraph_rewriter_nodes_with_kwargs (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0207601Z   test_subgraph_rewriter_pattern_is_entire_graph (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0208032Z   test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0208459Z   test_subgraph_rewriter_placeholder_matching (fx.test_subgraph_rewriter.TestSubgraphRewriter)
2023-01-11T22:05:04.0208806Z This tests that a placeholder Node can be matched to a Node with ... ok (0.007s)
2023-01-11T22:05:04.0209317Z   test_subgraph_rewriter_preserves_logic (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:04.0209719Z   test_subgraph_rewriter_replace_consecutive_submodules (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0210144Z   test_subgraph_rewriter_replace_with_duplicated_outputs (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:04.0210560Z   test_subgraph_rewriter_replace_with_multiple_outputs (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:04.0210982Z   test_subgraph_rewriter_replaces_referenced_submodules (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0211382Z   test_subgraph_rewriter_single_pattern_match (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0211776Z   test_subgraph_rewriter_traced_as_callable (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.008s)
2023-01-11T22:05:04.0212170Z   test_subgraph_rewriter_with_oneliner_pattern (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0212580Z   test_subgraph_rewriter_with_overlapping_matches (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0212962Z   test_subgraph_rewriter_with_unused_args (fx.test_subgraph_rewriter.TestSubgraphRewriter) ... ok (0.007s)
2023-01-11T22:05:04.0213311Z   test_torchvision_models_alexnet (__main__.TestVisionTracing) ... ok (0.486s)
2023-01-11T22:05:04.0213623Z   test_torchvision_models_convnext_base (__main__.TestVisionTracing) ... ok (2.229s)
2023-01-11T22:05:04.0213929Z   test_torchvision_models_convnext_large (__main__.TestVisionTracing) ... ok (3.748s)
2023-01-11T22:05:04.0214243Z   test_torchvision_models_convnext_small (__main__.TestVisionTracing) ... ok (1.372s)
2023-01-11T22:05:04.0214558Z   test_torchvision_models_convnext_tiny (__main__.TestVisionTracing) ... ok (0.756s)
2023-01-11T22:05:04.0214870Z   test_torchvision_models_densenet121 (__main__.TestVisionTracing) ... ok (1.582s)
2023-01-11T22:05:04.0215164Z   test_torchvision_models_densenet161 (__main__.TestVisionTracing) ... ok (2.584s)
2023-01-11T22:05:04.0215473Z   test_torchvision_models_densenet169 (__main__.TestVisionTracing) ... ok (2.202s)
2023-01-11T22:05:04.0215778Z   test_torchvision_models_densenet201 (__main__.TestVisionTracing) ... ok (2.800s)
2023-01-11T22:05:04.0216579Z   test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_320_fpn (__main__.TestVisionTracing) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.
2023-01-11T22:05:04.0217120Z   warnings.warn(
2023-01-11T22:05:04.0217777Z /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=None`.
2023-01-11T22:05:04.0218227Z   warnings.warn(msg)
2023-01-11T22:05:04.0218403Z ok (0.173s)
2023-01-11T22:05:04.0218670Z   test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_fpn (__main__.TestVisionTracing) ... ok (0.170s)
2023-01-11T22:05:04.0219080Z   test_torchvision_models_detection_fasterrcnn_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.390s)
2023-01-11T22:05:04.0219445Z   test_torchvision_models_detection_fasterrcnn_resnet50_fpn_v2 (__main__.TestVisionTracing) ... ok (0.416s)
2023-01-11T22:05:04.0219789Z   test_torchvision_models_detection_fcos_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.372s)
2023-01-11T22:05:04.0220144Z   test_torchvision_models_detection_keypointrcnn_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.574s)
2023-01-11T22:05:04.0220508Z   test_torchvision_models_detection_maskrcnn_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.419s)
2023-01-11T22:05:04.0220862Z   test_torchvision_models_detection_maskrcnn_resnet50_fpn_v2 (__main__.TestVisionTracing) ... ok (0.446s)
2023-01-11T22:05:04.0221206Z   test_torchvision_models_detection_retinanet_resnet50_fpn (__main__.TestVisionTracing) ... ok (0.374s)
2023-01-11T22:05:04.0221563Z   test_torchvision_models_detection_retinanet_resnet50_fpn_v2 (__main__.TestVisionTracing) ... ok (0.413s)
2023-01-11T22:05:04.0221905Z   test_torchvision_models_detection_ssd300_vgg16 (__main__.TestVisionTracing) ... ok (1.853s)
2023-01-11T22:05:04.0222258Z   test_torchvision_models_detection_ssdlite320_mobilenet_v3_large (__main__.TestVisionTracing) ... ok (0.143s)
2023-01-11T22:05:04.0222589Z   test_torchvision_models_efficientnet_b0 (__main__.TestVisionTracing) ... ok (1.017s)
2023-01-11T22:05:04.0222990Z   test_torchvision_models_efficientnet_b1 (__main__.TestVisionTracing) ... ok (1.426s)
2023-01-11T22:05:04.0223312Z   test_torchvision_models_efficientnet_b2 (__main__.TestVisionTracing) ... ok (1.403s)
2023-01-11T22:05:04.0223620Z   test_torchvision_models_efficientnet_b3 (__main__.TestVisionTracing) ... ok (1.656s)
2023-01-11T22:05:04.0223938Z   test_torchvision_models_efficientnet_b4 (__main__.TestVisionTracing) ... ok (2.243s)
2023-01-11T22:05:04.0224250Z   test_torchvision_models_efficientnet_b5 (__main__.TestVisionTracing) ... ok (2.718s)
2023-01-11T22:05:04.0224572Z   test_torchvision_models_efficientnet_b6 (__main__.TestVisionTracing) ... ok (3.438s)
2023-01-11T22:05:04.0224875Z   test_torchvision_models_efficientnet_b7 (__main__.TestVisionTracing) ... ok (4.375s)
2023-01-11T22:05:04.0225195Z   test_torchvision_models_efficientnet_v2_l (__main__.TestVisionTracing) ... ok (5.980s)
2023-01-11T22:05:04.0225519Z   test_torchvision_models_efficientnet_v2_m (__main__.TestVisionTracing) ... ok (4.021s)
2023-01-11T22:05:04.0225826Z   test_torchvision_models_efficientnet_v2_s (__main__.TestVisionTracing) ... ok (2.626s)
2023-01-11T22:05:04.0226720Z   test_torchvision_models_googlenet (__main__.TestVisionTracing) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/googlenet.py:47: FutureWarning: The default weight initialization of GoogleNet will be changed in future releases of torchvision. If you wish to keep the old behavior (which leads to long initialization times due to scipy/scipy#11299), please set init_weights=True.
2023-01-11T22:05:04.0227269Z   warnings.warn(
2023-01-11T22:05:04.0227444Z ok (1.003s)
2023-01-11T22:05:04.0228263Z   test_torchvision_models_inception_v3 (__main__.TestVisionTracing) ... /var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/models/inception.py:43: FutureWarning: The default weight initialization of inception_v3 will be changed in future releases of torchvision. If you wish to keep the old behavior (which leads to long initialization times due to scipy/scipy#11299), please set init_weights=True.
2023-01-11T22:05:04.0228825Z   warnings.warn(
2023-01-11T22:05:04.0228993Z ok (1.543s)
2023-01-11T22:05:04.0229695Z   test_torchvision_models_maxvit_t (__main__.TestVisionTracing) ... /opt/conda/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorShape.cpp:3452.)
2023-01-11T22:05:04.0230290Z   return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
2023-01-11T22:05:04.0230526Z ok (3.210s)
2023-01-11T22:05:04.0230767Z   test_torchvision_models_mnasnet0_5 (__main__.TestVisionTracing) ... ok (0.757s)
2023-01-11T22:05:04.0231079Z   test_torchvision_models_mnasnet0_75 (__main__.TestVisionTracing) ... ok (0.758s)
2023-01-11T22:05:04.0231374Z   test_torchvision_models_mnasnet1_0 (__main__.TestVisionTracing) ... ok (0.741s)
2023-01-11T22:05:04.0231680Z   test_torchvision_models_mnasnet1_3 (__main__.TestVisionTracing) ... ok (0.845s)
2023-01-11T22:05:04.0231985Z   test_torchvision_models_mobilenet_v2 (__main__.TestVisionTracing) ... ok (0.832s)
2023-01-11T22:05:04.0232306Z   test_torchvision_models_mobilenet_v3_large (__main__.TestVisionTracing) ... ok (1.178s)
2023-01-11T22:05:04.0232619Z   test_torchvision_models_mobilenet_v3_small (__main__.TestVisionTracing) ... ok (0.804s)
2023-01-11T22:05:04.0232935Z   test_torchvision_models_regnet_x_16gf (__main__.TestVisionTracing) ... ok (2.111s)
2023-01-11T22:05:04.0233247Z   test_torchvision_models_regnet_x_1_6gf (__main__.TestVisionTracing) ... ok (1.279s)
2023-01-11T22:05:04.0233544Z   test_torchvision_models_regnet_x_32gf (__main__.TestVisionTracing) ... ok (3.232s)
2023-01-11T22:05:04.0233853Z   test_torchvision_models_regnet_x_3_2gf (__main__.TestVisionTracing) ... ok (1.843s)
2023-01-11T22:05:04.0234161Z   test_torchvision_models_regnet_x_400mf (__main__.TestVisionTracing) ... ok (1.416s)
2023-01-11T22:05:04.0234467Z   test_torchvision_models_regnet_x_800mf (__main__.TestVisionTracing) ... ok (1.105s)
2023-01-11T22:05:04.0234760Z   test_torchvision_models_regnet_x_8gf (__main__.TestVisionTracing) ... ok (2.001s)
2023-01-11T22:05:04.0235071Z   test_torchvision_models_regnet_y_128gf (__main__.TestVisionTracing) ... ok (13.013s)
2023-01-11T22:05:04.0235382Z   test_torchvision_models_regnet_y_16gf (__main__.TestVisionTracing) ... ok (2.715s)
2023-01-11T22:05:04.0235679Z   test_torchvision_models_regnet_y_1_6gf (__main__.TestVisionTracing) ... ok (2.457s)
2023-01-11T22:05:04.0235989Z   test_torchvision_models_regnet_y_32gf (__main__.TestVisionTracing) ... ok (4.091s)
2023-01-11T22:05:04.0236298Z   test_torchvision_models_regnet_y_3_2gf (__main__.TestVisionTracing) ... ok (2.108s)
2023-01-11T22:05:04.0236605Z   test_torchvision_models_regnet_y_400mf (__main__.TestVisionTracing) ... ok (1.581s)
2023-01-11T22:05:04.0236891Z   test_torchvision_models_regnet_y_800mf (__main__.TestVisionTracing) ... ok (1.509s)
2023-01-11T22:05:04.0237197Z   test_torchvision_models_regnet_y_8gf (__main__.TestVisionTracing) ... ok (2.061s)
2023-01-11T22:05:04.0237505Z   test_torchvision_models_resnet101 (__main__.TestVisionTracing) ... ok (1.840s)
2023-01-11T22:05:04.0237809Z   test_torchvision_models_resnet152 (__main__.TestVisionTracing) ... ok (2.601s)
2023-01-11T22:05:04.0238096Z   test_torchvision_models_resnet18 (__main__.TestVisionTracing) ... ok (0.479s)
2023-01-11T22:05:04.0238396Z   test_torchvision_models_resnet34 (__main__.TestVisionTracing) ... ok (0.908s)
2023-01-11T22:05:04.0238697Z   test_torchvision_models_resnet50 (__main__.TestVisionTracing) ... ok (0.985s)
2023-01-11T22:05:04.0238996Z   test_torchvision_models_resnext101_32x8d (__main__.TestVisionTracing) ... ok (2.510s)
2023-01-11T22:05:04.0239348Z   test_torchvision_models_resnext101_64x4d (__main__.TestVisionTracing) ... ok (2.433s)
2023-01-11T22:05:04.0239662Z   test_torchvision_models_resnext50_32x4d (__main__.TestVisionTracing) ... ok (1.017s)
2023-01-11T22:05:04.0240016Z   test_torchvision_models_segmentation_deeplabv3_mobilenet_v3_large (__main__.TestVisionTracing) ... ok (1.277s)
2023-01-11T22:05:04.0240371Z   test_torchvision_models_segmentation_deeplabv3_resnet101 (__main__.TestVisionTracing) ... ok (2.163s)
2023-01-11T22:05:04.0240728Z   test_torchvision_models_segmentation_deeplabv3_resnet50 (__main__.TestVisionTracing) ... ok (1.135s)
2023-01-11T22:05:04.0241077Z   test_torchvision_models_segmentation_fcn_resnet101 (__main__.TestVisionTracing) ... ok (1.753s)
2023-01-11T22:05:04.0241407Z   test_torchvision_models_segmentation_fcn_resnet50 (__main__.TestVisionTracing) ... ok (0.976s)
2023-01-11T22:05:04.0241793Z   test_torchvision_models_segmentation_lraspp_mobilenet_v3_large (__main__.TestVisionTracing) ... ok (0.987s)
2023-01-11T22:05:04.0242140Z   test_torchvision_models_shufflenet_v2_x0_5 (__main__.TestVisionTracing) ... ok (0.945s)
2023-01-11T22:05:04.0242455Z   test_torchvision_models_shufflenet_v2_x1_0 (__main__.TestVisionTracing) ... ok (0.897s)
2023-01-11T22:05:04.0242759Z   test_torchvision_models_shufflenet_v2_x1_5 (__main__.TestVisionTracing) ... ok (0.898s)
2023-01-11T22:05:04.0243075Z   test_torchvision_models_shufflenet_v2_x2_0 (__main__.TestVisionTracing) ... ok (0.958s)
2023-01-11T22:05:04.0243386Z   test_torchvision_models_squeezenet1_0 (__main__.TestVisionTracing) ... ok (0.349s)
2023-01-11T22:05:04.0243698Z   test_torchvision_models_squeezenet1_1 (__main__.TestVisionTracing) ... ok (0.270s)
2023-01-11T22:05:04.0243993Z   test_torchvision_models_swin_b (__main__.TestVisionTracing) ... ok (3.026s)
2023-01-11T22:05:04.0244291Z   test_torchvision_models_swin_s (__main__.TestVisionTracing) ... ok (2.474s)
2023-01-11T22:05:04.0244585Z   test_torchvision_models_swin_t (__main__.TestVisionTracing) ... ok (1.250s)
2023-01-11T22:05:04.0244870Z   test_torchvision_models_swin_v2_b (__main__.TestVisionTracing) ... ok (3.545s)
2023-01-11T22:05:04.0245171Z   test_torchvision_models_swin_v2_s (__main__.TestVisionTracing) ... ok (2.960s)
2023-01-11T22:05:04.0245474Z   test_torchvision_models_swin_v2_t (__main__.TestVisionTracing) ... ok (1.511s)
2023-01-11T22:05:04.0245762Z   test_torchvision_models_vgg11 (__main__.TestVisionTracing) ... ok (1.592s)
2023-01-11T22:05:04.0246064Z   test_torchvision_models_vgg11_bn (__main__.TestVisionTracing) ... ok (1.784s)
2023-01-11T22:05:04.0246365Z   test_torchvision_models_vgg13 (__main__.TestVisionTracing) ... ok (1.826s)
2023-01-11T22:05:04.0246667Z   test_torchvision_models_vgg13_bn (__main__.TestVisionTracing) ... ok (1.846s)
2023-01-11T22:05:04.0246954Z   test_torchvision_models_vgg16 (__main__.TestVisionTracing) ... ok (1.929s)
2023-01-11T22:05:04.0247255Z   test_torchvision_models_vgg16_bn (__main__.TestVisionTracing) ... ok (1.982s)
2023-01-11T22:05:04.0247554Z   test_torchvision_models_vgg19 (__main__.TestVisionTracing) ... ok (2.050s)
2023-01-11T22:05:04.0247843Z   test_torchvision_models_vgg19_bn (__main__.TestVisionTracing) ... ok (2.145s)
2023-01-11T22:05:04.0248155Z   test_torchvision_models_video_mc3_18 (__main__.TestVisionTracing) ... ok (0.872s)
2023-01-11T22:05:04.0248469Z   test_torchvision_models_video_mvit_v1_b (__main__.TestVisionTracing) ... ok (4.569s)
2023-01-11T22:05:04.0248787Z   test_torchvision_models_video_mvit_v2_s (__main__.TestVisionTracing) ... ok (5.466s)
2023-01-11T22:05:04.0249202Z   test_torchvision_models_video_r2plus1d_18 (__main__.TestVisionTracing) ... ok (1.262s)
2023-01-11T22:05:04.0249523Z   test_torchvision_models_video_r3d_18 (__main__.TestVisionTracing) ... ok (1.228s)
2023-01-11T22:05:04.0249835Z   test_torchvision_models_video_s3d (__main__.TestVisionTracing) ... ok (2.589s)
2023-01-11T22:05:04.0250140Z   test_torchvision_models_video_swin3d_b (__main__.TestVisionTracing) ... ok (3.266s)
2023-01-11T22:05:04.0250456Z   test_torchvision_models_video_swin3d_s (__main__.TestVisionTracing) ... ok (2.641s)
2023-01-11T22:05:04.0250831Z   test_torchvision_models_video_swin3d_t (__main__.TestVisionTracing) ... ok (1.403s)
2023-01-11T22:05:04.0251137Z   test_torchvision_models_vit_b_16 (__main__.TestVisionTracing) ... ok (2.112s)
2023-01-11T22:05:04.0251425Z   test_torchvision_models_vit_b_32 (__main__.TestVisionTracing) ... ok (1.795s)
2023-01-11T22:05:04.0251725Z   test_torchvision_models_vit_h_14 (__main__.TestVisionTracing) ... ok (11.000s)
2023-01-11T22:05:04.0252027Z   test_torchvision_models_vit_l_16 (__main__.TestVisionTracing) ... ok (5.961s)
2023-01-11T22:05:04.0252314Z   test_torchvision_models_vit_l_32 (__main__.TestVisionTracing) ... ok (5.911s)
2023-01-11T22:05:04.0252628Z   test_torchvision_models_wide_resnet101_2 (__main__.TestVisionTracing) ... ok (2.951s)
2023-01-11T22:05:04.0252980Z   test_torchvision_models_wide_resnet50_2 (__main__.TestVisionTracing) ... ok (1.581s)
2023-01-11T22:05:04.0253308Z   test_flatten_fully_static (fx.test_gradual_type.TypeCheckerTest) ... ok (0.016s)
2023-01-11T22:05:04.0253607Z   test_resnet50 (fx.test_gradual_type.TypeCheckerTest) ... ok (1.232s)
2023-01-11T22:05:04.0253921Z   test_symbolic_add_with_broadcast (fx.test_gradual_type.TypeCheckerTest) ... ok (0.004s)
2023-01-11T22:05:04.0254263Z   test_symbolic_add_with_broadcast_2 (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0254596Z   test_type_check_add_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0254901Z   test_type_check_add_true (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0255231Z   test_type_check_add_with_broadcast (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0255569Z   test_type_check_add_with_scalar (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0255889Z   test_type_check_batch_norm_2D (fx.test_gradual_type.TypeCheckerTest) ... ok (0.004s)
2023-01-11T22:05:04.0256229Z   test_type_check_batch_norm_2D_broadcast (fx.test_gradual_type.TypeCheckerTest) ... ok (0.007s)
2023-01-11T22:05:04.0256572Z   test_type_check_batch_norm_2D_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0256907Z   test_type_check_batch_norm_symbolic (fx.test_gradual_type.TypeCheckerTest) ... ok (0.004s)
2023-01-11T22:05:04.0257218Z   test_type_check_conv2D (fx.test_gradual_type.TypeCheckerTest) ... ok (0.004s)
2023-01-11T22:05:04.0257533Z   test_type_check_conv2D_2 (fx.test_gradual_type.TypeCheckerTest) ... ok (0.007s)
2023-01-11T22:05:04.0257865Z   test_type_check_conv2D_2_fully_static (fx.test_gradual_type.TypeCheckerTest) ... ok (0.028s)
2023-01-11T22:05:04.0258198Z   test_type_check_conv2D_maxpool2d_flatten (fx.test_gradual_type.TypeCheckerTest) ... ok (0.005s)
2023-01-11T22:05:04.0258528Z   test_type_check_conv2D_types (fx.test_gradual_type.TypeCheckerTest) ... ok (0.008s)
2023-01-11T22:05:04.0258845Z   test_type_check_flatten (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0259163Z   test_type_check_flatten3 (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0259477Z   test_type_check_flatten_2 (fx.test_gradual_type.TypeCheckerTest) ... ok (0.002s)
2023-01-11T22:05:04.0259803Z   test_type_check_reshape_dyn_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.002s)
2023-01-11T22:05:04.0260138Z   test_type_check_reshape_dyn_true (fx.test_gradual_type.TypeCheckerTest) ... ok (0.002s)
2023-01-11T22:05:04.0260471Z   test_type_check_reshape_dyn_true_param_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.002s)
2023-01-11T22:05:04.0260814Z   test_type_check_reshape_false (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0261137Z   test_type_check_reshape_true (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0261499Z   test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten (fx.test_gradual_type.TypeCheckerTest) ... ok (0.019s)
2023-01-11T22:05:04.0261853Z   test_type_check_transpose_False (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0262211Z   test_type_check_transpose_true (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0262539Z   test_type_maxpool2d_fully_static (fx.test_gradual_type.TypeCheckerTest) ... ok (0.024s)
2023-01-11T22:05:04.0262951Z   test_type_typechecl_maxpool2d_3dinput (fx.test_gradual_type.TypeCheckerTest) ... ok (0.003s)
2023-01-11T22:05:04.0263275Z   test_typecheck_basicblock (fx.test_gradual_type.TypeCheckerTest) ... ok (0.006s)
2023-01-11T22:05:04.0263456Z 
2023-01-11T22:05:04.0263676Z ----------------------------------------------------------------------
2023-01-11T22:05:04.0263920Z Ran 526 tests in 223.797s
2023-01-11T22:05:04.0264037Z 
2023-01-11T22:05:04.0264096Z OK (skipped=3)
2023-01-11T22:05:04.0264201Z 
2023-01-11T22:05:04.0264286Z Generating XML reports...
2023-01-11T22:05:04.0264754Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_gradual_type.AnnotationsTest-20230111220117.xml
2023-01-11T22:05:04.0265278Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_cse_pass.TestCSEPass-20230111220117.xml
2023-01-11T22:05:04.0265793Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_common_passes.TestCommonPass-20230111220117.xml
2023-01-11T22:05:04.0266315Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_fx_const_fold.TestConstFold-20230111220117.xml
2023-01-11T22:05:04.0266924Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow-20230111220117.xml
2023-01-11T22:05:04.0267492Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_dce_pass.TestDCE-20230111220117.xml
2023-01-11T22:05:04.0267927Z Generated XML report: test-reports/python-unittest/test_fx/TEST-TestFX-20230111220117.xml
2023-01-11T22:05:04.0268433Z Generated XML report: test-reports/python-unittest/test_fx/TEST-TestFXAPIBackwardCompatibility-20230111220117.xml
2023-01-11T22:05:04.0268967Z Generated XML report: test-reports/python-unittest/test_fx/TEST-TestFunctionalTracing-20230111220117.xml
2023-01-11T22:05:04.0269477Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_pass_infra.TestPassManager-20230111220117.xml
2023-01-11T22:05:04.0270035Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_subgraph_rewriter.TestSubgraphRewriter-20230111220117.xml
2023-01-11T22:05:04.0270564Z Generated XML report: test-reports/python-unittest/test_fx/TEST-TestVisionTracing-20230111220117.xml
2023-01-11T22:05:04.0271074Z Generated XML report: test-reports/python-unittest/test_fx/TEST-fx.test_gradual_type.TypeCheckerTest-20230111220117.xml
2023-01-11T22:05:04.0271309Z 
2023-01-11T22:05:04.0271622Z ##[endgroup]
2023-01-11T22:05:04.0271972Z FINISHED PRINTING LOG FILE of test_fx (/var/lib/jenkins/workspace/test/test-reports/test_fx_4tp8se8h)
2023-01-11T22:05:04.0272172Z 
2023-01-11T22:05:04.0272332Z Running test_indexing ... [2023-01-11 22:05:04.006066]
2023-01-11T22:05:04.0272799Z Executing ['/opt/conda/bin/python', '-bb', 'test_indexing.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:04.006291]
2023-01-11T22:05:05.9115796Z 
2023-01-11T22:05:05.9116348Z Expand the folded group to see the log file of test_indexing
2023-01-11T22:05:05.9117147Z ##[group]PRINTING LOG FILE of test_indexing (/var/lib/jenkins/workspace/test/test-reports/test_indexing_5q82la_z)
2023-01-11T22:05:05.9117798Z 
2023-01-11T22:05:05.9118097Z Running tests...
2023-01-11T22:05:05.9118720Z ----------------------------------------------------------------------
2023-01-11T22:05:05.9118984Z 
2023-01-11T22:05:05.9119282Z ----------------------------------------------------------------------
2023-01-11T22:05:05.9119647Z Ran 0 tests in 0.000s
2023-01-11T22:05:05.9119813Z 
2023-01-11T22:05:05.9119902Z OK
2023-01-11T22:05:05.9122196Z 
2023-01-11T22:05:05.9122459Z Generating XML reports...
2023-01-11T22:05:05.9122857Z Test results will be stored in test-reports/python-unittest/test_indexing
2023-01-11T22:05:05.9129464Z 
2023-01-11T22:05:05.9129915Z ##[endgroup]
2023-01-11T22:05:05.9130346Z FINISHED PRINTING LOG FILE of test_indexing (/var/lib/jenkins/workspace/test/test-reports/test_indexing_5q82la_z)
2023-01-11T22:05:05.9130559Z 
2023-01-11T22:05:05.9130720Z Running test_jit_cuda_fuser ... [2023-01-11 22:05:05.911673]
2023-01-11T22:05:05.9131199Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_cuda_fuser.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:05.911933]
2023-01-11T22:05:08.6794550Z 
2023-01-11T22:05:08.6794993Z Expand the folded group to see the log file of test_jit_cuda_fuser
2023-01-11T22:05:08.6795989Z ##[group]PRINTING LOG FILE of test_jit_cuda_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_cuda_fuser_cayni90c)
2023-01-11T22:05:08.6796398Z 
2023-01-11T22:05:08.6796521Z Running tests...
2023-01-11T22:05:08.6797336Z ----------------------------------------------------------------------
2023-01-11T22:05:08.6798011Z Test results will be stored in test-reports/python-unittest/test_jit_cuda_fuser
2023-01-11T22:05:08.6798511Z   test__softmax_function (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6798987Z   test__softmax_function_half_to_float (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6799427Z   test_addcmul_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6799947Z   test_alias_pass_fix (__main__.TestCudaFuser) ... skip: skipping this test since unsqueeze is disabled now (0.001s)
2023-01-11T22:05:08.6800447Z   test_autocast_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6800933Z   test_autocast_1_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6801401Z   test_autocast_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6801848Z   test_autocast_2_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6802303Z   test_backward_type (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6802687Z   test_batch_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6803020Z   test_batch_norm_impl_index_correctness (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6803360Z   test_batch_norm_impl_index_inner_bcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6803696Z   test_bfloat (__main__.TestCudaFuser) ... skip: device does not support BFloat16 (0.001s)
2023-01-11T22:05:08.6804150Z   test_binary_bitwise (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6804596Z   test_binary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6805098Z   test_binary_ops_channels_last_with_bcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6805758Z   test_binary_ops_complex (__main__.TestCudaFuser) ... skip: see issue https://github.com/csarofeen/pytorch/issues/1730 (0.001s)
2023-01-11T22:05:08.6806328Z   test_binary_ops_permutation (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6806901Z   test_branches (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6807509Z   test_broadcasting_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6808091Z   test_broadcasting_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6808595Z   test_broadcasting_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6809286Z   test_broadcasting_3 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6810155Z   test_broadcasting_multiple_output (__main__.TestCudaFuser) ... skip: broadcast on branches can't be resolved yet (0.001s)
2023-01-11T22:05:08.6810849Z   test_broadcasting_multiple_output_shape (__main__.TestCudaFuser) ... skip: Broadcast with different output not supported yet (0.001s)
2023-01-11T22:05:08.6811676Z   test_broadcasting_partition_logic_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6812258Z   test_broadcasting_partition_logic_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6812869Z   test_build_shape_expression_native_dropout (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6813401Z   test_category_rule (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T22:05:08.6813918Z   test_channels_last_with_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T22:05:08.6814444Z   test_chunk (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6814898Z   test_clamp (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6815499Z   test_clamp_reversed_bound (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6816051Z   test_clean_profile_ivalue (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6816540Z   test_const (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6817041Z   test_contiguous_on_broadcasted (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6817576Z   test_conv2d_bias (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6818114Z   test_conv2d_symbolic_shapes (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6818650Z   test_cpu_scalar (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6819174Z   test_cuda_fusion_guard (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6819750Z   test_cuda_fusion_guard_backward (__main__.TestCudaFuser) ... skip: requires NVFuser (0.001s)
2023-01-11T22:05:08.6820333Z   test_device_constant (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6820948Z   test_disable_const_chunk_propagation_for_normalization (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6821584Z   test_disable_sibling_fuse (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6822157Z   test_dropout_inference_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6822849Z   test_dropout_train_nograd_fusion (__main__.TestCudaFuser) ... skip: not enough memory (0.001s)
2023-01-11T22:05:08.6823444Z   test_dropout_train_nograd_prob_check (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6824046Z   test_dropout_training_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6824644Z   test_dropout_training_prob_check (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6825188Z   test_dynamic_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6825715Z   test_expand (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6826265Z   test_fix_shape_expression_bn (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6826885Z   test_flatten (__main__.TestCudaFuser) ... skip: skipping this test since flatten is disabled now (0.001s)
2023-01-11T22:05:08.6827431Z   test_gelu (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6827926Z   test_grad_sum_to_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6828486Z   test_graph_for_with_missing_optimized_engine (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6829060Z   test_graph_rng (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6829554Z   test_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6830058Z   test_high_rank_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6830588Z   test_inf_quick_patch (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6831097Z   test_inplace_removal (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6831776Z   test_input_output_passthrough (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6832350Z   test_int_tensor_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6832870Z   test_issue1445_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6833360Z   test_issue_1785 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6833901Z   test_layer_norm_autodiff (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6834458Z   test_layer_norm_parser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6838579Z   test_layer_norm_trivial_reduce_dim (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6839231Z   test_linear (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6839795Z   test_linear_symbolic_shapes (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6840407Z   test_multiple_device_pw (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6840973Z   test_native_batch_norm_backward (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6841550Z   test_native_layer_norm (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6842100Z   test_native_layer_norm_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6842653Z   test_native_layer_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6843250Z   test_nested_view (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.000s)
2023-01-11T22:05:08.6843828Z   test_no_tensor_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6844346Z   test_norm (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6844859Z   test_norm_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6845381Z   test_norm_channels_last (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6845919Z   test_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6846422Z   test_norm_half_layer (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6846910Z   test_norm_large (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6847459Z   test_normalization_partition (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6848084Z   test_nvfuser_comparison_callbacks_with_fallback (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6848819Z   test_nvfuser_comparison_callbacks_without_fallback (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6849563Z   test_overlapped_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6850136Z   test_permutation_preservation (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6850766Z   test_permutation_preservation_edge_case_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6851444Z   test_permutation_preservation_edge_case_1_broken (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6852044Z   test_permutation_preservation_edge_case_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6852613Z   test_permute (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6853158Z   test_pointwise_reference_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6853694Z   test_profile_ivalue (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6854240Z   test_profile_ivalue_multiple_profiles (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6854874Z   test_profiling_node (__main__.TestCudaFuser) ... skip: Skipped due to rand_like behavior change (0.000s)
2023-01-11T22:05:08.6855593Z   test_pw_single_reduction_partition (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6856143Z   test_random_topo (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6856615Z   test_reduction (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6857098Z   test_reduction_dtypes_axis (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6857600Z   test_reduction_empty_axes (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6858172Z   test_reduction_multiple_output (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6858762Z   test_reduction_permutation (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6859438Z   test_reduction_sizes_op (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6860002Z   test_remove_output_used_only_in_dtype (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6860540Z   test_rsub (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6861053Z   test_scalar_cuda_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6861591Z   test_scalar_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6862107Z   test_scalar_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6862638Z   test_scalar_tensor_permuted (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6863376Z   test_scheduler_with_polymorphic_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6864063Z   test_shape_expression (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.001s)
2023-01-11T22:05:08.6864673Z   test_sibling_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6865256Z   test_sibling_fusion_no_scalar_inputs (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6865831Z   test_single_reduction_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6866366Z   test_singleton_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6866913Z   test_skip_parser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6867437Z   test_softmax (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6867936Z   test_softmax_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6868451Z   test_softmax_dtype (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6868938Z   test_softmax_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6869450Z   test_softplus_fuser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6870060Z   test_squeeze (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.000s)
2023-01-11T22:05:08.6870768Z   test_squeeze_negative_dim (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.000s)
2023-01-11T22:05:08.6871476Z   test_squeeze_zero (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.001s)
2023-01-11T22:05:08.6872103Z   test_strict_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6872617Z   test_sum_to_one (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6873123Z   test_sum_to_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6873621Z   test_ternary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T22:05:08.6874179Z   test_ternary_ops_integer_compatibility (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6874783Z   test_ternary_ops_type_promotion (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6875441Z   test_to_boolean (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6875943Z   test_to_copy (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6876453Z   test_to_dtype_bf16_to_bf16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6877017Z   test_to_dtype_bf16_to_fp32 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6877613Z   test_to_dtype_fp16_to_fp16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6878135Z   test_to_dtype_fp16_to_fp32 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6878667Z   test_to_dtype_fp32_to_bf16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6879322Z   test_to_dtype_fp32_to_fp16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6879864Z   test_transpose (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6880399Z   test_transpose_default (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6880952Z   test_trivial_reduction (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6881465Z   test_type_as_op (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6882017Z   test_type_inference (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6882540Z   test_unary_bitwise (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6883067Z   test_unary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6883652Z   test_unsqueeze (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.000s)
2023-01-11T22:05:08.6884243Z   test_variance (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6884769Z   test_variance_profiling (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6885365Z   test_view (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.000s)
2023-01-11T22:05:08.6885935Z   test_view_before_permute (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6886540Z   test_view_copy_graph_guard (__main__.TestCudaFuser) ... skip: skipping this test since reshape is disabled now (0.000s)
2023-01-11T22:05:08.6887241Z   test_view_copy_graph_guard_double_fusion (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.001s)
2023-01-11T22:05:08.6887885Z   test_can_be_enabled_nvfuser (__main__.TestEnableDisableCudaFuser) ... ok (0.001s)
2023-01-11T22:05:08.6888491Z   test_context_manager_test (__main__.TestEnableDisableCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:05:08.6889242Z   test_register_fuser (__main__.TestEnableDisableCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:05:08.6889862Z   test_register_fuser_cpu (__main__.TestEnableDisableCudaFuser) ... ok (0.004s)
2023-01-11T22:05:08.6890441Z   test_autodiff_fallback (jit.test_fuser_common.TestFuserCommon) ... ok (0.054s)
2023-01-11T22:05:08.6890770Z 
2023-01-11T22:05:08.6891145Z ----------------------------------------------------------------------
2023-01-11T22:05:08.6891552Z Ran 158 tests in 0.165s
2023-01-11T22:05:08.6891747Z 
2023-01-11T22:05:08.6891868Z OK (skipped=155)
2023-01-11T22:05:08.6892054Z 
2023-01-11T22:05:08.6892207Z Generating XML reports...
2023-01-11T22:05:08.6893012Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestEnableDisableCudaFuser-20230111220508.xml
2023-01-11T22:05:08.6894031Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-jit.test_fuser_common.TestFuserCommon-20230111220508.xml
2023-01-11T22:05:08.6894996Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestCudaFuser-20230111220508.xml
2023-01-11T22:05:08.6895399Z 
2023-01-11T22:05:08.6895927Z ##[endgroup]
2023-01-11T22:05:08.6896780Z FINISHED PRINTING LOG FILE of test_jit_cuda_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_cuda_fuser_cayni90c)
2023-01-11T22:05:08.6897189Z 
2023-01-11T22:05:08.6897483Z Running test_jit_disabled ... [2023-01-11 22:05:08.679836]
2023-01-11T22:05:08.6898324Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_disabled.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:08.680090]
2023-01-11T22:05:10.5997931Z 
2023-01-11T22:05:10.5998375Z Expand the folded group to see the log file of test_jit_disabled
2023-01-11T22:05:10.5999350Z ##[group]PRINTING LOG FILE of test_jit_disabled (/var/lib/jenkins/workspace/test/test-reports/test_jit_disabled_7mzfsfj9)
2023-01-11T22:05:10.5999667Z 
2023-01-11T22:05:10.5999773Z Running tests...
2023-01-11T22:05:10.6000434Z ----------------------------------------------------------------------
2023-01-11T22:05:10.6000884Z Test results will be stored in test-reports/python-unittest/test_jit_disabled
2023-01-11T22:05:10.6001199Z   test_attribute (__main__.TestJitDisabled) ... ok (0.252s)
2023-01-11T22:05:10.6001521Z   test_recursive_script (__main__.TestJitDisabled) ... ok (0.032s)
2023-01-11T22:05:10.6002035Z   test_script_module_construction (__main__.TestJitDisabled) ... ok (0.032s)
2023-01-11T22:05:10.6002345Z 
2023-01-11T22:05:10.6002568Z ----------------------------------------------------------------------
2023-01-11T22:05:10.6002807Z Ran 3 tests in 0.317s
2023-01-11T22:05:10.6002922Z 
2023-01-11T22:05:10.6003014Z OK
2023-01-11T22:05:10.6003171Z 
2023-01-11T22:05:10.6003321Z Generating XML reports...
2023-01-11T22:05:10.6003958Z Generated XML report: test-reports/python-unittest/test_jit_disabled/TEST-TestJitDisabled-20230111220509.xml
2023-01-11T22:05:10.6004191Z 
2023-01-11T22:05:10.6004414Z ##[endgroup]
2023-01-11T22:05:10.6004806Z FINISHED PRINTING LOG FILE of test_jit_disabled (/var/lib/jenkins/workspace/test/test-reports/test_jit_disabled_7mzfsfj9)
2023-01-11T22:05:10.6005021Z 
2023-01-11T22:05:10.6005187Z Running test_linalg ... [2023-01-11 22:05:10.599895]
2023-01-11T22:05:10.6005642Z Executing ['/opt/conda/bin/python', '-bb', 'test_linalg.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:10.600144]
2023-01-11T22:05:12.4596104Z 
2023-01-11T22:05:12.4596627Z Expand the folded group to see the log file of test_linalg
2023-01-11T22:05:12.4597572Z ##[group]PRINTING LOG FILE of test_linalg (/var/lib/jenkins/workspace/test/test-reports/test_linalg_827cdfkd)
2023-01-11T22:05:12.4597977Z 
2023-01-11T22:05:12.4598108Z Running tests...
2023-01-11T22:05:12.4598769Z ----------------------------------------------------------------------
2023-01-11T22:05:12.4599069Z 
2023-01-11T22:05:12.4599406Z ----------------------------------------------------------------------
2023-01-11T22:05:12.4599823Z Ran 0 tests in 0.000s
2023-01-11T22:05:12.4599942Z 
2023-01-11T22:05:12.4600015Z OK
2023-01-11T22:05:12.4600105Z 
2023-01-11T22:05:12.4600190Z Generating XML reports...
2023-01-11T22:05:12.4600507Z Test results will be stored in test-reports/python-unittest/test_linalg
2023-01-11T22:05:12.4600772Z 
2023-01-11T22:05:12.4601161Z ##[endgroup]
2023-01-11T22:05:12.4601684Z FINISHED PRINTING LOG FILE of test_linalg (/var/lib/jenkins/workspace/test/test-reports/test_linalg_827cdfkd)
2023-01-11T22:05:12.4601874Z 
2023-01-11T22:05:12.4602059Z Running test_mobile_optimizer ... [2023-01-11 22:05:12.459641]
2023-01-11T22:05:12.4602539Z Executing ['/opt/conda/bin/python', '-bb', 'test_mobile_optimizer.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:12.459916]
2023-01-11T22:05:16.7910480Z 
2023-01-11T22:05:16.7910947Z Expand the folded group to see the log file of test_mobile_optimizer
2023-01-11T22:05:16.7911746Z ##[group]PRINTING LOG FILE of test_mobile_optimizer (/var/lib/jenkins/workspace/test/test-reports/test_mobile_optimizer_x_y6wabu)
2023-01-11T22:05:16.7911990Z 
2023-01-11T22:05:16.7912097Z Running tests...
2023-01-11T22:05:16.7912499Z ----------------------------------------------------------------------
2023-01-11T22:05:16.7913280Z Test results will be stored in test-reports/python-unittest/test_mobile_optimizer
2023-01-11T22:05:16.7913645Z   test_clone_module_with_class (__main__.TestOptimizer) ... ok (0.236s)
2023-01-11T22:05:16.7914566Z   test_generate_mobile_module_lints (__main__.TestOptimizer) ... /opt/conda/lib/python3.10/site-packages/torch/utils/bundled_inputs.py:394: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:16.7915227Z   if arg._typed_storage().size() <= MAX_RAW_TENSOR_SIZE or skip_size_check:
2023-01-11T22:05:16.7915564Z ok (0.030s)
2023-01-11T22:05:16.7915900Z   test_hoist_conv_packed_params (__main__.TestOptimizer) ... ok (0.446s)
2023-01-11T22:05:16.7916335Z   test_mobilenet_optimize_for_mobile (__main__.TestOptimizer) ... ok (1.384s)
2023-01-11T22:05:16.7916857Z   test_optimize_for_mobile (__main__.TestOptimizer) ... ok (0.182s)
2023-01-11T22:05:16.7917325Z   test_preserve_bundled_inputs_methods (__main__.TestOptimizer) ... ok (0.040s)
2023-01-11T22:05:16.7917795Z   test_quantized_conv_no_asan_failures (__main__.TestOptimizer) ... ok (0.180s)
2023-01-11T22:05:16.7918048Z 
2023-01-11T22:05:16.7918376Z ----------------------------------------------------------------------
2023-01-11T22:05:16.7918776Z Ran 7 tests in 2.500s
2023-01-11T22:05:16.7918954Z 
2023-01-11T22:05:16.7919045Z OK
2023-01-11T22:05:16.7919190Z 
2023-01-11T22:05:16.7919317Z Generating XML reports...
2023-01-11T22:05:16.7920090Z Generated XML report: test-reports/python-unittest/test_mobile_optimizer/TEST-TestOptimizer-20230111220513.xml
2023-01-11T22:05:16.7920551Z 
2023-01-11T22:05:16.7921028Z ##[endgroup]
2023-01-11T22:05:16.7921716Z FINISHED PRINTING LOG FILE of test_mobile_optimizer (/var/lib/jenkins/workspace/test/test-reports/test_mobile_optimizer_x_y6wabu)
2023-01-11T22:05:16.7922129Z 
2023-01-11T22:05:16.7922430Z Running test_modules ... [2023-01-11 22:05:16.791167]
2023-01-11T22:05:16.7923260Z Executing ['/opt/conda/bin/python', '-bb', 'test_modules.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:16.791455]
2023-01-11T22:05:19.2018101Z 
2023-01-11T22:05:19.2020083Z Expand the folded group to see the log file of test_modules
2023-01-11T22:05:19.2020854Z ##[group]PRINTING LOG FILE of test_modules (/var/lib/jenkins/workspace/test/test-reports/test_modules_izu92gho)
2023-01-11T22:05:19.2021124Z 
2023-01-11T22:05:19.2021203Z Running tests...
2023-01-11T22:05:19.2021600Z ----------------------------------------------------------------------
2023-01-11T22:05:19.2021771Z 
2023-01-11T22:05:19.2021958Z ----------------------------------------------------------------------
2023-01-11T22:05:19.2022244Z Ran 0 tests in 0.000s
2023-01-11T22:05:19.2022403Z 
2023-01-11T22:05:19.2022491Z OK
2023-01-11T22:05:19.2022687Z 
2023-01-11T22:05:19.2022784Z Generating XML reports...
2023-01-11T22:05:19.2023116Z Test results will be stored in test-reports/python-unittest/test_modules
2023-01-11T22:05:19.2023293Z 
2023-01-11T22:05:19.2023570Z ##[endgroup]
2023-01-11T22:05:19.2023938Z FINISHED PRINTING LOG FILE of test_modules (/var/lib/jenkins/workspace/test/test-reports/test_modules_izu92gho)
2023-01-11T22:05:19.2024149Z 
2023-01-11T22:05:19.2024333Z Running test_multiprocessing ... [2023-01-11 22:05:19.201929]
2023-01-11T22:05:19.2024819Z Executing ['/opt/conda/bin/python', '-bb', 'test_multiprocessing.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:19.202194]
2023-01-11T22:05:30.4867122Z 
2023-01-11T22:05:30.4867725Z Expand the folded group to see the log file of test_multiprocessing
2023-01-11T22:05:30.4868816Z ##[group]PRINTING LOG FILE of test_multiprocessing (/var/lib/jenkins/workspace/test/test-reports/test_multiprocessing_chv4aypo)
2023-01-11T22:05:30.4869207Z 
2023-01-11T22:05:30.4869327Z Running tests...
2023-01-11T22:05:30.4870136Z ----------------------------------------------------------------------
2023-01-11T22:05:30.4870726Z Test results will be stored in test-reports/python-unittest/test_multiprocessing
2023-01-11T22:05:30.4871221Z   test_autograd_errors (__main__.TestMultiprocessing) ... ok (0.238s)
2023-01-11T22:05:30.4872124Z   test_autograd_fine_with_spawn (__main__.TestMultiprocessing) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:30.4872716Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:30.4873384Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:30.4874084Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:30.4874974Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:30.4875511Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:30.4876241Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:30.4876808Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:30.4877659Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:30.4878298Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:30.4879109Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:30.4879726Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:30.4880133Z ok (1.349s)
2023-01-11T22:05:30.4880580Z   test_cuda_bad_call (__main__.TestMultiprocessing) ... skip: CUDA not available (0.000s)
2023-01-11T22:05:30.4881164Z   test_cuda_ipc_deadlock (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:05:30.4881787Z   test_cuda_memory_allocation (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:05:30.4882418Z   test_cuda_parameter_sharing (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:05:30.4883045Z   test_cuda_send_many (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:05:30.4883620Z   test_cuda_simple (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:05:30.4884201Z   test_cuda_small_tensors (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:05:30.4884777Z   test_cuda_variable_sharing (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:05:30.4885346Z   test_empty_shared (__main__.TestMultiprocessing) ... ok (0.001s)
2023-01-11T22:05:30.4885851Z   test_empty_tensor_sharing (__main__.TestMultiprocessing) ... ok (0.003s)
2023-01-11T22:05:30.4886409Z   test_empty_tensor_sharing_cuda (__main__.TestMultiprocessing) ... skip: CUDA not available (0.000s)
2023-01-11T22:05:30.4887000Z   test_event (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:05:30.4887595Z   test_event_handle_exporter (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:05:30.4888249Z   test_event_handle_importer (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:05:30.4888866Z   test_event_handle_multi_gpu (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:05:30.4889703Z   test_event_multiprocess (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:05:30.4890276Z   test_fd_pool (__main__.TestMultiprocessing) ... ok (0.607s)
2023-01-11T22:05:30.4891442Z   test_fd_preserve_sharing (__main__.TestMultiprocessing) ... /var/lib/jenkins/workspace/test/test_multiprocessing.py:304: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:30.4892643Z   data = [x.storage(), x, x[2], x[:, 1]]
2023-01-11T22:05:30.4894210Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:30.4895203Z   device=typed_storage.device,
2023-01-11T22:05:30.4896306Z /var/lib/jenkins/workspace/test/test_multiprocessing.py:312: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:30.4897347Z   self.assertEqual(t.storage()._cdata, storage_cdata)
2023-01-11T22:05:30.4897713Z ok (0.129s)
2023-01-11T22:05:30.4898725Z   test_fd_sharing (__main__.TestMultiprocessing) ... /var/lib/jenkins/workspace/test/test_multiprocessing.py:284: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:30.4899711Z   s1 = t1.storage()
2023-01-11T22:05:30.4900643Z /var/lib/jenkins/workspace/test/test_multiprocessing.py:285: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:30.4901565Z   s2 = t2.storage()
2023-01-11T22:05:30.4902596Z /var/lib/jenkins/workspace/test/test_multiprocessing.py:287: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:30.4903597Z   self.assertEqual(s1.data_ptr(), s1.data_ptr())
2023-01-11T22:05:30.4903972Z ok (0.616s)
2023-01-11T22:05:30.4905017Z   test_fs (__main__.TestMultiprocessing) ... /var/lib/jenkins/workspace/test/test_multiprocessing.py:378: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T22:05:30.4906028Z   x = torch.DoubleStorage(4)
2023-01-11T22:05:30.4906364Z ok (4.499s)
2023-01-11T22:05:30.4906763Z   test_fs_is_shared (__main__.TestMultiprocessing) ... ok (0.001s)
2023-01-11T22:05:30.4907248Z   test_fs_pool (__main__.TestMultiprocessing) ... ok (0.491s)
2023-01-11T22:05:30.4907736Z   test_fs_preserve_sharing (__main__.TestMultiprocessing) ... ok (0.096s)
2023-01-11T22:05:30.4909383Z   test_fs_sharing (__main__.TestMultiprocessing) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/67002 for platform(s) windows, mac, linux, dynamo, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.000s)
2023-01-11T22:05:30.4910464Z   test_inherit_tensor (__main__.TestMultiprocessing) ... ok (0.011s)
2023-01-11T22:05:30.4911539Z   test_integer_parameter_serialization_cpu (__main__.TestMultiprocessing) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:30.4912369Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:30.4913157Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:30.4913807Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:30.4914203Z ok (1.524s)
2023-01-11T22:05:30.4914713Z   test_integer_parameter_serialization_cuda (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.000s)
2023-01-11T22:05:30.4915321Z   test_is_shared (__main__.TestMultiprocessing) ... ok (0.001s)
2023-01-11T22:05:30.4915970Z   test_is_shared_cuda (__main__.TestMultiprocessing) ... skip: CUDA not available (0.000s)
2023-01-11T22:05:30.4916541Z   test_leaf_variable_sharing (__main__.TestMultiprocessing) ... ok (0.016s)
2023-01-11T22:05:30.4917139Z   test_mixed_types_cuda_sharing (__main__.TestMultiprocessing) ... skip: CUDA IPC not available (0.001s)
2023-01-11T22:05:30.4917751Z   test_non_leaf_variable_sharing (__main__.TestMultiprocessing) ... ok (0.001s)
2023-01-11T22:05:30.4919403Z   test_parameter_sharing (__main__.TestMultiprocessing) ... /opt/conda/lib/python3.10/site-packages/torch/utils/hooks.py:87: UserWarning: backward hook <function TestMultiprocessing._test_autograd_sharing.<locals>.hook at 0x7f2eb2d48c10> on tensor will not be serialized.  If this is expected, you can decorate the function with @torch.utils.hooks.unserializable_hook to suppress this warning
2023-01-11T22:05:30.4920504Z   warnings.warn("backward hook {} on tensor will not be "
2023-01-11T22:05:30.4920894Z ok (0.009s)
2023-01-11T22:05:30.4922411Z   test_variable_sharing (__main__.TestMultiprocessing) ... /opt/conda/lib/python3.10/site-packages/torch/utils/hooks.py:87: UserWarning: backward hook <function TestMultiprocessing._test_autograd_sharing.<locals>.hook at 0x7f2eb2d497e0> on tensor will not be serialized.  If this is expected, you can decorate the function with @torch.utils.hooks.unserializable_hook to suppress this warning
2023-01-11T22:05:30.4923513Z   warnings.warn("backward hook {} on tensor will not be "
2023-01-11T22:05:30.4923836Z ok (0.018s)
2023-01-11T22:05:30.4924272Z   test_wrong_cuda_fork (__main__.TestMultiprocessing) ... skip: CUDA not available (0.001s)
2023-01-11T22:05:30.4924618Z 
2023-01-11T22:05:30.4924993Z ----------------------------------------------------------------------
2023-01-11T22:05:30.4925392Z Ran 37 tests in 9.622s
2023-01-11T22:05:30.4925565Z 
2023-01-11T22:05:30.4925693Z OK (skipped=19)
2023-01-11T22:05:30.4925883Z 
2023-01-11T22:05:30.4926031Z Generating XML reports...
2023-01-11T22:05:30.4926786Z Generated XML report: test-reports/python-unittest/test_multiprocessing/TEST-TestMultiprocessing-20230111220520.xml
2023-01-11T22:05:30.4927180Z 
2023-01-11T22:05:30.4927604Z ##[endgroup]
2023-01-11T22:05:30.4928374Z FINISHED PRINTING LOG FILE of test_multiprocessing (/var/lib/jenkins/workspace/test/test-reports/test_multiprocessing_chv4aypo)
2023-01-11T22:05:30.4928795Z 
2023-01-11T22:05:30.4929308Z Running test_multiprocessing_spawn ... [2023-01-11 22:05:30.486882]
2023-01-11T22:05:30.4930185Z Executing ['/opt/conda/bin/python', '-bb', 'test_multiprocessing_spawn.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:30.487167]
2023-01-11T22:05:48.9691099Z 
2023-01-11T22:05:48.9691660Z Expand the folded group to see the log file of test_multiprocessing_spawn
2023-01-11T22:05:48.9692881Z ##[group]PRINTING LOG FILE of test_multiprocessing_spawn (/var/lib/jenkins/workspace/test/test-reports/test_multiprocessing_spawn_s150fkr1)
2023-01-11T22:05:48.9693349Z 
2023-01-11T22:05:48.9693453Z Running tests...
2023-01-11T22:05:48.9694032Z ----------------------------------------------------------------------
2023-01-11T22:05:48.9694609Z Test results will be stored in test-reports/python-unittest/test_multiprocessing_spawn
2023-01-11T22:05:48.9695354Z   test_errors_pickleable (__main__.ErrorTest) ... ok (0.217s)
2023-01-11T22:05:48.9695727Z   test_exception_all (__main__.ForkTest) ... ok (0.025s)
2023-01-11T22:05:48.9696096Z   test_exception_single (__main__.ForkTest) ... ok (0.021s)
2023-01-11T22:05:48.9696505Z   test_first_argument_index (__main__.ForkTest) ... ok (0.010s)
2023-01-11T22:05:48.9696864Z   test_success (__main__.ForkTest) ... ok (0.008s)
2023-01-11T22:05:48.9697241Z   test_success_first_then_exception (__main__.ForkTest) ... ok (0.110s)
2023-01-11T22:05:48.9729503Z   test_success_non_blocking (__main__.ForkTest) ... ok (0.009s)
2023-01-11T22:05:48.9730125Z   test_terminate_exit (__main__.ForkTest) ... ok (0.009s)
2023-01-11T22:05:48.9730581Z   test_terminate_signal (__main__.ForkTest) ... ok (0.232s)
2023-01-11T22:05:48.9731701Z   test_exception_all (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9732395Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9733212Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9733807Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9734598Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9735185Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9735912Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9736472Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9736870Z ok (1.694s)
2023-01-11T22:05:48.9737658Z   test_exception_raises (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9738329Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9739045Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9739588Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9740001Z ok (1.518s)
2023-01-11T22:05:48.9740813Z   test_exception_single (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9741474Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9742246Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9742953Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9743724Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9744318Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9745111Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9745706Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9746502Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9747097Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9747869Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9748494Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9749297Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9750046Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9750782Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9751416Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9751808Z ok (3.164s)
2023-01-11T22:05:48.9752613Z   test_first_argument_index (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9753249Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9754027Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9754712Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9755497Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9756102Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9756917Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9757540Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9757910Z ok (1.613s)
2023-01-11T22:05:48.9758274Z   test_signal_raises (__main__.SpawnTest) ... ok (0.002s)
2023-01-11T22:05:48.9759216Z   test_success (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9759864Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9760643Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9761274Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9762075Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9762649Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9763416Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9764036Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9764442Z ok (1.621s)
2023-01-11T22:05:48.9765269Z   test_success_first_then_exception (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9765962Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9766753Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9767340Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9768128Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9768722Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9769695Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9770292Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9770688Z ok (1.732s)
2023-01-11T22:05:48.9771541Z   test_success_non_blocking (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9772211Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9772978Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9773729Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9774530Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9775067Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9775822Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9776440Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9776809Z ok (1.638s)
2023-01-11T22:05:48.9777722Z   test_terminate_exit (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9778397Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9779193Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9779802Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9780589Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9781183Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9781960Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9782637Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9783007Z ok (1.583s)
2023-01-11T22:05:48.9783831Z   test_terminate_signal (__main__.SpawnTest) ... /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9784484Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9785283Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9785908Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9786709Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:122: UserWarning: loaded 76 slow tests
2023-01-11T22:05:48.9787283Z   warnings.warn(f"loaded {len(slow_tests_dict)} slow tests")
2023-01-11T22:05:48.9788082Z /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py:126: UserWarning: loaded 210 disabled tests
2023-01-11T22:05:48.9788712Z   warnings.warn(f"loaded {len(disabled_tests_dict)} disabled tests")
2023-01-11T22:05:48.9789100Z ok (1.609s)
2023-01-11T22:05:48.9789281Z 
2023-01-11T22:05:48.9789645Z ----------------------------------------------------------------------
2023-01-11T22:05:48.9790073Z Ran 19 tests in 16.816s
2023-01-11T22:05:48.9790277Z 
2023-01-11T22:05:48.9790386Z OK
2023-01-11T22:05:48.9790529Z 
2023-01-11T22:05:48.9790673Z Generating XML reports...
2023-01-11T22:05:48.9791420Z Generated XML report: test-reports/python-unittest/test_multiprocessing_spawn/TEST-ErrorTest-20230111220531.xml
2023-01-11T22:05:48.9792320Z Generated XML report: test-reports/python-unittest/test_multiprocessing_spawn/TEST-ForkTest-20230111220531.xml
2023-01-11T22:05:48.9793216Z Generated XML report: test-reports/python-unittest/test_multiprocessing_spawn/TEST-SpawnTest-20230111220531.xml
2023-01-11T22:05:48.9793610Z 
2023-01-11T22:05:48.9794072Z ##[endgroup]
2023-01-11T22:05:48.9794843Z FINISHED PRINTING LOG FILE of test_multiprocessing_spawn (/var/lib/jenkins/workspace/test/test-reports/test_multiprocessing_spawn_s150fkr1)
2023-01-11T22:05:48.9795285Z 
2023-01-11T22:05:48.9795642Z Running test_namedtuple_return_api ... [2023-01-11 22:05:48.969328]
2023-01-11T22:05:48.9796539Z Executing ['/opt/conda/bin/python', '-bb', 'test_namedtuple_return_api.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:48.969598]
2023-01-11T22:05:51.8925472Z 
2023-01-11T22:05:51.8925961Z Expand the folded group to see the log file of test_namedtuple_return_api
2023-01-11T22:05:51.8926685Z ##[group]PRINTING LOG FILE of test_namedtuple_return_api (/var/lib/jenkins/workspace/test/test-reports/test_namedtuple_return_api_qni51v39)
2023-01-11T22:05:51.8926941Z 
2023-01-11T22:05:51.8927058Z Running tests...
2023-01-11T22:05:51.8927478Z ----------------------------------------------------------------------
2023-01-11T22:05:51.8927907Z Test results will be stored in test-reports/python-unittest/test_namedtuple_return_api
2023-01-11T22:05:51.8928282Z   test_import_return_types (__main__.TestNamedTupleAPI) ... ok (0.205s)
2023-01-11T22:05:51.8929225Z   test_namedtuple_return (__main__.TestNamedTupleAPI) ... /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:149: UserWarning: torch.qr is deprecated in favor of torch.linalg.qr and will be removed in a future PyTorch release.
2023-01-11T22:05:51.8929829Z The boolean parameter 'some' has been replaced with a string parameter 'mode'.
2023-01-11T22:05:51.8930085Z Q, R = torch.qr(A, some)
2023-01-11T22:05:51.8930280Z should be replaced with
2023-01-11T22:05:51.8930754Z Q, R = torch.linalg.qr(A, 'reduced' if some else 'complete') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2459.)
2023-01-11T22:05:51.8931092Z   ret1 = func(a, *op.input)
2023-01-11T22:05:51.8931456Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:155: UserWarning: torch.qr is deprecated in favor of torch.linalg.qr and will be removed in a future PyTorch release.
2023-01-11T22:05:51.8931925Z The boolean parameter 'some' has been replaced with a string parameter 'mode'.
2023-01-11T22:05:51.8932161Z Q, R = torch.qr(A, some)
2023-01-11T22:05:51.8932354Z should be replaced with
2023-01-11T22:05:51.8932825Z Q, R = torch.linalg.qr(A, 'reduced' if some else 'complete') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2471.)
2023-01-11T22:05:51.8933176Z   ret2 = func(a, *op.input, out=tuple(ret1))
2023-01-11T22:05:51.8933569Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:149: UserWarning: torch.symeig is deprecated in favor of torch.linalg.eigh and will be removed in a future PyTorch release.
2023-01-11T22:05:51.8934053Z The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.
2023-01-11T22:05:51.8934370Z L, _ = torch.symeig(A, upper=upper)
2023-01-11T22:05:51.8934558Z should be replaced with
2023-01-11T22:05:51.8934845Z L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')
2023-01-11T22:05:51.8935053Z and
2023-01-11T22:05:51.8935229Z L, V = torch.symeig(A, eigenvectors=True)
2023-01-11T22:05:51.8935437Z should be replaced with
2023-01-11T22:05:51.8935899Z L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2910.)
2023-01-11T22:05:51.8936223Z   ret1 = func(a, *op.input)
2023-01-11T22:05:51.8936596Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:155: UserWarning: torch.symeig is deprecated in favor of torch.linalg.eigh and will be removed in a future PyTorch release.
2023-01-11T22:05:51.8937079Z The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.
2023-01-11T22:05:51.8937394Z L, _ = torch.symeig(A, upper=upper)
2023-01-11T22:05:51.8937583Z should be replaced with
2023-01-11T22:05:51.8937870Z L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')
2023-01-11T22:05:51.8938082Z and
2023-01-11T22:05:51.8938257Z L, V = torch.symeig(A, eigenvectors=True)
2023-01-11T22:05:51.8938466Z should be replaced with
2023-01-11T22:05:51.8938923Z L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L') (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2928.)
2023-01-11T22:05:51.8939359Z   ret2 = func(a, *op.input, out=tuple(ret1))
2023-01-11T22:05:51.8939780Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:149: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.
2023-01-11T22:05:51.8940253Z torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.
2023-01-11T22:05:51.8940555Z X = torch.triangular_solve(B, A).solution
2023-01-11T22:05:51.8940754Z should be replaced with
2023-01-11T22:05:51.8941095Z X = torch.linalg.solve_triangular(A, B). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2225.)
2023-01-11T22:05:51.8941421Z   ret1 = func(a, *op.input)
2023-01-11T22:05:51.8941869Z /var/lib/jenkins/workspace/test/test_namedtuple_return_api.py:149: UserWarning: torch.lu is deprecated in favor of torch.linalg.lu_factor / torch.linalg.lu_factor_ex and will be removed in a future PyTorch release.
2023-01-11T22:05:51.8942257Z LU, pivots = torch.lu(A, compute_pivots)
2023-01-11T22:05:51.8942572Z should be replaced with
2023-01-11T22:05:51.8942811Z LU, pivots = torch.linalg.lu_factor(A, compute_pivots)
2023-01-11T22:05:51.8943008Z and
2023-01-11T22:05:51.8943223Z LU, pivots, info = torch.lu(A, compute_pivots, get_infos=True)
2023-01-11T22:05:51.8943458Z should be replaced with
2023-01-11T22:05:51.8943803Z LU, pivots, info = torch.linalg.lu_factor_ex(A, compute_pivots) (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2029.)
2023-01-11T22:05:51.8944147Z   ret1 = func(a, *op.input)
2023-01-11T22:05:51.8944330Z ok (0.033s)
2023-01-11T22:05:51.8944564Z   test_native_functions_yaml (__main__.TestNamedTupleAPI) ... ok (1.087s)
2023-01-11T22:05:51.8944721Z 
2023-01-11T22:05:51.8944932Z ----------------------------------------------------------------------
2023-01-11T22:05:51.8945175Z Ran 3 tests in 1.325s
2023-01-11T22:05:51.8945289Z 
2023-01-11T22:05:51.8945352Z OK
2023-01-11T22:05:51.8945444Z 
2023-01-11T22:05:51.8945514Z Generating XML reports...
2023-01-11T22:05:51.8945944Z Generated XML report: test-reports/python-unittest/test_namedtuple_return_api/TEST-TestNamedTupleAPI-20230111220550.xml
2023-01-11T22:05:51.8946192Z 
2023-01-11T22:05:51.8946470Z ##[endgroup]
2023-01-11T22:05:51.8946880Z FINISHED PRINTING LOG FILE of test_namedtuple_return_api (/var/lib/jenkins/workspace/test/test-reports/test_namedtuple_return_api_qni51v39)
2023-01-11T22:05:51.8947119Z 
2023-01-11T22:05:51.8947287Z Running test_ops ... [2023-01-11 22:05:51.892707]
2023-01-11T22:05:53.4257696Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:05:53.4550215Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:05:53.4953639Z Ignoring disabled issues:  []
2023-01-11T22:05:53.5200132Z Ignoring disabled issues:  []
2023-01-11T22:05:53.7299320Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:53.729563]
2023-01-11T22:05:53.7300511Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:53.729685]
2023-01-11T22:05:56.8321326Z 
2023-01-11T22:05:56.8321823Z Expand the folded group to see the log file of test_ops
2023-01-11T22:05:56.8322803Z ##[group]PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_ej0y0p5c)
2023-01-11T22:05:56.8324205Z Test results will be stored in test-reports/python-pytest/test_ops/test_ops-02e8df424f6ccae0.xml
2023-01-11T22:05:56.8324759Z ============================= test session starts ==============================
2023-01-11T22:05:56.8325179Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:05:56.8325617Z cachedir: .pytest_cache
2023-01-11T22:05:56.8326033Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:05:56.8326453Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:05:56.8327025Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:05:56.8327338Z collecting ... collected 0 items
2023-01-11T22:05:56.8327576Z Running 0 items in this shard: 
2023-01-11T22:05:56.8327707Z 
2023-01-11T22:05:56.8327883Z =============================== warnings summary ===============================
2023-01-11T22:05:56.8328376Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:05:56.8329586Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:05:56.8330137Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:05:56.8330270Z 
2023-01-11T22:05:56.8368061Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:05:56.8368708Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops/test_ops-02e8df424f6ccae0.xml -
2023-01-11T22:05:56.8369227Z ============================== 1 warning in 0.04s ==============================
2023-01-11T22:05:56.8369765Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:05:56.8370015Z 
2023-01-11T22:05:56.8370533Z ##[endgroup]
2023-01-11T22:05:56.8371193Z FINISHED PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_ej0y0p5c)
2023-01-11T22:05:56.8371576Z 
2023-01-11T22:05:56.8371583Z 
2023-01-11T22:05:56.8371852Z Expand the folded group to see the log file of test_ops
2023-01-11T22:05:56.8372716Z ##[group]PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_65x6uh7i)
2023-01-11T22:05:56.8373544Z Test results will be stored in test-reports/python-pytest/test_ops/test_ops-eab6b2fd1107632b.xml
2023-01-11T22:05:56.8374080Z ============================= test session starts ==============================
2023-01-11T22:05:56.8374608Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:05:56.8374952Z cachedir: .pytest_cache
2023-01-11T22:05:56.8375781Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:05:56.8376226Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:05:56.8376661Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:05:56.8376950Z collecting ... collected 0 items
2023-01-11T22:05:56.8377157Z Running 0 items in this shard: 
2023-01-11T22:05:56.8377280Z 
2023-01-11T22:05:56.8377506Z =============================== warnings summary ===============================
2023-01-11T22:05:56.8377921Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:05:56.8378449Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:05:56.8378805Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:05:56.8378937Z 
2023-01-11T22:05:56.8379179Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:05:56.8379648Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops/test_ops-eab6b2fd1107632b.xml -
2023-01-11T22:05:56.8379979Z ============================== 1 warning in 0.04s ==============================
2023-01-11T22:05:56.8380287Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:05:56.8380620Z 
2023-01-11T22:05:56.8380857Z ##[endgroup]
2023-01-11T22:05:56.8381215Z FINISHED PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_65x6uh7i)
2023-01-11T22:05:56.8381416Z 
2023-01-11T22:05:57.1710438Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:05:57.170651]
2023-01-11T22:06:00.0732243Z 
2023-01-11T22:06:00.0732771Z Expand the folded group to see the log file of test_ops
2023-01-11T22:06:00.0733855Z ##[group]PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_ohcjuv29)
2023-01-11T22:06:00.0734823Z Test results will be stored in test-reports/python-pytest/test_ops/test_ops-775e32094a08d97f.xml
2023-01-11T22:06:00.0735388Z ============================= test session starts ==============================
2023-01-11T22:06:00.0735838Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:00.0736181Z cachedir: .pytest_cache
2023-01-11T22:06:00.0736887Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:00.0737409Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:00.0737858Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:00.0738155Z collecting ... collected 0 items
2023-01-11T22:06:00.0738360Z Running 0 items in this shard: 
2023-01-11T22:06:00.0738483Z 
2023-01-11T22:06:00.0738582Z =============================== warnings summary ===============================
2023-01-11T22:06:00.0738988Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:00.0739862Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:00.0740218Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:00.0740348Z 
2023-01-11T22:06:00.0740564Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:00.0741035Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops/test_ops-775e32094a08d97f.xml -
2023-01-11T22:06:00.0741370Z ============================== 1 warning in 0.11s ==============================
2023-01-11T22:06:00.0741669Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:00.0741861Z 
2023-01-11T22:06:00.0742089Z ##[endgroup]
2023-01-11T22:06:00.0742519Z FINISHED PRINTING LOG FILE of test_ops (/var/lib/jenkins/workspace/test/test-reports/test_ops_ohcjuv29)
2023-01-11T22:06:00.0742723Z 
2023-01-11T22:06:00.0742905Z Running test_ops_fwd_gradients ... [2023-01-11 22:06:00.073741]
2023-01-11T22:06:01.6685806Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:01.6813051Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:01.7464962Z Ignoring disabled issues:  []
2023-01-11T22:06:01.7567283Z Ignoring disabled issues:  []
2023-01-11T22:06:01.7653085Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:01.765044]
2023-01-11T22:06:01.7754059Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:01.775052]
2023-01-11T22:06:04.4371132Z 
2023-01-11T22:06:04.4372012Z Expand the folded group to see the log file of test_ops_fwd_gradients
2023-01-11T22:06:04.4373177Z ##[group]PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_cl8saevw)
2023-01-11T22:06:04.4374160Z Test results will be stored in test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-c852b6361f23a757.xml
2023-01-11T22:06:04.4375730Z ============================= test session starts ==============================
2023-01-11T22:06:04.4376370Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:04.4376788Z cachedir: .pytest_cache
2023-01-11T22:06:04.4377201Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:04.4377551Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:04.4378040Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:04.4378337Z collecting ... collected 0 items
2023-01-11T22:06:04.4378540Z Running 0 items in this shard: 
2023-01-11T22:06:04.4378668Z 
2023-01-11T22:06:04.4378767Z =============================== warnings summary ===============================
2023-01-11T22:06:04.4379118Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:04.4379638Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:04.4379995Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:04.4380126Z 
2023-01-11T22:06:04.4380342Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:04.4380860Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-c852b6361f23a757.xml -
2023-01-11T22:06:04.4381220Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:04.4381520Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:04.4381704Z 
2023-01-11T22:06:04.4381941Z ##[endgroup]
2023-01-11T22:06:04.4382341Z FINISHED PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_cl8saevw)
2023-01-11T22:06:04.4382653Z 
2023-01-11T22:06:04.5028453Z 
2023-01-11T22:06:04.5028926Z Expand the folded group to see the log file of test_ops_fwd_gradients
2023-01-11T22:06:04.5029793Z ##[group]PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_zmquy4l7)
2023-01-11T22:06:04.5030730Z Test results will be stored in test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-d9fcda48e67f6e06.xml
2023-01-11T22:06:04.5031274Z ============================= test session starts ==============================
2023-01-11T22:06:04.5031880Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:04.5032156Z cachedir: .pytest_cache
2023-01-11T22:06:04.5032708Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:04.5033066Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:04.5033476Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:04.5033773Z collecting ... collected 0 items
2023-01-11T22:06:04.5033978Z Running 0 items in this shard: 
2023-01-11T22:06:04.5034099Z 
2023-01-11T22:06:04.5034211Z =============================== warnings summary ===============================
2023-01-11T22:06:04.5034546Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:04.5035072Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:04.5035423Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:04.5035734Z 
2023-01-11T22:06:04.5035962Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:04.5036478Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-d9fcda48e67f6e06.xml -
2023-01-11T22:06:04.5036843Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:04.5037147Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:04.5037339Z 
2023-01-11T22:06:04.5037593Z ##[endgroup]
2023-01-11T22:06:04.5037991Z FINISHED PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_zmquy4l7)
2023-01-11T22:06:04.5038217Z 
2023-01-11T22:06:04.8384454Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:04.837926]
2023-01-11T22:06:07.4499990Z 
2023-01-11T22:06:07.4500552Z Expand the folded group to see the log file of test_ops_fwd_gradients
2023-01-11T22:06:07.4501551Z ##[group]PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_xicbc3_f)
2023-01-11T22:06:07.4502728Z Test results will be stored in test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-60a240aeadf13b46.xml
2023-01-11T22:06:07.4503238Z ============================= test session starts ==============================
2023-01-11T22:06:07.4503881Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:07.4504329Z cachedir: .pytest_cache
2023-01-11T22:06:07.4505090Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:07.4505735Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:07.4506394Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:07.4506828Z collecting ... collected 0 items
2023-01-11T22:06:07.4507120Z Running 0 items in this shard: 
2023-01-11T22:06:07.4507309Z 
2023-01-11T22:06:07.4507471Z =============================== warnings summary ===============================
2023-01-11T22:06:07.4508027Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:07.4508889Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:07.4509525Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:07.4509757Z 
2023-01-11T22:06:07.4510185Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:07.4511088Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-60a240aeadf13b46.xml -
2023-01-11T22:06:07.4511639Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:07.4512135Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:07.4512470Z 
2023-01-11T22:06:07.4512883Z ##[endgroup]
2023-01-11T22:06:07.4513519Z FINISHED PRINTING LOG FILE of test_ops_fwd_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_fwd_gradients_xicbc3_f)
2023-01-11T22:06:07.4513869Z 
2023-01-11T22:06:07.4514175Z Running test_ops_gradients ... [2023-01-11 22:06:07.450610]
2023-01-11T22:06:08.9743455Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:09.0065250Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:09.0393977Z Ignoring disabled issues:  []
2023-01-11T22:06:09.0578860Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:09.057545]
2023-01-11T22:06:09.0713003Z Ignoring disabled issues:  []
2023-01-11T22:06:09.0900468Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:09.089749]
2023-01-11T22:06:11.7535487Z 
2023-01-11T22:06:11.7536007Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T22:06:11.7537103Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_21qp6016)
2023-01-11T22:06:11.7538431Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-b637e26586d3453a.xml
2023-01-11T22:06:11.7538805Z ============================= test session starts ==============================
2023-01-11T22:06:11.7539252Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:11.7539567Z cachedir: .pytest_cache
2023-01-11T22:06:11.7540240Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:11.7540872Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:11.7541321Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:11.7541601Z collecting ... collected 0 items
2023-01-11T22:06:11.7541804Z Running 0 items in this shard: 
2023-01-11T22:06:11.7541927Z 
2023-01-11T22:06:11.7542039Z =============================== warnings summary ===============================
2023-01-11T22:06:11.7542445Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:11.7542964Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:11.7543325Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:11.7543458Z 
2023-01-11T22:06:11.7543692Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:11.7544208Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-b637e26586d3453a.xml -
2023-01-11T22:06:11.7544558Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:11.7544857Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:11.7545049Z 
2023-01-11T22:06:11.7545295Z ##[endgroup]
2023-01-11T22:06:11.7545677Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_21qp6016)
2023-01-11T22:06:11.7545901Z 
2023-01-11T22:06:11.7784967Z 
2023-01-11T22:06:11.7785460Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T22:06:11.7786533Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_cg8es9rt)
2023-01-11T22:06:11.7787613Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-ed1e446199e7e291.xml
2023-01-11T22:06:11.7788075Z ============================= test session starts ==============================
2023-01-11T22:06:11.7788566Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:11.7788923Z cachedir: .pytest_cache
2023-01-11T22:06:11.7789456Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:11.7789820Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:11.7790246Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:11.7790792Z collecting ... collected 0 items
2023-01-11T22:06:11.7791081Z Running 0 items in this shard: 
2023-01-11T22:06:11.7791259Z 
2023-01-11T22:06:11.7791425Z =============================== warnings summary ===============================
2023-01-11T22:06:11.7791970Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:11.7792510Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:11.7792858Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:11.7792991Z 
2023-01-11T22:06:11.7793224Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:11.7793844Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-ed1e446199e7e291.xml -
2023-01-11T22:06:11.7794192Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:11.7794501Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:11.7794693Z 
2023-01-11T22:06:11.7794984Z ##[endgroup]
2023-01-11T22:06:11.7795368Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_cg8es9rt)
2023-01-11T22:06:11.7795593Z 
2023-01-11T22:06:12.1105016Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:12.110020]
2023-01-11T22:06:14.6729584Z 
2023-01-11T22:06:14.6730129Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T22:06:14.6731251Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_oipp2vw4)
2023-01-11T22:06:14.6731880Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-3567e610b2a391c3.xml
2023-01-11T22:06:14.6732222Z ============================= test session starts ==============================
2023-01-11T22:06:14.6732589Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:14.6732850Z cachedir: .pytest_cache
2023-01-11T22:06:14.6733253Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:14.6733611Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:14.6734033Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:14.6734313Z collecting ... collected 0 items
2023-01-11T22:06:14.6734520Z Running 0 items in this shard: 
2023-01-11T22:06:14.6734669Z 
2023-01-11T22:06:14.6734835Z =============================== warnings summary ===============================
2023-01-11T22:06:14.6735236Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:14.6735746Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:14.6736138Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:14.6736269Z 
2023-01-11T22:06:14.6736496Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:14.6737007Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-3567e610b2a391c3.xml -
2023-01-11T22:06:14.6737344Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:14.6737647Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:14.6737837Z 
2023-01-11T22:06:14.6738239Z ##[endgroup]
2023-01-11T22:06:14.6738621Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_oipp2vw4)
2023-01-11T22:06:14.6738844Z 
2023-01-11T22:06:14.6739007Z Running test_ops_jit ... [2023-01-11 22:06:14.673403]
2023-01-11T22:06:16.2063123Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:16.2295576Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T22:06:16.2706325Z Ignoring disabled issues:  []
2023-01-11T22:06:16.2892055Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_jit.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:16.288805]
2023-01-11T22:06:16.2935921Z Ignoring disabled issues:  []
2023-01-11T22:06:16.3123132Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_jit.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:16.312023]
2023-01-11T22:06:19.0502913Z 
2023-01-11T22:06:19.0503496Z Expand the folded group to see the log file of test_ops_jit
2023-01-11T22:06:19.0504556Z ##[group]PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_67igmrjd)
2023-01-11T22:06:19.0505342Z Test results will be stored in test-reports/python-pytest/test_ops_jit/test_ops_jit-5431f0498166287b.xml
2023-01-11T22:06:19.0505661Z ============================= test session starts ==============================
2023-01-11T22:06:19.0506032Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:19.0506278Z cachedir: .pytest_cache
2023-01-11T22:06:19.0506708Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:19.0507063Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:19.0507479Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:19.0507774Z collecting ... collected 0 items
2023-01-11T22:06:19.0507980Z Running 0 items in this shard: 
2023-01-11T22:06:19.0508103Z 
2023-01-11T22:06:19.0508203Z =============================== warnings summary ===============================
2023-01-11T22:06:19.0508551Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:19.0509073Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:19.0509425Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:19.0509557Z 
2023-01-11T22:06:19.0509778Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:19.0510260Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_jit/test_ops_jit-5431f0498166287b.xml -
2023-01-11T22:06:19.0521299Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:19.0521744Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:19.0521948Z 
2023-01-11T22:06:19.0522238Z ##[endgroup]
2023-01-11T22:06:19.0522692Z FINISHED PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_67igmrjd)
2023-01-11T22:06:19.0522902Z 
2023-01-11T22:06:19.0558532Z 
2023-01-11T22:06:19.0558867Z Expand the folded group to see the log file of test_ops_jit
2023-01-11T22:06:19.0559693Z ##[group]PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_ueawpfyj)
2023-01-11T22:06:19.0560656Z Test results will be stored in test-reports/python-pytest/test_ops_jit/test_ops_jit-096e00faef340b2a.xml
2023-01-11T22:06:19.0561227Z ============================= test session starts ==============================
2023-01-11T22:06:19.0562080Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:19.0562434Z cachedir: .pytest_cache
2023-01-11T22:06:19.0563026Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:19.0563541Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:19.0563995Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:19.0564278Z collecting ... collected 0 items
2023-01-11T22:06:19.0564486Z Running 0 items in this shard: 
2023-01-11T22:06:19.0564613Z 
2023-01-11T22:06:19.0564736Z =============================== warnings summary ===============================
2023-01-11T22:06:19.0565221Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:19.0566034Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:19.0566582Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:19.0566721Z 
2023-01-11T22:06:19.0566963Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:19.0567447Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_jit/test_ops_jit-096e00faef340b2a.xml -
2023-01-11T22:06:19.0567787Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:19.0568088Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:19.0568280Z 
2023-01-11T22:06:19.0568516Z ##[endgroup]
2023-01-11T22:06:19.0568890Z FINISHED PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_ueawpfyj)
2023-01-11T22:06:19.0569351Z 
2023-01-11T22:06:19.3993208Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_jit.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:19.398894]
2023-01-11T22:06:22.0352917Z 
2023-01-11T22:06:22.0353473Z Expand the folded group to see the log file of test_ops_jit
2023-01-11T22:06:22.0354371Z ##[group]PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_lvu198_v)
2023-01-11T22:06:22.0355386Z Test results will be stored in test-reports/python-pytest/test_ops_jit/test_ops_jit-256d5705adf79135.xml
2023-01-11T22:06:22.0355707Z ============================= test session starts ==============================
2023-01-11T22:06:22.0356072Z platform linux -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T22:06:22.0356332Z cachedir: .pytest_cache
2023-01-11T22:06:22.0356745Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T22:06:22.0357107Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T22:06:22.0357515Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T22:06:22.0357813Z collecting ... collected 0 items
2023-01-11T22:06:22.0358017Z Running 0 items in this shard: 
2023-01-11T22:06:22.0358139Z 
2023-01-11T22:06:22.0358238Z =============================== warnings summary ===============================
2023-01-11T22:06:22.0358587Z ../../../../../opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171
2023-01-11T22:06:22.0359144Z   /opt/conda/lib/python3.10/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T22:06:22.0359697Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T22:06:22.0359830Z 
2023-01-11T22:06:22.0360089Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T22:06:22.0360796Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_jit/test_ops_jit-256d5705adf79135.xml -
2023-01-11T22:06:22.0361147Z ============================== 1 warning in 0.01s ==============================
2023-01-11T22:06:22.0361477Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T22:06:22.0361670Z 
2023-01-11T22:06:22.0361923Z ##[endgroup]
2023-01-11T22:06:22.0362309Z FINISHED PRINTING LOG FILE of test_ops_jit (/var/lib/jenkins/workspace/test/test-reports/test_ops_jit_lvu198_v)
2023-01-11T22:06:22.0362516Z 
2023-01-11T22:06:22.0362673Z Running test_prims ... [2023-01-11 22:06:22.035838]
2023-01-11T22:06:22.0363204Z Executing ['/opt/conda/bin/python', '-bb', 'test_prims.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:22.036051]
2023-01-11T22:06:24.5233416Z 
2023-01-11T22:06:24.5233904Z Expand the folded group to see the log file of test_prims
2023-01-11T22:06:24.5234995Z ##[group]PRINTING LOG FILE of test_prims (/var/lib/jenkins/workspace/test/test-reports/test_prims_av_xfdkt)
2023-01-11T22:06:24.5235275Z 
2023-01-11T22:06:24.5235364Z Running tests...
2023-01-11T22:06:24.5235813Z ----------------------------------------------------------------------
2023-01-11T22:06:24.5236227Z Test results will be stored in test-reports/python-unittest/test_prims
2023-01-11T22:06:24.5236520Z   test_mul_complex (__main__.TestPrimsBasic) ... ok (0.002s)
2023-01-11T22:06:24.5236811Z   test_torch_ops (__main__.TestPrimsBasic) ... ok (0.002s)
2023-01-11T22:06:24.5236999Z 
2023-01-11T22:06:24.5237197Z ----------------------------------------------------------------------
2023-01-11T22:06:24.5237426Z Ran 2 tests in 0.004s
2023-01-11T22:06:24.5237538Z 
2023-01-11T22:06:24.5237632Z OK
2023-01-11T22:06:24.5237736Z 
2023-01-11T22:06:24.5237831Z Generating XML reports...
2023-01-11T22:06:24.5238227Z Generated XML report: test-reports/python-unittest/test_prims/TEST-TestPrimsBasic-20230111220624.xml
2023-01-11T22:06:24.5238455Z 
2023-01-11T22:06:24.5238678Z ##[endgroup]
2023-01-11T22:06:24.5239043Z FINISHED PRINTING LOG FILE of test_prims (/var/lib/jenkins/workspace/test/test-reports/test_prims_av_xfdkt)
2023-01-11T22:06:24.5239248Z 
2023-01-11T22:06:24.5239406Z Running test_reductions ... [2023-01-11 22:06:24.523359]
2023-01-11T22:06:24.5239872Z Executing ['/opt/conda/bin/python', '-bb', 'test_reductions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:24.523603]
2023-01-11T22:06:26.9725641Z 
2023-01-11T22:06:26.9726270Z Expand the folded group to see the log file of test_reductions
2023-01-11T22:06:26.9727146Z ##[group]PRINTING LOG FILE of test_reductions (/var/lib/jenkins/workspace/test/test-reports/test_reductions_nhn_em26)
2023-01-11T22:06:26.9727477Z 
2023-01-11T22:06:26.9727596Z Running tests...
2023-01-11T22:06:26.9728057Z ----------------------------------------------------------------------
2023-01-11T22:06:26.9728229Z 
2023-01-11T22:06:26.9728435Z ----------------------------------------------------------------------
2023-01-11T22:06:26.9728693Z Ran 0 tests in 0.000s
2023-01-11T22:06:26.9728806Z 
2023-01-11T22:06:26.9728866Z OK
2023-01-11T22:06:26.9728955Z 
2023-01-11T22:06:26.9729190Z Generating XML reports...
2023-01-11T22:06:26.9729547Z Test results will be stored in test-reports/python-unittest/test_reductions
2023-01-11T22:06:26.9729743Z 
2023-01-11T22:06:26.9730025Z ##[endgroup]
2023-01-11T22:06:26.9730419Z FINISHED PRINTING LOG FILE of test_reductions (/var/lib/jenkins/workspace/test/test-reports/test_reductions_nhn_em26)
2023-01-11T22:06:26.9730760Z 
2023-01-11T22:06:26.9731134Z Running test_show_pickle ... [2023-01-11 22:06:26.972647]
2023-01-11T22:06:26.9731863Z Executing ['/opt/conda/bin/python', '-bb', 'test_show_pickle.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:26.972880]
2023-01-11T22:06:28.7800484Z 
2023-01-11T22:06:28.7801045Z Expand the folded group to see the log file of test_show_pickle
2023-01-11T22:06:28.7802097Z ##[group]PRINTING LOG FILE of test_show_pickle (/var/lib/jenkins/workspace/test/test-reports/test_show_pickle_3ky4oq1k)
2023-01-11T22:06:28.7802342Z 
2023-01-11T22:06:28.7802416Z Running tests...
2023-01-11T22:06:28.7802882Z ----------------------------------------------------------------------
2023-01-11T22:06:28.7803313Z Test results will be stored in test-reports/python-unittest/test_show_pickle
2023-01-11T22:06:28.7803661Z   test_scripted_model (__main__.TestShowPickle) ... ok (0.224s)
2023-01-11T22:06:28.7803817Z 
2023-01-11T22:06:28.7804016Z ----------------------------------------------------------------------
2023-01-11T22:06:28.7804243Z Ran 1 test in 0.224s
2023-01-11T22:06:28.7804391Z 
2023-01-11T22:06:28.7804481Z OK
2023-01-11T22:06:28.7804588Z 
2023-01-11T22:06:28.7804678Z Generating XML reports...
2023-01-11T22:06:28.7805269Z Generated XML report: test-reports/python-unittest/test_show_pickle/TEST-TestShowPickle-20230111220628.xml
2023-01-11T22:06:28.7805753Z 
2023-01-11T22:06:28.7806102Z ##[endgroup]
2023-01-11T22:06:28.7806710Z FINISHED PRINTING LOG FILE of test_show_pickle (/var/lib/jenkins/workspace/test/test-reports/test_show_pickle_3ky4oq1k)
2023-01-11T22:06:28.7807058Z 
2023-01-11T22:06:28.7807318Z Running test_spectral_ops ... [2023-01-11 22:06:28.780151]
2023-01-11T22:06:28.7808107Z Executing ['/opt/conda/bin/python', '-bb', 'test_spectral_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:28.780373]
2023-01-11T22:06:32.1832295Z 
2023-01-11T22:06:32.1832779Z Expand the folded group to see the log file of test_spectral_ops
2023-01-11T22:06:32.1833782Z ##[group]PRINTING LOG FILE of test_spectral_ops (/var/lib/jenkins/workspace/test/test-reports/test_spectral_ops__g34f1fu)
2023-01-11T22:06:32.1834420Z /var/lib/jenkins/workspace/test/test_spectral_ops.py:44: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
2023-01-11T22:06:32.1835280Z   if LooseVersion(np.__version__) >= '1.20.0' and (
2023-01-11T22:06:32.1835688Z /var/lib/jenkins/workspace/test/test_spectral_ops.py:45: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
2023-01-11T22:06:32.1836166Z   not has_scipy_fft or LooseVersion(scipy.__version__) >= '1.6.0')
2023-01-11T22:06:32.1836375Z 
2023-01-11T22:06:32.1836481Z Running tests...
2023-01-11T22:06:32.1837047Z ----------------------------------------------------------------------
2023-01-11T22:06:32.1837299Z 
2023-01-11T22:06:32.1837495Z ----------------------------------------------------------------------
2023-01-11T22:06:32.1837740Z Ran 0 tests in 0.000s
2023-01-11T22:06:32.1837854Z 
2023-01-11T22:06:32.1837914Z OK
2023-01-11T22:06:32.1838007Z 
2023-01-11T22:06:32.1838078Z Generating XML reports...
2023-01-11T22:06:32.1838411Z Test results will be stored in test-reports/python-unittest/test_spectral_ops
2023-01-11T22:06:32.1838593Z 
2023-01-11T22:06:32.1838828Z ##[endgroup]
2023-01-11T22:06:32.1839203Z FINISHED PRINTING LOG FILE of test_spectral_ops (/var/lib/jenkins/workspace/test/test-reports/test_spectral_ops__g34f1fu)
2023-01-11T22:06:32.1839424Z 
2023-01-11T22:06:32.1839612Z Running test_tensor_creation_ops ... [2023-01-11 22:06:32.183275]
2023-01-11T22:06:32.1840100Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensor_creation_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:32.183511]
2023-01-11T22:06:34.0284153Z 
2023-01-11T22:06:34.0284630Z Expand the folded group to see the log file of test_tensor_creation_ops
2023-01-11T22:06:34.0285756Z ##[group]PRINTING LOG FILE of test_tensor_creation_ops (/var/lib/jenkins/workspace/test/test-reports/test_tensor_creation_ops_0hgotwfy)
2023-01-11T22:06:34.0286150Z 
2023-01-11T22:06:34.0286233Z Running tests...
2023-01-11T22:06:34.0286720Z ----------------------------------------------------------------------
2023-01-11T22:06:34.0286914Z 
2023-01-11T22:06:34.0287184Z ----------------------------------------------------------------------
2023-01-11T22:06:34.0287630Z Ran 0 tests in 0.000s
2023-01-11T22:06:34.0287742Z 
2023-01-11T22:06:34.0287802Z OK
2023-01-11T22:06:34.0287893Z 
2023-01-11T22:06:34.0287964Z Generating XML reports...
2023-01-11T22:06:34.0288315Z Test results will be stored in test-reports/python-unittest/test_tensor_creation_ops
2023-01-11T22:06:34.0288507Z 
2023-01-11T22:06:34.0288747Z ##[endgroup]
2023-01-11T22:06:34.0289438Z FINISHED PRINTING LOG FILE of test_tensor_creation_ops (/var/lib/jenkins/workspace/test/test-reports/test_tensor_creation_ops_0hgotwfy)
2023-01-11T22:06:34.0289712Z 
2023-01-11T22:06:34.0289900Z Running test_tensorexpr ... [2023-01-11 22:06:34.028541]
2023-01-11T22:06:34.0290376Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensorexpr.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 22:06:34.028837]
2023-01-11T22:07:19.4561875Z 
2023-01-11T22:07:19.4562782Z Expand the folded group to see the log file of test_tensorexpr
2023-01-11T22:07:19.4564682Z ##[group]PRINTING LOG FILE of test_tensorexpr (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_3qers4rd)
2023-01-11T22:07:19.4565019Z 
2023-01-11T22:07:19.4565112Z Running tests...
2023-01-11T22:07:19.4565738Z ----------------------------------------------------------------------
2023-01-11T22:07:19.4566376Z Test results will be stored in test-reports/python-unittest/test_tensorexpr
2023-01-11T22:07:19.4569955Z   test_add_const_rhs (__main__.TestTensorExprFuser) ... ok (0.259s)
2023-01-11T22:07:19.4570311Z   test_add_sub (__main__.TestTensorExprFuser) ... ok (0.036s)
2023-01-11T22:07:19.4570608Z   test_alias_analysis_input_and_module (__main__.TestTensorExprFuser) ... ok (0.045s)
2023-01-11T22:07:19.4570917Z   test_alias_analysis_inputs (__main__.TestTensorExprFuser) ... ok (0.008s)
2023-01-11T22:07:19.4571220Z   test_alias_analysis_module (__main__.TestTensorExprFuser) ... ok (0.130s)
2023-01-11T22:07:19.4571519Z   test_all_combos (__main__.TestTensorExprFuser) ... ok (0.038s)
2023-01-11T22:07:19.4571793Z   test_alpha (__main__.TestTensorExprFuser) ... ok (0.025s)
2023-01-11T22:07:19.4573734Z   test_binary_ops (__main__.TestTensorExprFuser) ... ok (1.442s)
2023-01-11T22:07:19.4574067Z   test_bitwise_ops (__main__.TestTensorExprFuser) ... ok (0.160s)
2023-01-11T22:07:19.4574357Z   test_broadcast (__main__.TestTensorExprFuser) ... ok (0.039s)
2023-01-11T22:07:19.4574679Z   test_broadcast3 (__main__.TestTensorExprFuser) ... ok (0.145s)
2023-01-11T22:07:19.4574964Z   test_broadcast_2 (__main__.TestTensorExprFuser) ... ok (0.033s)
2023-01-11T22:07:19.4575320Z   test_broadcast_big2 (__main__.TestTensorExprFuser) ... ok (0.046s)
2023-01-11T22:07:19.4575818Z   test_cat (__main__.TestTensorExprFuser) ... ok (3.245s)
2023-01-11T22:07:19.4576280Z   test_cat_empty_tensors (__main__.TestTensorExprFuser) ... ok (0.115s)
2023-01-11T22:07:19.4576719Z   test_cat_negative_dim (__main__.TestTensorExprFuser) ... ok (0.069s)
2023-01-11T22:07:19.4577317Z   test_cat_only (__main__.TestTensorExprFuser) ... skip: cat is broken with fusion group inlining disabled (0.001s)
2023-01-11T22:07:19.4577858Z   test_cat_promote_inputs (__main__.TestTensorExprFuser) ... ok (0.129s)
2023-01-11T22:07:19.4578306Z   test_cat_with_constant_dim (__main__.TestTensorExprFuser) ... ok (0.046s)
2023-01-11T22:07:19.4578642Z   test_char (__main__.TestTensorExprFuser) ... ok (0.028s)
2023-01-11T22:07:19.4579087Z   test_chunk (__main__.TestTensorExprFuser) ... ok (0.196s)
2023-01-11T22:07:19.4579555Z   test_clamp (__main__.TestTensorExprFuser) ... ok (0.037s)
2023-01-11T22:07:19.4579922Z   test_constant (__main__.TestTensorExprFuser) ... ok (0.027s)
2023-01-11T22:07:19.4580229Z   test_double (__main__.TestTensorExprFuser) ... ok (0.026s)
2023-01-11T22:07:19.4580500Z   test_double_intrinsics (__main__.TestTensorExprFuser) ... ok (0.032s)
2023-01-11T22:07:19.4580892Z   test_dynamic_shape (__main__.TestTensorExprFuser) ... skip: dynamic shapes are not quite there yet (0.001s)
2023-01-11T22:07:19.4581252Z   test_easy (__main__.TestTensorExprFuser) ... ok (0.037s)
2023-01-11T22:07:19.4581918Z   test_eq (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:07:19.4582376Z   test_exp_pow (__main__.TestTensorExprFuser) ... ok (0.058s)
2023-01-11T22:07:19.4582798Z   test_four_arg (__main__.TestTensorExprFuser) ... ok (0.038s)
2023-01-11T22:07:19.4583123Z   test_ge (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:07:19.4583532Z   test_gt (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:07:19.4583937Z   test_guard_fails (__main__.TestTensorExprFuser) ... skip: requires CUDA (0.001s)
2023-01-11T22:07:19.4584375Z   test_half_bn_relu (__main__.TestTensorExprFuser) ... ok (0.001s)
2023-01-11T22:07:19.4584676Z   test_half_gelu (__main__.TestTensorExprFuser) ... ok (0.003s)
2023-01-11T22:07:19.4585073Z   test_int64_promotion (__main__.TestTensorExprFuser) ... ok (0.027s)
2023-01-11T22:07:19.4585553Z   test_int_output (__main__.TestTensorExprFuser) ... ok (0.027s)
2023-01-11T22:07:19.4586052Z   test_le (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:07:19.4586520Z   test_loop (__main__.TestTensorExprFuser) ... ok (0.075s)
2023-01-11T22:07:19.4586857Z   test_lt (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:07:19.4587115Z   test_mask (__main__.TestTensorExprFuser) ... ok (0.025s)
2023-01-11T22:07:19.4587380Z   test_min_max (__main__.TestTensorExprFuser) ... ok (0.039s)
2023-01-11T22:07:19.4587648Z   test_min_max_reduction (__main__.TestTensorExprFuser) ... ok (0.026s)
2023-01-11T22:07:19.4587942Z   test_min_max_reduction2 (__main__.TestTensorExprFuser) ... ok (0.025s)
2023-01-11T22:07:19.4588241Z   test_min_max_reduction_dim1 (__main__.TestTensorExprFuser) ... ok (0.028s)
2023-01-11T22:07:19.4588530Z   test_min_max_reduction_dim1_2 (__main__.TestTensorExprFuser) ... ok (0.040s)
2023-01-11T22:07:19.4588819Z   test_multi_rand (__main__.TestTensorExprFuser) ... ok (0.052s)
2023-01-11T22:07:19.4589102Z   test_multioutput (__main__.TestTensorExprFuser) ... ok (0.036s)
2023-01-11T22:07:19.4589390Z   test_multiple_outputs (__main__.TestTensorExprFuser) ... ok (0.178s)
2023-01-11T22:07:19.4589655Z   test_nans (__main__.TestTensorExprFuser) ... ok (0.100s)
2023-01-11T22:07:19.4589912Z   test_ne (__main__.TestTensorExprFuser) ... ok (0.063s)
2023-01-11T22:07:19.4590174Z   test_promotion (__main__.TestTensorExprFuser) ... ok (0.033s)
2023-01-11T22:07:19.4590454Z   test_propagated_mem_layout (__main__.TestTensorExprFuser) ... ok (30.499s)
2023-01-11T22:07:19.4590747Z   test_rand_like (__main__.TestTensorExprFuser) ... ok (0.064s)
2023-01-11T22:07:19.4591015Z   test_rank_two (__main__.TestTensorExprFuser) ... ok (0.044s)
2023-01-11T22:07:19.4591265Z   test_relu (__main__.TestTensorExprFuser) ... ok (0.036s)
2023-01-11T22:07:19.4591534Z   test_remainder (__main__.TestTensorExprFuser) ... ok (0.256s)
2023-01-11T22:07:19.4591801Z   test_reps (__main__.TestTensorExprFuser) ... ok (0.042s)
2023-01-11T22:07:19.4592063Z   test_scalar (__main__.TestTensorExprFuser) ... ok (0.108s)
2023-01-11T22:07:19.4592316Z   test_short (__main__.TestTensorExprFuser) ... ok (0.027s)
2023-01-11T22:07:19.4592588Z   test_simple_add (__main__.TestTensorExprFuser) ... ok (0.015s)
2023-01-11T22:07:19.4592863Z   test_sin_pow (__main__.TestTensorExprFuser) ... ok (0.311s)
2023-01-11T22:07:19.4593117Z   test_slice (__main__.TestTensorExprFuser) ... ok (0.058s)
2023-01-11T22:07:19.4593390Z   test_sliced_stride (__main__.TestTensorExprFuser) ... ok (0.107s)
2023-01-11T22:07:19.4593669Z   test_softmax_cpu (__main__.TestTensorExprFuser) ... ok (0.649s)
2023-01-11T22:07:19.4593957Z   test_softmax_cuda (__main__.TestTensorExprFuser) ... skip: requires CUDA (0.000s)
2023-01-11T22:07:19.4594274Z   test_strided_output_preserved (__main__.TestTensorExprFuser) ... ok (0.110s)
2023-01-11T22:07:19.4594564Z   test_three_arg (__main__.TestTensorExprFuser) ... ok (0.036s)
2023-01-11T22:07:19.4594833Z   test_three_arg2 (__main__.TestTensorExprFuser) ... ok (0.040s)
2023-01-11T22:07:19.4595099Z   test_transpose (__main__.TestTensorExprFuser) ... ok (0.160s)
2023-01-11T22:07:19.4595367Z   test_unary_ops (__main__.TestTensorExprFuser) ... ok (3.150s)
2023-01-11T22:07:19.4595696Z   test_unsqueeze (__main__.TestTensorExprFuser) ... ok (0.061s)
2023-01-11T22:07:19.4595950Z   test_where (__main__.TestTensorExprFuser) ... ok (0.159s)
2023-01-11T22:07:19.4596097Z 
2023-01-11T22:07:19.4596365Z ----------------------------------------------------------------------
2023-01-11T22:07:19.4596607Z Ran 73 tests in 43.521s
2023-01-11T22:07:19.4596720Z 
2023-01-11T22:07:19.4596794Z OK (skipped=4)
2023-01-11T22:07:19.4596887Z 
2023-01-11T22:07:19.4596971Z Generating XML reports...
2023-01-11T22:07:19.4597403Z Generated XML report: test-reports/python-unittest/test_tensorexpr/TEST-TestTensorExprFuser-20230111220635.xml
2023-01-11T22:07:19.4597647Z 
2023-01-11T22:07:19.4597942Z ##[endgroup]
2023-01-11T22:07:19.4598356Z FINISHED PRINTING LOG FILE of test_tensorexpr (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_3qers4rd)
2023-01-11T22:07:19.4598578Z 
2023-01-11T22:07:19.4598728Z Running doctests ... [2023-01-11 22:07:19.456394]
2023-01-11T22:07:19.4883400Z Start doctest_module('/opt/conda/lib/python3.10/site-packages/torch')
2023-01-11T22:07:19.4883646Z Listing tests
2023-01-11T22:07:25.5722946Z gathering tests
2023-01-11T22:07:25.5739599Z running 663 test(s)
2023-01-11T22:07:25.5793196Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::is_tensor:0, line 429 <- wrt source file
2023-01-11T22:07:25.5800409Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::is_tensor:0
2023-01-11T22:07:25.5800957Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::set_default_tensor_type:0, line 458 <- wrt source file
2023-01-11T22:07:25.5803019Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::set_default_tensor_type:0
2023-01-11T22:07:25.5803752Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::set_default_dtype:0, line 496 <- wrt source file
2023-01-11T22:07:25.5806051Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::set_default_dtype:0
2023-01-11T22:07:25.5806767Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 629 <- wrt source file
2023-01-11T22:07:25.5808194Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::use_deterministic_algorithms:0
2023-01-11T22:07:25.5809273Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/__init__.py::compile:0, line 1221 <- wrt source file
2023-01-11T22:07:25.5809958Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/__init__.py::compile:0
2023-01-11T22:07:25.5810545Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file
2023-01-11T22:07:25.5811307Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so::Generator:0
2023-01-11T22:07:25.5811891Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file
2023-01-11T22:07:25.5813080Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_C.cpython-310-x86_64-linux-gnu.so::_LinAlgError:0
2023-01-11T22:07:25.5813846Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_namedtensor_internals.py::update_names:0, line 125 <- wrt source file
2023-01-11T22:07:25.5815381Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_namedtensor_internals.py::update_names:0
2023-01-11T22:07:25.5816066Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 508 <- wrt source file
2023-01-11T22:07:25.5824395Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.register_hook:0
2023-01-11T22:07:25.5825052Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1096 <- wrt source file
2023-01-11T22:07:25.5944206Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.refine_names:0
2023-01-11T22:07:25.5948228Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1141 <- wrt source file
2023-01-11T22:07:25.5954076Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.align_to:0
2023-01-11T22:07:25.5954705Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.rename:0, line 1214 <- wrt source file
2023-01-11T22:07:25.5963282Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.rename:0
2023-01-11T22:07:25.5963994Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1244 <- wrt source file
2023-01-11T22:07:25.5971955Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0
2023-01-11T22:07:25.5972678Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py::set_printoptions:0, line 49 <- wrt source file
2023-01-11T22:07:25.6000874Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py::set_printoptions:0
2023-01-11T22:07:25.6001587Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::broadcast_tensors:0, line 61 <- wrt source file
2023-01-11T22:07:25.6009164Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::broadcast_tensors:0
2023-01-11T22:07:25.6009701Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::broadcast_shapes:0, line 89 <- wrt source file
2023-01-11T22:07:25.6013007Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::broadcast_shapes:0
2023-01-11T22:07:25.6013529Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::split:0, line 161 <- wrt source file
2023-01-11T22:07:25.6027350Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::split:0
2023-01-11T22:07:25.6027853Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::einsum:0, line 269 <- wrt source file
2023-01-11T22:07:25.6224602Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::einsum:0
2023-01-11T22:07:25.6225285Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::meshgrid:0, line 450 <- wrt source file
2023-01-11T22:07:25.6261376Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::meshgrid:0
2023-01-11T22:07:25.6261965Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::_unique_impl:0, line 764 <- wrt source file
2023-01-11T22:07:25.6276751Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::_unique_impl:0
2023-01-11T22:07:25.6277362Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 842 <- wrt source file
2023-01-11T22:07:25.6289882Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::_unique_consecutive_impl:0
2023-01-11T22:07:25.6290474Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::tensordot:0, line 1040 <- wrt source file
2023-01-11T22:07:25.6304196Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::tensordot:0
2023-01-11T22:07:25.6304737Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::cartesian_prod:0, line 1118 <- wrt source file
2023-01-11T22:07:25.6312515Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::cartesian_prod:0
2023-01-11T22:07:25.6313200Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::block_diag:0, line 1152 <- wrt source file
2023-01-11T22:07:25.6324216Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::block_diag:0
2023-01-11T22:07:25.6324796Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::cdist:0, line 1203 <- wrt source file
2023-01-11T22:07:25.6342137Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::cdist:0
2023-01-11T22:07:25.6342723Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_1d:0, line 1243 <- wrt source file
2023-01-11T22:07:25.6362106Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_1d:0
2023-01-11T22:07:25.6362747Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_2d:0, line 1279 <- wrt source file
2023-01-11T22:07:25.6383449Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_2d:0
2023-01-11T22:07:25.6383968Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_3d:0, line 1317 <- wrt source file
2023-01-11T22:07:25.6409481Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::atleast_3d:0
2023-01-11T22:07:25.6409968Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::norm:0, line 1455 <- wrt source file
2023-01-11T22:07:25.6451845Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/functional.py::norm:0
2023-01-11T22:07:25.6452380Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::chain_matmul:0, line 1606 <- wrt source file
2023-01-11T22:07:25.6453884Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/functional.py::chain_matmul:0
2023-01-11T22:07:25.6454388Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/functional.py::_lu_impl:0, line 1706 <- wrt source file
2023-01-11T22:07:25.6456624Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/functional.py::_lu_impl:0
2023-01-11T22:07:25.6457309Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::list:0, line 391 <- wrt source file
2023-01-11T22:07:25.6457880Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::list:0
2023-01-11T22:07:25.6458571Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::help:0, line 444 <- wrt source file
2023-01-11T22:07:25.6459243Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::help:0
2023-01-11T22:07:25.6459911Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::load:0, line 524 <- wrt source file
2023-01-11T22:07:25.6460513Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::load:0
2023-01-11T22:07:25.6461211Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::_load_local:0, line 563 <- wrt source file
2023-01-11T22:07:25.6461782Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::_load_local:0
2023-01-11T22:07:25.6462514Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::download_url_to_file:0, line 592 <- wrt source file
2023-01-11T22:07:25.6463065Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::download_url_to_file:0
2023-01-11T22:07:25.6463572Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/hub.py::load_state_dict_from_url:0, line 701 <- wrt source file
2023-01-11T22:07:25.6464132Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/hub.py::load_state_dict_from_url:0
2023-01-11T22:07:25.6464643Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/library.py::Library.define:0, line 61 <- wrt source file
2023-01-11T22:07:25.6465267Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/library.py::Library.define:0
2023-01-11T22:07:25.6465756Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/library.py::Library.impl:0, line 81 <- wrt source file
2023-01-11T22:07:25.6466240Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/library.py::Library.impl:0
2023-01-11T22:07:25.6466753Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::get_ignored_functions:0, line 67 <- wrt source file
2023-01-11T22:07:25.6469220Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::get_ignored_functions:0
2023-01-11T22:07:25.6469880Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::get_testing_overrides:0, line 336 <- wrt source file
2023-01-11T22:07:25.6507147Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::get_testing_overrides:0
2023-01-11T22:07:25.6507681Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::wrap_torch_function:0, line 1391 <- wrt source file
2023-01-11T22:07:25.6511396Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::wrap_torch_function:0
2023-01-11T22:07:25.6511913Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::handle_torch_function:0, line 1508 <- wrt source file
2023-01-11T22:07:25.6515453Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::handle_torch_function:0
2023-01-11T22:07:25.6516001Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1732 <- wrt source file
2023-01-11T22:07:25.6557117Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::is_tensor_method_or_property:0
2023-01-11T22:07:25.6557637Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/overrides.py::is_tensor_like:0, line 1750 <- wrt source file
2023-01-11T22:07:25.6565851Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/overrides.py::is_tensor_like:0
2023-01-11T22:07:25.6566507Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/quasirandom.py::SobolEngine:0, line 37 <- wrt source file
2023-01-11T22:07:25.6567318Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/quasirandom.py::SobolEngine:0
2023-01-11T22:07:25.6567982Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/serialization.py::save:0, line 429 <- wrt source file
2023-01-11T22:07:25.6568455Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/serialization.py::save:0
2023-01-11T22:07:25.6568959Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/serialization.py::load:0, line 754 <- wrt source file
2023-01-11T22:07:25.6572387Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/serialization.py::load:0
2023-01-11T22:07:25.6573148Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/torch_version.py::TorchVersion:0, line 49 <- wrt source file
2023-01-11T22:07:25.6573942Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/torch_version.py::TorchVersion:0
2023-01-11T22:07:25.6574505Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1495 <- wrt source file
2023-01-11T22:07:25.6579971Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0
2023-01-11T22:07:25.6580690Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.then:0, line 147 <- wrt source file
2023-01-11T22:07:25.6581587Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.then:0
2023-01-11T22:07:25.6582266Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 195 <- wrt source file
2023-01-11T22:07:25.6584582Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.add_done_callback:0
2023-01-11T22:07:25.6585316Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.set_result:0, line 228 <- wrt source file
2023-01-11T22:07:25.6586406Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.set_result:0
2023-01-11T22:07:25.6587423Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 257 <- wrt source file
2023-01-11T22:07:25.6588098Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::Future.set_exception:0
2023-01-11T22:07:25.6589008Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::collect_all:0, line 288 <- wrt source file
2023-01-11T22:07:25.6589832Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/futures/__init__.py::collect_all:0
2023-01-11T22:07:25.6590411Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py::annotate:0, line 103 <- wrt source file
2023-01-11T22:07:25.6590996Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py::annotate:0
2023-01-11T22:07:25.6591883Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py::strict_fusion:0, line 202 <- wrt source file
2023-01-11T22:07:25.6592481Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/__init__.py::strict_fusion:0
2023-01-11T22:07:25.6593021Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 21 <- wrt source file
2023-01-11T22:07:25.6608379Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0
2023-01-11T22:07:25.6609189Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 39 <- wrt source file
2023-01-11T22:07:25.6624738Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nested/__init__.py::as_nested_tensor:0
2023-01-11T22:07:25.6625723Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/sparse/__init__.py::sum:0, line 175 <- wrt source file
2023-01-11T22:07:25.6637864Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/sparse/__init__.py::sum:0
2023-01-11T22:07:25.6638901Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 2139 <- wrt source file
2023-01-11T22:07:25.7914711Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py::aot_function:0
2023-01-11T22:07:25.7915461Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 162 <- wrt source file
2023-01-11T22:07:25.7916030Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0
2023-01-11T22:07:25.7916588Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 195 <- wrt source file
2023-01-11T22:07:25.7999248Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::vjp:0
2023-01-11T22:07:25.7999953Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 382 <- wrt source file
2023-01-11T22:07:25.8161159Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jacrev:0
2023-01-11T22:07:25.8161962Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 882 <- wrt source file
2023-01-11T22:07:25.8847640Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jvp:0
2023-01-11T22:07:25.8848194Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1024 <- wrt source file
2023-01-11T22:07:25.9002622Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0
2023-01-11T22:07:25.9003302Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1173 <- wrt source file
2023-01-11T22:07:25.9047640Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::hessian:0
2023-01-11T22:07:25.9048341Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::grad:0, line 1290 <- wrt source file
2023-01-11T22:07:25.9048957Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::grad:0
2023-01-11T22:07:25.9049626Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1441 <- wrt source file
2023-01-11T22:07:25.9054646Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py::functionalize:0
2023-01-11T22:07:25.9055579Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 72 <- wrt source file
2023-01-11T22:07:25.9056149Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_functorch/fx_minifier.py::minifier:0
2023-01-11T22:07:25.9056671Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_functorch/vmap.py::vmap:0, line 306 <- wrt source file
2023-01-11T22:07:25.9100317Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/_functorch/vmap.py::vmap:0
2023-01-11T22:07:25.9101310Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_prims/context.py::NvfuserPrimsMode:0, line 90 <- wrt source file
2023-01-11T22:07:25.9102273Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_prims/context.py::NvfuserPrimsMode:0
2023-01-11T22:07:25.9103327Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 141 <- wrt source file
2023-01-11T22:07:25.9104267Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/_prims/context.py::TorchRefsMode:0
2023-01-11T22:07:25.9105321Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 21 <- wrt source file
2023-01-11T22:07:25.9106337Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0
2023-01-11T22:07:25.9107474Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 21 <- wrt source file
2023-01-11T22:07:25.9108847Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0
2023-01-11T22:07:25.9110019Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file
2023-01-11T22:07:25.9111108Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0
2023-01-11T22:07:25.9112213Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 59 <- wrt source file
2023-01-11T22:07:25.9113552Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0
2023-01-11T22:07:25.9114632Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 126 <- wrt source file
2023-01-11T22:07:25.9115681Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0
2023-01-11T22:07:25.9116613Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 24 <- wrt source file
2023-01-11T22:07:25.9140791Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0
2023-01-11T22:07:25.9142620Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 274 <- wrt source file
2023-01-11T22:07:25.9183234Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0
2023-01-11T22:07:25.9185049Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 166 <- wrt source file
2023-01-11T22:07:25.9186295Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0
2023-01-11T22:07:25.9187287Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 226 <- wrt source file
2023-01-11T22:07:25.9188255Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0
2023-01-11T22:07:25.9189297Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 287 <- wrt source file
2023-01-11T22:07:25.9190236Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0
2023-01-11T22:07:25.9191267Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 74 <- wrt source file
2023-01-11T22:07:25.9196858Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0
2023-01-11T22:07:25.9197893Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 114 <- wrt source file
2023-01-11T22:07:25.9203736Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0
2023-01-11T22:07:25.9204816Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 34 <- wrt source file
2023-01-11T22:07:25.9205838Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0
2023-01-11T22:07:25.9206872Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 105 <- wrt source file
2023-01-11T22:07:25.9207906Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0
2023-01-11T22:07:25.9208922Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 170 <- wrt source file
2023-01-11T22:07:25.9210134Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0
2023-01-11T22:07:25.9211212Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 236 <- wrt source file
2023-01-11T22:07:25.9212289Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0
2023-01-11T22:07:25.9213503Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 297 <- wrt source file
2023-01-11T22:07:25.9214565Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0
2023-01-11T22:07:25.9215647Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 358 <- wrt source file
2023-01-11T22:07:25.9216695Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0
2023-01-11T22:07:25.9217864Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 28 <- wrt source file
2023-01-11T22:07:25.9218884Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0
2023-01-11T22:07:25.9219929Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 391 <- wrt source file
2023-01-11T22:07:25.9220885Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0
2023-01-11T22:07:25.9221792Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 618 <- wrt source file
2023-01-11T22:07:25.9222777Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0
2023-01-11T22:07:25.9223793Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 940 <- wrt source file
2023-01-11T22:07:25.9224796Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0
2023-01-11T22:07:25.9225814Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 993 <- wrt source file
2023-01-11T22:07:25.9226987Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0
2023-01-11T22:07:25.9228005Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1036 <- wrt source file
2023-01-11T22:07:25.9229004Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0
2023-01-11T22:07:25.9230034Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 31 <- wrt source file
2023-01-11T22:07:25.9231031Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0
2023-01-11T22:07:25.9232029Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv1d:0, line 295 <- wrt source file
2023-01-11T22:07:25.9233042Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv1d:0
2023-01-11T22:07:25.9234045Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 403 <- wrt source file
2023-01-11T22:07:25.9234999Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0
2023-01-11T22:07:25.9235960Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 502 <- wrt source file
2023-01-11T22:07:25.9236925Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0
2023-01-11T22:07:25.9237849Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 685 <- wrt source file
2023-01-11T22:07:25.9238881Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0
2023-01-11T22:07:25.9239771Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 775 <- wrt source file
2023-01-11T22:07:25.9240657Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0
2023-01-11T22:07:25.9241575Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 869 <- wrt source file
2023-01-11T22:07:25.9242709Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0
2023-01-11T22:07:25.9243799Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 84 <- wrt source file
2023-01-11T22:07:25.9260944Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0
2023-01-11T22:07:25.9261761Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 209 <- wrt source file
2023-01-11T22:07:25.9286238Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0
2023-01-11T22:07:25.9287045Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 21 <- wrt source file
2023-01-11T22:07:25.9292183Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0
2023-01-11T22:07:25.9292997Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 141 <- wrt source file
2023-01-11T22:07:25.9297131Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0
2023-01-11T22:07:25.9298043Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 117 <- wrt source file
2023-01-11T22:07:25.9298881Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0
2023-01-11T22:07:25.9299732Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/rnn.py::LSTM:0, line 20 <- wrt source file
2023-01-11T22:07:25.9300430Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/rnn.py::LSTM:0
2023-01-11T22:07:25.9301428Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 59 <- wrt source file
2023-01-11T22:07:25.9302369Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0
2023-01-11T22:07:25.9303477Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 92 <- wrt source file
2023-01-11T22:07:25.9441560Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0
2023-01-11T22:07:25.9442296Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 54 <- wrt source file
2023-01-11T22:07:25.9442972Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0
2023-01-11T22:07:25.9443702Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 19 <- wrt source file
2023-01-11T22:07:25.9446299Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0
2023-01-11T22:07:25.9446896Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 45 <- wrt source file
2023-01-11T22:07:25.9447488Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0
2023-01-11T22:07:25.9448176Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier.squash_mask:0, line 237 <- wrt source file
2023-01-11T22:07:25.9452316Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier.squash_mask:0
2023-01-11T22:07:25.9452917Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 143 <- wrt source file
2023-01-11T22:07:25.9454185Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0
2023-01-11T22:07:25.9454792Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 27 <- wrt source file
2023-01-11T22:07:25.9462275Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0
2023-01-11T22:07:25.9463021Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 64 <- wrt source file
2023-01-11T22:07:25.9469756Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0
2023-01-11T22:07:25.9470409Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 111 <- wrt source file
2023-01-11T22:07:25.9476043Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0
2023-01-11T22:07:25.9476662Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 139 <- wrt source file
2023-01-11T22:07:25.9482775Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0
2023-01-11T22:07:25.9483545Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 85 <- wrt source file
2023-01-11T22:07:25.9484476Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py::_with_args:0
2023-01-11T22:07:25.9485105Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 106 <- wrt source file
2023-01-11T22:07:25.9485880Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0
2023-01-11T22:07:25.9486452Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 242 <- wrt source file
2023-01-11T22:07:25.9487359Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0
2023-01-11T22:07:25.9489818Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 301 <- wrt source file
2023-01-11T22:07:25.9490547Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0
2023-01-11T22:07:25.9492591Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 439 <- wrt source file
2023-01-11T22:07:25.9493310Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0
2023-01-11T22:07:25.9494298Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 604 <- wrt source file
2023-01-11T22:07:25.9495024Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0
2023-01-11T22:07:25.9496005Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 663 <- wrt source file
2023-01-11T22:07:25.9496906Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0
2023-01-11T22:07:25.9497748Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 715 <- wrt source file
2023-01-11T22:07:25.9498555Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0
2023-01-11T22:07:25.9499489Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 408 <- wrt source file
2023-01-11T22:07:25.9500226Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0
2023-01-11T22:07:25.9501081Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 429 <- wrt source file
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2023-01-11T22:07:25.9530411Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0
2023-01-11T22:07:25.9531176Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 215 <- wrt source file
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2023-01-11T22:07:25.9579621Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0
2023-01-11T22:07:25.9580317Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 406 <- wrt source file
2023-01-11T22:07:25.9597879Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0
2023-01-11T22:07:25.9598635Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/autograd/profiler.py::profile:0, line 123 <- wrt source file
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2023-01-11T22:07:25.9777407Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py::_RemoteModule.init_from_module_rref:0, line 524 <- wrt source file
2023-01-11T22:07:25.9778019Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py::_RemoteModule.init_from_module_rref:0
2023-01-11T22:07:25.9778615Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py::RemoteModule:0, line 646 <- wrt source file
2023-01-11T22:07:25.9779168Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py::RemoteModule:0
2023-01-11T22:07:25.9779782Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 27 <- wrt source file
2023-01-11T22:07:25.9780413Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0
2023-01-11T22:07:25.9781020Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0, line 38 <- wrt source file
2023-01-11T22:07:25.9781593Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0
2023-01-11T22:07:25.9782175Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py::DistributedOptimizer:0, line 160 <- wrt source file
2023-01-11T22:07:25.9782892Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py::DistributedOptimizer:0
2023-01-11T22:07:25.9783522Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py::PostLocalSGDOptimizer:0, line 18 <- wrt source file
2023-01-11T22:07:25.9784149Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py::PostLocalSGDOptimizer:0
2023-01-11T22:07:25.9784751Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 35 <- wrt source file
2023-01-11T22:07:25.9785299Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0
2023-01-11T22:07:25.9785966Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py::ZeroRedundancyOptimizer:0, line 325 <- wrt source file
2023-01-11T22:07:25.9786611Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py::ZeroRedundancyOptimizer:0
2023-01-11T22:07:25.9787213Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/pipeline/sync/pipe.py::WithDevice:0, line 152 <- wrt source file
2023-01-11T22:07:25.9787747Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/pipeline/sync/pipe.py::WithDevice:0
2023-01-11T22:07:25.9788308Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/pipeline/sync/pipe.py::Pipe:0, line 274 <- wrt source file
2023-01-11T22:07:25.9788850Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/pipeline/sync/pipe.py::Pipe:0
2023-01-11T22:07:25.9789477Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 160 <- wrt source file
2023-01-11T22:07:25.9790196Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::_wait_all:0
2023-01-11T22:07:25.9790909Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 333 <- wrt source file
2023-01-11T22:07:25.9791423Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::shutdown:0
2023-01-11T22:07:25.9791944Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::remote:0, line 582 <- wrt source file
2023-01-11T22:07:25.9792432Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::remote:0
2023-01-11T22:07:25.9792995Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::rpc_sync:0, line 766 <- wrt source file
2023-01-11T22:07:25.9793686Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::rpc_sync:0
2023-01-11T22:07:25.9794434Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::rpc_async:0, line 858 <- wrt source file
2023-01-11T22:07:25.9795071Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/api.py::rpc_async:0
2023-01-11T22:07:25.9795722Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/functions.py::async_execution:0, line 33 <- wrt source file
2023-01-11T22:07:25.9796280Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/functions.py::async_execution:0
2023-01-11T22:07:25.9796901Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/options.py::TensorPipeRpcBackendOptions.set_device_map:0, line 117 <- wrt source file
2023-01-11T22:07:25.9797538Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/options.py::TensorPipeRpcBackendOptions.set_device_map:0
2023-01-11T22:07:25.9798262Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0, line 58 <- wrt source file
2023-01-11T22:07:25.9798899Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0
2023-01-11T22:07:25.9799523Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py::_prepare_input_validate:0, line 33 <- wrt source file
2023-01-11T22:07:25.9800112Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py::_prepare_input_validate:0
2023-01-11T22:07:25.9800774Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py::_prepare_output_validate:0, line 78 <- wrt source file
2023-01-11T22:07:25.9801509Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py::_prepare_output_validate:0
2023-01-11T22:07:25.9802116Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 63 <- wrt source file
2023-01-11T22:07:25.9802686Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0
2023-01-11T22:07:25.9803229Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 21 <- wrt source file
2023-01-11T22:07:25.9803754Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/bernoulli.py::Bernoulli:0
2023-01-11T22:07:25.9804273Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/beta.py::Beta:0, line 16 <- wrt source file
2023-01-11T22:07:25.9808919Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/beta.py::Beta:0
2023-01-11T22:07:25.9809638Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/binomial.py::Binomial:0, line 20 <- wrt source file
2023-01-11T22:07:25.9818340Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/binomial.py::Binomial:0
2023-01-11T22:07:25.9818894Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/categorical.py::Categorical:0, line 37 <- wrt source file
2023-01-11T22:07:25.9825496Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/categorical.py::Categorical:0
2023-01-11T22:07:25.9826044Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 19 <- wrt source file
2023-01-11T22:07:25.9831466Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/cauchy.py::Cauchy:0
2023-01-11T22:07:25.9831991Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/chi2.py::Chi2:0, line 12 <- wrt source file
2023-01-11T22:07:25.9837260Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/chi2.py::Chi2:0
2023-01-11T22:07:25.9838092Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 152 <- wrt source file
2023-01-11T22:07:25.9838661Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/constraints.py::_DependentProperty:0
2023-01-11T22:07:25.9839271Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 24 <- wrt source file
2023-01-11T22:07:25.9845868Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0
2023-01-11T22:07:25.9846441Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 35 <- wrt source file
2023-01-11T22:07:25.9851941Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/dirichlet.py::Dirichlet:0
2023-01-11T22:07:25.9852481Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/exponential.py::Exponential:0, line 15 <- wrt source file
2023-01-11T22:07:25.9857009Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/exponential.py::Exponential:0
2023-01-11T22:07:25.9857586Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 16 <- wrt source file
2023-01-11T22:07:25.9864813Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0
2023-01-11T22:07:25.9865355Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/gamma.py::Gamma:0, line 19 <- wrt source file
2023-01-11T22:07:25.9870069Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/gamma.py::Gamma:0
2023-01-11T22:07:25.9870610Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/geometric.py::Geometric:0, line 21 <- wrt source file
2023-01-11T22:07:25.9875525Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/geometric.py::Geometric:0
2023-01-11T22:07:25.9876144Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 17 <- wrt source file
2023-01-11T22:07:25.9883473Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/gumbel.py::Gumbel:0
2023-01-11T22:07:25.9884024Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 20 <- wrt source file
2023-01-11T22:07:25.9890449Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0
2023-01-11T22:07:25.9891063Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 20 <- wrt source file
2023-01-11T22:07:25.9896278Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/half_normal.py::HalfNormal:0
2023-01-11T22:07:25.9896933Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/independent.py::Independent:0, line 18 <- wrt source file
2023-01-11T22:07:25.9937979Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/independent.py::Independent:0
2023-01-11T22:07:25.9939170Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 25 <- wrt source file
2023-01-11T22:07:25.9940175Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0
2023-01-11T22:07:25.9941271Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/laplace.py::Laplace:0, line 14 <- wrt source file
2023-01-11T22:07:25.9942310Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/laplace.py::Laplace:0
2023-01-11T22:07:25.9943267Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 38 <- wrt source file
2023-01-11T22:07:25.9944226Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0
2023-01-11T22:07:25.9945246Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 17 <- wrt source file
2023-01-11T22:07:25.9946287Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/log_normal.py::LogNormal:0
2023-01-11T22:07:25.9947430Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 22 <- wrt source file
2023-01-11T22:07:25.9949019Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0
2023-01-11T22:07:25.9950399Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0, line 56 <- wrt source file
2023-01-11T22:07:25.9951807Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0
2023-01-11T22:07:25.9952991Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/mixture_same_family.py::MixtureSameFamily:0, line 19 <- wrt source file
2023-01-11T22:07:25.9968242Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/mixture_same_family.py::MixtureSameFamily:0
2023-01-11T22:07:25.9969250Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 34 <- wrt source file
2023-01-11T22:07:25.9969801Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/multinomial.py::Multinomial:0
2023-01-11T22:07:25.9970395Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 94 <- wrt source file
2023-01-11T22:07:25.9971084Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0
2023-01-11T22:07:25.9971777Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/normal.py::Normal:0, line 18 <- wrt source file
2023-01-11T22:07:25.9972292Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/normal.py::Normal:0
2023-01-11T22:07:25.9972862Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 27 <- wrt source file
2023-01-11T22:07:25.9973447Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0
2023-01-11T22:07:25.9973982Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/pareto.py::Pareto:0, line 14 <- wrt source file
2023-01-11T22:07:25.9979306Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/pareto.py::Pareto:0
2023-01-11T22:07:25.9979979Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/poisson.py::Poisson:0, line 20 <- wrt source file
2023-01-11T22:07:25.9980711Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/poisson.py::Poisson:0
2023-01-11T22:07:25.9981277Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/relaxed_bernoulli.py::RelaxedBernoulli:0, line 102 <- wrt source file
2023-01-11T22:07:25.9988788Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/relaxed_bernoulli.py::RelaxedBernoulli:0
2023-01-11T22:07:25.9989407Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/relaxed_categorical.py::RelaxedOneHotCategorical:0, line 96 <- wrt source file
2023-01-11T22:07:25.9998379Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/relaxed_categorical.py::RelaxedOneHotCategorical:0
2023-01-11T22:07:25.9998952Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/studentT.py::StudentT:0, line 17 <- wrt source file
2023-01-11T22:07:26.0005921Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/studentT.py::StudentT:0
2023-01-11T22:07:26.0006666Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1002 <- wrt source file
2023-01-11T22:07:26.0007481Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::CatTransform:0
2023-01-11T22:07:26.0008361Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1105 <- wrt source file
2023-01-11T22:07:26.0009009Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::StackTransform:0
2023-01-11T22:07:26.0009914Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1178 <- wrt source file
2023-01-11T22:07:26.0010661Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0
2023-01-11T22:07:26.0011325Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/uniform.py::Uniform:0, line 17 <- wrt source file
2023-01-11T22:07:26.0014730Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/uniform.py::Uniform:0
2023-01-11T22:07:26.0015256Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/von_mises.py::VonMises:0, line 79 <- wrt source file
2023-01-11T22:07:26.0023540Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/von_mises.py::VonMises:0
2023-01-11T22:07:26.0024070Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/weibull.py::Weibull:0, line 16 <- wrt source file
2023-01-11T22:07:26.0030881Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/distributions/weibull.py::Weibull:0
2023-01-11T22:07:26.0031655Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py::Wishart:0, line 36 <- wrt source file
2023-01-11T22:07:26.0032264Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/distributions/wishart.py::Wishart:0
2023-01-11T22:07:26.0033026Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::_snake_case:0, line 79 <- wrt source file
2023-01-11T22:07:26.0033682Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::_snake_case:0
2023-01-11T22:07:26.0034215Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1363 <- wrt source file
2023-01-11T22:07:26.0034726Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0
2023-01-11T22:07:26.0035754Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1431 <- wrt source file
2023-01-11T22:07:26.0036278Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/graph.py::Graph.on_generate_code:0
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2023-01-11T22:07:26.0077843Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0
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2023-01-11T22:07:26.0106098Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0
2023-01-11T22:07:26.0106696Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0, line 189 <- wrt source file
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2023-01-11T22:07:26.0125323Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py::update_in:0
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2023-01-11T22:07:26.0134696Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py::get_in:0
2023-01-11T22:07:26.0135297Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py::groupby:0, line 357 <- wrt source file
2023-01-11T22:07:26.0139379Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py::groupby:0
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2023-01-11T22:07:26.0142321Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0
2023-01-11T22:07:26.0143060Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 12 <- wrt source file
2023-01-11T22:07:26.0146736Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0
2023-01-11T22:07:26.0147561Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 39 <- wrt source file
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2023-01-11T22:07:26.0149082Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 67 <- wrt source file
2023-01-11T22:07:26.0151565Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0
2023-01-11T22:07:26.0152198Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 92 <- wrt source file
2023-01-11T22:07:26.0157061Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py::freeze:0
2023-01-11T22:07:26.0157906Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 62 <- wrt source file
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2023-01-11T22:07:26.0159106Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 18 <- wrt source file
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2023-01-11T22:07:26.0165551Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0
2023-01-11T22:07:26.0166306Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0, line 176 <- wrt source file
2023-01-11T22:07:26.0167104Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0
2023-01-11T22:07:26.0167855Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 288 <- wrt source file
2023-01-11T22:07:26.0168514Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0
2023-01-11T22:07:26.0169343Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 418 <- wrt source file
2023-01-11T22:07:26.0171676Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0
2023-01-11T22:07:26.0172327Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 15 <- wrt source file
2023-01-11T22:07:26.0175771Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0
2023-01-11T22:07:26.0176400Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 38 <- wrt source file
2023-01-11T22:07:26.0179776Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0
2023-01-11T22:07:26.0180505Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 66 <- wrt source file
2023-01-11T22:07:26.0183377Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0
2023-01-11T22:07:26.0184191Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 85 <- wrt source file
2023-01-11T22:07:26.0187612Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0
2023-01-11T22:07:26.0188341Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 115 <- wrt source file
2023-01-11T22:07:26.0191216Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0
2023-01-11T22:07:26.0192230Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file
2023-01-11T22:07:26.0193125Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0
2023-01-11T22:07:26.0193767Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 80 <- wrt source file
2023-01-11T22:07:26.0195008Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0
2023-01-11T22:07:26.0196281Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 76 <- wrt source file
2023-01-11T22:07:26.0196933Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0
2023-01-11T22:07:26.0198270Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/fx/passes/split_module.py::split_module:0, line 68 <- wrt source file
2023-01-11T22:07:26.0198975Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/fx/passes/split_module.py::split_module:0
2023-01-11T22:07:26.0199804Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 35 <- wrt source file
2023-01-11T22:07:26.0200670Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0
2023-01-11T22:07:26.0201362Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/_serialization.py::save:0, line 53 <- wrt source file
2023-01-11T22:07:26.0202046Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/_serialization.py::save:0
2023-01-11T22:07:26.0202783Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/_serialization.py::load:0, line 111 <- wrt source file
2023-01-11T22:07:26.0203526Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/_serialization.py::load:0
2023-01-11T22:07:26.0204366Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/_serialization.py::save_jit_module_to_flatbuffer:0, line 235 <- wrt source file
2023-01-11T22:07:26.0205356Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/_serialization.py::save_jit_module_to_flatbuffer:0
2023-01-11T22:07:26.0206142Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 23 <- wrt source file
2023-01-11T22:07:26.0207016Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0
2023-01-11T22:07:26.0208014Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_model_bytecode_version:0, line 89 <- wrt source file
2023-01-11T22:07:26.0208802Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_model_bytecode_version:0
2023-01-11T22:07:26.0209607Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 119 <- wrt source file
2023-01-11T22:07:26.0210179Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0
2023-01-11T22:07:26.0210739Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 199 <- wrt source file
2023-01-11T22:07:26.0211257Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0
2023-01-11T22:07:26.0211816Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 22 <- wrt source file
2023-01-11T22:07:26.0212360Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0
2023-01-11T22:07:26.0212928Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 452 <- wrt source file
2023-01-11T22:07:26.0284550Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0
2023-01-11T22:07:26.0285254Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 553 <- wrt source file
2023-01-11T22:07:26.1414324Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0
2023-01-11T22:07:26.1431509Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 1878 <- wrt source file
2023-01-11T22:07:26.1441446Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::gumbel_softmax:0
2023-01-11T22:07:26.1442284Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::embedding:0, line 2149 <- wrt source file
2023-01-11T22:07:26.1452905Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::embedding:0
2023-01-11T22:07:26.1453612Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::embedding_bag:0, line 2286 <- wrt source file
2023-01-11T22:07:26.1465312Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::embedding_bag:0
2023-01-11T22:07:26.1466007Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::ctc_loss:0, line 2615 <- wrt source file
2023-01-11T22:07:26.1482889Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::ctc_loss:0
2023-01-11T22:07:26.1483579Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::nll_loss:0, line 2681 <- wrt source file
2023-01-11T22:07:26.1489875Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::nll_loss:0
2023-01-11T22:07:26.1490560Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::cross_entropy:0, line 3000 <- wrt source file
2023-01-11T22:07:26.1499277Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::cross_entropy:0
2023-01-11T22:07:26.1500047Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3066 <- wrt source file
2023-01-11T22:07:26.1505547Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::binary_cross_entropy:0
2023-01-11T22:07:26.1506294Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3138 <- wrt source file
2023-01-11T22:07:26.1512590Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0
2023-01-11T22:07:26.1513317Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv1d_input:0, line 23 <- wrt source file
2023-01-11T22:07:26.1521909Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv1d_input:0
2023-01-11T22:07:26.1522582Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv1d_weight:0, line 53 <- wrt source file
2023-01-11T22:07:26.1528608Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv1d_weight:0
2023-01-11T22:07:26.1529409Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv2d_input:0, line 86 <- wrt source file
2023-01-11T22:07:26.1537177Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv2d_input:0
2023-01-11T22:07:26.1537838Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv2d_weight:0, line 116 <- wrt source file
2023-01-11T22:07:26.1543786Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv2d_weight:0
2023-01-11T22:07:26.1544458Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv3d_input:0, line 149 <- wrt source file
2023-01-11T22:07:26.1574335Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv3d_input:0
2023-01-11T22:07:26.1575208Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv3d_weight:0, line 179 <- wrt source file
2023-01-11T22:07:26.1594723Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/grad.py::conv3d_weight:0
2023-01-11T22:07:26.1595495Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::calculate_gain:0, line 96 <- wrt source file
2023-01-11T22:07:26.1599434Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::calculate_gain:0
2023-01-11T22:07:26.1600094Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::uniform_:0, line 132 <- wrt source file
2023-01-11T22:07:26.1603544Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::uniform_:0
2023-01-11T22:07:26.1604186Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::normal_:0, line 150 <- wrt source file
2023-01-11T22:07:26.1607847Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::normal_:0
2023-01-11T22:07:26.1608511Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::trunc_normal_:0, line 173 <- wrt source file
2023-01-11T22:07:26.1612432Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::trunc_normal_:0
2023-01-11T22:07:26.1613099Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::constant_:0, line 187 <- wrt source file
2023-01-11T22:07:26.1616808Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::constant_:0
2023-01-11T22:07:26.1617632Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::ones_:0, line 202 <- wrt source file
2023-01-11T22:07:26.1620464Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::ones_:0
2023-01-11T22:07:26.1621322Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::zeros_:0, line 215 <- wrt source file
2023-01-11T22:07:26.1624137Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::zeros_:0
2023-01-11T22:07:26.1624954Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::eye_:0, line 230 <- wrt source file
2023-01-11T22:07:26.1628264Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::eye_:0
2023-01-11T22:07:26.1629154Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::dirac_:0, line 251 <- wrt source file
2023-01-11T22:07:26.1633523Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::dirac_:0
2023-01-11T22:07:26.1634364Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::xavier_uniform_:0, line 320 <- wrt source file
2023-01-11T22:07:26.1637585Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::xavier_uniform_:0
2023-01-11T22:07:26.1638157Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::xavier_normal_:0, line 347 <- wrt source file
2023-01-11T22:07:26.1641483Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::xavier_normal_:0
2023-01-11T22:07:26.1641995Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 392 <- wrt source file
2023-01-11T22:07:26.1645430Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::kaiming_uniform_:0
2023-01-11T22:07:26.1646016Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::kaiming_normal_:0, line 441 <- wrt source file
2023-01-11T22:07:26.1650086Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::kaiming_normal_:0
2023-01-11T22:07:26.1651003Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::orthogonal_:0, line 466 <- wrt source file
2023-01-11T22:07:26.1651674Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::orthogonal_:0
2023-01-11T22:07:26.1652159Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::sparse_:0, line 512 <- wrt source file
2023-01-11T22:07:26.1656428Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/init.py::sparse_:0
2023-01-11T22:07:26.1657219Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Threshold:0, line 40 <- wrt source file
2023-01-11T22:07:26.1661457Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Threshold:0
2023-01-11T22:07:26.1662230Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ReLU:0, line 83 <- wrt source file
2023-01-11T22:07:26.1668135Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ReLU:0
2023-01-11T22:07:26.1668851Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::RReLU:0, line 142 <- wrt source file
2023-01-11T22:07:26.1672722Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::RReLU:0
2023-01-11T22:07:26.1673435Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 202 <- wrt source file
2023-01-11T22:07:26.1677714Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardtanh:0
2023-01-11T22:07:26.1678305Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 260 <- wrt source file
2023-01-11T22:07:26.1681737Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ReLU6:0
2023-01-11T22:07:26.1682405Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 288 <- wrt source file
2023-01-11T22:07:26.1685808Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Sigmoid:0
2023-01-11T22:07:26.1686502Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 320 <- wrt source file
2023-01-11T22:07:26.1690398Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0
2023-01-11T22:07:26.1691210Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Tanh:0, line 352 <- wrt source file
2023-01-11T22:07:26.1695089Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Tanh:0
2023-01-11T22:07:26.1695734Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::SiLU:0, line 383 <- wrt source file
2023-01-11T22:07:26.1700159Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::SiLU:0
2023-01-11T22:07:26.1700819Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Mish:0, line 419 <- wrt source file
2023-01-11T22:07:26.1704781Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Mish:0
2023-01-11T22:07:26.1705458Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 461 <- wrt source file
2023-01-11T22:07:26.1708785Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardswish:0
2023-01-11T22:07:26.1709311Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ELU:0, line 502 <- wrt source file
2023-01-11T22:07:26.1713256Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::ELU:0
2023-01-11T22:07:26.1713770Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::CELU:0, line 543 <- wrt source file
2023-01-11T22:07:26.1717508Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::CELU:0
2023-01-11T22:07:26.1718028Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::SELU:0, line 595 <- wrt source file
2023-01-11T22:07:26.1721833Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::SELU:0
2023-01-11T22:07:26.1722344Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::GLU:0, line 631 <- wrt source file
2023-01-11T22:07:26.1726336Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::GLU:0
2023-01-11T22:07:26.1726855Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::GELU:0, line 672 <- wrt source file
2023-01-11T22:07:26.1733187Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::GELU:0
2023-01-11T22:07:26.1733942Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 714 <- wrt source file
2023-01-11T22:07:26.1738408Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Hardshrink:0
2023-01-11T22:07:26.1739147Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 761 <- wrt source file
2023-01-11T22:07:26.1743459Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LeakyReLU:0
2023-01-11T22:07:26.1744337Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 796 <- wrt source file
2023-01-11T22:07:26.1747958Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LogSigmoid:0
2023-01-11T22:07:26.1748682Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softplus:0, line 827 <- wrt source file
2023-01-11T22:07:26.1752832Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softplus:0
2023-01-11T22:07:26.1753557Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 869 <- wrt source file
2023-01-11T22:07:26.1757584Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softshrink:0
2023-01-11T22:07:26.1758359Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 938 <- wrt source file
2023-01-11T22:07:26.1759117Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0
2023-01-11T22:07:26.1759853Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1274 <- wrt source file
2023-01-11T22:07:26.1763386Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::PReLU:0
2023-01-11T22:07:26.1764096Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1309 <- wrt source file
2023-01-11T22:07:26.1768208Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softsign:0
2023-01-11T22:07:26.1768942Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1332 <- wrt source file
2023-01-11T22:07:26.1773048Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Tanhshrink:0
2023-01-11T22:07:26.1773757Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1366 <- wrt source file
2023-01-11T22:07:26.1778408Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmin:0
2023-01-11T22:07:26.1779128Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1421 <- wrt source file
2023-01-11T22:07:26.1783481Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmax:0
2023-01-11T22:07:26.1784203Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1461 <- wrt source file
2023-01-11T22:07:26.1788407Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::Softmax2d:0
2023-01-11T22:07:26.1789102Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1493 <- wrt source file
2023-01-11T22:07:26.1793599Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/activation.py::LogSoftmax:0
2023-01-11T22:07:26.1794162Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 290 <- wrt source file
2023-01-11T22:07:26.1802533Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0
2023-01-11T22:07:26.1803277Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 399 <- wrt source file
2023-01-11T22:07:26.2043422Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0
2023-01-11T22:07:26.2044490Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 505 <- wrt source file
2023-01-11T22:07:26.4642152Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0
2023-01-11T22:07:26.4757726Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::SyncBatchNorm:0, line 627 <- wrt source file
2023-01-11T22:07:26.4760686Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::SyncBatchNorm:0
2023-01-11T22:07:26.4761598Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::SyncBatchNorm.convert_sync_batchnorm:0, line 782 <- wrt source file
2023-01-11T22:07:26.4762434Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py::SyncBatchNorm.convert_sync_batchnorm:0
2023-01-11T22:07:26.4763032Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 17 <- wrt source file
2023-01-11T22:07:26.4780584Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0
2023-01-11T22:07:26.4781363Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::Sequential:0, line 63 <- wrt source file
2023-01-11T22:07:26.4782060Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::Sequential:0
2023-01-11T22:07:26.4783914Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ModuleList:0, line 261 <- wrt source file
2023-01-11T22:07:26.4784716Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ModuleList:0
2023-01-11T22:07:26.4785616Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 433 <- wrt source file
2023-01-11T22:07:26.4786460Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ModuleDict:0
2023-01-11T22:07:26.4787551Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ParameterList:0, line 567 <- wrt source file
2023-01-11T22:07:26.4788293Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ParameterList:0
2023-01-11T22:07:26.4789053Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 707 <- wrt source file
2023-01-11T22:07:26.4789776Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py::ParameterDict:0
2023-01-11T22:07:26.4790535Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 36 <- wrt source file
2023-01-11T22:07:26.4796293Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0
2023-01-11T22:07:26.4797058Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 72 <- wrt source file
2023-01-11T22:07:26.4805785Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0
2023-01-11T22:07:26.4806525Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 49 <- wrt source file
2023-01-11T22:07:26.4812252Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout:0
2023-01-11T22:07:26.4812959Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 91 <- wrt source file
2023-01-11T22:07:26.4819811Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout1d:0
2023-01-11T22:07:26.4820538Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 140 <- wrt source file
2023-01-11T22:07:26.4844776Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout2d:0
2023-01-11T22:07:26.4845488Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 182 <- wrt source file
2023-01-11T22:07:26.4923458Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::Dropout3d:0
2023-01-11T22:07:26.4924303Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 225 <- wrt source file
2023-01-11T22:07:26.4929809Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0
2023-01-11T22:07:26.4930562Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 272 <- wrt source file
2023-01-11T22:07:26.5008043Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0
2023-01-11T22:07:26.5008915Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 24 <- wrt source file
2023-01-11T22:07:26.5016209Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/flatten.py::Flatten:0
2023-01-11T22:07:26.5016928Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/flatten.py::Unflatten:0, line 76 <- wrt source file
2023-01-11T22:07:26.5035761Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/flatten.py::Unflatten:0
2023-01-11T22:07:26.5036470Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file
2023-01-11T22:07:26.5042974Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/fold.py::Fold:0
2023-01-11T22:07:26.5043674Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/fold.py::Unfold:0, line 253 <- wrt source file
2023-01-11T22:07:26.5059724Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/fold.py::Unfold:0
2023-01-11T22:07:26.5060458Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 135 <- wrt source file
2023-01-11T22:07:26.5073805Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0
2023-01-11T22:07:26.5074572Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 251 <- wrt source file
2023-01-11T22:07:26.5267970Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0
2023-01-11T22:07:26.5268836Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 367 <- wrt source file
2023-01-11T22:07:26.7960484Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0
2023-01-11T22:07:26.8078833Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file
2023-01-11T22:07:26.8083881Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0
2023-01-11T22:07:26.8084721Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Identity:0, line 33 <- wrt source file
2023-01-11T22:07:26.8093485Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Identity:0
2023-01-11T22:07:26.8094512Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Linear:0, line 78 <- wrt source file
2023-01-11T22:07:26.8104349Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Linear:0
2023-01-11T22:07:26.8105477Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 164 <- wrt source file
2023-01-11T22:07:26.8128339Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py::Bilinear:0
2023-01-11T22:07:26.8129162Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 88 <- wrt source file
2023-01-11T22:07:26.8137000Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::L1Loss:0
2023-01-11T22:07:26.8138064Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 184 <- wrt source file
2023-01-11T22:07:26.8162443Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::NLLLoss:0
2023-01-11T22:07:26.8163078Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 271 <- wrt source file
2023-01-11T22:07:26.8170833Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0
2023-01-11T22:07:26.8171525Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 343 <- wrt source file
2023-01-11T22:07:26.8187446Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0
2023-01-11T22:07:26.8188150Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 451 <- wrt source file
2023-01-11T22:07:26.8198344Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::KLDivLoss:0
2023-01-11T22:07:26.8199473Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 523 <- wrt source file
2023-01-11T22:07:26.8206318Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MSELoss:0
2023-01-11T22:07:26.8207311Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 605 <- wrt source file
2023-01-11T22:07:26.8214621Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::BCELoss:0
2023-01-11T22:07:26.8215656Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 668 <- wrt source file
2023-01-11T22:07:26.8228711Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0
2023-01-11T22:07:26.8229756Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 831 <- wrt source file
2023-01-11T22:07:26.8239095Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0
2023-01-11T22:07:26.8240183Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1149 <- wrt source file
2023-01-11T22:07:26.8249703Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0
2023-01-11T22:07:26.8250458Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1317 <- wrt source file
2023-01-11T22:07:26.8257641Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0
2023-01-11T22:07:26.8258356Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1388 <- wrt source file
2023-01-11T22:07:26.8266685Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0
2023-01-11T22:07:26.8267588Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1468 <- wrt source file
2023-01-11T22:07:26.8279536Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0
2023-01-11T22:07:26.8280295Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::TripletMarginWithDistanceLoss:0, line 1559 <- wrt source file
2023-01-11T22:07:26.8303249Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::TripletMarginWithDistanceLoss:0
2023-01-11T22:07:26.8304235Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1670 <- wrt source file
2023-01-11T22:07:26.8339601Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py::CTCLoss:0
2023-01-11T22:07:26.8340338Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 491 <- wrt source file
2023-01-11T22:07:26.8341060Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.register_buffer:0
2023-01-11T22:07:26.8341786Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.apply:0, line 847 <- wrt source file
2023-01-11T22:07:26.8356911Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.apply:0
2023-01-11T22:07:26.8357617Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.to:0, line 1072 <- wrt source file
2023-01-11T22:07:26.8366112Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.to:0
2023-01-11T22:07:26.8366833Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 1763 <- wrt source file
2023-01-11T22:07:26.8369269Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.state_dict:0
2023-01-11T22:07:26.8370515Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2050 <- wrt source file
2023-01-11T22:07:26.8371347Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.parameters:0
2023-01-11T22:07:26.8372115Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2082 <- wrt source file
2023-01-11T22:07:26.8372797Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_parameters:0
2023-01-11T22:07:26.8373411Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2107 <- wrt source file
2023-01-11T22:07:26.8373981Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.buffers:0
2023-01-11T22:07:26.8374553Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2133 <- wrt source file
2023-01-11T22:07:26.8375121Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_buffers:0
2023-01-11T22:07:26.8375729Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2163 <- wrt source file
2023-01-11T22:07:26.8376308Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_children:0
2023-01-11T22:07:26.8376869Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2187 <- wrt source file
2023-01-11T22:07:26.8386475Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.modules:0
2023-01-11T22:07:26.8387035Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2221 <- wrt source file
2023-01-11T22:07:26.8392446Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py::Module.named_modules:0
2023-01-11T22:07:26.8393006Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 34 <- wrt source file
2023-01-11T22:07:26.8447134Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0
2023-01-11T22:07:26.8447874Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 140 <- wrt source file
2023-01-11T22:07:26.8457611Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::LayerNorm:0
2023-01-11T22:07:26.8458172Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 230 <- wrt source file
2023-01-11T22:07:26.8467479Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/normalization.py::GroupNorm:0
2023-01-11T22:07:26.8468235Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 48 <- wrt source file
2023-01-11T22:07:26.8477346Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0
2023-01-11T22:07:26.8478073Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 101 <- wrt source file
2023-01-11T22:07:26.8484148Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0
2023-01-11T22:07:26.8484890Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 157 <- wrt source file
2023-01-11T22:07:26.8509870Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0
2023-01-11T22:07:26.8510607Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 201 <- wrt source file
2023-01-11T22:07:26.8517349Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0
2023-01-11T22:07:26.8518063Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 244 <- wrt source file
2023-01-11T22:07:26.8523769Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0
2023-01-11T22:07:26.8524493Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 301 <- wrt source file
2023-01-11T22:07:26.8528412Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0
2023-01-11T22:07:26.8529230Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 358 <- wrt source file
2023-01-11T22:07:26.8535124Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0
2023-01-11T22:07:26.8535891Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 401 <- wrt source file
2023-01-11T22:07:26.8541909Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0
2023-01-11T22:07:26.8542711Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 458 <- wrt source file
2023-01-11T22:07:27.5083077Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0
2023-01-11T22:07:27.5300896Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 494 <- wrt source file
2023-01-11T22:07:27.5309276Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0
2023-01-11T22:07:27.5309832Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 36 <- wrt source file
2023-01-11T22:07:27.5317019Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0
2023-01-11T22:07:27.5318014Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 86 <- wrt source file
2023-01-11T22:07:27.5323809Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0
2023-01-11T22:07:27.5324548Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 76 <- wrt source file
2023-01-11T22:07:27.5331396Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0
2023-01-11T22:07:27.5332099Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 148 <- wrt source file
2023-01-11T22:07:27.5382121Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0
2023-01-11T22:07:27.5382907Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 226 <- wrt source file
2023-01-11T22:07:27.7557189Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0
2023-01-11T22:07:27.7625838Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 293 <- wrt source file
2023-01-11T22:07:27.7645912Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0
2023-01-11T22:07:27.7646652Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool2d:0, line 366 <- wrt source file
2023-01-11T22:07:27.7675484Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool2d:0
2023-01-11T22:07:27.7676217Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 451 <- wrt source file
2023-01-11T22:07:27.8424948Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0
2023-01-11T22:07:27.8425702Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 524 <- wrt source file
2023-01-11T22:07:27.8436267Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0
2023-01-11T22:07:27.8437014Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 600 <- wrt source file
2023-01-11T22:07:27.8478765Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0
2023-01-11T22:07:27.8479497Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 686 <- wrt source file
2023-01-11T22:07:28.0257420Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0
2023-01-11T22:07:28.0300930Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 749 <- wrt source file
2023-01-11T22:07:28.0352760Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0
2023-01-11T22:07:28.0354148Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 819 <- wrt source file
2023-01-11T22:07:28.1036397Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0
2023-01-11T22:07:28.1037150Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 909 <- wrt source file
2023-01-11T22:07:28.1045873Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::LPPool1d:0
2023-01-11T22:07:28.1046579Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 960 <- wrt source file
2023-01-11T22:07:28.1106929Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::LPPool2d:0
2023-01-11T22:07:28.1107803Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1011 <- wrt source file
2023-01-11T22:07:28.1112818Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0
2023-01-11T22:07:28.1113570Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1045 <- wrt source file
2023-01-11T22:07:28.1123899Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0
2023-01-11T22:07:28.1124688Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1088 <- wrt source file
2023-01-11T22:07:28.1191633Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0
2023-01-11T22:07:28.1192428Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1135 <- wrt source file
2023-01-11T22:07:28.1197397Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0
2023-01-11T22:07:28.1198175Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1166 <- wrt source file
2023-01-11T22:07:28.1207342Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0
2023-01-11T22:07:28.1208112Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1205 <- wrt source file
2023-01-11T22:07:28.1235565Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0
2023-01-11T22:07:28.1236527Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::RNN:0, line 436 <- wrt source file
2023-01-11T22:07:28.1249731Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::RNN:0
2023-01-11T22:07:28.1250527Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 702 <- wrt source file
2023-01-11T22:07:28.1268050Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::LSTM:0
2023-01-11T22:07:28.1269437Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::GRU:0, line 933 <- wrt source file
2023-01-11T22:07:28.1287012Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::GRU:0
2023-01-11T22:07:28.1288783Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1106 <- wrt source file
2023-01-11T22:07:28.1300354Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::RNNCell:0
2023-01-11T22:07:28.1301152Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1207 <- wrt source file
2023-01-11T22:07:28.1312439Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::LSTMCell:0
2023-01-11T22:07:28.1313225Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1300 <- wrt source file
2023-01-11T22:07:28.1327717Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/rnn.py::GRUCell:0
2023-01-11T22:07:28.1328913Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 67 <- wrt source file
2023-01-11T22:07:28.1343691Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::Embedding:0
2023-01-11T22:07:28.1344541Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 200 <- wrt source file
2023-01-11T22:07:28.1350009Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0
2023-01-11T22:07:28.1350701Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::EmbeddingBag:0, line 278 <- wrt source file
2023-01-11T22:07:28.1366946Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::EmbeddingBag:0
2023-01-11T22:07:28.1367724Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 429 <- wrt source file
2023-01-11T22:07:28.1375000Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0
2023-01-11T22:07:28.1375780Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 42 <- wrt source file
2023-01-11T22:07:28.8052000Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::Transformer:0
2023-01-11T22:07:28.8066451Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 134 <- wrt source file
2023-01-11T22:07:28.8067879Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0
2023-01-11T22:07:28.8068987Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 181 <- wrt source file
2023-01-11T22:07:28.8696849Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0
2023-01-11T22:07:28.8702205Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 325 <- wrt source file
2023-01-11T22:07:28.9953402Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0
2023-01-11T22:07:28.9961144Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 391 <- wrt source file
2023-01-11T22:07:29.0206739Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0
2023-01-11T22:07:29.0237048Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 608 <- wrt source file
2023-01-11T22:07:29.0635897Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0
2023-01-11T22:07:29.0636859Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 74 <- wrt source file
2023-01-11T22:07:29.0664265Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::Upsample:0
2023-01-11T22:07:29.0665341Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 196 <- wrt source file
2023-01-11T22:07:29.0678441Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0
2023-01-11T22:07:29.0679072Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 242 <- wrt source file
2023-01-11T22:07:29.0687721Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0
2023-01-11T22:07:29.0688947Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 116 <- wrt source file
2023-01-11T22:07:29.0690108Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0
2023-01-11T22:07:29.0691193Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 534 <- wrt source file
2023-01-11T22:07:29.0692255Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0
2023-01-11T22:07:29.0693381Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1051 <- wrt source file
2023-01-11T22:07:29.0694525Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0
2023-01-11T22:07:29.0695188Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.join:0, line 1377 <- wrt source file
2023-01-11T22:07:29.0695805Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.join:0
2023-01-11T22:07:29.0696446Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1550 <- wrt source file
2023-01-11T22:07:29.0697096Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0
2023-01-11T22:07:29.0697757Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1560 <- wrt source file
2023-01-11T22:07:29.0698394Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1
2023-01-11T22:07:29.0699048Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 1594 <- wrt source file
2023-01-11T22:07:29.0699703Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0
2023-01-11T22:07:29.0700358Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_fused_optim:0, line 1652 <- wrt source file
2023-01-11T22:07:29.0700992Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_fused_optim:0
2023-01-11T22:07:29.0701600Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 32 <- wrt source file
2023-01-11T22:07:29.0702148Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0
2023-01-11T22:07:29.0702743Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/init.py::skip_init:0, line 30 <- wrt source file
2023-01-11T22:07:29.0707196Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/init.py::skip_init:0
2023-01-11T22:07:29.0708013Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/memory_format.py::convert_conv2d_weight_memory_format:0, line 54 <- wrt source file
2023-01-11T22:07:29.0708904Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/memory_format.py::convert_conv2d_weight_memory_format:0
2023-01-11T22:07:29.0709649Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 245 <- wrt source file
2023-01-11T22:07:29.0710769Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0
2023-01-11T22:07:29.0712014Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 462 <- wrt source file
2023-01-11T22:07:29.0713092Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0
2023-01-11T22:07:29.0714178Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 463 <- wrt source file
2023-01-11T22:07:29.0719635Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0
2023-01-11T22:07:29.0720661Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::identity:0, line 846 <- wrt source file
2023-01-11T22:07:29.0721568Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::identity:0
2023-01-11T22:07:29.0722529Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 880 <- wrt source file
2023-01-11T22:07:29.0723533Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::random_unstructured:0
2023-01-11T22:07:29.0724483Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 921 <- wrt source file
2023-01-11T22:07:29.0725449Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::l1_unstructured:0
2023-01-11T22:07:29.0726407Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::random_structured:0, line 959 <- wrt source file
2023-01-11T22:07:29.0727370Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::random_structured:0
2023-01-11T22:07:29.0728209Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::ln_structured:0, line 1005 <- wrt source file
2023-01-11T22:07:29.0757038Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::ln_structured:0
2023-01-11T22:07:29.0757609Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::global_unstructured:0, line 1058 <- wrt source file
2023-01-11T22:07:29.0776797Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::global_unstructured:0
2023-01-11T22:07:29.0777435Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::custom_from_mask:0, line 1160 <- wrt source file
2023-01-11T22:07:29.0787904Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::custom_from_mask:0
2023-01-11T22:07:29.0788431Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::remove:0, line 1188 <- wrt source file
2023-01-11T22:07:29.0795265Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::remove:0
2023-01-11T22:07:29.0795786Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1215 <- wrt source file
2023-01-11T22:07:29.0804978Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/prune.py::is_pruned:0
2023-01-11T22:07:29.0805511Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 282 <- wrt source file
2023-01-11T22:07:29.0822225Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0
2023-01-11T22:07:29.0823111Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 359 <- wrt source file
2023-01-11T22:07:29.0829061Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pad_sequence:0
2023-01-11T22:07:29.0829863Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 412 <- wrt source file
2023-01-11T22:07:29.0846704Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0
2023-01-11T22:07:29.0847712Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 467 <- wrt source file
2023-01-11T22:07:29.0857492Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::pack_sequence:0
2023-01-11T22:07:29.0858170Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 495 <- wrt source file
2023-01-11T22:07:29.0879182Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0
2023-01-11T22:07:29.0879863Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 267 <- wrt source file
2023-01-11T22:07:29.0887587Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0
2023-01-11T22:07:29.0888263Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 294 <- wrt source file
2023-01-11T22:07:29.0895115Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0
2023-01-11T22:07:29.0895866Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 123 <- wrt source file
2023-01-11T22:07:29.0899624Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/stateless.py::functional_call:0
2023-01-11T22:07:29.0900352Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 99 <- wrt source file
2023-01-11T22:07:29.0909047Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0
2023-01-11T22:07:29.0909603Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 121 <- wrt source file
2023-01-11T22:07:29.0915567Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0
2023-01-11T22:07:29.0916504Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 203 <- wrt source file
2023-01-11T22:07:29.0918420Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0
2023-01-11T22:07:29.0919385Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 108 <- wrt source file
2023-01-11T22:07:29.1007222Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0
2023-01-11T22:07:29.1007917Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/onnx/_type_utils.py::JitScalarType:0, line 66 <- wrt source file
2023-01-11T22:07:29.1010680Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/onnx/_type_utils.py::JitScalarType:0
2023-01-11T22:07:29.1011442Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/onnx/verification.py::find_mismatch:0, line 1746 <- wrt source file
2023-01-11T22:07:29.1011970Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/onnx/verification.py::find_mismatch:0
2023-01-11T22:07:29.1012649Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/engine.py::DiagnosticEngine:0, line 20 <- wrt source file
2023-01-11T22:07:29.1013645Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/engine.py::DiagnosticEngine:0
2023-01-11T22:07:29.1014444Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 200 <- wrt source file
2023-01-11T22:07:29.1015181Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0
2023-01-11T22:07:29.1016030Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 286 <- wrt source file
2023-01-11T22:07:29.1016892Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0
2023-01-11T22:07:29.1017884Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 369 <- wrt source file
2023-01-11T22:07:29.1018811Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::StepLR:0
2023-01-11T22:07:29.1019662Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 418 <- wrt source file
2023-01-11T22:07:29.1020637Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0
2023-01-11T22:07:29.1021477Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 467 <- wrt source file
2023-01-11T22:07:29.1022299Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0
2023-01-11T22:07:29.1023332Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 529 <- wrt source file
2023-01-11T22:07:29.1024258Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::LinearLR:0
2023-01-11T22:07:29.1025206Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 621 <- wrt source file
2023-01-11T22:07:29.1026172Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0
2023-01-11T22:07:29.1027132Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 729 <- wrt source file
2023-01-11T22:07:29.1028054Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0
2023-01-11T22:07:29.1029054Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 849 <- wrt source file
2023-01-11T22:07:29.1030051Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0
2023-01-11T22:07:29.1030838Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 953 <- wrt source file
2023-01-11T22:07:29.1031369Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0
2023-01-11T22:07:29.1031906Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1168 <- wrt source file
2023-01-11T22:07:29.1032526Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0
2023-01-11T22:07:29.1033106Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1389 <- wrt source file
2023-01-11T22:07:29.1033712Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0
2023-01-11T22:07:29.1034323Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1405 <- wrt source file
2023-01-11T22:07:29.1034991Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1
2023-01-11T22:07:29.1035559Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1547 <- wrt source file
2023-01-11T22:07:29.1036075Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0
2023-01-11T22:07:29.1036562Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/sgd.py::SGD:0, line 58 <- wrt source file
2023-01-11T22:07:29.1037024Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/sgd.py::SGD:0
2023-01-11T22:07:29.1037534Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::AveragedModel:0, line 38 <- wrt source file
2023-01-11T22:07:29.1038041Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::AveragedModel:0
2023-01-11T22:07:29.1038556Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::AveragedModel:1, line 64 <- wrt source file
2023-01-11T22:07:29.1039060Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::AveragedModel:1
2023-01-11T22:07:29.1039577Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::update_bn:0, line 161 <- wrt source file
2023-01-11T22:07:29.1040069Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::update_bn:0
2023-01-11T22:07:29.1040565Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::SWALR:0, line 222 <- wrt source file
2023-01-11T22:07:29.1041042Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/optim/swa_utils.py::SWALR:0
2023-01-11T22:07:29.1041558Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/package/glob_group.py::GlobGroup:0, line 19 <- wrt source file
2023-01-11T22:07:29.1042068Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/package/glob_group.py::GlobGroup:0
2023-01-11T22:07:29.1042579Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/profiler/profiler.py::profile:0, line 363 <- wrt source file
2023-01-11T22:07:29.1043079Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/profiler/profiler.py::profile:0
2023-01-11T22:07:29.1043608Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_comparison.py::assert_close:0, line 1395 <- wrt source file
2023-01-11T22:07:29.1088079Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/testing/_comparison.py::assert_close:0
2023-01-11T22:07:29.1088864Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_creation.py::make_tensor:0, line 93 <- wrt source file
2023-01-11T22:07:29.1089931Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_creation.py::make_tensor:0
2023-01-11T22:07:29.1090884Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 305 <- wrt source file
2023-01-11T22:07:29.1091841Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::parametrize:0
2023-01-11T22:07:29.1092572Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 3447 <- wrt source file
2023-01-11T22:07:29.1093354Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0
2023-01-11T22:07:29.1094080Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 3461 <- wrt source file
2023-01-11T22:07:29.1094822Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0
2023-01-11T22:07:29.1095526Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 3491 <- wrt source file
2023-01-11T22:07:29.1096326Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0
2023-01-11T22:07:29.1097163Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 57 <- wrt source file
2023-01-11T22:07:29.1098069Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0
2023-01-11T22:07:29.1099092Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 24 <- wrt source file
2023-01-11T22:07:29.1100053Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0
2023-01-11T22:07:29.1100752Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 306 <- wrt source file
2023-01-11T22:07:29.1101301Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0
2023-01-11T22:07:29.1101848Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CppExtension:0, line 912 <- wrt source file
2023-01-11T22:07:29.1102426Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CppExtension:0
2023-01-11T22:07:29.1102978Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CUDAExtension:0, line 960 <- wrt source file
2023-01-11T22:07:29.1103500Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CUDAExtension:0
2023-01-11T22:07:29.1104047Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CUDAExtension:1, line 1037 <- wrt source file
2023-01-11T22:07:29.1104557Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::CUDAExtension:1
2023-01-11T22:07:29.1105082Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::load:0, line 1273 <- wrt source file
2023-01-11T22:07:29.1105582Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::load:0
2023-01-11T22:07:29.1106105Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::load_inline:0, line 1364 <- wrt source file
2023-01-11T22:07:29.1106606Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/cpp_extension.py::load_inline:0
2023-01-11T22:07:29.1107123Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 71 <- wrt source file
2023-01-11T22:07:29.1117768Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/dlpack.py::from_dlpack:0
2023-01-11T22:07:29.1118405Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/throughput_benchmark.py::ThroughputBenchmark:0, line 77 <- wrt source file
2023-01-11T22:07:29.1118988Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/throughput_benchmark.py::ThroughputBenchmark:0
2023-01-11T22:07:29.1119541Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 84 <- wrt source file
2023-01-11T22:07:29.1126262Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py::IterableDataset:0
2023-01-11T22:07:29.1127358Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py::random_split:0, line 320 <- wrt source file
2023-01-11T22:07:29.1128094Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py::random_split:0
2023-01-11T22:07:29.1128641Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/distributed.py::DistributedSampler:0, line 51 <- wrt source file
2023-01-11T22:07:29.1129304Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/distributed.py::DistributedSampler:0
2023-01-11T22:07:29.1129879Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 172 <- wrt source file
2023-01-11T22:07:29.1134992Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0
2023-01-11T22:07:29.1135590Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 220 <- wrt source file
2023-01-11T22:07:29.1140872Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/data/sampler.py::BatchSampler:0
2023-01-11T22:07:29.1141503Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 36 <- wrt source file
2023-01-11T22:07:29.1144639Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::default_convert:0
2023-01-11T22:07:29.1145201Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 102 <- wrt source file
2023-01-11T22:07:29.1149968Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::collate:0
2023-01-11T22:07:29.1150546Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 231 <- wrt source file
2023-01-11T22:07:29.1155752Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py::default_collate:0
2023-01-11T22:07:29.1156380Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 84 <- wrt source file
2023-01-11T22:07:29.1159370Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0
2023-01-11T22:07:29.1159952Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 232 <- wrt source file
2023-01-11T22:07:29.1161707Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0
2023-01-11T22:07:29.1162307Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 46 <- wrt source file
2023-01-11T22:07:29.1164480Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0
2023-01-11T22:07:29.1165103Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 187 <- wrt source file
2023-01-11T22:07:29.1167268Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0
2023-01-11T22:07:29.1167967Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 80 <- wrt source file
2023-01-11T22:07:29.1168657Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0
2023-01-11T22:07:29.1169450Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 33 <- wrt source file
2023-01-11T22:07:29.1193897Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0
2023-01-11T22:07:29.1194745Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 75 <- wrt source file
2023-01-11T22:07:29.1195793Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0
2023-01-11T22:07:29.1196822Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 250 <- wrt source file
2023-01-11T22:07:29.1197407Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0
2023-01-11T22:07:29.1198033Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 329 <- wrt source file
2023-01-11T22:07:29.1199859Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0
2023-01-11T22:07:29.1200565Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 507 <- wrt source file
2023-01-11T22:07:29.1201681Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0
2023-01-11T22:07:29.1202397Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 572 <- wrt source file
2023-01-11T22:07:29.1203342Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0
2023-01-11T22:07:29.1204442Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 29 <- wrt source file
2023-01-11T22:07:29.1205414Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0
2023-01-11T22:07:29.1206408Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 33 <- wrt source file
2023-01-11T22:07:29.1207211Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0
2023-01-11T22:07:29.1208263Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 102 <- wrt source file
2023-01-11T22:07:29.1208872Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0
2023-01-11T22:07:29.1209704Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 159 <- wrt source file
2023-01-11T22:07:29.1210415Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0
2023-01-11T22:07:29.1211026Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 226 <- wrt source file
2023-01-11T22:07:29.1213412Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0
2023-01-11T22:07:29.1214028Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 34 <- wrt source file
2023-01-11T22:07:29.1214939Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0
2023-01-11T22:07:29.1216569Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 20 <- wrt source file
2023-01-11T22:07:29.1217221Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0
2023-01-11T22:07:29.1217868Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 23 <- wrt source file
2023-01-11T22:07:29.1218496Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0
2023-01-11T22:07:29.1219294Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 30 <- wrt source file
2023-01-11T22:07:29.1219893Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0
2023-01-11T22:07:29.1220673Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 31 <- wrt source file
2023-01-11T22:07:29.1221410Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0
2023-01-11T22:07:29.1222277Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 24 <- wrt source file
2023-01-11T22:07:29.1223089Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0
2023-01-11T22:07:29.1223869Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 66 <- wrt source file
2023-01-11T22:07:29.1224534Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0
2023-01-11T22:07:29.1225162Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 23 <- wrt source file
2023-01-11T22:07:29.1225994Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0
2023-01-11T22:07:29.1226875Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 23 <- wrt source file
2023-01-11T22:07:29.1227561Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0
2023-01-11T22:07:29.1228279Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 33 <- wrt source file
2023-01-11T22:07:29.1228913Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0
2023-01-11T22:07:29.1229562Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 415 <- wrt source file
2023-01-11T22:07:29.1232363Z * SUCCESS: /opt/conda/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0
2023-01-11T22:07:29.1233301Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 511 <- wrt source file
2023-01-11T22:07:29.1234238Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0
2023-01-11T22:07:29.1235173Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 213 <- wrt source file
2023-01-11T22:07:29.1236093Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0
2023-01-11T22:07:29.1237019Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 320 <- wrt source file
2023-01-11T22:07:29.1237846Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0
2023-01-11T22:07:29.1238782Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 368 <- wrt source file
2023-01-11T22:07:29.1239622Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0
2023-01-11T22:07:29.1240376Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 404 <- wrt source file
2023-01-11T22:07:29.1241083Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0
2023-01-11T22:07:29.1241865Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 462 <- wrt source file
2023-01-11T22:07:29.1242588Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0
2023-01-11T22:07:29.1243313Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 519 <- wrt source file
2023-01-11T22:07:29.1243943Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0
2023-01-11T22:07:29.1244585Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 585 <- wrt source file
2023-01-11T22:07:29.1245238Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0
2023-01-11T22:07:29.1246389Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 638 <- wrt source file
2023-01-11T22:07:29.1247167Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0
2023-01-11T22:07:29.1247842Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 810 <- wrt source file
2023-01-11T22:07:29.1248615Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0
2023-01-11T22:07:29.1249584Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 896 <- wrt source file
2023-01-11T22:07:29.1250566Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0
2023-01-11T22:07:29.1251347Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 1001 <- wrt source file
2023-01-11T22:07:29.1252111Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0
2023-01-11T22:07:29.1252936Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1076 <- wrt source file
2023-01-11T22:07:29.1253815Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0
2023-01-11T22:07:29.1254550Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1095 <- wrt source file
2023-01-11T22:07:29.1255167Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0
2023-01-11T22:07:29.1255907Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1117 <- wrt source file
2023-01-11T22:07:29.1256745Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0
2023-01-11T22:07:29.1257777Z * DOCTEST : /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1161 <- wrt source file
2023-01-11T22:07:29.1258453Z * SKIPPED: /opt/conda/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0
2023-01-11T22:07:29.1258776Z ============
2023-01-11T22:07:29.1258966Z Finished doctests
2023-01-11T22:07:29.1259221Z 287 / 663 passed
2023-01-11T22:07:29.1259422Z 
2023-01-11T22:07:29.1259761Z === Found 3 run-time warnings ===
2023-01-11T22:07:29.1260082Z --- Runtime Warning: 1 / 3 ---
2023-01-11T22:07:29.1260534Z example = <DocTest(torch._tensor Tensor.refine_names:0 ln 1096) at 0x7f2be7e2f670>
2023-01-11T22:07:29.1261959Z     /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:1114: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1816.)
2023-01-11T22:07:29.1262699Z       return super(Tensor, self).refine_names(names)
2023-01-11T22:07:29.1262900Z     
2023-01-11T22:07:29.1263126Z --- Runtime Warning: 2 / 3 ---
2023-01-11T22:07:29.1263398Z example = <DocTest(torch.nested as_nested_tensor:0 ln 39) at 0x7f2be5585390>
2023-01-11T22:07:29.1264102Z     /opt/conda/lib/python3.10/site-packages/torch/nested/__init__.py:58: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T22:07:29.1264638Z       return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)
2023-01-11T22:07:29.1264962Z     
2023-01-11T22:07:29.1265276Z --- Runtime Warning: 3 / 3 ---
2023-01-11T22:07:29.1265770Z example = <DocTest(torch._functorch.aot_autograd aot_function:0 ln 2139) at 0x7f2be7e2fbb0>
2023-01-11T22:07:29.1266862Z     /opt/conda/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py:921: UserWarning: Your compiler for AOTAutograd is returning a a function that doesn't take boxed arguments. Please wrap it with functorch.compile.make_boxed_func or handle the boxed arguments yourself. See https://github.com/pytorch/pytorch/pull/83137#issuecomment-1211320670 for rationale.
2023-01-11T22:07:29.1267378Z       warnings.warn(
2023-01-11T22:07:29.1267628Z     
2023-01-11T22:07:29.1267907Z === 287 passed, 376 skipped, 3 warnings in 9.64 seconds ===
2023-01-11T22:07:29.4686814Z 
2023-01-11T22:07:29.4687190Z real	45m2.992s
2023-01-11T22:07:29.4687525Z user	84m48.991s
2023-01-11T22:07:29.4687825Z sys	10m6.893s
2023-01-11T22:07:29.4688086Z + assert_git_not_dirty
2023-01-11T22:07:29.4688471Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:07:29.4688797Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:07:29.4690608Z ++ git status --porcelain
2023-01-11T22:07:40.2253119Z + git_status=
2023-01-11T22:07:40.2253506Z + [[ -n '' ]]
2023-01-11T22:07:40.2253731Z + test_aten
2023-01-11T22:07:40.2254184Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *asan* ]]
2023-01-11T22:07:40.2254503Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:07:40.2255002Z + echo 'Running ATen tests with pytorch lib'
2023-01-11T22:07:40.2255235Z Running ATen tests with pytorch lib
2023-01-11T22:07:40.2255462Z + [[ -n '' ]]
2023-01-11T22:07:40.2255684Z + echo 'Running test with the build folder'
2023-01-11T22:07:40.2255905Z Running test with the build folder
2023-01-11T22:07:40.2256108Z + TEST_BASE_DIR=build/bin
2023-01-11T22:07:40.2256638Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10d_cuda_test.so build/bin
2023-01-11T22:07:40.2324562Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libcaffe2_nvrtc.so build/bin
2023-01-11T22:07:40.2333606Z + ln -sf '/opt/conda/lib/python3.10/site-packages/torch/lib/libmkldnn*' build/bin
2023-01-11T22:07:40.2342613Z + ln -sf '/opt/conda/lib/python3.10/site-packages/torch/lib/libnccl*' build/bin
2023-01-11T22:07:40.2352989Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda_linalg.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorchbind_test.so build/bin
2023-01-11T22:07:40.2361679Z + ln -sf '/opt/conda/lib/python3.10/site-packages/torch/lib/libtbb*' build/bin
2023-01-11T22:07:40.2369584Z + ls build/bin
2023-01-11T22:07:40.2453465Z  CppSignature_test
2023-01-11T22:07:40.2453735Z  Dict_test
2023-01-11T22:07:40.2454038Z  Dimname_test
2023-01-11T22:07:40.2454324Z  FileStoreTest
2023-01-11T22:07:40.2454601Z  HashStoreTest
2023-01-11T22:07:40.2455278Z  IListRef_test
2023-01-11T22:07:40.2455617Z  KernelFunction_test
2023-01-11T22:07:40.2455925Z  List_test
2023-01-11T22:07:40.2456157Z  MaybeOwned_test
2023-01-11T22:07:40.2456626Z  NamedTensor_test
2023-01-11T22:07:40.2456952Z  ProcessGroupGlooAsyncTest
2023-01-11T22:07:40.2457265Z  ProcessGroupGlooTest
2023-01-11T22:07:40.2457581Z  ProcessGroupMPITest
2023-01-11T22:07:40.2458024Z  ProcessGroupNCCLErrorsTest
2023-01-11T22:07:40.2458396Z  ProcessGroupNCCLTest
2023-01-11T22:07:40.2458671Z  ProcessGroupUCCTest
2023-01-11T22:07:40.2458952Z  TCPStoreTest
2023-01-11T22:07:40.2459134Z  aot_model_compiler_test
2023-01-11T22:07:40.2459308Z  apply_utils_test
2023-01-11T22:07:40.2459469Z  atest
2023-01-11T22:07:40.2459632Z  backend_fallback_test
2023-01-11T22:07:40.2459786Z  basic
2023-01-11T22:07:40.2459942Z  broadcast_test
2023-01-11T22:07:40.2460108Z  c10_Array_test
2023-01-11T22:07:40.2460262Z  c10_Bitset_test
2023-01-11T22:07:40.2460434Z  c10_C++17_test
2023-01-11T22:07:40.2460788Z  c10_CompileTimeFunctionPointer_test
2023-01-11T22:07:40.2461166Z  c10_ConstexprCrc_test
2023-01-11T22:07:40.2461391Z  c10_DeadlockDetection_test
2023-01-11T22:07:40.2461582Z  c10_DeviceGuard_test
2023-01-11T22:07:40.2461748Z  c10_Device_test
2023-01-11T22:07:40.2461929Z  c10_DispatchKeySet_test
2023-01-11T22:07:40.2462105Z  c10_Half_test
2023-01-11T22:07:40.2462501Z  c10_InlineDeviceGuard_test
2023-01-11T22:07:40.2462704Z  c10_InlineStreamGuard_test
2023-01-11T22:07:40.2462893Z  c10_LeftRight_test
2023-01-11T22:07:40.2463069Z  c10_Metaprogramming_test
2023-01-11T22:07:40.2463265Z  c10_SizesAndStrides_test
2023-01-11T22:07:40.2463453Z  c10_SmallVectorTest
2023-01-11T22:07:40.2463639Z  c10_StreamGuard_test
2023-01-11T22:07:40.2463802Z  c10_SymInt_test
2023-01-11T22:07:40.2463978Z  c10_Synchronized_test
2023-01-11T22:07:40.2464162Z  c10_ThreadLocal_test
2023-01-11T22:07:40.2464327Z  c10_TypeIndex_test
2023-01-11T22:07:40.2464502Z  c10_TypeList_test
2023-01-11T22:07:40.2464678Z  c10_TypeTraits_test
2023-01-11T22:07:40.2464843Z  c10_accumulate_test
2023-01-11T22:07:40.2465015Z  c10_bfloat16_test
2023-01-11T22:07:40.2465191Z  c10_complex_math_test
2023-01-11T22:07:40.2465352Z  c10_complex_test
2023-01-11T22:07:40.2465622Z  c10_cuda_CUDAAssertionsTest_1_var_test
2023-01-11T22:07:40.2465863Z  c10_cuda_CUDAAssertionsTest_catches_stream
2023-01-11T22:07:40.2466119Z  c10_cuda_CUDAAssertionsTest_catches_thread_and_block_and_device
2023-01-11T22:07:40.2466385Z  c10_cuda_CUDAAssertionsTest_from_2_processes
2023-01-11T22:07:40.2466661Z  c10_cuda_CUDAAssertionsTest_multiple_writes_from_blocks_and_threads
2023-01-11T22:07:40.2466945Z  c10_cuda_CUDAAssertionsTest_multiple_writes_from_multiple_blocks
2023-01-11T22:07:40.2467238Z  c10_cuda_CUDAAssertionsTest_multiple_writes_from_same_block
2023-01-11T22:07:40.2467458Z  c10_cuda_CUDATest
2023-01-11T22:07:40.2467616Z  c10_either_test
2023-01-11T22:07:40.2467787Z  c10_exception_test
2023-01-11T22:07:40.2467962Z  c10_flags_test
2023-01-11T22:07:40.2468128Z  c10_intrusive_ptr_benchmark
2023-01-11T22:07:40.2468316Z  c10_intrusive_ptr_test
2023-01-11T22:07:40.2468489Z  c10_irange_test
2023-01-11T22:07:40.2468645Z  c10_logging_test
2023-01-11T22:07:40.2468816Z  c10_optional_test
2023-01-11T22:07:40.2469010Z  c10_ordered_preserving_dict_test
2023-01-11T22:07:40.2469188Z  c10_registry_test
2023-01-11T22:07:40.2469362Z  c10_string_view_test
2023-01-11T22:07:40.2469536Z  c10_tempfile_test
2023-01-11T22:07:40.2469694Z  c10_typeid_test
2023-01-11T22:07:40.2469865Z  cpu_generator_test
2023-01-11T22:07:40.2470062Z  cpu_profiling_allocator_test
2023-01-11T22:07:40.2470234Z  cpu_rng_test
2023-01-11T22:07:40.2470400Z  cuda_apply_test
2023-01-11T22:07:40.2470577Z  cuda_atomic_ops_test
2023-01-11T22:07:40.2470762Z  cuda_caching_host_allocator_test
2023-01-11T22:07:40.2470956Z  cuda_complex_math_test
2023-01-11T22:07:40.2471137Z  cuda_complex_test
2023-01-11T22:07:40.2471294Z  cuda_cub_test
2023-01-11T22:07:40.2471459Z  cuda_cudnn_test
2023-01-11T22:07:40.2471629Z  cuda_device_test
2023-01-11T22:07:40.2471799Z  cuda_distributions_test
2023-01-11T22:07:40.2471986Z  cuda_dlconvertor_test
2023-01-11T22:07:40.2472169Z  cuda_generator_test
2023-01-11T22:07:40.2472333Z  cuda_half_test
2023-01-11T22:07:40.2472513Z  cuda_integer_divider_test
2023-01-11T22:07:40.2472699Z  cuda_optional_test
2023-01-11T22:07:40.2472896Z  cuda_packedtensoraccessor_test
2023-01-11T22:07:40.2473100Z  cuda_reportMemoryUsage_test
2023-01-11T22:07:40.2473288Z  cuda_stream_test
2023-01-11T22:07:40.2473465Z  cuda_vectorized_test
2023-01-11T22:07:40.2473635Z  dispatch_key_set_test
2023-01-11T22:07:40.2473818Z  dlconvertor_test
2023-01-11T22:07:40.2473992Z  example_allreduce
2023-01-11T22:07:40.2474159Z  extension_backend_test
2023-01-11T22:07:40.2474332Z  half_test
2023-01-11T22:07:40.2474500Z  inline_container_test
2023-01-11T22:07:40.2474661Z  ivalue_test
2023-01-11T22:07:40.2474839Z  kernel_function_legacy_test
2023-01-11T22:07:40.2475027Z  kernel_function_test
2023-01-11T22:07:40.2475202Z  kernel_lambda_legacy_test
2023-01-11T22:07:40.2475386Z  kernel_lambda_test
2023-01-11T22:07:40.2475567Z  kernel_stackbased_test
2023-01-11T22:07:40.2475734Z  lazy_tensor_test
2023-01-11T22:07:40.2475905Z  legacy_vmap_test
2023-01-11T22:07:40.2476071Z  libc10.so
2023-01-11T22:07:40.2476220Z  libc10_cuda.so
2023-01-11T22:07:40.2476389Z  libc10d_cuda_test.so
2023-01-11T22:07:40.2476619Z  libcaffe2_nvrtc.so
2023-01-11T22:07:40.2476888Z 'libmkldnn*'
2023-01-11T22:07:40.2477148Z 'libnccl*'
2023-01-11T22:07:40.2477460Z 'libtbb*'
2023-01-11T22:07:40.2477729Z  libtorch.so
2023-01-11T22:07:40.2478001Z  libtorch_cpu.so
2023-01-11T22:07:40.2478174Z  libtorch_cuda.so
2023-01-11T22:07:40.2478339Z  libtorch_cuda_linalg.so
2023-01-11T22:07:40.2478526Z  libtorch_global_deps.so
2023-01-11T22:07:40.2478712Z  libtorch_python.so
2023-01-11T22:07:40.2478882Z  libtorchbind_test.so
2023-01-11T22:07:40.2479082Z  make_boxed_from_unboxed_functor_test
2023-01-11T22:07:40.2479272Z  math_kernel_test
2023-01-11T22:07:40.2479433Z  memory_format_test
2023-01-11T22:07:40.2479616Z  memory_overlapping_test
2023-01-11T22:07:40.2479839Z  mobile_memory_cleanup
2023-01-11T22:07:40.2480041Z  native_test
2023-01-11T22:07:40.2480241Z  op_allowlist_test
2023-01-11T22:07:40.2480557Z  op_registration_test
2023-01-11T22:07:40.2480942Z  operator_name_test
2023-01-11T22:07:40.2481174Z  operators_test
2023-01-11T22:07:40.2481371Z  packedtensoraccessor_test
2023-01-11T22:07:40.2481557Z  parallel_benchmark
2023-01-11T22:07:40.2481730Z  pow_test
2023-01-11T22:07:40.2481891Z  protoc
2023-01-11T22:07:40.2482076Z  protoc-3.13.0.0
2023-01-11T22:07:40.2482254Z  quantized_test
2023-01-11T22:07:40.2482425Z  reduce_ops_test
2023-01-11T22:07:40.2482595Z  reportMemoryUsage_test
2023-01-11T22:07:40.2482783Z  scalar_tensor_test
2023-01-11T22:07:40.2482952Z  scalar_test
2023-01-11T22:07:40.2483113Z  stride_properties_test
2023-01-11T22:07:40.2483297Z  tensor_iterator_test
2023-01-11T22:07:40.2483467Z  test_api
2023-01-11T22:07:40.2483614Z  test_cpp_rpc
2023-01-11T22:07:40.2483781Z  test_dist_autograd
2023-01-11T22:07:40.2483967Z  test_edge_op_registration
2023-01-11T22:07:40.2484134Z  test_jit
2023-01-11T22:07:40.2484289Z  test_lazy
2023-01-11T22:07:40.2484452Z  test_mobile_nnc
2023-01-11T22:07:40.2484607Z  test_parallel
2023-01-11T22:07:40.2484778Z  test_tensorexpr
2023-01-11T22:07:40.2484950Z  thread_init_test
2023-01-11T22:07:40.2485109Z  torch_shm_manager
2023-01-11T22:07:40.2485288Z  tutorial_tensorexpr
2023-01-11T22:07:40.2485465Z  type_ptr_test
2023-01-11T22:07:40.2485614Z  type_test
2023-01-11T22:07:40.2485783Z  undefined_tensor_test
2023-01-11T22:07:40.2485956Z  variant_test
2023-01-11T22:07:40.2486119Z  vec_test_all_types_AVX2
2023-01-11T22:07:40.2486313Z  vec_test_all_types_DEFAULT
2023-01-11T22:07:40.2486506Z  verify_api_visibility
2023-01-11T22:07:40.2486669Z  weakref_test
2023-01-11T22:07:40.2486836Z  wrapdim_test
2023-01-11T22:07:40.2487001Z  xla_tensor_test
2023-01-11T22:07:40.2487185Z + aten/tools/run_tests.sh build/bin
2023-01-11T22:07:40.2487382Z + set -e
2023-01-11T22:07:40.2487561Z ++ dirname aten/tools/run_tests.sh
2023-01-11T22:07:40.2521036Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup
2023-01-11T22:07:40.2521593Z + pushd build/bin
2023-01-11T22:07:40.2521900Z ~/workspace/build/bin ~/workspace
2023-01-11T22:07:40.2522083Z + VALGRIND=ON
2023-01-11T22:07:40.2522244Z + ./basic
2023-01-11T22:07:41.5563083Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:41.5563593Z [==========] Running 5 tests from 1 test suite.
2023-01-11T22:07:41.5563899Z [----------] Global test environment set-up.
2023-01-11T22:07:41.5564169Z [----------] 5 tests from BasicTest
2023-01-11T22:07:41.5564443Z [ RUN      ] BasicTest.BasicTestCPU
2023-01-11T22:07:41.6802552Z    6 ms
2023-01-11T22:07:41.7538520Z    70 ms
2023-01-11T22:07:41.8620182Z    106 ms
2023-01-11T22:07:41.9166133Z [       OK ] BasicTest.BasicTestCPU (360 ms)
2023-01-11T22:07:41.9166545Z [ RUN      ] BasicTest.BasicTestHalfCPU
2023-01-11T22:07:41.9229547Z    3 ms
2023-01-11T22:07:42.0150332Z    91 ms
2023-01-11T22:07:42.1443096Z    129 ms
2023-01-11T22:07:42.1575704Z [       OK ] BasicTest.BasicTestHalfCPU (240 ms)
2023-01-11T22:07:42.1576296Z [ RUN      ] BasicTest.BasicTestCUDA
2023-01-11T22:07:42.1576595Z [       OK ] BasicTest.BasicTestCUDA (0 ms)
2023-01-11T22:07:42.1577154Z [ RUN      ] BasicTest.FactoryMethodsTest
2023-01-11T22:07:42.1629676Z [       OK ] BasicTest.FactoryMethodsTest (5 ms)
2023-01-11T22:07:42.1630085Z [ RUN      ] BasicTest.BasicStdTestCPU
2023-01-11T22:07:42.1630450Z Simple example: called once
2023-01-11T22:07:42.1634136Z throw: call_once will retry
2023-01-11T22:07:42.1635523Z throw: call_once will retry
2023-01-11T22:07:42.1637304Z Didn't throw, call_once will not attempt again
2023-01-11T22:07:42.1637844Z [       OK ] BasicTest.BasicStdTestCPU (0 ms)
2023-01-11T22:07:42.1638185Z [----------] 5 tests from BasicTest (607 ms total)
2023-01-11T22:07:42.1638374Z 
2023-01-11T22:07:42.1638535Z [----------] Global test environment tear-down
2023-01-11T22:07:42.1638853Z [==========] 5 tests from 1 test suite ran. (607 ms total)
2023-01-11T22:07:42.1639260Z [  PASSED  ] 5 tests.
2023-01-11T22:07:42.3832731Z + ./atest
2023-01-11T22:07:42.6969673Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:42.6970406Z [==========] Running 16 tests from 1 test suite.
2023-01-11T22:07:42.6970856Z [----------] Global test environment set-up.
2023-01-11T22:07:42.6971249Z [----------] 16 tests from atest
2023-01-11T22:07:42.6971619Z [ RUN      ] atest.operators
2023-01-11T22:07:42.6998093Z [       OK ] atest.operators (0 ms)
2023-01-11T22:07:42.6998590Z [ RUN      ] atest.logical_and_operators
2023-01-11T22:07:42.6998927Z [       OK ] atest.logical_and_operators (0 ms)
2023-01-11T22:07:42.6999213Z [ RUN      ] atest.logical_or_operators
2023-01-11T22:07:42.6999503Z [       OK ] atest.logical_or_operators (0 ms)
2023-01-11T22:07:42.6999783Z [ RUN      ] atest.logical_xor_operators
2023-01-11T22:07:42.7000072Z [       OK ] atest.logical_xor_operators (0 ms)
2023-01-11T22:07:42.7000337Z [ RUN      ] atest.lt_operators
2023-01-11T22:07:42.7000592Z [       OK ] atest.lt_operators (0 ms)
2023-01-11T22:07:42.7000899Z [ RUN      ] atest.le_operators
2023-01-11T22:07:42.7001346Z [       OK ] atest.le_operators (0 ms)
2023-01-11T22:07:42.7001689Z [ RUN      ] atest.gt_operators
2023-01-11T22:07:42.7002138Z [       OK ] atest.gt_operators (0 ms)
2023-01-11T22:07:42.7002497Z [ RUN      ] atest.ge_operators
2023-01-11T22:07:42.7002914Z [       OK ] atest.ge_operators (0 ms)
2023-01-11T22:07:42.7003363Z [ RUN      ] atest.eq_operators
2023-01-11T22:07:42.7003726Z [       OK ] atest.eq_operators (0 ms)
2023-01-11T22:07:42.7004108Z [ RUN      ] atest.ne_operators
2023-01-11T22:07:42.7004499Z [       OK ] atest.ne_operators (0 ms)
2023-01-11T22:07:42.7004840Z [ RUN      ] atest.add_operators
2023-01-11T22:07:42.7005339Z [       OK ] atest.add_operators (0 ms)
2023-01-11T22:07:42.7005780Z [ RUN      ] atest.max_operators
2023-01-11T22:07:42.7012187Z [       OK ] atest.max_operators (2 ms)
2023-01-11T22:07:42.7012547Z [ RUN      ] atest.min_operators
2023-01-11T22:07:42.7012919Z [       OK ] atest.min_operators (0 ms)
2023-01-11T22:07:42.7013232Z [ RUN      ] atest.sigmoid_backward_operator
2023-01-11T22:07:42.7013743Z [       OK ] atest.sigmoid_backward_operator (0 ms)
2023-01-11T22:07:42.7014034Z [ RUN      ] atest.fmod_tensor_operators
2023-01-11T22:07:42.7015306Z [       OK ] atest.fmod_tensor_operators (0 ms)
2023-01-11T22:07:42.7015608Z [ RUN      ] atest.atest
2023-01-11T22:07:42.7136039Z [       OK ] atest.atest (12 ms)
2023-01-11T22:07:42.7136903Z [----------] 16 tests from atest (16 ms total)
2023-01-11T22:07:42.7137046Z 
2023-01-11T22:07:42.7137217Z [----------] Global test environment tear-down
2023-01-11T22:07:42.7137528Z [==========] 16 tests from 1 test suite ran. (16 ms total)
2023-01-11T22:07:42.7137965Z [  PASSED  ] 16 tests.
2023-01-11T22:07:42.7870612Z + ./scalar_test
2023-01-11T22:07:43.0984325Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:43.0985194Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:07:43.0985541Z [----------] Global test environment set-up.
2023-01-11T22:07:43.0985846Z [----------] 4 tests from TestScalar
2023-01-11T22:07:43.0986122Z [ RUN      ] TestScalar.TestScalar
2023-01-11T22:07:43.0986320Z H2: 3 257 3 1
2023-01-11T22:07:43.1125979Z [       OK ] TestScalar.TestScalar (14 ms)
2023-01-11T22:07:43.1126471Z [ RUN      ] TestScalar.TestConj
2023-01-11T22:07:43.1126998Z [       OK ] TestScalar.TestConj (0 ms)
2023-01-11T22:07:43.1127555Z [ RUN      ] TestScalar.TestEqual
2023-01-11T22:07:43.1127842Z [       OK ] TestScalar.TestEqual (0 ms)
2023-01-11T22:07:43.1128172Z [ RUN      ] TestScalar.TestFormatting
2023-01-11T22:07:43.1128497Z [       OK ] TestScalar.TestFormatting (0 ms)
2023-01-11T22:07:43.1128818Z [----------] 4 tests from TestScalar (14 ms total)
2023-01-11T22:07:43.1129010Z 
2023-01-11T22:07:43.1129423Z [----------] Global test environment tear-down
2023-01-11T22:07:43.1129741Z [==========] 4 tests from 1 test suite ran. (14 ms total)
2023-01-11T22:07:43.1129991Z [  PASSED  ] 4 tests.
2023-01-11T22:07:43.1831784Z + ./broadcast_test
2023-01-11T22:07:43.5157244Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:43.5157817Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:07:43.5158143Z [----------] Global test environment set-up.
2023-01-11T22:07:43.5158461Z [----------] 1 test from BroadcastTest
2023-01-11T22:07:43.5158790Z [ RUN      ] BroadcastTest.Broadcast
2023-01-11T22:07:43.6027283Z [       OK ] BroadcastTest.Broadcast (86 ms)
2023-01-11T22:07:43.6027915Z [----------] 1 test from BroadcastTest (86 ms total)
2023-01-11T22:07:43.6028187Z 
2023-01-11T22:07:43.6028405Z [----------] Global test environment tear-down
2023-01-11T22:07:43.6028728Z [==========] 1 test from 1 test suite ran. (87 ms total)
2023-01-11T22:07:43.6028986Z [  PASSED  ] 1 test.
2023-01-11T22:07:43.6759184Z + ./wrapdim_test
2023-01-11T22:07:43.9881000Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:43.9881750Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:07:43.9882087Z [----------] Global test environment set-up.
2023-01-11T22:07:43.9882375Z [----------] 1 test from TestWrapdim
2023-01-11T22:07:43.9882650Z [ RUN      ] TestWrapdim.TestWrapdim
2023-01-11T22:07:43.9886864Z [       OK ] TestWrapdim.TestWrapdim (0 ms)
2023-01-11T22:07:43.9887288Z [----------] 1 test from TestWrapdim (0 ms total)
2023-01-11T22:07:43.9887447Z 
2023-01-11T22:07:43.9887617Z [----------] Global test environment tear-down
2023-01-11T22:07:43.9887974Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:07:43.9888227Z [  PASSED  ] 1 test.
2023-01-11T22:07:44.0572355Z + ./apply_utils_test
2023-01-11T22:07:44.3703741Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:44.3704256Z [==========] Running 6 tests from 1 test suite.
2023-01-11T22:07:44.3704564Z [----------] Global test environment set-up.
2023-01-11T22:07:44.3704874Z [----------] 6 tests from ApplyUtilsTest
2023-01-11T22:07:44.3705155Z [ RUN      ] ApplyUtilsTest.Contiguous2D
2023-01-11T22:07:44.3743716Z [       OK ] ApplyUtilsTest.Contiguous2D (4 ms)
2023-01-11T22:07:44.3744274Z [ RUN      ] ApplyUtilsTest.Small2D
2023-01-11T22:07:44.3745007Z [       OK ] ApplyUtilsTest.Small2D (0 ms)
2023-01-11T22:07:44.3745437Z [ RUN      ] ApplyUtilsTest._2D
2023-01-11T22:07:44.3756588Z [       OK ] ApplyUtilsTest._2D (1 ms)
2023-01-11T22:07:44.3756883Z [ RUN      ] ApplyUtilsTest._3D
2023-01-11T22:07:44.3758267Z [       OK ] ApplyUtilsTest._3D (0 ms)
2023-01-11T22:07:44.3758578Z [ RUN      ] ApplyUtilsTest.Medium3D
2023-01-11T22:07:44.3772751Z [       OK ] ApplyUtilsTest.Medium3D (1 ms)
2023-01-11T22:07:44.3773031Z [ RUN      ] ApplyUtilsTest._10D
2023-01-11T22:07:44.4637397Z [       OK ] ApplyUtilsTest._10D (86 ms)
2023-01-11T22:07:44.4637782Z [----------] 6 tests from ApplyUtilsTest (93 ms total)
2023-01-11T22:07:44.4637951Z 
2023-01-11T22:07:44.4638288Z [----------] Global test environment tear-down
2023-01-11T22:07:44.4638593Z [==========] 6 tests from 1 test suite ran. (93 ms total)
2023-01-11T22:07:44.4638859Z [  PASSED  ] 6 tests.
2023-01-11T22:07:44.5350286Z + ./dlconvertor_test
2023-01-11T22:07:44.8683474Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:44.8684180Z [==========] Running 2 tests from 1 test suite.
2023-01-11T22:07:44.8684486Z [----------] Global test environment set-up.
2023-01-11T22:07:44.8684828Z [----------] 2 tests from TestDlconvertor
2023-01-11T22:07:44.8685146Z [ RUN      ] TestDlconvertor.TestDlconvertor
2023-01-11T22:07:44.8687415Z [       OK ] TestDlconvertor.TestDlconvertor (0 ms)
2023-01-11T22:07:44.8688046Z [ RUN      ] TestDlconvertor.TestDlconvertorNoStrides
2023-01-11T22:07:44.8688580Z [       OK ] TestDlconvertor.TestDlconvertorNoStrides (0 ms)
2023-01-11T22:07:44.8688953Z [----------] 2 tests from TestDlconvertor (0 ms total)
2023-01-11T22:07:44.8689322Z 
2023-01-11T22:07:44.8689492Z [----------] Global test environment tear-down
2023-01-11T22:07:44.8689800Z [==========] 2 tests from 1 test suite ran. (0 ms total)
2023-01-11T22:07:44.8690054Z [  PASSED  ] 2 tests.
2023-01-11T22:07:44.9390895Z + ./native_test
2023-01-11T22:07:45.2522460Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:45.2523215Z [==========] Running 2 tests from 1 test suite.
2023-01-11T22:07:45.2523676Z [----------] Global test environment set-up.
2023-01-11T22:07:45.2524020Z [----------] 2 tests from TestNative
2023-01-11T22:07:45.2524289Z [ RUN      ] TestNative.NativeTestCPU
2023-01-11T22:07:45.2731967Z [W TensorCompare.cpp:493] Warning: where received a uint8 condition tensor. This behavior is deprecated and will be removed in a future version of PyTorch. Use a boolean condition instead. (function operator())
2023-01-11T22:07:45.2745595Z [       OK ] TestNative.NativeTestCPU (22 ms)
2023-01-11T22:07:45.2746013Z [ RUN      ] TestNative.NativeTestGPU
2023-01-11T22:07:45.2746447Z [       OK ] TestNative.NativeTestGPU (0 ms)
2023-01-11T22:07:45.2746785Z [----------] 2 tests from TestNative (22 ms total)
2023-01-11T22:07:45.2746960Z 
2023-01-11T22:07:45.2747167Z [----------] Global test environment tear-down
2023-01-11T22:07:45.2747466Z [==========] 2 tests from 1 test suite ran. (22 ms total)
2023-01-11T22:07:45.2747741Z [  PASSED  ] 2 tests.
2023-01-11T22:07:45.3458587Z + ./scalar_tensor_test
2023-01-11T22:07:45.6599900Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:45.6600651Z [==========] Running 3 tests from 1 test suite.
2023-01-11T22:07:45.6600943Z [----------] Global test environment set-up.
2023-01-11T22:07:45.6601254Z [----------] 3 tests from TestScalarTensor
2023-01-11T22:07:45.6601588Z [ RUN      ] TestScalarTensor.TestScalarTensorCPU
2023-01-11T22:07:45.8844919Z [       OK ] TestScalarTensor.TestScalarTensorCPU (224 ms)
2023-01-11T22:07:45.8845742Z [ RUN      ] TestScalarTensor.TestScalarTensorCUDA
2023-01-11T22:07:45.8846170Z [       OK ] TestScalarTensor.TestScalarTensorCUDA (0 ms)
2023-01-11T22:07:45.8846555Z [ RUN      ] TestScalarTensor.TestScalarTensorMPS
2023-01-11T22:07:45.8846909Z [       OK ] TestScalarTensor.TestScalarTensorMPS (0 ms)
2023-01-11T22:07:45.8847276Z [----------] 3 tests from TestScalarTensor (224 ms total)
2023-01-11T22:07:45.8847437Z 
2023-01-11T22:07:45.8847603Z [----------] Global test environment tear-down
2023-01-11T22:07:45.8847906Z [==========] 3 tests from 1 test suite ran. (224 ms total)
2023-01-11T22:07:45.8848165Z [  PASSED  ] 3 tests.
2023-01-11T22:07:45.9585528Z + [[ -x ./tensor_interop_test ]]
2023-01-11T22:07:45.9585790Z + ./undefined_tensor_test
2023-01-11T22:07:46.2720475Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:46.2721226Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:07:46.2721538Z [----------] Global test environment set-up.
2023-01-11T22:07:46.2721837Z [----------] 1 test from TestUndefined
2023-01-11T22:07:46.2722127Z [ RUN      ] TestUndefined.UndefinedTest
2023-01-11T22:07:46.3050893Z [       OK ] TestUndefined.UndefinedTest (33 ms)
2023-01-11T22:07:46.3051512Z [----------] 1 test from TestUndefined (33 ms total)
2023-01-11T22:07:46.3051792Z 
2023-01-11T22:07:46.3052013Z [----------] Global test environment tear-down
2023-01-11T22:07:46.3052322Z [==========] 1 test from 1 test suite ran. (33 ms total)
2023-01-11T22:07:46.3052566Z [  PASSED  ] 1 test.
2023-01-11T22:07:46.3768427Z + ./extension_backend_test
2023-01-11T22:07:46.6893964Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:46.6894608Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:07:46.6895051Z [----------] Global test environment set-up.
2023-01-11T22:07:46.6895549Z [----------] 1 test from BackendExtensionTest
2023-01-11T22:07:46.6896065Z [ RUN      ] BackendExtensionTest.TestRegisterOp
2023-01-11T22:07:46.6896610Z [       OK ] BackendExtensionTest.TestRegisterOp (0 ms)
2023-01-11T22:07:46.6897174Z [----------] 1 test from BackendExtensionTest (0 ms total)
2023-01-11T22:07:46.6897445Z 
2023-01-11T22:07:46.6897700Z [----------] Global test environment tear-down
2023-01-11T22:07:46.6898195Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:07:46.6898602Z [  PASSED  ] 1 test.
2023-01-11T22:07:46.7592178Z + ./lazy_tensor_test
2023-01-11T22:07:47.0709755Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:47.0710520Z [==========] Running 2 tests from 2 test suites.
2023-01-11T22:07:47.0711079Z [----------] Global test environment set-up.
2023-01-11T22:07:47.0711641Z [----------] 1 test from XlaTensorTest
2023-01-11T22:07:47.0712011Z [ RUN      ] XlaTensorTest.TestNoStorage
2023-01-11T22:07:47.0712325Z [       OK ] XlaTensorTest.TestNoStorage (0 ms)
2023-01-11T22:07:47.0712648Z [----------] 1 test from XlaTensorTest (0 ms total)
2023-01-11T22:07:47.0712799Z 
2023-01-11T22:07:47.0712946Z [----------] 1 test from LazyTensorTest
2023-01-11T22:07:47.0713224Z [ RUN      ] LazyTensorTest.TestNoStorage
2023-01-11T22:07:47.0713541Z [       OK ] LazyTensorTest.TestNoStorage (0 ms)
2023-01-11T22:07:47.0713864Z [----------] 1 test from LazyTensorTest (0 ms total)
2023-01-11T22:07:47.0714016Z 
2023-01-11T22:07:47.0714174Z [----------] Global test environment tear-down
2023-01-11T22:07:47.0714480Z [==========] 2 tests from 2 test suites ran. (0 ms total)
2023-01-11T22:07:47.0714976Z [  PASSED  ] 2 tests.
2023-01-11T22:07:47.1391726Z + ./tensor_iterator_test
2023-01-11T22:07:47.4806689Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:47.4807457Z [==========] Running 65 tests from 1 test suite.
2023-01-11T22:07:47.4807999Z [----------] Global test environment set-up.
2023-01-11T22:07:47.4808438Z [----------] 65 tests from TensorIteratorTest
2023-01-11T22:07:47.4808744Z [ RUN      ] TensorIteratorTest.CPUScalar
2023-01-11T22:07:47.4809245Z [       OK ] TensorIteratorTest.CPUScalar (0 ms)
2023-01-11T22:07:47.4809568Z [ RUN      ] TensorIteratorTest.CPUScalarInputs
2023-01-11T22:07:47.4809918Z [       OK ] TensorIteratorTest.CPUScalarInputs (0 ms)
2023-01-11T22:07:47.4810485Z [ RUN      ] TensorIteratorTest.MixedDevices
2023-01-11T22:07:47.4810811Z [       OK ] TensorIteratorTest.MixedDevices (0 ms)
2023-01-11T22:07:47.4811167Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Byte
2023-01-11T22:07:47.4831669Z [       OK ] TensorIteratorTest.SerialLoopUnary_Byte (2 ms)
2023-01-11T22:07:47.4832211Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Char
2023-01-11T22:07:47.4849188Z [       OK ] TensorIteratorTest.SerialLoopUnary_Char (1 ms)
2023-01-11T22:07:47.4849831Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Short
2023-01-11T22:07:47.4866790Z [       OK ] TensorIteratorTest.SerialLoopUnary_Short (1 ms)
2023-01-11T22:07:47.4867362Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Int
2023-01-11T22:07:47.4884266Z [       OK ] TensorIteratorTest.SerialLoopUnary_Int (1 ms)
2023-01-11T22:07:47.4884859Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Long
2023-01-11T22:07:47.4901737Z [       OK ] TensorIteratorTest.SerialLoopUnary_Long (1 ms)
2023-01-11T22:07:47.4902405Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Float
2023-01-11T22:07:47.4919531Z [       OK ] TensorIteratorTest.SerialLoopUnary_Float (1 ms)
2023-01-11T22:07:47.4920135Z [ RUN      ] TensorIteratorTest.SerialLoopUnary_Double
2023-01-11T22:07:47.4937348Z [       OK ] TensorIteratorTest.SerialLoopUnary_Double (1 ms)
2023-01-11T22:07:47.4937953Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Byte
2023-01-11T22:07:47.4954934Z [       OK ] TensorIteratorTest.SerialLoopBinary_Byte (1 ms)
2023-01-11T22:07:47.4955520Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Char
2023-01-11T22:07:47.4972497Z [       OK ] TensorIteratorTest.SerialLoopBinary_Char (1 ms)
2023-01-11T22:07:47.4973091Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Short
2023-01-11T22:07:47.4989966Z [       OK ] TensorIteratorTest.SerialLoopBinary_Short (1 ms)
2023-01-11T22:07:47.4990546Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Int
2023-01-11T22:07:47.5007193Z [       OK ] TensorIteratorTest.SerialLoopBinary_Int (1 ms)
2023-01-11T22:07:47.5007782Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Long
2023-01-11T22:07:47.5024733Z [       OK ] TensorIteratorTest.SerialLoopBinary_Long (1 ms)
2023-01-11T22:07:47.5025328Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Float
2023-01-11T22:07:47.5042046Z [       OK ] TensorIteratorTest.SerialLoopBinary_Float (1 ms)
2023-01-11T22:07:47.5042649Z [ RUN      ] TensorIteratorTest.SerialLoopBinary_Double
2023-01-11T22:07:47.5059653Z [       OK ] TensorIteratorTest.SerialLoopBinary_Double (1 ms)
2023-01-11T22:07:47.5060243Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Byte
2023-01-11T22:07:47.5077096Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Byte (1 ms)
2023-01-11T22:07:47.5077709Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Char
2023-01-11T22:07:47.5094526Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Char (1 ms)
2023-01-11T22:07:47.5095236Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Short
2023-01-11T22:07:47.5111946Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Short (1 ms)
2023-01-11T22:07:47.5112542Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Int
2023-01-11T22:07:47.5129554Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Int (1 ms)
2023-01-11T22:07:47.5130182Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Long
2023-01-11T22:07:47.5147061Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Long (1 ms)
2023-01-11T22:07:47.5147671Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Float
2023-01-11T22:07:47.5164492Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Float (1 ms)
2023-01-11T22:07:47.5165203Z [ RUN      ] TensorIteratorTest.SerialLoopPointwise_Double
2023-01-11T22:07:47.5182890Z [       OK ] TensorIteratorTest.SerialLoopPointwise_Double (1 ms)
2023-01-11T22:07:47.5183622Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Byte
2023-01-11T22:07:47.5184289Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Byte (0 ms)
2023-01-11T22:07:47.5184959Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Char
2023-01-11T22:07:47.5185629Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Char (0 ms)
2023-01-11T22:07:47.5186271Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Short
2023-01-11T22:07:47.5186909Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Short (0 ms)
2023-01-11T22:07:47.5187412Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Int
2023-01-11T22:07:47.5187827Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Int (0 ms)
2023-01-11T22:07:47.5188231Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Long
2023-01-11T22:07:47.5188643Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Long (0 ms)
2023-01-11T22:07:47.5189057Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Float
2023-01-11T22:07:47.5189480Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Float (0 ms)
2023-01-11T22:07:47.5189894Z [ RUN      ] TensorIteratorTest.SerialLoopUnaryNoOutput_Double
2023-01-11T22:07:47.5190312Z [       OK ] TensorIteratorTest.SerialLoopUnaryNoOutput_Double (0 ms)
2023-01-11T22:07:47.5190730Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Byte
2023-01-11T22:07:47.5191137Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Byte (0 ms)
2023-01-11T22:07:47.5191549Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Char
2023-01-11T22:07:47.5191968Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Char (0 ms)
2023-01-11T22:07:47.5192370Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Short
2023-01-11T22:07:47.5192792Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Short (0 ms)
2023-01-11T22:07:47.5193327Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Int
2023-01-11T22:07:47.5193979Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Int (0 ms)
2023-01-11T22:07:47.5194604Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Long
2023-01-11T22:07:47.5195248Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Long (0 ms)
2023-01-11T22:07:47.5195914Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Float
2023-01-11T22:07:47.5196577Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Float (0 ms)
2023-01-11T22:07:47.5197254Z [ RUN      ] TensorIteratorTest.SerialLoopBinaryNoOutput_Double
2023-01-11T22:07:47.5197939Z [       OK ] TensorIteratorTest.SerialLoopBinaryNoOutput_Double (0 ms)
2023-01-11T22:07:47.5198626Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Byte
2023-01-11T22:07:47.5199496Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Byte (0 ms)
2023-01-11T22:07:47.5200218Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Char
2023-01-11T22:07:47.5200850Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Char (0 ms)
2023-01-11T22:07:47.5201457Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Short
2023-01-11T22:07:47.5201909Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Short (0 ms)
2023-01-11T22:07:47.5202334Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Int
2023-01-11T22:07:47.5202766Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Int (0 ms)
2023-01-11T22:07:47.5203178Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Long
2023-01-11T22:07:47.5203717Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Long (0 ms)
2023-01-11T22:07:47.5204154Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Float
2023-01-11T22:07:47.5204581Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Float (0 ms)
2023-01-11T22:07:47.5205017Z [ RUN      ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Double
2023-01-11T22:07:47.5205454Z [       OK ] TensorIteratorTest.SerialLoopPoinwiseNoOutput_Double (0 ms)
2023-01-11T22:07:47.5205861Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Byte
2023-01-11T22:07:47.5206269Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Byte (0 ms)
2023-01-11T22:07:47.5206657Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Char
2023-01-11T22:07:47.5207051Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Char (0 ms)
2023-01-11T22:07:47.5207428Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Short
2023-01-11T22:07:47.5207830Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Short (0 ms)
2023-01-11T22:07:47.5208218Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Int
2023-01-11T22:07:47.5208612Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Int (0 ms)
2023-01-11T22:07:47.5208990Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Long
2023-01-11T22:07:47.5209752Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Long (0 ms)
2023-01-11T22:07:47.5210147Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Float
2023-01-11T22:07:47.5210533Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Float (0 ms)
2023-01-11T22:07:47.5210925Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Double
2023-01-11T22:07:47.5211326Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Double (0 ms)
2023-01-11T22:07:47.5211705Z [ RUN      ] TensorIteratorTest.ComparisonLoopBinary_Bool
2023-01-11T22:07:47.5212099Z [       OK ] TensorIteratorTest.ComparisonLoopBinary_Bool (0 ms)
2023-01-11T22:07:47.5212482Z [ RUN      ] TensorIteratorTest.SerialLoopSingleThread
2023-01-11T22:07:47.5255504Z [       OK ] TensorIteratorTest.SerialLoopSingleThread (5 ms)
2023-01-11T22:07:47.5256121Z [ RUN      ] TensorIteratorTest.InputDType
2023-01-11T22:07:47.5256564Z [       OK ] TensorIteratorTest.InputDType (0 ms)
2023-01-11T22:07:47.5257064Z [ RUN      ] TensorIteratorTest.ComputeCommonDTypeInputOnly
2023-01-11T22:07:47.5257609Z [       OK ] TensorIteratorTest.ComputeCommonDTypeInputOnly (0 ms)
2023-01-11T22:07:47.5258183Z [ RUN      ] TensorIteratorTest.DoNotComputeCommonDTypeInputOnly
2023-01-11T22:07:47.5258778Z [       OK ] TensorIteratorTest.DoNotComputeCommonDTypeInputOnly (0 ms)
2023-01-11T22:07:47.5259334Z [ RUN      ] TensorIteratorTest.FailNonPromotingBinaryOp
2023-01-11T22:07:47.5268733Z [       OK ] TensorIteratorTest.FailNonPromotingBinaryOp (1 ms)
2023-01-11T22:07:47.5269444Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Byte
2023-01-11T22:07:47.5270338Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Byte (0 ms)
2023-01-11T22:07:47.5270893Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Char
2023-01-11T22:07:47.5271431Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Char (0 ms)
2023-01-11T22:07:47.5271984Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Short
2023-01-11T22:07:47.5272548Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Short (0 ms)
2023-01-11T22:07:47.5273078Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Int
2023-01-11T22:07:47.5273637Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Int (0 ms)
2023-01-11T22:07:47.5274182Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Long
2023-01-11T22:07:47.5281439Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Long (0 ms)
2023-01-11T22:07:47.5282281Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Float
2023-01-11T22:07:47.5282850Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Float (0 ms)
2023-01-11T22:07:47.5283394Z [ RUN      ] TensorIteratorTest.CpuKernelMultipleOutputs_Double
2023-01-11T22:07:47.5283932Z [       OK ] TensorIteratorTest.CpuKernelMultipleOutputs_Double (0 ms)
2023-01-11T22:07:47.5284445Z [----------] 65 tests from TensorIteratorTest (47 ms total)
2023-01-11T22:07:47.5284661Z 
2023-01-11T22:07:47.5284875Z [----------] Global test environment tear-down
2023-01-11T22:07:47.5285281Z [==========] 65 tests from 1 test suite ran. (47 ms total)
2023-01-11T22:07:47.5285596Z [  PASSED  ] 65 tests.
2023-01-11T22:07:47.6010827Z + ./Dimname_test
2023-01-11T22:07:47.9356042Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:47.9356703Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:07:47.9357008Z [----------] Global test environment set-up.
2023-01-11T22:07:47.9357326Z [----------] 4 tests from DimnameTest
2023-01-11T22:07:47.9357620Z [ RUN      ] DimnameTest.isValidIdentifier
2023-01-11T22:07:47.9357933Z [       OK ] DimnameTest.isValidIdentifier (0 ms)
2023-01-11T22:07:47.9358235Z [ RUN      ] DimnameTest.wildcardName
2023-01-11T22:07:47.9358533Z [       OK ] DimnameTest.wildcardName (0 ms)
2023-01-11T22:07:47.9358823Z [ RUN      ] DimnameTest.createNormalName
2023-01-11T22:07:47.9374885Z [       OK ] DimnameTest.createNormalName (1 ms)
2023-01-11T22:07:47.9375483Z [ RUN      ] DimnameTest.unifyAndMatch
2023-01-11T22:07:47.9375829Z [       OK ] DimnameTest.unifyAndMatch (0 ms)
2023-01-11T22:07:47.9376147Z [----------] 4 tests from DimnameTest (2 ms total)
2023-01-11T22:07:47.9376303Z 
2023-01-11T22:07:47.9376466Z [----------] Global test environment tear-down
2023-01-11T22:07:47.9376776Z [==========] 4 tests from 1 test suite ran. (2 ms total)
2023-01-11T22:07:47.9377025Z [  PASSED  ] 4 tests.
2023-01-11T22:07:48.0070918Z + ./Dict_test
2023-01-11T22:07:48.3221505Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:48.3222256Z [==========] Running 47 tests from 2 test suites.
2023-01-11T22:07:48.3222869Z [----------] Global test environment set-up.
2023-01-11T22:07:48.3223345Z [----------] 46 tests from DictTest
2023-01-11T22:07:48.3223914Z [ RUN      ] DictTest.givenEmptyDict_whenCallingEmpty_thenReturnsTrue
2023-01-11T22:07:48.3224595Z [       OK ] DictTest.givenEmptyDict_whenCallingEmpty_thenReturnsTrue (0 ms)
2023-01-11T22:07:48.3225274Z [ RUN      ] DictTest.givenNonemptyDict_whenCallingEmpty_thenReturnsFalse
2023-01-11T22:07:48.3225944Z [       OK ] DictTest.givenNonemptyDict_whenCallingEmpty_thenReturnsFalse (0 ms)
2023-01-11T22:07:48.3226573Z [ RUN      ] DictTest.givenEmptyDict_whenCallingSize_thenReturnsZero
2023-01-11T22:07:48.3227485Z [       OK ] DictTest.givenEmptyDict_whenCallingSize_thenReturnsZero (0 ms)
2023-01-11T22:07:48.3228193Z [ RUN      ] DictTest.givenNonemptyDict_whenCallingSize_thenReturnsNumberOfElements
2023-01-11T22:07:48.3228801Z [       OK ] DictTest.givenNonemptyDict_whenCallingSize_thenReturnsNumberOfElements (0 ms)
2023-01-11T22:07:48.3229396Z [ RUN      ] DictTest.givenNonemptyDict_whenCallingClear_thenIsEmpty
2023-01-11T22:07:48.3230033Z [       OK ] DictTest.givenNonemptyDict_whenCallingClear_thenIsEmpty (0 ms)
2023-01-11T22:07:48.3230734Z [ RUN      ] DictTest.whenInsertingNewKey_thenReturnsTrueAndIteratorToNewElement
2023-01-11T22:07:48.3231679Z [       OK ] DictTest.whenInsertingNewKey_thenReturnsTrueAndIteratorToNewElement (0 ms)
2023-01-11T22:07:48.3232540Z [ RUN      ] DictTest.whenInsertingExistingKey_thenReturnsFalseAndIteratorToExistingElement
2023-01-11T22:07:48.3233472Z [       OK ] DictTest.whenInsertingExistingKey_thenReturnsFalseAndIteratorToExistingElement (0 ms)
2023-01-11T22:07:48.3234273Z [ RUN      ] DictTest.whenInsertingExistingKey_thenDoesNotModifyDict
2023-01-11T22:07:48.3234959Z [       OK ] DictTest.whenInsertingExistingKey_thenDoesNotModifyDict (0 ms)
2023-01-11T22:07:48.3235750Z [ RUN      ] DictTest.whenInsertOrAssigningNewKey_thenReturnsTrueAndIteratorToNewElement
2023-01-11T22:07:48.3236598Z [       OK ] DictTest.whenInsertOrAssigningNewKey_thenReturnsTrueAndIteratorToNewElement (0 ms)
2023-01-11T22:07:48.3237470Z [ RUN      ] DictTest.whenInsertOrAssigningExistingKey_thenReturnsFalseAndIteratorToChangedElement
2023-01-11T22:07:48.3238406Z [       OK ] DictTest.whenInsertOrAssigningExistingKey_thenReturnsFalseAndIteratorToChangedElement (0 ms)
2023-01-11T22:07:48.3239227Z [ RUN      ] DictTest.whenInsertOrAssigningExistingKey_thenDoesModifyDict
2023-01-11T22:07:48.3239955Z [       OK ] DictTest.whenInsertOrAssigningExistingKey_thenDoesModifyDict (0 ms)
2023-01-11T22:07:48.3240603Z [ RUN      ] DictTest.givenEmptyDict_whenIterating_thenBeginIsEnd
2023-01-11T22:07:48.3241231Z [       OK ] DictTest.givenEmptyDict_whenIterating_thenBeginIsEnd (0 ms)
2023-01-11T22:07:48.3241853Z [ RUN      ] DictTest.givenMutableDict_whenIterating_thenFindsElements
2023-01-11T22:07:48.3242494Z [       OK ] DictTest.givenMutableDict_whenIterating_thenFindsElements (0 ms)
2023-01-11T22:07:48.3243191Z [ RUN      ] DictTest.givenMutableDict_whenIteratingWithForeach_thenFindsElements
2023-01-11T22:07:48.3243906Z [       OK ] DictTest.givenMutableDict_whenIteratingWithForeach_thenFindsElements (0 ms)
2023-01-11T22:07:48.3244568Z [ RUN      ] DictTest.givenConstDict_whenIterating_thenFindsElements
2023-01-11T22:07:48.3245252Z [       OK ] DictTest.givenConstDict_whenIterating_thenFindsElements (0 ms)
2023-01-11T22:07:48.3245909Z [ RUN      ] DictTest.givenConstDict_whenIteratingWithForeach_thenFindsElements
2023-01-11T22:07:48.3246611Z [       OK ] DictTest.givenConstDict_whenIteratingWithForeach_thenFindsElements (0 ms)
2023-01-11T22:07:48.3247219Z [ RUN      ] DictTest.givenIterator_thenCanModifyValue
2023-01-11T22:07:48.3247770Z [       OK ] DictTest.givenIterator_thenCanModifyValue (0 ms)
2023-01-11T22:07:48.3248400Z [ RUN      ] DictTest.givenOneElementDict_whenErasingByIterator_thenDictIsEmpty
2023-01-11T22:07:48.3249315Z [       OK ] DictTest.givenOneElementDict_whenErasingByIterator_thenDictIsEmpty (0 ms)
2023-01-11T22:07:48.3250046Z [ RUN      ] DictTest.givenOneElementDict_whenErasingByKey_thenReturnsOneAndDictIsEmpty
2023-01-11T22:07:48.3250796Z [       OK ] DictTest.givenOneElementDict_whenErasingByKey_thenReturnsOneAndDictIsEmpty (0 ms)
2023-01-11T22:07:48.3251600Z [ RUN      ] DictTest.givenOneElementDict_whenErasingByNonexistingKey_thenReturnsZeroAndDictIsUnchanged
2023-01-11T22:07:48.3252626Z [       OK ] DictTest.givenOneElementDict_whenErasingByNonexistingKey_thenReturnsZeroAndDictIsUnchanged (0 ms)
2023-01-11T22:07:48.3253394Z [ RUN      ] DictTest.whenCallingAtWithExistingKey_thenReturnsCorrectElement
2023-01-11T22:07:48.3254092Z [       OK ] DictTest.whenCallingAtWithExistingKey_thenReturnsCorrectElement (0 ms)
2023-01-11T22:07:48.3254863Z [ RUN      ] DictTest.whenCallingAtWithNonExistingKey_thenReturnsCorrectElement
2023-01-11T22:07:48.3255719Z [       OK ] DictTest.whenCallingAtWithNonExistingKey_thenReturnsCorrectElement (0 ms)
2023-01-11T22:07:48.3256624Z [ RUN      ] DictTest.givenMutableDict_whenCallingFindOnExistingKey_thenFindsCorrectElement
2023-01-11T22:07:48.3257578Z [       OK ] DictTest.givenMutableDict_whenCallingFindOnExistingKey_thenFindsCorrectElement (0 ms)
2023-01-11T22:07:48.3258397Z [ RUN      ] DictTest.givenMutableDict_whenCallingFindOnNonExistingKey_thenReturnsEnd
2023-01-11T22:07:48.3259200Z [       OK ] DictTest.givenMutableDict_whenCallingFindOnNonExistingKey_thenReturnsEnd (0 ms)
2023-01-11T22:07:48.3259980Z [ RUN      ] DictTest.givenConstDict_whenCallingFindOnExistingKey_thenFindsCorrectElement
2023-01-11T22:07:48.3260796Z [       OK ] DictTest.givenConstDict_whenCallingFindOnExistingKey_thenFindsCorrectElement (0 ms)
2023-01-11T22:07:48.3261571Z [ RUN      ] DictTest.givenConstDict_whenCallingFindOnNonExistingKey_thenReturnsEnd
2023-01-11T22:07:48.3262422Z [       OK ] DictTest.givenConstDict_whenCallingFindOnNonExistingKey_thenReturnsEnd (0 ms)
2023-01-11T22:07:48.3263140Z [ RUN      ] DictTest.whenCallingContainsWithExistingKey_thenReturnsTrue
2023-01-11T22:07:48.3263898Z [       OK ] DictTest.whenCallingContainsWithExistingKey_thenReturnsTrue (0 ms)
2023-01-11T22:07:48.3264646Z [ RUN      ] DictTest.whenCallingContainsWithNonExistingKey_thenReturnsFalse
2023-01-11T22:07:48.3265420Z [       OK ] DictTest.whenCallingContainsWithNonExistingKey_thenReturnsFalse (0 ms)
2023-01-11T22:07:48.3266078Z [ RUN      ] DictTest.whenCallingReserve_thenDoesntCrash
2023-01-11T22:07:48.3266659Z [       OK ] DictTest.whenCallingReserve_thenDoesntCrash (0 ms)
2023-01-11T22:07:48.3267272Z [ RUN      ] DictTest.whenCopyConstructingDict_thenAreEqual
2023-01-11T22:07:48.3267896Z [       OK ] DictTest.whenCopyConstructingDict_thenAreEqual (0 ms)
2023-01-11T22:07:48.3268485Z [ RUN      ] DictTest.whenCopyAssigningDict_thenAreEqual
2023-01-11T22:07:48.3269059Z [       OK ] DictTest.whenCopyAssigningDict_thenAreEqual (0 ms)
2023-01-11T22:07:48.3269546Z [ RUN      ] DictTest.whenCopyingDict_thenAreEqual
2023-01-11T22:07:48.3270010Z [       OK ] DictTest.whenCopyingDict_thenAreEqual (0 ms)
2023-01-11T22:07:48.3270572Z [ RUN      ] DictTest.whenMoveConstructingDict_thenNewIsCorrect
2023-01-11T22:07:48.3271181Z [       OK ] DictTest.whenMoveConstructingDict_thenNewIsCorrect (0 ms)
2023-01-11T22:07:48.3271758Z [ RUN      ] DictTest.whenMoveAssigningDict_thenNewIsCorrect
2023-01-11T22:07:48.3272286Z [       OK ] DictTest.whenMoveAssigningDict_thenNewIsCorrect (0 ms)
2023-01-11T22:07:48.3272808Z [ RUN      ] DictTest.whenMoveConstructingDict_thenOldIsUnchanged
2023-01-11T22:07:48.3273335Z [       OK ] DictTest.whenMoveConstructingDict_thenOldIsUnchanged (0 ms)
2023-01-11T22:07:48.3273871Z [ RUN      ] DictTest.whenMoveAssigningDict_thenOldIsUnchanged
2023-01-11T22:07:48.3274501Z [       OK ] DictTest.whenMoveAssigningDict_thenOldIsUnchanged (0 ms)
2023-01-11T22:07:48.3275221Z [ RUN      ] DictTest.givenIterator_whenPostfixIncrementing_thenMovesToNextAndReturnsOldPosition
2023-01-11T22:07:48.3275998Z [       OK ] DictTest.givenIterator_whenPostfixIncrementing_thenMovesToNextAndReturnsOldPosition (0 ms)
2023-01-11T22:07:48.3276942Z [ RUN      ] DictTest.givenIterator_whenPrefixIncrementing_thenMovesToNextAndReturnsNewPosition
2023-01-11T22:07:48.3277748Z [       OK ] DictTest.givenIterator_whenPrefixIncrementing_thenMovesToNextAndReturnsNewPosition (0 ms)
2023-01-11T22:07:48.3278381Z [ RUN      ] DictTest.givenEqualIterators_thenAreEqual
2023-01-11T22:07:48.3278957Z [       OK ] DictTest.givenEqualIterators_thenAreEqual (0 ms)
2023-01-11T22:07:48.3279538Z [ RUN      ] DictTest.givenDifferentIterators_thenAreNotEqual
2023-01-11T22:07:48.3280159Z [       OK ] DictTest.givenDifferentIterators_thenAreNotEqual (0 ms)
2023-01-11T22:07:48.3280795Z [ RUN      ] DictTest.givenIterator_whenDereferencing_thenPointsToCorrectElement
2023-01-11T22:07:48.3281608Z [       OK ] DictTest.givenIterator_whenDereferencing_thenPointsToCorrectElement (0 ms)
2023-01-11T22:07:48.3282285Z [ RUN      ] DictTest.givenIterator_whenWritingToValue_thenChangesValue
2023-01-11T22:07:48.3282897Z [       OK ] DictTest.givenIterator_whenWritingToValue_thenChangesValue (0 ms)
2023-01-11T22:07:48.3283433Z [ RUN      ] DictTest.isReferenceType
2023-01-11T22:07:48.3283903Z [       OK ] DictTest.isReferenceType (0 ms)
2023-01-11T22:07:48.3284403Z [ RUN      ] DictTest.copyHasSeparateStorage
2023-01-11T22:07:48.3284923Z [       OK ] DictTest.copyHasSeparateStorage (0 ms)
2023-01-11T22:07:48.3285415Z [ RUN      ] DictTest.dictTensorAsKey
2023-01-11T22:07:48.3285877Z [       OK ] DictTest.dictTensorAsKey (0 ms)
2023-01-11T22:07:48.3286371Z [ RUN      ] DictTest.dictEquality
2023-01-11T22:07:48.3286794Z [       OK ] DictTest.dictEquality (0 ms)
2023-01-11T22:07:48.3287258Z [----------] 46 tests from DictTest (0 ms total)
2023-01-11T22:07:48.3287471Z 
2023-01-11T22:07:48.3287712Z [----------] 1 test from ListTest_IValueBasedList
2023-01-11T22:07:48.3288360Z [ RUN      ] ListTest_IValueBasedList.givenIterator_whenWritingToValueFromIterator_thenChangesValue
2023-01-11T22:07:48.3289266Z [       OK ] ListTest_IValueBasedList.givenIterator_whenWritingToValueFromIterator_thenChangesValue (0 ms)
2023-01-11T22:07:48.3289916Z [----------] 1 test from ListTest_IValueBasedList (0 ms total)
2023-01-11T22:07:48.3290176Z 
2023-01-11T22:07:48.3290415Z [----------] Global test environment tear-down
2023-01-11T22:07:48.3290895Z [==========] 47 tests from 2 test suites ran. (0 ms total)
2023-01-11T22:07:48.3291280Z [  PASSED  ] 47 tests.
2023-01-11T22:07:48.3950709Z + ./NamedTensor_test
2023-01-11T22:07:48.7064427Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:48.7065214Z [==========] Running 10 tests from 1 test suite.
2023-01-11T22:07:48.7065576Z [----------] Global test environment set-up.
2023-01-11T22:07:48.7066022Z [----------] 10 tests from NamedTensorTest
2023-01-11T22:07:48.7066357Z [ RUN      ] NamedTensorTest.isNamed
2023-01-11T22:07:48.7066866Z [W TensorImpl.h:1816] Warning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (function operator())
2023-01-11T22:07:48.7067428Z [       OK ] NamedTensorTest.isNamed (0 ms)
2023-01-11T22:07:48.7067789Z [ RUN      ] NamedTensorTest.attachMetadata
2023-01-11T22:07:48.7068106Z [       OK ] NamedTensorTest.attachMetadata (0 ms)
2023-01-11T22:07:48.7068490Z [ RUN      ] NamedTensorTest.internalSetNamesInplace
2023-01-11T22:07:48.7068899Z [       OK ] NamedTensorTest.internalSetNamesInplace (0 ms)
2023-01-11T22:07:48.7069216Z [ RUN      ] NamedTensorTest.empty
2023-01-11T22:07:48.7102085Z [       OK ] NamedTensorTest.empty (3 ms)
2023-01-11T22:07:48.7102690Z [ RUN      ] NamedTensorTest.dimnameToPosition
2023-01-11T22:07:48.7107603Z [       OK ] NamedTensorTest.dimnameToPosition (0 ms)
2023-01-11T22:07:48.7107990Z [ RUN      ] NamedTensorTest.unifyFromRight
2023-01-11T22:07:48.7176568Z [       OK ] NamedTensorTest.unifyFromRight (6 ms)
2023-01-11T22:07:48.7177016Z [ RUN      ] NamedTensorTest.alias
2023-01-11T22:07:48.7177300Z [       OK ] NamedTensorTest.alias (0 ms)
2023-01-11T22:07:48.7177593Z [ RUN      ] NamedTensorTest.NoNamesGuard
2023-01-11T22:07:48.7177913Z [       OK ] NamedTensorTest.NoNamesGuard (0 ms)
2023-01-11T22:07:48.7178222Z [ RUN      ] NamedTensorTest.TensorNamePrint
2023-01-11T22:07:48.7178558Z [       OK ] NamedTensorTest.TensorNamePrint (0 ms)
2023-01-11T22:07:48.7179021Z [ RUN      ] NamedTensorTest.TensorNamesCheckUnique
2023-01-11T22:07:48.7182860Z [       OK ] NamedTensorTest.TensorNamesCheckUnique (0 ms)
2023-01-11T22:07:48.7183233Z [----------] 10 tests from NamedTensorTest (11 ms total)
2023-01-11T22:07:48.7183398Z 
2023-01-11T22:07:48.7183563Z [----------] Global test environment tear-down
2023-01-11T22:07:48.7183870Z [==========] 10 tests from 1 test suite ran. (11 ms total)
2023-01-11T22:07:48.7184130Z [  PASSED  ] 10 tests.
2023-01-11T22:07:48.7881777Z + ./cpu_generator_test
2023-01-11T22:07:49.0984712Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:49.0985344Z [==========] Running 15 tests from 1 test suite.
2023-01-11T22:07:49.0985757Z [----------] Global test environment set-up.
2023-01-11T22:07:49.0986226Z [----------] 15 tests from CPUGeneratorImpl
2023-01-11T22:07:49.0986727Z [ RUN      ] CPUGeneratorImpl.TestGeneratorDynamicCast
2023-01-11T22:07:49.0987361Z [       OK ] CPUGeneratorImpl.TestGeneratorDynamicCast (0 ms)
2023-01-11T22:07:49.0987949Z [ RUN      ] CPUGeneratorImpl.TestDefaultGenerator
2023-01-11T22:07:49.0988486Z [       OK ] CPUGeneratorImpl.TestDefaultGenerator (0 ms)
2023-01-11T22:07:49.0989024Z [ RUN      ] CPUGeneratorImpl.TestCloning
2023-01-11T22:07:49.0989519Z [       OK ] CPUGeneratorImpl.TestCloning (0 ms)
2023-01-11T22:07:49.0990126Z [ RUN      ] CPUGeneratorImpl.TestMultithreadingGetEngineOperator
2023-01-11T22:07:49.0991010Z [       OK ] CPUGeneratorImpl.TestMultithreadingGetEngineOperator (0 ms)
2023-01-11T22:07:49.0991832Z [ RUN      ] CPUGeneratorImpl.TestGetSetCurrentSeed
2023-01-11T22:07:49.0992559Z [       OK ] CPUGeneratorImpl.TestGetSetCurrentSeed (0 ms)
2023-01-11T22:07:49.0993390Z [ RUN      ] CPUGeneratorImpl.TestMultithreadingGetSetCurrentSeed
2023-01-11T22:07:49.0994327Z [       OK ] CPUGeneratorImpl.TestMultithreadingGetSetCurrentSeed (0 ms)
2023-01-11T22:07:49.0995075Z [ RUN      ] CPUGeneratorImpl.TestRNGForking
2023-01-11T22:07:49.1004961Z [       OK ] CPUGeneratorImpl.TestRNGForking (1 ms)
2023-01-11T22:07:49.1005568Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineReproducibility
2023-01-11T22:07:49.1006027Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineReproducibility (0 ms)
2023-01-11T22:07:49.1006427Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineOffset1
2023-01-11T22:07:49.1006812Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineOffset1 (0 ms)
2023-01-11T22:07:49.1007196Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineOffset2
2023-01-11T22:07:49.1007567Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineOffset2 (0 ms)
2023-01-11T22:07:49.1007941Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineOffset3
2023-01-11T22:07:49.1008329Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineOffset3 (0 ms)
2023-01-11T22:07:49.1008774Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineIndex
2023-01-11T22:07:49.1009712Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineIndex (0 ms)
2023-01-11T22:07:49.1010334Z [ RUN      ] CPUGeneratorImpl.TestMT19937EngineReproducibility
2023-01-11T22:07:49.1010979Z [       OK ] CPUGeneratorImpl.TestMT19937EngineReproducibility (0 ms)
2023-01-11T22:07:49.1011414Z [ RUN      ] CPUGeneratorImpl.TestPhiloxEngineReproducibilityRandN
2023-01-11T22:07:49.1011888Z [       OK ] CPUGeneratorImpl.TestPhiloxEngineReproducibilityRandN (0 ms)
2023-01-11T22:07:49.1012309Z [ RUN      ] CPUGeneratorImpl.TestPhiloxDeterministic
2023-01-11T22:07:49.1012695Z [       OK ] CPUGeneratorImpl.TestPhiloxDeterministic (0 ms)
2023-01-11T22:07:49.1013052Z [----------] 15 tests from CPUGeneratorImpl (2 ms total)
2023-01-11T22:07:49.1013214Z 
2023-01-11T22:07:49.1013473Z [----------] Global test environment tear-down
2023-01-11T22:07:49.1013788Z [==========] 15 tests from 1 test suite ran. (2 ms total)
2023-01-11T22:07:49.1014036Z [  PASSED  ] 15 tests.
2023-01-11T22:07:49.1728314Z + ./legacy_vmap_test
2023-01-11T22:07:49.4850782Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:49.4851518Z [==========] Running 23 tests from 1 test suite.
2023-01-11T22:07:49.4851814Z [----------] Global test environment set-up.
2023-01-11T22:07:49.4852115Z [----------] 23 tests from VmapTest
2023-01-11T22:07:49.4852403Z [ RUN      ] VmapTest.TestBatchedTensor
2023-01-11T22:07:49.4873624Z [       OK ] VmapTest.TestBatchedTensor (2 ms)
2023-01-11T22:07:49.4874041Z [ RUN      ] VmapTest.TestBatchedTensorMaxLevel
2023-01-11T22:07:49.4888205Z [       OK ] VmapTest.TestBatchedTensorMaxLevel (1 ms)
2023-01-11T22:07:49.4888587Z [ RUN      ] VmapTest.TestBatchedTensorActualDim
2023-01-11T22:07:49.4913280Z [       OK ] VmapTest.TestBatchedTensorActualDim (2 ms)
2023-01-11T22:07:49.4913659Z [ RUN      ] VmapTest.TestMultiBatchVmapTransform
2023-01-11T22:07:49.4923225Z [       OK ] VmapTest.TestMultiBatchVmapTransform (0 ms)
2023-01-11T22:07:49.4923603Z [ RUN      ] VmapTest.TestVmapPhysicalViewGetPhysicalDim
2023-01-11T22:07:49.4932914Z [       OK ] VmapTest.TestVmapPhysicalViewGetPhysicalDim (0 ms)
2023-01-11T22:07:49.4933329Z [ RUN      ] VmapTest.TestVmapPhysicalViewGetPhysicalDims
2023-01-11T22:07:49.4942610Z [       OK ] VmapTest.TestVmapPhysicalViewGetPhysicalDims (0 ms)
2023-01-11T22:07:49.4943051Z [ RUN      ] VmapTest.TestVmapPhysicalViewNewLogicalFromPhysical
2023-01-11T22:07:49.4943518Z [       OK ] VmapTest.TestVmapPhysicalViewNewLogicalFromPhysical (0 ms)
2023-01-11T22:07:49.4943902Z [ RUN      ] VmapTest.TestBatchedTensorSum
2023-01-11T22:07:49.4946724Z [       OK ] VmapTest.TestBatchedTensorSum (0 ms)
2023-01-11T22:07:49.4947114Z [ RUN      ] VmapTest.TestBroadcastingVmapTransformBatchedBatched
2023-01-11T22:07:49.4953918Z [       OK ] VmapTest.TestBroadcastingVmapTransformBatchedBatched (0 ms)
2023-01-11T22:07:49.4954472Z [ RUN      ] VmapTest.TestBroadcastingVmapTransformBatchedUnbatched
2023-01-11T22:07:49.4960332Z [       OK ] VmapTest.TestBroadcastingVmapTransformBatchedUnbatched (0 ms)
2023-01-11T22:07:49.4960844Z [ RUN      ] VmapTest.TestBroadcastingVmapTransformMaxLevels
2023-01-11T22:07:49.4964334Z [       OK ] VmapTest.TestBroadcastingVmapTransformMaxLevels (0 ms)
2023-01-11T22:07:49.4964739Z [ RUN      ] VmapTest.TestBatchedTensorMul
2023-01-11T22:07:49.4966750Z [       OK ] VmapTest.TestBatchedTensorMul (0 ms)
2023-01-11T22:07:49.4967163Z [ RUN      ] VmapTest.TestBatchedTensorSize
2023-01-11T22:07:49.4972892Z [       OK ] VmapTest.TestBatchedTensorSize (0 ms)
2023-01-11T22:07:49.4973283Z [ RUN      ] VmapTest.TestVmapPhysicalViewGetPhysicalShape
2023-01-11T22:07:49.4973908Z [       OK ] VmapTest.TestVmapPhysicalViewGetPhysicalShape (0 ms)
2023-01-11T22:07:49.4974284Z [ RUN      ] VmapTest.TestBatchedTensorExpand
2023-01-11T22:07:49.5115219Z [       OK ] VmapTest.TestBatchedTensorExpand (14 ms)
2023-01-11T22:07:49.5115669Z [ RUN      ] VmapTest.TestBatchedTensorUnsqueeze
2023-01-11T22:07:49.5116530Z [       OK ] VmapTest.TestBatchedTensorUnsqueeze (0 ms)
2023-01-11T22:07:49.5116920Z [ RUN      ] VmapTest.TestBatchedTensorSqueeze
2023-01-11T22:07:49.5118208Z [       OK ] VmapTest.TestBatchedTensorSqueeze (0 ms)
2023-01-11T22:07:49.5118547Z [ RUN      ] VmapTest.TestBatchedTensorTranspose
2023-01-11T22:07:49.5120773Z [       OK ] VmapTest.TestBatchedTensorTranspose (0 ms)
2023-01-11T22:07:49.5121292Z [ RUN      ] VmapTest.TestBatchedTensorPermute
2023-01-11T22:07:49.5122941Z [       OK ] VmapTest.TestBatchedTensorPermute (0 ms)
2023-01-11T22:07:49.5123347Z [ RUN      ] VmapTest.TestMultiBatchVmapTransformBatchedBatched
2023-01-11T22:07:49.5148119Z [       OK ] VmapTest.TestMultiBatchVmapTransformBatchedBatched (2 ms)
2023-01-11T22:07:49.5148624Z [ RUN      ] VmapTest.TestMultiBatchVmapTransformBatchedUnbatched
2023-01-11T22:07:49.5154661Z [       OK ] VmapTest.TestMultiBatchVmapTransformBatchedUnbatched (0 ms)
2023-01-11T22:07:49.5155175Z [ RUN      ] VmapTest.TestMultiBatchVmapTransformMaxLevels
2023-01-11T22:07:49.5159621Z [       OK ] VmapTest.TestMultiBatchVmapTransformMaxLevels (0 ms)
2023-01-11T22:07:49.5160130Z [ RUN      ] VmapTest.TestMultiBatchVmapTransformMultipleTensors
2023-01-11T22:07:49.5164450Z [       OK ] VmapTest.TestMultiBatchVmapTransformMultipleTensors (0 ms)
2023-01-11T22:07:49.5164952Z [----------] 23 tests from VmapTest (31 ms total)
2023-01-11T22:07:49.5165112Z 
2023-01-11T22:07:49.5165278Z [----------] Global test environment tear-down
2023-01-11T22:07:49.5165583Z [==========] 23 tests from 1 test suite ran. (31 ms total)
2023-01-11T22:07:49.5165842Z [  PASSED  ] 23 tests.
2023-01-11T22:07:49.5872845Z + ./operators_test
2023-01-11T22:07:49.9014154Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:49.9014850Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:07:49.9015144Z [----------] Global test environment set-up.
2023-01-11T22:07:49.9015449Z [----------] 4 tests from OperatorsTest
2023-01-11T22:07:49.9015762Z [ RUN      ] OperatorsTest.TestFunctionDecltype
2023-01-11T22:07:49.9020250Z [       OK ] OperatorsTest.TestFunctionDecltype (0 ms)
2023-01-11T22:07:49.9020757Z [ RUN      ] OperatorsTest.TestMethodOnlyDecltype
2023-01-11T22:07:49.9021396Z [       OK ] OperatorsTest.TestMethodOnlyDecltype (0 ms)
2023-01-11T22:07:49.9021724Z [ RUN      ] OperatorsTest.Test_ATEN_FN
2023-01-11T22:07:49.9049995Z [       OK ] OperatorsTest.Test_ATEN_FN (2 ms)
2023-01-11T22:07:49.9050393Z [ RUN      ] OperatorsTest.TestOutVariantIsFaithful
2023-01-11T22:07:49.9050842Z [       OK ] OperatorsTest.TestOutVariantIsFaithful (0 ms)
2023-01-11T22:07:49.9051380Z [----------] 4 tests from OperatorsTest (3 ms total)
2023-01-11T22:07:49.9051524Z 
2023-01-11T22:07:49.9051691Z [----------] Global test environment tear-down
2023-01-11T22:07:49.9052005Z [==========] 4 tests from 1 test suite ran. (3 ms total)
2023-01-11T22:07:49.9052263Z [  PASSED  ] 4 tests.
2023-01-11T22:07:49.9746302Z + [[ -x ./cudnn_test ]]
2023-01-11T22:07:49.9746824Z + [[ -x ./cuda_generator_test ]]
2023-01-11T22:07:49.9747113Z + ./cuda_generator_test
2023-01-11T22:07:50.3131763Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:50.3132539Z [==========] Running 11 tests from 1 test suite.
2023-01-11T22:07:50.3133321Z [----------] Global test environment set-up.
2023-01-11T22:07:50.3133661Z [----------] 11 tests from CUDAGeneratorImpl
2023-01-11T22:07:50.3134042Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineReproducibility
2023-01-11T22:07:50.3134549Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineReproducibility (0 ms)
2023-01-11T22:07:50.3134963Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineOffset1
2023-01-11T22:07:50.3135400Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineOffset1 (0 ms)
2023-01-11T22:07:50.3135794Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineOffset2
2023-01-11T22:07:50.3136222Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineOffset2 (0 ms)
2023-01-11T22:07:50.3136611Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineOffset3
2023-01-11T22:07:50.3137099Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineOffset3 (0 ms)
2023-01-11T22:07:50.3137535Z [ RUN      ] CUDAGeneratorImpl.TestPhiloxEngineIndex
2023-01-11T22:07:50.3137918Z [       OK ] CUDAGeneratorImpl.TestPhiloxEngineIndex (0 ms)
2023-01-11T22:07:50.3138344Z [ RUN      ] CUDAGeneratorImpl.TestGeneratorDynamicCast
2023-01-11T22:07:50.3138746Z [       OK ] CUDAGeneratorImpl.TestGeneratorDynamicCast (0 ms)
2023-01-11T22:07:50.3139182Z [ RUN      ] CUDAGeneratorImpl.TestDefaultGenerator
2023-01-11T22:07:50.3139543Z [       OK ] CUDAGeneratorImpl.TestDefaultGenerator (0 ms)
2023-01-11T22:07:50.3139938Z [ RUN      ] CUDAGeneratorImpl.TestCloning
2023-01-11T22:07:50.3140267Z [       OK ] CUDAGeneratorImpl.TestCloning (0 ms)
2023-01-11T22:07:50.3140708Z [ RUN      ] CUDAGeneratorImpl.TestMultithreadingGetSetCurrentSeed
2023-01-11T22:07:50.3141179Z [       OK ] CUDAGeneratorImpl.TestMultithreadingGetSetCurrentSeed (0 ms)
2023-01-11T22:07:50.3141637Z [ RUN      ] CUDAGeneratorImpl.TestRNGForking
2023-01-11T22:07:50.3141977Z [       OK ] CUDAGeneratorImpl.TestRNGForking (0 ms)
2023-01-11T22:07:50.3142448Z [ RUN      ] CUDAGeneratorImpl.TestMultithreadRNG
2023-01-11T22:07:50.3142814Z [       OK ] CUDAGeneratorImpl.TestMultithreadRNG (0 ms)
2023-01-11T22:07:50.3143232Z [----------] 11 tests from CUDAGeneratorImpl (0 ms total)
2023-01-11T22:07:50.3143400Z 
2023-01-11T22:07:50.3143556Z [----------] Global test environment tear-down
2023-01-11T22:07:50.3143920Z [==========] 11 tests from 1 test suite ran. (0 ms total)
2023-01-11T22:07:50.3144182Z [  PASSED  ] 11 tests.
2023-01-11T22:07:50.3855051Z + [[ -x ./apply_test ]]
2023-01-11T22:07:50.3855360Z + [[ -x ./stream_test ]]
2023-01-11T22:07:50.3855587Z + [[ -x ./cuda_half_test ]]
2023-01-11T22:07:50.3855774Z + ./cuda_half_test
2023-01-11T22:07:50.6966617Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:50.6967442Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:07:50.6967871Z [----------] Global test environment set-up.
2023-01-11T22:07:50.6968138Z [----------] 1 test from HalfCuda
2023-01-11T22:07:50.6968411Z [ RUN      ] HalfCuda.HalfCuda
2023-01-11T22:07:50.6968682Z [       OK ] HalfCuda.HalfCuda (0 ms)
2023-01-11T22:07:50.6968976Z [----------] 1 test from HalfCuda (0 ms total)
2023-01-11T22:07:50.6969227Z 
2023-01-11T22:07:50.6969394Z [----------] Global test environment tear-down
2023-01-11T22:07:50.6969699Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:07:50.6969957Z [  PASSED  ] 1 test.
2023-01-11T22:07:50.7654705Z + [[ -x ./cuda_vectorized_test ]]
2023-01-11T22:07:50.7654991Z + ./cuda_vectorized_test
2023-01-11T22:07:51.0793425Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:51.0794190Z [==========] Running 2 tests from 1 test suite.
2023-01-11T22:07:51.0794939Z [----------] Global test environment set-up.
2023-01-11T22:07:51.0795469Z [----------] 2 tests from TestVectorizedMemoryAccess
2023-01-11T22:07:51.0796036Z [ RUN      ] TestVectorizedMemoryAccess.CanVectorizeUpTo
2023-01-11T22:07:51.0796702Z [       OK ] TestVectorizedMemoryAccess.CanVectorizeUpTo (0 ms)
2023-01-11T22:07:51.0797342Z [ RUN      ] TestVectorizedMemoryAccess.CopyKernel
2023-01-11T22:07:51.0797948Z [       OK ] TestVectorizedMemoryAccess.CopyKernel (0 ms)
2023-01-11T22:07:51.0798586Z [----------] 2 tests from TestVectorizedMemoryAccess (0 ms total)
2023-01-11T22:07:51.0798885Z 
2023-01-11T22:07:51.0799161Z [----------] Global test environment tear-down
2023-01-11T22:07:51.0799668Z [==========] 2 tests from 1 test suite ran. (0 ms total)
2023-01-11T22:07:51.0800209Z [  PASSED  ] 2 tests.
2023-01-11T22:07:51.1491165Z + [[ -x ./cuda_distributions_test ]]
2023-01-11T22:07:51.1491480Z + ./cuda_distributions_test
2023-01-11T22:07:51.4629416Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:51.4630115Z [==========] Running 4 tests from 2 test suites.
2023-01-11T22:07:51.4630638Z [----------] Global test environment set-up.
2023-01-11T22:07:51.4631132Z [----------] 3 tests from DistributionsTest
2023-01-11T22:07:51.4631529Z [ RUN      ] DistributionsTest.TestPhiloxIncrementSmallUniformTensor
2023-01-11T22:07:51.4632000Z [       OK ] DistributionsTest.TestPhiloxIncrementSmallUniformTensor (0 ms)
2023-01-11T22:07:51.4632471Z [ RUN      ] DistributionsTest.TestPhiloxIncrementBigUniformTensor
2023-01-11T22:07:51.4632943Z [       OK ] DistributionsTest.TestPhiloxIncrementBigUniformTensor (0 ms)
2023-01-11T22:07:51.4633426Z [ RUN      ] DistributionsTest.TestPhiloxIncrementSmallMultinomialTensor
2023-01-11T22:07:51.4633937Z [       OK ] DistributionsTest.TestPhiloxIncrementSmallMultinomialTensor (0 ms)
2023-01-11T22:07:51.4634367Z [----------] 3 tests from DistributionsTest (0 ms total)
2023-01-11T22:07:51.4634532Z 
2023-01-11T22:07:51.4634704Z [----------] 1 test from RandomPermutationTest
2023-01-11T22:07:51.4635035Z [ RUN      ] RandomPermutationTest.TestIslandShuffle
2023-01-11T22:07:51.4635414Z [       OK ] RandomPermutationTest.TestIslandShuffle (0 ms)
2023-01-11T22:07:51.4635787Z [----------] 1 test from RandomPermutationTest (0 ms total)
2023-01-11T22:07:51.4635953Z 
2023-01-11T22:07:51.4636107Z [----------] Global test environment tear-down
2023-01-11T22:07:51.4636416Z [==========] 4 tests from 2 test suites ran. (0 ms total)
2023-01-11T22:07:51.4636666Z [  PASSED  ] 4 tests.
2023-01-11T22:07:51.5321113Z + [[ -x ./cuda_optional_test ]]
2023-01-11T22:07:51.8634876Z + ./cuda_optional_test
2023-01-11T22:07:51.8635468Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:51.8636270Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:07:51.8636790Z [----------] Global test environment set-up.
2023-01-11T22:07:51.8637085Z [----------] 1 test from OptionalTest
2023-01-11T22:07:51.8637396Z [ RUN      ] OptionalTest.OptionalTestCUDA
2023-01-11T22:07:51.8637722Z [       OK ] OptionalTest.OptionalTestCUDA (0 ms)
2023-01-11T22:07:51.8638038Z [----------] 1 test from OptionalTest (0 ms total)
2023-01-11T22:07:51.8638186Z 
2023-01-11T22:07:51.8638347Z [----------] Global test environment tear-down
2023-01-11T22:07:51.8638650Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:07:51.8638894Z [  PASSED  ] 1 test.
2023-01-11T22:07:51.9339347Z + [[ -x ./cuda_tensor_interop_test ]]
2023-01-11T22:07:51.9339757Z + [[ -x ./cuda_complex_test ]]
2023-01-11T22:07:51.9340070Z + ./cuda_complex_test
2023-01-11T22:07:52.2477147Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:52.2477908Z [==========] Running 11 tests from 7 test suites.
2023-01-11T22:07:52.2478286Z [----------] Global test environment set-up.
2023-01-11T22:07:52.2478601Z [----------] 2 tests from TestMemory
2023-01-11T22:07:52.2478944Z [ RUN      ] TestMemory.ReinterpretCast
2023-01-11T22:07:52.2479470Z [       OK ] TestMemory.ReinterpretCast (0 ms)
2023-01-11T22:07:52.2479945Z [ RUN      ] TestMemory.ThrustReinterpretCast
2023-01-11T22:07:52.2480369Z [       OK ] TestMemory.ThrustReinterpretCast (0 ms)
2023-01-11T22:07:52.2480830Z [----------] 2 tests from TestMemory (0 ms total)
2023-01-11T22:07:52.2481024Z 
2023-01-11T22:07:52.2481493Z [----------] 2 tests from TestConstructors
2023-01-11T22:07:52.2481953Z [ RUN      ] TestConstructors.FromThrust
2023-01-11T22:07:52.2482454Z [       OK ] TestConstructors.FromThrust (0 ms)
2023-01-11T22:07:52.2482956Z [ RUN      ] TestConstructors.UnorderedMap
2023-01-11T22:07:52.2483521Z [       OK ] TestConstructors.UnorderedMap (0 ms)
2023-01-11T22:07:52.2484120Z [----------] 2 tests from TestConstructors (0 ms total)
2023-01-11T22:07:52.2484288Z 
2023-01-11T22:07:52.2484440Z [----------] 1 test from TestAssignment
2023-01-11T22:07:52.2484741Z [ RUN      ] TestAssignment.FromThrust
2023-01-11T22:07:52.2485030Z [       OK ] TestAssignment.FromThrust (0 ms)
2023-01-11T22:07:52.2485351Z [----------] 1 test from TestAssignment (0 ms total)
2023-01-11T22:07:52.2485502Z 
2023-01-11T22:07:52.2485675Z [----------] 1 test from TestArithmeticIntScalar
2023-01-11T22:07:52.2485981Z [ RUN      ] TestArithmeticIntScalar.All
2023-01-11T22:07:52.2486283Z [       OK ] TestArithmeticIntScalar.All (0 ms)
2023-01-11T22:07:52.2486630Z [----------] 1 test from TestArithmeticIntScalar (0 ms total)
2023-01-11T22:07:52.2486805Z 
2023-01-11T22:07:52.2486937Z [----------] 1 test from TestIO
2023-01-11T22:07:52.2487161Z [ RUN      ] TestIO.All
2023-01-11T22:07:52.2487398Z [       OK ] TestIO.All (0 ms)
2023-01-11T22:07:52.2487669Z [----------] 1 test from TestIO (0 ms total)
2023-01-11T22:07:52.2487807Z 
2023-01-11T22:07:52.2487939Z [----------] 1 test from TestStd
2023-01-11T22:07:52.2488192Z [ RUN      ] TestStd.BasicFunctions
2023-01-11T22:07:52.2488481Z [       OK ] TestStd.BasicFunctions (0 ms)
2023-01-11T22:07:52.2488776Z [----------] 1 test from TestStd (0 ms total)
2023-01-11T22:07:52.2488903Z 
2023-01-11T22:07:52.2489443Z [----------] 3 tests from DeviceTests
2023-01-11T22:07:52.2489801Z [ RUN      ] DeviceTests.ThrustConversion
2023-01-11T22:07:52.2490125Z [       OK ] DeviceTests.ThrustConversion (0 ms)
2023-01-11T22:07:52.2490414Z [ RUN      ] DeviceTests.StdFunctions
2023-01-11T22:07:52.2490711Z [       OK ] DeviceTests.StdFunctions (0 ms)
2023-01-11T22:07:52.2491013Z [ RUN      ] DeviceTests.ReinterpretCast
2023-01-11T22:07:52.2491317Z [       OK ] DeviceTests.ReinterpretCast (0 ms)
2023-01-11T22:07:52.2491640Z [----------] 3 tests from DeviceTests (0 ms total)
2023-01-11T22:07:52.2491788Z 
2023-01-11T22:07:52.2491954Z [----------] Global test environment tear-down
2023-01-11T22:07:52.2492265Z [==========] 11 tests from 7 test suites ran. (0 ms total)
2023-01-11T22:07:52.2492507Z [  PASSED  ] 11 tests.
2023-01-11T22:07:52.3187270Z + [[ -x ./cuda_complex_math_test ]]
2023-01-11T22:07:52.3187505Z + ./cuda_complex_math_test
2023-01-11T22:07:52.6304704Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:52.6305578Z [==========] Running 30 tests from 24 test suites.
2023-01-11T22:07:52.6306697Z [----------] Global test environment set-up.
2023-01-11T22:07:52.6307237Z [----------] 2 tests from TestExponentialDevice
2023-01-11T22:07:52.6307817Z [ RUN      ] TestExponentialDevice.IPi
2023-01-11T22:07:52.6308322Z [       OK ] TestExponentialDevice.IPi (0 ms)
2023-01-11T22:07:52.6308820Z [ RUN      ] TestExponentialDevice.EulerFormula
2023-01-11T22:07:52.6309289Z [       OK ] TestExponentialDevice.EulerFormula (0 ms)
2023-01-11T22:07:52.6309658Z [----------] 2 tests from TestExponentialDevice (0 ms total)
2023-01-11T22:07:52.6309886Z 
2023-01-11T22:07:52.6310069Z [----------] 1 test from TestLogDevice
2023-01-11T22:07:52.6310414Z [ RUN      ] TestLogDevice.Definition
2023-01-11T22:07:52.6310795Z [       OK ] TestLogDevice.Definition (0 ms)
2023-01-11T22:07:52.6311567Z [----------] 1 test from TestLogDevice (0 ms total)
2023-01-11T22:07:52.6311741Z 
2023-01-11T22:07:52.6311897Z [----------] 1 test from TestLog10Device
2023-01-11T22:07:52.6312172Z [ RUN      ] TestLog10Device.Rev
2023-01-11T22:07:52.6312444Z [       OK ] TestLog10Device.Rev (0 ms)
2023-01-11T22:07:52.6312748Z [----------] 1 test from TestLog10Device (0 ms total)
2023-01-11T22:07:52.6312989Z 
2023-01-11T22:07:52.6313286Z [----------] 1 test from TestLog2Device
2023-01-11T22:07:52.6313767Z [ RUN      ] TestLog2Device.Rev
2023-01-11T22:07:52.6314185Z [       OK ] TestLog2Device.Rev (0 ms)
2023-01-11T22:07:52.6314676Z [----------] 1 test from TestLog2Device (0 ms total)
2023-01-11T22:07:52.6314932Z 
2023-01-11T22:07:52.6315184Z [----------] 3 tests from TestLog1pDevice
2023-01-11T22:07:52.6315628Z [ RUN      ] TestLog1pDevice.Normal
2023-01-11T22:07:52.6316126Z [       OK ] TestLog1pDevice.Normal (0 ms)
2023-01-11T22:07:52.6316636Z [ RUN      ] TestLog1pDevice.Small
2023-01-11T22:07:52.6317150Z [       OK ] TestLog1pDevice.Small (0 ms)
2023-01-11T22:07:52.6317653Z [ RUN      ] TestLog1pDevice.Extreme
2023-01-11T22:07:52.6318033Z [       OK ] TestLog1pDevice.Extreme (0 ms)
2023-01-11T22:07:52.6318348Z [----------] 3 tests from TestLog1pDevice (0 ms total)
2023-01-11T22:07:52.6318503Z 
2023-01-11T22:07:52.6318647Z [----------] 1 test from TestPowSqrtDevice
2023-01-11T22:07:52.6318929Z [ RUN      ] TestPowSqrtDevice.Equal
2023-01-11T22:07:52.6319218Z [       OK ] TestPowSqrtDevice.Equal (0 ms)
2023-01-11T22:07:52.6319536Z [----------] 1 test from TestPowSqrtDevice (0 ms total)
2023-01-11T22:07:52.6319681Z 
2023-01-11T22:07:52.6319825Z [----------] 1 test from TestPowDevice
2023-01-11T22:07:52.6320092Z [ RUN      ] TestPowDevice.Square
2023-01-11T22:07:52.6320371Z [       OK ] TestPowDevice.Square (0 ms)
2023-01-11T22:07:52.6320703Z [----------] 1 test from TestPowDevice (0 ms total)
2023-01-11T22:07:52.6320853Z 
2023-01-11T22:07:52.6321014Z [----------] 1 test from TestSinCosSinhCoshDevice
2023-01-11T22:07:52.6321332Z [ RUN      ] TestSinCosSinhCoshDevice.Identity
2023-01-11T22:07:52.6321672Z [       OK ] TestSinCosSinhCoshDevice.Identity (0 ms)
2023-01-11T22:07:52.6322032Z [----------] 1 test from TestSinCosSinhCoshDevice (0 ms total)
2023-01-11T22:07:52.6322191Z 
2023-01-11T22:07:52.6322334Z [----------] 1 test from TestTanDevice
2023-01-11T22:07:52.6322605Z [ RUN      ] TestTanDevice.Identity
2023-01-11T22:07:52.6322893Z [       OK ] TestTanDevice.Identity (0 ms)
2023-01-11T22:07:52.6323186Z [----------] 1 test from TestTanDevice (0 ms total)
2023-01-11T22:07:52.6323335Z 
2023-01-11T22:07:52.6323479Z [----------] 1 test from TestTanhDevice
2023-01-11T22:07:52.6323752Z [ RUN      ] TestTanhDevice.Identity
2023-01-11T22:07:52.6324032Z [       OK ] TestTanhDevice.Identity (0 ms)
2023-01-11T22:07:52.6324428Z [----------] 1 test from TestTanhDevice (0 ms total)
2023-01-11T22:07:52.6324579Z 
2023-01-11T22:07:52.6324759Z [----------] 1 test from TestRevTrigonometricDevice
2023-01-11T22:07:52.6325075Z [ RUN      ] TestRevTrigonometricDevice.Rev
2023-01-11T22:07:52.6325391Z [       OK ] TestRevTrigonometricDevice.Rev (0 ms)
2023-01-11T22:07:52.6325748Z [----------] 1 test from TestRevTrigonometricDevice (0 ms total)
2023-01-11T22:07:52.6325922Z 
2023-01-11T22:07:52.6326093Z [----------] 1 test from TestRevHyperbolicDevice
2023-01-11T22:07:52.6326386Z [ RUN      ] TestRevHyperbolicDevice.Rev
2023-01-11T22:07:52.6326698Z [       OK ] TestRevHyperbolicDevice.Rev (0 ms)
2023-01-11T22:07:52.6327048Z [----------] 1 test from TestRevHyperbolicDevice (0 ms total)
2023-01-11T22:07:52.6327220Z 
2023-01-11T22:07:52.6327427Z [----------] 2 tests from TestExponentialHost
2023-01-11T22:07:52.6327710Z [ RUN      ] TestExponentialHost.IPi
2023-01-11T22:07:52.6328010Z [       OK ] TestExponentialHost.IPi (0 ms)
2023-01-11T22:07:52.6328327Z [ RUN      ] TestExponentialHost.EulerFormula
2023-01-11T22:07:52.6328654Z [       OK ] TestExponentialHost.EulerFormula (0 ms)
2023-01-11T22:07:52.6329009Z [----------] 2 tests from TestExponentialHost (0 ms total)
2023-01-11T22:07:52.6329395Z 
2023-01-11T22:07:52.6329614Z [----------] 1 test from TestLogHost
2023-01-11T22:07:52.6329961Z [ RUN      ] TestLogHost.Definition
2023-01-11T22:07:52.6330378Z [       OK ] TestLogHost.Definition (0 ms)
2023-01-11T22:07:52.6330964Z [----------] 1 test from TestLogHost (0 ms total)
2023-01-11T22:07:52.6331180Z 
2023-01-11T22:07:52.6331329Z [----------] 1 test from TestLog10Host
2023-01-11T22:07:52.6331587Z [ RUN      ] TestLog10Host.Rev
2023-01-11T22:07:52.6331854Z [       OK ] TestLog10Host.Rev (0 ms)
2023-01-11T22:07:52.6332258Z [----------] 1 test from TestLog10Host (0 ms total)
2023-01-11T22:07:52.6332486Z 
2023-01-11T22:07:52.6332732Z [----------] 1 test from TestLog2Host
2023-01-11T22:07:52.6333209Z [ RUN      ] TestLog2Host.Rev
2023-01-11T22:07:52.6333495Z [       OK ] TestLog2Host.Rev (0 ms)
2023-01-11T22:07:52.6333779Z [----------] 1 test from TestLog2Host (0 ms total)
2023-01-11T22:07:52.6333930Z 
2023-01-11T22:07:52.6334080Z [----------] 3 tests from TestLog1pHost
2023-01-11T22:07:52.6334355Z [ RUN      ] TestLog1pHost.Normal
2023-01-11T22:07:52.6334635Z [       OK ] TestLog1pHost.Normal (0 ms)
2023-01-11T22:07:52.6334896Z [ RUN      ] TestLog1pHost.Small
2023-01-11T22:07:52.6335165Z [       OK ] TestLog1pHost.Small (0 ms)
2023-01-11T22:07:52.6335438Z [ RUN      ] TestLog1pHost.Extreme
2023-01-11T22:07:52.6335709Z [       OK ] TestLog1pHost.Extreme (0 ms)
2023-01-11T22:07:52.6336019Z [----------] 3 tests from TestLog1pHost (0 ms total)
2023-01-11T22:07:52.6336169Z 
2023-01-11T22:07:52.6336319Z [----------] 1 test from TestPowSqrtHost
2023-01-11T22:07:52.6336583Z [ RUN      ] TestPowSqrtHost.Equal
2023-01-11T22:07:52.6336869Z [       OK ] TestPowSqrtHost.Equal (0 ms)
2023-01-11T22:07:52.6337178Z [----------] 1 test from TestPowSqrtHost (0 ms total)
2023-01-11T22:07:52.6337328Z 
2023-01-11T22:07:52.6337471Z [----------] 1 test from TestPowHost
2023-01-11T22:07:52.6337716Z [ RUN      ] TestPowHost.Square
2023-01-11T22:07:52.6337989Z [       OK ] TestPowHost.Square (0 ms)
2023-01-11T22:07:52.6338284Z [----------] 1 test from TestPowHost (0 ms total)
2023-01-11T22:07:52.6338428Z 
2023-01-11T22:07:52.6338583Z [----------] 1 test from TestSinCosSinhCoshHost
2023-01-11T22:07:52.6338894Z [ RUN      ] TestSinCosSinhCoshHost.Identity
2023-01-11T22:07:52.6339222Z [       OK ] TestSinCosSinhCoshHost.Identity (0 ms)
2023-01-11T22:07:52.6339684Z [----------] 1 test from TestSinCosSinhCoshHost (0 ms total)
2023-01-11T22:07:52.6339839Z 
2023-01-11T22:07:52.6339977Z [----------] 1 test from TestTanHost
2023-01-11T22:07:52.6340240Z [ RUN      ] TestTanHost.Identity
2023-01-11T22:07:52.6340514Z [       OK ] TestTanHost.Identity (0 ms)
2023-01-11T22:07:52.6340802Z [----------] 1 test from TestTanHost (0 ms total)
2023-01-11T22:07:52.6340946Z 
2023-01-11T22:07:52.6341086Z [----------] 1 test from TestTanhHost
2023-01-11T22:07:52.6341371Z [ RUN      ] TestTanhHost.Identity
2023-01-11T22:07:52.6341650Z [       OK ] TestTanhHost.Identity (0 ms)
2023-01-11T22:07:52.6341939Z [----------] 1 test from TestTanhHost (0 ms total)
2023-01-11T22:07:52.6342084Z 
2023-01-11T22:07:52.6342387Z [----------] 1 test from TestRevTrigonometricHost
2023-01-11T22:07:52.6342705Z [ RUN      ] TestRevTrigonometricHost.Rev
2023-01-11T22:07:52.6343013Z [       OK ] TestRevTrigonometricHost.Rev (0 ms)
2023-01-11T22:07:52.6343362Z [----------] 1 test from TestRevTrigonometricHost (0 ms total)
2023-01-11T22:07:52.6343534Z 
2023-01-11T22:07:52.6343701Z [----------] 1 test from TestRevHyperbolicHost
2023-01-11T22:07:52.6343994Z [ RUN      ] TestRevHyperbolicHost.Rev
2023-01-11T22:07:52.6344282Z [       OK ] TestRevHyperbolicHost.Rev (0 ms)
2023-01-11T22:07:52.6344616Z [----------] 1 test from TestRevHyperbolicHost (0 ms total)
2023-01-11T22:07:52.6344779Z 
2023-01-11T22:07:52.6344944Z [----------] Global test environment tear-down
2023-01-11T22:07:52.6345241Z [==========] 30 tests from 24 test suites ran. (0 ms total)
2023-01-11T22:07:52.6345497Z [  PASSED  ] 30 tests.
2023-01-11T22:07:52.7017516Z + [[ -x ./cuda_cub_test ]]
2023-01-11T22:07:52.7017852Z + ./cuda_cub_test
2023-01-11T22:07:53.0140026Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:53.0140791Z [==========] Running 3 tests from 3 test suites.
2023-01-11T22:07:53.0141099Z [----------] Global test environment set-up.
2023-01-11T22:07:53.0141387Z [----------] 1 test from NumBits
2023-01-11T22:07:53.0141638Z [ RUN      ] NumBits.CubTest
2023-01-11T22:07:53.0141897Z [       OK ] NumBits.CubTest (0 ms)
2023-01-11T22:07:53.0142169Z [----------] 1 test from NumBits (0 ms total)
2023-01-11T22:07:53.0142395Z 
2023-01-11T22:07:53.0142567Z [----------] 1 test from InclusiveScanSplit
2023-01-11T22:07:53.0142861Z [ RUN      ] InclusiveScanSplit.CubTest
2023-01-11T22:07:53.0143171Z [       OK ] InclusiveScanSplit.CubTest (0 ms)
2023-01-11T22:07:53.0143494Z [----------] 1 test from InclusiveScanSplit (0 ms total)
2023-01-11T22:07:53.0143663Z 
2023-01-11T22:07:53.0143822Z [----------] 1 test from ExclusiveScanSplit
2023-01-11T22:07:53.0144113Z [ RUN      ] ExclusiveScanSplit.CubTest
2023-01-11T22:07:53.0144410Z [       OK ] ExclusiveScanSplit.CubTest (0 ms)
2023-01-11T22:07:53.0144742Z [----------] 1 test from ExclusiveScanSplit (0 ms total)
2023-01-11T22:07:53.0144901Z 
2023-01-11T22:07:53.0145067Z [----------] Global test environment tear-down
2023-01-11T22:07:53.0145373Z [==========] 3 tests from 3 test suites ran. (0 ms total)
2023-01-11T22:07:53.0145618Z [  PASSED  ] 3 tests.
2023-01-11T22:07:53.0832025Z + [[ -x ./cuda_atomic_ops_test ]]
2023-01-11T22:07:53.0832312Z + ./cuda_atomic_ops_test
2023-01-11T22:07:53.3949915Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:07:53.3950606Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:07:53.3950935Z [----------] Global test environment set-up.
2023-01-11T22:07:53.3951247Z [----------] 4 tests from TestAtomicOps
2023-01-11T22:07:53.3951785Z [ RUN      ] TestAtomicOps.TestAtomicAdd
2023-01-11T22:07:53.3952109Z [       OK ] TestAtomicOps.TestAtomicAdd (0 ms)
2023-01-11T22:07:53.3952423Z [ RUN      ] TestAtomicOps.TestAtomicMul
2023-01-11T22:07:53.3952726Z [       OK ] TestAtomicOps.TestAtomicMul (0 ms)
2023-01-11T22:07:53.3953035Z [ RUN      ] TestAtomicOps.TestAtomicMax
2023-01-11T22:07:53.3953351Z [       OK ] TestAtomicOps.TestAtomicMax (0 ms)
2023-01-11T22:07:53.3953648Z [ RUN      ] TestAtomicOps.TestAtomicMin
2023-01-11T22:07:53.3953963Z [       OK ] TestAtomicOps.TestAtomicMin (0 ms)
2023-01-11T22:07:53.3954291Z [----------] 4 tests from TestAtomicOps (0 ms total)
2023-01-11T22:07:53.3954446Z 
2023-01-11T22:07:53.3954600Z [----------] Global test environment tear-down
2023-01-11T22:07:53.3954972Z [==========] 4 tests from 1 test suite ran. (0 ms total)
2023-01-11T22:07:53.3955237Z [  PASSED  ] 4 tests.
2023-01-11T22:07:53.4641268Z + '[' ON == ON ']'
2023-01-11T22:07:53.4641732Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 ./basic '--gtest_filter=-*CUDA'
2023-01-11T22:07:53.5011602Z ==24418== Memcheck, a memory error detector
2023-01-11T22:07:53.5012318Z ==24418== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
2023-01-11T22:07:53.5012742Z ==24418== Using Valgrind-3.16.1 and LibVEX; rerun with -h for copyright info
2023-01-11T22:07:53.5013062Z ==24418== Command: ./basic --gtest_filter=-*CUDA
2023-01-11T22:07:53.5013260Z ==24418== 
2023-01-11T22:07:56.8168451Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8169179Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8169804Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8170446Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8171078Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8171676Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8172203Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8172763Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8174280Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8174747Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8175270Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8175735Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8176189Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8176728Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8177251Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8177666Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8178050Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8178519Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8178957Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8179353Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8179708Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8180066Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8180473Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8180871Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8181175Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8181610Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8181938Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8182660Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8183035Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8183500Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8183872Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8184308Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8184692Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8185088Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8185613Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8186020Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8186397Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8186944Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8187376Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8187834Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8188228Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8188618Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8188966Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8189434Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8189906Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8190234Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8190740Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8191243Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8191618Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8192037Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8192438Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8192799Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8193342Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8193735Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8194157Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8194683Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8195206Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8195550Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8195975Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8196438Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8196870Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8197419Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8197871Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8198328Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8198776Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8199258Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8199620Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8200000Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8200324Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8200616Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8200957Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8201280Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8201664Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8202007Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8202333Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8202640Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8202964Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8203285Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8203591Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8203916Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8204237Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8204576Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8204901Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8205225Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8205523Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8205856Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8206162Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8206461Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8206795Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8207102Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8207403Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8207741Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8208060Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8208346Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8208678Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8209001Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8209595Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8210080Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8210611Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8211090Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8211659Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8212201Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8212503Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8212826Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8213151Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8213559Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8214131Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8214713Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8215247Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8215572Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8215890Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8216192Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8216750Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8217315Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8217838Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8218445Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8219012Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8219693Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8220083Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8220390Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8220694Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8221031Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8221358Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8221647Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8222022Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8222583Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8223126Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8223768Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8224311Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8224888Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8225631Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8226333Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8227353Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8228066Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8228754Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8229397Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8230103Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8230794Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8231445Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8232182Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8232862Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8233507Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8234236Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8234909Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8235553Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8236281Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8236951Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8237599Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8238336Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8239028Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8239660Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8240383Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8241066Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8241689Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8242419Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8243104Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8243722Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8244448Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8245138Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8245788Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8246499Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8247188Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8247942Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8248662Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8249420Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8249993Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8250396Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8250722Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8251023Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8251358Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8251665Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8252044Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8252383Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8252695Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8252997Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8253336Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8253642Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8253943Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8254279Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8254602Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8254887Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8255222Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8255543Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8255827Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8256165Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8256482Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8256769Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8257103Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8257419Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8257717Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8258040Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8258358Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8258659Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8258982Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8259297Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8259595Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8259919Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8260237Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8260536Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8260871Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8261176Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8261473Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8261809Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8262113Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8262488Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8262829Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8263135Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8263543Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8263882Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8264201Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8264487Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8264822Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8265146Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8265435Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8265771Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8266090Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8266408Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8266745Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8267066Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8267364Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8267687Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8268005Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8268302Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8268625Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8268946Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8269245Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8269568Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8269889Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8270187Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8270526Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8270829Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8271126Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8271459Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8271765Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8272062Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8272399Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8272702Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8272999Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8273336Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8273654Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8273946Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8274295Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8274612Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8274900Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8275236Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8275556Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8275842Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8276176Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8276492Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8276790Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8277115Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8277434Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8277769Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8278090Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8278408Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8278707Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8279029Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8279344Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8279642Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8279974Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8280280Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8280607Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8280944Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8281250Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8281551Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8281884Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8282189Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8282489Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8282821Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8283137Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8283422Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8283753Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8284074Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8284358Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8284694Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8285010Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8285296Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8285630Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8285947Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8286244Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8286562Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8286880Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8287176Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8287499Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8287816Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8288116Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8288433Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8288748Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8289174Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8289537Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8289842Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8290143Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8290479Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8290787Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8291094Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8291431Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8291803Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8292107Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8292444Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8292765Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8293054Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8293393Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8293713Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8293999Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8294333Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8294653Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8294977Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8295317Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8295638Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8295936Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8296256Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8296574Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8296871Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8297191Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8297510Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8297808Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8298131Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8298449Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8298750Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8299085Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8299390Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8299690Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8300024Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8300328Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8300627Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8300959Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8301265Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8301562Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8301898Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8302216Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8302576Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8302912Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8303233Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8303521Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8303860Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8304181Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8304466Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8304803Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8305125Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8305470Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8305794Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8306155Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8306453Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8306775Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8307093Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8307393Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8307713Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8308031Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8308330Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8308666Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8309003Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8309307Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8309642Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8309951Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8310249Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8310586Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8310890Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8311188Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8311524Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8311841Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8312129Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8312463Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8312782Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8313069Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8313402Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8313721Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8314007Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8314344Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8314662Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8314957Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8315277Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8315595Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8315895Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8316220Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8316537Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8316836Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8317158Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8317474Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8317773Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8318108Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8318412Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8318707Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8319040Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8319346Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8319647Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8319980Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8320338Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8320626Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8320959Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8321276Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8321560Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8321896Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8322212Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8322498Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8322836Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8323186Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8323487Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8323812Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8324128Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8324428Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8324751Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8325067Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8325364Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8325686Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8326004Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8326303Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8326641Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8326949Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8327244Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8327580Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8327884Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8328182Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8328515Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8328819Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8329249Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8329594Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8329917Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8330205Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8330546Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8330867Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8331159Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8331496Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8331813Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8332098Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8332432Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8332748Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8333046Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8333365Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8333683Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8333983Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8334306Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8334699Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8335004Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8335326Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8335646Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8335950Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8336288Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8336595Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8336900Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8337236Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8337585Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8337888Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8338224Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8338528Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8338827Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8339160Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8339477Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8339762Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8340101Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8340426Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8340711Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8341051Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8341370Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8341657Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8341990Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8342373Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8342681Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8343005Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8343327Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8343633Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8343958Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8344278Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8344581Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8344907Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8345234Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8345539Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8345872Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8346176Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8346478Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8346811Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8347114Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8347414Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8347746Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8348049Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8348350Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8348686Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8349044Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8349328Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8349663Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8349982Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8350266Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8350600Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8350916Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8351200Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8351534Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8351887Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8352189Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8352514Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8352831Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8353129Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8353451Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8353770Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8354068Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8354388Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8354705Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8355003Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8355340Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8355645Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8355947Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8356279Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8356584Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8356882Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8357215Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8357524Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8357821Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8358153Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8358472Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8358762Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8359096Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8359416Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8359702Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8360033Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8360350Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8360635Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8360968Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8361285Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8361583Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8361903Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8362220Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8362520Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8362839Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8363208Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8363502Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8363824Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8364142Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8364442Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8364782Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8365090Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8365392Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8365728Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8366065Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8366368Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8366702Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8367008Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8367305Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8367639Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8367954Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8368239Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8368573Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8368890Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8369308Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8369653Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8369973Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8370262Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8370602Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8370920Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8371222Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8371545Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8371862Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8372162Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8372481Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8372800Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:56.8373099Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:56.8373422Z --24418-- When reading debug info from /opt/conda/lib/libstdc++.so.6.0.29:
2023-01-11T22:07:56.8373772Z --24418-- parse_CU_Header: is neither DWARF2 nor DWARF3 nor DWARF4
2023-01-11T22:07:57.2822160Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2822770Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2823380Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2823784Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2824100Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2824422Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2824728Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2825057Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2825371Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2825675Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2826000Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2826485Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2826784Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2827111Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2827414Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2827717Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2828042Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2828359Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2828644Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2828967Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2829336Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2834969Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2835594Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2836102Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2836642Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2837241Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2837827Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2838377Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2838960Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2839527Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2840075Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2840570Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2841040Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2841496Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2841982Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2842440Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2842917Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2857178Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2857497Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2857801Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2858129Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2858433Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2858737Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2859067Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2859386Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2859677Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2860000Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2860312Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2860599Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2860921Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2861235Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2861640Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2861964Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2862278Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2862659Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2862972Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2863442Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2863740Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2864052Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2864364Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2864662Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2864973Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2865283Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2865585Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2865912Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2866213Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2866565Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2866893Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2867195Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2867496Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2867821Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2868120Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2868419Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2868743Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2869052Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2869341Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2869663Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2869977Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2870265Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2870590Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2870902Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2871188Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2871508Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2871817Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2872115Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2872423Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2872732Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2873027Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2873338Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2873650Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2873946Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2874257Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2874567Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2874863Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2875170Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2875479Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2875777Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2876096Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2876396Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2876691Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2877014Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2877311Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2877650Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2877973Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2878272Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2878571Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2878893Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2879200Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2879487Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2879810Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2880120Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2880407Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2880761Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2881075Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2881363Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2881685Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2881997Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2882293Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2882603Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2882913Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2883212Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2883522Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2883828Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2884127Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2884436Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2884748Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2885047Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2885368Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2885667Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2885966Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2886285Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2886583Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2886881Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2887203Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2887498Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2887800Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2888118Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2888428Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2888711Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2889144Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2889456Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2889740Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2890062Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2890370Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2890657Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2890979Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2891293Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2891578Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2891994Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2892304Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2892602Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2892910Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2893219Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2893515Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2893824Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2894133Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2894433Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2894781Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2895095Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2895389Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2895715Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2896014Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2896310Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2896631Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2896927Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2897225Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2897548Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2897847Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2898145Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2898473Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2898782Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2899071Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2899397Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2899706Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2899990Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2900311Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2900619Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2900903Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2901219Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2901526Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2901824Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2902134Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2902528Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2902834Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2903142Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2903452Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2903750Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2904061Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2904375Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2904672Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2904992Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2905288Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2905619Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2906004Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2906351Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2906649Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2906974Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2907274Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2907603Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2907928Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2908243Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2908533Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2908856Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2909202Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2909489Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2909812Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2910125Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2910414Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2910739Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2911050Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2911336Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2911656Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2911965Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2912264Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2912574Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2912884Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2913180Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2913491Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2913802Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2914100Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2914408Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2914719Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2915018Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2915337Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2915635Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2915932Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2916259Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2916555Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2916855Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2917179Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2917476Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2917781Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2918105Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2918417Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2918701Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2919020Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2919331Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2919616Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2919942Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2920249Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2920570Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2920889Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2921195Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2921491Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2921798Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2922104Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2922397Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2922707Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2923021Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2923390Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2923700Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2924011Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2924309Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2924630Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2924924Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2925223Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2925544Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2925843Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2926141Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2926461Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2926755Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2927056Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2927376Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2927688Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2927971Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2928292Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2928600Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2928886Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2929316Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2929628Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2929913Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2930236Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2930550Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2930836Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2931167Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2931477Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2931776Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2932083Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2932389Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2932687Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2932998Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2933307Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2933603Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2933914Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2934226Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2934525Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2934914Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2935211Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2935507Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2935828Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2936126Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2936422Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2936746Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2937046Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2937341Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2937731Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2938044Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2938330Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2938649Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2938958Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2939241Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2939560Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2939867Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2940152Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2940473Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2940779Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2941075Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2941385Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2941693Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2941990Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2942345Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2942666Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2942963Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2943274Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2943588Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2943886Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2944207Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2944504Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2944805Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2945128Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2945432Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2945730Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2946051Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2946347Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2946644Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2946964Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2947272Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2947555Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2947870Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2948179Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.2948460Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.2948783Z --24418-- When reading debug info from /opt/conda/lib/libgcc_s.so.1:
2023-01-11T22:07:57.2949163Z --24418-- parse_CU_Header: is neither DWARF2 nor DWARF3 nor DWARF4
2023-01-11T22:07:57.6679893Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6680534Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6680885Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6681202Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6681549Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6681878Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6682169Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6682506Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6682999Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6683290Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6683632Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6683953Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6690729Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6691438Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6691791Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6692104Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6692456Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6692790Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6693092Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6693440Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6693751Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6694060Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6694582Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6694893Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6695217Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6695591Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6695894Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6696199Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6696531Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6696844Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6697136Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6697464Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6697782Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6698067Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6698393Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6698706Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6698988Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6699323Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6699635Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6699937Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6700256Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6700569Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6700865Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6701182Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6701655Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6701954Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6702271Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6702669Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6702976Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6703492Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6703833Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6704182Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6704538Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6705020Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6705405Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6705747Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6706095Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6706419Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6706753Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6707141Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6707431Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6707827Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6708144Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6714486Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6715101Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6715691Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6716000Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6716474Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6716903Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6717350Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6717962Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6718309Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6718610Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6718930Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6719253Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6719552Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6719892Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6720196Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6720511Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6720845Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6721150Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6721451Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6721786Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6722102Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6722389Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6722718Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6723033Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6723322Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6723650Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6724072Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6727667Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6728176Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6728667Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6729002Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6729459Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6729940Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6730466Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6730938Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6731494Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6731860Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6732197Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6732513Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6732816Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6733151Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6733456Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6733758Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6734080Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6734379Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6734682Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6735010Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6735395Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6735685Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6736019Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6736333Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6736619Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6736947Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6737260Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6737542Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6737873Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6738186Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6738482Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6738799Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6739127Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6739429Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6739744Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6740055Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6740356Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6740687Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6740988Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6741285Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6741617Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6741917Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6742257Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6742675Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6743066Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6743367Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6743698Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6744014Z --24418-- Ignoring non-Dwarf2/3/4 block in .debug_info
2023-01-11T22:07:57.6744299Z --24418-- WARNING: Serious error when reading debug info
2023-01-11T22:07:57.6744628Z --24418-- When reading debug info from /opt/conda/lib/libgomp.so.1.0.0:
2023-01-11T22:07:57.6744977Z --24418-- parse_CU_Header: is neither DWARF2 nor DWARF3 nor DWARF4
2023-01-11T22:07:58.6589932Z ==24418== Warning: set address range perms: large range [0x59eb4000, 0x6ed02000) (defined)
2023-01-11T22:08:41.4424500Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2023-01-11T22:08:41.4633758Z Note: Google Test filter = -*CUDA
2023-01-11T22:08:41.4686831Z [==========] Running 4 tests from 1 test suite.
2023-01-11T22:08:41.4703739Z [----------] Global test environment set-up.
2023-01-11T22:08:41.4732093Z [----------] 4 tests from BasicTest
2023-01-11T22:08:41.4747258Z [ RUN      ] BasicTest.BasicTestCPU
2023-01-11T22:08:42.5886128Z    311 ms
2023-01-11T22:08:47.5406735Z    4920 ms
2023-01-11T22:08:54.1207601Z    6572 ms
2023-01-11T22:08:54.7627033Z [       OK ] BasicTest.BasicTestCPU (13285 ms)
2023-01-11T22:08:54.7632814Z [ RUN      ] BasicTest.BasicTestHalfCPU
2023-01-11T22:08:54.9529402Z    134 ms
2023-01-11T22:09:00.0073506Z    5046 ms
2023-01-11T22:09:06.9574277Z    6945 ms
2023-01-11T22:09:07.0852700Z [       OK ] BasicTest.BasicTestHalfCPU (12321 ms)
2023-01-11T22:09:07.0853044Z [ RUN      ] BasicTest.FactoryMethodsTest
2023-01-11T22:09:07.1213999Z [       OK ] BasicTest.FactoryMethodsTest (35 ms)
2023-01-11T22:09:07.1214616Z [ RUN      ] BasicTest.BasicStdTestCPU
2023-01-11T22:09:07.1612507Z Simple example: called once
2023-01-11T22:09:07.2827688Z throw: call_once will retry
2023-01-11T22:09:07.2859348Z throw: call_once will retry
2023-01-11T22:09:07.3266175Z throw: call_once will retry
2023-01-11T22:09:07.3288345Z Didn't throw, call_once will not attempt again
2023-01-11T22:09:07.3318266Z [       OK ] BasicTest.BasicStdTestCPU (209 ms)
2023-01-11T22:09:07.3327803Z [----------] 4 tests from BasicTest (25857 ms total)
2023-01-11T22:09:07.3327990Z 
2023-01-11T22:09:07.3338718Z [----------] Global test environment tear-down
2023-01-11T22:09:07.3366060Z [==========] 4 tests from 1 test suite ran. (25874 ms total)
2023-01-11T22:09:07.3380285Z [  PASSED  ] 4 tests.
2023-01-11T22:09:09.4213793Z ==24418== 
2023-01-11T22:09:09.4217499Z ==24418== HEAP SUMMARY:
2023-01-11T22:09:09.4217792Z ==24418==     in use at exit: 687,403 bytes in 4,089 blocks
2023-01-11T22:09:09.4220453Z ==24418==   total heap usage: 1,929,215 allocs, 1,925,126 frees, 311,460,255 bytes allocated
2023-01-11T22:09:09.4220872Z ==24418== 
2023-01-11T22:09:09.9631578Z ==24418== LEAK SUMMARY:
2023-01-11T22:09:09.9632021Z ==24418==    definitely lost: 0 bytes in 0 blocks
2023-01-11T22:09:09.9632417Z ==24418==    indirectly lost: 0 bytes in 0 blocks
2023-01-11T22:09:09.9632664Z ==24418==      possibly lost: 1,584 bytes in 3 blocks
2023-01-11T22:09:09.9632888Z ==24418==    still reachable: 685,819 bytes in 4,086 blocks
2023-01-11T22:09:09.9633121Z ==24418==         suppressed: 0 bytes in 0 blocks
2023-01-11T22:09:09.9633546Z ==24418== Rerun with --leak-check=full to see details of leaked memory
2023-01-11T22:09:09.9633759Z ==24418== 
2023-01-11T22:09:09.9634017Z ==24418== For lists of detected and suppressed errors, rerun with: -s
2023-01-11T22:09:09.9634311Z ==24418== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 4 from 4)
2023-01-11T22:09:10.0118626Z + [[ -x ./tensor_interop_test ]]
2023-01-11T22:09:10.0118934Z + popd
2023-01-11T22:09:10.0119179Z ~/workspace
2023-01-11T22:09:10.0120896Z + [[ -n '' ]]
2023-01-11T22:09:10.0121357Z + assert_git_not_dirty
2023-01-11T22:09:10.0121756Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:09:10.0122080Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:09:10.0125025Z ++ git status --porcelain
2023-01-11T22:09:10.0958022Z + git_status=
2023-01-11T22:09:10.0958463Z + [[ -n '' ]]
2023-01-11T22:09:10.0958745Z + test_vec256
2023-01-11T22:09:10.0959197Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *asan* ]]
2023-01-11T22:09:10.0959667Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:09:10.0959951Z + echo 'Testing vec256 instructions'
2023-01-11T22:09:10.0960153Z Testing vec256 instructions
2023-01-11T22:09:10.0960409Z + mkdir -p test/test-reports/vec256
2023-01-11T22:09:10.1005066Z + pushd build/bin
2023-01-11T22:09:10.1005293Z ~/workspace/build/bin ~/workspace
2023-01-11T22:09:10.1008203Z ++ find . -maxdepth 1 -executable -name 'vec256_test*'
2023-01-11T22:09:10.1074057Z + vec256_tests=
2023-01-11T22:09:10.1074320Z + popd
2023-01-11T22:09:10.1074618Z ~/workspace
2023-01-11T22:09:10.1074804Z + assert_git_not_dirty
2023-01-11T22:09:10.1075141Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:09:10.1075450Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:09:10.1077906Z ++ git status --porcelain
2023-01-11T22:09:10.1883399Z + git_status=
2023-01-11T22:09:10.1883836Z + [[ -n '' ]]
2023-01-11T22:09:10.1884104Z + test_libtorch
2023-01-11T22:09:10.1884541Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:09:10.1884941Z + echo 'Testing libtorch'
2023-01-11T22:09:10.1885264Z Testing libtorch
2023-01-11T22:09:10.1885940Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libbackend_with_compiler.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:10.1893693Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libjitbackend_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:10.1901963Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10d_cuda_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:10.1910853Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libshm.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:10.1920698Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda_linalg.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorchbind_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:10.1928802Z + ln -sf '/opt/conda/lib/python3.10/site-packages/torch/lib/libtbb*' /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:09:10.1938012Z + TEST_REPORTS_DIR=test/test-reports/cpp-unittest/test_libtorch
2023-01-11T22:09:10.1938663Z + mkdir -p test/test-reports/cpp-unittest/test_libtorch
2023-01-11T22:09:10.1939305Z + python tools/download_mnist.py --quiet -d test/cpp/api/mnist
2023-01-11T22:09:10.1962065Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *-tsan* ]]
2023-01-11T22:09:10.1962564Z + python test/cpp/jit/tests_setup.py setup
2023-01-11T22:09:10.2364312Z Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz ...
2023-01-11T22:09:10.5387389Z Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz ...
2023-01-11T22:09:10.5585400Z Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz ...
2023-01-11T22:09:10.6792875Z Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ...
2023-01-11T22:09:11.5734933Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *cuda* ]]
2023-01-11T22:09:11.5735486Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_jit --gtest_output=xml:test/test-reports/cpp-unittest/test_libtorch/test_jit.xml
2023-01-11T22:09:11.9884458Z CUDA not available. Disabling CUDA and MultiCUDA tests
2023-01-11T22:09:11.9892217Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:09:11.9892777Z [==========] Running 569 tests from 119 test suites.
2023-01-11T22:09:11.9893113Z [----------] Global test environment set-up.
2023-01-11T22:09:11.9893425Z [----------] 2 tests from AddIfThenElseOpTest
2023-01-11T22:09:11.9893761Z [ RUN      ] AddIfThenElseOpTest.AddIfThenElseOpSimple
2023-01-11T22:09:11.9954380Z [       OK ] AddIfThenElseOpTest.AddIfThenElseOpSimple (6 ms)
2023-01-11T22:09:11.9955024Z [ RUN      ] AddIfThenElseOpTest.NoIfThenElseOpMultipleOutputs
2023-01-11T22:09:11.9955704Z [       OK ] AddIfThenElseOpTest.NoIfThenElseOpMultipleOutputs (0 ms)
2023-01-11T22:09:11.9956103Z [----------] 2 tests from AddIfThenElseOpTest (6 ms total)
2023-01-11T22:09:11.9956285Z 
2023-01-11T22:09:11.9956515Z [----------] 15 tests from TopologicalMoveTest
2023-01-11T22:09:11.9956899Z [ RUN      ] TopologicalMoveTest.SplitsDeps
2023-01-11T22:09:11.9957493Z [       OK ] TopologicalMoveTest.SplitsDeps (0 ms)
2023-01-11T22:09:11.9957987Z [ RUN      ] TopologicalMoveTest.MoveAfterBackwardSimple
2023-01-11T22:09:11.9958404Z [       OK ] TopologicalMoveTest.MoveAfterBackwardSimple (0 ms)
2023-01-11T22:09:11.9958985Z [ RUN      ] TopologicalMoveTest.MoveAfterBackwardInvalid
2023-01-11T22:09:11.9959687Z [       OK ] TopologicalMoveTest.MoveAfterBackwardInvalid (0 ms)
2023-01-11T22:09:11.9960356Z [ RUN      ] TopologicalMoveTest.MoveAfterNoOp
2023-01-11T22:09:11.9961006Z [       OK ] TopologicalMoveTest.MoveAfterNoOp (0 ms)
2023-01-11T22:09:11.9961485Z [ RUN      ] TopologicalMoveTest.MoveAfterBackwardMultipleDeps
2023-01-11T22:09:11.9962254Z [       OK ] TopologicalMoveTest.MoveAfterBackwardMultipleDeps (0 ms)
2023-01-11T22:09:11.9963026Z [ RUN      ] TopologicalMoveTest.MoveAfterBackwardNonZeroWorkingSet
2023-01-11T22:09:11.9963758Z [       OK ] TopologicalMoveTest.MoveAfterBackwardNonZeroWorkingSet (0 ms)
2023-01-11T22:09:11.9964505Z [ RUN      ] TopologicalMoveTest.MoveAfterForwardSimple
2023-01-11T22:09:11.9965020Z [       OK ] TopologicalMoveTest.MoveAfterForwardSimple (0 ms)
2023-01-11T22:09:11.9965676Z [ RUN      ] TopologicalMoveTest.MoveAfterForwardNonZeroWorkingSet
2023-01-11T22:09:11.9966452Z [       OK ] TopologicalMoveTest.MoveAfterForwardNonZeroWorkingSet (0 ms)
2023-01-11T22:09:11.9967163Z [ RUN      ] TopologicalMoveTest.MoveBeforeForwardSimple
2023-01-11T22:09:11.9967907Z [       OK ] TopologicalMoveTest.MoveBeforeForwardSimple (0 ms)
2023-01-11T22:09:11.9968438Z [ RUN      ] TopologicalMoveTest.MoveBeforeBackwardSimple
2023-01-11T22:09:11.9969331Z [       OK ] TopologicalMoveTest.MoveBeforeBackwardSimple (0 ms)
2023-01-11T22:09:11.9969991Z [ RUN      ] TopologicalMoveTest.MoveBeforeNoOp
2023-01-11T22:09:11.9970602Z [       OK ] TopologicalMoveTest.MoveBeforeNoOp (0 ms)
2023-01-11T22:09:11.9971150Z [ RUN      ] TopologicalMoveTest.MoveBeforeForwardWithDeps
2023-01-11T22:09:11.9971854Z [       OK ] TopologicalMoveTest.MoveBeforeForwardWithDeps (0 ms)
2023-01-11T22:09:11.9972274Z [ RUN      ] TopologicalMoveTest.MoveBeforeBackwardWithDeps
2023-01-11T22:09:11.9973011Z [       OK ] TopologicalMoveTest.MoveBeforeBackwardWithDeps (0 ms)
2023-01-11T22:09:11.9973523Z [ RUN      ] TopologicalMoveTest.DepsDisallowMove
2023-01-11T22:09:11.9974268Z [       OK ] TopologicalMoveTest.DepsDisallowMove (0 ms)
2023-01-11T22:09:11.9974921Z [ RUN      ] TopologicalMoveTest.MoveAfterBeforeWithDeps
2023-01-11T22:09:11.9975336Z [       OK ] TopologicalMoveTest.MoveAfterBeforeWithDeps (0 ms)
2023-01-11T22:09:11.9975858Z [----------] 15 tests from TopologicalMoveTest (1 ms total)
2023-01-11T22:09:11.9976029Z 
2023-01-11T22:09:11.9976188Z [----------] 6 tests from AliasAnalysisTest
2023-01-11T22:09:11.9976544Z [ RUN      ] AliasAnalysisTest.AliasingMutationBlocksMoves
2023-01-11T22:09:12.0013557Z [       OK ] AliasAnalysisTest.AliasingMutationBlocksMoves (3 ms)
2023-01-11T22:09:12.0014140Z [ RUN      ] AliasAnalysisTest.AliasingMutationBlocksMoves2
2023-01-11T22:09:12.0014720Z [       OK ] AliasAnalysisTest.AliasingMutationBlocksMoves2 (0 ms)
2023-01-11T22:09:12.0015227Z [ RUN      ] AliasAnalysisTest.SideEffectsBlockMoves
2023-01-11T22:09:12.0015789Z [       OK ] AliasAnalysisTest.SideEffectsBlockMoves (0 ms)
2023-01-11T22:09:12.0016450Z [ RUN      ] AliasAnalysisTest.MovingAcrossInnerBlocks
2023-01-11T22:09:12.0016857Z [       OK ] AliasAnalysisTest.MovingAcrossInnerBlocks (0 ms)
2023-01-11T22:09:12.0017228Z [ RUN      ] AliasAnalysisTest.NoneHasNoWriters
2023-01-11T22:09:12.0017702Z [       OK ] AliasAnalysisTest.NoneHasNoWriters (0 ms)
2023-01-11T22:09:12.0018220Z [ RUN      ] AliasAnalysisTest.SafeToChangeAliasingRelationship
2023-01-11T22:09:12.0018895Z [       OK ] AliasAnalysisTest.SafeToChangeAliasingRelationship (0 ms)
2023-01-11T22:09:12.0019404Z [----------] 6 tests from AliasAnalysisTest (4 ms total)
2023-01-11T22:09:12.0019574Z 
2023-01-11T22:09:12.0019735Z [----------] 4 tests from WriteTrackingTest
2023-01-11T22:09:12.0020077Z [ RUN      ] WriteTrackingTest.Basic
2023-01-11T22:09:12.0020855Z [       OK ] WriteTrackingTest.Basic (0 ms)
2023-01-11T22:09:12.0021638Z [ RUN      ] WriteTrackingTest.IsMutable
2023-01-11T22:09:12.0024608Z [       OK ] WriteTrackingTest.IsMutable (0 ms)
2023-01-11T22:09:12.0025144Z [ RUN      ] WriteTrackingTest.IsImmutable
2023-01-11T22:09:12.0025739Z [       OK ] WriteTrackingTest.IsImmutable (0 ms)
2023-01-11T22:09:12.0026268Z [ RUN      ] WriteTrackingTest.HasWriters
2023-01-11T22:09:12.0026806Z [       OK ] WriteTrackingTest.HasWriters (0 ms)
2023-01-11T22:09:12.0027415Z [----------] 4 tests from WriteTrackingTest (0 ms total)
2023-01-11T22:09:12.0027702Z 
2023-01-11T22:09:12.0028041Z [----------] 13 tests from ContainerAliasingTest
2023-01-11T22:09:12.0028568Z [ RUN      ] ContainerAliasingTest.MayContainAlias
2023-01-11T22:09:12.0029007Z [       OK ] ContainerAliasingTest.MayContainAlias (0 ms)
2023-01-11T22:09:12.0029492Z [ RUN      ] ContainerAliasingTest.MayContainAlias_cast
2023-01-11T22:09:12.0030071Z [       OK ] ContainerAliasingTest.MayContainAlias_cast (0 ms)
2023-01-11T22:09:12.0030858Z [ RUN      ] ContainerAliasingTest.PrimitveValuesDontAliasContainers
2023-01-11T22:09:12.0031699Z [       OK ] ContainerAliasingTest.PrimitveValuesDontAliasContainers (0 ms)
2023-01-11T22:09:12.0032290Z [ RUN      ] ContainerAliasingTest.UnionAliasing
2023-01-11T22:09:12.0032929Z [       OK ] ContainerAliasingTest.UnionAliasing (0 ms)
2023-01-11T22:09:12.0033363Z [ RUN      ] ContainerAliasingTest.InputsCanAliasOutputs
2023-01-11T22:09:12.0033784Z [       OK ] ContainerAliasingTest.InputsCanAliasOutputs (0 ms)
2023-01-11T22:09:12.0034350Z [ RUN      ] ContainerAliasingTest.NestedTupleConstruct
2023-01-11T22:09:12.0035096Z [       OK ] ContainerAliasingTest.NestedTupleConstruct (0 ms)
2023-01-11T22:09:12.0035498Z [ RUN      ] ContainerAliasingTest.NestedTypes
2023-01-11T22:09:12.0035977Z [       OK ] ContainerAliasingTest.NestedTypes (0 ms)
2023-01-11T22:09:12.0036343Z [ RUN      ] ContainerAliasingTest.Simple
2023-01-11T22:09:12.0036945Z [       OK ] ContainerAliasingTest.Simple (0 ms)
2023-01-11T22:09:12.0037387Z [ RUN      ] ContainerAliasingTest.Lists
2023-01-11T22:09:12.0037894Z [       OK ] ContainerAliasingTest.Lists (0 ms)
2023-01-11T22:09:12.0038386Z [ RUN      ] ContainerAliasingTest.Lists2
2023-01-11T22:09:12.0038974Z [       OK ] ContainerAliasingTest.Lists2 (0 ms)
2023-01-11T22:09:12.0039538Z [ RUN      ] ContainerAliasingTest.Conservative
2023-01-11T22:09:12.0040135Z [       OK ] ContainerAliasingTest.Conservative (0 ms)
2023-01-11T22:09:12.0040846Z [ RUN      ] ContainerAliasingTest.MovesAcrossContainedWrites
2023-01-11T22:09:12.0041639Z [       OK ] ContainerAliasingTest.MovesAcrossContainedWrites (0 ms)
2023-01-11T22:09:12.0042112Z [ RUN      ] ContainerAliasingTest.MovesAcrossContainedWritesNested
2023-01-11T22:09:12.0042786Z [       OK ] ContainerAliasingTest.MovesAcrossContainedWritesNested (0 ms)
2023-01-11T22:09:12.0043469Z [----------] 13 tests from ContainerAliasingTest (1 ms total)
2023-01-11T22:09:12.0043644Z 
2023-01-11T22:09:12.0043834Z [----------] 3 tests from WildcardsTest
2023-01-11T22:09:12.0044108Z [ RUN      ] WildcardsTest.Basic
2023-01-11T22:09:12.0044541Z [       OK ] WildcardsTest.Basic (0 ms)
2023-01-11T22:09:12.0045214Z [ RUN      ] WildcardsTest.TypeIsolation
2023-01-11T22:09:12.0045524Z [       OK ] WildcardsTest.TypeIsolation (0 ms)
2023-01-11T22:09:12.0045897Z [ RUN      ] WildcardsTest.InvariantContainerAliasing
2023-01-11T22:09:12.0046610Z [       OK ] WildcardsTest.InvariantContainerAliasing (0 ms)
2023-01-11T22:09:12.0047029Z [----------] 3 tests from WildcardsTest (0 ms total)
2023-01-11T22:09:12.0047186Z 
2023-01-11T22:09:12.0047360Z [----------] 18 tests from AliasRegistrationTest
2023-01-11T22:09:12.0047745Z [ RUN      ] AliasRegistrationTest.ConservativeWithInferredSchema
2023-01-11T22:09:12.0048206Z [       OK ] AliasRegistrationTest.ConservativeWithInferredSchema (0 ms)
2023-01-11T22:09:12.0048654Z [ RUN      ] AliasRegistrationTest.ConservativeWithSpecifiedSchema
2023-01-11T22:09:12.0049299Z [       OK ] AliasRegistrationTest.ConservativeWithSpecifiedSchema (0 ms)
2023-01-11T22:09:12.0049825Z [ RUN      ] AliasRegistrationTest.ConservativeWithAliasingAnnotationsShouldError
2023-01-11T22:09:12.0138347Z [       OK ] AliasRegistrationTest.ConservativeWithAliasingAnnotationsShouldError (9 ms)
2023-01-11T22:09:12.0138998Z [ RUN      ] AliasRegistrationTest.ConservativeWithAliasingAnnotationsShouldError2
2023-01-11T22:09:12.0185338Z [       OK ] AliasRegistrationTest.ConservativeWithAliasingAnnotationsShouldError2 (4 ms)
2023-01-11T22:09:12.0185894Z [ RUN      ] AliasRegistrationTest.FromSchemaWithInferredSchemaShouldError
2023-01-11T22:09:12.0199377Z [       OK ] AliasRegistrationTest.FromSchemaWithInferredSchemaShouldError (1 ms)
2023-01-11T22:09:12.0199844Z [ RUN      ] AliasRegistrationTest.FromSchemaInferredPure
2023-01-11T22:09:12.0201207Z [       OK ] AliasRegistrationTest.FromSchemaInferredPure (0 ms)
2023-01-11T22:09:12.0201602Z [ RUN      ] AliasRegistrationTest.FromSchemaAliased
2023-01-11T22:09:12.0202647Z [       OK ] AliasRegistrationTest.FromSchemaAliased (0 ms)
2023-01-11T22:09:12.0203016Z [ RUN      ] AliasRegistrationTest.FromSchemaPure
2023-01-11T22:09:12.0204405Z [       OK ] AliasRegistrationTest.FromSchemaPure (0 ms)
2023-01-11T22:09:12.0204824Z [ RUN      ] AliasRegistrationTest.PureNoSchema
2023-01-11T22:09:12.0205725Z [       OK ] AliasRegistrationTest.PureNoSchema (0 ms)
2023-01-11T22:09:12.0206161Z [ RUN      ] AliasRegistrationTest.PureWithSchema
2023-01-11T22:09:12.0207138Z [       OK ] AliasRegistrationTest.PureWithSchema (0 ms)
2023-01-11T22:09:12.0207543Z [ RUN      ] AliasRegistrationTest.PureWithAnnotationsShouldError
2023-01-11T22:09:12.0240777Z [       OK ] AliasRegistrationTest.PureWithAnnotationsShouldError (3 ms)
2023-01-11T22:09:12.0241459Z [ RUN      ] AliasRegistrationTest.AliasMoveAtenListOp
2023-01-11T22:09:12.0242048Z [       OK ] AliasRegistrationTest.AliasMoveAtenListOp (0 ms)
2023-01-11T22:09:12.0242802Z [ RUN      ] AliasRegistrationTest.AliasMoveForTupleConstructWithSingleUseAsGraphOutput
2023-01-11T22:09:12.0243791Z [       OK ] AliasRegistrationTest.AliasMoveForTupleConstructWithSingleUseAsGraphOutput (0 ms)
2023-01-11T22:09:12.0244635Z [ RUN      ] AliasRegistrationTest.RecursiveSubgraphTupleContainment
2023-01-11T22:09:12.0245230Z [       OK ] AliasRegistrationTest.RecursiveSubgraphTupleContainment (0 ms)
2023-01-11T22:09:12.0245902Z [ RUN      ] AliasRegistrationTest.WildcardAliasForTupleConstructWithUses
2023-01-11T22:09:12.0246517Z [       OK ] AliasRegistrationTest.WildcardAliasForTupleConstructWithUses (0 ms)
2023-01-11T22:09:12.0246988Z [ RUN      ] AliasRegistrationTest.ATenSplitIntListAliasCheck
2023-01-11T22:09:12.0247415Z [       OK ] AliasRegistrationTest.ATenSplitIntListAliasCheck (0 ms)
2023-01-11T22:09:12.0247830Z [ RUN      ] AliasRegistrationTest.ATenSplitIntAliasCheck
2023-01-11T22:09:12.0248246Z [       OK ] AliasRegistrationTest.ATenSplitIntAliasCheck (0 ms)
2023-01-11T22:09:12.0248684Z [ RUN      ] AliasRegistrationTest.PureWithAnnotationsShouldError2
2023-01-11T22:09:12.0280178Z [       OK ] AliasRegistrationTest.PureWithAnnotationsShouldError2 (3 ms)
2023-01-11T22:09:12.0280995Z [----------] 18 tests from AliasRegistrationTest (23 ms total)
2023-01-11T22:09:12.0281303Z 
2023-01-11T22:09:12.0281628Z [----------] 2 tests from IRNonDeterminismTest
2023-01-11T22:09:12.0282167Z [ RUN      ] IRNonDeterminismTest.Basic
2023-01-11T22:09:12.0282740Z [       OK ] IRNonDeterminismTest.Basic (0 ms)
2023-01-11T22:09:12.0283307Z [ RUN      ] IRNonDeterminismTest.DropoutSpecialCase
2023-01-11T22:09:12.0283934Z [       OK ] IRNonDeterminismTest.DropoutSpecialCase (0 ms)
2023-01-11T22:09:12.0284503Z [----------] 2 tests from IRNonDeterminismTest (0 ms total)
2023-01-11T22:09:12.0284764Z 
2023-01-11T22:09:12.0285118Z [----------] 1 test from NonDeterminismBackwardsCompatibility
2023-01-11T22:09:12.0286034Z [ RUN      ] NonDeterminismBackwardsCompatibility.BackwardsCompatibility
2023-01-11T22:09:12.0286991Z [       OK ] NonDeterminismBackwardsCompatibility.BackwardsCompatibility (0 ms)
2023-01-11T22:09:12.0287852Z [----------] 1 test from NonDeterminismBackwardsCompatibility (0 ms total)
2023-01-11T22:09:12.0288221Z 
2023-01-11T22:09:12.0288486Z [----------] 3 tests from AutodiffTest
2023-01-11T22:09:12.0289007Z [ RUN      ] AutodiffTest.ADFormulas
2023-01-11T22:09:12.0752168Z [       OK ] AutodiffTest.ADFormulas (46 ms)
2023-01-11T22:09:12.0752510Z [ RUN      ] AutodiffTest.Differentiate
2023-01-11T22:09:12.0757837Z [       OK ] AutodiffTest.Differentiate (0 ms)
2023-01-11T22:09:12.0758203Z [ RUN      ] AutodiffTest.DifferentiateWithRequiresGrad
2023-01-11T22:09:12.0776789Z [       OK ] AutodiffTest.DifferentiateWithRequiresGrad (1 ms)
2023-01-11T22:09:12.0777471Z [----------] 3 tests from AutodiffTest (49 ms total)
2023-01-11T22:09:12.0777630Z 
2023-01-11T22:09:12.0777843Z [----------] 1 test from AutodiffRemoveUnusedGradientsTest
2023-01-11T22:09:12.0778214Z [ RUN      ] AutodiffRemoveUnusedGradientsTest.Linear
2023-01-11T22:09:12.0790153Z [       OK ] AutodiffRemoveUnusedGradientsTest.Linear (1 ms)
2023-01-11T22:09:12.0790926Z [----------] 1 test from AutodiffRemoveUnusedGradientsTest (1 ms total)
2023-01-11T22:09:12.0791168Z 
2023-01-11T22:09:12.0791329Z [----------] 1 test from UpgraderLoad
2023-01-11T22:09:12.0791711Z [ RUN      ] UpgraderLoad.CanPopulateUpgradersGraph
2023-01-11T22:09:12.0832683Z [       OK ] UpgraderLoad.CanPopulateUpgradersGraph (4 ms)
2023-01-11T22:09:12.0833090Z [----------] 1 test from UpgraderLoad (4 ms total)
2023-01-11T22:09:12.0833245Z 
2023-01-11T22:09:12.0833407Z [----------] 4 tests from OpReplacementTest
2023-01-11T22:09:12.0833747Z [ RUN      ] OpReplacementTest.ReplaceDivInSimpleFunction
2023-01-11T22:09:12.0834934Z [       OK ] OpReplacementTest.ReplaceDivInSimpleFunction (0 ms)
2023-01-11T22:09:12.0835387Z [ RUN      ] OpReplacementTest.ReplaceTwoOpsInSimpleFunction
2023-01-11T22:09:12.0836942Z [       OK ] OpReplacementTest.ReplaceTwoOpsInSimpleFunction (0 ms)
2023-01-11T22:09:12.0837353Z [ RUN      ] OpReplacementTest.ReplaceDivInNestedFunction
2023-01-11T22:09:12.0839228Z [       OK ] OpReplacementTest.ReplaceDivInNestedFunction (0 ms)
2023-01-11T22:09:12.0839739Z [ RUN      ] OpReplacementTest.ReplaceTestSubcmulInSimpleFunction
2023-01-11T22:09:12.0840751Z [       OK ] OpReplacementTest.ReplaceTestSubcmulInSimpleFunction (0 ms)
2023-01-11T22:09:12.0841373Z [----------] 4 tests from OpReplacementTest (0 ms total)
2023-01-11T22:09:12.0841634Z 
2023-01-11T22:09:12.0841799Z [----------] 4 tests from UpgraderUtils
2023-01-11T22:09:12.0842110Z [ RUN      ] UpgraderUtils.FindCorrectUpgrader
2023-01-11T22:09:12.0842445Z [       OK ] UpgraderUtils.FindCorrectUpgrader (0 ms)
2023-01-11T22:09:12.0842784Z [ RUN      ] UpgraderUtils.IsVersionMapSorted
2023-01-11T22:09:12.0843125Z [       OK ] UpgraderUtils.IsVersionMapSorted (0 ms)
2023-01-11T22:09:12.0843444Z [ RUN      ] UpgraderUtils.FindIfOpIsCurrent
2023-01-11T22:09:12.0843903Z [       OK ] UpgraderUtils.FindIfOpIsCurrent (0 ms)
2023-01-11T22:09:12.0844378Z [ RUN      ] UpgraderUtils.CanLoadHistoricOp
2023-01-11T22:09:12.0844825Z [       OK ] UpgraderUtils.CanLoadHistoricOp (0 ms)
2023-01-11T22:09:12.0845399Z [----------] 4 tests from UpgraderUtils (0 ms total)
2023-01-11T22:09:12.0845570Z 
2023-01-11T22:09:12.0845725Z [----------] 9 tests from BackendTest
2023-01-11T22:09:12.0845996Z [ RUN      ] BackendTest.ToBackend
2023-01-11T22:09:12.0890885Z [       OK ] BackendTest.ToBackend (4 ms)
2023-01-11T22:09:12.0891208Z [ RUN      ] BackendTest.ToBackendNotAvailable
2023-01-11T22:09:12.0912721Z [W backend_detail.cpp:393] Warning: Backend [test_backend_unavailable] is not available. Execution of this Module is still possible by saving and loading on a device where the backend is available. (function codegen_backend_module)
2023-01-11T22:09:12.0929969Z [       OK ] BackendTest.ToBackendNotAvailable (3 ms)
2023-01-11T22:09:12.0930290Z [ RUN      ] BackendTest.TestCompiler
2023-01-11T22:09:12.0987999Z [       OK ] BackendTest.TestCompiler (5 ms)
2023-01-11T22:09:12.0988393Z [ RUN      ] BackendTest.TestCompilerWithStringTable
2023-01-11T22:09:12.1042407Z [       OK ] BackendTest.TestCompilerWithStringTable (5 ms)
2023-01-11T22:09:12.1042801Z [ RUN      ] BackendTest.TestComposite
2023-01-11T22:09:12.1146627Z [       OK ] BackendTest.TestComposite (10 ms)
2023-01-11T22:09:12.1147126Z [ RUN      ] BackendTest.TestPrimDtype
2023-01-11T22:09:12.1152793Z [       OK ] BackendTest.TestPrimDtype (0 ms)
2023-01-11T22:09:12.1153229Z [ RUN      ] BackendTest.TestCompositeWithSetStates
2023-01-11T22:09:12.1255866Z [       OK ] BackendTest.TestCompositeWithSetStates (10 ms)
2023-01-11T22:09:12.1256377Z [ RUN      ] BackendTest.TestConsistencyOfCompositeWithSetStates
2023-01-11T22:09:12.1445658Z [       OK ] BackendTest.TestConsistencyOfCompositeWithSetStates (18 ms)
2023-01-11T22:09:12.1446153Z [ RUN      ] BackendTest.TestCompilerNotSupport
2023-01-11T22:09:12.1465437Z [       OK ] BackendTest.TestCompilerNotSupport (1 ms)
2023-01-11T22:09:12.1466082Z [----------] 9 tests from BackendTest (62 ms total)
2023-01-11T22:09:12.1466241Z 
2023-01-11T22:09:12.1466411Z [----------] 6 tests from BackendTestDebugInfo
2023-01-11T22:09:12.1466724Z [ RUN      ] BackendTestDebugInfo.TestCompiler
2023-01-11T22:09:12.1592883Z [       OK ] BackendTestDebugInfo.TestCompiler (12 ms)
2023-01-11T22:09:12.1593306Z [ RUN      ] BackendTestDebugInfo.TestCompilerWithStringTable
2023-01-11T22:09:12.1733108Z [       OK ] BackendTestDebugInfo.TestCompilerWithStringTable (14 ms)
2023-01-11T22:09:12.1733639Z [ RUN      ] BackendTestDebugInfo.TestExceptionStackForCompilerWithModuleHierarchy
2023-01-11T22:09:12.1870653Z [       OK ] BackendTestDebugInfo.TestExceptionStackForCompilerWithModuleHierarchy (13 ms)
2023-01-11T22:09:12.1871417Z [ RUN      ] BackendTestDebugInfo.TestExceptionStackForCompilerWithTwoLevelModuleHierarchy
2023-01-11T22:09:12.2002784Z [       OK ] BackendTestDebugInfo.TestExceptionStackForCompilerWithTwoLevelModuleHierarchy (13 ms)
2023-01-11T22:09:12.2003426Z [ RUN      ] BackendTestDebugInfo.TestExceptionStackForCompilerWithLoweredSubModule
2023-01-11T22:09:12.2141331Z [       OK ] BackendTestDebugInfo.TestExceptionStackForCompilerWithLoweredSubModule (13 ms)
2023-01-11T22:09:12.2142025Z [ RUN      ] BackendTestDebugInfo.TestExceptionStackForCompilerWithSelectiveLoweredSubModule
2023-01-11T22:09:12.2273856Z [       OK ] BackendTestDebugInfo.TestExceptionStackForCompilerWithSelectiveLoweredSubModule (13 ms)
2023-01-11T22:09:12.2274384Z [----------] 6 tests from BackendTestDebugInfo (80 ms total)
2023-01-11T22:09:12.2274548Z 
2023-01-11T22:09:12.2274704Z [----------] 4 tests from ClassImportTest
2023-01-11T22:09:12.2274989Z [ RUN      ] ClassImportTest.Basic
2023-01-11T22:09:12.2280224Z [       OK ] ClassImportTest.Basic (0 ms)
2023-01-11T22:09:12.2280558Z [ RUN      ] ClassImportTest.ScriptObject
2023-01-11T22:09:12.2306418Z [       OK ] ClassImportTest.ScriptObject (2 ms)
2023-01-11T22:09:12.2306862Z [ RUN      ] ClassImportTest.ClassDerive
2023-01-11T22:09:12.2307559Z [       OK ] ClassImportTest.ClassDerive (0 ms)
2023-01-11T22:09:12.2307905Z [ RUN      ] ClassImportTest.CustomClass
2023-01-11T22:09:12.2309622Z [       OK ] ClassImportTest.CustomClass (0 ms)
2023-01-11T22:09:12.2310307Z [----------] 4 tests from ClassImportTest (3 ms total)
2023-01-11T22:09:12.2310611Z 
2023-01-11T22:09:12.2310879Z [----------] 1 test from ClassParserTest
2023-01-11T22:09:12.2311364Z [ RUN      ] ClassParserTest.Basic
2023-01-11T22:09:12.2311662Z [       OK ] ClassParserTest.Basic (0 ms)
2023-01-11T22:09:12.2311982Z [----------] 1 test from ClassParserTest (0 ms total)
2023-01-11T22:09:12.2312123Z 
2023-01-11T22:09:12.2312272Z [----------] 3 tests from ClassTypeTest
2023-01-11T22:09:12.2312564Z [ RUN      ] ClassTypeTest.AddRemoveAttr
2023-01-11T22:09:12.2312878Z [       OK ] ClassTypeTest.AddRemoveAttr (0 ms)
2023-01-11T22:09:12.2313181Z [ RUN      ] ClassTypeTest.AddRemoveConstant
2023-01-11T22:09:12.2313512Z [       OK ] ClassTypeTest.AddRemoveConstant (0 ms)
2023-01-11T22:09:12.2313869Z [ RUN      ] ClassTypeTest.IdenticalTypesDifferentCus
2023-01-11T22:09:12.2336408Z [       OK ] ClassTypeTest.IdenticalTypesDifferentCus (2 ms)
2023-01-11T22:09:12.2337026Z [----------] 3 tests from ClassTypeTest (2 ms total)
2023-01-11T22:09:12.2337326Z 
2023-01-11T22:09:12.2337532Z [----------] 2 tests from TestCodeTemplate
2023-01-11T22:09:12.2337821Z [ RUN      ] TestCodeTemplate.Copying
2023-01-11T22:09:12.2338113Z [       OK ] TestCodeTemplate.Copying (0 ms)
2023-01-11T22:09:12.2338416Z [ RUN      ] TestCodeTemplate.Formatting
2023-01-11T22:09:12.2338842Z [       OK ] TestCodeTemplate.Formatting (0 ms)
2023-01-11T22:09:12.2339180Z [----------] 2 tests from TestCodeTemplate (0 ms total)
2023-01-11T22:09:12.2339325Z 
2023-01-11T22:09:12.2339477Z [----------] 13 tests from ConcatOptTest
2023-01-11T22:09:12.2339838Z [ RUN      ] ConcatOptTest.SimpleCommonInputsEliminationPrefix
2023-01-11T22:09:12.2392790Z [       OK ] ConcatOptTest.SimpleCommonInputsEliminationPrefix (5 ms)
2023-01-11T22:09:12.2393396Z [ RUN      ] ConcatOptTest.SimpleCommonInputsEliminationSuffix
2023-01-11T22:09:12.2432529Z [       OK ] ConcatOptTest.SimpleCommonInputsEliminationSuffix (3 ms)
2023-01-11T22:09:12.2433296Z [ RUN      ] ConcatOptTest.CommonInputsEliminationWithDifferentOrderInputs
2023-01-11T22:09:12.2461601Z [       OK ] ConcatOptTest.CommonInputsEliminationWithDifferentOrderInputs (2 ms)
2023-01-11T22:09:12.2462292Z [ RUN      ] ConcatOptTest.MoreCommonInputsElimination
2023-01-11T22:09:12.2529902Z [       OK ] ConcatOptTest.MoreCommonInputsElimination (6 ms)
2023-01-11T22:09:12.2530385Z [ RUN      ] ConcatOptTest.ExpandConcat
2023-01-11T22:09:12.2554238Z [       OK ] ConcatOptTest.ExpandConcat (2 ms)
2023-01-11T22:09:12.2554774Z [ RUN      ] ConcatOptTest.ConcatWithoutResultShape
2023-01-11T22:09:12.2575366Z [       OK ] ConcatOptTest.ConcatWithoutResultShape (2 ms)
2023-01-11T22:09:12.2575957Z [ RUN      ] ConcatOptTest.ConcatWithoutInputShape
2023-01-11T22:09:12.2597450Z [       OK ] ConcatOptTest.ConcatWithoutInputShape (2 ms)
2023-01-11T22:09:12.2597991Z [ RUN      ] ConcatOptTest.UseVariadicCat
2023-01-11T22:09:12.2647592Z [       OK ] ConcatOptTest.UseVariadicCat (5 ms)
2023-01-11T22:09:12.2648097Z [ RUN      ] ConcatOptTest.UseVariadicCatWithMultipleListUses
2023-01-11T22:09:12.2667098Z [       OK ] ConcatOptTest.UseVariadicCatWithMultipleListUses (1 ms)
2023-01-11T22:09:12.2667784Z [ RUN      ] ConcatOptTest.UseVariadicCatWithListMutationAfterCat
2023-01-11T22:09:12.2692669Z [       OK ] ConcatOptTest.UseVariadicCatWithListMutationAfterCat (2 ms)
2023-01-11T22:09:12.2693398Z [ RUN      ] ConcatOptTest.UseVariadicCatWithListMutationBeforeCat
2023-01-11T22:09:12.2721219Z [       OK ] ConcatOptTest.UseVariadicCatWithListMutationBeforeCat (2 ms)
2023-01-11T22:09:12.2721942Z [ RUN      ] ConcatOptTest.UseVariadicCatWithMultipleListMutations
2023-01-11T22:09:12.2764421Z [       OK ] ConcatOptTest.UseVariadicCatWithMultipleListMutations (4 ms)
2023-01-11T22:09:12.2765216Z [ RUN      ] ConcatOptTest.RemoveListMutationUseVariadicCatAndCommonInputsElimination
2023-01-11T22:09:12.2800984Z [       OK ] ConcatOptTest.RemoveListMutationUseVariadicCatAndCommonInputsElimination (3 ms)
2023-01-11T22:09:12.2801609Z [----------] 13 tests from ConcatOptTest (46 ms total)
2023-01-11T22:09:12.2801830Z 
2023-01-11T22:09:12.2802042Z [----------] 1 test from OptimizeConcatTest
2023-01-11T22:09:12.2802505Z [ RUN      ] OptimizeConcatTest.UseVariadicCatReplaceMultiple
2023-01-11T22:09:12.2832714Z [       OK ] OptimizeConcatTest.UseVariadicCatReplaceMultiple (3 ms)
2023-01-11T22:09:12.2834448Z [----------] 1 test from OptimizeConcatTest (3 ms total)
2023-01-11T22:09:12.2834767Z 
2023-01-11T22:09:12.2835046Z [----------] 3 tests from ConcatOpt
2023-01-11T22:09:12.2835752Z [ RUN      ] ConcatOpt.CombineConcatsSimpleCase
2023-01-11T22:09:12.2836348Z [       OK ] ConcatOpt.CombineConcatsSimpleCase (0 ms)
2023-01-11T22:09:12.2836937Z [ RUN      ] ConcatOpt.CombineConcatsLongChain
2023-01-11T22:09:12.2837917Z [       OK ] ConcatOpt.CombineConcatsLongChain (0 ms)
2023-01-11T22:09:12.2838478Z [ RUN      ] ConcatOpt.CombineConcatsMutation
2023-01-11T22:09:12.2839982Z [       OK ] ConcatOpt.CombineConcatsMutation (0 ms)
2023-01-11T22:09:12.2840759Z [----------] 3 tests from ConcatOpt (0 ms total)
2023-01-11T22:09:12.2840956Z 
2023-01-11T22:09:12.2841184Z [----------] 4 tests from ConstantPoolingTest
2023-01-11T22:09:12.2841550Z [ RUN      ] ConstantPoolingTest.Int
2023-01-11T22:09:12.2841929Z [       OK ] ConstantPoolingTest.Int (0 ms)
2023-01-11T22:09:12.2842368Z [ RUN      ] ConstantPoolingTest.PoolingAcrossBlocks
2023-01-11T22:09:12.2842850Z [       OK ] ConstantPoolingTest.PoolingAcrossBlocks (0 ms)
2023-01-11T22:09:12.2843344Z [ RUN      ] ConstantPoolingTest.PoolingDifferentDevices
2023-01-11T22:09:12.2845553Z [       OK ] ConstantPoolingTest.PoolingDifferentDevices (0 ms)
2023-01-11T22:09:12.2846067Z [ RUN      ] ConstantPoolingTest.DictConstantPooling
2023-01-11T22:09:12.2846627Z [       OK ] ConstantPoolingTest.DictConstantPooling (0 ms)
2023-01-11T22:09:12.2847970Z [----------] 4 tests from ConstantPoolingTest (0 ms total)
2023-01-11T22:09:12.2848200Z 
2023-01-11T22:09:12.2848408Z [----------] 1 test from CleanupPassTest
2023-01-11T22:09:12.2848759Z [ RUN      ] CleanupPassTest.Basic
2023-01-11T22:09:12.2849442Z [       OK ] CleanupPassTest.Basic (0 ms)
2023-01-11T22:09:12.2849862Z [----------] 1 test from CleanupPassTest (0 ms total)
2023-01-11T22:09:12.2850061Z 
2023-01-11T22:09:12.2850305Z [----------] 1 test from CreateAutodiffSubgraphsTest
2023-01-11T22:09:12.2850726Z [ RUN      ] CreateAutodiffSubgraphsTest.Basic
2023-01-11T22:09:12.2853548Z [       OK ] CreateAutodiffSubgraphsTest.Basic (0 ms)
2023-01-11T22:09:12.2854049Z [----------] 1 test from CreateAutodiffSubgraphsTest (0 ms total)
2023-01-11T22:09:12.2854285Z 
2023-01-11T22:09:12.2854491Z [----------] 4 tests from CustomClassTest
2023-01-11T22:09:12.2854885Z [ RUN      ] CustomClassTest.TorchbindIValueAPI
2023-01-11T22:09:12.2859114Z [       OK ] CustomClassTest.TorchbindIValueAPI (0 ms)
2023-01-11T22:09:12.2859555Z [ RUN      ] CustomClassTest.ScalarTypeClass
2023-01-11T22:09:12.2861902Z [       OK ] CustomClassTest.ScalarTypeClass (0 ms)
2023-01-11T22:09:12.2862375Z [ RUN      ] CustomClassTest.TestDocString
2023-01-11T22:09:12.2862796Z [       OK ] CustomClassTest.TestDocString (0 ms)
2023-01-11T22:09:12.2863202Z [ RUN      ] CustomClassTest.Serialization
2023-01-11T22:09:12.2883847Z [       OK ] CustomClassTest.Serialization (2 ms)
2023-01-11T22:09:12.2884201Z [----------] 4 tests from CustomClassTest (3 ms total)
2023-01-11T22:09:12.2884363Z 
2023-01-11T22:09:12.2884529Z [----------] 5 tests from CustomOperatorTest
2023-01-11T22:09:12.2884835Z [ RUN      ] CustomOperatorTest.InferredSchema
2023-01-11T22:09:12.2886935Z [       OK ] CustomOperatorTest.InferredSchema (0 ms)
2023-01-11T22:09:12.2887281Z [ RUN      ] CustomOperatorTest.ExplicitSchema
2023-01-11T22:09:12.2889262Z [       OK ] CustomOperatorTest.ExplicitSchema (0 ms)
2023-01-11T22:09:12.2889627Z [ RUN      ] CustomOperatorTest.ListParameters
2023-01-11T22:09:12.2890582Z [       OK ] CustomOperatorTest.ListParameters (0 ms)
2023-01-11T22:09:12.2890994Z [ RUN      ] CustomOperatorTest.ListParameters2
2023-01-11T22:09:12.2892714Z [       OK ] CustomOperatorTest.ListParameters2 (0 ms)
2023-01-11T22:09:12.2893110Z [ RUN      ] CustomOperatorTest.Aliasing
2023-01-11T22:09:12.2894808Z [       OK ] CustomOperatorTest.Aliasing (0 ms)
2023-01-11T22:09:12.2895530Z [----------] 5 tests from CustomOperatorTest (1 ms total)
2023-01-11T22:09:12.2895812Z 
2023-01-11T22:09:12.2896205Z [----------] 2 tests from TestCustomOperator
2023-01-11T22:09:12.2896856Z [ RUN      ] TestCustomOperator.OperatorGeneratorUndeclared
2023-01-11T22:09:12.2897285Z [       OK ] TestCustomOperator.OperatorGeneratorUndeclared (0 ms)
2023-01-11T22:09:12.2897912Z [ RUN      ] TestCustomOperator.OperatorGeneratorBasic
2023-01-11T22:09:12.2898462Z [       OK ] TestCustomOperator.OperatorGeneratorBasic (0 ms)
2023-01-11T22:09:12.2899108Z [----------] 2 tests from TestCustomOperator (0 ms total)
2023-01-11T22:09:12.2899413Z 
2023-01-11T22:09:12.2899732Z [----------] 1 test from EliminateDeadCodeTest
2023-01-11T22:09:12.2900047Z [ RUN      ] EliminateDeadCodeTest.Basic
2023-01-11T22:09:12.2900369Z [       OK ] EliminateDeadCodeTest.Basic (0 ms)
2023-01-11T22:09:12.2900710Z [----------] 1 test from EliminateDeadCodeTest (0 ms total)
2023-01-11T22:09:12.2900876Z 
2023-01-11T22:09:12.2901016Z [----------] 2 tests from FuserTest
2023-01-11T22:09:12.2901417Z [ RUN      ] FuserTest.FusionAliasing
2023-01-11T22:09:12.2902609Z [       OK ] FuserTest.FusionAliasing (0 ms)
2023-01-11T22:09:12.2902952Z [ RUN      ] FuserTest.KernelCaching
2023-01-11T22:09:12.2905631Z [       OK ] FuserTest.KernelCaching (0 ms)
2023-01-11T22:09:12.2906211Z [----------] 2 tests from FuserTest (0 ms total)
2023-01-11T22:09:12.2906452Z 
2023-01-11T22:09:12.2906614Z [----------] 1 test from GraphExecutorTest
2023-01-11T22:09:12.2906927Z [ RUN      ] GraphExecutorTest.runAsync_executor
2023-01-11T22:09:12.2936907Z [       OK ] GraphExecutorTest.runAsync_executor (3 ms)
2023-01-11T22:09:12.2937722Z [----------] 1 test from GraphExecutorTest (3 ms total)
2023-01-11T22:09:12.2938121Z 
2023-01-11T22:09:12.2938498Z [----------] 5 tests from GraphIteratorTest
2023-01-11T22:09:12.2939296Z [ RUN      ] GraphIteratorTest.ConstantReturnGraph
2023-01-11T22:09:12.2940120Z [       OK ] GraphIteratorTest.ConstantReturnGraph (0 ms)
2023-01-11T22:09:12.2940769Z [ RUN      ] GraphIteratorTest.GraphWithParameters
2023-01-11T22:09:12.2941299Z [       OK ] GraphIteratorTest.GraphWithParameters (0 ms)
2023-01-11T22:09:12.2941751Z [ RUN      ] GraphIteratorTest.GraphWithIf
2023-01-11T22:09:12.2942388Z [       OK ] GraphIteratorTest.GraphWithIf (0 ms)
2023-01-11T22:09:12.2942916Z [ RUN      ] GraphIteratorTest.GraphWithNestedIf
2023-01-11T22:09:12.2943258Z [       OK ] GraphIteratorTest.GraphWithNestedIf (0 ms)
2023-01-11T22:09:12.2943598Z [ RUN      ] GraphIteratorTest.GraphWithLoop
2023-01-11T22:09:12.2943932Z [       OK ] GraphIteratorTest.GraphWithLoop (0 ms)
2023-01-11T22:09:12.2944279Z [----------] 5 tests from GraphIteratorTest (0 ms total)
2023-01-11T22:09:12.2944426Z 
2023-01-11T22:09:12.2944617Z [----------] 1 test from CSDebugInfoSerializaitionTest
2023-01-11T22:09:12.2944987Z [ RUN      ] CSDebugInfoSerializaitionTest.TwoSubmodules
2023-01-11T22:09:12.2947141Z [       OK ] CSDebugInfoSerializaitionTest.TwoSubmodules (0 ms)
2023-01-11T22:09:12.2947872Z [----------] 1 test from CSDebugInfoSerializaitionTest (0 ms total)
2023-01-11T22:09:12.2948065Z 
2023-01-11T22:09:12.2948210Z [----------] 1 test from InlinerTest
2023-01-11T22:09:12.2948467Z [ RUN      ] InlinerTest.Basic
2023-01-11T22:09:12.2950168Z [       OK ] InlinerTest.Basic (0 ms)
2023-01-11T22:09:12.2950695Z [----------] 1 test from InlinerTest (0 ms total)
2023-01-11T22:09:12.2950960Z 
2023-01-11T22:09:12.2951117Z [----------] 1 test from InterfaceTest
2023-01-11T22:09:12.2951452Z [ RUN      ] InterfaceTest.ModuleInterfaceSerialization
2023-01-11T22:09:12.2963697Z [       OK ] InterfaceTest.ModuleInterfaceSerialization (1 ms)
2023-01-11T22:09:12.2964357Z [----------] 1 test from InterfaceTest (1 ms total)
2023-01-11T22:09:12.2964522Z 
2023-01-11T22:09:12.2964749Z [----------] 4 tests from TypeCheckTest
2023-01-11T22:09:12.2965114Z [ RUN      ] TypeCheckTest.MatchingType
2023-01-11T22:09:12.2965722Z [       OK ] TypeCheckTest.MatchingType (0 ms)
2023-01-11T22:09:12.2966272Z [ RUN      ] TypeCheckTest.SizeMismatch
2023-01-11T22:09:12.2966825Z [       OK ] TypeCheckTest.SizeMismatch (0 ms)
2023-01-11T22:09:12.2967358Z [ RUN      ] TypeCheckTest.GradientMismatch
2023-01-11T22:09:12.2967915Z [       OK ] TypeCheckTest.GradientMismatch (0 ms)
2023-01-11T22:09:12.2968513Z [ RUN      ] TypeCheckTest.ScalarTypeMismatch
2023-01-11T22:09:12.2969290Z [       OK ] TypeCheckTest.ScalarTypeMismatch (0 ms)
2023-01-11T22:09:12.2969890Z [----------] 4 tests from TypeCheckTest (0 ms total)
2023-01-11T22:09:12.2970123Z 
2023-01-11T22:09:12.2970284Z [----------] 3 tests from InterpreterTest
2023-01-11T22:09:12.2970610Z [ RUN      ] InterpreterTest.IgnorableArgsInSchema
2023-01-11T22:09:12.2972692Z [       OK ] InterpreterTest.IgnorableArgsInSchema (0 ms)
2023-01-11T22:09:12.2973086Z [ RUN      ] InterpreterTest.IgnorableArgsInSchemaWithOut
2023-01-11T22:09:12.2973496Z [       OK ] InterpreterTest.IgnorableArgsInSchemaWithOut (0 ms)
2023-01-11T22:09:12.2973866Z [ RUN      ] InterpreterTest.runAsyncBasicTest
2023-01-11T22:09:12.2992275Z [       OK ] InterpreterTest.runAsyncBasicTest (1 ms)
2023-01-11T22:09:12.2992735Z [----------] 3 tests from InterpreterTest (2 ms total)
2023-01-11T22:09:12.2992952Z 
2023-01-11T22:09:12.2993218Z [----------] 1 test from EnableRethrowCaughtExceptionTest
2023-01-11T22:09:12.2993966Z [ RUN      ] EnableRethrowCaughtExceptionTest.EnableRethrowCaughtExceptionTestRethrowsCaughtException
2023-01-11T22:09:12.3205386Z [       OK ] EnableRethrowCaughtExceptionTest.EnableRethrowCaughtExceptionTestRethrowsCaughtException (21 ms)
2023-01-11T22:09:12.3206030Z [----------] 1 test from EnableRethrowCaughtExceptionTest (21 ms total)
2023-01-11T22:09:12.3206337Z 
2023-01-11T22:09:12.3206514Z [----------] 4 tests from IRTest
2023-01-11T22:09:12.3206818Z [ RUN      ] IRTest.Attributes
2023-01-11T22:09:12.3207232Z [       OK ] IRTest.Attributes (0 ms)
2023-01-11T22:09:12.3207615Z [ RUN      ] IRTest.Blocks
2023-01-11T22:09:12.3207961Z [       OK ] IRTest.Blocks (0 ms)
2023-01-11T22:09:12.3208344Z [ RUN      ] IRTest.CommonAncestor
2023-01-11T22:09:12.3208738Z [       OK ] IRTest.CommonAncestor (0 ms)
2023-01-11T22:09:12.3209303Z [ RUN      ] IRTest.OperatorMap
2023-01-11T22:09:12.3209763Z [       OK ] IRTest.OperatorMap (0 ms)
2023-01-11T22:09:12.3210171Z [----------] 4 tests from IRTest (0 ms total)
2023-01-11T22:09:12.3210375Z 
2023-01-11T22:09:12.3210581Z [----------] 21 tests from IRParserTest
2023-01-11T22:09:12.3210967Z [ RUN      ] IRParserTest.Basic
2023-01-11T22:09:12.3211395Z [       OK ] IRParserTest.Basic (0 ms)
2023-01-11T22:09:12.3211792Z [ RUN      ] IRParserTest.NestedBlock
2023-01-11T22:09:12.3212242Z [       OK ] IRParserTest.NestedBlock (0 ms)
2023-01-11T22:09:12.3212512Z [ RUN      ] IRParserTest.If
2023-01-11T22:09:12.3212888Z [       OK ] IRParserTest.If (0 ms)
2023-01-11T22:09:12.3213281Z [ RUN      ] IRParserTest.If2
2023-01-11T22:09:12.3213691Z [       OK ] IRParserTest.If2 (0 ms)
2023-01-11T22:09:12.3214152Z [ RUN      ] IRParserTest.InferredTypeIsTensor
2023-01-11T22:09:12.3214657Z [       OK ] IRParserTest.InferredTypeIsTensor (0 ms)
2023-01-11T22:09:12.3215114Z [ RUN      ] IRParserTest.ValueReuse
2023-01-11T22:09:12.3215487Z [       OK ] IRParserTest.ValueReuse (0 ms)
2023-01-11T22:09:12.3215826Z [ RUN      ] IRParserTest.Attributes
2023-01-11T22:09:12.3216209Z [       OK ] IRParserTest.Attributes (0 ms)
2023-01-11T22:09:12.3216591Z [ RUN      ] IRParserTest.OptionalTypes
2023-01-11T22:09:12.3216994Z [       OK ] IRParserTest.OptionalTypes (0 ms)
2023-01-11T22:09:12.3217562Z [ RUN      ] IRParserTest.StarTensor
2023-01-11T22:09:12.3218043Z [       OK ] IRParserTest.StarTensor (0 ms)
2023-01-11T22:09:12.3218384Z [ RUN      ] IRParserTest.UnshapedTensor
2023-01-11T22:09:12.3218830Z [       OK ] IRParserTest.UnshapedTensor (0 ms)
2023-01-11T22:09:12.3219306Z [ RUN      ] IRParserTest.ShapedTensor
2023-01-11T22:09:12.3219779Z [       OK ] IRParserTest.ShapedTensor (0 ms)
2023-01-11T22:09:12.3220320Z [ RUN      ] IRParserTest.NestedContrainer
2023-01-11T22:09:12.3220947Z [       OK ] IRParserTest.NestedContrainer (0 ms)
2023-01-11T22:09:12.3221432Z [ RUN      ] IRParserTest.MalformedShapeAnnotation
2023-01-11T22:09:12.3222064Z [       OK ] IRParserTest.MalformedShapeAnnotation (0 ms)
2023-01-11T22:09:12.3222543Z [ RUN      ] IRParserTest.FileCheck
2023-01-11T22:09:12.3222842Z [       OK ] IRParserTest.FileCheck (0 ms)
2023-01-11T22:09:12.3223110Z [ RUN      ] IRParserTest.Strides
2023-01-11T22:09:12.3223388Z [       OK ] IRParserTest.Strides (0 ms)
2023-01-11T22:09:12.3223685Z [ RUN      ] IRParserTest.MalformedStrides
2023-01-11T22:09:12.3223994Z [       OK ] IRParserTest.MalformedStrides (0 ms)
2023-01-11T22:09:12.3224297Z [ RUN      ] IRParserTest.TensorShapes
2023-01-11T22:09:12.3224598Z [       OK ] IRParserTest.TensorShapes (0 ms)
2023-01-11T22:09:12.3224944Z [ RUN      ] IRParserTest.DeviceAndRequiresGradTensors
2023-01-11T22:09:12.3225323Z [       OK ] IRParserTest.DeviceAndRequiresGradTensors (0 ms)
2023-01-11T22:09:12.3225657Z [ RUN      ] IRParserTest.ListConstant
2023-01-11T22:09:12.3225957Z [       OK ] IRParserTest.ListConstant (0 ms)
2023-01-11T22:09:12.3226261Z [ RUN      ] IRParserTest.PartialStarTensor
2023-01-11T22:09:12.3226589Z [       OK ] IRParserTest.PartialStarTensor (0 ms)
2023-01-11T22:09:12.3226931Z [ RUN      ] IRParserTest.ComplexTensorAttributes
2023-01-11T22:09:12.3227277Z [       OK ] IRParserTest.ComplexTensorAttributes (0 ms)
2023-01-11T22:09:12.3227680Z [----------] 21 tests from IRParserTest (1 ms total)
2023-01-11T22:09:12.3227832Z 
2023-01-11T22:09:12.3227977Z [----------] 2 tests from JitTypeTest
2023-01-11T22:09:12.3228306Z [ RUN      ] JitTypeTest.IsComplete
2023-01-11T22:09:12.3228586Z [       OK ] JitTypeTest.IsComplete (0 ms)
2023-01-11T22:09:12.3228869Z [ RUN      ] JitTypeTest.UnifyTypes
2023-01-11T22:09:12.3229163Z [       OK ] JitTypeTest.UnifyTypes (0 ms)
2023-01-11T22:09:12.3229458Z [----------] 2 tests from JitTypeTest (0 ms total)
2023-01-11T22:09:12.3229606Z 
2023-01-11T22:09:12.3229777Z [----------] 42 tests from LiteInterpreterTest
2023-01-11T22:09:12.3230106Z [ RUN      ] LiteInterpreterTest.UpsampleNearest2d
2023-01-11T22:09:12.3234379Z [       OK ] LiteInterpreterTest.UpsampleNearest2d (1 ms)
2023-01-11T22:09:12.3234834Z [ RUN      ] LiteInterpreterTest.CheckAttrAccess
2023-01-11T22:09:12.3235788Z [       OK ] LiteInterpreterTest.CheckAttrAccess (0 ms)
2023-01-11T22:09:12.3236152Z [ RUN      ] LiteInterpreterTest.MethodInvocation
2023-01-11T22:09:12.3260851Z [       OK ] LiteInterpreterTest.MethodInvocation (2 ms)
2023-01-11T22:09:12.3261182Z [ RUN      ] LiteInterpreterTest.Conv
2023-01-11T22:09:12.3285474Z [       OK ] LiteInterpreterTest.Conv (2 ms)
2023-01-11T22:09:12.3285886Z [ RUN      ] LiteInterpreterTest.Inline
2023-01-11T22:09:12.3294975Z [       OK ] LiteInterpreterTest.Inline (0 ms)
2023-01-11T22:09:12.3295283Z [ RUN      ] LiteInterpreterTest.Tuple
2023-01-11T22:09:12.3301639Z [       OK ] LiteInterpreterTest.Tuple (0 ms)
2023-01-11T22:09:12.3302000Z [ RUN      ] LiteInterpreterTest.AtenFormat
2023-01-11T22:09:12.3307610Z [       OK ] LiteInterpreterTest.AtenFormat (0 ms)
2023-01-11T22:09:12.3308006Z [ RUN      ] LiteInterpreterTest.PrimDevice
2023-01-11T22:09:12.3311591Z [       OK ] LiteInterpreterTest.PrimDevice (0 ms)
2023-01-11T22:09:12.3311942Z [ RUN      ] LiteInterpreterTest.Dict
2023-01-11T22:09:12.3318230Z [       OK ] LiteInterpreterTest.Dict (0 ms)
2023-01-11T22:09:12.3318560Z [ RUN      ] LiteInterpreterTest.List
2023-01-11T22:09:12.3326728Z [       OK ] LiteInterpreterTest.List (0 ms)
2023-01-11T22:09:12.3327313Z [ RUN      ] LiteInterpreterTest.PrimOverload
2023-01-11T22:09:12.3327790Z [       OK ] LiteInterpreterTest.PrimOverload (0 ms)
2023-01-11T22:09:12.3328103Z [ RUN      ] LiteInterpreterTest.Prim
2023-01-11T22:09:12.3331408Z [       OK ] LiteInterpreterTest.Prim (0 ms)
2023-01-11T22:09:12.3331928Z [ RUN      ] LiteInterpreterTest.PrimScalar
2023-01-11T22:09:12.3335954Z [       OK ] LiteInterpreterTest.PrimScalar (0 ms)
2023-01-11T22:09:12.3336583Z [ RUN      ] LiteInterpreterTest.LoadOrigJit
2023-01-11T22:09:12.3391937Z [       OK ] LiteInterpreterTest.LoadOrigJit (5 ms)
2023-01-11T22:09:12.3392530Z [ RUN      ] LiteInterpreterTest.WrongMethodName
2023-01-11T22:09:12.3411990Z [       OK ] LiteInterpreterTest.WrongMethodName (1 ms)
2023-01-11T22:09:12.3412549Z [ RUN      ] LiteInterpreterTest.SetState
2023-01-11T22:09:12.3436056Z [       OK ] LiteInterpreterTest.SetState (2 ms)
2023-01-11T22:09:12.3436650Z [ RUN      ] LiteInterpreterTest.BuiltinClass
2023-01-11T22:09:12.3443844Z [       OK ] LiteInterpreterTest.BuiltinClass (0 ms)
2023-01-11T22:09:12.3444453Z [ RUN      ] LiteInterpreterTest.BuiltinFunction
2023-01-11T22:09:12.3447533Z [       OK ] LiteInterpreterTest.BuiltinFunction (0 ms)
2023-01-11T22:09:12.3448206Z [ RUN      ] LiteInterpreterTest.GetRuntimeByteCodeVersion
2023-01-11T22:09:12.3448742Z [       OK ] LiteInterpreterTest.GetRuntimeByteCodeVersion (0 ms)
2023-01-11T22:09:12.3449294Z [ RUN      ] LiteInterpreterTest.GetRuntimeOperatorsVersion
2023-01-11T22:09:12.3449718Z [       OK ] LiteInterpreterTest.GetRuntimeOperatorsVersion (0 ms)
2023-01-11T22:09:12.3450140Z [ RUN      ] LiteInterpreterTest.GetByteCodeVersion
2023-01-11T22:09:12.3452173Z [       OK ] LiteInterpreterTest.GetByteCodeVersion (0 ms)
2023-01-11T22:09:12.3452574Z [ RUN      ] LiteInterpreterTest.GetContainTypes
2023-01-11T22:09:12.3454548Z [       OK ] LiteInterpreterTest.GetContainTypes (0 ms)
2023-01-11T22:09:12.3454988Z [ RUN      ] LiteInterpreterTest.BackPortByteCodeModelAllVersions
2023-01-11T22:09:12.4338693Z [       OK ] LiteInterpreterTest.BackPortByteCodeModelAllVersions (88 ms)
2023-01-11T22:09:12.4339216Z [ RUN      ] LiteInterpreterTest.GetRuntimeOpsAndInfo
2023-01-11T22:09:12.4401262Z [       OK ] LiteInterpreterTest.GetRuntimeOpsAndInfo (6 ms)
2023-01-11T22:09:12.4401646Z [ RUN      ] LiteInterpreterTest.isCompatibleSuccess
2023-01-11T22:09:12.4455596Z [       OK ] LiteInterpreterTest.isCompatibleSuccess (5 ms)
2023-01-11T22:09:12.4455987Z [ RUN      ] LiteInterpreterTest.isCompatibleFail
2023-01-11T22:09:12.4546490Z [       OK ] LiteInterpreterTest.isCompatibleFail (9 ms)
2023-01-11T22:09:12.4546843Z [ RUN      ] LiteInterpreterTest.Eval
2023-01-11T22:09:12.4556436Z [       OK ] LiteInterpreterTest.Eval (1 ms)
2023-01-11T22:09:12.4556797Z [ RUN      ] LiteInterpreterTest.FindWrongMethodName
2023-01-11T22:09:12.4560683Z [       OK ] LiteInterpreterTest.FindWrongMethodName (0 ms)
2023-01-11T22:09:12.4561071Z [ RUN      ] LiteInterpreterTest.FindAndRunMethod
2023-01-11T22:09:12.4567735Z [       OK ] LiteInterpreterTest.FindAndRunMethod (0 ms)
2023-01-11T22:09:12.4568346Z [ RUN      ] LiteInterpreterTest.RunMethodVariadic
2023-01-11T22:09:12.4574681Z [       OK ] LiteInterpreterTest.RunMethodVariadic (0 ms)
2023-01-11T22:09:12.4575058Z [ RUN      ] LiteInterpreterTest.DuplicateSetState
2023-01-11T22:09:12.4585050Z [       OK ] LiteInterpreterTest.DuplicateSetState (1 ms)
2023-01-11T22:09:12.4585398Z [ RUN      ] LiteInterpreterTest.ExtraFiles
2023-01-11T22:09:12.4590333Z [       OK ] LiteInterpreterTest.ExtraFiles (0 ms)
2023-01-11T22:09:12.4590868Z [ RUN      ] LiteInterpreterTest.OpNameExportFetchRootOperators
2023-01-11T22:09:12.4599660Z [       OK ] LiteInterpreterTest.OpNameExportFetchRootOperators (0 ms)
2023-01-11T22:09:12.4600131Z [ RUN      ] LiteInterpreterTest.DefaultArgsConv
2023-01-11T22:09:12.4613699Z [       OK ] LiteInterpreterTest.DefaultArgsConv (1 ms)
2023-01-11T22:09:12.4614203Z [ RUN      ] LiteInterpreterTest.DefaultArgsPinv
2023-01-11T22:09:12.4662096Z [       OK ] LiteInterpreterTest.DefaultArgsPinv (4 ms)
2023-01-11T22:09:12.4662989Z [ RUN      ] LiteInterpreterTest.DefaultArgsTensorinvSpecifyDefault
2023-01-11T22:09:12.4705005Z [       OK ] LiteInterpreterTest.DefaultArgsTensorinvSpecifyDefault (4 ms)
2023-01-11T22:09:12.4705653Z [ RUN      ] LiteInterpreterTest.DefaultArgsPinvWithOutArg
2023-01-11T22:09:12.4724221Z [       OK ] LiteInterpreterTest.DefaultArgsPinvWithOutArg (1 ms)
2023-01-11T22:09:12.4724945Z [ RUN      ] LiteInterpreterTest.DefaultArgsWithOutArg
2023-01-11T22:09:12.4732576Z [       OK ] LiteInterpreterTest.DefaultArgsWithOutArg (0 ms)
2023-01-11T22:09:12.4733228Z [ RUN      ] LiteInterpreterTest.TestExceptionStackWithTwoLevelModuleHierarchy
2023-01-11T22:09:12.4830964Z [       OK ] LiteInterpreterTest.TestExceptionStackWithTwoLevelModuleHierarchy (9 ms)
2023-01-11T22:09:12.4831499Z [ RUN      ] LiteInterpreterTest.OperatorCacheDifferentiatesDefaultArgs
2023-01-11T22:09:12.4863654Z [       OK ] LiteInterpreterTest.OperatorCacheDifferentiatesDefaultArgs (3 ms)
2023-01-11T22:09:12.4864150Z [ RUN      ] LiteInterpreterTest.OperatorSize1
2023-01-11T22:09:12.4868055Z [       OK ] LiteInterpreterTest.OperatorSize1 (0 ms)
2023-01-11T22:09:12.4868523Z [ RUN      ] LiteInterpreterTest.OperatorTest2
2023-01-11T22:09:12.4885418Z [       OK ] LiteInterpreterTest.OperatorTest2 (1 ms)
2023-01-11T22:09:12.4885909Z [----------] 42 tests from LiteInterpreterTest (166 ms total)
2023-01-11T22:09:12.4886086Z 
2023-01-11T22:09:12.4886234Z [----------] 3 tests from RunTimeTest
2023-01-11T22:09:12.4886524Z [ RUN      ] RunTimeTest.ParseBytecode
2023-01-11T22:09:12.4886821Z [       OK ] RunTimeTest.ParseBytecode (0 ms)
2023-01-11T22:09:12.4887134Z [ RUN      ] RunTimeTest.ParseOperator
2023-01-11T22:09:12.4887440Z [       OK ] RunTimeTest.ParseOperator (0 ms)
2023-01-11T22:09:12.4887740Z [ RUN      ] RunTimeTest.RuntimeCall
2023-01-11T22:09:12.4888019Z [       OK ] RunTimeTest.RuntimeCall (0 ms)
2023-01-11T22:09:12.4888331Z [----------] 3 tests from RunTimeTest (0 ms total)
2023-01-11T22:09:12.4888480Z 
2023-01-11T22:09:12.4888668Z [----------] 11 tests from LiteInterpreterUpgraderTest
2023-01-11T22:09:12.4889014Z [ RUN      ] LiteInterpreterUpgraderTest.DivTensorV2
2023-01-11T22:09:12.4891954Z [       OK ] LiteInterpreterUpgraderTest.DivTensorV2 (0 ms)
2023-01-11T22:09:12.4892348Z [ RUN      ] LiteInterpreterUpgraderTest.DivTensorOutV2
2023-01-11T22:09:12.4896478Z [       OK ] LiteInterpreterUpgraderTest.DivTensorOutV2 (0 ms)
2023-01-11T22:09:12.4896878Z [ RUN      ] LiteInterpreterUpgraderTest.DivTensorInplaceV2
2023-01-11T22:09:12.4905665Z [       OK ] LiteInterpreterUpgraderTest.DivTensorInplaceV2 (0 ms)
2023-01-11T22:09:12.4906082Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarFloatV2
2023-01-11T22:09:12.4916698Z [       OK ] LiteInterpreterUpgraderTest.DivScalarFloatV2 (1 ms)
2023-01-11T22:09:12.4917248Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarReciprocalFloatV2
2023-01-11T22:09:12.4921276Z expect output:  0.5000
2023-01-11T22:09:12.4921571Z [ CPUFloatType{1} ]actual output:  0.5000
2023-01-11T22:09:12.4922001Z [ CPUFloatType{1} ][       OK ] LiteInterpreterUpgraderTest.DivScalarReciprocalFloatV2 (0 ms)
2023-01-11T22:09:12.4922483Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarReciprocalIntV2
2023-01-11T22:09:12.4925781Z [       OK ] LiteInterpreterUpgraderTest.DivScalarReciprocalIntV2 (0 ms)
2023-01-11T22:09:12.4926228Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarScalarV2
2023-01-11T22:09:12.4929528Z [       OK ] LiteInterpreterUpgraderTest.DivScalarScalarV2 (0 ms)
2023-01-11T22:09:12.4929951Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarIntV2
2023-01-11T22:09:12.4942496Z [       OK ] LiteInterpreterUpgraderTest.DivScalarIntV2 (1 ms)
2023-01-11T22:09:12.4942919Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarInplaceFloatV2
2023-01-11T22:09:12.4946738Z [       OK ] LiteInterpreterUpgraderTest.DivScalarInplaceFloatV2 (0 ms)
2023-01-11T22:09:12.4947189Z [ RUN      ] LiteInterpreterUpgraderTest.DivScalarInplaceIntV2
2023-01-11T22:09:12.4950818Z [       OK ] LiteInterpreterUpgraderTest.DivScalarInplaceIntV2 (0 ms)
2023-01-11T22:09:12.4951231Z [ RUN      ] LiteInterpreterUpgraderTest.Upgrader
2023-01-11T22:09:12.4951603Z [       OK ] LiteInterpreterUpgraderTest.Upgrader (0 ms)
2023-01-11T22:09:12.4951997Z [----------] 11 tests from LiteInterpreterUpgraderTest (6 ms total)
2023-01-11T22:09:12.4952182Z 
2023-01-11T22:09:12.4952371Z [----------] 29 tests from LiteInterpreterDirectTest
2023-01-11T22:09:12.4952722Z [ RUN      ] LiteInterpreterDirectTest.UpsampleNearest2d
2023-01-11T22:09:12.4959442Z [       OK ] LiteInterpreterDirectTest.UpsampleNearest2d (0 ms)
2023-01-11T22:09:12.4959893Z [ RUN      ] LiteInterpreterDirectTest.CheckAttrAccess
2023-01-11T22:09:12.4960275Z [       OK ] LiteInterpreterDirectTest.CheckAttrAccess (0 ms)
2023-01-11T22:09:12.4960664Z [ RUN      ] LiteInterpreterDirectTest.MethodInvocation
2023-01-11T22:09:12.4965089Z hello 
2023-01-11T22:09:12.4965277Z hello 3
2023-01-11T22:09:12.4972600Z hello 
2023-01-11T22:09:12.4972819Z hello 3
2023-01-11T22:09:12.4977918Z hello 
2023-01-11T22:09:12.4978225Z hello 3
2023-01-11T22:09:12.4978591Z [       OK ] LiteInterpreterDirectTest.MethodInvocation (1 ms)
2023-01-11T22:09:12.4978963Z [ RUN      ] LiteInterpreterDirectTest.Conv
2023-01-11T22:09:12.4992120Z [       OK ] LiteInterpreterDirectTest.Conv (1 ms)
2023-01-11T22:09:12.4992552Z [ RUN      ] LiteInterpreterDirectTest.Inline
2023-01-11T22:09:12.4998531Z [       OK ] LiteInterpreterDirectTest.Inline (0 ms)
2023-01-11T22:09:12.4998893Z [ RUN      ] LiteInterpreterDirectTest.Tuple
2023-01-11T22:09:12.5002437Z [       OK ] LiteInterpreterDirectTest.Tuple (0 ms)
2023-01-11T22:09:12.5002777Z [ RUN      ] LiteInterpreterDirectTest.Dict
2023-01-11T22:09:12.5006252Z [       OK ] LiteInterpreterDirectTest.Dict (0 ms)
2023-01-11T22:09:12.5006588Z [ RUN      ] LiteInterpreterDirectTest.Prim
2023-01-11T22:09:12.5008996Z [       OK ] LiteInterpreterDirectTest.Prim (0 ms)
2023-01-11T22:09:12.5009529Z [ RUN      ] LiteInterpreterDirectTest.PrimScalar
2023-01-11T22:09:12.5012432Z [       OK ] LiteInterpreterDirectTest.PrimScalar (0 ms)
2023-01-11T22:09:12.5012808Z [ RUN      ] LiteInterpreterDirectTest.WrongMethodName
2023-01-11T22:09:12.5030717Z [       OK ] LiteInterpreterDirectTest.WrongMethodName (1 ms)
2023-01-11T22:09:12.5031093Z [ RUN      ] LiteInterpreterDirectTest.SetState
2023-01-11T22:09:12.5047499Z [       OK ] LiteInterpreterDirectTest.SetState (1 ms)
2023-01-11T22:09:12.5047873Z [ RUN      ] LiteInterpreterDirectTest.BuiltinFunction
2023-01-11T22:09:12.5049870Z [       OK ] LiteInterpreterDirectTest.BuiltinFunction (0 ms)
2023-01-11T22:09:12.5050650Z [ RUN      ] LiteInterpreterDirectTest.GetRuntimeByteCodeVersion
2023-01-11T22:09:12.5051103Z [       OK ] LiteInterpreterDirectTest.GetRuntimeByteCodeVersion (0 ms)
2023-01-11T22:09:12.5051551Z [ RUN      ] LiteInterpreterDirectTest.GetRuntimeOperatorsVersion
2023-01-11T22:09:12.5052015Z [       OK ] LiteInterpreterDirectTest.GetRuntimeOperatorsVersion (0 ms)
2023-01-11T22:09:12.5052440Z [ RUN      ] LiteInterpreterDirectTest.GetByteCodeVersion
2023-01-11T22:09:12.5052916Z [       OK ] LiteInterpreterDirectTest.GetByteCodeVersion (0 ms)
2023-01-11T22:09:12.5053330Z [ RUN      ] LiteInterpreterDirectTest.GetRuntimeOpsAndInfo
2023-01-11T22:09:12.5096552Z [       OK ] LiteInterpreterDirectTest.GetRuntimeOpsAndInfo (4 ms)
2023-01-11T22:09:12.5096926Z [ RUN      ] LiteInterpreterDirectTest.Eval
2023-01-11T22:09:12.5102816Z [       OK ] LiteInterpreterDirectTest.Eval (0 ms)
2023-01-11T22:09:12.5103191Z [ RUN      ] LiteInterpreterDirectTest.FindWrongMethodName
2023-01-11T22:09:12.5105124Z [       OK ] LiteInterpreterDirectTest.FindWrongMethodName (0 ms)
2023-01-11T22:09:12.5105512Z [ RUN      ] LiteInterpreterDirectTest.FindAndRunMethod
2023-01-11T22:09:12.5110635Z [       OK ] LiteInterpreterDirectTest.FindAndRunMethod (0 ms)
2023-01-11T22:09:12.5111066Z [ RUN      ] LiteInterpreterDirectTest.RunMethodVariadic
2023-01-11T22:09:12.5115276Z [       OK ] LiteInterpreterDirectTest.RunMethodVariadic (0 ms)
2023-01-11T22:09:12.5115828Z [ RUN      ] LiteInterpreterDirectTest.DuplicateSetState
2023-01-11T22:09:12.5120240Z [       OK ] LiteInterpreterDirectTest.DuplicateSetState (0 ms)
2023-01-11T22:09:12.5120915Z [ RUN      ] LiteInterpreterDirectTest.OpNameExportFetchRootOperators
2023-01-11T22:09:12.5125286Z [       OK ] LiteInterpreterDirectTest.OpNameExportFetchRootOperators (0 ms)
2023-01-11T22:09:12.5125908Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsConv
2023-01-11T22:09:12.5135591Z [       OK ] LiteInterpreterDirectTest.DefaultArgsConv (0 ms)
2023-01-11T22:09:12.5136391Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsPinv
2023-01-11T22:09:12.5173059Z [       OK ] LiteInterpreterDirectTest.DefaultArgsPinv (3 ms)
2023-01-11T22:09:12.5173858Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsTensorinvSpecifyDefault
2023-01-11T22:09:12.5180640Z [       OK ] LiteInterpreterDirectTest.DefaultArgsTensorinvSpecifyDefault (0 ms)
2023-01-11T22:09:12.5181277Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsPinvWithOutArg
2023-01-11T22:09:12.5200394Z [       OK ] LiteInterpreterDirectTest.DefaultArgsPinvWithOutArg (1 ms)
2023-01-11T22:09:12.5201111Z [ RUN      ] LiteInterpreterDirectTest.DefaultArgsWithOutArg
2023-01-11T22:09:12.5205974Z [       OK ] LiteInterpreterDirectTest.DefaultArgsWithOutArg (0 ms)
2023-01-11T22:09:12.5206702Z [ RUN      ] LiteInterpreterDirectTest.TestExceptionStackWithTwoLevelModuleHierarchy
2023-01-11T22:09:12.5304026Z [       OK ] LiteInterpreterDirectTest.TestExceptionStackWithTwoLevelModuleHierarchy (9 ms)
2023-01-11T22:09:12.5304709Z [ RUN      ] LiteInterpreterDirectTest.OperatorCacheDifferentiatesDefaultArgs
2023-01-11T22:09:12.5323192Z [       OK ] LiteInterpreterDirectTest.OperatorCacheDifferentiatesDefaultArgs (1 ms)
2023-01-11T22:09:12.5323877Z [----------] 29 tests from LiteInterpreterDirectTest (37 ms total)
2023-01-11T22:09:12.5324051Z 
2023-01-11T22:09:12.5324205Z [----------] 7 tests from LiteTrainerTest
2023-01-11T22:09:12.5324595Z [ RUN      ] LiteTrainerTest.Params
2023-01-11T22:09:12.5405726Z [       OK ] LiteTrainerTest.Params (8 ms)
2023-01-11T22:09:12.5406230Z [ RUN      ] LiteTrainerTest.SGD
2023-01-11T22:09:12.5476433Z [       OK ] LiteTrainerTest.SGD (7 ms)
2023-01-11T22:09:12.5476904Z [ RUN      ] LiteTrainerTest.SequentialSampler
2023-01-11T22:09:12.5477415Z [       OK ] LiteTrainerTest.SequentialSampler (0 ms)
2023-01-11T22:09:12.5478175Z [ RUN      ] LiteTrainerTest.RandomSamplerReturnsIndicesInCorrectRange
2023-01-11T22:09:12.5478727Z [       OK ] LiteTrainerTest.RandomSamplerReturnsIndicesInCorrectRange (0 ms)
2023-01-11T22:09:12.5479221Z [ RUN      ] LiteTrainerTest.RandomSamplerReturnsLessValuesForLastBatch
2023-01-11T22:09:12.5479807Z [       OK ] LiteTrainerTest.RandomSamplerReturnsLessValuesForLastBatch (0 ms)
2023-01-11T22:09:12.5480249Z [ RUN      ] LiteTrainerTest.RandomSamplerResetsWell
2023-01-11T22:09:12.5480630Z [       OK ] LiteTrainerTest.RandomSamplerResetsWell (0 ms)
2023-01-11T22:09:12.5481040Z [ RUN      ] LiteTrainerTest.RandomSamplerResetsWithNewSizeWell
2023-01-11T22:09:12.5481476Z [       OK ] LiteTrainerTest.RandomSamplerResetsWithNewSizeWell (0 ms)
2023-01-11T22:09:12.5481973Z [----------] 7 tests from LiteTrainerTest (15 ms total)
2023-01-11T22:09:12.5482136Z 
2023-01-11T22:09:12.5482279Z [----------] 6 tests from MobileTest
2023-01-11T22:09:12.5482624Z [ RUN      ] MobileTest.SaveLoadParametersEmpty
2023-01-11T22:09:12.5483251Z [       OK ] MobileTest.SaveLoadParametersEmpty (0 ms)
2023-01-11T22:09:12.5483915Z [ RUN      ] MobileTest.SaveParametersDefaultsToZip
2023-01-11T22:09:12.5484411Z [       OK ] MobileTest.SaveParametersDefaultsToZip (0 ms)
2023-01-11T22:09:12.5484777Z [ RUN      ] MobileTest.SaveParametersCanUseFlatbuffer
2023-01-11T22:09:12.5485174Z [       OK ] MobileTest.SaveParametersCanUseFlatbuffer (0 ms)
2023-01-11T22:09:12.5485619Z [ RUN      ] MobileTest.SaveLoadParametersUsingFlatbuffers
2023-01-11T22:09:12.5486026Z [       OK ] MobileTest.SaveLoadParametersUsingFlatbuffers (0 ms)
2023-01-11T22:09:12.5486459Z [ RUN      ] MobileTest.LoadParametersUnexpectedFormatShouldThrow
2023-01-11T22:09:12.5507838Z [       OK ] MobileTest.LoadParametersUnexpectedFormatShouldThrow (2 ms)
2023-01-11T22:09:12.5508274Z [ RUN      ] MobileTest.LoadParametersEmptyDataShouldThrow
2023-01-11T22:09:12.5529966Z [       OK ] MobileTest.LoadParametersEmptyDataShouldThrow (2 ms)
2023-01-11T22:09:12.5530671Z [----------] 6 tests from MobileTest (5 ms total)
2023-01-11T22:09:12.5530917Z 
2023-01-11T22:09:12.5531078Z [----------] 1 test from MemoryDAGTest
2023-01-11T22:09:12.5531350Z [ RUN      ] MemoryDAGTest.Basic
2023-01-11T22:09:12.5531616Z [       OK ] MemoryDAGTest.Basic (0 ms)
2023-01-11T22:09:12.5531930Z [----------] 1 test from MemoryDAGTest (0 ms total)
2023-01-11T22:09:12.5532081Z 
2023-01-11T22:09:12.5532241Z [----------] 1 test from InternedStringsTest
2023-01-11T22:09:12.5532524Z [ RUN      ] InternedStringsTest.Basic
2023-01-11T22:09:12.5556106Z [       OK ] InternedStringsTest.Basic (2 ms)
2023-01-11T22:09:12.5556731Z [----------] 1 test from InternedStringsTest (2 ms total)
2023-01-11T22:09:12.5556917Z 
2023-01-11T22:09:12.5557078Z [----------] 1 test from FromQualStringTest
2023-01-11T22:09:12.5557353Z [ RUN      ] FromQualStringTest.Basic
2023-01-11T22:09:12.5566099Z [       OK ] FromQualStringTest.Basic (0 ms)
2023-01-11T22:09:12.5566714Z [----------] 1 test from FromQualStringTest (0 ms total)
2023-01-11T22:09:12.5567028Z 
2023-01-11T22:09:12.5567186Z [----------] 1 test from THNNConvTest
2023-01-11T22:09:12.5567489Z [ RUN      ] THNNConvTest.Basic
2023-01-11T22:09:12.5579555Z [       OK ] THNNConvTest.Basic (1 ms)
2023-01-11T22:09:12.5580256Z [----------] 1 test from THNNConvTest (1 ms total)
2023-01-11T22:09:12.5580491Z 
2023-01-11T22:09:12.5580726Z [----------] 1 test from ATenNativeBatchNormTest
2023-01-11T22:09:12.5581045Z [ RUN      ] ATenNativeBatchNormTest.Basic
2023-01-11T22:09:12.5590698Z [       OK ] ATenNativeBatchNormTest.Basic (1 ms)
2023-01-11T22:09:12.5591614Z [----------] 1 test from ATenNativeBatchNormTest (1 ms total)
2023-01-11T22:09:12.5591873Z 
2023-01-11T22:09:12.5592170Z [----------] 2 tests from CustomFusionTest
2023-01-11T22:09:12.5592875Z [ RUN      ] CustomFusionTest.Basic
2023-01-11T22:09:12.5593568Z [       OK ] CustomFusionTest.Basic (0 ms)
2023-01-11T22:09:12.5594190Z [ RUN      ] CustomFusionTest.NestedBlocks
2023-01-11T22:09:12.5594725Z [       OK ] CustomFusionTest.NestedBlocks (0 ms)
2023-01-11T22:09:12.5595336Z [----------] 2 tests from CustomFusionTest (0 ms total)
2023-01-11T22:09:12.5595643Z 
2023-01-11T22:09:12.5595840Z [----------] 1 test from ControlFlowTest
2023-01-11T22:09:12.5596111Z [ RUN      ] ControlFlowTest.Basic
2023-01-11T22:09:12.5603244Z [       OK ] ControlFlowTest.Basic (0 ms)
2023-01-11T22:09:12.5603844Z [----------] 1 test from ControlFlowTest (0 ms total)
2023-01-11T22:09:12.5604135Z 
2023-01-11T22:09:12.5604285Z [----------] 1 test from ProtoTest
2023-01-11T22:09:12.5604533Z [ RUN      ] ProtoTest.Basic
2023-01-11T22:09:12.5604797Z [       OK ] ProtoTest.Basic (0 ms)
2023-01-11T22:09:12.5605088Z [----------] 1 test from ProtoTest (0 ms total)
2023-01-11T22:09:12.5605234Z 
2023-01-11T22:09:12.5605380Z [----------] 9 tests from SchemaParserTest
2023-01-11T22:09:12.5605687Z [ RUN      ] SchemaParserTest.NestedArrays
2023-01-11T22:09:12.5606013Z [       OK ] SchemaParserTest.NestedArrays (0 ms)
2023-01-11T22:09:12.5606314Z [ RUN      ] SchemaParserTest.OutVariant
2023-01-11T22:09:12.5606626Z [       OK ] SchemaParserTest.OutVariant (0 ms)
2023-01-11T22:09:12.5606938Z [ RUN      ] SchemaParserTest.NamedReturns
2023-01-11T22:09:12.5607261Z [       OK ] SchemaParserTest.NamedReturns (0 ms)
2023-01-11T22:09:12.5607549Z [ RUN      ] SchemaParserTest.Futures
2023-01-11T22:09:12.5607848Z [       OK ] SchemaParserTest.Futures (0 ms)
2023-01-11T22:09:12.5608172Z [ RUN      ] SchemaParserTest.AnnotatedAliasSets
2023-01-11T22:09:12.5608512Z [       OK ] SchemaParserTest.AnnotatedAliasSets (0 ms)
2023-01-11T22:09:12.5608895Z [ RUN      ] SchemaParserTest.TensorListAnnotatedAliasSets
2023-01-11T22:09:12.5609456Z [       OK ] SchemaParserTest.TensorListAnnotatedAliasSets (0 ms)
2023-01-11T22:09:12.5609860Z [ RUN      ] SchemaParserTest.AnnotatedAliasWithoutBeforeSet
2023-01-11T22:09:12.5611841Z [       OK ] SchemaParserTest.AnnotatedAliasWithoutBeforeSet (0 ms)
2023-01-11T22:09:12.5612535Z [ RUN      ] SchemaParserTest.BeforeAfterSets
2023-01-11T22:09:12.5613172Z [       OK ] SchemaParserTest.BeforeAfterSets (0 ms)
2023-01-11T22:09:12.5613768Z [ RUN      ] SchemaParserTest.BeforeAfterSets2
2023-01-11T22:09:12.5614189Z [       OK ] SchemaParserTest.BeforeAfterSets2 (0 ms)
2023-01-11T22:09:12.5614796Z [----------] 9 tests from SchemaParserTest (0 ms total)
2023-01-11T22:09:12.5615091Z 
2023-01-11T22:09:12.5615269Z [----------] 2 tests from TopologicalIndexTest
2023-01-11T22:09:12.5615573Z [ RUN      ] TopologicalIndexTest.Basic
2023-01-11T22:09:12.5615880Z [       OK ] TopologicalIndexTest.Basic (0 ms)
2023-01-11T22:09:12.5616190Z [ RUN      ] TopologicalIndexTest.Reindex
2023-01-11T22:09:12.5616496Z [       OK ] TopologicalIndexTest.Reindex (0 ms)
2023-01-11T22:09:12.5616961Z [----------] 2 tests from TopologicalIndexTest (0 ms total)
2023-01-11T22:09:12.5617130Z 
2023-01-11T22:09:12.5617290Z [----------] 7 tests from RecordFunctionTest
2023-01-11T22:09:12.5617622Z [ RUN      ] RecordFunctionTest.TracedTestInputsOutputs
2023-01-11T22:09:12.5618022Z [       OK ] RecordFunctionTest.TracedTestInputsOutputs (0 ms)
2023-01-11T22:09:12.5618393Z [ RUN      ] RecordFunctionTest.SampledCallbacks
2023-01-11T22:09:12.5675202Z [       OK ] RecordFunctionTest.SampledCallbacks (6 ms)
2023-01-11T22:09:12.5675837Z [ RUN      ] RecordFunctionTest.RecordFunctionGuard
2023-01-11T22:09:12.5676214Z [       OK ] RecordFunctionTest.RecordFunctionGuard (0 ms)
2023-01-11T22:09:12.5676551Z [ RUN      ] RecordFunctionTest.Callbacks
2023-01-11T22:09:12.5677887Z [       OK ] RecordFunctionTest.Callbacks (0 ms)
2023-01-11T22:09:12.5678353Z [ RUN      ] RecordFunctionTest.ShouldRun
2023-01-11T22:09:12.5678772Z [       OK ] RecordFunctionTest.ShouldRun (0 ms)
2023-01-11T22:09:12.5679167Z [ RUN      ] RecordFunctionTest.Basic
2023-01-11T22:09:12.5680185Z [       OK ] RecordFunctionTest.Basic (0 ms)
2023-01-11T22:09:12.5680798Z [ RUN      ] RecordFunctionTest.OperatorNameOverload
2023-01-11T22:09:12.5681401Z [       OK ] RecordFunctionTest.OperatorNameOverload (0 ms)
2023-01-11T22:09:12.5681915Z [----------] 7 tests from RecordFunctionTest (7 ms total)
2023-01-11T22:09:12.5682084Z 
2023-01-11T22:09:12.5682264Z [----------] 1 test from ThreadLocalDebugInfoTest
2023-01-11T22:09:12.5682769Z [ RUN      ] ThreadLocalDebugInfoTest.Basic
2023-01-11T22:09:12.5683200Z [       OK ] ThreadLocalDebugInfoTest.Basic (0 ms)
2023-01-11T22:09:12.5683722Z [----------] 1 test from ThreadLocalDebugInfoTest (0 ms total)
2023-01-11T22:09:12.5684046Z 
2023-01-11T22:09:12.5684301Z [----------] 1 test from TestSymIntArrayRef
2023-01-11T22:09:12.5684818Z [ RUN      ] TestSymIntArrayRef.BasicConversion
2023-01-11T22:09:12.5685406Z [       OK ] TestSymIntArrayRef.BasicConversion (0 ms)
2023-01-11T22:09:12.5686035Z [----------] 1 test from TestSymIntArrayRef (0 ms total)
2023-01-11T22:09:12.5686259Z 
2023-01-11T22:09:12.5686452Z [----------] 4 tests from TestSymInt
2023-01-11T22:09:12.5686808Z [ RUN      ] TestSymInt.NarrowCopyWithSymbolicInt
2023-01-11T22:09:12.5687153Z [       OK ] TestSymInt.NarrowCopyWithSymbolicInt (0 ms)
2023-01-11T22:09:12.5687469Z [ RUN      ] TestSymInt.NarrowCopy
2023-01-11T22:09:12.5687762Z [       OK ] TestSymInt.NarrowCopy (0 ms)
2023-01-11T22:09:12.5688050Z [ RUN      ] TestSymInt.AddSymbolicInt
2023-01-11T22:09:12.5688340Z [       OK ] TestSymInt.AddSymbolicInt (0 ms)
2023-01-11T22:09:12.5688673Z [ RUN      ] TestSymInt.TestSymIntToSymNodeDispatch
2023-01-11T22:09:12.5689194Z [       OK ] TestSymInt.TestSymIntToSymNodeDispatch (0 ms)
2023-01-11T22:09:12.5689537Z [----------] 4 tests from TestSymInt (0 ms total)
2023-01-11T22:09:12.5689682Z 
2023-01-11T22:09:12.5689840Z [----------] 1 test from FallbackGraphsTest
2023-01-11T22:09:12.5690134Z [ RUN      ] FallbackGraphsTest.Basic
2023-01-11T22:09:12.5694143Z [       OK ] FallbackGraphsTest.Basic (0 ms)
2023-01-11T22:09:12.5694829Z [----------] 1 test from FallbackGraphsTest (0 ms total)
2023-01-11T22:09:12.5695144Z 
2023-01-11T22:09:12.5695321Z [----------] 1 test from NoneSchemaMatchTest
2023-01-11T22:09:12.5695682Z [ RUN      ] NoneSchemaMatchTest.Basic
2023-01-11T22:09:12.5696176Z [       OK ] NoneSchemaMatchTest.Basic (0 ms)
2023-01-11T22:09:12.5696532Z [----------] 1 test from NoneSchemaMatchTest (0 ms total)
2023-01-11T22:09:12.5696699Z 
2023-01-11T22:09:12.5696863Z [----------] 1 test from PassManagementTest
2023-01-11T22:09:12.5697277Z [ RUN      ] PassManagementTest.Basic
2023-01-11T22:09:12.5697590Z [       OK ] PassManagementTest.Basic (0 ms)
2023-01-11T22:09:12.5697922Z [----------] 1 test from PassManagementTest (0 ms total)
2023-01-11T22:09:12.5698084Z 
2023-01-11T22:09:12.5698238Z [----------] 5 tests from LoopPeelerTest
2023-01-11T22:09:12.5698551Z [ RUN      ] LoopPeelerTest.NoInductionVariableUse
2023-01-11T22:09:12.5700103Z [       OK ] LoopPeelerTest.NoInductionVariableUse (0 ms)
2023-01-11T22:09:12.5700494Z [ RUN      ] LoopPeelerTest.YesInductionVariableUse
2023-01-11T22:09:12.5703362Z [       OK ] LoopPeelerTest.YesInductionVariableUse (0 ms)
2023-01-11T22:09:12.5703752Z [ RUN      ] LoopPeelerTest.LoopWithTerminationCondition
2023-01-11T22:09:12.5707643Z [       OK ] LoopPeelerTest.LoopWithTerminationCondition (0 ms)
2023-01-11T22:09:12.5708035Z [ RUN      ] LoopPeelerTest.SimpleNestedLoops
2023-01-11T22:09:12.5713304Z [       OK ] LoopPeelerTest.SimpleNestedLoops (0 ms)
2023-01-11T22:09:12.5713664Z [ RUN      ] LoopPeelerTest.SimpleNestedLoops2
2023-01-11T22:09:12.5721369Z [       OK ] LoopPeelerTest.SimpleNestedLoops2 (0 ms)
2023-01-11T22:09:12.5721797Z [----------] 5 tests from LoopPeelerTest (2 ms total)
2023-01-11T22:09:12.5721951Z 
2023-01-11T22:09:12.5722097Z [----------] 1 test from JitTracing
2023-01-11T22:09:12.5722383Z [ RUN      ] JitTracing.Basic
2023-01-11T22:09:12.5857343Z [       OK ] JitTracing.Basic (13 ms)
2023-01-11T22:09:12.5857763Z [----------] 1 test from JitTracing (13 ms total)
2023-01-11T22:09:12.5857924Z 
2023-01-11T22:09:12.5858148Z [----------] 1 test from InsertAndEliminateRedundantGuardsTest
2023-01-11T22:09:12.5858606Z [ RUN      ] InsertAndEliminateRedundantGuardsTest.Basic
2023-01-11T22:09:12.5863718Z [       OK ] InsertAndEliminateRedundantGuardsTest.Basic (0 ms)
2023-01-11T22:09:12.5864538Z [----------] 1 test from InsertAndEliminateRedundantGuardsTest (0 ms total)
2023-01-11T22:09:12.5864915Z 
2023-01-11T22:09:12.5865208Z [----------] 1 test from InsertBailOutsTest
2023-01-11T22:09:12.5865561Z [ RUN      ] InsertBailOutsTest.Basic
2023-01-11T22:09:12.5872860Z [       OK ] InsertBailOutsTest.Basic (0 ms)
2023-01-11T22:09:12.5873465Z [----------] 1 test from InsertBailOutsTest (0 ms total)
2023-01-11T22:09:12.5873674Z 
2023-01-11T22:09:12.5873827Z [----------] 2 tests from ProfilerTest
2023-01-11T22:09:12.5874095Z [ RUN      ] ProfilerTest.Basic
2023-01-11T22:09:12.5980708Z [       OK ] ProfilerTest.Basic (10 ms)
2023-01-11T22:09:12.5981121Z [ RUN      ] ProfilerTest.OptionalProfiling
2023-01-11T22:09:12.5982896Z [       OK ] ProfilerTest.OptionalProfiling (0 ms)
2023-01-11T22:09:12.5983267Z [----------] 2 tests from ProfilerTest (10 ms total)
2023-01-11T22:09:12.5983428Z 
2023-01-11T22:09:12.5983566Z [----------] 2 tests from CallStackTest
2023-01-11T22:09:12.5983831Z [ RUN      ] CallStackTest.Basic
2023-01-11T22:09:12.5987557Z [       OK ] CallStackTest.Basic (0 ms)
2023-01-11T22:09:12.5987820Z [ RUN      ] CallStackTest.Caching
2023-01-11T22:09:12.5991127Z [       OK ] CallStackTest.Caching (0 ms)
2023-01-11T22:09:12.5991501Z [----------] 2 tests from CallStackTest (0 ms total)
2023-01-11T22:09:12.5991653Z 
2023-01-11T22:09:12.5991822Z [----------] 2 tests from InlinedCallStackTest
2023-01-11T22:09:12.5992136Z [ RUN      ] InlinedCallStackTest.BlockAnnotation
2023-01-11T22:09:12.5997770Z [       OK ] InlinedCallStackTest.BlockAnnotation (0 ms)
2023-01-11T22:09:12.5998225Z [ RUN      ] InlinedCallStackTest.SelfCallMethods
2023-01-11T22:09:12.6007571Z [       OK ] InlinedCallStackTest.SelfCallMethods (0 ms)
2023-01-11T22:09:12.6008033Z [----------] 2 tests from InlinedCallStackTest (1 ms total)
2023-01-11T22:09:12.6008381Z 
2023-01-11T22:09:12.6008644Z [----------] 1 test from AutogradSymbolsTest
2023-01-11T22:09:12.6009216Z [ RUN      ] AutogradSymbolsTest.Basic
2023-01-11T22:09:12.6009635Z [       OK ] AutogradSymbolsTest.Basic (0 ms)
2023-01-11T22:09:12.6010075Z [----------] 1 test from AutogradSymbolsTest (0 ms total)
2023-01-11T22:09:12.6010238Z 
2023-01-11T22:09:12.6010420Z [----------] 1 test from DefaultArgTypeHintingTest
2023-01-11T22:09:12.6010742Z [ RUN      ] DefaultArgTypeHintingTest.Basic
2023-01-11T22:09:12.6011069Z [       OK ] DefaultArgTypeHintingTest.Basic (0 ms)
2023-01-11T22:09:12.6011433Z [----------] 1 test from DefaultArgTypeHintingTest (0 ms total)
2023-01-11T22:09:12.6011608Z 
2023-01-11T22:09:12.6011825Z [----------] 5 tests from FuturesTest
2023-01-11T22:09:12.6012075Z [ RUN      ] FuturesTest.Basic
2023-01-11T22:09:12.6012344Z [       OK ] FuturesTest.Basic (0 ms)
2023-01-11T22:09:12.6012604Z [ RUN      ] FuturesTest.Error
2023-01-11T22:09:12.6022503Z [       OK ] FuturesTest.Error (1 ms)
2023-01-11T22:09:12.6022968Z [ RUN      ] FuturesTest.Then
2023-01-11T22:09:12.6023501Z [       OK ] FuturesTest.Then (0 ms)
2023-01-11T22:09:12.6023914Z [ RUN      ] FuturesTest.CollectAll
2023-01-11T22:09:12.6024195Z [       OK ] FuturesTest.CollectAll (0 ms)
2023-01-11T22:09:12.6024485Z [ RUN      ] FuturesTest.CollectAny
2023-01-11T22:09:12.6024770Z [       OK ] FuturesTest.CollectAny (0 ms)
2023-01-11T22:09:12.6025109Z [----------] 5 tests from FuturesTest (1 ms total)
2023-01-11T22:09:12.6025335Z 
2023-01-11T22:09:12.6025601Z [----------] 1 test from TLSFutureCallbacksTest
2023-01-11T22:09:12.6026096Z [ RUN      ] TLSFutureCallbacksTest.Basic
2023-01-11T22:09:12.6026538Z [       OK ] TLSFutureCallbacksTest.Basic (0 ms)
2023-01-11T22:09:12.6026884Z [----------] 1 test from TLSFutureCallbacksTest (0 ms total)
2023-01-11T22:09:12.6027056Z 
2023-01-11T22:09:12.6027250Z [----------] 1 test from ProfilerDisableInCallbackTest
2023-01-11T22:09:12.6027602Z [ RUN      ] ProfilerDisableInCallbackTest.Basic
2023-01-11T22:09:12.6029237Z [       OK ] ProfilerDisableInCallbackTest.Basic (0 ms)
2023-01-11T22:09:12.6029932Z [----------] 1 test from ProfilerDisableInCallbackTest (0 ms total)
2023-01-11T22:09:12.6030259Z 
2023-01-11T22:09:12.6030431Z [----------] 2 tests from RecordDebugHandles
2023-01-11T22:09:12.6030734Z [ RUN      ] RecordDebugHandles.Basic
2023-01-11T22:09:12.6031921Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T22:09:12.6033678Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T22:09:12.6034387Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T22:09:12.6034818Z [       OK ] RecordDebugHandles.Basic (0 ms)
2023-01-11T22:09:12.6035238Z [ RUN      ] RecordDebugHandles.ScopedCallbacks
2023-01-11T22:09:12.6035871Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T22:09:12.6039962Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T22:09:12.6040610Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T22:09:12.6041264Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T22:09:12.6045134Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T22:09:12.6045785Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T22:09:12.6046352Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T22:09:12.6052592Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T22:09:12.6053414Z STAGE:2023-01-11 22:09:12 24550:24550 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T22:09:12.6053824Z [       OK ] RecordDebugHandles.ScopedCallbacks (1 ms)
2023-01-11T22:09:12.6054186Z [----------] 2 tests from RecordDebugHandles (2 ms total)
2023-01-11T22:09:12.6054352Z 
2023-01-11T22:09:12.6054494Z [----------] 1 test from IValueKWargsTest
2023-01-11T22:09:12.6054775Z [ RUN      ] IValueKWargsTest.Basic
2023-01-11T22:09:12.6058277Z [       OK ] IValueKWargsTest.Basic (0 ms)
2023-01-11T22:09:12.6058919Z [----------] 1 test from IValueKWargsTest (0 ms total)
2023-01-11T22:09:12.6059120Z 
2023-01-11T22:09:12.6059299Z [----------] 1 test from ComputeFlopsTest
2023-01-11T22:09:12.6059611Z [ RUN      ] ComputeFlopsTest.Basic
2023-01-11T22:09:12.6060047Z [W util.cpp:501] Warning: Failed to compute flops for op aten::conv2d because both input and weight must be size 4. (function computeFlops)
2023-01-11T22:09:12.6060665Z [W util.cpp:516] Warning: Failed to compute flops for op aten::conv2d because stride must be size 2 and cannot be 0. (function computeFlops)
2023-01-11T22:09:12.6061232Z [W util.cpp:472] Warning: Calculating flops for aten::conv2d requires groups, padding, stride, dilation, input_size, and weight_size in saved arguments. (function computeFlops)
2023-01-11T22:09:12.6062044Z [W util.cpp:545] Warning: Calculating flops for aten::mm requires mat1_size and mat2_size in saved arguments. (function computeFlops)
2023-01-11T22:09:12.6062839Z [       OK ] ComputeFlopsTest.Basic (0 ms)
2023-01-11T22:09:12.6063169Z [----------] 1 test from ComputeFlopsTest (0 ms total)
2023-01-11T22:09:12.6063335Z 
2023-01-11T22:09:12.6063486Z [----------] 1 test from TestConstant
2023-01-11T22:09:12.6063766Z [ RUN      ] TestConstant.TensorGrad
2023-01-11T22:09:12.6064064Z [       OK ] TestConstant.TensorGrad (0 ms)
2023-01-11T22:09:12.6064364Z [----------] 1 test from TestConstant (0 ms total)
2023-01-11T22:09:12.6064514Z 
2023-01-11T22:09:12.6064661Z [----------] 1 test from TestMutation
2023-01-11T22:09:12.6064936Z [ RUN      ] TestMutation.Basic
2023-01-11T22:09:12.6065197Z [       OK ] TestMutation.Basic (0 ms)
2023-01-11T22:09:12.6065501Z [----------] 1 test from TestMutation (0 ms total)
2023-01-11T22:09:12.6065649Z 
2023-01-11T22:09:12.6065857Z [----------] 1 test from TestInplaceToFunctionalActivation
2023-01-11T22:09:12.6066232Z [ RUN      ] TestInplaceToFunctionalActivation.Basic
2023-01-11T22:09:12.6066601Z [       OK ] TestInplaceToFunctionalActivation.Basic (0 ms)
2023-01-11T22:09:12.6067016Z [----------] 1 test from TestInplaceToFunctionalActivation (0 ms total)
2023-01-11T22:09:12.6067210Z 
2023-01-11T22:09:12.6067377Z [----------] 1 test from TestRegisterShapeOp
2023-01-11T22:09:12.6067660Z [ RUN      ] TestRegisterShapeOp.Basic
2023-01-11T22:09:12.7243974Z [       OK ] TestRegisterShapeOp.Basic (117 ms)
2023-01-11T22:09:12.7244454Z [----------] 1 test from TestRegisterShapeOp (117 ms total)
2023-01-11T22:09:12.7244634Z 
2023-01-11T22:09:12.7244917Z [----------] 1 test from TestFunctionalToInplaceActivation
2023-01-11T22:09:12.7245305Z [ RUN      ] TestFunctionalToInplaceActivation.Basic
2023-01-11T22:09:12.7245759Z [       OK ] TestFunctionalToInplaceActivation.Basic (0 ms)
2023-01-11T22:09:12.7246174Z [----------] 1 test from TestFunctionalToInplaceActivation (0 ms total)
2023-01-11T22:09:12.7246421Z 
2023-01-11T22:09:12.7246612Z [----------] 2 tests from TestFunctionExecutor
2023-01-11T22:09:12.7247143Z [ RUN      ] TestFunctionExecutor.SimpleExecutorTest
2023-01-11T22:09:12.7248551Z [       OK ] TestFunctionExecutor.SimpleExecutorTest (0 ms)
2023-01-11T22:09:12.7248932Z [ RUN      ] TestFunctionExecutor.RunDecompositionTest
2023-01-11T22:09:12.7265447Z [       OK ] TestFunctionExecutor.RunDecompositionTest (1 ms)
2023-01-11T22:09:12.7265938Z [----------] 2 tests from TestFunctionExecutor (2 ms total)
2023-01-11T22:09:12.7266113Z 
2023-01-11T22:09:12.7266298Z [----------] 1 test from TestShapeGraphLinting
2023-01-11T22:09:12.7266656Z [ RUN      ] TestShapeGraphLinting.Basic
2023-01-11T22:09:12.7270836Z [       OK ] TestShapeGraphLinting.Basic (0 ms)
2023-01-11T22:09:12.7271468Z [----------] 1 test from TestShapeGraphLinting (0 ms total)
2023-01-11T22:09:12.7271919Z 
2023-01-11T22:09:12.7272104Z [----------] 1 test from Composed
2023-01-11T22:09:12.7272377Z [ RUN      ] Composed.ComposedOp
2023-01-11T22:09:13.0254292Z [       OK ] Composed.ComposedOp (298 ms)
2023-01-11T22:09:13.0254777Z [----------] 1 test from Composed (298 ms total)
2023-01-11T22:09:13.0254929Z 
2023-01-11T22:09:13.0255085Z [----------] 1 test from ConstantPropagation
2023-01-11T22:09:13.0255464Z [ RUN      ] ConstantPropagation.CustomClassesCanBePropagated
2023-01-11T22:09:13.0260356Z [       OK ] ConstantPropagation.CustomClassesCanBePropagated (0 ms)
2023-01-11T22:09:13.0260997Z [----------] 1 test from ConstantPropagation (0 ms total)
2023-01-11T22:09:13.0261247Z 
2023-01-11T22:09:13.0261540Z [----------] 19 tests from MobileTypeParserTest
2023-01-11T22:09:13.0262047Z [ RUN      ] MobileTypeParserTest.Int
2023-01-11T22:09:13.0262705Z [       OK ] MobileTypeParserTest.Int (0 ms)
2023-01-11T22:09:13.0263201Z [ RUN      ] MobileTypeParserTest.NestedContainersAnnotationStr
2023-01-11T22:09:13.0263639Z [       OK ] MobileTypeParserTest.NestedContainersAnnotationStr (0 ms)
2023-01-11T22:09:13.0264048Z [ RUN      ] MobileTypeParserTest.TorchBindClass
2023-01-11T22:09:13.0264407Z [       OK ] MobileTypeParserTest.TorchBindClass (0 ms)
2023-01-11T22:09:13.0264765Z [ RUN      ] MobileTypeParserTest.ListOfTorchBindClass
2023-01-11T22:09:13.0265160Z [       OK ] MobileTypeParserTest.ListOfTorchBindClass (0 ms)
2023-01-11T22:09:13.0265616Z [ RUN      ] MobileTypeParserTest.NestedContainersAnnotationStrWithSpaces
2023-01-11T22:09:13.0266149Z [       OK ] MobileTypeParserTest.NestedContainersAnnotationStrWithSpaces (0 ms)
2023-01-11T22:09:13.0279224Z [ RUN      ] MobileTypeParserTest.NamedTuple
2023-01-11T22:09:13.0279867Z [       OK ] MobileTypeParserTest.NamedTuple (0 ms)
2023-01-11T22:09:13.0280292Z [ RUN      ] MobileTypeParserTest.DictNestedNamedTupleTypeList
2023-01-11T22:09:13.0280750Z [       OK ] MobileTypeParserTest.DictNestedNamedTupleTypeList (0 ms)
2023-01-11T22:09:13.0281229Z [ RUN      ] MobileTypeParserTest.NamedTupleNestedNamedTupleTypeList
2023-01-11T22:09:13.0281704Z [       OK ] MobileTypeParserTest.NamedTupleNestedNamedTupleTypeList (0 ms)
2023-01-11T22:09:13.0282158Z [ RUN      ] MobileTypeParserTest.NamedTupleNestedNamedTuple
2023-01-11T22:09:13.0282592Z [       OK ] MobileTypeParserTest.NamedTupleNestedNamedTuple (0 ms)
2023-01-11T22:09:13.0282938Z [ RUN      ] MobileTypeParserTest.Empty
2023-01-11T22:09:13.0295886Z [       OK ] MobileTypeParserTest.Empty (3 ms)
2023-01-11T22:09:13.0296224Z [ RUN      ] MobileTypeParserTest.TypoRaises
2023-01-11T22:09:13.0333760Z [       OK ] MobileTypeParserTest.TypoRaises (3 ms)
2023-01-11T22:09:13.0334219Z [ RUN      ] MobileTypeParserTest.MismatchBracketRaises
2023-01-11T22:09:13.0384067Z [       OK ] MobileTypeParserTest.MismatchBracketRaises (5 ms)
2023-01-11T22:09:13.0384717Z [ RUN      ] MobileTypeParserTest.MismatchBracketRaises2
2023-01-11T22:09:13.0421770Z [       OK ] MobileTypeParserTest.MismatchBracketRaises2 (3 ms)
2023-01-11T22:09:13.0422285Z [ RUN      ] MobileTypeParserTest.DictWithoutValueRaises
2023-01-11T22:09:13.0454255Z [       OK ] MobileTypeParserTest.DictWithoutValueRaises (3 ms)
2023-01-11T22:09:13.0454705Z [ RUN      ] MobileTypeParserTest.ListArgCountMismatchRaises
2023-01-11T22:09:13.0492357Z [       OK ] MobileTypeParserTest.ListArgCountMismatchRaises (3 ms)
2023-01-11T22:09:13.0492831Z [ RUN      ] MobileTypeParserTest.DictArgCountMismatchRaises
2023-01-11T22:09:13.0524354Z [       OK ] MobileTypeParserTest.DictArgCountMismatchRaises (3 ms)
2023-01-11T22:09:13.0524923Z [ RUN      ] MobileTypeParserTest.ValidTypeWithExtraStuffRaises
2023-01-11T22:09:13.0545875Z [       OK ] MobileTypeParserTest.ValidTypeWithExtraStuffRaises (2 ms)
2023-01-11T22:09:13.0546302Z [ RUN      ] MobileTypeParserTest.NonIdentifierRaises
2023-01-11T22:09:13.0567014Z [       OK ] MobileTypeParserTest.NonIdentifierRaises (2 ms)
2023-01-11T22:09:13.0567466Z [ RUN      ] MobileTypeParserTest.DictNestedNamedTupleTypeListRaises
2023-01-11T22:09:13.0610744Z [       OK ] MobileTypeParserTest.DictNestedNamedTupleTypeListRaises (4 ms)
2023-01-11T22:09:13.0611452Z [----------] 19 tests from MobileTypeParserTest (35 ms total)
2023-01-11T22:09:13.0611628Z 
2023-01-11T22:09:13.0611782Z [----------] 13 tests from ModuleAPITest
2023-01-11T22:09:13.0612066Z [ RUN      ] ModuleAPITest.MethodRunAsync
2023-01-11T22:09:13.0635835Z [       OK ] ModuleAPITest.MethodRunAsync (2 ms)
2023-01-11T22:09:13.0636240Z [ RUN      ] ModuleAPITest.Clone
2023-01-11T22:09:13.0636598Z [       OK ] ModuleAPITest.Clone (0 ms)
2023-01-11T22:09:13.0637024Z [ RUN      ] ModuleAPITest.CloneWithModuleInterface
2023-01-11T22:09:13.0643943Z [       OK ] ModuleAPITest.CloneWithModuleInterface (0 ms)
2023-01-11T22:09:13.0644425Z [ RUN      ] ModuleAPITest.Copy
2023-01-11T22:09:13.0644805Z [       OK ] ModuleAPITest.Copy (0 ms)
2023-01-11T22:09:13.0645100Z [ RUN      ] ModuleAPITest.DeepCopy
2023-01-11T22:09:13.0645496Z [       OK ] ModuleAPITest.DeepCopy (0 ms)
2023-01-11T22:09:13.0645934Z [ RUN      ] ModuleAPITest.DeepCopyString
2023-01-11T22:09:13.0646492Z [       OK ] ModuleAPITest.DeepCopyString (0 ms)
2023-01-11T22:09:13.0646993Z [ RUN      ] ModuleAPITest.DeepCopyEnum
2023-01-11T22:09:13.0647306Z [       OK ] ModuleAPITest.DeepCopyEnum (0 ms)
2023-01-11T22:09:13.0647640Z [ RUN      ] ModuleAPITest.DeepCopyPreservesAliasing
2023-01-11T22:09:13.0648026Z [       OK ] ModuleAPITest.DeepCopyPreservesAliasing (0 ms)
2023-01-11T22:09:13.0648352Z [ RUN      ] ModuleAPITest.Constants
2023-01-11T22:09:13.0648637Z [       OK ] ModuleAPITest.Constants (0 ms)
2023-01-11T22:09:13.0648932Z [ RUN      ] ModuleAPITest.Parameters
2023-01-11T22:09:13.0649382Z [       OK ] ModuleAPITest.Parameters (0 ms)
2023-01-11T22:09:13.0649654Z [ RUN      ] ModuleAPITest.Define
2023-01-11T22:09:13.0652669Z [       OK ] ModuleAPITest.Define (0 ms)
2023-01-11T22:09:13.0652957Z [ RUN      ] ModuleAPITest.Freezing
2023-01-11T22:09:13.0681434Z [       OK ] ModuleAPITest.Freezing (2 ms)
2023-01-11T22:09:13.0681739Z [ RUN      ] ModuleAPITest.OfiFreezesTraining
2023-01-11T22:09:13.0709767Z [       OK ] ModuleAPITest.OfiFreezesTraining (2 ms)
2023-01-11T22:09:13.0710286Z [----------] 13 tests from ModuleAPITest (9 ms total)
2023-01-11T22:09:13.0710540Z 
2023-01-11T22:09:13.0710869Z [----------] 6 tests from PeepholeOptimizeTest
2023-01-11T22:09:13.0711336Z [ RUN      ] PeepholeOptimizeTest.IsAndIsNot
2023-01-11T22:09:13.0711922Z [       OK ] PeepholeOptimizeTest.IsAndIsNot (0 ms)
2023-01-11T22:09:13.0712447Z [ RUN      ] PeepholeOptimizeTest.IsAndIsNot2
2023-01-11T22:09:13.0712959Z [       OK ] PeepholeOptimizeTest.IsAndIsNot2 (0 ms)
2023-01-11T22:09:13.0713300Z [ RUN      ] PeepholeOptimizeTest.IsAndIsNot3
2023-01-11T22:09:13.0713637Z [       OK ] PeepholeOptimizeTest.IsAndIsNot3 (0 ms)
2023-01-11T22:09:13.0714095Z [ RUN      ] PeepholeOptimizeTest.UnwrapOptional
2023-01-11T22:09:13.0714642Z [       OK ] PeepholeOptimizeTest.UnwrapOptional (0 ms)
2023-01-11T22:09:13.0715174Z [ RUN      ] PeepholeOptimizeTest.UnwrapOptional2
2023-01-11T22:09:13.0715869Z [       OK ] PeepholeOptimizeTest.UnwrapOptional2 (0 ms)
2023-01-11T22:09:13.0716231Z [ RUN      ] PeepholeOptimizeTest.AddMMFusion
2023-01-11T22:09:13.0716667Z [       OK ] PeepholeOptimizeTest.AddMMFusion (0 ms)
2023-01-11T22:09:13.0717031Z [----------] 6 tests from PeepholeOptimizeTest (0 ms total)
2023-01-11T22:09:13.0717205Z 
2023-01-11T22:09:13.0717352Z [----------] 5 tests from QualifiedNameTest
2023-01-11T22:09:13.0717675Z [ RUN      ] QualifiedNameTest.PrefixConstruction
2023-01-11T22:09:13.0718033Z [       OK ] QualifiedNameTest.PrefixConstruction (0 ms)
2023-01-11T22:09:13.0718386Z [ RUN      ] QualifiedNameTest.DottedConstruction
2023-01-11T22:09:13.0718732Z [       OK ] QualifiedNameTest.DottedConstruction (0 ms)
2023-01-11T22:09:13.0719073Z [ RUN      ] QualifiedNameTest.BadInputRaises
2023-01-11T22:09:13.0762816Z [       OK ] QualifiedNameTest.BadInputRaises (4 ms)
2023-01-11T22:09:13.0763240Z [ RUN      ] QualifiedNameTest.Equality
2023-01-11T22:09:13.0763557Z [       OK ] QualifiedNameTest.Equality (0 ms)
2023-01-11T22:09:13.0763868Z [ RUN      ] QualifiedNameTest.IsPrefixOf
2023-01-11T22:09:13.0764188Z [       OK ] QualifiedNameTest.IsPrefixOf (0 ms)
2023-01-11T22:09:13.0764512Z [----------] 5 tests from QualifiedNameTest (4 ms total)
2023-01-11T22:09:13.0764673Z 
2023-01-11T22:09:13.0764862Z [----------] 6 tests from SerializationTest
2023-01-11T22:09:13.0765215Z [ RUN      ] SerializationTest.ExtraFilesHookPreference
2023-01-11T22:09:13.0765744Z [W export_module.cpp:587] Warning: An extra files hook attempted to write metadata.json but this is already written in extra files and so will be skipped. This warning will only appear once per process. (function operator())
2023-01-11T22:09:13.0770674Z [       OK ] SerializationTest.ExtraFilesHookPreference (0 ms)
2023-01-11T22:09:13.0771149Z [ RUN      ] SerializationTest.ExtraFileHooksNoSecret
2023-01-11T22:09:13.0771934Z [       OK ] SerializationTest.ExtraFileHooksNoSecret (0 ms)
2023-01-11T22:09:13.0772470Z [ RUN      ] SerializationTest.ExtraFileHooksWithSecret
2023-01-11T22:09:13.0772993Z [       OK ] SerializationTest.ExtraFileHooksWithSecret (0 ms)
2023-01-11T22:09:13.0773337Z [ RUN      ] SerializationTest.TypeTags
2023-01-11T22:09:13.0775892Z [       OK ] SerializationTest.TypeTags (0 ms)
2023-01-11T22:09:13.0776216Z [ RUN      ] SerializationTest.ParentDirNotExist
2023-01-11T22:09:13.0829977Z [       OK ] SerializationTest.ParentDirNotExist (5 ms)
2023-01-11T22:09:13.0830691Z [ RUN      ] SerializationTest.CalculateNecessaryArgsTest
2023-01-11T22:09:13.0831132Z [       OK ] SerializationTest.CalculateNecessaryArgsTest (0 ms)
2023-01-11T22:09:13.0831510Z [----------] 6 tests from SerializationTest (6 ms total)
2023-01-11T22:09:13.0831672Z 
2023-01-11T22:09:13.0831838Z [----------] 3 tests from TestSourceRoundTrip
2023-01-11T22:09:13.0832177Z [ RUN      ] TestSourceRoundTrip.UpsampleNearest2d
2023-01-11T22:09:13.0845320Z [       OK ] TestSourceRoundTrip.UpsampleNearest2d (1 ms)
2023-01-11T22:09:13.0845712Z [ RUN      ] TestSourceRoundTrip.CheckAttrAccess
2023-01-11T22:09:13.0846252Z [       OK ] TestSourceRoundTrip.CheckAttrAccess (0 ms)
2023-01-11T22:09:13.0846594Z [ RUN      ] TestSourceRoundTrip.MethodInvocation
2023-01-11T22:09:13.0894967Z [       OK ] TestSourceRoundTrip.MethodInvocation (4 ms)
2023-01-11T22:09:13.0895443Z [----------] 3 tests from TestSourceRoundTrip (6 ms total)
2023-01-11T22:09:13.0895617Z 
2023-01-11T22:09:13.0895762Z [----------] 1 test from TestSaveLoad
2023-01-11T22:09:13.0896055Z [ RUN      ] TestSaveLoad.LoadWithoutDebugInfo
2023-01-11T22:09:13.0915886Z [       OK ] TestSaveLoad.LoadWithoutDebugInfo (2 ms)
2023-01-11T22:09:13.0916230Z [----------] 1 test from TestSaveLoad (2 ms total)
2023-01-11T22:09:13.0916386Z 
2023-01-11T22:09:13.0916667Z [----------] 2 tests from FunctionSchemaIsAliasingTest
2023-01-11T22:09:13.0916996Z [ RUN      ] FunctionSchemaIsAliasingTest.Basic
2023-01-11T22:09:13.0917351Z [       OK ] FunctionSchemaIsAliasingTest.Basic (0 ms)
2023-01-11T22:09:13.0917725Z [ RUN      ] FunctionSchemaIsAliasingTest.InvalidArgument
2023-01-11T22:09:13.0938412Z [       OK ] FunctionSchemaIsAliasingTest.InvalidArgument (2 ms)
2023-01-11T22:09:13.0938845Z [----------] 2 tests from FunctionSchemaIsAliasingTest (2 ms total)
2023-01-11T22:09:13.0939073Z 
2023-01-11T22:09:13.0939289Z [----------] 2 tests from FunctionSchemaIsMutableTest
2023-01-11T22:09:13.0939627Z [ RUN      ] FunctionSchemaIsMutableTest.Basic
2023-01-11T22:09:13.0939962Z [       OK ] FunctionSchemaIsMutableTest.Basic (0 ms)
2023-01-11T22:09:13.0940332Z [ RUN      ] FunctionSchemaIsMutableTest.InvalidArgument
2023-01-11T22:09:13.0971327Z [       OK ] FunctionSchemaIsMutableTest.InvalidArgument (3 ms)
2023-01-11T22:09:13.0971777Z [----------] 2 tests from FunctionSchemaIsMutableTest (3 ms total)
2023-01-11T22:09:13.0972008Z 
2023-01-11T22:09:13.0972222Z [----------] 5 tests from SchemaInfoIsMutableTest
2023-01-11T22:09:13.0972699Z [ RUN      ] SchemaInfoIsMutableTest.Basic
2023-01-11T22:09:13.0973163Z [       OK ] SchemaInfoIsMutableTest.Basic (0 ms)
2023-01-11T22:09:13.0973507Z [ RUN      ] SchemaInfoIsMutableTest.InvalidArgument
2023-01-11T22:09:13.1005602Z [       OK ] SchemaInfoIsMutableTest.InvalidArgument (3 ms)
2023-01-11T22:09:13.1006311Z [ RUN      ] SchemaInfoIsMutableTest.AliasingInputs
2023-01-11T22:09:13.1006917Z [       OK ] SchemaInfoIsMutableTest.AliasingInputs (0 ms)
2023-01-11T22:09:13.1007529Z [ RUN      ] SchemaInfoIsMutableTest.InstanceNorm
2023-01-11T22:09:13.1008139Z [       OK ] SchemaInfoIsMutableTest.InstanceNorm (0 ms)
2023-01-11T22:09:13.1008776Z [ RUN      ] SchemaInfoIsMutableTest.BatchNorm
2023-01-11T22:09:13.1009401Z [       OK ] SchemaInfoIsMutableTest.BatchNorm (0 ms)
2023-01-11T22:09:13.1009770Z [----------] 5 tests from SchemaInfoIsMutableTest (3 ms total)
2023-01-11T22:09:13.1009946Z 
2023-01-11T22:09:13.1010150Z [----------] 2 tests from SchemaInfoIsNonDeterministicTest
2023-01-11T22:09:13.1010519Z [ RUN      ] SchemaInfoIsNonDeterministicTest.Basic
2023-01-11T22:09:13.1010876Z [       OK ] SchemaInfoIsNonDeterministicTest.Basic (0 ms)
2023-01-11T22:09:13.1011255Z [ RUN      ] SchemaInfoIsNonDeterministicTest.Dropout
2023-01-11T22:09:13.1011645Z [       OK ] SchemaInfoIsNonDeterministicTest.Dropout (0 ms)
2023-01-11T22:09:13.1012049Z [----------] 2 tests from SchemaInfoIsNonDeterministicTest (0 ms total)
2023-01-11T22:09:13.1012243Z 
2023-01-11T22:09:13.1012428Z [----------] 3 tests from FunctionSchemaMayAliasTest
2023-01-11T22:09:13.1012762Z [ RUN      ] FunctionSchemaMayAliasTest.Basic
2023-01-11T22:09:13.1013100Z [       OK ] FunctionSchemaMayAliasTest.Basic (0 ms)
2023-01-11T22:09:13.1013552Z [ RUN      ] FunctionSchemaMayAliasTest.InvalidArgument
2023-01-11T22:09:13.1036956Z [       OK ] FunctionSchemaMayAliasTest.InvalidArgument (2 ms)
2023-01-11T22:09:13.1037646Z [ RUN      ] FunctionSchemaMayAliasTest.Wildcard
2023-01-11T22:09:13.1038082Z [       OK ] FunctionSchemaMayAliasTest.Wildcard (0 ms)
2023-01-11T22:09:13.1038679Z [----------] 3 tests from FunctionSchemaMayAliasTest (2 ms total)
2023-01-11T22:09:13.1038981Z 
2023-01-11T22:09:13.1039278Z [----------] 7 tests from SchemaInfoMayAliasTest
2023-01-11T22:09:13.1039832Z [ RUN      ] SchemaInfoMayAliasTest.AliasingInputs
2023-01-11T22:09:13.1040405Z [       OK ] SchemaInfoMayAliasTest.AliasingInputs (0 ms)
2023-01-11T22:09:13.1040945Z [ RUN      ] SchemaInfoMayAliasTest.AliasingOutputs
2023-01-11T22:09:13.1041607Z [       OK ] SchemaInfoMayAliasTest.AliasingOutputs (0 ms)
2023-01-11T22:09:13.1042148Z [ RUN      ] SchemaInfoMayAliasTest.AliasingInputOutput
2023-01-11T22:09:13.1042775Z [       OK ] SchemaInfoMayAliasTest.AliasingInputOutput (0 ms)
2023-01-11T22:09:13.1043356Z [ RUN      ] SchemaInfoMayAliasTest.MultipleWildcardInputs
2023-01-11T22:09:13.1043941Z [       OK ] SchemaInfoMayAliasTest.MultipleWildcardInputs (0 ms)
2023-01-11T22:09:13.1044526Z [ RUN      ] SchemaInfoMayAliasTest.MultipleNonWildcardInputs
2023-01-11T22:09:13.1045084Z [W schema_info.cpp:333] Warning: alias::a appears twice in same argument list which will make aliasing checks more conservative. (function operator())
2023-01-11T22:09:13.1045674Z [       OK ] SchemaInfoMayAliasTest.MultipleNonWildcardInputs (0 ms)
2023-01-11T22:09:13.1046271Z [ RUN      ] SchemaInfoMayAliasTest.MultipleNonWildcardOutputs
2023-01-11T22:09:13.1046766Z [W schema_info.cpp:333] Warning: alias::a appears twice in same argument list which will make aliasing checks more conservative. (function operator())
2023-01-11T22:09:13.1047247Z [       OK ] SchemaInfoMayAliasTest.MultipleNonWildcardOutputs (0 ms)
2023-01-11T22:09:13.1047649Z [ RUN      ] SchemaInfoMayAliasTest.MismatchingTypes
2023-01-11T22:09:13.1048027Z [       OK ] SchemaInfoMayAliasTest.MismatchingTypes (0 ms)
2023-01-11T22:09:13.1048396Z [----------] 7 tests from SchemaInfoMayAliasTest (0 ms total)
2023-01-11T22:09:13.1048568Z 
2023-01-11T22:09:13.1048773Z [----------] 3 tests from FunctionSchemaMayContainAliasTest
2023-01-11T22:09:13.1049299Z [ RUN      ] FunctionSchemaMayContainAliasTest.Basic
2023-01-11T22:09:13.1049664Z [       OK ] FunctionSchemaMayContainAliasTest.Basic (0 ms)
2023-01-11T22:09:13.1050043Z [ RUN      ] FunctionSchemaMayContainAliasTest.Wildcard
2023-01-11T22:09:13.1050542Z [       OK ] FunctionSchemaMayContainAliasTest.Wildcard (0 ms)
2023-01-11T22:09:13.1051137Z [ RUN      ] FunctionSchemaMayContainAliasTest.InputAndOutputContainers
2023-01-11T22:09:13.1051698Z [       OK ] FunctionSchemaMayContainAliasTest.InputAndOutputContainers (0 ms)
2023-01-11T22:09:13.1052168Z [----------] 3 tests from FunctionSchemaMayContainAliasTest (0 ms total)
2023-01-11T22:09:13.1052361Z 
2023-01-11T22:09:13.1052552Z [----------] 6 tests from SchemaInfoMayContainAliasTest
2023-01-11T22:09:13.1052943Z [ RUN      ] SchemaInfoMayContainAliasTest.ContainAliasInputsEqual
2023-01-11T22:09:13.1053410Z [       OK ] SchemaInfoMayContainAliasTest.ContainAliasInputsEqual (0 ms)
2023-01-11T22:09:13.1053889Z [ RUN      ] SchemaInfoMayContainAliasTest.ContainAliasInputsContained
2023-01-11T22:09:13.1054512Z [       OK ] SchemaInfoMayContainAliasTest.ContainAliasInputsContained (0 ms)
2023-01-11T22:09:13.1055227Z [ RUN      ] SchemaInfoMayContainAliasTest.ContainAliasOutputs
2023-01-11T22:09:13.1055672Z [       OK ] SchemaInfoMayContainAliasTest.ContainAliasOutputs (0 ms)
2023-01-11T22:09:13.1056216Z [ RUN      ] SchemaInfoMayContainAliasTest.ContainAliasInputOutput
2023-01-11T22:09:13.1056671Z [       OK ] SchemaInfoMayContainAliasTest.ContainAliasInputOutput (0 ms)
2023-01-11T22:09:13.1057135Z [ RUN      ] SchemaInfoMayContainAliasTest.InputAndOutputContainers
2023-01-11T22:09:13.1057604Z [       OK ] SchemaInfoMayContainAliasTest.InputAndOutputContainers (0 ms)
2023-01-11T22:09:13.1058018Z [ RUN      ] SchemaInfoMayContainAliasTest.Wildcard
2023-01-11T22:09:13.1058375Z [       OK ] SchemaInfoMayContainAliasTest.Wildcard (0 ms)
2023-01-11T22:09:13.1058770Z [----------] 6 tests from SchemaInfoMayContainAliasTest (0 ms total)
2023-01-11T22:09:13.1058954Z 
2023-01-11T22:09:13.1059114Z [----------] 2 tests from SchemaMatchingTest
2023-01-11T22:09:13.1059447Z [ RUN      ] SchemaMatchingTest.VarType
2023-01-11T22:09:13.1059770Z [       OK ] SchemaMatchingTest.VarType (0 ms)
2023-01-11T22:09:13.1060075Z [ RUN      ] SchemaMatchingTest.VarType2
2023-01-11T22:09:13.1060388Z [       OK ] SchemaMatchingTest.VarType2 (0 ms)
2023-01-11T22:09:13.1060712Z [----------] 2 tests from SchemaMatchingTest (0 ms total)
2023-01-11T22:09:13.1060875Z 
2023-01-11T22:09:13.1061022Z [----------] 6 tests from StackOptTest
2023-01-11T22:09:13.1061315Z [ RUN      ] StackOptTest.UseVariadicStack
2023-01-11T22:09:13.1159884Z [       OK ] StackOptTest.UseVariadicStack (10 ms)
2023-01-11T22:09:13.1160444Z [ RUN      ] StackOptTest.UseVariadicStackReplaceMultiple
2023-01-11T22:09:13.1217120Z [       OK ] StackOptTest.UseVariadicStackReplaceMultiple (5 ms)
2023-01-11T22:09:13.1217561Z [ RUN      ] StackOptTest.UseVariadicStackWithMultipleListUses
2023-01-11T22:09:13.1241183Z [       OK ] StackOptTest.UseVariadicStackWithMultipleListUses (2 ms)
2023-01-11T22:09:13.1241642Z [ RUN      ] StackOptTest.UseVariadicStackWithListMutationAfterCat
2023-01-11T22:09:13.1275645Z [       OK ] StackOptTest.UseVariadicStackWithListMutationAfterCat (3 ms)
2023-01-11T22:09:13.1276110Z [ RUN      ] StackOptTest.UseVariadicStackWithListMutationBeforeCat
2023-01-11T22:09:13.1321223Z [       OK ] StackOptTest.UseVariadicStackWithListMutationBeforeCat (4 ms)
2023-01-11T22:09:13.1321715Z [ RUN      ] StackOptTest.UseVariadicStackWithMultipleListMutations
2023-01-11T22:09:13.1391862Z [       OK ] StackOptTest.UseVariadicStackWithMultipleListMutations (6 ms)
2023-01-11T22:09:13.1392733Z [----------] 6 tests from StackOptTest (33 ms total)
2023-01-11T22:09:13.1393058Z 
2023-01-11T22:09:13.1393417Z [----------] 16 tests from SubgraphMatcherTest
2023-01-11T22:09:13.1394095Z [ RUN      ] SubgraphMatcherTest.Trivial1
2023-01-11T22:09:13.1394713Z [       OK ] SubgraphMatcherTest.Trivial1 (0 ms)
2023-01-11T22:09:13.1395267Z [ RUN      ] SubgraphMatcherTest.Trivial2
2023-01-11T22:09:13.1395858Z [       OK ] SubgraphMatcherTest.Trivial2 (0 ms)
2023-01-11T22:09:13.1396422Z [ RUN      ] SubgraphMatcherTest.Trivial3
2023-01-11T22:09:13.1396992Z [       OK ] SubgraphMatcherTest.Trivial3 (0 ms)
2023-01-11T22:09:13.1397552Z [ RUN      ] SubgraphMatcherTest.Trivial4
2023-01-11T22:09:13.1398145Z [       OK ] SubgraphMatcherTest.Trivial4 (0 ms)
2023-01-11T22:09:13.1398684Z [ RUN      ] SubgraphMatcherTest.Linear1
2023-01-11T22:09:13.1399214Z [       OK ] SubgraphMatcherTest.Linear1 (0 ms)
2023-01-11T22:09:13.1399796Z [ RUN      ] SubgraphMatcherTest.Linear2
2023-01-11T22:09:13.1400356Z [       OK ] SubgraphMatcherTest.Linear2 (0 ms)
2023-01-11T22:09:13.1400888Z [ RUN      ] SubgraphMatcherTest.Diamond1
2023-01-11T22:09:13.1401472Z [       OK ] SubgraphMatcherTest.Diamond1 (0 ms)
2023-01-11T22:09:13.1402033Z [ RUN      ] SubgraphMatcherTest.Diamond2
2023-01-11T22:09:13.1402821Z [       OK ] SubgraphMatcherTest.Diamond2 (0 ms)
2023-01-11T22:09:13.1403368Z [ RUN      ] SubgraphMatcherTest.XPattern
2023-01-11T22:09:13.1403932Z [       OK ] SubgraphMatcherTest.XPattern (0 ms)
2023-01-11T22:09:13.1404518Z [ RUN      ] SubgraphMatcherTest.MultipleMatches
2023-01-11T22:09:13.1405139Z [       OK ] SubgraphMatcherTest.MultipleMatches (0 ms)
2023-01-11T22:09:13.1405782Z [ RUN      ] SubgraphMatcherTest.OverlappingMatches
2023-01-11T22:09:13.1406453Z [       OK ] SubgraphMatcherTest.OverlappingMatches (0 ms)
2023-01-11T22:09:13.1407104Z [ RUN      ] SubgraphMatcherTest.MatchInBasicBlocks1
2023-01-11T22:09:13.1407775Z [       OK ] SubgraphMatcherTest.MatchInBasicBlocks1 (0 ms)
2023-01-11T22:09:13.1408573Z [ RUN      ] SubgraphMatcherTest.MatchInBasicBlocks2
2023-01-11T22:09:13.1409427Z [       OK ] SubgraphMatcherTest.MatchInBasicBlocks2 (0 ms)
2023-01-11T22:09:13.1410057Z [ RUN      ] SubgraphMatcherTest.MatchesAttributes
2023-01-11T22:09:13.1410477Z [       OK ] SubgraphMatcherTest.MatchesAttributes (0 ms)
2023-01-11T22:09:13.1410821Z [ RUN      ] SubgraphMatcherTest.BadPattern
2023-01-11T22:09:13.1444466Z [       OK ] SubgraphMatcherTest.BadPattern (4 ms)
2023-01-11T22:09:13.1445067Z [ RUN      ] SubgraphMatcherTest.MultiOutput
2023-01-11T22:09:13.1445651Z [       OK ] SubgraphMatcherTest.MultiOutput (0 ms)
2023-01-11T22:09:13.1446294Z [----------] 16 tests from SubgraphMatcherTest (5 ms total)
2023-01-11T22:09:13.1446565Z 
2023-01-11T22:09:13.1446873Z [----------] 4 tests from SubgraphRewriterTest
2023-01-11T22:09:13.1447434Z [ RUN      ] SubgraphRewriterTest.FilterMatch
2023-01-11T22:09:13.1448075Z [       OK ] SubgraphRewriterTest.FilterMatch (0 ms)
2023-01-11T22:09:13.1448656Z [ RUN      ] SubgraphRewriterTest.FilterNoMatch
2023-01-11T22:09:13.1449435Z [       OK ] SubgraphRewriterTest.FilterNoMatch (0 ms)
2023-01-11T22:09:13.1450058Z [ RUN      ] SubgraphRewriterTest.MultiOutput
2023-01-11T22:09:13.1454142Z [       OK ] SubgraphRewriterTest.MultiOutput (0 ms)
2023-01-11T22:09:13.1454722Z [ RUN      ] SubgraphRewriterTest.OutputType
2023-01-11T22:09:13.1455325Z [       OK ] SubgraphRewriterTest.OutputType (0 ms)
2023-01-11T22:09:13.1455917Z [----------] 4 tests from SubgraphRewriterTest (0 ms total)
2023-01-11T22:09:13.1456197Z 
2023-01-11T22:09:13.1456462Z [----------] 3 tests from SubgraphUtilsTest
2023-01-11T22:09:13.1456956Z [ RUN      ] SubgraphUtilsTest.Basic
2023-01-11T22:09:13.1460276Z [       OK ] SubgraphUtilsTest.Basic (0 ms)
2023-01-11T22:09:13.1460860Z [ RUN      ] SubgraphUtilsTest.MergeSubgraphs
2023-01-11T22:09:13.1464304Z [       OK ] SubgraphUtilsTest.MergeSubgraphs (0 ms)
2023-01-11T22:09:13.1465108Z [ RUN      ] SubgraphUtilsTest.GraphName
2023-01-11T22:09:13.1466540Z [       OK ] SubgraphUtilsTest.GraphName (0 ms)
2023-01-11T22:09:13.1468372Z [----------] 3 tests from SubgraphUtilsTest (0 ms total)
2023-01-11T22:09:13.1468526Z 
2023-01-11T22:09:13.1468684Z [----------] 8 tests from UnionTypeTest
2023-01-11T22:09:13.1469001Z [ RUN      ] UnionTypeTest.UnionOperatorEquals
2023-01-11T22:09:13.1469370Z [       OK ] UnionTypeTest.UnionOperatorEquals (0 ms)
2023-01-11T22:09:13.1469737Z [ RUN      ] UnionTypeTest.UnionCreate_OptionalT1AndOptionalT2
2023-01-11T22:09:13.1470136Z [       OK ] UnionTypeTest.UnionCreate_OptionalT1AndOptionalT2 (0 ms)
2023-01-11T22:09:13.1470505Z [ RUN      ] UnionTypeTest.UnionCreate_OptionalTAndT
2023-01-11T22:09:13.1470865Z [       OK ] UnionTypeTest.UnionCreate_OptionalTAndT (0 ms)
2023-01-11T22:09:13.1471243Z [ RUN      ] UnionTypeTest.UnionCreate_TupleWithSubtypingRelationship
2023-01-11T22:09:13.1471813Z [       OK ] UnionTypeTest.UnionCreate_TupleWithSubtypingRelationship (0 ms)
2023-01-11T22:09:13.1472195Z [ RUN      ] UnionTypeTest.UnionCreate_ContainerTAndT
2023-01-11T22:09:13.1472539Z [       OK ] UnionTypeTest.UnionCreate_ContainerTAndT (0 ms)
2023-01-11T22:09:13.1472941Z [ RUN      ] UnionTypeTest.UnionCreate_OptionalContainerTAndContainerTAndT
2023-01-11T22:09:13.1473390Z [       OK ] UnionTypeTest.UnionCreate_OptionalContainerTAndContainerTAndT (0 ms)
2023-01-11T22:09:13.1473772Z [ RUN      ] UnionTypeTest.Subtyping_NumberType
2023-01-11T22:09:13.1474091Z [       OK ] UnionTypeTest.Subtyping_NumberType (0 ms)
2023-01-11T22:09:13.1474427Z [ RUN      ] UnionTypeTest.Subtyping_OptionalType
2023-01-11T22:09:13.1474767Z [       OK ] UnionTypeTest.Subtyping_OptionalType (0 ms)
2023-01-11T22:09:13.1475195Z [----------] 8 tests from UnionTypeTest (0 ms total)
2023-01-11T22:09:13.1475365Z 
2023-01-11T22:09:13.1475539Z [----------] 2 tests from ScriptProfileTest
2023-01-11T22:09:13.1475835Z [ RUN      ] ScriptProfileTest.Basic
2023-01-11T22:09:13.1476135Z [       OK ] ScriptProfileTest.Basic (0 ms)
2023-01-11T22:09:13.1476435Z [ RUN      ] ScriptProfileTest.CallingOrder
2023-01-11T22:09:13.1499932Z [       OK ] ScriptProfileTest.CallingOrder (3 ms)
2023-01-11T22:09:13.1500604Z [----------] 2 tests from ScriptProfileTest (3 ms total)
2023-01-11T22:09:13.1500844Z 
2023-01-11T22:09:13.1500999Z [----------] 7 tests from ShapeAnalysisTest
2023-01-11T22:09:13.1501333Z [ RUN      ] ShapeAnalysisTest.DynamicShapesFusion
2023-01-11T22:09:13.1572958Z [       OK ] ShapeAnalysisTest.DynamicShapesFusion (7 ms)
2023-01-11T22:09:13.1573446Z [ RUN      ] ShapeAnalysisTest.MovingConstantOutOfFusionGroups
2023-01-11T22:09:13.1589685Z [       OK ] ShapeAnalysisTest.MovingConstantOutOfFusionGroups (1 ms)
2023-01-11T22:09:13.1590107Z [ RUN      ] ShapeAnalysisTest.SymbolicShapeAPI
2023-01-11T22:09:13.1662275Z [       OK ] ShapeAnalysisTest.SymbolicShapeAPI (7 ms)
2023-01-11T22:09:13.1662696Z [ RUN      ] ShapeAnalysisTest.BoundedSymbolicShapes
2023-01-11T22:09:13.1668540Z [       OK ] ShapeAnalysisTest.BoundedSymbolicShapes (0 ms)
2023-01-11T22:09:13.1668922Z [ RUN      ] ShapeAnalysisTest.SymbolicShapeCaching
2023-01-11T22:09:13.1674970Z [       OK ] ShapeAnalysisTest.SymbolicShapeCaching (0 ms)
2023-01-11T22:09:13.1675353Z [ RUN      ] ShapeAnalysisTest.ShapeCacheMultipleFns
2023-01-11T22:09:13.1703434Z [       OK ] ShapeAnalysisTest.ShapeCacheMultipleFns (2 ms)
2023-01-11T22:09:13.1703824Z [ RUN      ] ShapeAnalysisTest.TestShapeMultipleReturns
2023-01-11T22:09:13.1716284Z [       OK ] ShapeAnalysisTest.TestShapeMultipleReturns (1 ms)
2023-01-11T22:09:13.1717618Z [----------] 7 tests from ShapeAnalysisTest (21 ms total)
2023-01-11T22:09:13.1717915Z 
2023-01-11T22:09:13.1718164Z [----------] 5 tests from JitLoggingTest
2023-01-11T22:09:13.1718677Z [ RUN      ] JitLoggingTest.CheckSetLoggingLevel
2023-01-11T22:09:13.1719250Z [       OK ] JitLoggingTest.CheckSetLoggingLevel (0 ms)
2023-01-11T22:09:13.1719832Z [ RUN      ] JitLoggingTest.CheckSetMultipleLogLevels
2023-01-11T22:09:13.1720511Z [       OK ] JitLoggingTest.CheckSetMultipleLogLevels (0 ms)
2023-01-11T22:09:13.1721183Z [ RUN      ] JitLoggingTest.CheckLoggingLevelAfterUnset
2023-01-11T22:09:13.1721858Z [       OK ] JitLoggingTest.CheckLoggingLevelAfterUnset (0 ms)
2023-01-11T22:09:13.1722523Z [ RUN      ] JitLoggingTest.CheckAfterChangingLevel
2023-01-11T22:09:13.1723192Z [       OK ] JitLoggingTest.CheckAfterChangingLevel (0 ms)
2023-01-11T22:09:13.1723860Z [ RUN      ] JitLoggingTest.CheckOutputStreamSetting
2023-01-11T22:09:13.1724521Z [       OK ] JitLoggingTest.CheckOutputStreamSetting (0 ms)
2023-01-11T22:09:13.1725274Z [----------] 5 tests from JitLoggingTest (0 ms total)
2023-01-11T22:09:13.1725516Z 
2023-01-11T22:09:13.1725768Z [----------] 9 tests from FileFormatTest
2023-01-11T22:09:13.1726333Z [ RUN      ] FileFormatTest.IdentifiesFlatbufferStream
2023-01-11T22:09:13.1727027Z [       OK ] FileFormatTest.IdentifiesFlatbufferStream (0 ms)
2023-01-11T22:09:13.1727619Z [ RUN      ] FileFormatTest.IdentifiesZipStream
2023-01-11T22:09:13.1728116Z [       OK ] FileFormatTest.IdentifiesZipStream (0 ms)
2023-01-11T22:09:13.1728772Z [ RUN      ] FileFormatTest.FlatbufferTakesPrecedence
2023-01-11T22:09:13.1729562Z [       OK ] FileFormatTest.FlatbufferTakesPrecedence (0 ms)
2023-01-11T22:09:13.1730284Z [ RUN      ] FileFormatTest.HandlesUnknownStream
2023-01-11T22:09:13.1730867Z [       OK ] FileFormatTest.HandlesUnknownStream (0 ms)
2023-01-11T22:09:13.1731453Z [ RUN      ] FileFormatTest.ShortStreamIsUnknown
2023-01-11T22:09:13.1732040Z [       OK ] FileFormatTest.ShortStreamIsUnknown (0 ms)
2023-01-11T22:09:13.1732628Z [ RUN      ] FileFormatTest.EmptyStreamIsUnknown
2023-01-11T22:09:13.1733241Z [       OK ] FileFormatTest.EmptyStreamIsUnknown (0 ms)
2023-01-11T22:09:13.1733813Z [ RUN      ] FileFormatTest.BadStreamIsUnknown
2023-01-11T22:09:13.1734303Z [       OK ] FileFormatTest.BadStreamIsUnknown (0 ms)
2023-01-11T22:09:13.1734873Z [ RUN      ] FileFormatTest.StreamOffsetIsObservedAndRestored
2023-01-11T22:09:13.1735504Z [       OK ] FileFormatTest.StreamOffsetIsObservedAndRestored (0 ms)
2023-01-11T22:09:13.1736177Z [ RUN      ] FileFormatTest.HandlesMissingFile
2023-01-11T22:09:13.1736680Z [       OK ] FileFormatTest.HandlesMissingFile (0 ms)
2023-01-11T22:09:13.1737197Z [----------] 9 tests from FileFormatTest (0 ms total)
2023-01-11T22:09:13.1737432Z 
2023-01-11T22:09:13.1737706Z [----------] 35 tests from FlatbufferTest
2023-01-11T22:09:13.1738228Z [ RUN      ] FlatbufferTest.UpsampleNearest2d
2023-01-11T22:09:13.1740462Z [       OK ] FlatbufferTest.UpsampleNearest2d (2 ms)
2023-01-11T22:09:13.1741152Z [ RUN      ] FlatbufferTest.UpsampleNearest2dWithCopyTensorMemory
2023-01-11T22:09:13.1751557Z [       OK ] FlatbufferTest.UpsampleNearest2dWithCopyTensorMemory (1 ms)
2023-01-11T22:09:13.1752018Z [ RUN      ] FlatbufferTest.CheckAttrAccess
2023-01-11T22:09:13.1752345Z [       OK ] FlatbufferTest.CheckAttrAccess (0 ms)
2023-01-11T22:09:13.1752674Z [ RUN      ] FlatbufferTest.MethodInvocation
2023-01-11T22:09:13.1772096Z [       OK ] FlatbufferTest.MethodInvocation (2 ms)
2023-01-11T22:09:13.1772441Z [ RUN      ] FlatbufferTest.FlatbufferBackPortTest
2023-01-11T22:09:13.1801496Z [       OK ] FlatbufferTest.FlatbufferBackPortTest (2 ms)
2023-01-11T22:09:13.1801926Z [ RUN      ] FlatbufferTest.ExtraFiles
2023-01-11T22:09:13.1805106Z [       OK ] FlatbufferTest.ExtraFiles (0 ms)
2023-01-11T22:09:13.1805381Z [ RUN      ] FlatbufferTest.Conv
2023-01-11T22:09:13.1824929Z [       OK ] FlatbufferTest.Conv (1 ms)
2023-01-11T22:09:13.1825377Z [ RUN      ] FlatbufferTest.ConvWithCopyTensorMemory
2023-01-11T22:09:13.1844936Z [       OK ] FlatbufferTest.ConvWithCopyTensorMemory (1 ms)
2023-01-11T22:09:13.1845371Z [ RUN      ] FlatbufferTest.Inline
2023-01-11T22:09:13.1852377Z [       OK ] FlatbufferTest.Inline (0 ms)
2023-01-11T22:09:13.1852767Z [ RUN      ] FlatbufferTest.InlineWithCopyTensorMemory
2023-01-11T22:09:13.1858937Z [       OK ] FlatbufferTest.InlineWithCopyTensorMemory (0 ms)
2023-01-11T22:09:13.1859300Z [ RUN      ] FlatbufferTest.Tuple
2023-01-11T22:09:13.1864014Z [       OK ] FlatbufferTest.Tuple (0 ms)
2023-01-11T22:09:13.1864444Z [ RUN      ] FlatbufferTest.Dict
2023-01-11T22:09:13.1868588Z [       OK ] FlatbufferTest.Dict (0 ms)
2023-01-11T22:09:13.1868886Z [ RUN      ] FlatbufferTest.Prim
2023-01-11T22:09:13.1872015Z [       OK ] FlatbufferTest.Prim (0 ms)
2023-01-11T22:09:13.1872311Z [ RUN      ] FlatbufferTest.PrimScalar
2023-01-11T22:09:13.1875908Z [       OK ] FlatbufferTest.PrimScalar (0 ms)
2023-01-11T22:09:13.1876548Z [ RUN      ] FlatbufferTest.WrongMethodName
2023-01-11T22:09:13.1910964Z [       OK ] FlatbufferTest.WrongMethodName (3 ms)
2023-01-11T22:09:13.1911531Z [ RUN      ] FlatbufferTest.SetState
2023-01-11T22:09:13.1930589Z [       OK ] FlatbufferTest.SetState (1 ms)
2023-01-11T22:09:13.1931171Z [ RUN      ] FlatbufferTest.BuiltinClass
2023-01-11T22:09:13.1936347Z [       OK ] FlatbufferTest.BuiltinClass (0 ms)
2023-01-11T22:09:13.1936883Z [ RUN      ] FlatbufferTest.BuiltinFunction
2023-01-11T22:09:13.1938679Z [       OK ] FlatbufferTest.BuiltinFunction (0 ms)
2023-01-11T22:09:13.1939235Z [ RUN      ] FlatbufferTest.Eval
2023-01-11T22:09:13.1944312Z [       OK ] FlatbufferTest.Eval (0 ms)
2023-01-11T22:09:13.1944912Z [ RUN      ] FlatbufferTest.FindWrongMethodName
2023-01-11T22:09:13.1947442Z [       OK ] FlatbufferTest.FindWrongMethodName (0 ms)
2023-01-11T22:09:13.1948018Z [ RUN      ] FlatbufferTest.FindAndRunMethod
2023-01-11T22:09:13.1953317Z [       OK ] FlatbufferTest.FindAndRunMethod (0 ms)
2023-01-11T22:09:13.1953669Z [ RUN      ] FlatbufferTest.RunMethodVariadic
2023-01-11T22:09:13.1958550Z [       OK ] FlatbufferTest.RunMethodVariadic (0 ms)
2023-01-11T22:09:13.1958875Z [ RUN      ] FlatbufferTest.DuplicateSetState
2023-01-11T22:09:13.1967062Z [       OK ] FlatbufferTest.DuplicateSetState (0 ms)
2023-01-11T22:09:13.1967467Z [ RUN      ] FlatbufferTest.OpNameExportFetchRootOperators
2023-01-11T22:09:13.1973410Z [       OK ] FlatbufferTest.OpNameExportFetchRootOperators (0 ms)
2023-01-11T22:09:13.1973771Z [ RUN      ] FlatbufferTest.DefaultArgsConv
2023-01-11T22:09:13.1986477Z [       OK ] FlatbufferTest.DefaultArgsConv (1 ms)
2023-01-11T22:09:13.1986915Z [ RUN      ] FlatbufferTest.DefaultArgsPinv
2023-01-11T22:09:13.2040126Z [       OK ] FlatbufferTest.DefaultArgsPinv (5 ms)
2023-01-11T22:09:13.2040586Z [ RUN      ] FlatbufferTest.DefaultArgsTensorinvSpecifyDefault
2023-01-11T22:09:13.2050503Z [       OK ] FlatbufferTest.DefaultArgsTensorinvSpecifyDefault (1 ms)
2023-01-11T22:09:13.2050918Z [ RUN      ] FlatbufferTest.DefaultArgsPinvWithOutArg
2023-01-11T22:09:13.2070619Z [       OK ] FlatbufferTest.DefaultArgsPinvWithOutArg (1 ms)
2023-01-11T22:09:13.2071062Z [ RUN      ] FlatbufferTest.DefaultArgsWithOutArg
2023-01-11T22:09:13.2080121Z [       OK ] FlatbufferTest.DefaultArgsWithOutArg (0 ms)
2023-01-11T22:09:13.2080554Z [ RUN      ] FlatbufferTest.OperatorCacheDifferentiatesDefaultArgs
2023-01-11T22:09:13.2103571Z [       OK ] FlatbufferTest.OperatorCacheDifferentiatesDefaultArgs (2 ms)
2023-01-11T22:09:13.2103987Z [ RUN      ] FlatbufferTest.OperatorSize1
2023-01-11T22:09:13.2106743Z [       OK ] FlatbufferTest.OperatorSize1 (0 ms)
2023-01-11T22:09:13.2107163Z [ RUN      ] FlatbufferTest.BoolAndDoubleList
2023-01-11T22:09:13.2107512Z [       OK ] FlatbufferTest.BoolAndDoubleList (0 ms)
2023-01-11T22:09:13.2107888Z [ RUN      ] FlatbufferTest.OperatorTest2
2023-01-11T22:09:13.2117710Z [       OK ] FlatbufferTest.OperatorTest2 (1 ms)
2023-01-11T22:09:13.2118312Z [ RUN      ] FlatbufferTest.DetachedBufferSmoke
2023-01-11T22:09:13.2118832Z [       OK ] FlatbufferTest.DetachedBufferSmoke (0 ms)
2023-01-11T22:09:13.2119465Z [ RUN      ] FlatbufferTest.DetachedBufferNullOwner
2023-01-11T22:09:13.2120231Z [       OK ] FlatbufferTest.DetachedBufferNullOwner (0 ms)
2023-01-11T22:09:13.2120595Z [----------] 35 tests from FlatbufferTest (39 ms total)
2023-01-11T22:09:13.2120755Z 
2023-01-11T22:09:13.2120923Z [----------] 3 tests from TestSourceFlatbuffer
2023-01-11T22:09:13.2121260Z [ RUN      ] TestSourceFlatbuffer.UpsampleNearest2d
2023-01-11T22:09:13.2134435Z [       OK ] TestSourceFlatbuffer.UpsampleNearest2d (1 ms)
2023-01-11T22:09:13.2134840Z [ RUN      ] TestSourceFlatbuffer.CheckAttrAccess
2023-01-11T22:09:13.2135388Z [       OK ] TestSourceFlatbuffer.CheckAttrAccess (0 ms)
2023-01-11T22:09:13.2135784Z [ RUN      ] TestSourceFlatbuffer.MethodInvocation
2023-01-11T22:09:13.2187749Z [       OK ] TestSourceFlatbuffer.MethodInvocation (5 ms)
2023-01-11T22:09:13.2188356Z [----------] 3 tests from TestSourceFlatbuffer (6 ms total)
2023-01-11T22:09:13.2188529Z 
2023-01-11T22:09:13.2188718Z [----------] 10 tests from FlatbufferUpgraderTest
2023-01-11T22:09:13.2189048Z [ RUN      ] FlatbufferUpgraderTest.DivTensorV2
2023-01-11T22:09:13.2201853Z [       OK ] FlatbufferUpgraderTest.DivTensorV2 (1 ms)
2023-01-11T22:09:13.2202468Z [ RUN      ] FlatbufferUpgraderTest.DivTensorOutV2
2023-01-11T22:09:13.2206895Z [       OK ] FlatbufferUpgraderTest.DivTensorOutV2 (0 ms)
2023-01-11T22:09:13.2207500Z [ RUN      ] FlatbufferUpgraderTest.DivTensorInplaceV2
2023-01-11T22:09:13.2213737Z [       OK ] FlatbufferUpgraderTest.DivTensorInplaceV2 (0 ms)
2023-01-11T22:09:13.2214343Z [ RUN      ] FlatbufferUpgraderTest.DivScalarFloatV2
2023-01-11T22:09:13.2218205Z [       OK ] FlatbufferUpgraderTest.DivScalarFloatV2 (0 ms)
2023-01-11T22:09:13.2218839Z [ RUN      ] FlatbufferUpgraderTest.DivScalarReciprocalFloatV2
2023-01-11T22:09:13.2230638Z [       OK ] FlatbufferUpgraderTest.DivScalarReciprocalFloatV2 (1 ms)
2023-01-11T22:09:13.2231278Z [ RUN      ] FlatbufferUpgraderTest.DivScalarReciprocalIntV2
2023-01-11T22:09:13.2237214Z [       OK ] FlatbufferUpgraderTest.DivScalarReciprocalIntV2 (0 ms)
2023-01-11T22:09:13.2237821Z [ RUN      ] FlatbufferUpgraderTest.DivScalarScalarV2
2023-01-11T22:09:13.2241096Z [       OK ] FlatbufferUpgraderTest.DivScalarScalarV2 (0 ms)
2023-01-11T22:09:13.2241708Z [ RUN      ] FlatbufferUpgraderTest.DivScalarIntV2
2023-01-11T22:09:13.2250849Z [       OK ] FlatbufferUpgraderTest.DivScalarIntV2 (0 ms)
2023-01-11T22:09:13.2251499Z [ RUN      ] FlatbufferUpgraderTest.DivScalarInplaceFloatV2
2023-01-11T22:09:13.2259531Z [       OK ] FlatbufferUpgraderTest.DivScalarInplaceFloatV2 (0 ms)
2023-01-11T22:09:13.2260163Z [ RUN      ] FlatbufferUpgraderTest.DivScalarInplaceIntV2
2023-01-11T22:09:13.2263137Z [       OK ] FlatbufferUpgraderTest.DivScalarInplaceIntV2 (0 ms)
2023-01-11T22:09:13.2263818Z [----------] 10 tests from FlatbufferUpgraderTest (7 ms total)
2023-01-11T22:09:13.2264140Z 
2023-01-11T22:09:13.2264570Z [----------] 12 tests from AliasAnalysisTest/BatchAndInstanceNormFixture
2023-01-11T22:09:13.2265237Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/0
2023-01-11T22:09:13.2266045Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/0 (0 ms)
2023-01-11T22:09:13.2266907Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/1
2023-01-11T22:09:13.2267762Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/1 (0 ms)
2023-01-11T22:09:13.2268628Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/2
2023-01-11T22:09:13.2269529Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/2 (0 ms)
2023-01-11T22:09:13.2270394Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/3
2023-01-11T22:09:13.2271492Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNorm/3 (0 ms)
2023-01-11T22:09:13.2272460Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/0
2023-01-11T22:09:13.2273567Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/0 (0 ms)
2023-01-11T22:09:13.2274625Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/1
2023-01-11T22:09:13.2275416Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/1 (0 ms)
2023-01-11T22:09:13.2276052Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/2
2023-01-11T22:09:13.2276654Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/2 (0 ms)
2023-01-11T22:09:13.2277251Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/3
2023-01-11T22:09:13.2277846Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchAndInstanceNormTrainingUnknown/3 (0 ms)
2023-01-11T22:09:13.2278417Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/0
2023-01-11T22:09:13.2278992Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/0 (0 ms)
2023-01-11T22:09:13.2279561Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/1
2023-01-11T22:09:13.2280133Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/1 (0 ms)
2023-01-11T22:09:13.2280690Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/2
2023-01-11T22:09:13.2281264Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/2 (0 ms)
2023-01-11T22:09:13.2281837Z [ RUN      ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/3
2023-01-11T22:09:13.2282416Z [       OK ] AliasAnalysisTest/BatchAndInstanceNormFixture.BatchNormTrainingWithNoMeanOrVar/3 (0 ms)
2023-01-11T22:09:13.2282907Z [----------] 12 tests from AliasAnalysisTest/BatchAndInstanceNormFixture (1 ms total)
2023-01-11T22:09:13.2283106Z 
2023-01-11T22:09:13.2283348Z [----------] 10 tests from PyTorch/LiteInterpreterDynamicTypeTestFixture
2023-01-11T22:09:13.2283792Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/0
2023-01-11T22:09:14.0474656Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/0 (819 ms)
2023-01-11T22:09:14.0475203Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/1
2023-01-11T22:09:15.2095088Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/1 (1162 ms)
2023-01-11T22:09:15.2095672Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/2
2023-01-11T22:09:16.6073740Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/2 (1397 ms)
2023-01-11T22:09:16.6074282Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/3
2023-01-11T22:09:17.9647122Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/3 (1357 ms)
2023-01-11T22:09:17.9647675Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/4
2023-01-11T22:09:19.3075843Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/4 (1342 ms)
2023-01-11T22:09:19.3076384Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/5
2023-01-11T22:09:20.6770294Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/5 (1369 ms)
2023-01-11T22:09:20.6771066Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/6
2023-01-11T22:09:22.0333967Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/6 (1356 ms)
2023-01-11T22:09:22.0334475Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/7
2023-01-11T22:09:23.3440545Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/7 (1310 ms)
2023-01-11T22:09:23.3441060Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/8
2023-01-11T22:09:24.5271054Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/8 (1183 ms)
2023-01-11T22:09:24.5271578Z [ RUN      ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/9
2023-01-11T22:09:25.7089279Z [       OK ] PyTorch/LiteInterpreterDynamicTypeTestFixture.Conformance/9 (1181 ms)
2023-01-11T22:09:25.7089833Z [----------] 10 tests from PyTorch/LiteInterpreterDynamicTypeTestFixture (12481 ms total)
2023-01-11T22:09:25.7090072Z 
2023-01-11T22:09:25.7090250Z [----------] Global test environment tear-down
2023-01-11T22:09:25.7155676Z [==========] 569 tests from 119 test suites ran. (13719 ms total)
2023-01-11T22:09:25.7155958Z [  PASSED  ] 569 tests.
2023-01-11T22:09:25.8000962Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *cuda* ]]
2023-01-11T22:09:25.8001260Z + [[ nogpu_AVX512 != *nogpu* ]]
2023-01-11T22:09:25.8001703Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_lazy --gtest_output=xml:test/test-reports/cpp-unittest/test_libtorch/test_lazy.xml
2023-01-11T22:09:26.1539354Z CUDA not available. Disabling CUDA and MultiCUDA tests
2023-01-11T22:09:26.1542195Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:09:26.1542708Z [==========] Running 611 tests from 10 test suites.
2023-01-11T22:09:26.1543257Z [----------] Global test environment set-up.
2023-01-11T22:09:26.1543719Z [----------] 11 tests from BackendDeviceTest
2023-01-11T22:09:26.1544048Z [ RUN      ] BackendDeviceTest.BackendDeviceType
2023-01-11T22:09:26.1544404Z [       OK ] BackendDeviceTest.BackendDeviceType (0 ms)
2023-01-11T22:09:26.1544716Z [ RUN      ] BackendDeviceTest.Basic1
2023-01-11T22:09:26.1545013Z [       OK ] BackendDeviceTest.Basic1 (0 ms)
2023-01-11T22:09:26.1545304Z [ RUN      ] BackendDeviceTest.Basic2
2023-01-11T22:09:26.1545601Z [       OK ] BackendDeviceTest.Basic2 (0 ms)
2023-01-11T22:09:26.1545876Z [ RUN      ] BackendDeviceTest.Basic3
2023-01-11T22:09:26.1546169Z [       OK ] BackendDeviceTest.Basic3 (0 ms)
2023-01-11T22:09:26.1546456Z [ RUN      ] BackendDeviceTest.Basic4
2023-01-11T22:09:26.1546735Z [       OK ] BackendDeviceTest.Basic4 (0 ms)
2023-01-11T22:09:26.1547028Z [ RUN      ] BackendDeviceTest.Compare
2023-01-11T22:09:26.1547327Z [       OK ] BackendDeviceTest.Compare (0 ms)
2023-01-11T22:09:26.1547612Z [ RUN      ] BackendDeviceTest.Ostream
2023-01-11T22:09:26.1547917Z [       OK ] BackendDeviceTest.Ostream (0 ms)
2023-01-11T22:09:26.1548215Z [ RUN      ] BackendDeviceTest.FromAten
2023-01-11T22:09:26.1597707Z [       OK ] BackendDeviceTest.FromAten (5 ms)
2023-01-11T22:09:26.1598214Z [ RUN      ] BackendDeviceTest.ToAten
2023-01-11T22:09:26.1598698Z [       OK ] BackendDeviceTest.ToAten (0 ms)
2023-01-11T22:09:26.1599201Z [ RUN      ] BackendDeviceTest.GetBackendDevice1
2023-01-11T22:09:26.1600105Z [       OK ] BackendDeviceTest.GetBackendDevice1 (0 ms)
2023-01-11T22:09:26.1600691Z [ RUN      ] BackendDeviceTest.GetBackendDevice2
2023-01-11T22:09:26.1601122Z [       OK ] BackendDeviceTest.GetBackendDevice2 (0 ms)
2023-01-11T22:09:26.1601623Z [----------] 11 tests from BackendDeviceTest (5 ms total)
2023-01-11T22:09:26.1601878Z 
2023-01-11T22:09:26.1602097Z [----------] 2 tests from CacheTest
2023-01-11T22:09:26.1602784Z [ RUN      ] CacheTest.BasicTest
2023-01-11T22:09:26.1603236Z [       OK ] CacheTest.BasicTest (0 ms)
2023-01-11T22:09:26.1603570Z [ RUN      ] CacheTest.ShapeCacheTestForDynamicShape
2023-01-11T22:09:26.1603937Z [       OK ] CacheTest.ShapeCacheTestForDynamicShape (0 ms)
2023-01-11T22:09:26.1604351Z [----------] 2 tests from CacheTest (0 ms total)
2023-01-11T22:09:26.1605541Z 
2023-01-11T22:09:26.1605714Z [----------] 5 tests from IrTest
2023-01-11T22:09:26.1605957Z [ RUN      ] IrTest.BasicTest
2023-01-11T22:09:26.1606228Z [       OK ] IrTest.BasicTest (0 ms)
2023-01-11T22:09:26.1606493Z [ RUN      ] IrTest.MetaDataTest
2023-01-11T22:09:26.1606857Z [       OK ] IrTest.MetaDataTest (0 ms)
2023-01-11T22:09:26.1607192Z [ RUN      ] IrTest.TsNodeTest
2023-01-11T22:09:26.1607461Z [       OK ] IrTest.TsNodeTest (0 ms)
2023-01-11T22:09:26.1607738Z [ RUN      ] IrTest.DimensionNodeTest
2023-01-11T22:09:26.1608020Z [       OK ] IrTest.DimensionNodeTest (0 ms)
2023-01-11T22:09:26.1608328Z [ RUN      ] IrTest.DimensionIsDynamicTest
2023-01-11T22:09:26.1608649Z [       OK ] IrTest.DimensionIsDynamicTest (0 ms)
2023-01-11T22:09:26.1608944Z [----------] 5 tests from IrTest (0 ms total)
2023-01-11T22:09:26.1609202Z 
2023-01-11T22:09:26.1609347Z [----------] 2 tests from IrUtilTest
2023-01-11T22:09:26.1609615Z [ RUN      ] IrUtilTest.BasicTest
2023-01-11T22:09:26.1609893Z [       OK ] IrUtilTest.BasicTest (0 ms)
2023-01-11T22:09:26.1610154Z [ RUN      ] IrUtilTest.TestCircle
2023-01-11T22:09:26.1626325Z [       OK ] IrUtilTest.TestCircle (2 ms)
2023-01-11T22:09:26.1627043Z [----------] 2 tests from IrUtilTest (2 ms total)
2023-01-11T22:09:26.1627316Z 
2023-01-11T22:09:26.1627547Z [----------] 2 tests from HashTest
2023-01-11T22:09:26.1627939Z [ RUN      ] HashTest.Scalar
2023-01-11T22:09:26.1628195Z [       OK ] HashTest.Scalar (0 ms)
2023-01-11T22:09:26.1628433Z [ RUN      ] HashTest.Sanity
2023-01-11T22:09:26.1628689Z [       OK ] HashTest.Sanity (0 ms)
2023-01-11T22:09:26.1628972Z [----------] 2 tests from HashTest (0 ms total)
2023-01-11T22:09:26.1629113Z 
2023-01-11T22:09:26.1629280Z [----------] 3 tests from PermutationUtilTest
2023-01-11T22:09:26.1629615Z [ RUN      ] PermutationUtilTest.TestInversePermutation
2023-01-11T22:09:26.1657006Z [       OK ] PermutationUtilTest.TestInversePermutation (2 ms)
2023-01-11T22:09:26.1657557Z [ RUN      ] PermutationUtilTest.TestIsPermutation
2023-01-11T22:09:26.1657939Z [       OK ] PermutationUtilTest.TestIsPermutation (0 ms)
2023-01-11T22:09:26.1658312Z [ RUN      ] PermutationUtilTest.TestPermute
2023-01-11T22:09:26.1677371Z [       OK ] PermutationUtilTest.TestPermute (2 ms)
2023-01-11T22:09:26.1677944Z [----------] 3 tests from PermutationUtilTest (4 ms total)
2023-01-11T22:09:26.1678211Z 
2023-01-11T22:09:26.1678409Z [----------] 7 tests from ShapeTest
2023-01-11T22:09:26.1678785Z [ RUN      ] ShapeTest.Basic1
2023-01-11T22:09:26.1679201Z [       OK ] ShapeTest.Basic1 (0 ms)
2023-01-11T22:09:26.1679486Z [ RUN      ] ShapeTest.Basic2
2023-01-11T22:09:26.1679755Z [       OK ] ShapeTest.Basic2 (0 ms)
2023-01-11T22:09:26.1680039Z [ RUN      ] ShapeTest.Basic3
2023-01-11T22:09:26.1680419Z [       OK ] ShapeTest.Basic3 (0 ms)
2023-01-11T22:09:26.1680831Z [ RUN      ] ShapeTest.SetScalarType
2023-01-11T22:09:26.1681297Z [       OK ] ShapeTest.SetScalarType (0 ms)
2023-01-11T22:09:26.1681812Z [ RUN      ] ShapeTest.SetSize
2023-01-11T22:09:26.1682174Z [       OK ] ShapeTest.SetSize (0 ms)
2023-01-11T22:09:26.1682417Z [ RUN      ] ShapeTest.Equal
2023-01-11T22:09:26.1682831Z [       OK ] ShapeTest.Equal (0 ms)
2023-01-11T22:09:26.1683085Z [ RUN      ] ShapeTest.Ostream
2023-01-11T22:09:26.1683335Z [       OK ] ShapeTest.Ostream (0 ms)
2023-01-11T22:09:26.1683629Z [----------] 7 tests from ShapeTest (0 ms total)
2023-01-11T22:09:26.1683777Z 
2023-01-11T22:09:26.1683924Z [----------] 2 tests from TrieCacheTest
2023-01-11T22:09:26.1684213Z [ RUN      ] TrieCacheTest.TestSinglePath
2023-01-11T22:09:26.1684521Z [       OK ] TrieCacheTest.TestSinglePath (0 ms)
2023-01-11T22:09:26.1684891Z [ RUN      ] TrieCacheTest.TestTwoPaths
2023-01-11T22:09:26.1685199Z [       OK ] TrieCacheTest.TestTwoPaths (0 ms)
2023-01-11T22:09:26.1685511Z [----------] 2 tests from TrieCacheTest (0 ms total)
2023-01-11T22:09:26.1685662Z 
2023-01-11T22:09:26.1685861Z [----------] 3 tests from UtilTest
2023-01-11T22:09:26.1686139Z [ RUN      ] UtilTest.ExceptionCleanup
2023-01-11T22:09:26.1686447Z [       OK ] UtilTest.ExceptionCleanup (0 ms)
2023-01-11T22:09:26.1686707Z [ RUN      ] UtilTest.MaybeRef
2023-01-11T22:09:26.1686967Z [       OK ] UtilTest.MaybeRef (0 ms)
2023-01-11T22:09:26.1687213Z [ RUN      ] UtilTest.Iota
2023-01-11T22:09:26.1687466Z [       OK ] UtilTest.Iota (0 ms)
2023-01-11T22:09:26.1687801Z [----------] 3 tests from UtilTest (0 ms total)
2023-01-11T22:09:26.1687972Z 
2023-01-11T22:09:26.1688140Z [----------] 574 tests from LazyOpsTest
2023-01-11T22:09:26.1688530Z [ RUN      ] LazyOpsTest.TestScalarTensor
2023-01-11T22:09:26.1816130Z [       OK ] LazyOpsTest.TestScalarTensor (12 ms)
2023-01-11T22:09:26.1816429Z [ RUN      ] LazyOpsTest.TestClone
2023-01-11T22:09:26.1846458Z [       OK ] LazyOpsTest.TestClone (2 ms)
2023-01-11T22:09:26.1846993Z [ RUN      ] LazyOpsTest.TestTo
2023-01-11T22:09:26.1847443Z [       OK ] LazyOpsTest.TestTo (0 ms)
2023-01-11T22:09:26.1847989Z [ RUN      ] LazyOpsTest.TestIsFloatingPoint
2023-01-11T22:09:26.1848578Z [       OK ] LazyOpsTest.TestIsFloatingPoint (0 ms)
2023-01-11T22:09:26.1848901Z [ RUN      ] LazyOpsTest.TestIsSigned
2023-01-11T22:09:26.1849474Z [       OK ] LazyOpsTest.TestIsSigned (0 ms)
2023-01-11T22:09:26.1849759Z [ RUN      ] LazyOpsTest.TestCastByte
2023-01-11T22:09:26.2221431Z [       OK ] LazyOpsTest.TestCastByte (37 ms)
2023-01-11T22:09:26.2221957Z [ RUN      ] LazyOpsTest.TestCastChar
2023-01-11T22:09:26.2224270Z [       OK ] LazyOpsTest.TestCastChar (0 ms)
2023-01-11T22:09:26.2224824Z [ RUN      ] LazyOpsTest.TestCastShort
2023-01-11T22:09:26.2227208Z [       OK ] LazyOpsTest.TestCastShort (0 ms)
2023-01-11T22:09:26.2227787Z [ RUN      ] LazyOpsTest.TestCastInt
2023-01-11T22:09:26.2230316Z [       OK ] LazyOpsTest.TestCastInt (0 ms)
2023-01-11T22:09:26.2230898Z [ RUN      ] LazyOpsTest.TestCastLong
2023-01-11T22:09:26.2233401Z [       OK ] LazyOpsTest.TestCastLong (0 ms)
2023-01-11T22:09:26.2233782Z [ RUN      ] LazyOpsTest.TestCastFloat
2023-01-11T22:09:26.2234128Z [       OK ] LazyOpsTest.TestCastFloat (0 ms)
2023-01-11T22:09:26.2234520Z [ RUN      ] LazyOpsTest.TestRetainType
2023-01-11T22:09:26.2235205Z [       OK ] LazyOpsTest.TestRetainType (0 ms)
2023-01-11T22:09:26.2235577Z [ RUN      ] LazyOpsTest.TestLogicalTypeWithInterop
2023-01-11T22:09:26.2276029Z [       OK ] LazyOpsTest.TestLogicalTypeWithInterop (4 ms)
2023-01-11T22:09:26.2276353Z [ RUN      ] LazyOpsTest.TestAdd
2023-01-11T22:09:26.2278941Z [       OK ] LazyOpsTest.TestAdd (0 ms)
2023-01-11T22:09:26.2279243Z [ RUN      ] LazyOpsTest.TestAddHalf
2023-01-11T22:09:26.2281999Z [       OK ] LazyOpsTest.TestAddHalf (0 ms)
2023-01-11T22:09:26.2282324Z [ RUN      ] LazyOpsTest.TestAddMixedPrecision
2023-01-11T22:09:26.2287105Z [       OK ] LazyOpsTest.TestAddMixedPrecision (0 ms)
2023-01-11T22:09:26.2287434Z [ RUN      ] LazyOpsTest.TestAddInPlace
2023-01-11T22:09:26.2291923Z [       OK ] LazyOpsTest.TestAddInPlace (0 ms)
2023-01-11T22:09:26.2292319Z [ RUN      ] LazyOpsTest.TestAddScalar
2023-01-11T22:09:26.2294884Z [       OK ] LazyOpsTest.TestAddScalar (0 ms)
2023-01-11T22:09:26.2295207Z [ RUN      ] LazyOpsTest.TestAddScalarInPlace
2023-01-11T22:09:26.2298929Z [       OK ] LazyOpsTest.TestAddScalarInPlace (0 ms)
2023-01-11T22:09:26.2299253Z [ RUN      ] LazyOpsTest.TestAddZeroSizeDim
2023-01-11T22:09:26.2301811Z [       OK ] LazyOpsTest.TestAddZeroSizeDim (0 ms)
2023-01-11T22:09:26.2302108Z [ RUN      ] LazyOpsTest.TestSub
2023-01-11T22:09:26.2305204Z [       OK ] LazyOpsTest.TestSub (0 ms)
2023-01-11T22:09:26.2305495Z [ RUN      ] LazyOpsTest.TestSubInPlace
2023-01-11T22:09:26.2320105Z [       OK ] LazyOpsTest.TestSubInPlace (1 ms)
2023-01-11T22:09:26.2320411Z [ RUN      ] LazyOpsTest.TestSubScalar
2023-01-11T22:09:26.2323071Z [       OK ] LazyOpsTest.TestSubScalar (0 ms)
2023-01-11T22:09:26.2323397Z [ RUN      ] LazyOpsTest.TestSubScalarInPlace
2023-01-11T22:09:26.2327043Z [       OK ] LazyOpsTest.TestSubScalarInPlace (0 ms)
2023-01-11T22:09:26.2327344Z [ RUN      ] LazyOpsTest.TestMul
2023-01-11T22:09:26.2329802Z [       OK ] LazyOpsTest.TestMul (0 ms)
2023-01-11T22:09:26.2330111Z [ RUN      ] LazyOpsTest.TestMulInPlace
2023-01-11T22:09:26.2333893Z [       OK ] LazyOpsTest.TestMulInPlace (0 ms)
2023-01-11T22:09:26.2334271Z [ RUN      ] LazyOpsTest.TestMulScalar
2023-01-11T22:09:26.2336524Z [       OK ] LazyOpsTest.TestMulScalar (0 ms)
2023-01-11T22:09:26.2336848Z [ RUN      ] LazyOpsTest.TestMulScalarInPlace
2023-01-11T22:09:26.2340401Z [       OK ] LazyOpsTest.TestMulScalarInPlace (0 ms)
2023-01-11T22:09:26.2340693Z [ RUN      ] LazyOpsTest.TestDiv
2023-01-11T22:09:26.2425445Z [       OK ] LazyOpsTest.TestDiv (8 ms)
2023-01-11T22:09:26.2426032Z [ RUN      ] LazyOpsTest.TestDivWithRoundingMode
2023-01-11T22:09:26.2731598Z [       OK ] LazyOpsTest.TestDivWithRoundingMode (30 ms)
2023-01-11T22:09:26.2732199Z [ RUN      ] LazyOpsTest.TestDivInPlace
2023-01-11T22:09:26.2735432Z [       OK ] LazyOpsTest.TestDivInPlace (0 ms)
2023-01-11T22:09:26.2735964Z [ RUN      ] LazyOpsTest.TestDivInPlaceWithRoundingMode
2023-01-11T22:09:26.2745687Z [       OK ] LazyOpsTest.TestDivInPlaceWithRoundingMode (1 ms)
2023-01-11T22:09:26.2746280Z [ RUN      ] LazyOpsTest.TestDivScalar
2023-01-11T22:09:26.2773878Z [       OK ] LazyOpsTest.TestDivScalar (2 ms)
2023-01-11T22:09:26.2774463Z [ RUN      ] LazyOpsTest.TestDivScalarInPlace
2023-01-11T22:09:26.2780353Z [       OK ] LazyOpsTest.TestDivScalarInPlace (0 ms)
2023-01-11T22:09:26.2780905Z [ RUN      ] LazyOpsTest.TestDivOut
2023-01-11T22:09:26.2784499Z [       OK ] LazyOpsTest.TestDivOut (0 ms)
2023-01-11T22:09:26.2785125Z [ RUN      ] LazyOpsTest.TestRsubScalar
2023-01-11T22:09:26.2787701Z [       OK ] LazyOpsTest.TestRsubScalar (0 ms)
2023-01-11T22:09:26.2788218Z [ RUN      ] LazyOpsTest.TestNe
2023-01-11T22:09:26.2790252Z [       OK ] LazyOpsTest.TestNe (0 ms)
2023-01-11T22:09:26.2790766Z [ RUN      ] LazyOpsTest.TestNeInplace
2023-01-11T22:09:26.2794820Z [       OK ] LazyOpsTest.TestNeInplace (0 ms)
2023-01-11T22:09:26.2795332Z [ RUN      ] LazyOpsTest.TestEq
2023-01-11T22:09:26.2797546Z [       OK ] LazyOpsTest.TestEq (0 ms)
2023-01-11T22:09:26.2798077Z [ RUN      ] LazyOpsTest.TestEqInplace
2023-01-11T22:09:26.2801806Z [       OK ] LazyOpsTest.TestEqInplace (0 ms)
2023-01-11T22:09:26.2802231Z [ RUN      ] LazyOpsTest.TestGe
2023-01-11T22:09:26.2804363Z [       OK ] LazyOpsTest.TestGe (0 ms)
2023-01-11T22:09:26.2804722Z [ RUN      ] LazyOpsTest.TestGeInplace
2023-01-11T22:09:26.2818566Z [       OK ] LazyOpsTest.TestGeInplace (1 ms)
2023-01-11T22:09:26.2818857Z [ RUN      ] LazyOpsTest.TestLe
2023-01-11T22:09:26.2821108Z [       OK ] LazyOpsTest.TestLe (0 ms)
2023-01-11T22:09:26.2821440Z [ RUN      ] LazyOpsTest.TestLeInplace
2023-01-11T22:09:26.2825716Z [       OK ] LazyOpsTest.TestLeInplace (0 ms)
2023-01-11T22:09:26.2826027Z [ RUN      ] LazyOpsTest.TestGt
2023-01-11T22:09:26.2828605Z [       OK ] LazyOpsTest.TestGt (0 ms)
2023-01-11T22:09:26.2828926Z [ RUN      ] LazyOpsTest.TestGtInplace
2023-01-11T22:09:26.2833154Z [       OK ] LazyOpsTest.TestGtInplace (0 ms)
2023-01-11T22:09:26.2833457Z [ RUN      ] LazyOpsTest.TestLt
2023-01-11T22:09:26.2835732Z [       OK ] LazyOpsTest.TestLt (0 ms)
2023-01-11T22:09:26.2836067Z [ RUN      ] LazyOpsTest.TestLtInplace
2023-01-11T22:09:26.2840230Z [       OK ] LazyOpsTest.TestLtInplace (0 ms)
2023-01-11T22:09:26.2840594Z [ RUN      ] LazyOpsTest.TestNeScalar
2023-01-11T22:09:26.2842869Z [       OK ] LazyOpsTest.TestNeScalar (0 ms)
2023-01-11T22:09:26.2843217Z [ RUN      ] LazyOpsTest.TestEqScalar
2023-01-11T22:09:26.2845524Z [       OK ] LazyOpsTest.TestEqScalar (0 ms)
2023-01-11T22:09:26.2845836Z [ RUN      ] LazyOpsTest.TestGeScalar
2023-01-11T22:09:26.2847818Z [       OK ] LazyOpsTest.TestGeScalar (0 ms)
2023-01-11T22:09:26.2848194Z [ RUN      ] LazyOpsTest.TestGeScalarInplace
2023-01-11T22:09:26.2863630Z [       OK ] LazyOpsTest.TestGeScalarInplace (1 ms)
2023-01-11T22:09:26.2864218Z [ RUN      ] LazyOpsTest.TestLeScalar
2023-01-11T22:09:26.2866588Z [       OK ] LazyOpsTest.TestLeScalar (0 ms)
2023-01-11T22:09:26.2867147Z [ RUN      ] LazyOpsTest.TestLeScalarInplace
2023-01-11T22:09:26.2871834Z [       OK ] LazyOpsTest.TestLeScalarInplace (0 ms)
2023-01-11T22:09:26.2872373Z [ RUN      ] LazyOpsTest.TestGtScalar
2023-01-11T22:09:26.2874783Z [       OK ] LazyOpsTest.TestGtScalar (0 ms)
2023-01-11T22:09:26.2875337Z [ RUN      ] LazyOpsTest.TestGtScalarInplace
2023-01-11T22:09:26.2880176Z [       OK ] LazyOpsTest.TestGtScalarInplace (0 ms)
2023-01-11T22:09:26.2880701Z [ RUN      ] LazyOpsTest.TestLtScalar
2023-01-11T22:09:26.2882887Z [       OK ] LazyOpsTest.TestLtScalar (0 ms)
2023-01-11T22:09:26.2883444Z [ RUN      ] LazyOpsTest.TestLtScalarInplace
2023-01-11T22:09:26.2888161Z [       OK ] LazyOpsTest.TestLtScalarInplace (0 ms)
2023-01-11T22:09:26.2888736Z [ RUN      ] LazyOpsTest.TestIntegerAdd
2023-01-11T22:09:26.2900631Z [       OK ] LazyOpsTest.TestIntegerAdd (1 ms)
2023-01-11T22:09:26.2901172Z [ RUN      ] LazyOpsTest.TestSVD
2023-01-11T22:09:26.2981860Z [       OK ] LazyOpsTest.TestSVD (8 ms)
2023-01-11T22:09:26.2982400Z [ RUN      ] LazyOpsTest.TestQR
2023-01-11T22:09:26.2983064Z [W BatchLinearAlgebra.cpp:2459] Warning: torch.qr is deprecated in favor of torch.linalg.qr and will be removed in a future PyTorch release.
2023-01-11T22:09:26.2983521Z The boolean parameter 'some' has been replaced with a string parameter 'mode'.
2023-01-11T22:09:26.2983760Z Q, R = torch.qr(A, some)
2023-01-11T22:09:26.2984006Z should be replaced with
2023-01-11T22:09:26.2984429Z Q, R = torch.linalg.qr(A, 'reduced' if some else 'complete') (function operator())
2023-01-11T22:09:26.3002333Z [       OK ] LazyOpsTest.TestQR (2 ms)
2023-01-11T22:09:26.3002970Z [ RUN      ] LazyOpsTest.TestSymEig
2023-01-11T22:09:26.3003519Z [W BatchLinearAlgebra.cpp:2910] Warning: torch.symeig is deprecated in favor of torch.linalg.eigh and will be removed in a future PyTorch release.
2023-01-11T22:09:26.3004294Z The default behavior has changed from using the upper triangular portion of the matrix by default to using the lower triangular portion.
2023-01-11T22:09:26.3004715Z L, _ = torch.symeig(A, upper=upper)
2023-01-11T22:09:26.3004981Z should be replaced with
2023-01-11T22:09:26.3005377Z L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')
2023-01-11T22:09:26.3005653Z and
2023-01-11T22:09:26.3005891Z L, V = torch.symeig(A, eigenvectors=True)
2023-01-11T22:09:26.3006173Z should be replaced with
2023-01-11T22:09:26.3006596Z L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L') (function operator())
2023-01-11T22:09:26.3021233Z [       OK ] LazyOpsTest.TestSymEig (1 ms)
2023-01-11T22:09:26.3021617Z [ RUN      ] LazyOpsTest.TestCholesky
2023-01-11T22:09:26.3022830Z [W BatchLinearAlgebra.cpp:1730] Warning: torch.cholesky is deprecated in favor of torch.linalg.cholesky and will be removed in a future PyTorch release.
2023-01-11T22:09:26.3023278Z L = torch.cholesky(A)
2023-01-11T22:09:26.3023521Z should be replaced with
2023-01-11T22:09:26.3023794Z L = torch.linalg.cholesky(A)
2023-01-11T22:09:26.3024035Z and
2023-01-11T22:09:26.3025075Z U = torch.cholesky(A, upper=True)
2023-01-11T22:09:26.3025351Z should be replaced with
2023-01-11T22:09:26.3025627Z U = torch.linalg.cholesky(A).mH().
2023-01-11T22:09:26.3026051Z This transform will produce equivalent results for all valid (symmetric positive definite) inputs. (function operator())
2023-01-11T22:09:26.3030749Z [       OK ] LazyOpsTest.TestCholesky (0 ms)
2023-01-11T22:09:26.3031222Z [ RUN      ] LazyOpsTest.TestLogDet
2023-01-11T22:09:26.3058970Z [       OK ] LazyOpsTest.TestLogDet (2 ms)
2023-01-11T22:09:26.3059516Z [ RUN      ] LazyOpsTest.TestTriangularSolve
2023-01-11T22:09:26.3060062Z [W BatchLinearAlgebra.cpp:2225] Warning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.
2023-01-11T22:09:26.3060659Z torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.
2023-01-11T22:09:26.3061055Z X = torch.triangular_solve(B, A).solution
2023-01-11T22:09:26.3061334Z should be replaced with
2023-01-11T22:09:26.3061637Z X = torch.linalg.solve_triangular(A, B). (function operator())
2023-01-11T22:09:26.3276323Z [       OK ] LazyOpsTest.TestTriangularSolve (21 ms)
2023-01-11T22:09:26.3276652Z [ RUN      ] LazyOpsTest.TestKthValue
2023-01-11T22:09:26.3308894Z [       OK ] LazyOpsTest.TestKthValue (3 ms)
2023-01-11T22:09:26.3309248Z [ RUN      ] LazyOpsTest.TestTopK
2023-01-11T22:09:26.3486656Z [       OK ] LazyOpsTest.TestTopK (17 ms)
2023-01-11T22:09:26.3486956Z [ RUN      ] LazyOpsTest.TestSort
2023-01-11T22:09:26.3553711Z [       OK ] LazyOpsTest.TestSort (6 ms)
2023-01-11T22:09:26.3554039Z [ RUN      ] LazyOpsTest.TestSortDescWithMinValue
2023-01-11T22:09:26.3558894Z [       OK ] LazyOpsTest.TestSortDescWithMinValue (0 ms)
2023-01-11T22:09:26.3559204Z [ RUN      ] LazyOpsTest.TestArgSort
2023-01-11T22:09:26.3570994Z [       OK ] LazyOpsTest.TestArgSort (1 ms)
2023-01-11T22:09:26.3571276Z [ RUN      ] LazyOpsTest.TestMin
2023-01-11T22:09:26.3574534Z [       OK ] LazyOpsTest.TestMin (0 ms)
2023-01-11T22:09:26.3574791Z [ RUN      ] LazyOpsTest.TestMax
2023-01-11T22:09:26.3577784Z [       OK ] LazyOpsTest.TestMax (0 ms)
2023-01-11T22:09:26.3578124Z [ RUN      ] LazyOpsTest.TestUnaryMin
2023-01-11T22:09:26.3580995Z [       OK ] LazyOpsTest.TestUnaryMin (0 ms)
2023-01-11T22:09:26.3581383Z [ RUN      ] LazyOpsTest.TestUnaryMax
2023-01-11T22:09:26.3583970Z [       OK ] LazyOpsTest.TestUnaryMax (0 ms)
2023-01-11T22:09:26.3584307Z [ RUN      ] LazyOpsTest.TestAll
2023-01-11T22:09:26.3594473Z [       OK ] LazyOpsTest.TestAll (1 ms)
2023-01-11T22:09:26.3594928Z [ RUN      ] LazyOpsTest.TestAllDim
2023-01-11T22:09:26.3596992Z [       OK ] LazyOpsTest.TestAllDim (0 ms)
2023-01-11T22:09:26.3597333Z [ RUN      ] LazyOpsTest.TestAllDimKeep
2023-01-11T22:09:26.3599245Z [       OK ] LazyOpsTest.TestAllDimKeep (0 ms)
2023-01-11T22:09:26.3599593Z [ RUN      ] LazyOpsTest.TestAmax
2023-01-11T22:09:26.3634916Z [       OK ] LazyOpsTest.TestAmax (3 ms)
2023-01-11T22:09:26.3635272Z [ RUN      ] LazyOpsTest.TestAmin
2023-01-11T22:09:26.3670071Z [       OK ] LazyOpsTest.TestAmin (3 ms)
2023-01-11T22:09:26.3670400Z [ RUN      ] LazyOpsTest.TestAny
2023-01-11T22:09:26.3691133Z [       OK ] LazyOpsTest.TestAny (2 ms)
2023-01-11T22:09:26.3691459Z [ RUN      ] LazyOpsTest.TestAnyDim
2023-01-11T22:09:26.3693873Z [       OK ] LazyOpsTest.TestAnyDim (0 ms)
2023-01-11T22:09:26.3694174Z [ RUN      ] LazyOpsTest.TestAnyDimKeep
2023-01-11T22:09:26.3696036Z [       OK ] LazyOpsTest.TestAnyDimKeep (0 ms)
2023-01-11T22:09:26.3696568Z [ RUN      ] LazyOpsTest.TestMean
2023-01-11T22:09:26.3699330Z [       OK ] LazyOpsTest.TestMean (0 ms)
2023-01-11T22:09:26.3699844Z [ RUN      ] LazyOpsTest.TestMeanCast
2023-01-11T22:09:26.3701992Z [       OK ] LazyOpsTest.TestMeanCast (0 ms)
2023-01-11T22:09:26.3702609Z [ RUN      ] LazyOpsTest.TestMeanInDim
2023-01-11T22:09:26.3719829Z [       OK ] LazyOpsTest.TestMeanInDim (1 ms)
2023-01-11T22:09:26.3720387Z [ RUN      ] LazyOpsTest.TestMeanInDims
2023-01-11T22:09:26.3725878Z [       OK ] LazyOpsTest.TestMeanInDims (0 ms)
2023-01-11T22:09:26.3726464Z [ RUN      ] LazyOpsTest.TestMeanInDimsKeepCast
2023-01-11T22:09:26.3732401Z [       OK ] LazyOpsTest.TestMeanInDimsKeepCast (0 ms)
2023-01-11T22:09:26.3732979Z [ RUN      ] LazyOpsTest.TestMeanInDimOut
2023-01-11T22:09:26.3761410Z [       OK ] LazyOpsTest.TestMeanInDimOut (2 ms)
2023-01-11T22:09:26.3761930Z [ RUN      ] LazyOpsTest.TestStd
2023-01-11T22:09:26.3767813Z [       OK ] LazyOpsTest.TestStd (0 ms)
2023-01-11T22:09:26.3768342Z [ RUN      ] LazyOpsTest.TestStdInDim
2023-01-11T22:09:26.3837945Z [       OK ] LazyOpsTest.TestStdInDim (6 ms)
2023-01-11T22:09:26.3838515Z [ RUN      ] LazyOpsTest.TestStdWithCorrection
2023-01-11T22:09:26.3872002Z [       OK ] LazyOpsTest.TestStdWithCorrection (3 ms)
2023-01-11T22:09:26.3872551Z [ RUN      ] LazyOpsTest.TestStdMeanWithCorrection
2023-01-11T22:09:26.3884961Z [       OK ] LazyOpsTest.TestStdMeanWithCorrection (1 ms)
2023-01-11T22:09:26.3885522Z [ RUN      ] LazyOpsTest.TestSum
2023-01-11T22:09:26.3887798Z [       OK ] LazyOpsTest.TestSum (0 ms)
2023-01-11T22:09:26.3888332Z [ RUN      ] LazyOpsTest.TestSumCast
2023-01-11T22:09:26.3891255Z [       OK ] LazyOpsTest.TestSumCast (0 ms)
2023-01-11T22:09:26.3891789Z [ RUN      ] LazyOpsTest.TestSumU8
2023-01-11T22:09:26.3893289Z [       OK ] LazyOpsTest.TestSumU8 (0 ms)
2023-01-11T22:09:26.3893569Z [ RUN      ] LazyOpsTest.TestSumInDim
2023-01-11T22:09:26.3910042Z [       OK ] LazyOpsTest.TestSumInDim (1 ms)
2023-01-11T22:09:26.3910400Z [ RUN      ] LazyOpsTest.TestSumInDims
2023-01-11T22:09:26.3915942Z [       OK ] LazyOpsTest.TestSumInDims (0 ms)
2023-01-11T22:09:26.3916275Z [ RUN      ] LazyOpsTest.TestSumInDimsKeep
2023-01-11T22:09:26.3921937Z [       OK ] LazyOpsTest.TestSumInDimsKeep (0 ms)
2023-01-11T22:09:26.3922315Z [ RUN      ] LazyOpsTest.TestSumInDimsKeepCast
2023-01-11T22:09:26.3927835Z [       OK ] LazyOpsTest.TestSumInDimsKeepCast (0 ms)
2023-01-11T22:09:26.3928180Z [ RUN      ] LazyOpsTest.TestVar
2023-01-11T22:09:26.3930095Z [       OK ] LazyOpsTest.TestVar (0 ms)
2023-01-11T22:09:26.3930424Z [ RUN      ] LazyOpsTest.TestVarWithDim
2023-01-11T22:09:26.3935883Z [       OK ] LazyOpsTest.TestVarWithDim (0 ms)
2023-01-11T22:09:26.3936209Z [ RUN      ] LazyOpsTest.TestVarWithCorrection
2023-01-11T22:09:26.3943595Z [       OK ] LazyOpsTest.TestVarWithCorrection (0 ms)
2023-01-11T22:09:26.3943973Z [ RUN      ] LazyOpsTest.TestVarMeanWithCorrection
2023-01-11T22:09:26.3956658Z [       OK ] LazyOpsTest.TestVarMeanWithCorrection (1 ms)
2023-01-11T22:09:26.3957102Z [ RUN      ] LazyOpsTest.TestMaxInDim
2023-01-11T22:09:26.4015818Z [       OK ] LazyOpsTest.TestMaxInDim (5 ms)
2023-01-11T22:09:26.4016214Z [ RUN      ] LazyOpsTest.TestMinInDim
2023-01-11T22:09:26.4027708Z [       OK ] LazyOpsTest.TestMinInDim (1 ms)
2023-01-11T22:09:26.4028122Z [ RUN      ] LazyOpsTest.TestNorm
2023-01-11T22:09:26.4031606Z [       OK ] LazyOpsTest.TestNorm (0 ms)
2023-01-11T22:09:26.4032012Z [ RUN      ] LazyOpsTest.TestNormInDim
2023-01-11T22:09:26.4038772Z [       OK ] LazyOpsTest.TestNormInDim (0 ms)
2023-01-11T22:09:26.4039348Z [ RUN      ] LazyOpsTest.TestNormInDims
2023-01-11T22:09:26.4045658Z [       OK ] LazyOpsTest.TestNormInDims (0 ms)
2023-01-11T22:09:26.4046240Z [ RUN      ] LazyOpsTest.TestNormInDimsKeep
2023-01-11T22:09:26.4053361Z [       OK ] LazyOpsTest.TestNormInDimsKeep (0 ms)
2023-01-11T22:09:26.4054008Z [ RUN      ] LazyOpsTest.TestNormalTwoTensor
2023-01-11T22:09:26.4060114Z [       OK ] LazyOpsTest.TestNormalTwoTensor (0 ms)
2023-01-11T22:09:26.4060556Z [ RUN      ] LazyOpsTest.TestNormalDoubleMean
2023-01-11T22:09:26.4066811Z [       OK ] LazyOpsTest.TestNormalDoubleMean (0 ms)
2023-01-11T22:09:26.4067129Z [ RUN      ] LazyOpsTest.TestNormalDoubleStd
2023-01-11T22:09:26.4069628Z [       OK ] LazyOpsTest.TestNormalDoubleStd (0 ms)
2023-01-11T22:09:26.4069972Z [ RUN      ] LazyOpsTest.TestNormalInPlace
2023-01-11T22:09:26.4073014Z [       OK ] LazyOpsTest.TestNormalInPlace (0 ms)
2023-01-11T22:09:26.4073352Z [ RUN      ] LazyOpsTest.TestUniformInPlace
2023-01-11T22:09:26.4075927Z [       OK ] LazyOpsTest.TestUniformInPlace (0 ms)
2023-01-11T22:09:26.4076256Z [ RUN      ] LazyOpsTest.TestRandomInPlace
2023-01-11T22:09:26.4128843Z [       OK ] LazyOpsTest.TestRandomInPlace (5 ms)
2023-01-11T22:09:26.4129374Z [ RUN      ] LazyOpsTest.TestRandomInPlaceDefaultFrom
2023-01-11T22:09:26.4179927Z [       OK ] LazyOpsTest.TestRandomInPlaceDefaultFrom (5 ms)
2023-01-11T22:09:26.4180299Z [ RUN      ] LazyOpsTest.TestRandomInPlaceDefault
2023-01-11T22:09:26.4192867Z [       OK ] LazyOpsTest.TestRandomInPlaceDefault (1 ms)
2023-01-11T22:09:26.4193214Z [ RUN      ] LazyOpsTest.TestNormGeneral
2023-01-11T22:09:26.4196665Z [       OK ] LazyOpsTest.TestNormGeneral (0 ms)
2023-01-11T22:09:26.4196966Z [ RUN      ] LazyOpsTest.TestNormNuclear
2023-01-11T22:09:26.4200818Z [       OK ] LazyOpsTest.TestNormNuclear (0 ms)
2023-01-11T22:09:26.4201163Z [ RUN      ] LazyOpsTest.TestFrobeniusNormInDim
2023-01-11T22:09:26.4201669Z [W LinearAlgebra.cpp:2783] Warning: at::frobenius_norm is deprecated and it is just left for JIT compatibility. It will be removed in a future PyTorch release. Please use `linalg.vector_norm(A, 2., dim, keepdim)` instead (function operator())
2023-01-11T22:09:26.4207220Z [       OK ] LazyOpsTest.TestFrobeniusNormInDim (0 ms)
2023-01-11T22:09:26.4207663Z [ RUN      ] LazyOpsTest.TestFrobeniusNormInDims
2023-01-11T22:09:26.4214083Z [       OK ] LazyOpsTest.TestFrobeniusNormInDims (0 ms)
2023-01-11T22:09:26.4214423Z [ RUN      ] LazyOpsTest.TestGroupNorm
2023-01-11T22:09:26.4253434Z [       OK ] LazyOpsTest.TestGroupNorm (3 ms)
2023-01-11T22:09:26.4254054Z [ RUN      ] LazyOpsTest.TestGroupNormBackward
2023-01-11T22:09:26.4725688Z [       OK ] LazyOpsTest.TestGroupNormBackward (47 ms)
2023-01-11T22:09:26.4726148Z [ RUN      ] LazyOpsTest.TestInstanceNorm
2023-01-11T22:09:26.4752032Z [       OK ] LazyOpsTest.TestInstanceNorm (2 ms)
2023-01-11T22:09:26.4752446Z [ RUN      ] LazyOpsTest.TestLayerNorm
2023-01-11T22:09:26.4788308Z [       OK ] LazyOpsTest.TestLayerNorm (3 ms)
2023-01-11T22:09:26.4788801Z [ RUN      ] LazyOpsTest.TestLayerNormBackward
2023-01-11T22:09:26.5027818Z [       OK ] LazyOpsTest.TestLayerNormBackward (23 ms)
2023-01-11T22:09:26.5028270Z [ RUN      ] LazyOpsTest.TestNuclearNorm
2023-01-11T22:09:26.5029295Z [W LinearAlgebra.cpp:2837] Warning: at::nuclear_norm is deprecated and it is just left for JIT compatibility. It will be removed in a future PyTorch release. Please use `linalg.matrix_norm(A, 'nuc', dim, keepdim)` instead (function operator())
2023-01-11T22:09:26.5036040Z [       OK ] LazyOpsTest.TestNuclearNorm (0 ms)
2023-01-11T22:09:26.5036726Z [ RUN      ] LazyOpsTest.TestPairwiseDistance
2023-01-11T22:09:26.5699026Z [       OK ] LazyOpsTest.TestPairwiseDistance (66 ms)
2023-01-11T22:09:26.5699698Z [ RUN      ] LazyOpsTest.TestCosineSimilarity
2023-01-11T22:09:26.5744469Z [       OK ] LazyOpsTest.TestCosineSimilarity (4 ms)
2023-01-11T22:09:26.5745146Z [ RUN      ] LazyOpsTest.TestCosineEmbeddingLoss
2023-01-11T22:09:26.6352048Z [       OK ] LazyOpsTest.TestCosineEmbeddingLoss (60 ms)
2023-01-11T22:09:26.6352637Z [ RUN      ] LazyOpsTest.TestHingeEmbeddingLoss
2023-01-11T22:09:26.6390440Z [       OK ] LazyOpsTest.TestHingeEmbeddingLoss (3 ms)
2023-01-11T22:09:26.6391054Z [ RUN      ] LazyOpsTest.TestTripletMarginLoss
2023-01-11T22:09:26.9149955Z [       OK ] LazyOpsTest.TestTripletMarginLoss (275 ms)
2023-01-11T22:09:26.9150728Z [ RUN      ] LazyOpsTest.TestBinaryCrossEntropy
2023-01-11T22:09:26.9169386Z [       OK ] LazyOpsTest.TestBinaryCrossEntropy (2 ms)
2023-01-11T22:09:26.9169997Z [ RUN      ] LazyOpsTest.TestMarginRankingLoss
2023-01-11T22:09:26.9851597Z [       OK ] LazyOpsTest.TestMarginRankingLoss (68 ms)
2023-01-11T22:09:26.9852249Z [ RUN      ] LazyOpsTest.TestBCEWithLogits
2023-01-11T22:09:26.9866494Z [       OK ] LazyOpsTest.TestBCEWithLogits (1 ms)
2023-01-11T22:09:26.9867129Z [ RUN      ] LazyOpsTest.TestKlDiv
2023-01-11T22:09:26.9887465Z [       OK ] LazyOpsTest.TestKlDiv (2 ms)
2023-01-11T22:09:26.9887954Z [ RUN      ] LazyOpsTest.TestProd
2023-01-11T22:09:26.9889279Z [       OK ] LazyOpsTest.TestProd (0 ms)
2023-01-11T22:09:26.9889775Z [ RUN      ] LazyOpsTest.TestProdCast
2023-01-11T22:09:26.9890305Z [       OK ] LazyOpsTest.TestProdCast (0 ms)
2023-01-11T22:09:26.9890796Z [ RUN      ] LazyOpsTest.TestProdInDim
2023-01-11T22:09:26.9895481Z [       OK ] LazyOpsTest.TestProdInDim (0 ms)
2023-01-11T22:09:26.9896071Z [ RUN      ] LazyOpsTest.TestProdInDimKeepCast
2023-01-11T22:09:26.9900172Z [       OK ] LazyOpsTest.TestProdInDimKeepCast (0 ms)
2023-01-11T22:09:26.9900740Z [ RUN      ] LazyOpsTest.TestProdInDimKeep
2023-01-11T22:09:26.9905129Z [       OK ] LazyOpsTest.TestProdInDimKeep (0 ms)
2023-01-11T22:09:26.9905673Z [ RUN      ] LazyOpsTest.TestCumSum
2023-01-11T22:09:26.9920025Z [       OK ] LazyOpsTest.TestCumSum (1 ms)
2023-01-11T22:09:26.9920575Z [ RUN      ] LazyOpsTest.TestCumSumCast
2023-01-11T22:09:26.9935966Z [       OK ] LazyOpsTest.TestCumSumCast (1 ms)
2023-01-11T22:09:26.9936509Z [ RUN      ] LazyOpsTest.TestCumSumLong
2023-01-11T22:09:26.9948999Z [       OK ] LazyOpsTest.TestCumSumLong (1 ms)
2023-01-11T22:09:26.9949603Z [ RUN      ] LazyOpsTest.TestCumSumCastLong
2023-01-11T22:09:26.9962087Z [       OK ] LazyOpsTest.TestCumSumCastLong (1 ms)
2023-01-11T22:09:26.9962914Z [ RUN      ] LazyOpsTest.TestCumProd
2023-01-11T22:09:26.9967373Z [       OK ] LazyOpsTest.TestCumProd (0 ms)
2023-01-11T22:09:26.9972211Z [ RUN      ] LazyOpsTest.TestCumProdCast
2023-01-11T22:09:26.9972773Z [       OK ] LazyOpsTest.TestCumProdCast (0 ms)
2023-01-11T22:09:26.9973241Z [ RUN      ] LazyOpsTest.TestCumProdLong
2023-01-11T22:09:26.9981463Z [       OK ] LazyOpsTest.TestCumProdLong (0 ms)
2023-01-11T22:09:26.9982054Z [ RUN      ] LazyOpsTest.TestCumProdCastLong
2023-01-11T22:09:26.9990848Z [       OK ] LazyOpsTest.TestCumProdCastLong (0 ms)
2023-01-11T22:09:26.9991383Z [ RUN      ] LazyOpsTest.TestArgMin
2023-01-11T22:09:26.9991872Z [       OK ] LazyOpsTest.TestArgMin (0 ms)
2023-01-11T22:09:26.9992550Z [ RUN      ] LazyOpsTest.TestArgMinDim
2023-01-11T22:09:26.9993229Z [       OK ] LazyOpsTest.TestArgMinDim (0 ms)
2023-01-11T22:09:26.9993730Z [ RUN      ] LazyOpsTest.TestArgMinDimKeep
2023-01-11T22:09:26.9994482Z [       OK ] LazyOpsTest.TestArgMinDimKeep (0 ms)
2023-01-11T22:09:26.9995037Z [ RUN      ] LazyOpsTest.TestArgMinSameValue
2023-01-11T22:09:26.9995594Z [       OK ] LazyOpsTest.TestArgMinSameValue (0 ms)
2023-01-11T22:09:26.9996108Z [ RUN      ] LazyOpsTest.TestArgMinWrapper
2023-01-11T22:09:26.9996901Z [       OK ] LazyOpsTest.TestArgMinWrapper (0 ms)
2023-01-11T22:09:26.9997420Z [ RUN      ] LazyOpsTest.TestArgMax
2023-01-11T22:09:26.9997905Z [       OK ] LazyOpsTest.TestArgMax (0 ms)
2023-01-11T22:09:26.9998396Z [ RUN      ] LazyOpsTest.TestArgMaxDim
2023-01-11T22:09:26.9999183Z [       OK ] LazyOpsTest.TestArgMaxDim (0 ms)
2023-01-11T22:09:26.9999674Z [ RUN      ] LazyOpsTest.TestArgMaxDimKeep
2023-01-11T22:09:27.0000588Z [       OK ] LazyOpsTest.TestArgMaxDimKeep (0 ms)
2023-01-11T22:09:27.0001122Z [ RUN      ] LazyOpsTest.TestArgMaxSameValue
2023-01-11T22:09:27.0001712Z [       OK ] LazyOpsTest.TestArgMaxSameValue (0 ms)
2023-01-11T22:09:27.0002202Z [ RUN      ] LazyOpsTest.TestArgMaxWrapper
2023-01-11T22:09:27.0003195Z [       OK ] LazyOpsTest.TestArgMaxWrapper (0 ms)
2023-01-11T22:09:27.0003745Z [ RUN      ] LazyOpsTest.TestAsin
2023-01-11T22:09:27.0005044Z [       OK ] LazyOpsTest.TestAsin (0 ms)
2023-01-11T22:09:27.0005582Z [ RUN      ] LazyOpsTest.TestAsinh
2023-01-11T22:09:27.0006879Z [       OK ] LazyOpsTest.TestAsinh (0 ms)
2023-01-11T22:09:27.0007447Z [ RUN      ] LazyOpsTest.TestAsinhInPlace
2023-01-11T22:09:27.0009681Z [       OK ] LazyOpsTest.TestAsinhInPlace (0 ms)
2023-01-11T22:09:27.0010249Z [ RUN      ] LazyOpsTest.TestSin
2023-01-11T22:09:27.0011547Z [       OK ] LazyOpsTest.TestSin (0 ms)
2023-01-11T22:09:27.0012051Z [ RUN      ] LazyOpsTest.TestSinh
2023-01-11T22:09:27.0013197Z [       OK ] LazyOpsTest.TestSinh (0 ms)
2023-01-11T22:09:27.0013727Z [ RUN      ] LazyOpsTest.TestAcos
2023-01-11T22:09:27.0014810Z [       OK ] LazyOpsTest.TestAcos (0 ms)
2023-01-11T22:09:27.0015349Z [ RUN      ] LazyOpsTest.TestAcosh
2023-01-11T22:09:27.0016501Z [       OK ] LazyOpsTest.TestAcosh (0 ms)
2023-01-11T22:09:27.0017094Z [ RUN      ] LazyOpsTest.TestAcoshInPlace
2023-01-11T22:09:27.0019060Z [       OK ] LazyOpsTest.TestAcoshInPlace (0 ms)
2023-01-11T22:09:27.0019642Z [ RUN      ] LazyOpsTest.TestCos
2023-01-11T22:09:27.0023399Z [       OK ] LazyOpsTest.TestCos (0 ms)
2023-01-11T22:09:27.0023926Z [ RUN      ] LazyOpsTest.TestCosh
2023-01-11T22:09:27.0024617Z [       OK ] LazyOpsTest.TestCosh (0 ms)
2023-01-11T22:09:27.0025135Z [ RUN      ] LazyOpsTest.TestAtan
2023-01-11T22:09:27.0026178Z [       OK ] LazyOpsTest.TestAtan (0 ms)
2023-01-11T22:09:27.0026851Z [ RUN      ] LazyOpsTest.TestAtanh
2023-01-11T22:09:27.0027860Z [       OK ] LazyOpsTest.TestAtanh (0 ms)
2023-01-11T22:09:27.0028443Z [ RUN      ] LazyOpsTest.TestAtanhInPlace
2023-01-11T22:09:27.0030532Z [       OK ] LazyOpsTest.TestAtanhInPlace (0 ms)
2023-01-11T22:09:27.0031152Z [ RUN      ] LazyOpsTest.TestAtan2
2023-01-11T22:09:27.0032433Z [       OK ] LazyOpsTest.TestAtan2 (0 ms)
2023-01-11T22:09:27.0032962Z [ RUN      ] LazyOpsTest.TestTan
2023-01-11T22:09:27.0034231Z [       OK ] LazyOpsTest.TestTan (0 ms)
2023-01-11T22:09:27.0034755Z [ RUN      ] LazyOpsTest.TestTanh
2023-01-11T22:09:27.0038922Z [       OK ] LazyOpsTest.TestTanh (0 ms)
2023-01-11T22:09:27.0039529Z [ RUN      ] LazyOpsTest.TestClampMinMax
2023-01-11T22:09:27.0043334Z [       OK ] LazyOpsTest.TestClampMinMax (0 ms)
2023-01-11T22:09:27.0043887Z [ RUN      ] LazyOpsTest.TestClampMin
2023-01-11T22:09:27.0046900Z [       OK ] LazyOpsTest.TestClampMin (0 ms)
2023-01-11T22:09:27.0047421Z [ RUN      ] LazyOpsTest.TestClampMax
2023-01-11T22:09:27.0050733Z [       OK ] LazyOpsTest.TestClampMax (0 ms)
2023-01-11T22:09:27.0051278Z [ RUN      ] LazyOpsTest.TestClampMinExplicit
2023-01-11T22:09:27.0054088Z [       OK ] LazyOpsTest.TestClampMinExplicit (0 ms)
2023-01-11T22:09:27.0054635Z [ RUN      ] LazyOpsTest.TestClampMaxExplicit
2023-01-11T22:09:27.0055230Z [       OK ] LazyOpsTest.TestClampMaxExplicit (0 ms)
2023-01-11T22:09:27.0055880Z [ RUN      ] LazyOpsTest.TestClampMinExplicitInPlace
2023-01-11T22:09:27.0059631Z [       OK ] LazyOpsTest.TestClampMinExplicitInPlace (0 ms)
2023-01-11T22:09:27.0060245Z [ RUN      ] LazyOpsTest.TestClampMaxExplicitInPlace
2023-01-11T22:09:27.0061050Z [       OK ] LazyOpsTest.TestClampMaxExplicitInPlace (0 ms)
2023-01-11T22:09:27.0061560Z [ RUN      ] LazyOpsTest.TestCeil
2023-01-11T22:09:27.0062327Z [       OK ] LazyOpsTest.TestCeil (0 ms)
2023-01-11T22:09:27.0062858Z [ RUN      ] LazyOpsTest.TestFloor
2023-01-11T22:09:27.0066419Z [       OK ] LazyOpsTest.TestFloor (0 ms)
2023-01-11T22:09:27.0066883Z [ RUN      ] LazyOpsTest.TestRound
2023-01-11T22:09:27.0067755Z [       OK ] LazyOpsTest.TestRound (0 ms)
2023-01-11T22:09:27.0068272Z [ RUN      ] LazyOpsTest.TestTrunc
2023-01-11T22:09:27.0072374Z [       OK ] LazyOpsTest.TestTrunc (0 ms)
2023-01-11T22:09:27.0072931Z [ RUN      ] LazyOpsTest.TestFrac
2023-01-11T22:09:27.0076227Z [       OK ] LazyOpsTest.TestFrac (0 ms)
2023-01-11T22:09:27.0076869Z [ RUN      ] LazyOpsTest.TestNeg
2023-01-11T22:09:27.0079727Z [       OK ] LazyOpsTest.TestNeg (0 ms)
2023-01-11T22:09:27.0080225Z [ RUN      ] LazyOpsTest.TestBitwiseNot
2023-01-11T22:09:27.0082375Z [       OK ] LazyOpsTest.TestBitwiseNot (0 ms)
2023-01-11T22:09:27.0082946Z [ RUN      ] LazyOpsTest.TestBitwiseNotInPlace
2023-01-11T22:09:27.0085532Z [       OK ] LazyOpsTest.TestBitwiseNotInPlace (0 ms)
2023-01-11T22:09:27.0086034Z [ RUN      ] LazyOpsTest.TestSign
2023-01-11T22:09:27.0086623Z [       OK ] LazyOpsTest.TestSign (0 ms)
2023-01-11T22:09:27.0087059Z [ RUN      ] LazyOpsTest.TestSignByte
2023-01-11T22:09:27.0087532Z [       OK ] LazyOpsTest.TestSignByte (0 ms)
2023-01-11T22:09:27.0087998Z [ RUN      ] LazyOpsTest.TestAbs
2023-01-11T22:09:27.0091298Z [       OK ] LazyOpsTest.TestAbs (0 ms)
2023-01-11T22:09:27.0091762Z [ RUN      ] LazyOpsTest.TestAbsByte
2023-01-11T22:09:27.0093671Z [       OK ] LazyOpsTest.TestAbsByte (0 ms)
2023-01-11T22:09:27.0094141Z [ RUN      ] LazyOpsTest.TestEmptyLike
2023-01-11T22:09:27.0094645Z [       OK ] LazyOpsTest.TestEmptyLike (0 ms)
2023-01-11T22:09:27.0095164Z [ RUN      ] LazyOpsTest.TestEmptyLikeOptions
2023-01-11T22:09:27.0095854Z [       OK ] LazyOpsTest.TestEmptyLikeOptions (0 ms)
2023-01-11T22:09:27.0096333Z [ RUN      ] LazyOpsTest.TestEmpty
2023-01-11T22:09:27.0096795Z [       OK ] LazyOpsTest.TestEmpty (0 ms)
2023-01-11T22:09:27.0097230Z [ RUN      ] LazyOpsTest.TestZeroInPlace
2023-01-11T22:09:27.0098123Z [       OK ] LazyOpsTest.TestZeroInPlace (0 ms)
2023-01-11T22:09:27.0098612Z [ RUN      ] LazyOpsTest.TestZerosLike
2023-01-11T22:09:27.0099584Z [       OK ] LazyOpsTest.TestZerosLike (0 ms)
2023-01-11T22:09:27.0100098Z [ RUN      ] LazyOpsTest.TestZerosLikeOptions
2023-01-11T22:09:27.0100802Z [       OK ] LazyOpsTest.TestZerosLikeOptions (0 ms)
2023-01-11T22:09:27.0101345Z [ RUN      ] LazyOpsTest.TestZeros
2023-01-11T22:09:27.0104882Z [       OK ] LazyOpsTest.TestZeros (0 ms)
2023-01-11T22:09:27.0105400Z [ RUN      ] LazyOpsTest.TestOnes
2023-01-11T22:09:27.0109317Z [       OK ] LazyOpsTest.TestOnes (0 ms)
2023-01-11T22:09:27.0109799Z [ RUN      ] LazyOpsTest.TestOnesLike
2023-01-11T22:09:27.0110278Z [       OK ] LazyOpsTest.TestOnesLike (0 ms)
2023-01-11T22:09:27.0110784Z [ RUN      ] LazyOpsTest.TestOnesLikeOptions
2023-01-11T22:09:27.0111668Z [       OK ] LazyOpsTest.TestOnesLikeOptions (0 ms)
2023-01-11T22:09:27.0112019Z [ RUN      ] LazyOpsTest.TestFull
2023-01-11T22:09:27.0116269Z [       OK ] LazyOpsTest.TestFull (0 ms)
2023-01-11T22:09:27.0116713Z [ RUN      ] LazyOpsTest.TestFullLike
2023-01-11T22:09:27.0117277Z [       OK ] LazyOpsTest.TestFullLike (0 ms)
2023-01-11T22:09:27.0117762Z [ RUN      ] LazyOpsTest.TestFullLikeOptions
2023-01-11T22:09:27.0118300Z [       OK ] LazyOpsTest.TestFullLikeOptions (0 ms)
2023-01-11T22:09:27.0118694Z [ RUN      ] LazyOpsTest.TestARange
2023-01-11T22:09:27.0133694Z [       OK ] LazyOpsTest.TestARange (1 ms)
2023-01-11T22:09:27.0134011Z [ RUN      ] LazyOpsTest.TestARangeOut
2023-01-11T22:09:27.0134483Z [W RangeFactories.cpp:216] Warning: The number of elements in the out tensor of shape [4] is 4 which does not match the computed number of elements 200. Note that this may occur as a result of rounding error. The out tensor will be resized to a tensor of shape (200,). (function operator())
2023-01-11T22:09:27.0136560Z [       OK ] LazyOpsTest.TestARangeOut (0 ms)
2023-01-11T22:09:27.0136897Z [ RUN      ] LazyOpsTest.TestDimARange
2023-01-11T22:09:27.0140828Z [       OK ] LazyOpsTest.TestDimARange (0 ms)
2023-01-11T22:09:27.0141158Z [ RUN      ] LazyOpsTest.TestBartlettWindow
2023-01-11T22:09:27.0168034Z [       OK ] LazyOpsTest.TestBartlettWindow (2 ms)
2023-01-11T22:09:27.0168390Z [ RUN      ] LazyOpsTest.TestBlackmanWindow
2023-01-11T22:09:27.0190351Z [       OK ] LazyOpsTest.TestBlackmanWindow (2 ms)
2023-01-11T22:09:27.0190719Z [ RUN      ] LazyOpsTest.TestHammingWindow
2023-01-11T22:09:27.0206146Z [       OK ] LazyOpsTest.TestHammingWindow (1 ms)
2023-01-11T22:09:27.0206473Z [ RUN      ] LazyOpsTest.TestHannWindow
2023-01-11T22:09:27.0778973Z [       OK ] LazyOpsTest.TestHannWindow (57 ms)
2023-01-11T22:09:27.0779402Z [ RUN      ] LazyOpsTest.TestLogSigmoid
2023-01-11T22:09:27.0783372Z [       OK ] LazyOpsTest.TestLogSigmoid (0 ms)
2023-01-11T22:09:27.0783828Z [ RUN      ] LazyOpsTest.TestLogSigmoidForward
2023-01-11T22:09:27.0789031Z [       OK ] LazyOpsTest.TestLogSigmoidForward (0 ms)
2023-01-11T22:09:27.0789467Z [ RUN      ] LazyOpsTest.TestLogsumexp
2023-01-11T22:09:27.0827782Z [       OK ] LazyOpsTest.TestLogsumexp (3 ms)
2023-01-11T22:09:27.0828090Z [ RUN      ] LazyOpsTest.TestSiLU
2023-01-11T22:09:27.0831059Z [       OK ] LazyOpsTest.TestSiLU (0 ms)
2023-01-11T22:09:27.0831349Z [ RUN      ] LazyOpsTest.TestSigmoid
2023-01-11T22:09:27.0834354Z [       OK ] LazyOpsTest.TestSigmoid (0 ms)
2023-01-11T22:09:27.0834644Z [ RUN      ] LazyOpsTest.TestMatmul_1x1
2023-01-11T22:09:27.0836394Z [       OK ] LazyOpsTest.TestMatmul_1x1 (0 ms)
2023-01-11T22:09:27.0836698Z [ RUN      ] LazyOpsTest.TestMatmul_2x1
2023-01-11T22:09:27.0840188Z [       OK ] LazyOpsTest.TestMatmul_2x1 (0 ms)
2023-01-11T22:09:27.0840471Z [ RUN      ] LazyOpsTest.TestMatmul_1x2
2023-01-11T22:09:27.0845724Z [       OK ] LazyOpsTest.TestMatmul_1x2 (0 ms)
2023-01-11T22:09:27.0846020Z [ RUN      ] LazyOpsTest.TestMatmul_2x2
2023-01-11T22:09:27.0849865Z [       OK ] LazyOpsTest.TestMatmul_2x2 (0 ms)
2023-01-11T22:09:27.0850276Z [ RUN      ] LazyOpsTest.TestMatmulBcast
2023-01-11T22:09:27.0858841Z [       OK ] LazyOpsTest.TestMatmulBcast (0 ms)
2023-01-11T22:09:27.0860040Z [ RUN      ] LazyOpsTest.TestDot
2023-01-11T22:09:27.0860413Z [       OK ] LazyOpsTest.TestDot (0 ms)
2023-01-11T22:09:27.0860797Z [ RUN      ] LazyOpsTest.TestTensorDot
2023-01-11T22:09:27.0869466Z [       OK ] LazyOpsTest.TestTensorDot (0 ms)
2023-01-11T22:09:27.0869832Z [ RUN      ] LazyOpsTest.TestGer
2023-01-11T22:09:27.0882011Z [       OK ] LazyOpsTest.TestGer (1 ms)
2023-01-11T22:09:27.0882461Z [ RUN      ] LazyOpsTest.TestMv
2023-01-11T22:09:27.0885605Z [       OK ] LazyOpsTest.TestMv (0 ms)
2023-01-11T22:09:27.0885964Z [ RUN      ] LazyOpsTest.TestMvOut
2023-01-11T22:09:27.0898925Z [       OK ] LazyOpsTest.TestMvOut (1 ms)
2023-01-11T22:09:27.0899362Z [ RUN      ] LazyOpsTest.TestBatchAddBatchMatMul
2023-01-11T22:09:27.0903420Z [       OK ] LazyOpsTest.TestBatchAddBatchMatMul (0 ms)
2023-01-11T22:09:27.0903924Z [ RUN      ] LazyOpsTest.TestBatchAddBatchMatMulInPlace
2023-01-11T22:09:27.0908374Z [       OK ] LazyOpsTest.TestBatchAddBatchMatMulInPlace (0 ms)
2023-01-11T22:09:27.0908844Z [ RUN      ] LazyOpsTest.TestBatchMatMul
2023-01-11T22:09:27.0911949Z [       OK ] LazyOpsTest.TestBatchMatMul (0 ms)
2023-01-11T22:09:27.0913548Z [ RUN      ] LazyOpsTest.TestChainMatMul
2023-01-11T22:09:27.0914331Z [W LinearAlgebra.cpp:1077] Warning: torch.chain_matmul is deprecated and will be removed in a future PyTorch release. Use torch.linalg.multi_dot instead, which accepts a list of two or more tensors rather than multiple parameters. (function operator())
2023-01-11T22:09:27.0919274Z [       OK ] LazyOpsTest.TestChainMatMul (0 ms)
2023-01-11T22:09:27.0919637Z [ RUN      ] LazyOpsTest.TestLinear
2023-01-11T22:09:27.0927960Z [       OK ] LazyOpsTest.TestLinear (0 ms)
2023-01-11T22:09:27.0928508Z [ RUN      ] LazyOpsTest.TestPinverse
2023-01-11T22:09:27.0946928Z [       OK ] LazyOpsTest.TestPinverse (1 ms)
2023-01-11T22:09:27.0947499Z [ RUN      ] LazyOpsTest.TestEinsumOuter
2023-01-11T22:09:27.0953726Z [       OK ] LazyOpsTest.TestEinsumOuter (0 ms)
2023-01-11T22:09:27.0954345Z [ RUN      ] LazyOpsTest.TestEinsumOuterBackward
2023-01-11T22:09:27.0976575Z [       OK ] LazyOpsTest.TestEinsumOuterBackward (2 ms)
2023-01-11T22:09:27.0977203Z [ RUN      ] LazyOpsTest.TestEinsumBatchMatMul
2023-01-11T22:09:27.0988036Z [       OK ] LazyOpsTest.TestEinsumBatchMatMul (1 ms)
2023-01-11T22:09:27.0988543Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerBilinear
2023-01-11T22:09:27.1005357Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerBilinear (1 ms)
2023-01-11T22:09:27.1005871Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerDiagonal
2023-01-11T22:09:27.1010271Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerDiagonal (0 ms)
2023-01-11T22:09:27.1010795Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerBatchDiagonal
2023-01-11T22:09:27.1015589Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerBatchDiagonal (0 ms)
2023-01-11T22:09:27.1016292Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerBatchPermute
2023-01-11T22:09:27.1019391Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerBatchPermute (0 ms)
2023-01-11T22:09:27.1019919Z [ RUN      ] LazyOpsTest.TestEinsumPyTorchLowerRepeatedAxis
2023-01-11T22:09:27.1029750Z [       OK ] LazyOpsTest.TestEinsumPyTorchLowerRepeatedAxis (1 ms)
2023-01-11T22:09:27.1030205Z [ RUN      ] LazyOpsTest.TestBilinear
2023-01-11T22:09:27.1162542Z [       OK ] LazyOpsTest.TestBilinear (13 ms)
2023-01-11T22:09:27.1163013Z [ RUN      ] LazyOpsTest.TestUpsampleNearest2D
2023-01-11T22:09:27.1167138Z [       OK ] LazyOpsTest.TestUpsampleNearest2D (0 ms)
2023-01-11T22:09:27.1167768Z [ RUN      ] LazyOpsTest.TestUpsampleNearest2DBackward
2023-01-11T22:09:27.1178749Z [       OK ] LazyOpsTest.TestUpsampleNearest2DBackward (1 ms)
2023-01-11T22:09:27.1179282Z [ RUN      ] LazyOpsTest.TestUpsampleNearest2DWithScale
2023-01-11T22:09:27.1183845Z [       OK ] LazyOpsTest.TestUpsampleNearest2DWithScale (0 ms)
2023-01-11T22:09:27.1184718Z [ RUN      ] LazyOpsTest.TestUpsampleNearest2DBackwardWithScale
2023-01-11T22:09:27.1195416Z [       OK ] LazyOpsTest.TestUpsampleNearest2DBackwardWithScale (1 ms)
2023-01-11T22:09:27.1195948Z [ RUN      ] LazyOpsTest.TestUpsampleBilinear2D
2023-01-11T22:09:27.1203797Z [       OK ] LazyOpsTest.TestUpsampleBilinear2D (0 ms)
2023-01-11T22:09:27.1204161Z [ RUN      ] LazyOpsTest.TestUpsampleBilinear2DBackward
2023-01-11T22:09:27.1225808Z [       OK ] LazyOpsTest.TestUpsampleBilinear2DBackward (2 ms)
2023-01-11T22:09:27.1226285Z [ RUN      ] LazyOpsTest.TestAddCMul
2023-01-11T22:09:27.1229259Z [       OK ] LazyOpsTest.TestAddCMul (0 ms)
2023-01-11T22:09:27.1229602Z [ RUN      ] LazyOpsTest.TestAddCDiv
2023-01-11T22:09:27.1232634Z [       OK ] LazyOpsTest.TestAddCDiv (0 ms)
2023-01-11T22:09:27.1233028Z [ RUN      ] LazyOpsTest.TestAddCDivWithBroadcast
2023-01-11T22:09:27.1235812Z [       OK ] LazyOpsTest.TestAddCDivWithBroadcast (0 ms)
2023-01-11T22:09:27.1236238Z [ RUN      ] LazyOpsTest.TestSize
2023-01-11T22:09:27.1236520Z [       OK ] LazyOpsTest.TestSize (0 ms)
2023-01-11T22:09:27.1236785Z [ RUN      ] LazyOpsTest.TestSelect
2023-01-11T22:09:27.1323168Z [       OK ] LazyOpsTest.TestSelect (8 ms)
2023-01-11T22:09:27.1323532Z [ RUN      ] LazyOpsTest.TestBernoulliScalarProb
2023-01-11T22:09:27.1328464Z [       OK ] LazyOpsTest.TestBernoulliScalarProb (0 ms)
2023-01-11T22:09:27.1328941Z [ RUN      ] LazyOpsTest.TestBernoulliTensorProb
2023-01-11T22:09:27.1332416Z [       OK ] LazyOpsTest.TestBernoulliTensorProb (0 ms)
2023-01-11T22:09:27.1332918Z [ RUN      ] LazyOpsTest.TestBernoulliScalarProbInPlace
2023-01-11T22:09:27.1336372Z [       OK ] LazyOpsTest.TestBernoulliScalarProbInPlace (0 ms)
2023-01-11T22:09:27.1336880Z [ RUN      ] LazyOpsTest.TestBernoulliTensorProbInPlace
2023-01-11T22:09:27.1340530Z [       OK ] LazyOpsTest.TestBernoulliTensorProbInPlace (0 ms)
2023-01-11T22:09:27.1341059Z [ RUN      ] LazyOpsTest.TestDropout
2023-01-11T22:09:27.1344432Z [       OK ] LazyOpsTest.TestDropout (0 ms)
2023-01-11T22:09:27.1344855Z [ RUN      ] LazyOpsTest.TestDropoutInPlace
2023-01-11T22:09:27.1349665Z [       OK ] LazyOpsTest.TestDropoutInPlace (0 ms)
2023-01-11T22:09:27.1350315Z [ RUN      ] LazyOpsTest.TestRandperm
2023-01-11T22:09:27.1351790Z [       OK ] LazyOpsTest.TestRandperm (0 ms)
2023-01-11T22:09:27.1352558Z [ RUN      ] LazyOpsTest.TestSlice
2023-01-11T22:09:27.1361369Z [       OK ] LazyOpsTest.TestSlice (0 ms)
2023-01-11T22:09:27.1361745Z [ RUN      ] LazyOpsTest.TestTake
2023-01-11T22:09:27.1363266Z [       OK ] LazyOpsTest.TestTake (0 ms)
2023-01-11T22:09:27.1364177Z [ RUN      ] LazyOpsTest.TestTakeBackward
2023-01-11T22:09:27.1374231Z [       OK ] LazyOpsTest.TestTakeBackward (1 ms)
2023-01-11T22:09:27.1374615Z [ RUN      ] LazyOpsTest.TestStack
2023-01-11T22:09:27.1398036Z [       OK ] LazyOpsTest.TestStack (2 ms)
2023-01-11T22:09:27.1398359Z [ RUN      ] LazyOpsTest.TestCat
2023-01-11T22:09:27.1405212Z [       OK ] LazyOpsTest.TestCat (0 ms)
2023-01-11T22:09:27.1405492Z [ RUN      ] LazyOpsTest.TestUnbind
2023-01-11T22:09:27.1417060Z [       OK ] LazyOpsTest.TestUnbind (1 ms)
2023-01-11T22:09:27.1417369Z [ RUN      ] LazyOpsTest.TestRepeat
2023-01-11T22:09:27.1428328Z [       OK ] LazyOpsTest.TestRepeat (1 ms)
2023-01-11T22:09:27.1428793Z [ RUN      ] LazyOpsTest.TestGather
2023-01-11T22:09:27.1435320Z [       OK ] LazyOpsTest.TestGather (0 ms)
2023-01-11T22:09:27.1435627Z [ RUN      ] LazyOpsTest.TestScatter
2023-01-11T22:09:27.1438855Z [       OK ] LazyOpsTest.TestScatter (0 ms)
2023-01-11T22:09:27.1439335Z [ RUN      ] LazyOpsTest.TestScatterR1
2023-01-11T22:09:27.1440148Z [       OK ] LazyOpsTest.TestScatterR1 (0 ms)
2023-01-11T22:09:27.1440482Z [ RUN      ] LazyOpsTest.TestScatterR3
2023-01-11T22:09:27.1443060Z [       OK ] LazyOpsTest.TestScatterR3 (0 ms)
2023-01-11T22:09:27.1443493Z [ RUN      ] LazyOpsTest.TestScatterBiggerSource
2023-01-11T22:09:27.1446009Z [       OK ] LazyOpsTest.TestScatterBiggerSource (0 ms)
2023-01-11T22:09:27.1446346Z [ RUN      ] LazyOpsTest.TestScatterScalar
2023-01-11T22:09:27.1448666Z [       OK ] LazyOpsTest.TestScatterScalar (0 ms)
2023-01-11T22:09:27.1449249Z [ RUN      ] LazyOpsTest.TestScatterReduceAdd
2023-01-11T22:09:27.1452177Z [       OK ] LazyOpsTest.TestScatterReduceAdd (0 ms)
2023-01-11T22:09:27.1452518Z [ RUN      ] LazyOpsTest.TestScatterAdd
2023-01-11T22:09:27.1459252Z [       OK ] LazyOpsTest.TestScatterAdd (0 ms)
2023-01-11T22:09:27.1459598Z [ RUN      ] LazyOpsTest.TestScatterAddInPlace
2023-01-11T22:09:27.1467056Z [       OK ] LazyOpsTest.TestScatterAddInPlace (0 ms)
2023-01-11T22:09:27.1467479Z [ RUN      ] LazyOpsTest.TestIndexSelect
2023-01-11T22:09:27.1556876Z [       OK ] LazyOpsTest.TestIndexSelect (8 ms)
2023-01-11T22:09:27.1557320Z [ RUN      ] LazyOpsTest.TestIndexSelectRank0
2023-01-11T22:09:27.1579912Z [       OK ] LazyOpsTest.TestIndexSelectRank0 (2 ms)
2023-01-11T22:09:27.1580305Z [ RUN      ] LazyOpsTest.TestInverse
2023-01-11T22:09:27.1585172Z [       OK ] LazyOpsTest.TestInverse (0 ms)
2023-01-11T22:09:27.1585702Z [ RUN      ] LazyOpsTest.TestIsnan
2023-01-11T22:09:27.1586268Z [       OK ] LazyOpsTest.TestIsnan (0 ms)
2023-01-11T22:09:27.1586606Z [ RUN      ] LazyOpsTest.TestExpand
2023-01-11T22:09:27.1589550Z [       OK ] LazyOpsTest.TestExpand (0 ms)
2023-01-11T22:09:27.1590228Z [ RUN      ] LazyOpsTest.TestExpandBack
2023-01-11T22:09:27.1593198Z [       OK ] LazyOpsTest.TestExpandBack (0 ms)
2023-01-11T22:09:27.1593544Z [ RUN      ] LazyOpsTest.TestExpandAs
2023-01-11T22:09:27.1596872Z [       OK ] LazyOpsTest.TestExpandAs (0 ms)
2023-01-11T22:09:27.1597285Z [ RUN      ] LazyOpsTest.TestEye
2023-01-11T22:09:27.1597855Z [       OK ] LazyOpsTest.TestEye (0 ms)
2023-01-11T22:09:27.1598251Z [ RUN      ] LazyOpsTest.TestEyeWide
2023-01-11T22:09:27.1599011Z [       OK ] LazyOpsTest.TestEyeWide (0 ms)
2023-01-11T22:09:27.1599429Z [ RUN      ] LazyOpsTest.TestEyeNarrow
2023-01-11T22:09:27.1599778Z [       OK ] LazyOpsTest.TestEyeNarrow (0 ms)
2023-01-11T22:09:27.1600128Z [ RUN      ] LazyOpsTest.TestBroadcastTensors
2023-01-11T22:09:27.1605957Z [       OK ] LazyOpsTest.TestBroadcastTensors (0 ms)
2023-01-11T22:09:27.1606394Z [ RUN      ] LazyOpsTest.TestOneIndex
2023-01-11T22:09:27.1620589Z [       OK ] LazyOpsTest.TestOneIndex (1 ms)
2023-01-11T22:09:27.1621028Z [ RUN      ] LazyOpsTest.TestOneIndexTransfer
2023-01-11T22:09:27.1631941Z [       OK ] LazyOpsTest.TestOneIndexTransfer (1 ms)
2023-01-11T22:09:27.1632432Z [ RUN      ] LazyOpsTest.TestNonzero
2023-01-11T22:09:27.1633431Z [       OK ] LazyOpsTest.TestNonzero (0 ms)
2023-01-11T22:09:27.1633842Z [ RUN      ] LazyOpsTest.TestMaskedSelect
2023-01-11T22:09:27.1634927Z [       OK ] LazyOpsTest.TestMaskedSelect (0 ms)
2023-01-11T22:09:27.1635340Z [ RUN      ] LazyOpsTest.TestMaskedScatter
2023-01-11T22:09:27.1638003Z [       OK ] LazyOpsTest.TestMaskedScatter (0 ms)
2023-01-11T22:09:27.1638569Z [ RUN      ] LazyOpsTest.TestMultiIndexHeadNull
2023-01-11T22:09:27.1647433Z [       OK ] LazyOpsTest.TestMultiIndexHeadNull (0 ms)
2023-01-11T22:09:27.1647915Z [ RUN      ] LazyOpsTest.TestMultiIndexMiddleNull
2023-01-11T22:09:27.1657249Z [       OK ] LazyOpsTest.TestMultiIndexMiddleNull (0 ms)
2023-01-11T22:09:27.1657776Z [ RUN      ] LazyOpsTest.TestMultiIndexTailNull
2023-01-11T22:09:27.1666267Z [       OK ] LazyOpsTest.TestMultiIndexTailNull (0 ms)
2023-01-11T22:09:27.1666936Z [ RUN      ] LazyOpsTest.TestMultiIndexMiddleBroadcast
2023-01-11T22:09:27.1675705Z [       OK ] LazyOpsTest.TestMultiIndexMiddleBroadcast (0 ms)
2023-01-11T22:09:27.1676200Z [ RUN      ] LazyOpsTest.TestMultiIndexTailBroadcast
2023-01-11T22:09:27.1684459Z [       OK ] LazyOpsTest.TestMultiIndexTailBroadcast (0 ms)
2023-01-11T22:09:27.1685023Z [ RUN      ] LazyOpsTest.TestMaskIndex
2023-01-11T22:09:27.1688977Z [       OK ] LazyOpsTest.TestMaskIndex (0 ms)
2023-01-11T22:09:27.1689506Z [ RUN      ] LazyOpsTest.TestOneIndexPut
2023-01-11T22:09:27.1721190Z [       OK ] LazyOpsTest.TestOneIndexPut (3 ms)
2023-01-11T22:09:27.1721599Z [ RUN      ] LazyOpsTest.TestOneIndexPutInPlace
2023-01-11T22:09:27.1746647Z [       OK ] LazyOpsTest.TestOneIndexPutInPlace (2 ms)
2023-01-11T22:09:27.1747015Z [ RUN      ] LazyOpsTest.TestOneIndexPutTransfer
2023-01-11T22:09:27.1764849Z [       OK ] LazyOpsTest.TestOneIndexPutTransfer (1 ms)
2023-01-11T22:09:27.1765199Z [ RUN      ] LazyOpsTest.TestMultiIndexPut
2023-01-11T22:09:27.1785087Z [       OK ] LazyOpsTest.TestMultiIndexPut (2 ms)
2023-01-11T22:09:27.1785717Z [ RUN      ] LazyOpsTest.TestMultiIndexPutHeadNull
2023-01-11T22:09:27.1814829Z [       OK ] LazyOpsTest.TestMultiIndexPutHeadNull (2 ms)
2023-01-11T22:09:27.1815336Z [ RUN      ] LazyOpsTest.TestMultiIndexPutMiddleNull
2023-01-11T22:09:27.1831886Z [       OK ] LazyOpsTest.TestMultiIndexPutMiddleNull (1 ms)
2023-01-11T22:09:27.1832391Z [ RUN      ] LazyOpsTest.TestMultiIndexPutTailNull
2023-01-11T22:09:27.1848990Z [       OK ] LazyOpsTest.TestMultiIndexPutTailNull (1 ms)
2023-01-11T22:09:27.1849635Z [ RUN      ] LazyOpsTest.TestMultiIndexPutMiddleBroadcast
2023-01-11T22:09:27.1869110Z [       OK ] LazyOpsTest.TestMultiIndexPutMiddleBroadcast (1 ms)
2023-01-11T22:09:27.1869811Z [ RUN      ] LazyOpsTest.TestMultiIndexPutTailBroadcast
2023-01-11T22:09:27.1888185Z [       OK ] LazyOpsTest.TestMultiIndexPutTailBroadcast (1 ms)
2023-01-11T22:09:27.1888670Z [ RUN      ] LazyOpsTest.TestMaskIndexPut
2023-01-11T22:09:27.1909092Z [       OK ] LazyOpsTest.TestMaskIndexPut (2 ms)
2023-01-11T22:09:27.1909509Z [ RUN      ] LazyOpsTest.TestIndexPutImpl
2023-01-11T22:09:27.1934752Z [       OK ] LazyOpsTest.TestIndexPutImpl (2 ms)
2023-01-11T22:09:27.1935177Z [ RUN      ] LazyOpsTest.TestIndexFillWithScalar
2023-01-11T22:09:27.1966980Z [       OK ] LazyOpsTest.TestIndexFillWithScalar (3 ms)
2023-01-11T22:09:27.1967465Z [ RUN      ] LazyOpsTest.TestIndexFillWithScalarInPlace
2023-01-11T22:09:27.1996738Z [       OK ] LazyOpsTest.TestIndexFillWithScalarInPlace (2 ms)
2023-01-11T22:09:27.1997148Z [ RUN      ] LazyOpsTest.TestIndexFillWithTensor
2023-01-11T22:09:27.2025250Z [       OK ] LazyOpsTest.TestIndexFillWithTensor (2 ms)
2023-01-11T22:09:27.2025666Z [ RUN      ] LazyOpsTest.TestIndexFillWithTensorInPlace
2023-01-11T22:09:27.2058755Z [       OK ] LazyOpsTest.TestIndexFillWithTensorInPlace (3 ms)
2023-01-11T22:09:27.2059122Z [ RUN      ] LazyOpsTest.TestIndexFillRank0
2023-01-11T22:09:27.2086437Z [       OK ] LazyOpsTest.TestIndexFillRank0 (2 ms)
2023-01-11T22:09:27.2086868Z [ RUN      ] LazyOpsTest.TestIndexAdd
2023-01-11T22:09:27.2157812Z [       OK ] LazyOpsTest.TestIndexAdd (7 ms)
2023-01-11T22:09:27.2158162Z [ RUN      ] LazyOpsTest.TestIndexAddInPlace
2023-01-11T22:09:27.2210539Z [       OK ] LazyOpsTest.TestIndexAddInPlace (5 ms)
2023-01-11T22:09:27.2210868Z [ RUN      ] LazyOpsTest.TestIndexAddRank0
2023-01-11T22:09:27.2232443Z [       OK ] LazyOpsTest.TestIndexAddRank0 (2 ms)
2023-01-11T22:09:27.2232779Z [ RUN      ] LazyOpsTest.TestIndexCopy
2023-01-11T22:09:27.2255121Z [       OK ] LazyOpsTest.TestIndexCopy (2 ms)
2023-01-11T22:09:27.2255482Z [ RUN      ] LazyOpsTest.TestIndexCopyInPlace
2023-01-11T22:09:27.2283592Z [       OK ] LazyOpsTest.TestIndexCopyInPlace (2 ms)
2023-01-11T22:09:27.2283947Z [ RUN      ] LazyOpsTest.TestIndexCopyRank0
2023-01-11T22:09:27.2304742Z [       OK ] LazyOpsTest.TestIndexCopyRank0 (2 ms)
2023-01-11T22:09:27.2305085Z [ RUN      ] LazyOpsTest.TestRelu
2023-01-11T22:09:27.2307738Z [       OK ] LazyOpsTest.TestRelu (0 ms)
2023-01-11T22:09:27.2308083Z [ RUN      ] LazyOpsTest.TestReluInPlace
2023-01-11T22:09:27.2311755Z [       OK ] LazyOpsTest.TestReluInPlace (0 ms)
2023-01-11T22:09:27.2312177Z [ RUN      ] LazyOpsTest.TestHardshrink
2023-01-11T22:09:27.2312960Z [       OK ] LazyOpsTest.TestHardshrink (0 ms)
2023-01-11T22:09:27.2313284Z [ RUN      ] LazyOpsTest.TestHardSigmoid
2023-01-11T22:09:27.2316392Z [       OK ] LazyOpsTest.TestHardSigmoid (0 ms)
2023-01-11T22:09:27.2316755Z [ RUN      ] LazyOpsTest.TestHardSigmoidInPlace
2023-01-11T22:09:27.2319926Z [       OK ] LazyOpsTest.TestHardSigmoidInPlace (0 ms)
2023-01-11T22:09:27.2320276Z [ RUN      ] LazyOpsTest.TestHardSigmoidBackward
2023-01-11T22:09:27.2329284Z [       OK ] LazyOpsTest.TestHardSigmoidBackward (0 ms)
2023-01-11T22:09:27.2329625Z [ RUN      ] LazyOpsTest.TestSoftshrink
2023-01-11T22:09:27.2330741Z [       OK ] LazyOpsTest.TestSoftshrink (0 ms)
2023-01-11T22:09:27.2331166Z [ RUN      ] LazyOpsTest.TestHardtanh
2023-01-11T22:09:27.2331978Z [       OK ] LazyOpsTest.TestHardtanh (0 ms)
2023-01-11T22:09:27.2332381Z [ RUN      ] LazyOpsTest.TestHardtanhInPlace
2023-01-11T22:09:27.2334132Z [       OK ] LazyOpsTest.TestHardtanhInPlace (0 ms)
2023-01-11T22:09:27.2334464Z [ RUN      ] LazyOpsTest.TestLeakyRelu
2023-01-11T22:09:27.2337375Z [       OK ] LazyOpsTest.TestLeakyRelu (0 ms)
2023-01-11T22:09:27.2337696Z [ RUN      ] LazyOpsTest.TestLeakyReluInPlace
2023-01-11T22:09:27.2341438Z [       OK ] LazyOpsTest.TestLeakyReluInPlace (0 ms)
2023-01-11T22:09:27.2341755Z [ RUN      ] LazyOpsTest.TestExp
2023-01-11T22:09:27.2344809Z [       OK ] LazyOpsTest.TestExp (0 ms)
2023-01-11T22:09:27.2345087Z [ RUN      ] LazyOpsTest.TestExpm1
2023-01-11T22:09:27.2346894Z [       OK ] LazyOpsTest.TestExpm1 (0 ms)
2023-01-11T22:09:27.2347469Z [ RUN      ] LazyOpsTest.TestLog
2023-01-11T22:09:27.2350655Z [       OK ] LazyOpsTest.TestLog (0 ms)
2023-01-11T22:09:27.2351073Z [ RUN      ] LazyOpsTest.TestLog2
2023-01-11T22:09:27.2353923Z [       OK ] LazyOpsTest.TestLog2 (0 ms)
2023-01-11T22:09:27.2354299Z [ RUN      ] LazyOpsTest.TestLog10
2023-01-11T22:09:27.2355446Z [       OK ] LazyOpsTest.TestLog10 (0 ms)
2023-01-11T22:09:27.2355838Z [ RUN      ] LazyOpsTest.TestLog1p
2023-01-11T22:09:27.2356728Z [       OK ] LazyOpsTest.TestLog1p (0 ms)
2023-01-11T22:09:27.2357058Z [ RUN      ] LazyOpsTest.TestErf
2023-01-11T22:09:27.2358387Z [       OK ] LazyOpsTest.TestErf (0 ms)
2023-01-11T22:09:27.2358718Z [ RUN      ] LazyOpsTest.TestErfc
2023-01-11T22:09:27.2359592Z [       OK ] LazyOpsTest.TestErfc (0 ms)
2023-01-11T22:09:27.2360007Z [ RUN      ] LazyOpsTest.TestErfinv
2023-01-11T22:09:27.2361062Z [       OK ] LazyOpsTest.TestErfinv (0 ms)
2023-01-11T22:09:27.2361355Z [ RUN      ] LazyOpsTest.TestSqrt
2023-01-11T22:09:27.2364413Z [       OK ] LazyOpsTest.TestSqrt (0 ms)
2023-01-11T22:09:27.2364709Z [ RUN      ] LazyOpsTest.TestRsqrt
2023-01-11T22:09:27.2367683Z [       OK ] LazyOpsTest.TestRsqrt (0 ms)
2023-01-11T22:09:27.2368236Z [ RUN      ] LazyOpsTest.TestReciprocal
2023-01-11T22:09:27.2371081Z [       OK ] LazyOpsTest.TestReciprocal (0 ms)
2023-01-11T22:09:27.2371672Z [ RUN      ] LazyOpsTest.TestPowTensorScalar
2023-01-11T22:09:27.2374201Z [       OK ] LazyOpsTest.TestPowTensorScalar (0 ms)
2023-01-11T22:09:27.2374800Z [ RUN      ] LazyOpsTest.TestPowTensorScalarInPlace
2023-01-11T22:09:27.2378128Z [       OK ] LazyOpsTest.TestPowTensorScalarInPlace (0 ms)
2023-01-11T22:09:27.2378731Z [ RUN      ] LazyOpsTest.TestPowTensorTensor
2023-01-11T22:09:27.2381366Z [       OK ] LazyOpsTest.TestPowTensorTensor (0 ms)
2023-01-11T22:09:27.2381955Z [ RUN      ] LazyOpsTest.TestPowTensorTensorInPlace
2023-01-11T22:09:27.2395021Z [       OK ] LazyOpsTest.TestPowTensorTensorInPlace (1 ms)
2023-01-11T22:09:27.2395648Z [ RUN      ] LazyOpsTest.TestPowTensorTensorBroadcast
2023-01-11T22:09:27.2398301Z [       OK ] LazyOpsTest.TestPowTensorTensorBroadcast (0 ms)
2023-01-11T22:09:27.2398879Z [ RUN      ] LazyOpsTest.TestPowScalarTensor
2023-01-11T22:09:27.2399503Z [       OK ] LazyOpsTest.TestPowScalarTensor (0 ms)
2023-01-11T22:09:27.2400099Z [ RUN      ] LazyOpsTest.TestPowIntExponent
2023-01-11T22:09:27.2402523Z [       OK ] LazyOpsTest.TestPowIntExponent (0 ms)
2023-01-11T22:09:27.2403102Z [ RUN      ] LazyOpsTest.TestFmodScalar
2023-01-11T22:09:27.2403903Z [       OK ] LazyOpsTest.TestFmodScalar (0 ms)
2023-01-11T22:09:27.2404501Z [ RUN      ] LazyOpsTest.TestFmodScalarInPlace
2023-01-11T22:09:27.2405580Z [       OK ] LazyOpsTest.TestFmodScalarInPlace (0 ms)
2023-01-11T22:09:27.2406160Z [ RUN      ] LazyOpsTest.TestFmodTensor
2023-01-11T22:09:27.2406972Z [       OK ] LazyOpsTest.TestFmodTensor (0 ms)
2023-01-11T22:09:27.2407544Z [ RUN      ] LazyOpsTest.TestFmodTensorInPlace
2023-01-11T22:09:27.2421312Z [       OK ] LazyOpsTest.TestFmodTensorInPlace (1 ms)
2023-01-11T22:09:27.2421910Z [ RUN      ] LazyOpsTest.TestRemainderScalar
2023-01-11T22:09:27.2424646Z [       OK ] LazyOpsTest.TestRemainderScalar (0 ms)
2023-01-11T22:09:27.2425240Z [ RUN      ] LazyOpsTest.TestRemainderScalarInPlace
2023-01-11T22:09:27.2429128Z [       OK ] LazyOpsTest.TestRemainderScalarInPlace (0 ms)
2023-01-11T22:09:27.2429635Z [ RUN      ] LazyOpsTest.TestRemainderTensor
2023-01-11T22:09:27.2431903Z [       OK ] LazyOpsTest.TestRemainderTensor (0 ms)
2023-01-11T22:09:27.2432539Z [ RUN      ] LazyOpsTest.TestRemainderTensorInPlace
2023-01-11T22:09:27.2436495Z [       OK ] LazyOpsTest.TestRemainderTensorInPlace (0 ms)
2023-01-11T22:09:27.2437108Z [ RUN      ] LazyOpsTest.TestWhere
2023-01-11T22:09:27.2437639Z [W TensorCompare.cpp:493] Warning: where received a uint8 condition tensor. This behavior is deprecated and will be removed in a future version of PyTorch. Use a boolean condition instead. (function operator())
2023-01-11T22:09:27.2438197Z [       OK ] LazyOpsTest.TestWhere (0 ms)
2023-01-11T22:09:27.2438531Z [ RUN      ] LazyOpsTest.TestWhereBroadcast
2023-01-11T22:09:27.2439615Z [       OK ] LazyOpsTest.TestWhereBroadcast (0 ms)
2023-01-11T22:09:27.2439914Z [ RUN      ] LazyOpsTest.TestThreshold
2023-01-11T22:09:27.2442668Z [       OK ] LazyOpsTest.TestThreshold (0 ms)
2023-01-11T22:09:27.2443121Z [ RUN      ] LazyOpsTest.TestThresholdBackward
2023-01-11T22:09:27.2451631Z [       OK ] LazyOpsTest.TestThresholdBackward (0 ms)
2023-01-11T22:09:27.2451972Z [ RUN      ] LazyOpsTest.TestThresholdInPlace
2023-01-11T22:09:27.2454959Z [       OK ] LazyOpsTest.TestThresholdInPlace (0 ms)
2023-01-11T22:09:27.2455265Z [ RUN      ] LazyOpsTest.TestElu
2023-01-11T22:09:27.2458909Z [       OK ] LazyOpsTest.TestElu (0 ms)
2023-01-11T22:09:27.2459211Z [ RUN      ] LazyOpsTest.TestEluInPlace
2023-01-11T22:09:27.2462544Z [       OK ] LazyOpsTest.TestEluInPlace (0 ms)
2023-01-11T22:09:27.2462894Z [ RUN      ] LazyOpsTest.TestSelu
2023-01-11T22:09:27.2465625Z [       OK ] LazyOpsTest.TestSelu (0 ms)
2023-01-11T22:09:27.2465930Z [ RUN      ] LazyOpsTest.TestSeluInPlace
2023-01-11T22:09:27.2469355Z [       OK ] LazyOpsTest.TestSeluInPlace (0 ms)
2023-01-11T22:09:27.2469692Z [ RUN      ] LazyOpsTest.TestCelu
2023-01-11T22:09:27.2470950Z [       OK ] LazyOpsTest.TestCelu (0 ms)
2023-01-11T22:09:27.2471259Z [ RUN      ] LazyOpsTest.TestCeluInPlace
2023-01-11T22:09:27.2472933Z [       OK ] LazyOpsTest.TestCeluInPlace (0 ms)
2023-01-11T22:09:27.2473338Z [ RUN      ] LazyOpsTest.TestGelu
2023-01-11T22:09:27.2480548Z [       OK ] LazyOpsTest.TestGelu (0 ms)
2023-01-11T22:09:27.2480924Z [ RUN      ] LazyOpsTest.TestAddMatMul
2023-01-11T22:09:27.2499469Z [       OK ] LazyOpsTest.TestAddMatMul (1 ms)
2023-01-11T22:09:27.2500004Z [ RUN      ] LazyOpsTest.TestEmbedding
2023-01-11T22:09:27.2504195Z [       OK ] LazyOpsTest.TestEmbedding (0 ms)
2023-01-11T22:09:27.2504586Z [ RUN      ] LazyOpsTest.TestOneHot
2023-01-11T22:09:27.2512030Z [       OK ] LazyOpsTest.TestOneHot (0 ms)
2023-01-11T22:09:27.2512415Z [ RUN      ] LazyOpsTest.TestTranspose
2023-01-11T22:09:27.2515038Z [       OK ] LazyOpsTest.TestTranspose (0 ms)
2023-01-11T22:09:27.2515463Z [ RUN      ] LazyOpsTest.TestTransposeInPlace
2023-01-11T22:09:27.2519263Z [       OK ] LazyOpsTest.TestTransposeInPlace (0 ms)
2023-01-11T22:09:27.2519725Z [ RUN      ] LazyOpsTest.TestReshape
2023-01-11T22:09:27.2526042Z [       OK ] LazyOpsTest.TestReshape (0 ms)
2023-01-11T22:09:27.2526440Z [ RUN      ] LazyOpsTest.TestResize
2023-01-11T22:09:27.2531376Z [       OK ] LazyOpsTest.TestResize (0 ms)
2023-01-11T22:09:27.2531788Z [ RUN      ] LazyOpsTest.TestViewResize
2023-01-11T22:09:27.2537666Z [       OK ] LazyOpsTest.TestViewResize (0 ms)
2023-01-11T22:09:27.2538046Z [ RUN      ] LazyOpsTest.TestView
2023-01-11T22:09:27.2544756Z [       OK ] LazyOpsTest.TestView (0 ms)
2023-01-11T22:09:27.2545128Z [ RUN      ] LazyOpsTest.TestViewMod
2023-01-11T22:09:27.2561434Z [       OK ] LazyOpsTest.TestViewMod (1 ms)
2023-01-11T22:09:27.2561823Z [ RUN      ] LazyOpsTest.TestViewModComplex
2023-01-11T22:09:27.2580962Z [       OK ] LazyOpsTest.TestViewModComplex (1 ms)
2023-01-11T22:09:27.2581411Z [ RUN      ] LazyOpsTest.TestViewOfViewMod
2023-01-11T22:09:27.2604171Z [       OK ] LazyOpsTest.TestViewOfViewMod (2 ms)
2023-01-11T22:09:27.2604526Z [ RUN      ] LazyOpsTest.TestViewSqueezeAddInPlace
2023-01-11T22:09:27.2615769Z [       OK ] LazyOpsTest.TestViewSqueezeAddInPlace (1 ms)
2023-01-11T22:09:27.2616098Z [ RUN      ] LazyOpsTest.TestUnsafeView
2023-01-11T22:09:27.2621972Z [       OK ] LazyOpsTest.TestUnsafeView (0 ms)
2023-01-11T22:09:27.2622308Z [ RUN      ] LazyOpsTest.TestNarrow
2023-01-11T22:09:27.2638988Z [       OK ] LazyOpsTest.TestNarrow (1 ms)
2023-01-11T22:09:27.2639328Z [ RUN      ] LazyOpsTest.TestNarrowUpdate
2023-01-11T22:09:27.2667026Z [       OK ] LazyOpsTest.TestNarrowUpdate (2 ms)
2023-01-11T22:09:27.2667475Z [ RUN      ] LazyOpsTest.TestNarrowUpdateBaseCheck
2023-01-11T22:09:27.2690432Z [       OK ] LazyOpsTest.TestNarrowUpdateBaseCheck (2 ms)
2023-01-11T22:09:27.2690808Z [ RUN      ] LazyOpsTest.TestNarrowUpdateTwoSlices
2023-01-11T22:09:27.2807422Z [       OK ] LazyOpsTest.TestNarrowUpdateTwoSlices (11 ms)
2023-01-11T22:09:27.2807776Z [ RUN      ] LazyOpsTest.TestNarrowUpdateView
2023-01-11T22:09:27.2841875Z [       OK ] LazyOpsTest.TestNarrowUpdateView (3 ms)
2023-01-11T22:09:27.2842246Z [ RUN      ] LazyOpsTest.TestNarrowInNarrowUpdate
2023-01-11T22:09:27.2885202Z [       OK ] LazyOpsTest.TestNarrowInNarrowUpdate (4 ms)
2023-01-11T22:09:27.2885529Z [ RUN      ] LazyOpsTest.TestNarrowCopy
2023-01-11T22:09:27.2894173Z [       OK ] LazyOpsTest.TestNarrowCopy (0 ms)
2023-01-11T22:09:27.2894479Z [ RUN      ] LazyOpsTest.TestViewAs
2023-01-11T22:09:27.2903327Z [       OK ] LazyOpsTest.TestViewAs (0 ms)
2023-01-11T22:09:27.2903663Z [ RUN      ] LazyOpsTest.TestLogSoftmax
2023-01-11T22:09:27.2926711Z [       OK ] LazyOpsTest.TestLogSoftmax (2 ms)
2023-01-11T22:09:27.2927140Z [ RUN      ] LazyOpsTest.TestLogSoftmaxCast
2023-01-11T22:09:27.2957458Z [       OK ] LazyOpsTest.TestLogSoftmaxCast (3 ms)
2023-01-11T22:09:27.2957896Z [ RUN      ] LazyOpsTest.TestLogSoftmaxWrapper
2023-01-11T22:09:27.2980722Z [       OK ] LazyOpsTest.TestLogSoftmaxWrapper (2 ms)
2023-01-11T22:09:27.2981125Z [ RUN      ] LazyOpsTest.TestSoftmax
2023-01-11T22:09:27.3003535Z [       OK ] LazyOpsTest.TestSoftmax (2 ms)
2023-01-11T22:09:27.3003930Z [ RUN      ] LazyOpsTest.TestSoftmaxCast
2023-01-11T22:09:27.3037458Z [       OK ] LazyOpsTest.TestSoftmaxCast (3 ms)
2023-01-11T22:09:27.3037821Z [ RUN      ] LazyOpsTest.TestSoftmaxWrapper
2023-01-11T22:09:27.3061990Z [       OK ] LazyOpsTest.TestSoftmaxWrapper (2 ms)
2023-01-11T22:09:27.3062309Z [ RUN      ] LazyOpsTest.TestSoftplus
2023-01-11T22:09:27.3065497Z [       OK ] LazyOpsTest.TestSoftplus (0 ms)
2023-01-11T22:09:27.3065916Z [ RUN      ] LazyOpsTest.TestMaxPool1D
2023-01-11T22:09:27.3162750Z [       OK ] LazyOpsTest.TestMaxPool1D (9 ms)
2023-01-11T22:09:27.3163176Z [ RUN      ] LazyOpsTest.TestMaxPool2D
2023-01-11T22:09:27.3227773Z [       OK ] LazyOpsTest.TestMaxPool2D (6 ms)
2023-01-11T22:09:27.3228234Z [ RUN      ] LazyOpsTest.TestMaxPool2DWithIndices
2023-01-11T22:09:27.3359591Z [       OK ] LazyOpsTest.TestMaxPool2DWithIndices (13 ms)
2023-01-11T22:09:27.3360125Z [ RUN      ] LazyOpsTest.TestMaxPool2DNonSquare
2023-01-11T22:09:27.3423390Z [       OK ] LazyOpsTest.TestMaxPool2DNonSquare (6 ms)
2023-01-11T22:09:27.3423822Z [ RUN      ] LazyOpsTest.TestMaxPool3D
2023-01-11T22:09:27.3442186Z [       OK ] LazyOpsTest.TestMaxPool3D (1 ms)
2023-01-11T22:09:27.3442643Z [ RUN      ] LazyOpsTest.TestMaxPool3DWithIndices
2023-01-11T22:09:27.3468091Z [       OK ] LazyOpsTest.TestMaxPool3DWithIndices (2 ms)
2023-01-11T22:09:27.3468838Z [ RUN      ] LazyOpsTest.TestMaxPool3DIncompleteAttributes
2023-01-11T22:09:27.3480636Z [       OK ] LazyOpsTest.TestMaxPool3DIncompleteAttributes (1 ms)
2023-01-11T22:09:27.3481024Z [ RUN      ] LazyOpsTest.TestMaxPool3DNonSquare
2023-01-11T22:09:27.3497463Z [       OK ] LazyOpsTest.TestMaxPool3DNonSquare (1 ms)
2023-01-11T22:09:27.3498076Z [ RUN      ] LazyOpsTest.TestMaxPool2DNoBatch
2023-01-11T22:09:27.3561310Z [       OK ] LazyOpsTest.TestMaxPool2DNoBatch (6 ms)
2023-01-11T22:09:27.3561776Z [ RUN      ] LazyOpsTest.TestMaxPool3DNoBatch
2023-01-11T22:09:27.3580089Z [       OK ] LazyOpsTest.TestMaxPool3DNoBatch (1 ms)
2023-01-11T22:09:27.3580492Z [ RUN      ] LazyOpsTest.TestAvgPool1D
2023-01-11T22:09:27.3656484Z [       OK ] LazyOpsTest.TestAvgPool1D (7 ms)
2023-01-11T22:09:27.3656884Z [ RUN      ] LazyOpsTest.TestAvgPool2D
2023-01-11T22:09:27.3714129Z [       OK ] LazyOpsTest.TestAvgPool2D (5 ms)
2023-01-11T22:09:27.3714970Z [ RUN      ] LazyOpsTest.TestAvgPool2DNonSquare
2023-01-11T22:09:27.3770403Z [       OK ] LazyOpsTest.TestAvgPool2DNonSquare (5 ms)
2023-01-11T22:09:27.3770831Z [ RUN      ] LazyOpsTest.TestAvgPool3D
2023-01-11T22:09:27.3784993Z [       OK ] LazyOpsTest.TestAvgPool3D (1 ms)
2023-01-11T22:09:27.3785641Z [ RUN      ] LazyOpsTest.TestAvgPool3DIncompleteAttributes
2023-01-11T22:09:27.3796391Z [       OK ] LazyOpsTest.TestAvgPool3DIncompleteAttributes (1 ms)
2023-01-11T22:09:27.3796885Z [ RUN      ] LazyOpsTest.TestAvgPool3DNonSquare
2023-01-11T22:09:27.3808093Z [       OK ] LazyOpsTest.TestAvgPool3DNonSquare (1 ms)
2023-01-11T22:09:27.3808520Z [ RUN      ] LazyOpsTest.TestAvgPool2DNoBatch
2023-01-11T22:09:27.3860831Z [       OK ] LazyOpsTest.TestAvgPool2DNoBatch (5 ms)
2023-01-11T22:09:27.3861402Z [ RUN      ] LazyOpsTest.TestAvgPool3DNoBatch
2023-01-11T22:09:27.3874937Z [       OK ] LazyOpsTest.TestAvgPool3DNoBatch (1 ms)
2023-01-11T22:09:27.3875388Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool2D
2023-01-11T22:09:27.3882419Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool2D (0 ms)
2023-01-11T22:09:27.3882914Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool3D
2023-01-11T22:09:27.4082005Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool3D (19 ms)
2023-01-11T22:09:27.4082518Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool3DNoBatch
2023-01-11T22:09:27.4099323Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool3DNoBatch (1 ms)
2023-01-11T22:09:27.4099820Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool2DNoBatch
2023-01-11T22:09:27.4107468Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool2DNoBatch (0 ms)
2023-01-11T22:09:27.4107963Z [ RUN      ] LazyOpsTest.TestMaxUnpool2D
2023-01-11T22:09:27.4124851Z [       OK ] LazyOpsTest.TestMaxUnpool2D (1 ms)
2023-01-11T22:09:27.4125277Z [ RUN      ] LazyOpsTest.TestMaxUnpool3D
2023-01-11T22:09:27.4140421Z [       OK ] LazyOpsTest.TestMaxUnpool3D (1 ms)
2023-01-11T22:09:27.4141105Z [ RUN      ] LazyOpsTest.TestNllLoss
2023-01-11T22:09:27.4141518Z /var/lib/jenkins/workspace/test/cpp/lazy/test_lazy_ops.cpp:8173: Skipped
2023-01-11T22:09:27.4141792Z 
2023-01-11T22:09:27.4142060Z [  SKIPPED ] LazyOpsTest.TestNllLoss (0 ms)
2023-01-11T22:09:27.4142642Z [ RUN      ] LazyOpsTest.TestNllLoss2d
2023-01-11T22:09:27.4217872Z [       OK ] LazyOpsTest.TestNllLoss2d (7 ms)
2023-01-11T22:09:27.4218414Z [ RUN      ] LazyOpsTest.TestSmoothL1Loss
2023-01-11T22:09:27.4235838Z [       OK ] LazyOpsTest.TestSmoothL1Loss (1 ms)
2023-01-11T22:09:27.4236237Z [ RUN      ] LazyOpsTest.TestL1Loss
2023-01-11T22:09:27.4246999Z [       OK ] LazyOpsTest.TestL1Loss (1 ms)
2023-01-11T22:09:27.4247421Z [ RUN      ] LazyOpsTest.TestL1LossBackward
2023-01-11T22:09:27.4283616Z [       OK ] LazyOpsTest.TestL1LossBackward (3 ms)
2023-01-11T22:09:27.4284144Z [ RUN      ] LazyOpsTest.TestMseLoss
2023-01-11T22:09:27.4286765Z [       OK ] LazyOpsTest.TestMseLoss (0 ms)
2023-01-11T22:09:27.4287255Z [ RUN      ] LazyOpsTest.TestMseLossBackward
2023-01-11T22:09:27.4309216Z [       OK ] LazyOpsTest.TestMseLossBackward (2 ms)
2023-01-11T22:09:27.4309581Z [ RUN      ] LazyOpsTest.TestBatchNorm1D
2023-01-11T22:09:27.4328916Z [       OK ] LazyOpsTest.TestBatchNorm1D (1 ms)
2023-01-11T22:09:27.4329407Z [ RUN      ] LazyOpsTest.TestBatchNorm2D
2023-01-11T22:09:27.4348595Z [       OK ] LazyOpsTest.TestBatchNorm2D (1 ms)
2023-01-11T22:09:27.4349123Z [ RUN      ] LazyOpsTest.TestDim
2023-01-11T22:09:27.4349781Z [       OK ] LazyOpsTest.TestDim (0 ms)
2023-01-11T22:09:27.4350286Z [ RUN      ] LazyOpsTest.TestContiguous
2023-01-11T22:09:27.4350766Z [       OK ] LazyOpsTest.TestContiguous (0 ms)
2023-01-11T22:09:27.4351082Z [ RUN      ] LazyOpsTest.TestSqueezeAll
2023-01-11T22:09:27.4352699Z [       OK ] LazyOpsTest.TestSqueezeAll (0 ms)
2023-01-11T22:09:27.4353096Z [ RUN      ] LazyOpsTest.TestSqueezeAllInPlace
2023-01-11T22:09:27.4356594Z [       OK ] LazyOpsTest.TestSqueezeAllInPlace (0 ms)
2023-01-11T22:09:27.4357151Z [ RUN      ] LazyOpsTest.TestSqueezeOne
2023-01-11T22:09:27.4378382Z [       OK ] LazyOpsTest.TestSqueezeOne (2 ms)
2023-01-11T22:09:27.4378750Z [ RUN      ] LazyOpsTest.TestSqueezeOneInPlace
2023-01-11T22:09:27.4402887Z [       OK ] LazyOpsTest.TestSqueezeOneInPlace (2 ms)
2023-01-11T22:09:27.4403219Z [ RUN      ] LazyOpsTest.TestUnsqueeze
2023-01-11T22:09:27.4417395Z [       OK ] LazyOpsTest.TestUnsqueeze (1 ms)
2023-01-11T22:09:27.4417724Z [ RUN      ] LazyOpsTest.TestUnsqueezeInPlace
2023-01-11T22:09:27.4434530Z [       OK ] LazyOpsTest.TestUnsqueezeInPlace (1 ms)
2023-01-11T22:09:27.4434848Z [ RUN      ] LazyOpsTest.TestMaskedFill
2023-01-11T22:09:27.4437646Z [       OK ] LazyOpsTest.TestMaskedFill (0 ms)
2023-01-11T22:09:27.4437975Z [ RUN      ] LazyOpsTest.TestMaskedFillInPlace
2023-01-11T22:09:27.4441195Z [       OK ] LazyOpsTest.TestMaskedFillInPlace (0 ms)
2023-01-11T22:09:27.4441535Z [ RUN      ] LazyOpsTest.TestMaskedFillBroadcast
2023-01-11T22:09:27.4444354Z [       OK ] LazyOpsTest.TestMaskedFillBroadcast (0 ms)
2023-01-11T22:09:27.4444669Z [ RUN      ] LazyOpsTest.TestFill
2023-01-11T22:09:27.4448396Z [       OK ] LazyOpsTest.TestFill (0 ms)
2023-01-11T22:09:27.4448689Z [ RUN      ] LazyOpsTest.TestFillWithRank0
2023-01-11T22:09:27.4450312Z [       OK ] LazyOpsTest.TestFillWithRank0 (0 ms)
2023-01-11T22:09:27.4450676Z [ RUN      ] LazyOpsTest.TestPermute
2023-01-11T22:09:27.4481237Z [       OK ] LazyOpsTest.TestPermute (3 ms)
2023-01-11T22:09:27.4481654Z [ RUN      ] LazyOpsTest.TestPermuteMod
2023-01-11T22:09:27.4591647Z [       OK ] LazyOpsTest.TestPermuteMod (10 ms)
2023-01-11T22:09:27.4591939Z [ RUN      ] LazyOpsTest.TestFlip
2023-01-11T22:09:27.4624881Z [       OK ] LazyOpsTest.TestFlip (3 ms)
2023-01-11T22:09:27.4625305Z [ RUN      ] LazyOpsTest.TestPixelShuffle
2023-01-11T22:09:27.4631669Z [       OK ] LazyOpsTest.TestPixelShuffle (0 ms)
2023-01-11T22:09:27.4632074Z [ RUN      ] LazyOpsTest.TestSumToSize
2023-01-11T22:09:27.4636108Z [       OK ] LazyOpsTest.TestSumToSize (0 ms)
2023-01-11T22:09:27.4636575Z [ RUN      ] LazyOpsTest.TestTransposeDims
2023-01-11T22:09:27.4640181Z [       OK ] LazyOpsTest.TestTransposeDims (0 ms)
2023-01-11T22:09:27.4640636Z [ RUN      ] LazyOpsTest.TestTransposeDimsMod
2023-01-11T22:09:27.4650565Z [       OK ] LazyOpsTest.TestTransposeDimsMod (0 ms)
2023-01-11T22:09:27.4651187Z [ RUN      ] LazyOpsTest.TestTransposeDimsInPlace
2023-01-11T22:09:27.4655028Z [       OK ] LazyOpsTest.TestTransposeDimsInPlace (0 ms)
2023-01-11T22:09:27.4655456Z [ RUN      ] LazyOpsTest.TestSplit
2023-01-11T22:09:27.4676945Z [       OK ] LazyOpsTest.TestSplit (2 ms)
2023-01-11T22:09:27.4677373Z [ RUN      ] LazyOpsTest.TestSplitEmpty
2023-01-11T22:09:27.4678096Z [       OK ] LazyOpsTest.TestSplitEmpty (0 ms)
2023-01-11T22:09:27.4678430Z [ RUN      ] LazyOpsTest.TestSplitWithSizes
2023-01-11T22:09:27.4691905Z [       OK ] LazyOpsTest.TestSplitWithSizes (1 ms)
2023-01-11T22:09:27.4692329Z [ RUN      ] LazyOpsTest.TestCrossImplicitDim
2023-01-11T22:09:27.4695177Z [       OK ] LazyOpsTest.TestCrossImplicitDim (0 ms)
2023-01-11T22:09:27.4695727Z [ RUN      ] LazyOpsTest.TestCrossExplicitDim
2023-01-11T22:09:27.4698851Z [       OK ] LazyOpsTest.TestCrossExplicitDim (0 ms)
2023-01-11T22:09:27.4699512Z [ RUN      ] LazyOpsTest.TestCrossZeroDim
2023-01-11T22:09:27.4699972Z [       OK ] LazyOpsTest.TestCrossZeroDim (0 ms)
2023-01-11T22:09:27.4700349Z [ RUN      ] LazyOpsTest.TestTriu
2023-01-11T22:09:27.4728561Z [       OK ] LazyOpsTest.TestTriu (2 ms)
2023-01-11T22:09:27.4729245Z [ RUN      ] LazyOpsTest.TestTriuNonSquare
2023-01-11T22:09:27.4756733Z [       OK ] LazyOpsTest.TestTriuNonSquare (2 ms)
2023-01-11T22:09:27.4757277Z [ RUN      ] LazyOpsTest.TestTriuBatch
2023-01-11T22:09:27.4785038Z [       OK ] LazyOpsTest.TestTriuBatch (2 ms)
2023-01-11T22:09:27.4785572Z [ RUN      ] LazyOpsTest.TestTril
2023-01-11T22:09:27.4813141Z [       OK ] LazyOpsTest.TestTril (2 ms)
2023-01-11T22:09:27.4813521Z [ RUN      ] LazyOpsTest.TestTrilNonSquare
2023-01-11T22:09:27.4841647Z [       OK ] LazyOpsTest.TestTrilNonSquare (2 ms)
2023-01-11T22:09:27.4842099Z [ RUN      ] LazyOpsTest.TestTrilBatch
2023-01-11T22:09:27.4869974Z [       OK ] LazyOpsTest.TestTrilBatch (2 ms)
2023-01-11T22:09:27.4870397Z [ RUN      ] LazyOpsTest.TestTriuInPlace
2023-01-11T22:09:27.4906159Z [       OK ] LazyOpsTest.TestTriuInPlace (3 ms)
2023-01-11T22:09:27.4906576Z [ RUN      ] LazyOpsTest.TestTrilInPlace
2023-01-11T22:09:27.4941697Z [       OK ] LazyOpsTest.TestTrilInPlace (3 ms)
2023-01-11T22:09:27.4942098Z [ RUN      ] LazyOpsTest.TestTrace
2023-01-11T22:09:27.4944532Z [       OK ] LazyOpsTest.TestTrace (0 ms)
2023-01-11T22:09:27.4944918Z [ RUN      ] LazyOpsTest.TestTraceWide
2023-01-11T22:09:27.4947203Z [       OK ] LazyOpsTest.TestTraceWide (0 ms)
2023-01-11T22:09:27.4947540Z [ RUN      ] LazyOpsTest.TestTraceNarrow
2023-01-11T22:09:27.4949724Z [       OK ] LazyOpsTest.TestTraceNarrow (0 ms)
2023-01-11T22:09:27.4950130Z [ RUN      ] LazyOpsTest.TestDiagRank1
2023-01-11T22:09:27.5136856Z [       OK ] LazyOpsTest.TestDiagRank1 (18 ms)
2023-01-11T22:09:27.5137222Z [ RUN      ] LazyOpsTest.TestDiagRank2
2023-01-11T22:09:27.5175719Z [       OK ] LazyOpsTest.TestDiagRank2 (3 ms)
2023-01-11T22:09:27.5176273Z [ RUN      ] LazyOpsTest.TestDiagFlat
2023-01-11T22:09:27.5705001Z [       OK ] LazyOpsTest.TestDiagFlat (52 ms)
2023-01-11T22:09:27.5705439Z [ RUN      ] LazyOpsTest.TestDiagonal
2023-01-11T22:09:27.5733875Z [       OK ] LazyOpsTest.TestDiagonal (3 ms)
2023-01-11T22:09:27.5734358Z [ RUN      ] LazyOpsTest.TestDiagonalUpdate
2023-01-11T22:09:27.5828717Z [       OK ] LazyOpsTest.TestDiagonalUpdate (9 ms)
2023-01-11T22:09:27.5829094Z [ RUN      ] LazyOpsTest.TestDiagonalNonSquare
2023-01-11T22:09:27.5856845Z [       OK ] LazyOpsTest.TestDiagonalNonSquare (2 ms)
2023-01-11T22:09:27.5857203Z [ RUN      ] LazyOpsTest.TestDiagonalBatch
2023-01-11T22:09:27.5884833Z [       OK ] LazyOpsTest.TestDiagonalBatch (2 ms)
2023-01-11T22:09:27.5885205Z [ RUN      ] LazyOpsTest.TestFlatten
2023-01-11T22:09:27.5951941Z [       OK ] LazyOpsTest.TestFlatten (6 ms)
2023-01-11T22:09:27.5952254Z [ RUN      ] LazyOpsTest.TestLogicalAnd
2023-01-11T22:09:27.5971101Z [       OK ] LazyOpsTest.TestLogicalAnd (1 ms)
2023-01-11T22:09:27.5971408Z [ RUN      ] LazyOpsTest.TestBitwiseAnd
2023-01-11T22:09:27.5973965Z [       OK ] LazyOpsTest.TestBitwiseAnd (0 ms)
2023-01-11T22:09:27.5974310Z [ RUN      ] LazyOpsTest.TestBitwiseAndInPlace
2023-01-11T22:09:27.5977041Z [       OK ] LazyOpsTest.TestBitwiseAndInPlace (0 ms)
2023-01-11T22:09:27.5977385Z [ RUN      ] LazyOpsTest.TestBitwiseAndScalar
2023-01-11T22:09:27.5980024Z [       OK ] LazyOpsTest.TestBitwiseAndScalar (0 ms)
2023-01-11T22:09:27.5980398Z [ RUN      ] LazyOpsTest.TestBitwiseAndScalarInPlace
2023-01-11T22:09:27.5983528Z [       OK ] LazyOpsTest.TestBitwiseAndScalarInPlace (0 ms)
2023-01-11T22:09:27.5983908Z [ RUN      ] LazyOpsTest.TestBitwiseAndPromotion
2023-01-11T22:09:27.5988303Z [       OK ] LazyOpsTest.TestBitwiseAndPromotion (0 ms)
2023-01-11T22:09:27.5988647Z [ RUN      ] LazyOpsTest.TestBitwiseOr
2023-01-11T22:09:27.5991270Z [       OK ] LazyOpsTest.TestBitwiseOr (0 ms)
2023-01-11T22:09:27.5991602Z [ RUN      ] LazyOpsTest.TestBitwiseOrInPlace
2023-01-11T22:09:27.5994269Z [       OK ] LazyOpsTest.TestBitwiseOrInPlace (0 ms)
2023-01-11T22:09:27.5994595Z [ RUN      ] LazyOpsTest.TestBitwiseOrScalar
2023-01-11T22:09:27.5997024Z [       OK ] LazyOpsTest.TestBitwiseOrScalar (0 ms)
2023-01-11T22:09:27.5997388Z [ RUN      ] LazyOpsTest.TestBitwiseOrScalarInPlace
2023-01-11T22:09:27.6000248Z [       OK ] LazyOpsTest.TestBitwiseOrScalarInPlace (0 ms)
2023-01-11T22:09:27.6000613Z [ RUN      ] LazyOpsTest.TestBitwiseXor
2023-01-11T22:09:27.6001262Z [       OK ] LazyOpsTest.TestBitwiseXor (0 ms)
2023-01-11T22:09:27.6001628Z [ RUN      ] LazyOpsTest.TestBitwiseXorInPlace
2023-01-11T22:09:27.6002507Z [       OK ] LazyOpsTest.TestBitwiseXorInPlace (0 ms)
2023-01-11T22:09:27.6003005Z [ RUN      ] LazyOpsTest.TestBitwiseXorScalar
2023-01-11T22:09:27.6003509Z [       OK ] LazyOpsTest.TestBitwiseXorScalar (0 ms)
2023-01-11T22:09:27.6004024Z [ RUN      ] LazyOpsTest.TestBitwiseXorScalarInPlace
2023-01-11T22:09:27.6004601Z [       OK ] LazyOpsTest.TestBitwiseXorScalarInPlace (0 ms)
2023-01-11T22:09:27.6004999Z [ RUN      ] LazyOpsTest.TestLshift
2023-01-11T22:09:27.6005910Z [       OK ] LazyOpsTest.TestLshift (0 ms)
2023-01-11T22:09:27.6006233Z [ RUN      ] LazyOpsTest.TestLshiftInPlace
2023-01-11T22:09:27.6007773Z [       OK ] LazyOpsTest.TestLshiftInPlace (0 ms)
2023-01-11T22:09:27.6008279Z [ RUN      ] LazyOpsTest.TestLshiftScalar
2023-01-11T22:09:27.6009016Z [       OK ] LazyOpsTest.TestLshiftScalar (0 ms)
2023-01-11T22:09:27.6009665Z [ RUN      ] LazyOpsTest.TestLshiftScalarInPlace
2023-01-11T22:09:27.6010754Z [       OK ] LazyOpsTest.TestLshiftScalarInPlace (0 ms)
2023-01-11T22:09:27.6011350Z [ RUN      ] LazyOpsTest.TestRshift
2023-01-11T22:09:27.6012026Z [       OK ] LazyOpsTest.TestRshift (0 ms)
2023-01-11T22:09:27.6012616Z [ RUN      ] LazyOpsTest.TestRshiftInPlace
2023-01-11T22:09:27.6013902Z [       OK ] LazyOpsTest.TestRshiftInPlace (0 ms)
2023-01-11T22:09:27.6014495Z [ RUN      ] LazyOpsTest.TestRshiftScalar
2023-01-11T22:09:27.6015189Z [       OK ] LazyOpsTest.TestRshiftScalar (0 ms)
2023-01-11T22:09:27.6015808Z [ RUN      ] LazyOpsTest.TestRshiftScalarInPlace
2023-01-11T22:09:27.6016608Z [       OK ] LazyOpsTest.TestRshiftScalarInPlace (0 ms)
2023-01-11T22:09:27.6017344Z [ RUN      ] LazyOpsTest.TestMeshgrid
2023-01-11T22:09:27.6017723Z [W TensorShape.cpp:3452] Warning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (function operator())
2023-01-11T22:09:27.6027369Z [       OK ] LazyOpsTest.TestMeshgrid (1 ms)
2023-01-11T22:09:27.6027948Z [ RUN      ] LazyOpsTest.TestConstantPad
2023-01-11T22:09:27.6031775Z [       OK ] LazyOpsTest.TestConstantPad (0 ms)
2023-01-11T22:09:27.6032426Z [ RUN      ] LazyOpsTest.TestConstantPadIncomplete
2023-01-11T22:09:27.6036420Z [       OK ] LazyOpsTest.TestConstantPadIncomplete (0 ms)
2023-01-11T22:09:27.6037176Z [ RUN      ] LazyOpsTest.TestReflectionPad2dRank3
2023-01-11T22:09:27.6037956Z [       OK ] LazyOpsTest.TestReflectionPad2dRank3 (0 ms)
2023-01-11T22:09:27.6038629Z [ RUN      ] LazyOpsTest.TestReflectionPad2dRank4
2023-01-11T22:09:27.6039861Z [       OK ] LazyOpsTest.TestReflectionPad2dRank4 (0 ms)
2023-01-11T22:09:27.6040351Z [ RUN      ] LazyOpsTest.TestReflectionPad2dBackward
2023-01-11T22:09:27.6049938Z [       OK ] LazyOpsTest.TestReflectionPad2dBackward (0 ms)
2023-01-11T22:09:27.6050404Z [ RUN      ] LazyOpsTest.TestReplicationPad1d
2023-01-11T22:09:27.6051986Z [       OK ] LazyOpsTest.TestReplicationPad1d (0 ms)
2023-01-11T22:09:27.6053279Z [ RUN      ] LazyOpsTest.TestReplicationPad1dZeroPad
2023-01-11T22:09:27.6053771Z [       OK ] LazyOpsTest.TestReplicationPad1dZeroPad (0 ms)
2023-01-11T22:09:27.6054244Z [ RUN      ] LazyOpsTest.TestReplicationPad1dBackward
2023-01-11T22:09:27.6060073Z [       OK ] LazyOpsTest.TestReplicationPad1dBackward (0 ms)
2023-01-11T22:09:27.6060601Z [ RUN      ] LazyOpsTest.TestReplicationPad2d
2023-01-11T22:09:27.6062427Z [       OK ] LazyOpsTest.TestReplicationPad2d (0 ms)
2023-01-11T22:09:27.6063257Z [ RUN      ] LazyOpsTest.TestReplicationPad2dZeroPad
2023-01-11T22:09:27.6063968Z [       OK ] LazyOpsTest.TestReplicationPad2dZeroPad (0 ms)
2023-01-11T22:09:27.6064660Z [ RUN      ] LazyOpsTest.TestReplicationPad2dBackward
2023-01-11T22:09:27.6070958Z [       OK ] LazyOpsTest.TestReplicationPad2dBackward (0 ms)
2023-01-11T22:09:27.6071475Z [ RUN      ] LazyOpsTest.TestAsStrided
2023-01-11T22:09:27.6081823Z [       OK ] LazyOpsTest.TestAsStrided (1 ms)
2023-01-11T22:09:27.6082221Z [ RUN      ] LazyOpsTest.TestAsStridedInPlace
2023-01-11T22:09:27.6097729Z [       OK ] LazyOpsTest.TestAsStridedInPlace (1 ms)
2023-01-11T22:09:27.6098189Z [ RUN      ] LazyOpsTest.TestAsStridedWithOffset
2023-01-11T22:09:27.6103197Z [       OK ] LazyOpsTest.TestAsStridedWithOffset (0 ms)
2023-01-11T22:09:27.6103767Z [ RUN      ] LazyOpsTest.TestAsStridedWithInplaceCopy
2023-01-11T22:09:27.6111893Z [       OK ] LazyOpsTest.TestAsStridedWithInplaceCopy (0 ms)
2023-01-11T22:09:27.6112352Z [ RUN      ] LazyOpsTest.TestEmptyStrided
2023-01-11T22:09:27.6112777Z [       OK ] LazyOpsTest.TestEmptyStrided (0 ms)
2023-01-11T22:09:27.6113186Z [ RUN      ] LazyOpsTest.TestAvgPool2DBackward
2023-01-11T22:09:27.6270934Z [       OK ] LazyOpsTest.TestAvgPool2DBackward (15 ms)
2023-01-11T22:09:27.6271582Z [ RUN      ] LazyOpsTest.TestAvgPool3DBackward
2023-01-11T22:09:27.6369013Z [       OK ] LazyOpsTest.TestAvgPool3DBackward (9 ms)
2023-01-11T22:09:27.6369707Z [ RUN      ] LazyOpsTest.TestAvgPool2DNoBatchBackward
2023-01-11T22:09:27.6560451Z [       OK ] LazyOpsTest.TestAvgPool2DNoBatchBackward (19 ms)
2023-01-11T22:09:27.6560967Z [ RUN      ] LazyOpsTest.TestAvgPool3DNoBatchBackward
2023-01-11T22:09:27.6666731Z [       OK ] LazyOpsTest.TestAvgPool3DNoBatchBackward (10 ms)
2023-01-11T22:09:27.6667185Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool3DNoBatchBackward
2023-01-11T22:09:27.6706321Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool3DNoBatchBackward (3 ms)
2023-01-11T22:09:27.6706851Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool3DBackward
2023-01-11T22:09:27.6765188Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool3DBackward (5 ms)
2023-01-11T22:09:27.6765778Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool2DBackward
2023-01-11T22:09:27.6790210Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool2DBackward (2 ms)
2023-01-11T22:09:27.6790738Z [ RUN      ] LazyOpsTest.TestAdaptiveAvgPool2DNoBatchBackward
2023-01-11T22:09:27.6814152Z [       OK ] LazyOpsTest.TestAdaptiveAvgPool2DNoBatchBackward (2 ms)
2023-01-11T22:09:27.6814590Z [ RUN      ] LazyOpsTest.TestConv2D
2023-01-11T22:09:27.7923465Z [       OK ] LazyOpsTest.TestConv2D (110 ms)
2023-01-11T22:09:27.7924502Z [ RUN      ] LazyOpsTest.TestConv2DBackward
2023-01-11T22:09:28.2066894Z [       OK ] LazyOpsTest.TestConv2DBackward (414 ms)
2023-01-11T22:09:28.2067756Z [ RUN      ] LazyOpsTest.TestTransposedConv2DBackward
2023-01-11T22:09:28.5081052Z [       OK ] LazyOpsTest.TestTransposedConv2DBackward (301 ms)
2023-01-11T22:09:28.5081937Z [ RUN      ] LazyOpsTest.TestConv3DBackward
2023-01-11T22:09:28.7895438Z [       OK ] LazyOpsTest.TestConv3DBackward (281 ms)
2023-01-11T22:09:28.7896381Z [ RUN      ] LazyOpsTest.TestTransposedConv3DBackward
2023-01-11T22:09:29.4320422Z [       OK ] LazyOpsTest.TestTransposedConv3DBackward (642 ms)
2023-01-11T22:09:29.4320926Z [ RUN      ] LazyOpsTest.TestMaxPool2DBackward
2023-01-11T22:09:29.4419990Z [       OK ] LazyOpsTest.TestMaxPool2DBackward (10 ms)
2023-01-11T22:09:29.4420445Z [ RUN      ] LazyOpsTest.TestMaxPool3DBackward
2023-01-11T22:09:29.4474085Z [       OK ] LazyOpsTest.TestMaxPool3DBackward (5 ms)
2023-01-11T22:09:29.4474539Z [ RUN      ] LazyOpsTest.TestMaxPool2DNoBatchBackward
2023-01-11T22:09:29.4572606Z [       OK ] LazyOpsTest.TestMaxPool2DNoBatchBackward (9 ms)
2023-01-11T22:09:29.4573096Z [ RUN      ] LazyOpsTest.TestMaxPool3DNoBatchBackward
2023-01-11T22:09:29.4626041Z [       OK ] LazyOpsTest.TestMaxPool3DNoBatchBackward (5 ms)
2023-01-11T22:09:29.4626551Z [ RUN      ] LazyOpsTest.TestMaxUnpool2DBackward
2023-01-11T22:09:29.4822662Z [       OK ] LazyOpsTest.TestMaxUnpool2DBackward (19 ms)
2023-01-11T22:09:29.4823147Z [ RUN      ] LazyOpsTest.TestMaxUnpool3DBackward
2023-01-11T22:09:29.4972654Z [       OK ] LazyOpsTest.TestMaxUnpool3DBackward (15 ms)
2023-01-11T22:09:29.4973110Z [ RUN      ] LazyOpsTest.TestTanhBackward
2023-01-11T22:09:29.4983219Z [       OK ] LazyOpsTest.TestTanhBackward (1 ms)
2023-01-11T22:09:29.4983676Z [ RUN      ] LazyOpsTest.TestSigmoidBackward
2023-01-11T22:09:29.4993539Z [       OK ] LazyOpsTest.TestSigmoidBackward (1 ms)
2023-01-11T22:09:29.4994006Z [ RUN      ] LazyOpsTest.TestLogSigmoidBackward
2023-01-11T22:09:29.5003693Z [       OK ] LazyOpsTest.TestLogSigmoidBackward (1 ms)
2023-01-11T22:09:29.5004134Z [ RUN      ] LazyOpsTest.TestLogSoftmaxBackward
2023-01-11T22:09:29.5090901Z [       OK ] LazyOpsTest.TestLogSoftmaxBackward (8 ms)
2023-01-11T22:09:29.5091338Z [ RUN      ] LazyOpsTest.TestSoftmaxBackward
2023-01-11T22:09:29.5175827Z [       OK ] LazyOpsTest.TestSoftmaxBackward (8 ms)
2023-01-11T22:09:29.5176252Z [ RUN      ] LazyOpsTest.TestSoftplusBackward
2023-01-11T22:09:29.5186794Z [       OK ] LazyOpsTest.TestSoftplusBackward (1 ms)
2023-01-11T22:09:29.5187208Z [ RUN      ] LazyOpsTest.TestReluBackward
2023-01-11T22:09:29.5196368Z [       OK ] LazyOpsTest.TestReluBackward (0 ms)
2023-01-11T22:09:29.5196791Z [ RUN      ] LazyOpsTest.TestRreluBackward
2023-01-11T22:09:29.5207333Z [       OK ] LazyOpsTest.TestRreluBackward (1 ms)
2023-01-11T22:09:29.5207896Z [ RUN      ] LazyOpsTest.TestHardshrinkBackward
2023-01-11T22:09:29.5217245Z [       OK ] LazyOpsTest.TestHardshrinkBackward (0 ms)
2023-01-11T22:09:29.5217595Z [ RUN      ] LazyOpsTest.TestSoftshrinkBackward
2023-01-11T22:09:29.5226692Z [       OK ] LazyOpsTest.TestSoftshrinkBackward (0 ms)
2023-01-11T22:09:29.5227029Z [ RUN      ] LazyOpsTest.TestHardtanhBackward
2023-01-11T22:09:29.5232094Z [       OK ] LazyOpsTest.TestHardtanhBackward (0 ms)
2023-01-11T22:09:29.5232418Z [ RUN      ] LazyOpsTest.TestEluBackward
2023-01-11T22:09:29.5242522Z [       OK ] LazyOpsTest.TestEluBackward (1 ms)
2023-01-11T22:09:29.5242857Z [ RUN      ] LazyOpsTest.TestGeluBackward
2023-01-11T22:09:29.5259706Z [       OK ] LazyOpsTest.TestGeluBackward (1 ms)
2023-01-11T22:09:29.5260358Z [ RUN      ] LazyOpsTest.TestLeakyReluBackward
2023-01-11T22:09:29.5270247Z [       OK ] LazyOpsTest.TestLeakyReluBackward (1 ms)
2023-01-11T22:09:29.5270741Z [ RUN      ] LazyOpsTest.TestTransposeBackward
2023-01-11T22:09:29.5279155Z [       OK ] LazyOpsTest.TestTransposeBackward (0 ms)
2023-01-11T22:09:29.5279596Z [ RUN      ] LazyOpsTest.TestAddMatMulBackward
2023-01-11T22:09:29.5348072Z [       OK ] LazyOpsTest.TestAddMatMulBackward (6 ms)
2023-01-11T22:09:29.5348640Z [ RUN      ] LazyOpsTest.TestBinaryCrossEntropyBackward
2023-01-11T22:09:29.5413100Z [       OK ] LazyOpsTest.TestBinaryCrossEntropyBackward (6 ms)
2023-01-11T22:09:29.5413766Z [ RUN      ] LazyOpsTest.TestNllLossBackward
2023-01-11T22:09:29.5414102Z /var/lib/jenkins/workspace/test/cpp/lazy/test_lazy_ops.cpp:10954: Skipped
2023-01-11T22:09:29.5414275Z 
2023-01-11T22:09:29.5414479Z [  SKIPPED ] LazyOpsTest.TestNllLossBackward (0 ms)
2023-01-11T22:09:29.5414817Z [ RUN      ] LazyOpsTest.TestNllLoss2dBackward
2023-01-11T22:09:29.5698636Z [       OK ] LazyOpsTest.TestNllLoss2dBackward (28 ms)
2023-01-11T22:09:29.5699124Z [ RUN      ] LazyOpsTest.TestSmoothL1LossBackward
2023-01-11T22:09:29.5759509Z [       OK ] LazyOpsTest.TestSmoothL1LossBackward (6 ms)
2023-01-11T22:09:29.5759848Z [ RUN      ] LazyOpsTest.TestViewBackward
2023-01-11T22:09:29.5781793Z [       OK ] LazyOpsTest.TestViewBackward (2 ms)
2023-01-11T22:09:29.5782191Z [ RUN      ] LazyOpsTest.TestBatchNorm2DBackward
2023-01-11T22:09:29.5835141Z [       OK ] LazyOpsTest.TestBatchNorm2DBackward (5 ms)
2023-01-11T22:09:29.5835641Z [ RUN      ] LazyOpsTest.TestBatchNorm3DBackward
2023-01-11T22:09:29.5887259Z [       OK ] LazyOpsTest.TestBatchNorm3DBackward (5 ms)
2023-01-11T22:09:29.5887753Z [ RUN      ] LazyOpsTest.TestBCEWithLogitsBackward
2023-01-11T22:09:29.6251576Z [       OK ] LazyOpsTest.TestBCEWithLogitsBackward (36 ms)
2023-01-11T22:09:29.6252048Z [ RUN      ] LazyOpsTest.TestKlDivBackward
2023-01-11T22:09:29.6329200Z [       OK ] LazyOpsTest.TestKlDivBackward (7 ms)
2023-01-11T22:09:29.6329547Z [ RUN      ] LazyOpsTest.TestEmbeddingBackward
2023-01-11T22:09:29.7112623Z [       OK ] LazyOpsTest.TestEmbeddingBackward (78 ms)
2023-01-11T22:09:29.7113098Z [ RUN      ] LazyOpsTest.TestAmpForeachNonFiniteCheckAndUnscale
2023-01-11T22:09:29.7113465Z /var/lib/jenkins/workspace/test/cpp/lazy/test_lazy_ops.cpp:11351: Skipped
2023-01-11T22:09:29.7113636Z 
2023-01-11T22:09:29.7113900Z [  SKIPPED ] LazyOpsTest.TestAmpForeachNonFiniteCheckAndUnscale (0 ms)
2023-01-11T22:09:29.7114430Z [ RUN      ] LazyOpsTest.TestAmpUpdateScale
2023-01-11T22:09:29.7114960Z /var/lib/jenkins/workspace/test/cpp/lazy/test_lazy_ops.cpp:11400: Skipped
2023-01-11T22:09:29.7115173Z 
2023-01-11T22:09:29.7115377Z [  SKIPPED ] LazyOpsTest.TestAmpUpdateScale (0 ms)
2023-01-11T22:09:29.7115744Z [ RUN      ] LazyOpsTest.TestEarlySyncLiveTensors
2023-01-11T22:09:29.7116355Z [       OK ] LazyOpsTest.TestEarlySyncLiveTensors (0 ms)
2023-01-11T22:09:29.7116669Z [ RUN      ] LazyOpsTest.TestLerp
2023-01-11T22:09:29.7116964Z [       OK ] LazyOpsTest.TestLerp (0 ms)
2023-01-11T22:09:29.7117311Z [ RUN      ] LazyOpsTest.TestLerpScalar
2023-01-11T22:09:29.7117697Z [       OK ] LazyOpsTest.TestLerpScalar (0 ms)
2023-01-11T22:09:29.7118181Z [ RUN      ] LazyOpsTest.TestLerpInplace
2023-01-11T22:09:29.7118501Z [       OK ] LazyOpsTest.TestLerpInplace (0 ms)
2023-01-11T22:09:29.7118829Z [ RUN      ] LazyOpsTest.TestLerpScalarInplace
2023-01-11T22:09:29.7120281Z [       OK ] LazyOpsTest.TestLerpScalarInplace (0 ms)
2023-01-11T22:09:29.7120587Z [ RUN      ] LazyOpsTest.TestLerpOut
2023-01-11T22:09:29.7122334Z [       OK ] LazyOpsTest.TestLerpOut (0 ms)
2023-01-11T22:09:29.7122723Z [ RUN      ] LazyOpsTest.TestLerpScalarOut
2023-01-11T22:09:29.7124040Z [       OK ] LazyOpsTest.TestLerpScalarOut (0 ms)
2023-01-11T22:09:29.7124506Z [ RUN      ] LazyOpsTest.IsAliasOf
2023-01-11T22:09:29.7125049Z [       OK ] LazyOpsTest.IsAliasOf (0 ms)
2023-01-11T22:09:29.7125523Z [----------] 574 tests from LazyOpsTest (3543 ms total)
2023-01-11T22:09:29.7125687Z 
2023-01-11T22:09:29.7125843Z [----------] Global test environment tear-down
2023-01-11T22:09:29.7184511Z [==========] 611 tests from 10 test suites ran. (3558 ms total)
2023-01-11T22:09:29.7184843Z [  PASSED  ] 607 tests.
2023-01-11T22:09:29.7185113Z [  SKIPPED ] 4 tests, listed below:
2023-01-11T22:09:29.7185383Z [  SKIPPED ] LazyOpsTest.TestNllLoss
2023-01-11T22:09:29.7185697Z [  SKIPPED ] LazyOpsTest.TestNllLossBackward
2023-01-11T22:09:29.7186095Z [  SKIPPED ] LazyOpsTest.TestAmpForeachNonFiniteCheckAndUnscale
2023-01-11T22:09:29.7186459Z [  SKIPPED ] LazyOpsTest.TestAmpUpdateScale
2023-01-11T22:09:29.8057665Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *-tsan* ]]
2023-01-11T22:09:29.8057959Z + python test/cpp/jit/tests_setup.py shutdown
2023-01-11T22:09:31.1415499Z + wait
2023-01-11T22:09:31.1415904Z + OMP_NUM_THREADS=2
2023-01-11T22:09:31.1416323Z + TORCH_CPP_TEST_MNIST_PATH=test/cpp/api/mnist
2023-01-11T22:09:31.1416962Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_api '--gtest_filter=-IMethodTest.*' --gtest_output=xml:test/test-reports/cpp-unittest/test_libtorch/test_api.xml
2023-01-11T22:09:31.5326457Z CUDA not available. Disabling CUDA and MultiCUDA tests
2023-01-11T22:09:31.5334194Z Note: Google Test filter = -IMethodTest.*:*_CUDA:*_MultiCUDA
2023-01-11T22:09:31.5334807Z [==========] Running 992 tests from 48 test suites.
2023-01-11T22:09:31.5335141Z [----------] Global test environment set-up.
2023-01-11T22:09:31.5335459Z [----------] 9 tests from AutogradAPITests
2023-01-11T22:09:31.5335768Z [ RUN      ] AutogradAPITests.BackwardSimpleTest
2023-01-11T22:09:31.5346527Z [       OK ] AutogradAPITests.BackwardSimpleTest (1 ms)
2023-01-11T22:09:31.5347138Z [ RUN      ] AutogradAPITests.BackwardTest
2023-01-11T22:09:31.5347939Z [W engine.cpp:1134] Warning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (function operator())
2023-01-11T22:09:31.5350215Z [       OK ] AutogradAPITests.BackwardTest (0 ms)
2023-01-11T22:09:31.5350796Z [ RUN      ] AutogradAPITests.GradSimpleTest
2023-01-11T22:09:31.5352448Z [       OK ] AutogradAPITests.GradSimpleTest (0 ms)
2023-01-11T22:09:31.5353024Z [ RUN      ] AutogradAPITests.GradTest
2023-01-11T22:09:31.5368565Z [       OK ] AutogradAPITests.GradTest (1 ms)
2023-01-11T22:09:31.5369507Z [ RUN      ] AutogradAPITests.GradNonLeafTest
2023-01-11T22:09:31.5372117Z [W TensorBody.h:485] Warning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (function grad)
2023-01-11T22:09:31.5374239Z [W TensorBody.h:485] Warning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (function grad)
2023-01-11T22:09:31.5375579Z [W TensorBody.h:485] Warning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (function grad)
2023-01-11T22:09:31.5377022Z [W TensorBody.h:485] Warning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (function grad)
2023-01-11T22:09:31.5377746Z [       OK ] AutogradAPITests.GradNonLeafTest (0 ms)
2023-01-11T22:09:31.5378101Z [ RUN      ] AutogradAPITests.GradUnreachableTest
2023-01-11T22:09:31.5438633Z [       OK ] AutogradAPITests.GradUnreachableTest (6 ms)
2023-01-11T22:09:31.5438976Z [ RUN      ] AutogradAPITests.EmptyInput
2023-01-11T22:09:31.5465839Z [       OK ] AutogradAPITests.EmptyInput (2 ms)
2023-01-11T22:09:31.5466223Z [ RUN      ] AutogradAPITests.RetainGrad
2023-01-11T22:09:31.5470472Z [       OK ] AutogradAPITests.RetainGrad (0 ms)
2023-01-11T22:09:31.5470861Z [ RUN      ] AutogradAPITests.AnomalyMode
2023-01-11T22:09:31.5471253Z [W anomaly_mode.cpp:27] Warning: This mode should be enabled only for debugging as the different tests will slow down your program execution. (function operator())
2023-01-11T22:09:31.7079919Z [       OK ] AutogradAPITests.AnomalyMode (160 ms)
2023-01-11T22:09:31.7080315Z [----------] 9 tests from AutogradAPITests (174 ms total)
2023-01-11T22:09:31.7080491Z 
2023-01-11T22:09:31.7080645Z [----------] 33 tests from CustomAutogradTest
2023-01-11T22:09:31.7081013Z [ RUN      ] CustomAutogradTest.GradUnreachableDiscoveryTest
2023-01-11T22:09:31.7095294Z [       OK ] CustomAutogradTest.GradUnreachableDiscoveryTest (1 ms)
2023-01-11T22:09:31.7095681Z [ RUN      ] CustomAutogradTest.CustomFunction
2023-01-11T22:09:31.7098256Z [       OK ] CustomAutogradTest.CustomFunction (0 ms)
2023-01-11T22:09:31.7098700Z [ RUN      ] CustomAutogradTest.CustomFunctionWithTensorList
2023-01-11T22:09:31.7100481Z [       OK ] CustomAutogradTest.CustomFunctionWithTensorList (0 ms)
2023-01-11T22:09:31.7100968Z [ RUN      ] CustomAutogradTest.GraphTaskTrimEdges
2023-01-11T22:09:31.7104303Z [       OK ] CustomAutogradTest.GraphTaskTrimEdges (0 ms)
2023-01-11T22:09:31.7104932Z [ RUN      ] CustomAutogradTest.FunctionReturnsInput
2023-01-11T22:09:31.7105584Z [       OK ] CustomAutogradTest.FunctionReturnsInput (0 ms)
2023-01-11T22:09:31.7105960Z [ RUN      ] CustomAutogradTest.FunctionReturnsUndefined
2023-01-11T22:09:31.7108375Z [       OK ] CustomAutogradTest.FunctionReturnsUndefined (0 ms)
2023-01-11T22:09:31.7108800Z [ RUN      ] CustomAutogradTest.MaterializeGrads
2023-01-11T22:09:31.7109253Z [       OK ] CustomAutogradTest.MaterializeGrads (0 ms)
2023-01-11T22:09:31.7109795Z [ RUN      ] CustomAutogradTest.DontMaterializeGrads
2023-01-11T22:09:31.7110625Z [       OK ] CustomAutogradTest.DontMaterializeGrads (0 ms)
2023-01-11T22:09:31.7111038Z [ RUN      ] CustomAutogradTest.NoGradCustomFunction
2023-01-11T22:09:31.7111570Z [       OK ] CustomAutogradTest.NoGradCustomFunction (0 ms)
2023-01-11T22:09:31.7111907Z [ RUN      ] CustomAutogradTest.MarkDirty
2023-01-11T22:09:31.7124435Z [       OK ] CustomAutogradTest.MarkDirty (1 ms)
2023-01-11T22:09:31.7124921Z [ RUN      ] CustomAutogradTest.MarkNonDifferentiable
2023-01-11T22:09:31.7125782Z [       OK ] CustomAutogradTest.MarkNonDifferentiable (0 ms)
2023-01-11T22:09:31.7126246Z [ RUN      ] CustomAutogradTest.MarkNonDifferentiableMixed
2023-01-11T22:09:31.7128591Z [       OK ] CustomAutogradTest.MarkNonDifferentiableMixed (0 ms)
2023-01-11T22:09:31.7129263Z [ RUN      ] CustomAutogradTest.MarkNonDifferentiableNone
2023-01-11T22:09:31.7129732Z [       OK ] CustomAutogradTest.MarkNonDifferentiableNone (0 ms)
2023-01-11T22:09:31.7130124Z [ RUN      ] CustomAutogradTest.ReturnLeafInplace
2023-01-11T22:09:31.7131914Z [       OK ] CustomAutogradTest.ReturnLeafInplace (0 ms)
2023-01-11T22:09:31.7132388Z [ RUN      ] CustomAutogradTest.ReturnDuplicateInplace
2023-01-11T22:09:31.7176066Z [       OK ] CustomAutogradTest.ReturnDuplicateInplace (4 ms)
2023-01-11T22:09:31.7176740Z [ RUN      ] CustomAutogradTest.ReturnDuplicate
2023-01-11T22:09:31.7177205Z [       OK ] CustomAutogradTest.ReturnDuplicate (0 ms)
2023-01-11T22:09:31.7177621Z [ RUN      ] CustomAutogradTest.SaveEmptyForBackward
2023-01-11T22:09:31.7178458Z [       OK ] CustomAutogradTest.SaveEmptyForBackward (0 ms)
2023-01-11T22:09:31.7178939Z [ RUN      ] CustomAutogradTest.InvalidGradients
2023-01-11T22:09:31.7237679Z [       OK ] CustomAutogradTest.InvalidGradients (5 ms)
2023-01-11T22:09:31.7238414Z [ RUN      ] CustomAutogradTest.NoGradInput
2023-01-11T22:09:31.7238882Z [       OK ] CustomAutogradTest.NoGradInput (0 ms)
2023-01-11T22:09:31.7239393Z [ RUN      ] CustomAutogradTest.TooManyGrads
2023-01-11T22:09:31.7239993Z [       OK ] CustomAutogradTest.TooManyGrads (0 ms)
2023-01-11T22:09:31.7240465Z [ RUN      ] CustomAutogradTest.DepNoGrad
2023-01-11T22:09:31.7240902Z [       OK ] CustomAutogradTest.DepNoGrad (0 ms)
2023-01-11T22:09:31.7241374Z [ RUN      ] CustomAutogradTest.Reentrant
2023-01-11T22:09:31.7241957Z [       OK ] CustomAutogradTest.Reentrant (0 ms)
2023-01-11T22:09:31.7242371Z [ RUN      ] CustomAutogradTest.DeepReentrant
2023-01-11T22:09:32.1825776Z [       OK ] CustomAutogradTest.DeepReentrant (458 ms)
2023-01-11T22:09:32.1826180Z [ RUN      ] CustomAutogradTest.ReentrantPriority
2023-01-11T22:09:32.1831816Z [       OK ] CustomAutogradTest.ReentrantPriority (0 ms)
2023-01-11T22:09:32.1832150Z [ RUN      ] CustomAutogradTest.Hooks
2023-01-11T22:09:32.1873963Z [       OK ] CustomAutogradTest.Hooks (4 ms)
2023-01-11T22:09:32.1874290Z [ RUN      ] CustomAutogradTest.HooksInplace
2023-01-11T22:09:32.1877084Z [       OK ] CustomAutogradTest.HooksInplace (0 ms)
2023-01-11T22:09:32.1877470Z [ RUN      ] CustomAutogradTest.HooksInplaceWithRetainsGrad
2023-01-11T22:09:32.1880552Z [       OK ] CustomAutogradTest.HooksInplaceWithRetainsGrad (0 ms)
2023-01-11T22:09:32.1880984Z [ RUN      ] CustomAutogradTest.HooksInplaceTwiceWithRetainsGrad
2023-01-11T22:09:32.1884117Z [       OK ] CustomAutogradTest.HooksInplaceTwiceWithRetainsGrad (0 ms)
2023-01-11T22:09:32.1884553Z [ RUN      ] CustomAutogradTest.HookNone
2023-01-11T22:09:32.1885505Z [       OK ] CustomAutogradTest.HookNone (0 ms)
2023-01-11T22:09:32.1885834Z [ RUN      ] CustomAutogradTest.BackwardWithInputs
2023-01-11T22:09:32.1887852Z [       OK ] CustomAutogradTest.BackwardWithInputs (0 ms)
2023-01-11T22:09:32.1888308Z [ RUN      ] CustomAutogradTest.BackwardWithEmptyInputs
2023-01-11T22:09:32.1900850Z [       OK ] CustomAutogradTest.BackwardWithEmptyInputs (1 ms)
2023-01-11T22:09:32.1901299Z [ RUN      ] CustomAutogradTest.BackwardWithNonLeafInputs
2023-01-11T22:09:32.1903559Z [       OK ] CustomAutogradTest.BackwardWithNonLeafInputs (0 ms)
2023-01-11T22:09:32.1904195Z [ RUN      ] CustomAutogradTest.BackwardWithCreateGraphWarns
2023-01-11T22:09:32.1904947Z [       OK ] CustomAutogradTest.BackwardWithCreateGraphWarns (0 ms)
2023-01-11T22:09:32.1905420Z [----------] 33 tests from CustomAutogradTest (482 ms total)
2023-01-11T22:09:32.1905591Z 
2023-01-11T22:09:32.1905803Z [----------] 13 tests from TestAutogradNotImplementedFallback
2023-01-11T22:09:32.1906226Z [ RUN      ] TestAutogradNotImplementedFallback.RetSingleNonTensor
2023-01-11T22:09:32.1909254Z [       OK ] TestAutogradNotImplementedFallback.RetSingleNonTensor (0 ms)
2023-01-11T22:09:32.1909743Z [ RUN      ] TestAutogradNotImplementedFallback.InplaceOp
2023-01-11T22:09:32.1954711Z [       OK ] TestAutogradNotImplementedFallback.InplaceOp (4 ms)
2023-01-11T22:09:32.1955153Z [ RUN      ] TestAutogradNotImplementedFallback.DoubleInplaceOp
2023-01-11T22:09:32.1994184Z [       OK ] TestAutogradNotImplementedFallback.DoubleInplaceOp (3 ms)
2023-01-11T22:09:32.1994606Z [ RUN      ] TestAutogradNotImplementedFallback.OptOp
2023-01-11T22:09:32.1998006Z [       OK ] TestAutogradNotImplementedFallback.OptOp (0 ms)
2023-01-11T22:09:32.1998458Z [ RUN      ] TestAutogradNotImplementedFallback.OutOfPlaceAddition
2023-01-11T22:09:32.2031767Z [       OK ] TestAutogradNotImplementedFallback.OutOfPlaceAddition (3 ms)
2023-01-11T22:09:32.2032244Z [ RUN      ] TestAutogradNotImplementedFallback.RetTupleNonTensor
2023-01-11T22:09:32.2065764Z [       OK ] TestAutogradNotImplementedFallback.RetTupleNonTensor (3 ms)
2023-01-11T22:09:32.2066185Z [ RUN      ] TestAutogradNotImplementedFallback.ViewOp
2023-01-11T22:09:32.2130888Z [       OK ] TestAutogradNotImplementedFallback.ViewOp (6 ms)
2023-01-11T22:09:32.2131380Z [ RUN      ] TestAutogradNotImplementedFallback.ViewOpWithExtraArg
2023-01-11T22:09:32.2164851Z [       OK ] TestAutogradNotImplementedFallback.ViewOpWithExtraArg (3 ms)
2023-01-11T22:09:32.2165323Z [ RUN      ] TestAutogradNotImplementedFallback.RetTensorVectorView
2023-01-11T22:09:32.2167062Z [       OK ] TestAutogradNotImplementedFallback.RetTensorVectorView (0 ms)
2023-01-11T22:09:32.2167514Z [ RUN      ] TestAutogradNotImplementedFallback.DoubleViewOP
2023-01-11T22:09:32.2187083Z [       OK ] TestAutogradNotImplementedFallback.DoubleViewOP (1 ms)
2023-01-11T22:09:32.2187531Z [ RUN      ] TestAutogradNotImplementedFallback.NonFirstViewOP
2023-01-11T22:09:32.2217443Z [       OK ] TestAutogradNotImplementedFallback.NonFirstViewOP (3 ms)
2023-01-11T22:09:32.2217892Z [ RUN      ] TestAutogradNotImplementedFallback.RetTensorVector
2023-01-11T22:09:32.2251340Z [       OK ] TestAutogradNotImplementedFallback.RetTensorVector (3 ms)
2023-01-11T22:09:32.2251787Z [ RUN      ] TestAutogradNotImplementedFallback.TensorlistOp
2023-01-11T22:09:32.2276731Z [       OK ] TestAutogradNotImplementedFallback.TensorlistOp (2 ms)
2023-01-11T22:09:32.2277262Z [----------] 13 tests from TestAutogradNotImplementedFallback (37 ms total)
2023-01-11T22:09:32.2277593Z 
2023-01-11T22:09:32.2277859Z [----------] 18 tests from AnyModuleTest
2023-01-11T22:09:32.2278364Z [ RUN      ] AnyModuleTest.SimpleReturnType
2023-01-11T22:09:32.2278699Z [       OK ] AnyModuleTest.SimpleReturnType (0 ms)
2023-01-11T22:09:32.2279084Z [ RUN      ] AnyModuleTest.SimpleReturnTypeAndSingleArgument
2023-01-11T22:09:32.2279499Z [       OK ] AnyModuleTest.SimpleReturnTypeAndSingleArgument (0 ms)
2023-01-11T22:09:32.2279925Z [ RUN      ] AnyModuleTest.StringLiteralReturnTypeAndArgument
2023-01-11T22:09:32.2280500Z [       OK ] AnyModuleTest.StringLiteralReturnTypeAndArgument (0 ms)
2023-01-11T22:09:32.2280918Z [ RUN      ] AnyModuleTest.StringReturnTypeWithConstArgument
2023-01-11T22:09:32.2281348Z [       OK ] AnyModuleTest.StringReturnTypeWithConstArgument (0 ms)
2023-01-11T22:09:32.2281860Z [ RUN      ] AnyModuleTest.TensorReturnTypeAndStringArgumentsWithFunkyQualifications
2023-01-11T22:09:32.2284074Z [       OK ] AnyModuleTest.TensorReturnTypeAndStringArgumentsWithFunkyQualifications (0 ms)
2023-01-11T22:09:32.2284648Z [ RUN      ] AnyModuleTest.WrongArgumentType
2023-01-11T22:09:32.2290818Z [       OK ] AnyModuleTest.WrongArgumentType (1 ms)
2023-01-11T22:09:32.2291429Z [ RUN      ] AnyModuleTest.WrongNumberOfArguments
2023-01-11T22:09:32.2326843Z [       OK ] AnyModuleTest.WrongNumberOfArguments (3 ms)
2023-01-11T22:09:32.2327610Z [ RUN      ] AnyModuleTest.PassingArgumentsToModuleWithDefaultArgumentsInForwardMethod
2023-01-11T22:09:32.2407763Z [       OK ] AnyModuleTest.PassingArgumentsToModuleWithDefaultArgumentsInForwardMethod (8 ms)
2023-01-11T22:09:32.2408469Z [ RUN      ] AnyModuleTest.GetWithCorrectTypeSucceeds
2023-01-11T22:09:32.2408850Z [       OK ] AnyModuleTest.GetWithCorrectTypeSucceeds (0 ms)
2023-01-11T22:09:32.2409373Z [ RUN      ] AnyModuleTest.GetWithIncorrectTypeThrows
2023-01-11T22:09:32.2418249Z [       OK ] AnyModuleTest.GetWithIncorrectTypeThrows (1 ms)
2023-01-11T22:09:32.2418943Z [ RUN      ] AnyModuleTest.PtrWithBaseClassSucceeds
2023-01-11T22:09:32.2419404Z [       OK ] AnyModuleTest.PtrWithBaseClassSucceeds (0 ms)
2023-01-11T22:09:32.2419788Z [ RUN      ] AnyModuleTest.PtrWithGoodDowncastSuccceeds
2023-01-11T22:09:32.2420186Z [       OK ] AnyModuleTest.PtrWithGoodDowncastSuccceeds (0 ms)
2023-01-11T22:09:32.2420555Z [ RUN      ] AnyModuleTest.PtrWithBadDowncastThrows
2023-01-11T22:09:32.2439358Z [       OK ] AnyModuleTest.PtrWithBadDowncastThrows (2 ms)
2023-01-11T22:09:32.2439752Z [ RUN      ] AnyModuleTest.DefaultStateIsEmpty
2023-01-11T22:09:32.2440112Z [       OK ] AnyModuleTest.DefaultStateIsEmpty (0 ms)
2023-01-11T22:09:32.2440484Z [ RUN      ] AnyModuleTest.AllMethodsThrowForEmptyAnyModule
2023-01-11T22:09:32.2492536Z [       OK ] AnyModuleTest.AllMethodsThrowForEmptyAnyModule (5 ms)
2023-01-11T22:09:32.2493283Z [ RUN      ] AnyModuleTest.CanMoveAssignDifferentModules
2023-01-11T22:09:32.2493959Z [       OK ] AnyModuleTest.CanMoveAssignDifferentModules (0 ms)
2023-01-11T22:09:32.2494550Z [ RUN      ] AnyModuleTest.ConstructsFromModuleHolder
2023-01-11T22:09:32.2495101Z [       OK ] AnyModuleTest.ConstructsFromModuleHolder (0 ms)
2023-01-11T22:09:32.2495786Z [ RUN      ] AnyModuleTest.ConvertsVariableToTensorCorrectly
2023-01-11T22:09:32.2496206Z [       OK ] AnyModuleTest.ConvertsVariableToTensorCorrectly (0 ms)
2023-01-11T22:09:32.2496590Z [----------] 18 tests from AnyModuleTest (21 ms total)
2023-01-11T22:09:32.2496925Z 
2023-01-11T22:09:32.2497073Z [----------] 12 tests from AnyValueTest
2023-01-11T22:09:32.2497420Z [ RUN      ] AnyValueTest.CorrectlyAccessesIntWhenCorrectType
2023-01-11T22:09:32.2497870Z [       OK ] AnyValueTest.CorrectlyAccessesIntWhenCorrectType (0 ms)
2023-01-11T22:09:32.2498337Z [ RUN      ] AnyValueTest.CorrectlyAccessesStringLiteralWhenCorrectType
2023-01-11T22:09:32.2498843Z [       OK ] AnyValueTest.CorrectlyAccessesStringLiteralWhenCorrectType (0 ms)
2023-01-11T22:09:32.2499300Z [ RUN      ] AnyValueTest.CorrectlyAccessesStringWhenCorrectType
2023-01-11T22:09:32.2499751Z [       OK ] AnyValueTest.CorrectlyAccessesStringWhenCorrectType (0 ms)
2023-01-11T22:09:32.2500201Z [ RUN      ] AnyValueTest.CorrectlyAccessesPointersWhenCorrectType
2023-01-11T22:09:32.2500728Z [       OK ] AnyValueTest.CorrectlyAccessesPointersWhenCorrectType (0 ms)
2023-01-11T22:09:32.2501187Z [ RUN      ] AnyValueTest.CorrectlyAccessesReferencesWhenCorrectType
2023-01-11T22:09:32.2501670Z [       OK ] AnyValueTest.CorrectlyAccessesReferencesWhenCorrectType (0 ms)
2023-01-11T22:09:32.2502120Z [ RUN      ] AnyValueTest.TryGetReturnsNullptrForTheWrongType
2023-01-11T22:09:32.2502601Z [       OK ] AnyValueTest.TryGetReturnsNullptrForTheWrongType (0 ms)
2023-01-11T22:09:32.2502999Z [ RUN      ] AnyValueTest.GetThrowsForTheWrongType
2023-01-11T22:09:32.2517280Z [       OK ] AnyValueTest.GetThrowsForTheWrongType (2 ms)
2023-01-11T22:09:32.2517957Z [ RUN      ] AnyValueTest.MoveConstructionIsAllowed
2023-01-11T22:09:32.2518364Z [       OK ] AnyValueTest.MoveConstructionIsAllowed (0 ms)
2023-01-11T22:09:32.2518727Z [ RUN      ] AnyValueTest.MoveAssignmentIsAllowed
2023-01-11T22:09:32.2519090Z [       OK ] AnyValueTest.MoveAssignmentIsAllowed (0 ms)
2023-01-11T22:09:32.2519430Z [ RUN      ] AnyValueTest.TypeInfoIsCorrectForInt
2023-01-11T22:09:32.2519787Z [       OK ] AnyValueTest.TypeInfoIsCorrectForInt (0 ms)
2023-01-11T22:09:32.2520177Z [ RUN      ] AnyValueTest.TypeInfoIsCorrectForStringLiteral
2023-01-11T22:09:32.2520598Z [       OK ] AnyValueTest.TypeInfoIsCorrectForStringLiteral (0 ms)
2023-01-11T22:09:32.2520978Z [ RUN      ] AnyValueTest.TypeInfoIsCorrectForString
2023-01-11T22:09:32.2521355Z [       OK ] AnyValueTest.TypeInfoIsCorrectForString (0 ms)
2023-01-11T22:09:32.2521712Z [----------] 12 tests from AnyValueTest (2 ms total)
2023-01-11T22:09:32.2521866Z 
2023-01-11T22:09:32.2521994Z [----------] 50 tests from DataTest
2023-01-11T22:09:32.2522290Z [ RUN      ] DataTest.DatasetCallsGetCorrectly
2023-01-11T22:09:32.2532743Z [       OK ] DataTest.DatasetCallsGetCorrectly (1 ms)
2023-01-11T22:09:32.2533425Z [ RUN      ] DataTest.TransformCallsGetApplyCorrectly
2023-01-11T22:09:32.2533842Z [       OK ] DataTest.TransformCallsGetApplyCorrectly (0 ms)
2023-01-11T22:09:32.2534255Z [ RUN      ] DataTest.ChunkDataSetWithInvalidInitParameter
2023-01-11T22:09:32.2601941Z [       OK ] DataTest.ChunkDataSetWithInvalidInitParameter (6 ms)
2023-01-11T22:09:32.2602324Z [ RUN      ] DataTest.InfiniteStreamDataset
2023-01-11T22:09:32.2602661Z [       OK ] DataTest.InfiniteStreamDataset (0 ms)
2023-01-11T22:09:32.2602984Z [ RUN      ] DataTest.NoSequencerIsIdentity
2023-01-11T22:09:32.2615127Z [       OK ] DataTest.NoSequencerIsIdentity (1 ms)
2023-01-11T22:09:32.2615566Z [ RUN      ] DataTest.OrderedSequencerIsSetUpWell
2023-01-11T22:09:32.2615932Z [       OK ] DataTest.OrderedSequencerIsSetUpWell (0 ms)
2023-01-11T22:09:32.2616301Z [ RUN      ] DataTest.OrderedSequencerReOrdersValues
2023-01-11T22:09:32.2616674Z [       OK ] DataTest.OrderedSequencerReOrdersValues (0 ms)
2023-01-11T22:09:32.2617056Z [ RUN      ] DataTest.BatchLambdaAppliesFunctionToBatch
2023-01-11T22:09:32.2617577Z [       OK ] DataTest.BatchLambdaAppliesFunctionToBatch (0 ms)
2023-01-11T22:09:32.2617961Z [ RUN      ] DataTest.LambdaAppliesFunctionToExample
2023-01-11T22:09:32.2618325Z [       OK ] DataTest.LambdaAppliesFunctionToExample (0 ms)
2023-01-11T22:09:32.2618664Z [ RUN      ] DataTest.CollateReducesBatch
2023-01-11T22:09:32.2618981Z [       OK ] DataTest.CollateReducesBatch (0 ms)
2023-01-11T22:09:32.2619285Z [ RUN      ] DataTest.CollationReducesBatch
2023-01-11T22:09:32.2619610Z [       OK ] DataTest.CollationReducesBatch (0 ms)
2023-01-11T22:09:32.2619989Z [ RUN      ] DataTest.SequentialSamplerReturnsIndicesInOrder
2023-01-11T22:09:32.2620464Z [       OK ] DataTest.SequentialSamplerReturnsIndicesInOrder (0 ms)
2023-01-11T22:09:32.2620920Z [ RUN      ] DataTest.SequentialSamplerReturnsLessValuesForLastBatch
2023-01-11T22:09:32.2621402Z [       OK ] DataTest.SequentialSamplerReturnsLessValuesForLastBatch (0 ms)
2023-01-11T22:09:32.2621814Z [ RUN      ] DataTest.SequentialSamplerResetsWell
2023-01-11T22:09:32.2622160Z [       OK ] DataTest.SequentialSamplerResetsWell (0 ms)
2023-01-11T22:09:32.2622621Z [ RUN      ] DataTest.SequentialSamplerResetsWithNewSizeWell
2023-01-11T22:09:32.2623053Z [       OK ] DataTest.SequentialSamplerResetsWithNewSizeWell (0 ms)
2023-01-11T22:09:32.2623471Z [ RUN      ] DataTest.CanSaveAndLoadSequentialSampler
2023-01-11T22:09:32.2766965Z [       OK ] DataTest.CanSaveAndLoadSequentialSampler (15 ms)
2023-01-11T22:09:32.2767637Z [ RUN      ] DataTest.RandomSamplerReturnsIndicesInCorrectRange
2023-01-11T22:09:32.2768217Z [       OK ] DataTest.RandomSamplerReturnsIndicesInCorrectRange (0 ms)
2023-01-11T22:09:32.2768720Z [ RUN      ] DataTest.RandomSamplerReturnsLessValuesForLastBatch
2023-01-11T22:09:32.2769470Z [       OK ] DataTest.RandomSamplerReturnsLessValuesForLastBatch (0 ms)
2023-01-11T22:09:32.2769866Z [ RUN      ] DataTest.RandomSamplerResetsWell
2023-01-11T22:09:32.2770251Z [       OK ] DataTest.RandomSamplerResetsWell (0 ms)
2023-01-11T22:09:32.2770626Z [ RUN      ] DataTest.RandomSamplerResetsWithNewSizeWell
2023-01-11T22:09:32.2771088Z [       OK ] DataTest.RandomSamplerResetsWithNewSizeWell (0 ms)
2023-01-11T22:09:32.2771550Z [ RUN      ] DataTest.SavingAndLoadingRandomSamplerYieldsSameSequence
2023-01-11T22:09:32.2773294Z [       OK ] DataTest.SavingAndLoadingRandomSamplerYieldsSameSequence (0 ms)
2023-01-11T22:09:32.2773897Z [ RUN      ] DataTest.StreamSamplerReturnsTheBatchSizeAndThenRemainder
2023-01-11T22:09:32.2774626Z [       OK ] DataTest.StreamSamplerReturnsTheBatchSizeAndThenRemainder (0 ms)
2023-01-11T22:09:32.2775059Z [ RUN      ] DataTest.StreamSamplerResetsWell
2023-01-11T22:09:32.2775502Z [       OK ] DataTest.StreamSamplerResetsWell (0 ms)
2023-01-11T22:09:32.2775870Z [ RUN      ] DataTest.StreamSamplerResetsWithNewSizeWell
2023-01-11T22:09:32.2776435Z [       OK ] DataTest.StreamSamplerResetsWithNewSizeWell (0 ms)
2023-01-11T22:09:32.2776939Z [ RUN      ] DataTest.TensorDatasetConstructsFromSingleTensor
2023-01-11T22:09:32.2777473Z [       OK ] DataTest.TensorDatasetConstructsFromSingleTensor (0 ms)
2023-01-11T22:09:32.2777992Z [ RUN      ] DataTest.TensorDatasetConstructsFromInitializerListOfTensors
2023-01-11T22:09:32.2778503Z [       OK ] DataTest.TensorDatasetConstructsFromInitializerListOfTensors (0 ms)
2023-01-11T22:09:32.2778923Z [ RUN      ] DataTest.StackTransformWorksForExample
2023-01-11T22:09:32.2780728Z [       OK ] DataTest.StackTransformWorksForExample (0 ms)
2023-01-11T22:09:32.2781190Z [ RUN      ] DataTest.StackTransformWorksForTensorExample
2023-01-11T22:09:32.2782084Z [       OK ] DataTest.StackTransformWorksForTensorExample (0 ms)
2023-01-11T22:09:32.2782801Z [ RUN      ] DataTest.TensorTransformWorksForAnyTargetType
2023-01-11T22:09:32.2784206Z [       OK ] DataTest.TensorTransformWorksForAnyTargetType (0 ms)
2023-01-11T22:09:32.2784781Z [ RUN      ] DataTest.TensorLambdaWorksforAnyTargetType
2023-01-11T22:09:32.2785311Z [       OK ] DataTest.TensorLambdaWorksforAnyTargetType (0 ms)
2023-01-11T22:09:32.2785659Z [ RUN      ] DataTest.NormalizeTransform
2023-01-11T22:09:32.2791893Z [       OK ] DataTest.NormalizeTransform (0 ms)
2023-01-11T22:09:32.2792238Z [ RUN      ] DataTest.MapDoesNotCopy
2023-01-11T22:09:32.2792555Z [       OK ] DataTest.MapDoesNotCopy (0 ms)
2023-01-11T22:09:32.2793052Z [ RUN      ] DataTest.QueuePushAndPopFromSameThread
2023-01-11T22:09:32.2793435Z [       OK ] DataTest.QueuePushAndPopFromSameThread (0 ms)
2023-01-11T22:09:32.2793819Z [ RUN      ] DataTest.QueuePopWithTimeoutThrowsUponTimeout
2023-01-11T22:09:32.2904574Z [       OK ] DataTest.QueuePopWithTimeoutThrowsUponTimeout (11 ms)
2023-01-11T22:09:32.2905029Z [ RUN      ] DataTest.QueuePushAndPopFromDifferentThreads
2023-01-11T22:09:32.3110862Z [       OK ] DataTest.QueuePushAndPopFromDifferentThreads (20 ms)
2023-01-11T22:09:32.3111287Z [ RUN      ] DataTest.QueueClearEmptiesTheQueue
2023-01-11T22:09:32.3132852Z [       OK ] DataTest.QueueClearEmptiesTheQueue (2 ms)
2023-01-11T22:09:32.3133545Z [ RUN      ] DataTest.DataShuttleCanPushAndPopJob
2023-01-11T22:09:32.3133967Z [       OK ] DataTest.DataShuttleCanPushAndPopJob (0 ms)
2023-01-11T22:09:32.3134343Z [ RUN      ] DataTest.DataShuttleCanPushAndPopResult
2023-01-11T22:09:32.3134732Z [       OK ] DataTest.DataShuttleCanPushAndPopResult (0 ms)
2023-01-11T22:09:32.3135190Z [ RUN      ] DataTest.DataShuttlePopResultReturnsNulloptWhenNoJobsInFlight
2023-01-11T22:09:32.3135714Z [       OK ] DataTest.DataShuttlePopResultReturnsNulloptWhenNoJobsInFlight (0 ms)
2023-01-11T22:09:32.3136211Z [ RUN      ] DataTest.DataShuttleDrainMeansPopResultReturnsNullopt
2023-01-11T22:09:32.3136685Z [       OK ] DataTest.DataShuttleDrainMeansPopResultReturnsNullopt (0 ms)
2023-01-11T22:09:32.3137083Z [ RUN      ] DataTest.DataShuttlePopResultTimesOut
2023-01-11T22:09:32.3245550Z [       OK ] DataTest.DataShuttlePopResultTimesOut (11 ms)
2023-01-11T22:09:32.3246163Z [ RUN      ] DataTest.SharedBatchDatasetReallyIsShared
2023-01-11T22:09:32.3260185Z [       OK ] DataTest.SharedBatchDatasetReallyIsShared (1 ms)
2023-01-11T22:09:32.3260689Z [ RUN      ] DataTest.SharedBatchDatasetDoesNotIncurCopyWhenPassedDatasetObject
2023-01-11T22:09:32.3261278Z [       OK ] DataTest.SharedBatchDatasetDoesNotIncurCopyWhenPassedDatasetObject (0 ms)
2023-01-11T22:09:32.3261743Z [ RUN      ] DataTest.CanUseCustomTypeAsIndexType
2023-01-11T22:09:32.3262117Z [       OK ] DataTest.CanUseCustomTypeAsIndexType (0 ms)
2023-01-11T22:09:32.3262655Z [ RUN      ] DataTest.DistributedRandomSamplerSingleReplicaProduceCorrectSamples
2023-01-11T22:09:32.3275463Z [       OK ] DataTest.DistributedRandomSamplerSingleReplicaProduceCorrectSamples (1 ms)
2023-01-11T22:09:32.3276335Z [ RUN      ] DataTest.DistributedRandomSamplerMultiReplicaProduceCorrectSamples
2023-01-11T22:09:32.3277258Z [       OK ] DataTest.DistributedRandomSamplerMultiReplicaProduceCorrectSamples (0 ms)
2023-01-11T22:09:32.3278034Z [ RUN      ] DataTest.CanSaveAndLoadDistributedRandomSampler
2023-01-11T22:09:32.3283837Z [       OK ] DataTest.CanSaveAndLoadDistributedRandomSampler (0 ms)
2023-01-11T22:09:32.3284720Z [ RUN      ] DataTest.DistributedSequentialSamplerSingleReplicaProduceCorrectSamples
2023-01-11T22:09:32.3285710Z [       OK ] DataTest.DistributedSequentialSamplerSingleReplicaProduceCorrectSamples (0 ms)
2023-01-11T22:09:32.3286888Z [ RUN      ] DataTest.DistributedSequentialSamplerMultiReplicaProduceCorrectSamples
2023-01-11T22:09:32.3288038Z [       OK ] DataTest.DistributedSequentialSamplerMultiReplicaProduceCorrectSamples (0 ms)
2023-01-11T22:09:32.3289005Z [ RUN      ] DataTest.CanSaveAndLoadDistributedSequentialSampler
2023-01-11T22:09:32.3290049Z [       OK ] DataTest.CanSaveAndLoadDistributedSequentialSampler (0 ms)
2023-01-11T22:09:32.3290772Z [----------] 50 tests from DataTest (76 ms total)
2023-01-11T22:09:32.3291039Z 
2023-01-11T22:09:32.3291318Z [----------] 37 tests from DataLoaderTest
2023-01-11T22:09:32.3291990Z [ RUN      ] DataLoaderTest.DataLoaderOptionsDefaultAsExpected
2023-01-11T22:09:32.3292964Z [       OK ] DataLoaderTest.DataLoaderOptionsDefaultAsExpected (0 ms)
2023-01-11T22:09:32.3293825Z [ RUN      ] DataLoaderTest.DataLoaderOptionsCoalesceOptionalValues
2023-01-11T22:09:32.3294726Z [       OK ] DataLoaderTest.DataLoaderOptionsCoalesceOptionalValues (0 ms)
2023-01-11T22:09:32.3295543Z [ RUN      ] DataLoaderTest.MakeDataLoaderDefaultsAsExpected
2023-01-11T22:09:32.3296344Z [       OK ] DataLoaderTest.MakeDataLoaderDefaultsAsExpected (0 ms)
2023-01-11T22:09:32.3297363Z [ RUN      ] DataLoaderTest.MakeDataLoaderThrowsWhenConstructingSamplerWithUnsizedDataset
2023-01-11T22:09:32.3302127Z [       OK ] DataLoaderTest.MakeDataLoaderThrowsWhenConstructingSamplerWithUnsizedDataset (1 ms)
2023-01-11T22:09:32.3302991Z [ RUN      ] DataLoaderTest.IteratorsCompareEqualToThemselves
2023-01-11T22:09:32.3303744Z [       OK ] DataLoaderTest.IteratorsCompareEqualToThemselves (0 ms)
2023-01-11T22:09:32.3304574Z [ RUN      ] DataLoaderTest.ValidIteratorsCompareUnequalToEachOther
2023-01-11T22:09:32.3305212Z [       OK ] DataLoaderTest.ValidIteratorsCompareUnequalToEachOther (0 ms)
2023-01-11T22:09:32.3305682Z [ RUN      ] DataLoaderTest.SentinelIteratorsCompareEqualToEachOther
2023-01-11T22:09:32.3306164Z [       OK ] DataLoaderTest.SentinelIteratorsCompareEqualToEachOther (0 ms)
2023-01-11T22:09:32.3306658Z [ RUN      ] DataLoaderTest.IteratorsCompareEqualToSentinelWhenExhausted
2023-01-11T22:09:32.3307154Z [       OK ] DataLoaderTest.IteratorsCompareEqualToSentinelWhenExhausted (0 ms)
2023-01-11T22:09:32.3307570Z [ RUN      ] DataLoaderTest.IteratorsShareState
2023-01-11T22:09:32.3307920Z [       OK ] DataLoaderTest.IteratorsShareState (0 ms)
2023-01-11T22:09:32.3308317Z [ RUN      ] DataLoaderTest.CanDereferenceIteratorMultipleTimes
2023-01-11T22:09:32.3308758Z [       OK ] DataLoaderTest.CanDereferenceIteratorMultipleTimes (0 ms)
2023-01-11T22:09:32.3309167Z [ RUN      ] DataLoaderTest.CanUseIteratorAlgorithms
2023-01-11T22:09:32.3309547Z [       OK ] DataLoaderTest.CanUseIteratorAlgorithms (0 ms)
2023-01-11T22:09:32.3309990Z [ RUN      ] DataLoaderTest.CallingBeginWhileOtherIteratorIsInFlightThrows
2023-01-11T22:09:32.3319064Z [       OK ] DataLoaderTest.CallingBeginWhileOtherIteratorIsInFlightThrows (1 ms)
2023-01-11T22:09:32.3319573Z [ RUN      ] DataLoaderTest.IncrementingExhaustedValidIteratorThrows
2023-01-11T22:09:32.3331125Z [       OK ] DataLoaderTest.IncrementingExhaustedValidIteratorThrows (1 ms)
2023-01-11T22:09:32.3331599Z [ RUN      ] DataLoaderTest.DereferencingExhaustedValidIteratorThrows
2023-01-11T22:09:32.3343098Z [       OK ] DataLoaderTest.DereferencingExhaustedValidIteratorThrows (1 ms)
2023-01-11T22:09:32.3343565Z [ RUN      ] DataLoaderTest.IncrementingSentinelIteratorThrows
2023-01-11T22:09:32.3354811Z [       OK ] DataLoaderTest.IncrementingSentinelIteratorThrows (1 ms)
2023-01-11T22:09:32.3355246Z [ RUN      ] DataLoaderTest.DereferencingSentinelIteratorThrows
2023-01-11T22:09:32.3366692Z [       OK ] DataLoaderTest.DereferencingSentinelIteratorThrows (1 ms)
2023-01-11T22:09:32.3367326Z [ RUN      ] DataLoaderTest.YieldsCorrectBatchSize
2023-01-11T22:09:32.3367807Z [       OK ] DataLoaderTest.YieldsCorrectBatchSize (0 ms)
2023-01-11T22:09:32.3368389Z [ RUN      ] DataLoaderTest.ReturnsLastBatchWhenSmallerThanBatchSizeWhenDropLastIsFalse
2023-01-11T22:09:32.3369162Z [       OK ] DataLoaderTest.ReturnsLastBatchWhenSmallerThanBatchSizeWhenDropLastIsFalse (0 ms)
2023-01-11T22:09:32.3369815Z [ RUN      ] DataLoaderTest.DoesNotReturnLastBatchWhenSmallerThanBatchSizeWhenDropLastIsTrue
2023-01-11T22:09:32.3370476Z [       OK ] DataLoaderTest.DoesNotReturnLastBatchWhenSmallerThanBatchSizeWhenDropLastIsTrue (0 ms)
2023-01-11T22:09:32.3371084Z [ RUN      ] DataLoaderTest.RespectsTimeout
2023-01-11T22:09:32.3484334Z [       OK ] DataLoaderTest.RespectsTimeout (11 ms)
2023-01-11T22:09:32.3485014Z [ RUN      ] DataLoaderTest.EnforcesOrderingAmongThreadsWhenConfigured
2023-01-11T22:09:32.3510416Z [       OK ] DataLoaderTest.EnforcesOrderingAmongThreadsWhenConfigured (2 ms)
2023-01-11T22:09:32.3510857Z [ RUN      ] DataLoaderTest.Reset
2023-01-11T22:09:32.3511223Z [       OK ] DataLoaderTest.Reset (0 ms)
2023-01-11T22:09:32.3511606Z [ RUN      ] DataLoaderTest.TestExceptionsArePropagatedFromWorkers
2023-01-11T22:09:32.3515499Z [       OK ] DataLoaderTest.TestExceptionsArePropagatedFromWorkers (0 ms)
2023-01-11T22:09:32.3516237Z [ RUN      ] DataLoaderTest.StatefulDatasetWithNoWorkers
2023-01-11T22:09:32.3516722Z [       OK ] DataLoaderTest.StatefulDatasetWithNoWorkers (0 ms)
2023-01-11T22:09:32.3517155Z [ RUN      ] DataLoaderTest.StatefulDatasetWithManyWorkers
2023-01-11T22:09:32.3534280Z [       OK ] DataLoaderTest.StatefulDatasetWithManyWorkers (1 ms)
2023-01-11T22:09:32.3534975Z [ RUN      ] DataLoaderTest.StatefulDatasetWithMap
2023-01-11T22:09:32.3535678Z [       OK ] DataLoaderTest.StatefulDatasetWithMap (0 ms)
2023-01-11T22:09:32.3536057Z [ RUN      ] DataLoaderTest.StatefulDatasetWithCollate
2023-01-11T22:09:32.3537004Z [       OK ] DataLoaderTest.StatefulDatasetWithCollate (0 ms)
2023-01-11T22:09:32.3537616Z [ RUN      ] DataLoaderTest.ChunkDataSetGetBatch
2023-01-11T22:09:32.3638224Z [       OK ] DataLoaderTest.ChunkDataSetGetBatch (10 ms)
2023-01-11T22:09:32.3638949Z [ RUN      ] DataLoaderTest.ChunkDataSetWithBatchSizeMismatch
2023-01-11T22:09:32.3651983Z [       OK ] DataLoaderTest.ChunkDataSetWithBatchSizeMismatch (1 ms)
2023-01-11T22:09:32.3652625Z [ RUN      ] DataLoaderTest.ChunkDataSetWithEmptyBatch
2023-01-11T22:09:32.3653228Z [       OK ] DataLoaderTest.ChunkDataSetWithEmptyBatch (0 ms)
2023-01-11T22:09:32.3653700Z [ RUN      ] DataLoaderTest.ChunkDataSetGetBatchWithUnevenBatchSize
2023-01-11T22:09:32.3654625Z [       OK ] DataLoaderTest.ChunkDataSetGetBatchWithUnevenBatchSize (0 ms)
2023-01-11T22:09:32.3655476Z [ RUN      ] DataLoaderTest.CanAccessChunkSamplerWithChunkDataSet
2023-01-11T22:09:32.3656194Z [       OK ] DataLoaderTest.CanAccessChunkSamplerWithChunkDataSet (0 ms)
2023-01-11T22:09:32.3656940Z [ RUN      ] DataLoaderTest.ChunkDatasetDoesNotHang
2023-01-11T22:09:32.3657330Z [       OK ] DataLoaderTest.ChunkDatasetDoesNotHang (0 ms)
2023-01-11T22:09:32.3657668Z [ RUN      ] DataLoaderTest.ChunkDatasetSave
2023-01-11T22:09:32.3821547Z [       OK ] DataLoaderTest.ChunkDatasetSave (16 ms)
2023-01-11T22:09:32.3822099Z [ RUN      ] DataLoaderTest.ChunkDatasetLoad
2023-01-11T22:09:32.3825963Z [       OK ] DataLoaderTest.ChunkDatasetLoad (0 ms)
2023-01-11T22:09:32.3826567Z [ RUN      ] DataLoaderTest.ChunkDatasetCrossChunkShuffle
2023-01-11T22:09:32.3829681Z [       OK ] DataLoaderTest.ChunkDatasetCrossChunkShuffle (0 ms)
2023-01-11T22:09:32.3830334Z [ RUN      ] DataLoaderTest.CustomPreprocessPolicy
2023-01-11T22:09:32.3832601Z [       OK ] DataLoaderTest.CustomPreprocessPolicy (0 ms)
2023-01-11T22:09:32.3833451Z [----------] 37 tests from DataLoaderTest (54 ms total)
2023-01-11T22:09:32.3833743Z 
2023-01-11T22:09:32.3833991Z [----------] 1 test from EnumTest
2023-01-11T22:09:32.3834449Z [ RUN      ] EnumTest.AllEnums
2023-01-11T22:09:32.3834739Z [       OK ] EnumTest.AllEnums (0 ms)
2023-01-11T22:09:32.3835030Z [----------] 1 test from EnumTest (0 ms total)
2023-01-11T22:09:32.3835175Z 
2023-01-11T22:09:32.3835341Z [----------] 6 tests from ExpandingArrayTest
2023-01-11T22:09:32.3835823Z [ RUN      ] ExpandingArrayTest.CanConstructFromInitializerList
2023-01-11T22:09:32.3836278Z [       OK ] ExpandingArrayTest.CanConstructFromInitializerList (0 ms)
2023-01-11T22:09:32.3836698Z [ RUN      ] ExpandingArrayTest.CanConstructFromVector
2023-01-11T22:09:32.3837090Z [       OK ] ExpandingArrayTest.CanConstructFromVector (0 ms)
2023-01-11T22:09:32.3837461Z [ RUN      ] ExpandingArrayTest.CanConstructFromArray
2023-01-11T22:09:32.3837845Z [       OK ] ExpandingArrayTest.CanConstructFromArray (0 ms)
2023-01-11T22:09:32.3838244Z [ RUN      ] ExpandingArrayTest.CanConstructFromSingleValue
2023-01-11T22:09:32.3838654Z [       OK ] ExpandingArrayTest.CanConstructFromSingleValue (0 ms)
2023-01-11T22:09:32.3839240Z [ RUN      ] ExpandingArrayTest.ThrowsWhenConstructedWithIncorrectNumberOfArgumentsInInitializerList
2023-01-11T22:09:32.3844170Z [       OK ] ExpandingArrayTest.ThrowsWhenConstructedWithIncorrectNumberOfArgumentsInInitializerList (1 ms)
2023-01-11T22:09:32.3844918Z [ RUN      ] ExpandingArrayTest.ThrowsWhenConstructedWithIncorrectNumberOfArgumentsInVector
2023-01-11T22:09:32.3854499Z [       OK ] ExpandingArrayTest.ThrowsWhenConstructedWithIncorrectNumberOfArgumentsInVector (1 ms)
2023-01-11T22:09:32.3855017Z [----------] 6 tests from ExpandingArrayTest (2 ms total)
2023-01-11T22:09:32.3855185Z 
2023-01-11T22:09:32.3855324Z [----------] 10 tests from FFTTest
2023-01-11T22:09:32.3855569Z [ RUN      ] FFTTest.fft
2023-01-11T22:09:32.3974531Z [       OK ] FFTTest.fft (11 ms)
2023-01-11T22:09:32.3974795Z [ RUN      ] FFTTest.fft_real
2023-01-11T22:09:32.3976352Z [       OK ] FFTTest.fft_real (0 ms)
2023-01-11T22:09:32.3976634Z [ RUN      ] FFTTest.fft_pad
2023-01-11T22:09:32.3989024Z [       OK ] FFTTest.fft_pad (1 ms)
2023-01-11T22:09:32.3989333Z [ RUN      ] FFTTest.fft_norm
2023-01-11T22:09:32.3991949Z [       OK ] FFTTest.fft_norm (0 ms)
2023-01-11T22:09:32.3992258Z [ RUN      ] FFTTest.ifft
2023-01-11T22:09:32.3995057Z [       OK ] FFTTest.ifft (0 ms)
2023-01-11T22:09:32.3995315Z [ RUN      ] FFTTest.fft_ifft
2023-01-11T22:09:32.4172723Z [       OK ] FFTTest.fft_ifft (17 ms)
2023-01-11T22:09:32.4172984Z [ RUN      ] FFTTest.rfft
2023-01-11T22:09:32.4196378Z [       OK ] FFTTest.rfft (2 ms)
2023-01-11T22:09:32.4196669Z [ RUN      ] FFTTest.rfft_irfft
2023-01-11T22:09:32.4197979Z [       OK ] FFTTest.rfft_irfft (0 ms)
2023-01-11T22:09:32.4198234Z [ RUN      ] FFTTest.ihfft
2023-01-11T22:09:32.4202327Z [       OK ] FFTTest.ihfft (0 ms)
2023-01-11T22:09:32.4202573Z [ RUN      ] FFTTest.hfft_ihfft
2023-01-11T22:09:32.4246992Z [       OK ] FFTTest.hfft_ihfft (4 ms)
2023-01-11T22:09:32.4247381Z [----------] 10 tests from FFTTest (39 ms total)
2023-01-11T22:09:32.4247532Z 
2023-01-11T22:09:32.4247686Z [----------] 132 tests from FunctionalTest
2023-01-11T22:09:32.4247966Z [ RUN      ] FunctionalTest.Conv1d
2023-01-11T22:09:32.4269886Z [       OK ] FunctionalTest.Conv1d (2 ms)
2023-01-11T22:09:32.4270199Z [ RUN      ] FunctionalTest.Conv2dEven
2023-01-11T22:09:32.4275702Z [       OK ] FunctionalTest.Conv2dEven (0 ms)
2023-01-11T22:09:32.4276123Z [ RUN      ] FunctionalTest.Conv2dUneven
2023-01-11T22:09:32.4278244Z [       OK ] FunctionalTest.Conv2dUneven (0 ms)
2023-01-11T22:09:32.4278557Z [ RUN      ] FunctionalTest.Conv3d
2023-01-11T22:09:32.4281960Z [       OK ] FunctionalTest.Conv3d (0 ms)
2023-01-11T22:09:32.4282328Z [ RUN      ] FunctionalTest.MaxPool1d
2023-01-11T22:09:32.4308238Z [       OK ] FunctionalTest.MaxPool1d (2 ms)
2023-01-11T22:09:32.4308737Z [ RUN      ] FunctionalTest.MaxPool2d
2023-01-11T22:09:32.4309292Z [       OK ] FunctionalTest.MaxPool2d (0 ms)
2023-01-11T22:09:32.4309775Z [ RUN      ] FunctionalTest.MaxPool2dBackward
2023-01-11T22:09:32.4310733Z [       OK ] FunctionalTest.MaxPool2dBackward (0 ms)
2023-01-11T22:09:32.4311165Z [ RUN      ] FunctionalTest.MaxPool3d
2023-01-11T22:09:32.4311757Z [       OK ] FunctionalTest.MaxPool3d (0 ms)
2023-01-11T22:09:32.4312218Z [ RUN      ] FunctionalTest.AvgPool1d
2023-01-11T22:09:32.4313232Z [       OK ] FunctionalTest.AvgPool1d (0 ms)
2023-01-11T22:09:32.4313696Z [ RUN      ] FunctionalTest.AvgPool2d
2023-01-11T22:09:32.4314133Z [       OK ] FunctionalTest.AvgPool2d (0 ms)
2023-01-11T22:09:32.4314561Z [ RUN      ] FunctionalTest.AvgPool3d
2023-01-11T22:09:32.4314864Z [       OK ] FunctionalTest.AvgPool3d (0 ms)
2023-01-11T22:09:32.4315171Z [ RUN      ] FunctionalTest.FractionalMaxPool2d
2023-01-11T22:09:32.4317717Z [       OK ] FunctionalTest.FractionalMaxPool2d (0 ms)
2023-01-11T22:09:32.4318080Z [ RUN      ] FunctionalTest.FractionalMaxPool3d
2023-01-11T22:09:32.4319848Z [       OK ] FunctionalTest.FractionalMaxPool3d (0 ms)
2023-01-11T22:09:32.4320270Z [ RUN      ] FunctionalTest.LPPool1d
2023-01-11T22:09:32.4321873Z [       OK ] FunctionalTest.LPPool1d (0 ms)
2023-01-11T22:09:32.4322195Z [ RUN      ] FunctionalTest.LPPool2d
2023-01-11T22:09:32.4323092Z [       OK ] FunctionalTest.LPPool2d (0 ms)
2023-01-11T22:09:32.4323429Z [ RUN      ] FunctionalTest.CosineSimilarity
2023-01-11T22:09:32.4325094Z [       OK ] FunctionalTest.CosineSimilarity (0 ms)
2023-01-11T22:09:32.4325462Z [ RUN      ] FunctionalTest.SmoothL1LossDefaultOptions
2023-01-11T22:09:32.4327446Z [       OK ] FunctionalTest.SmoothL1LossDefaultOptions (0 ms)
2023-01-11T22:09:32.4327870Z [ RUN      ] FunctionalTest.SmoothL1LossBeta
2023-01-11T22:09:32.4329346Z [       OK ] FunctionalTest.SmoothL1LossBeta (0 ms)
2023-01-11T22:09:32.4329736Z [ RUN      ] FunctionalTest.SmoothL1LossNoReduction
2023-01-11T22:09:32.4330791Z [       OK ] FunctionalTest.SmoothL1LossNoReduction (0 ms)
2023-01-11T22:09:32.4331439Z [ RUN      ] FunctionalTest.HuberLossDefaultOptions
2023-01-11T22:09:32.4332635Z [       OK ] FunctionalTest.HuberLossDefaultOptions (0 ms)
2023-01-11T22:09:32.4333227Z [ RUN      ] FunctionalTest.HuberLossDelta
2023-01-11T22:09:32.4333872Z [       OK ] FunctionalTest.HuberLossDelta (0 ms)
2023-01-11T22:09:32.4334478Z [ RUN      ] FunctionalTest.HuberLossNoReduction
2023-01-11T22:09:32.4336098Z [       OK ] FunctionalTest.HuberLossNoReduction (0 ms)
2023-01-11T22:09:32.4336770Z [ RUN      ] FunctionalTest.SoftMarginLossDefaultOptions
2023-01-11T22:09:32.4338086Z [       OK ] FunctionalTest.SoftMarginLossDefaultOptions (0 ms)
2023-01-11T22:09:32.4338830Z [ RUN      ] FunctionalTest.MultiLabelSoftMarginLossDefaultOptions
2023-01-11T22:09:32.4342181Z [       OK ] FunctionalTest.MultiLabelSoftMarginLossDefaultOptions (0 ms)
2023-01-11T22:09:32.4342890Z [ RUN      ] FunctionalTest.SoftMarginLossNoReduction
2023-01-11T22:09:32.4344135Z [       OK ] FunctionalTest.SoftMarginLossNoReduction (0 ms)
2023-01-11T22:09:32.4344837Z [ RUN      ] FunctionalTest.MultiLabelSoftMarginLossWeightedNoReduction
2023-01-11T22:09:32.4347615Z [       OK ] FunctionalTest.MultiLabelSoftMarginLossWeightedNoReduction (0 ms)
2023-01-11T22:09:32.4348254Z [ RUN      ] FunctionalTest.PairwiseDistance
2023-01-11T22:09:32.4349191Z [       OK ] FunctionalTest.PairwiseDistance (0 ms)
2023-01-11T22:09:32.4349745Z [ RUN      ] FunctionalTest.PDist
2023-01-11T22:09:32.4351045Z [       OK ] FunctionalTest.PDist (0 ms)
2023-01-11T22:09:32.4351607Z [ RUN      ] FunctionalTest.AdaptiveMaxPool1d
2023-01-11T22:09:32.4352630Z [       OK ] FunctionalTest.AdaptiveMaxPool1d (0 ms)
2023-01-11T22:09:32.4353328Z [ RUN      ] FunctionalTest.AdaptiveMaxPool2d
2023-01-11T22:09:32.4353883Z [       OK ] FunctionalTest.AdaptiveMaxPool2d (0 ms)
2023-01-11T22:09:32.4354448Z [ RUN      ] FunctionalTest.AdaptiveMaxPool3d
2023-01-11T22:09:32.4355004Z [       OK ] FunctionalTest.AdaptiveMaxPool3d (0 ms)
2023-01-11T22:09:32.4355532Z [ RUN      ] FunctionalTest.AdaptiveAvgPool1d
2023-01-11T22:09:32.4356113Z [       OK ] FunctionalTest.AdaptiveAvgPool1d (0 ms)
2023-01-11T22:09:32.4356685Z [ RUN      ] FunctionalTest.AdaptiveAvgPool2d
2023-01-11T22:09:32.4357150Z [       OK ] FunctionalTest.AdaptiveAvgPool2d (0 ms)
2023-01-11T22:09:32.4357468Z [ RUN      ] FunctionalTest.AdaptiveAvgPool3d
2023-01-11T22:09:32.4357882Z [       OK ] FunctionalTest.AdaptiveAvgPool3d (0 ms)
2023-01-11T22:09:32.4358183Z [ RUN      ] FunctionalTest.L1Loss
2023-01-11T22:09:32.4359590Z [       OK ] FunctionalTest.L1Loss (0 ms)
2023-01-11T22:09:32.4360048Z [ RUN      ] FunctionalTest.MSELoss
2023-01-11T22:09:32.4360656Z [       OK ] FunctionalTest.MSELoss (0 ms)
2023-01-11T22:09:32.4361009Z [ RUN      ] FunctionalTest.BCELoss
2023-01-11T22:09:32.4362210Z [       OK ] FunctionalTest.BCELoss (0 ms)
2023-01-11T22:09:32.4362563Z [ RUN      ] FunctionalTest.KLDivLoss
2023-01-11T22:09:32.4363565Z [W loss.h:57] Warning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release. (function kl_div)
2023-01-11T22:09:32.4364111Z [       OK ] FunctionalTest.KLDivLoss (0 ms)
2023-01-11T22:09:32.4364416Z [ RUN      ] FunctionalTest.HingeEmbeddingLoss
2023-01-11T22:09:32.4365949Z [       OK ] FunctionalTest.HingeEmbeddingLoss (0 ms)
2023-01-11T22:09:32.4366276Z [ RUN      ] FunctionalTest.GridSample
2023-01-11T22:09:32.4368356Z [W vision.h:87] Warning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details. (function grid_sample)
2023-01-11T22:09:32.4370397Z [       OK ] FunctionalTest.GridSample (0 ms)
2023-01-11T22:09:32.4370711Z [ RUN      ] FunctionalTest.AffineGrid
2023-01-11T22:09:32.4509845Z [       OK ] FunctionalTest.AffineGrid (13 ms)
2023-01-11T22:09:32.4510273Z [ RUN      ] FunctionalTest.MultiMarginLoss
2023-01-11T22:09:32.4511507Z [       OK ] FunctionalTest.MultiMarginLoss (0 ms)
2023-01-11T22:09:32.4511897Z [ RUN      ] FunctionalTest.CosineEmbeddingLoss
2023-01-11T22:09:32.4513855Z [       OK ] FunctionalTest.CosineEmbeddingLoss (0 ms)
2023-01-11T22:09:32.4514305Z [ RUN      ] FunctionalTest.MultiLabelMarginLossDefaultOptions
2023-01-11T22:09:32.4516031Z [       OK ] FunctionalTest.MultiLabelMarginLossDefaultOptions (0 ms)
2023-01-11T22:09:32.4516622Z [ RUN      ] FunctionalTest.MultiLabelMarginLossNoReduction
2023-01-11T22:09:32.4517549Z [       OK ] FunctionalTest.MultiLabelMarginLossNoReduction (0 ms)
2023-01-11T22:09:32.4517988Z [ RUN      ] FunctionalTest.TripletMarginLoss
2023-01-11T22:09:32.4519210Z [       OK ] FunctionalTest.TripletMarginLoss (0 ms)
2023-01-11T22:09:32.4519696Z [ RUN      ] FunctionalTest.TripletMarginWithDistanceLossDefaultParity
2023-01-11T22:09:32.4659287Z [       OK ] FunctionalTest.TripletMarginWithDistanceLossDefaultParity (13 ms)
2023-01-11T22:09:32.4659713Z [ RUN      ] FunctionalTest.NLLLoss
2023-01-11T22:09:32.4661176Z [       OK ] FunctionalTest.NLLLoss (0 ms)
2023-01-11T22:09:32.4661550Z [ RUN      ] FunctionalTest.CrossEntropy
2023-01-11T22:09:32.4665544Z [       OK ] FunctionalTest.CrossEntropy (0 ms)
2023-01-11T22:09:32.4665872Z [ RUN      ] FunctionalTest.MaxUnpool1d
2023-01-11T22:09:32.4668677Z [       OK ] FunctionalTest.MaxUnpool1d (0 ms)
2023-01-11T22:09:32.4668974Z [ RUN      ] FunctionalTest.MaxUnpool2d
2023-01-11T22:09:32.4671012Z [       OK ] FunctionalTest.MaxUnpool2d (0 ms)
2023-01-11T22:09:32.4671355Z [ RUN      ] FunctionalTest.MaxUnpool3d
2023-01-11T22:09:32.4672619Z [       OK ] FunctionalTest.MaxUnpool3d (0 ms)
2023-01-11T22:09:32.4673100Z [ RUN      ] FunctionalTest.ELU
2023-01-11T22:09:32.4685585Z [       OK ] FunctionalTest.ELU (1 ms)
2023-01-11T22:09:32.4686075Z [ RUN      ] FunctionalTest.SELU
2023-01-11T22:09:32.4689652Z [       OK ] FunctionalTest.SELU (0 ms)
2023-01-11T22:09:32.4690147Z [ RUN      ] FunctionalTest.GLU
2023-01-11T22:09:32.4691391Z [       OK ] FunctionalTest.GLU (0 ms)
2023-01-11T22:09:32.4691884Z [ RUN      ] FunctionalTest.GELU
2023-01-11T22:09:32.4695999Z [       OK ] FunctionalTest.GELU (0 ms)
2023-01-11T22:09:32.4696516Z [ RUN      ] FunctionalTest.TanhGELU
2023-01-11T22:09:32.4697590Z [       OK ] FunctionalTest.TanhGELU (0 ms)
2023-01-11T22:09:32.4698115Z [ RUN      ] FunctionalTest.Hardshrink
2023-01-11T22:09:32.4705842Z [       OK ] FunctionalTest.Hardshrink (0 ms)
2023-01-11T22:09:32.4706381Z [ RUN      ] FunctionalTest.OneHot
2023-01-11T22:09:32.4709047Z [       OK ] FunctionalTest.OneHot (0 ms)
2023-01-11T22:09:32.4709427Z [ RUN      ] FunctionalTest.Hardtanh
2023-01-11T22:09:32.4732424Z [       OK ] FunctionalTest.Hardtanh (2 ms)
2023-01-11T22:09:32.4732785Z [ RUN      ] FunctionalTest.LeakyReLU
2023-01-11T22:09:32.4740072Z [       OK ] FunctionalTest.LeakyReLU (0 ms)
2023-01-11T22:09:32.4740622Z [ RUN      ] FunctionalTest.LogSigmoid
2023-01-11T22:09:32.4742212Z [       OK ] FunctionalTest.LogSigmoid (0 ms)
2023-01-11T22:09:32.4742828Z [ RUN      ] FunctionalTest.GumbelSoftmax
2023-01-11T22:09:32.4773422Z [       OK ] FunctionalTest.GumbelSoftmax (3 ms)
2023-01-11T22:09:32.4773823Z [ RUN      ] FunctionalTest.Softmax
2023-01-11T22:09:32.4774844Z [       OK ] FunctionalTest.Softmax (0 ms)
2023-01-11T22:09:32.4775143Z [ RUN      ] FunctionalTest.Softmin
2023-01-11T22:09:32.4776748Z [       OK ] FunctionalTest.Softmin (0 ms)
2023-01-11T22:09:32.4777099Z [ RUN      ] FunctionalTest.LogSoftmax
2023-01-11T22:09:32.4778403Z [       OK ] FunctionalTest.LogSoftmax (0 ms)
2023-01-11T22:09:32.4778734Z [ RUN      ] FunctionalTest.PReLU
2023-01-11T22:09:32.4780578Z [       OK ] FunctionalTest.PReLU (0 ms)
2023-01-11T22:09:32.4780932Z [ RUN      ] FunctionalTest.LayerNorm
2023-01-11T22:09:32.4781872Z [       OK ] FunctionalTest.LayerNorm (0 ms)
2023-01-11T22:09:32.4782232Z [ RUN      ] FunctionalTest.GroupNorm
2023-01-11T22:09:32.4782993Z [       OK ] FunctionalTest.GroupNorm (0 ms)
2023-01-11T22:09:32.4783580Z [ RUN      ] FunctionalTest.LocalResponseNorm
2023-01-11T22:09:32.4784962Z [       OK ] FunctionalTest.LocalResponseNorm (0 ms)
2023-01-11T22:09:32.4785372Z [ RUN      ] FunctionalTest.Linear
2023-01-11T22:09:32.4788101Z [       OK ] FunctionalTest.Linear (0 ms)
2023-01-11T22:09:32.4788870Z [ RUN      ] FunctionalTest.Embedding
2023-01-11T22:09:32.4789754Z [       OK ] FunctionalTest.Embedding (0 ms)
2023-01-11T22:09:32.4790382Z [ RUN      ] FunctionalTest.EmbeddingBag
2023-01-11T22:09:32.4795464Z [       OK ] FunctionalTest.EmbeddingBag (0 ms)
2023-01-11T22:09:32.4796059Z [ RUN      ] FunctionalTest.Bilinear
2023-01-11T22:09:32.4799112Z [       OK ] FunctionalTest.Bilinear (0 ms)
2023-01-11T22:09:32.4799891Z [ RUN      ] FunctionalTest.Normalize
2023-01-11T22:09:32.4804257Z [       OK ] FunctionalTest.Normalize (0 ms)
2023-01-11T22:09:32.4804748Z [ RUN      ] FunctionalTest.ReLU
2023-01-11T22:09:32.4807423Z [       OK ] FunctionalTest.ReLU (0 ms)
2023-01-11T22:09:32.4807894Z [ RUN      ] FunctionalTest.ReLUDefaultOptions
2023-01-11T22:09:32.4808578Z [       OK ] FunctionalTest.ReLUDefaultOptions (0 ms)
2023-01-11T22:09:32.4809273Z [ RUN      ] FunctionalTest.ReLU6
2023-01-11T22:09:32.4812521Z [       OK ] FunctionalTest.ReLU6 (0 ms)
2023-01-11T22:09:32.4813141Z [ RUN      ] FunctionalTest.ReLU6DefaultOptions
2023-01-11T22:09:32.4813516Z [       OK ] FunctionalTest.ReLU6DefaultOptions (0 ms)
2023-01-11T22:09:32.4813823Z [ RUN      ] FunctionalTest.RReLU
2023-01-11T22:09:32.4843953Z [       OK ] FunctionalTest.RReLU (3 ms)
2023-01-11T22:09:32.4844269Z [ RUN      ] FunctionalTest.RReLUDefaultOptions
2023-01-11T22:09:32.4846145Z [       OK ] FunctionalTest.RReLUDefaultOptions (0 ms)
2023-01-11T22:09:32.4846697Z [ RUN      ] FunctionalTest.CELU
2023-01-11T22:09:32.4856422Z [       OK ] FunctionalTest.CELU (1 ms)
2023-01-11T22:09:32.4856975Z [ RUN      ] FunctionalTest.CELUDefaultOptions
2023-01-11T22:09:32.4858936Z [       OK ] FunctionalTest.CELUDefaultOptions (0 ms)
2023-01-11T22:09:32.4859467Z [ RUN      ] FunctionalTest.PixelShuffle
2023-01-11T22:09:32.4859946Z [       OK ] FunctionalTest.PixelShuffle (0 ms)
2023-01-11T22:09:32.4860435Z [ RUN      ] FunctionalTest.PixelUnshuffle
2023-01-11T22:09:32.4861300Z [       OK ] FunctionalTest.PixelUnshuffle (0 ms)
2023-01-11T22:09:32.4861770Z [ RUN      ] FunctionalTest.Softplus
2023-01-11T22:09:32.4868503Z [       OK ] FunctionalTest.Softplus (0 ms)
2023-01-11T22:09:32.4868946Z [ RUN      ] FunctionalTest.SoftplusDefaultOptions
2023-01-11T22:09:32.4869498Z [       OK ] FunctionalTest.SoftplusDefaultOptions (0 ms)
2023-01-11T22:09:32.4869898Z [ RUN      ] FunctionalTest.Fold
2023-01-11T22:09:32.4871345Z [       OK ] FunctionalTest.Fold (0 ms)
2023-01-11T22:09:32.4871777Z [ RUN      ] FunctionalTest.Unfold
2023-01-11T22:09:32.4872822Z [       OK ] FunctionalTest.Unfold (0 ms)
2023-01-11T22:09:32.4873255Z [ RUN      ] FunctionalTest.Softshrink
2023-01-11T22:09:32.4879158Z [       OK ] FunctionalTest.Softshrink (0 ms)
2023-01-11T22:09:32.4879620Z [ RUN      ] FunctionalTest.SoftshrinkDefaultOptions
2023-01-11T22:09:32.4882055Z [       OK ] FunctionalTest.SoftshrinkDefaultOptions (0 ms)
2023-01-11T22:09:32.4882679Z [ RUN      ] FunctionalTest.Softsign
2023-01-11T22:09:32.4883206Z [       OK ] FunctionalTest.Softsign (0 ms)
2023-01-11T22:09:32.4883528Z [ RUN      ] FunctionalTest.Mish
2023-01-11T22:09:32.4884526Z [       OK ] FunctionalTest.Mish (0 ms)
2023-01-11T22:09:32.4885027Z [ RUN      ] FunctionalTest.Tanhshrink
2023-01-11T22:09:32.4885572Z [       OK ] FunctionalTest.Tanhshrink (0 ms)
2023-01-11T22:09:32.4886112Z [ RUN      ] FunctionalTest.Threshold
2023-01-11T22:09:32.4898345Z [       OK ] FunctionalTest.Threshold (1 ms)
2023-01-11T22:09:32.4899109Z [ RUN      ] FunctionalTest.BatchNorm1d
2023-01-11T22:09:32.4899633Z [       OK ] FunctionalTest.BatchNorm1d (0 ms)
2023-01-11T22:09:32.4900237Z [ RUN      ] FunctionalTest.BatchNorm1dDefaultOptions
2023-01-11T22:09:32.4901014Z [       OK ] FunctionalTest.BatchNorm1dDefaultOptions (0 ms)
2023-01-11T22:09:32.4901596Z [ RUN      ] FunctionalTest.BatchNorm2d
2023-01-11T22:09:32.4902137Z [       OK ] FunctionalTest.BatchNorm2d (0 ms)
2023-01-11T22:09:32.4902842Z [ RUN      ] FunctionalTest.BatchNorm2dDefaultOptions
2023-01-11T22:09:32.4904024Z [       OK ] FunctionalTest.BatchNorm2dDefaultOptions (0 ms)
2023-01-11T22:09:32.4904597Z [ RUN      ] FunctionalTest.BatchNorm3d
2023-01-11T22:09:32.4905341Z [       OK ] FunctionalTest.BatchNorm3d (0 ms)
2023-01-11T22:09:32.4905972Z [ RUN      ] FunctionalTest.BatchNorm3dDefaultOptions
2023-01-11T22:09:32.4906759Z [       OK ] FunctionalTest.BatchNorm3dDefaultOptions (0 ms)
2023-01-11T22:09:32.4907301Z [ RUN      ] FunctionalTest.InstanceNorm1d
2023-01-11T22:09:32.4909416Z [       OK ] FunctionalTest.InstanceNorm1d (0 ms)
2023-01-11T22:09:32.4910119Z [ RUN      ] FunctionalTest.InstanceNorm1dDefaultOptions
2023-01-11T22:09:32.4910940Z [       OK ] FunctionalTest.InstanceNorm1dDefaultOptions (0 ms)
2023-01-11T22:09:32.4911524Z [ RUN      ] FunctionalTest.InstanceNorm2d
2023-01-11T22:09:32.4913440Z [       OK ] FunctionalTest.InstanceNorm2d (0 ms)
2023-01-11T22:09:32.4913805Z [ RUN      ] FunctionalTest.InstanceNorm2dDefaultOptions
2023-01-11T22:09:32.4915405Z [       OK ] FunctionalTest.InstanceNorm2dDefaultOptions (0 ms)
2023-01-11T22:09:32.4915748Z [ RUN      ] FunctionalTest.InstanceNorm3d
2023-01-11T22:09:32.4918852Z [       OK ] FunctionalTest.InstanceNorm3d (0 ms)
2023-01-11T22:09:32.4919212Z [ RUN      ] FunctionalTest.InstanceNorm3dDefaultOptions
2023-01-11T22:09:32.4921742Z [       OK ] FunctionalTest.InstanceNorm3dDefaultOptions (0 ms)
2023-01-11T22:09:32.4922202Z [ RUN      ] FunctionalTest.Interpolate
2023-01-11T22:09:32.4923093Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:32.4924386Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:32.4925520Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:32.4926560Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:32.4927751Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:32.4928735Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:32.4930481Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:32.4932190Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:32.5007962Z [       OK ] FunctionalTest.Interpolate (8 ms)
2023-01-11T22:09:32.5008390Z [ RUN      ] FunctionalTest.Pad1
2023-01-11T22:09:32.5009345Z [       OK ] FunctionalTest.Pad1 (0 ms)
2023-01-11T22:09:32.5009703Z [ RUN      ] FunctionalTest.Pad2
2023-01-11T22:09:32.5011320Z [       OK ] FunctionalTest.Pad2 (0 ms)
2023-01-11T22:09:32.5011672Z [ RUN      ] FunctionalTest.Pad3
2023-01-11T22:09:32.5016230Z [       OK ] FunctionalTest.Pad3 (0 ms)
2023-01-11T22:09:32.5016696Z [ RUN      ] FunctionalTest.Pad4
2023-01-11T22:09:32.5017760Z [       OK ] FunctionalTest.Pad4 (0 ms)
2023-01-11T22:09:32.5018064Z [ RUN      ] FunctionalTest.Pad5
2023-01-11T22:09:32.5020663Z [       OK ] FunctionalTest.Pad5 (0 ms)
2023-01-11T22:09:32.5020981Z [ RUN      ] FunctionalTest.Pad6
2023-01-11T22:09:32.5024316Z [       OK ] FunctionalTest.Pad6 (0 ms)
2023-01-11T22:09:32.5024589Z [ RUN      ] FunctionalTest.Pad7
2023-01-11T22:09:32.5024869Z [       OK ] FunctionalTest.Pad7 (0 ms)
2023-01-11T22:09:32.5025151Z [ RUN      ] FunctionalTest.Pad8
2023-01-11T22:09:32.5025415Z [       OK ] FunctionalTest.Pad8 (0 ms)
2023-01-11T22:09:32.5025696Z [ RUN      ] FunctionalTest.CTCLoss
2023-01-11T22:09:32.5135534Z [       OK ] FunctionalTest.CTCLoss (11 ms)
2023-01-11T22:09:32.5135875Z [ RUN      ] FunctionalTest.PoissonNLLLoss
2023-01-11T22:09:32.5137784Z [       OK ] FunctionalTest.PoissonNLLLoss (0 ms)
2023-01-11T22:09:32.5138372Z [ RUN      ] FunctionalTest.MarginRankingLoss
2023-01-11T22:09:32.5141348Z [       OK ] FunctionalTest.MarginRankingLoss (0 ms)
2023-01-11T22:09:32.5141697Z [ RUN      ] FunctionalTest.ConvTranspose1d
2023-01-11T22:09:32.5144347Z [       OK ] FunctionalTest.ConvTranspose1d (0 ms)
2023-01-11T22:09:32.5144715Z [ RUN      ] FunctionalTest.ConvTranspose2dEven
2023-01-11T22:09:32.5149114Z [       OK ] FunctionalTest.ConvTranspose2dEven (0 ms)
2023-01-11T22:09:32.5149494Z [ RUN      ] FunctionalTest.ConvTranspose2dUneven
2023-01-11T22:09:32.5153175Z [       OK ] FunctionalTest.ConvTranspose2dUneven (0 ms)
2023-01-11T22:09:32.5153642Z [ RUN      ] FunctionalTest.ConvTranspose3d
2023-01-11T22:09:32.5156604Z [       OK ] FunctionalTest.ConvTranspose3d (0 ms)
2023-01-11T22:09:32.5156929Z [ RUN      ] FunctionalTest.AlphaDropout
2023-01-11T22:09:32.5166717Z [       OK ] FunctionalTest.AlphaDropout (0 ms)
2023-01-11T22:09:32.5167068Z [ RUN      ] FunctionalTest.FeatureAlphaDropout
2023-01-11T22:09:32.5175900Z [       OK ] FunctionalTest.FeatureAlphaDropout (0 ms)
2023-01-11T22:09:32.5176237Z [ RUN      ] FunctionalTest.Dropout
2023-01-11T22:09:32.5181196Z [       OK ] FunctionalTest.Dropout (0 ms)
2023-01-11T22:09:32.5181522Z [ RUN      ] FunctionalTest.Dropout2d
2023-01-11T22:09:32.5187714Z [       OK ] FunctionalTest.Dropout2d (0 ms)
2023-01-11T22:09:32.5188135Z [ RUN      ] FunctionalTest.Dropout3d
2023-01-11T22:09:32.5193258Z [       OK ] FunctionalTest.Dropout3d (0 ms)
2023-01-11T22:09:32.5193583Z [ RUN      ] FunctionalTest.isfinite
2023-01-11T22:09:32.5199291Z [       OK ] FunctionalTest.isfinite (0 ms)
2023-01-11T22:09:32.5199591Z [ RUN      ] FunctionalTest.isinf
2023-01-11T22:09:32.5203805Z [       OK ] FunctionalTest.isinf (0 ms)
2023-01-11T22:09:32.5204107Z [ RUN      ] FunctionalTest.AllClose
2023-01-11T22:09:32.5267514Z [       OK ] FunctionalTest.AllClose (6 ms)
2023-01-11T22:09:32.5267858Z [ RUN      ] FunctionalTest.BCEWithLogitsLoss
2023-01-11T22:09:32.5304989Z [       OK ] FunctionalTest.BCEWithLogitsLoss (3 ms)
2023-01-11T22:09:32.5305607Z [----------] 132 tests from FunctionalTest (105 ms total)
2023-01-11T22:09:32.5305868Z 
2023-01-11T22:09:32.5306023Z [----------] 1 test from IntegrationTest
2023-01-11T22:09:32.5306315Z [ RUN      ] IntegrationTest.CartPole
2023-01-11T22:09:45.0737636Z [       OK ] IntegrationTest.CartPole (12543 ms)
2023-01-11T22:09:45.0738305Z [----------] 1 test from IntegrationTest (12543 ms total)
2023-01-11T22:09:45.0738548Z 
2023-01-11T22:09:45.0738693Z [----------] 9 tests from InitTest
2023-01-11T22:09:45.0739012Z [ RUN      ] InitTest.ProducesPyTorchValues_XavierUniform
2023-01-11T22:09:45.0768985Z [       OK ] InitTest.ProducesPyTorchValues_XavierUniform (2 ms)
2023-01-11T22:09:45.0769793Z [ RUN      ] InitTest.ProducesPyTorchValues_XavierNormal
2023-01-11T22:09:45.0781972Z [       OK ] InitTest.ProducesPyTorchValues_XavierNormal (1 ms)
2023-01-11T22:09:45.0782697Z [ RUN      ] InitTest.ProducesPyTorchValues_KaimingNormal
2023-01-11T22:09:45.0796553Z [       OK ] InitTest.ProducesPyTorchValues_KaimingNormal (1 ms)
2023-01-11T22:09:45.0797226Z [ RUN      ] InitTest.ProducesPyTorchValues_KaimingUniform
2023-01-11T22:09:45.0810033Z [       OK ] InitTest.ProducesPyTorchValues_KaimingUniform (1 ms)
2023-01-11T22:09:45.0810762Z [ RUN      ] InitTest.CanInitializeTensorThatRequiresGrad
2023-01-11T22:09:45.0837228Z [       OK ] InitTest.CanInitializeTensorThatRequiresGrad (2 ms)
2023-01-11T22:09:45.0837728Z [ RUN      ] InitTest.CalculateGainWithTanh
2023-01-11T22:09:45.0838122Z [       OK ] InitTest.CalculateGainWithTanh (0 ms)
2023-01-11T22:09:45.0838480Z [ RUN      ] InitTest.CalculateGainWithRelu
2023-01-11T22:09:45.0838850Z [       OK ] InitTest.CalculateGainWithRelu (0 ms)
2023-01-11T22:09:45.0839230Z [ RUN      ] InitTest.CalculateGainWithLeakyRelu
2023-01-11T22:09:45.0839619Z [       OK ] InitTest.CalculateGainWithLeakyRelu (0 ms)
2023-01-11T22:09:45.0840033Z [ RUN      ] InitTest.CanInitializeCnnWithOrthogonal
2023-01-11T22:09:45.0851912Z [       OK ] InitTest.CanInitializeCnnWithOrthogonal (1 ms)
2023-01-11T22:09:45.0852530Z [----------] 9 tests from InitTest (11 ms total)
2023-01-11T22:09:45.0852783Z 
2023-01-11T22:09:45.0853310Z [----------] 6 tests from TorchScriptTest
2023-01-11T22:09:45.0853995Z [ RUN      ] TorchScriptTest.CanCompileMultipleFunctions
2023-01-11T22:09:45.1232263Z [       OK ] TorchScriptTest.CanCompileMultipleFunctions (37 ms)
2023-01-11T22:09:45.1233048Z [ RUN      ] TorchScriptTest.TestNestedIValueModuleArgMatching
2023-01-11T22:09:45.1270272Z [       OK ] TorchScriptTest.TestNestedIValueModuleArgMatching (3 ms)
2023-01-11T22:09:45.1270992Z [ RUN      ] TorchScriptTest.TestDictArgMatching
2023-01-11T22:09:45.1273956Z [       OK ] TorchScriptTest.TestDictArgMatching (0 ms)
2023-01-11T22:09:45.1274573Z [ RUN      ] TorchScriptTest.TestTupleArgMatching
2023-01-11T22:09:45.1275455Z [       OK ] TorchScriptTest.TestTupleArgMatching (0 ms)
2023-01-11T22:09:45.1276285Z [ RUN      ] TorchScriptTest.TestOptionalArgMatching
2023-01-11T22:09:45.1281847Z [       OK ] TorchScriptTest.TestOptionalArgMatching (0 ms)
2023-01-11T22:09:45.1282403Z [ RUN      ] TorchScriptTest.TestPickle
2023-01-11T22:09:45.1283001Z [       OK ] TorchScriptTest.TestPickle (0 ms)
2023-01-11T22:09:45.1283645Z [----------] 6 tests from TorchScriptTest (43 ms total)
2023-01-11T22:09:45.1283969Z 
2023-01-11T22:09:45.1284269Z [----------] 3 tests from MakeUniqueTest
2023-01-11T22:09:45.1284914Z [ RUN      ] MakeUniqueTest.ForwardRvaluesCorrectly
2023-01-11T22:09:45.1285681Z [       OK ] MakeUniqueTest.ForwardRvaluesCorrectly (0 ms)
2023-01-11T22:09:45.1286434Z [ RUN      ] MakeUniqueTest.ForwardLvaluesCorrectly
2023-01-11T22:09:45.1287183Z [       OK ] MakeUniqueTest.ForwardLvaluesCorrectly (0 ms)
2023-01-11T22:09:45.1287981Z [ RUN      ] MakeUniqueTest.CanConstructUniquePtrOfArray
2023-01-11T22:09:45.1288828Z [       OK ] MakeUniqueTest.CanConstructUniquePtrOfArray (0 ms)
2023-01-11T22:09:45.1289916Z [----------] 3 tests from MakeUniqueTest (0 ms total)
2023-01-11T22:09:45.1290181Z 
2023-01-11T22:09:45.1290441Z [----------] 2 tests from MetaTensorTest
2023-01-11T22:09:45.1290956Z [ RUN      ] MetaTensorTest.MetaDeviceApi
2023-01-11T22:09:45.1291537Z [       OK ] MetaTensorTest.MetaDeviceApi (0 ms)
2023-01-11T22:09:45.1292090Z [ RUN      ] MetaTensorTest.MetaNamespaceApi
2023-01-11T22:09:45.1292678Z [       OK ] MetaTensorTest.MetaNamespaceApi (0 ms)
2023-01-11T22:09:45.1293267Z [----------] 2 tests from MetaTensorTest (0 ms total)
2023-01-11T22:09:45.1293538Z 
2023-01-11T22:09:45.1293777Z [----------] 2 tests from UtilsTest
2023-01-11T22:09:45.1294209Z [ RUN      ] UtilsTest.WarnOnce
2023-01-11T22:09:45.1294675Z [       OK ] UtilsTest.WarnOnce (0 ms)
2023-01-11T22:09:45.1295232Z [ RUN      ] UtilsTest.AmbiguousOperatorDefaults
2023-01-11T22:09:45.1295849Z [       OK ] UtilsTest.AmbiguousOperatorDefaults (0 ms)
2023-01-11T22:09:45.1296449Z [----------] 2 tests from UtilsTest (0 ms total)
2023-01-11T22:09:45.1296700Z 
2023-01-11T22:09:45.1296942Z [----------] 1 test from NoGradTest
2023-01-11T22:09:45.1297446Z [ RUN      ] NoGradTest.SetsGradModeCorrectly
2023-01-11T22:09:45.1320647Z [       OK ] NoGradTest.SetsGradModeCorrectly (3 ms)
2023-01-11T22:09:45.1321198Z [----------] 1 test from NoGradTest (3 ms total)
2023-01-11T22:09:45.1321427Z 
2023-01-11T22:09:45.1321679Z [----------] 3 tests from AutogradTest
2023-01-11T22:09:45.1322157Z [ RUN      ] AutogradTest.CanTakeDerivatives
2023-01-11T22:09:45.1322713Z [       OK ] AutogradTest.CanTakeDerivatives (0 ms)
2023-01-11T22:09:45.1323356Z [ RUN      ] AutogradTest.CanTakeDerivativesOfZeroDimTensors
2023-01-11T22:09:45.1324071Z [       OK ] AutogradTest.CanTakeDerivativesOfZeroDimTensors (0 ms)
2023-01-11T22:09:45.1324708Z [ RUN      ] AutogradTest.CanPassCustomGradientInputs
2023-01-11T22:09:45.1325743Z [       OK ] AutogradTest.CanPassCustomGradientInputs (0 ms)
2023-01-11T22:09:45.1326406Z [----------] 3 tests from AutogradTest (0 ms total)
2023-01-11T22:09:45.1326681Z 
2023-01-11T22:09:45.1326972Z [----------] 1 test from OptionalArrayRefTest
2023-01-11T22:09:45.1327615Z [ RUN      ] OptionalArrayRefTest.DanglingPointerFix
2023-01-11T22:09:45.1328366Z [       OK ] OptionalArrayRefTest.DanglingPointerFix (0 ms)
2023-01-11T22:09:45.1329322Z [----------] 1 test from OptionalArrayRefTest (0 ms total)
2023-01-11T22:09:45.1329650Z 
2023-01-11T22:09:45.1329939Z [----------] 52 tests from ModuleTest
2023-01-11T22:09:45.1330620Z [ RUN      ] ModuleTest.CanEnableAndDisableTrainingMode
2023-01-11T22:09:45.1331596Z [       OK ] ModuleTest.CanEnableAndDisableTrainingMode (0 ms)
2023-01-11T22:09:45.1332326Z [ RUN      ] ModuleTest.ZeroGrad
2023-01-11T22:09:45.1332820Z [       OK ] ModuleTest.ZeroGrad (0 ms)
2023-01-11T22:09:45.1333361Z [ RUN      ] ModuleTest.ZeroGradWithUndefined
2023-01-11T22:09:45.1333949Z [       OK ] ModuleTest.ZeroGradWithUndefined (0 ms)
2023-01-11T22:09:45.1334668Z [ RUN      ] ModuleTest.RegisterModuleThrowsForEmptyOrDottedName
2023-01-11T22:09:45.1355397Z [       OK ] ModuleTest.RegisterModuleThrowsForEmptyOrDottedName (2 ms)
2023-01-11T22:09:45.1356275Z [ RUN      ] ModuleTest.RegisterModuleThrowsForDuplicateModuleName
2023-01-11T22:09:45.1375383Z [       OK ] ModuleTest.RegisterModuleThrowsForDuplicateModuleName (1 ms)
2023-01-11T22:09:45.1376256Z [ RUN      ] ModuleTest.ReplaceModuleThrowsForUnknownModuleName
2023-01-11T22:09:45.1388845Z [       OK ] ModuleTest.ReplaceModuleThrowsForUnknownModuleName (1 ms)
2023-01-11T22:09:45.1389536Z [ RUN      ] ModuleTest.ReplaceModule
2023-01-11T22:09:45.1390054Z [       OK ] ModuleTest.ReplaceModule (0 ms)
2023-01-11T22:09:45.1390592Z [ RUN      ] ModuleTest.UnregisterModule
2023-01-11T22:09:45.1406497Z [       OK ] ModuleTest.UnregisterModule (1 ms)
2023-01-11T22:09:45.1407242Z [ RUN      ] ModuleTest.RegisterParameterThrowsForEmptyOrDottedName
2023-01-11T22:09:45.1439470Z [       OK ] ModuleTest.RegisterParameterThrowsForEmptyOrDottedName (3 ms)
2023-01-11T22:09:45.1440384Z [ RUN      ] ModuleTest.RegisterParameterThrowsForDuplicateModuleName
2023-01-11T22:09:45.1462168Z [       OK ] ModuleTest.RegisterParameterThrowsForDuplicateModuleName (2 ms)
2023-01-11T22:09:45.1463089Z [ RUN      ] ModuleTest.RegisterParameterUndefinedTensor
2023-01-11T22:09:45.1463807Z [       OK ] ModuleTest.RegisterParameterUndefinedTensor (0 ms)
2023-01-11T22:09:45.1464591Z [ RUN      ] ModuleTest.RegisterBufferThrowsForEmptyOrDottedName
2023-01-11T22:09:45.1495536Z [       OK ] ModuleTest.RegisterBufferThrowsForEmptyOrDottedName (3 ms)
2023-01-11T22:09:45.1496393Z [ RUN      ] ModuleTest.RegisterBufferThrowsForDuplicateModuleName
2023-01-11T22:09:45.1518448Z [       OK ] ModuleTest.RegisterBufferThrowsForDuplicateModuleName (2 ms)
2023-01-11T22:09:45.1519149Z [ RUN      ] ModuleTest.CanGetName
2023-01-11T22:09:45.1519653Z [       OK ] ModuleTest.CanGetName (0 ms)
2023-01-11T22:09:45.1520200Z [ RUN      ] ModuleTest.AsCastsModulesCorrectly
2023-01-11T22:09:45.1520820Z [       OK ] ModuleTest.AsCastsModulesCorrectly (0 ms)
2023-01-11T22:09:45.1521568Z [ RUN      ] ModuleTest.DeviceOrDtypeConversionSkipsUndefinedTensor
2023-01-11T22:09:45.1522428Z [       OK ] ModuleTest.DeviceOrDtypeConversionSkipsUndefinedTensor (0 ms)
2023-01-11T22:09:45.1523331Z [ RUN      ] ModuleTest.ParametersAndBuffersAccessorSkipsUndefinedTensor
2023-01-11T22:09:45.1524277Z [       OK ] ModuleTest.ParametersAndBuffersAccessorSkipsUndefinedTensor (0 ms)
2023-01-11T22:09:45.1525385Z [ RUN      ] ModuleTest.CallingCloneOnModuleThatDoesNotOverrideCloneThrows
2023-01-11T22:09:45.1538300Z [       OK ] ModuleTest.CallingCloneOnModuleThatDoesNotOverrideCloneThrows (1 ms)
2023-01-11T22:09:45.1539238Z [ RUN      ] ModuleTest.CallingCloneOnModuleThatDoesOverrideCloneDoesNotThrow
2023-01-11T22:09:45.1540210Z [       OK ] ModuleTest.CallingCloneOnModuleThatDoesOverrideCloneDoesNotThrow (0 ms)
2023-01-11T22:09:45.1541176Z [ RUN      ] ModuleTest.CloneCreatesDistinctParameters
2023-01-11T22:09:45.1546702Z [       OK ] ModuleTest.CloneCreatesDistinctParameters (0 ms)
2023-01-11T22:09:45.1547411Z [ RUN      ] ModuleTest.ClonePreservesExternalReferences
2023-01-11T22:09:45.1548603Z [       OK ] ModuleTest.ClonePreservesExternalReferences (0 ms)
2023-01-11T22:09:45.1549591Z [ RUN      ] ModuleTest.CloneCopiesTheValuesOfVariablesOfSubmodules
2023-01-11T22:09:45.1550258Z [       OK ] ModuleTest.CloneCopiesTheValuesOfVariablesOfSubmodules (0 ms)
2023-01-11T22:09:45.1550770Z [ RUN      ] ModuleTest.HasCorrectNumberOfParameters
2023-01-11T22:09:45.1551270Z [       OK ] ModuleTest.HasCorrectNumberOfParameters (0 ms)
2023-01-11T22:09:45.1551720Z [ RUN      ] ModuleTest.ContainsParametersWithTheCorrectName
2023-01-11T22:09:45.1552195Z [       OK ] ModuleTest.ContainsParametersWithTheCorrectName (0 ms)
2023-01-11T22:09:45.1552582Z [ RUN      ] ModuleTest.HasCorrectNumberOfBuffers
2023-01-11T22:09:45.1552940Z [       OK ] ModuleTest.HasCorrectNumberOfBuffers (0 ms)
2023-01-11T22:09:45.1553312Z [ RUN      ] ModuleTest.ContainsBuffersWithTheCorrectName
2023-01-11T22:09:45.1553722Z [       OK ] ModuleTest.ContainsBuffersWithTheCorrectName (0 ms)
2023-01-11T22:09:45.1554238Z [ RUN      ] ModuleTest.DefaultConstructorOfModuleHolderCallsDefaultConstructorOfImpl
2023-01-11T22:09:45.1554838Z [       OK ] ModuleTest.DefaultConstructorOfModuleHolderCallsDefaultConstructorOfImpl (0 ms)
2023-01-11T22:09:45.1555435Z [ RUN      ] ModuleTest.ValueConstructorOfModuleHolderCallsCorrectConstructorInImpl
2023-01-11T22:09:45.1556033Z [       OK ] ModuleTest.ValueConstructorOfModuleHolderCallsCorrectConstructorInImpl (0 ms)
2023-01-11T22:09:45.1556587Z [ RUN      ] ModuleTest.NullptrConstructorLeavesTheModuleHolderInEmptyState
2023-01-11T22:09:45.1563474Z [       OK ] ModuleTest.NullptrConstructorLeavesTheModuleHolderInEmptyState (1 ms)
2023-01-11T22:09:45.1564174Z [ RUN      ] ModuleTest.ModulesReturnsExpectedSubmodulesForFlatModel
2023-01-11T22:09:45.1564753Z [       OK ] ModuleTest.ModulesReturnsExpectedSubmodulesForFlatModel (0 ms)
2023-01-11T22:09:45.1565523Z [ RUN      ] ModuleTest.ModulesExcludesSelfWhenIncludeSelfSetToFalse
2023-01-11T22:09:45.1566269Z [       OK ] ModuleTest.ModulesExcludesSelfWhenIncludeSelfSetToFalse (0 ms)
2023-01-11T22:09:45.1567013Z [ RUN      ] ModuleTest.NamedModulesReturnsExpectedNamedSubmodulesForFlatModel
2023-01-11T22:09:45.1567901Z [       OK ] ModuleTest.NamedModulesReturnsExpectedNamedSubmodulesForFlatModel (0 ms)
2023-01-11T22:09:45.1568819Z [ RUN      ] ModuleTest.NamedModulesExcludesSelfWhenIncludeSelfSetToFalse
2023-01-11T22:09:45.1569812Z [       OK ] ModuleTest.NamedModulesExcludesSelfWhenIncludeSelfSetToFalse (0 ms)
2023-01-11T22:09:45.1570714Z [ RUN      ] ModuleTest.ChildrenReturnsExpectedSubmodulesForFlatModel
2023-01-11T22:09:45.1571607Z [       OK ] ModuleTest.ChildrenReturnsExpectedSubmodulesForFlatModel (0 ms)
2023-01-11T22:09:45.1572573Z [ RUN      ] ModuleTest.NamedChildrenReturnsExpectedNamedSubmodulesForFlatModel
2023-01-11T22:09:45.1573610Z [       OK ] ModuleTest.NamedChildrenReturnsExpectedNamedSubmodulesForFlatModel (0 ms)
2023-01-11T22:09:45.1574147Z [ RUN      ] ModuleTest.ParametersReturnsExpectedTensorsForFlatModel
2023-01-11T22:09:45.1574762Z [       OK ] ModuleTest.ParametersReturnsExpectedTensorsForFlatModel (0 ms)
2023-01-11T22:09:45.1575250Z [ RUN      ] ModuleTest.NamedParametersReturnsExpectedTensorsForFlatModel
2023-01-11T22:09:45.1575772Z [       OK ] ModuleTest.NamedParametersReturnsExpectedTensorsForFlatModel (0 ms)
2023-01-11T22:09:45.1576259Z [ RUN      ] ModuleTest.BuffersReturnsExpectedTensorsForFlatModel
2023-01-11T22:09:45.1576708Z [       OK ] ModuleTest.BuffersReturnsExpectedTensorsForFlatModel (0 ms)
2023-01-11T22:09:45.1577183Z [ RUN      ] ModuleTest.NamedBuffersReturnsExpectedTensorsForFlatModel
2023-01-11T22:09:45.1577680Z [       OK ] ModuleTest.NamedBuffersReturnsExpectedTensorsForFlatModel (0 ms)
2023-01-11T22:09:45.1578211Z [ RUN      ] ModuleTest.ModulesReturnsExpectedSubmodulesForDeepModel
2023-01-11T22:09:45.1578706Z [       OK ] ModuleTest.ModulesReturnsExpectedSubmodulesForDeepModel (0 ms)
2023-01-11T22:09:45.1579225Z [ RUN      ] ModuleTest.NamedModulesReturnsExpectedNamedSubmodulesForDeepModel
2023-01-11T22:09:45.1579777Z [       OK ] ModuleTest.NamedModulesReturnsExpectedNamedSubmodulesForDeepModel (0 ms)
2023-01-11T22:09:45.1580295Z [ RUN      ] ModuleTest.ChildrensReturnsExpectedSubmodulesForDeepModel
2023-01-11T22:09:45.1580777Z [       OK ] ModuleTest.ChildrensReturnsExpectedSubmodulesForDeepModel (0 ms)
2023-01-11T22:09:45.1581308Z [ RUN      ] ModuleTest.NamedChildrensReturnsExpectedNamedSubmodulesForDeepModel
2023-01-11T22:09:45.1581879Z [       OK ] ModuleTest.NamedChildrensReturnsExpectedNamedSubmodulesForDeepModel (0 ms)
2023-01-11T22:09:45.1582330Z [ RUN      ] ModuleTest.ModuleApplyIteratesCorreclty
2023-01-11T22:09:45.1582807Z [       OK ] ModuleTest.ModuleApplyIteratesCorreclty (0 ms)
2023-01-11T22:09:45.1583204Z [ RUN      ] ModuleTest.ConstModuleApplyIteratesCorreclty
2023-01-11T22:09:45.1583623Z [       OK ] ModuleTest.ConstModuleApplyIteratesCorreclty (0 ms)
2023-01-11T22:09:45.1584017Z [ RUN      ] ModuleTest.NamedModuleApplyIteratesCorreclty
2023-01-11T22:09:45.1584428Z [       OK ] ModuleTest.NamedModuleApplyIteratesCorreclty (0 ms)
2023-01-11T22:09:45.1584853Z [ RUN      ] ModuleTest.ConstNamedModuleApplyIteratesCorreclty
2023-01-11T22:09:45.1585284Z [       OK ] ModuleTest.ConstNamedModuleApplyIteratesCorreclty (0 ms)
2023-01-11T22:09:45.1585716Z [ RUN      ] ModuleTest.ModulePointerApplyIteratesCorreclty
2023-01-11T22:09:45.1586142Z [       OK ] ModuleTest.ModulePointerApplyIteratesCorreclty (0 ms)
2023-01-11T22:09:45.1586576Z [ RUN      ] ModuleTest.NamedModulePointerApplyIteratesCorreclty
2023-01-11T22:09:45.1587018Z [       OK ] ModuleTest.NamedModulePointerApplyIteratesCorreclty (0 ms)
2023-01-11T22:09:45.1587507Z [ RUN      ] ModuleTest.ThrowsWhenAttemptingtoGetTopLevelModuleAsSharedPtr
2023-01-11T22:09:45.1601195Z [       OK ] ModuleTest.ThrowsWhenAttemptingtoGetTopLevelModuleAsSharedPtr (2 ms)
2023-01-11T22:09:45.1601826Z [ RUN      ] ModuleTest.PrettyPrint
2023-01-11T22:09:45.1602135Z [       OK ] ModuleTest.PrettyPrint (0 ms)
2023-01-11T22:09:45.1602548Z [ RUN      ] ModuleTest.CanCallForwardOnNonTensorForwardThroughPimpl
2023-01-11T22:09:45.1603033Z [       OK ] ModuleTest.CanCallForwardOnNonTensorForwardThroughPimpl (0 ms)
2023-01-11T22:09:45.1603429Z [----------] 52 tests from ModuleTest (27 ms total)
2023-01-11T22:09:45.1603583Z 
2023-01-11T22:09:45.1603736Z [----------] 11 tests from ModuleDictTest
2023-01-11T22:09:45.1604047Z [ RUN      ] ModuleDictTest.ConstructsFromList
2023-01-11T22:09:45.1604381Z [       OK ] ModuleDictTest.ConstructsFromList (0 ms)
2023-01-11T22:09:45.1604745Z [ RUN      ] ModuleDictTest.ConstructsFromordereddict
2023-01-11T22:09:45.1605126Z [       OK ] ModuleDictTest.ConstructsFromordereddict (0 ms)
2023-01-11T22:09:45.1605606Z [ RUN      ] ModuleDictTest.UpdatePopClearContains
2023-01-11T22:09:45.1614605Z [       OK ] ModuleDictTest.UpdatePopClearContains (1 ms)
2023-01-11T22:09:45.1615211Z [ RUN      ] ModuleDictTest.UpdateExist
2023-01-11T22:09:45.1615553Z [       OK ] ModuleDictTest.UpdateExist (0 ms)
2023-01-11T22:09:45.1615827Z [ RUN      ] ModuleDictTest.Keys
2023-01-11T22:09:45.1626984Z [       OK ] ModuleDictTest.Keys (1 ms)
2023-01-11T22:09:45.1627365Z [ RUN      ] ModuleDictTest.Values
2023-01-11T22:09:45.1627670Z [       OK ] ModuleDictTest.Values (0 ms)
2023-01-11T22:09:45.1628103Z [ RUN      ] ModuleDictTest.SanityCheckForHoldingStandardModules
2023-01-11T22:09:45.1629394Z [       OK ] ModuleDictTest.SanityCheckForHoldingStandardModules (0 ms)
2023-01-11T22:09:45.1630130Z [ RUN      ] ModuleDictTest.HasReferenceSemantics
2023-01-11T22:09:45.1630520Z [       OK ] ModuleDictTest.HasReferenceSemantics (0 ms)
2023-01-11T22:09:45.1630844Z [ RUN      ] ModuleDictTest.IsCloneable
2023-01-11T22:09:45.1633639Z [       OK ] ModuleDictTest.IsCloneable (0 ms)
2023-01-11T22:09:45.1634359Z [ RUN      ] ModuleDictTest.RegistersElementsAsSubmodules
2023-01-11T22:09:45.1635147Z [       OK ] ModuleDictTest.RegistersElementsAsSubmodules (0 ms)
2023-01-11T22:09:45.1635775Z [ RUN      ] ModuleDictTest.PrettyPrintModuleDict
2023-01-11T22:09:45.1636651Z [       OK ] ModuleDictTest.PrettyPrintModuleDict (0 ms)
2023-01-11T22:09:45.1637294Z [----------] 11 tests from ModuleDictTest (3 ms total)
2023-01-11T22:09:45.1637538Z 
2023-01-11T22:09:45.1637808Z [----------] 15 tests from ModuleListTest
2023-01-11T22:09:45.1638447Z [ RUN      ] ModuleListTest.ConstructsFromSharedPointer
2023-01-11T22:09:45.1638877Z [       OK ] ModuleListTest.ConstructsFromSharedPointer (0 ms)
2023-01-11T22:09:45.1639258Z [ RUN      ] ModuleListTest.ConstructsFromConcreteType
2023-01-11T22:09:45.1639644Z [       OK ] ModuleListTest.ConstructsFromConcreteType (0 ms)
2023-01-11T22:09:45.1640027Z [ RUN      ] ModuleListTest.ConstructsFromModuleHolder
2023-01-11T22:09:45.1640416Z [       OK ] ModuleListTest.ConstructsFromModuleHolder (0 ms)
2023-01-11T22:09:45.1640773Z [ RUN      ] ModuleListTest.PushBackAddsAnElement
2023-01-11T22:09:45.1641131Z [       OK ] ModuleListTest.PushBackAddsAnElement (0 ms)
2023-01-11T22:09:45.1641452Z [ RUN      ] ModuleListTest.Insertion
2023-01-11T22:09:45.1641735Z [       OK ] ModuleListTest.Insertion (0 ms)
2023-01-11T22:09:45.1642034Z [ RUN      ] ModuleListTest.AccessWithAt
2023-01-11T22:09:45.1662423Z [       OK ] ModuleListTest.AccessWithAt (2 ms)
2023-01-11T22:09:45.1663031Z [ RUN      ] ModuleListTest.AccessWithPtr
2023-01-11T22:09:45.1685059Z [       OK ] ModuleListTest.AccessWithPtr (2 ms)
2023-01-11T22:09:45.1685747Z [ RUN      ] ModuleListTest.SanityCheckForHoldingStandardModules
2023-01-11T22:09:45.1686614Z [       OK ] ModuleListTest.SanityCheckForHoldingStandardModules (0 ms)
2023-01-11T22:09:45.1687424Z [ RUN      ] ModuleListTest.ExtendPushesModulesFromOtherModuleList
2023-01-11T22:09:45.1688258Z [       OK ] ModuleListTest.ExtendPushesModulesFromOtherModuleList (0 ms)
2023-01-11T22:09:45.1688666Z [ RUN      ] ModuleListTest.HasReferenceSemantics
2023-01-11T22:09:45.1689155Z [       OK ] ModuleListTest.HasReferenceSemantics (0 ms)
2023-01-11T22:09:45.1689467Z [ RUN      ] ModuleListTest.IsCloneable
2023-01-11T22:09:45.1692156Z [       OK ] ModuleListTest.IsCloneable (0 ms)
2023-01-11T22:09:45.1692536Z [ RUN      ] ModuleListTest.RegistersElementsAsSubmodules
2023-01-11T22:09:45.1692994Z [       OK ] ModuleListTest.RegistersElementsAsSubmodules (0 ms)
2023-01-11T22:09:45.1693565Z [ RUN      ] ModuleListTest.NestingIsPossible
2023-01-11T22:09:45.1694087Z [       OK ] ModuleListTest.NestingIsPossible (0 ms)
2023-01-11T22:09:45.1694566Z [ RUN      ] ModuleListTest.PrettyPrintModuleList
2023-01-11T22:09:45.1695078Z [       OK ] ModuleListTest.PrettyPrintModuleList (0 ms)
2023-01-11T22:09:45.1695428Z [ RUN      ] ModuleListTest.RangeBasedForLoop
2023-01-11T22:09:45.1695773Z [       OK ] ModuleListTest.RangeBasedForLoop (0 ms)
2023-01-11T22:09:45.1696102Z [----------] 15 tests from ModuleListTest (5 ms total)
2023-01-11T22:09:45.1696259Z 
2023-01-11T22:09:45.1696409Z [----------] 256 tests from ModulesTest
2023-01-11T22:09:45.1696673Z [ RUN      ] ModulesTest.Conv1d
2023-01-11T22:09:45.1715916Z [       OK ] ModulesTest.Conv1d (2 ms)
2023-01-11T22:09:45.1716286Z [ RUN      ] ModulesTest.Conv1dSameStrided
2023-01-11T22:09:45.1752281Z [       OK ] ModulesTest.Conv1dSameStrided (3 ms)
2023-01-11T22:09:45.1752835Z [ RUN      ] ModulesTest.Conv2dEven
2023-01-11T22:09:45.1756451Z [       OK ] ModulesTest.Conv2dEven (0 ms)
2023-01-11T22:09:45.1756984Z [ RUN      ] ModulesTest.Conv2dUneven
2023-01-11T22:09:45.1759490Z [       OK ] ModulesTest.Conv2dUneven (0 ms)
2023-01-11T22:09:45.1760036Z [ RUN      ] ModulesTest.Conv2dSameStrided
2023-01-11T22:09:45.1806711Z [       OK ] ModulesTest.Conv2dSameStrided (4 ms)
2023-01-11T22:09:45.1807235Z [ RUN      ] ModulesTest.Conv3d
2023-01-11T22:09:45.1812123Z [       OK ] ModulesTest.Conv3d (0 ms)
2023-01-11T22:09:45.1812643Z [ RUN      ] ModulesTest.Conv3dSameStrided
2023-01-11T22:09:45.1874925Z [       OK ] ModulesTest.Conv3dSameStrided (6 ms)
2023-01-11T22:09:45.1875507Z [ RUN      ] ModulesTest.ConvTranspose1d
2023-01-11T22:09:45.1878538Z [       OK ] ModulesTest.ConvTranspose1d (0 ms)
2023-01-11T22:09:45.1878873Z [ RUN      ] ModulesTest.ConvTranspose2dEven
2023-01-11T22:09:45.1883667Z [       OK ] ModulesTest.ConvTranspose2dEven (0 ms)
2023-01-11T22:09:45.1884008Z [ RUN      ] ModulesTest.ConvTranspose2dUneven
2023-01-11T22:09:45.1887731Z [       OK ] ModulesTest.ConvTranspose2dUneven (0 ms)
2023-01-11T22:09:45.1888061Z [ RUN      ] ModulesTest.ConvTranspose3d
2023-01-11T22:09:45.1891617Z [       OK ] ModulesTest.ConvTranspose3d (0 ms)
2023-01-11T22:09:45.1891901Z [ RUN      ] ModulesTest.MaxPool1d
2023-01-11T22:09:45.1910180Z [       OK ] ModulesTest.MaxPool1d (1 ms)
2023-01-11T22:09:45.1910616Z [ RUN      ] ModulesTest.MaxPool1dReturnIndices
2023-01-11T22:09:45.1911279Z [       OK ] ModulesTest.MaxPool1dReturnIndices (0 ms)
2023-01-11T22:09:45.1911718Z [ RUN      ] ModulesTest.MaxPool2dEven
2023-01-11T22:09:45.1913040Z [       OK ] ModulesTest.MaxPool2dEven (0 ms)
2023-01-11T22:09:45.1913460Z [ RUN      ] ModulesTest.MaxPool2dUneven
2023-01-11T22:09:45.1914645Z [       OK ] ModulesTest.MaxPool2dUneven (0 ms)
2023-01-11T22:09:45.1915071Z [ RUN      ] ModulesTest.MaxPool2dReturnIndices
2023-01-11T22:09:45.1916286Z [       OK ] ModulesTest.MaxPool2dReturnIndices (0 ms)
2023-01-11T22:09:45.1916853Z [ RUN      ] ModulesTest.MaxPool3d
2023-01-11T22:09:45.1918139Z [       OK ] ModulesTest.MaxPool3d (0 ms)
2023-01-11T22:09:45.1918722Z [ RUN      ] ModulesTest.MaxPool3dReturnIndices
2023-01-11T22:09:45.1919684Z [       OK ] ModulesTest.MaxPool3dReturnIndices (0 ms)
2023-01-11T22:09:45.1920244Z [ RUN      ] ModulesTest.AvgPool1d
2023-01-11T22:09:45.1921510Z [       OK ] ModulesTest.AvgPool1d (0 ms)
2023-01-11T22:09:45.1922052Z [ RUN      ] ModulesTest.AvgPool2dEven
2023-01-11T22:09:45.1922901Z [       OK ] ModulesTest.AvgPool2dEven (0 ms)
2023-01-11T22:09:45.1923560Z [ RUN      ] ModulesTest.AvgPool2dUneven
2023-01-11T22:09:45.1924087Z [       OK ] ModulesTest.AvgPool2dUneven (0 ms)
2023-01-11T22:09:45.1924627Z [ RUN      ] ModulesTest.AvgPool3d
2023-01-11T22:09:45.1925444Z [       OK ] ModulesTest.AvgPool3d (0 ms)
2023-01-11T22:09:45.1925991Z [ RUN      ] ModulesTest.FractionalMaxPool2d
2023-01-11T22:09:45.1927359Z [       OK ] ModulesTest.FractionalMaxPool2d (0 ms)
2023-01-11T22:09:45.1928005Z [ RUN      ] ModulesTest.FractionalMaxPool2dReturnIndices
2023-01-11T22:09:45.1928848Z [       OK ] ModulesTest.FractionalMaxPool2dReturnIndices (0 ms)
2023-01-11T22:09:45.1929618Z [ RUN      ] ModulesTest.FractionalMaxPool3d
2023-01-11T22:09:45.1931158Z [       OK ] ModulesTest.FractionalMaxPool3d (0 ms)
2023-01-11T22:09:45.1931928Z [ RUN      ] ModulesTest.FractionalMaxPool3dReturnIndices
2023-01-11T22:09:45.1932618Z [       OK ] ModulesTest.FractionalMaxPool3dReturnIndices (0 ms)
2023-01-11T22:09:45.1933170Z [ RUN      ] ModulesTest.LPPool1d
2023-01-11T22:09:45.1934046Z [       OK ] ModulesTest.LPPool1d (0 ms)
2023-01-11T22:09:45.1934538Z [ RUN      ] ModulesTest.LPPool2d
2023-01-11T22:09:45.1935378Z [       OK ] ModulesTest.LPPool2d (0 ms)
2023-01-11T22:09:45.1935768Z [ RUN      ] ModulesTest.Identity
2023-01-11T22:09:45.1936640Z [       OK ] ModulesTest.Identity (0 ms)
2023-01-11T22:09:45.1936922Z [ RUN      ] ModulesTest.Flatten
2023-01-11T22:09:45.1939352Z [       OK ] ModulesTest.Flatten (0 ms)
2023-01-11T22:09:45.1939887Z [ RUN      ] ModulesTest.Unflatten
2023-01-11T22:09:45.1940595Z [W TensorImpl.h:1816] Warning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (function operator())
2023-01-11T22:09:45.1941088Z [       OK ] ModulesTest.Unflatten (0 ms)
2023-01-11T22:09:45.1941463Z [ RUN      ] ModulesTest.AdaptiveMaxPool1d
2023-01-11T22:09:45.1969830Z [       OK ] ModulesTest.AdaptiveMaxPool1d (0 ms)
2023-01-11T22:09:45.1970372Z [ RUN      ] ModulesTest.AdaptiveMaxPool1dReturnIndices
2023-01-11T22:09:45.1970827Z [       OK ] ModulesTest.AdaptiveMaxPool1dReturnIndices (0 ms)
2023-01-11T22:09:45.1971245Z [ RUN      ] ModulesTest.AdaptiveMaxPool2dEven
2023-01-11T22:09:45.1971583Z [       OK ] ModulesTest.AdaptiveMaxPool2dEven (0 ms)
2023-01-11T22:09:45.1971985Z [ RUN      ] ModulesTest.AdaptiveMaxPool2dUneven
2023-01-11T22:09:45.1972336Z [       OK ] ModulesTest.AdaptiveMaxPool2dUneven (0 ms)
2023-01-11T22:09:45.1972722Z [ RUN      ] ModulesTest.AdaptiveMaxPool2dReturnIndicesEven
2023-01-11T22:09:45.1973139Z [       OK ] ModulesTest.AdaptiveMaxPool2dReturnIndicesEven (0 ms)
2023-01-11T22:09:45.1973559Z [ RUN      ] ModulesTest.AdaptiveMaxPool2dReturnIndicesUneven
2023-01-11T22:09:45.1973992Z [       OK ] ModulesTest.AdaptiveMaxPool2dReturnIndicesUneven (0 ms)
2023-01-11T22:09:45.1974344Z [ RUN      ] ModulesTest.AdaptiveMaxPool3d
2023-01-11T22:09:45.1974665Z [       OK ] ModulesTest.AdaptiveMaxPool3d (0 ms)
2023-01-11T22:09:45.1975017Z [ RUN      ] ModulesTest.AdaptiveMaxPool3dReturnIndices
2023-01-11T22:09:45.1975396Z [       OK ] ModulesTest.AdaptiveMaxPool3dReturnIndices (0 ms)
2023-01-11T22:09:45.1975744Z [ RUN      ] ModulesTest.AdaptiveAvgPool1d
2023-01-11T22:09:45.1976064Z [       OK ] ModulesTest.AdaptiveAvgPool1d (0 ms)
2023-01-11T22:09:45.1976389Z [ RUN      ] ModulesTest.AdaptiveAvgPool2dEven
2023-01-11T22:09:45.1976718Z [       OK ] ModulesTest.AdaptiveAvgPool2dEven (0 ms)
2023-01-11T22:09:45.1977064Z [ RUN      ] ModulesTest.AdaptiveAvgPool2dUneven
2023-01-11T22:09:45.1977414Z [       OK ] ModulesTest.AdaptiveAvgPool2dUneven (0 ms)
2023-01-11T22:09:45.1977864Z [ RUN      ] ModulesTest.AdaptiveAvgPool3d
2023-01-11T22:09:45.1978187Z [       OK ] ModulesTest.AdaptiveAvgPool3d (0 ms)
2023-01-11T22:09:45.1978487Z [ RUN      ] ModulesTest.MaxUnpool1d
2023-01-11T22:09:45.1978771Z [       OK ] ModulesTest.MaxUnpool1d (0 ms)
2023-01-11T22:09:45.1979080Z [ RUN      ] ModulesTest.MaxPool1d_MaxUnpool1d
2023-01-11T22:09:45.1979398Z [       OK ] ModulesTest.MaxPool1d_MaxUnpool1d (0 ms)
2023-01-11T22:09:45.1979694Z [ RUN      ] ModulesTest.MaxUnpool2d
2023-01-11T22:09:45.1979973Z [       OK ] ModulesTest.MaxUnpool2d (0 ms)
2023-01-11T22:09:45.1980282Z [ RUN      ] ModulesTest.MaxPool2d_MaxUnpool2d
2023-01-11T22:09:45.1980598Z [       OK ] ModulesTest.MaxPool2d_MaxUnpool2d (0 ms)
2023-01-11T22:09:45.1980925Z [ RUN      ] ModulesTest.MaxUnpool3d
2023-01-11T22:09:45.1981223Z [       OK ] ModulesTest.MaxUnpool3d (0 ms)
2023-01-11T22:09:45.1981536Z [ RUN      ] ModulesTest.MaxUnpool3dOutputSize
2023-01-11T22:09:45.1985719Z [       OK ] ModulesTest.MaxUnpool3dOutputSize (1 ms)
2023-01-11T22:09:45.1986299Z [ RUN      ] ModulesTest.MaxPool3d_MaxUnpool3d
2023-01-11T22:09:45.3034555Z [       OK ] ModulesTest.MaxPool3d_MaxUnpool3d (104 ms)
2023-01-11T22:09:45.3034906Z [ RUN      ] ModulesTest.Linear
2023-01-11T22:09:45.3039247Z [       OK ] ModulesTest.Linear (0 ms)
2023-01-11T22:09:45.3039795Z [ RUN      ] ModulesTest.LocalResponseNorm
2023-01-11T22:09:45.3043608Z [       OK ] ModulesTest.LocalResponseNorm (0 ms)
2023-01-11T22:09:45.3044145Z [ RUN      ] ModulesTest.LayerNorm
2023-01-11T22:09:45.3045898Z [       OK ] ModulesTest.LayerNorm (0 ms)
2023-01-11T22:09:45.3046449Z [ RUN      ] ModulesTest.GroupNorm
2023-01-11T22:09:45.3047996Z [       OK ] ModulesTest.GroupNorm (0 ms)
2023-01-11T22:09:45.3048511Z [ RUN      ] ModulesTest.Bilinear
2023-01-11T22:09:45.3051825Z [       OK ] ModulesTest.Bilinear (0 ms)
2023-01-11T22:09:45.3052311Z [ RUN      ] ModulesTest.Fold
2023-01-11T22:09:45.3077843Z [       OK ] ModulesTest.Fold (2 ms)
2023-01-11T22:09:45.3078336Z [ RUN      ] ModulesTest.Unfold
2023-01-11T22:09:45.3144716Z [       OK ] ModulesTest.Unfold (6 ms)
2023-01-11T22:09:45.3145275Z [ RUN      ] ModulesTest.SimpleContainer
2023-01-11T22:09:45.3163166Z [       OK ] ModulesTest.SimpleContainer (1 ms)
2023-01-11T22:09:45.3163672Z [ RUN      ] ModulesTest.EmbeddingBasic
2023-01-11T22:09:45.3164762Z [       OK ] ModulesTest.EmbeddingBasic (0 ms)
2023-01-11T22:09:45.3165884Z [ RUN      ] ModulesTest.EmbeddingList
2023-01-11T22:09:45.3166455Z [       OK ] ModulesTest.EmbeddingList (0 ms)
2023-01-11T22:09:45.3167020Z [ RUN      ] ModulesTest.EmbeddingFromPretrained
2023-01-11T22:09:45.3167721Z [       OK ] ModulesTest.EmbeddingFromPretrained (0 ms)
2023-01-11T22:09:45.3168365Z [ RUN      ] ModulesTest.EmbeddingBagFromPretrained
2023-01-11T22:09:45.3169672Z [       OK ] ModulesTest.EmbeddingBagFromPretrained (0 ms)
2023-01-11T22:09:45.3170234Z [ RUN      ] ModulesTest.AlphaDropout
2023-01-11T22:09:45.3171393Z [       OK ] ModulesTest.AlphaDropout (0 ms)
2023-01-11T22:09:45.3171985Z [ RUN      ] ModulesTest.FeatureAlphaDropout
2023-01-11T22:09:45.3172785Z [       OK ] ModulesTest.FeatureAlphaDropout (0 ms)
2023-01-11T22:09:45.3173336Z [ RUN      ] ModulesTest.Dropout
2023-01-11T22:09:45.3174973Z [       OK ] ModulesTest.Dropout (0 ms)
2023-01-11T22:09:45.3175595Z [ RUN      ] ModulesTest.Dropout2d
2023-01-11T22:09:45.3180350Z [       OK ] ModulesTest.Dropout2d (0 ms)
2023-01-11T22:09:45.3180854Z [ RUN      ] ModulesTest.Dropout3d
2023-01-11T22:09:45.3187780Z [       OK ] ModulesTest.Dropout3d (0 ms)
2023-01-11T22:09:45.3188640Z [ RUN      ] ModulesTest.Parameters
2023-01-11T22:09:45.3189189Z [       OK ] ModulesTest.Parameters (0 ms)
2023-01-11T22:09:45.3189788Z [ RUN      ] ModulesTest.FunctionalCallsSuppliedFunction
2023-01-11T22:09:45.3190397Z [       OK ] ModulesTest.FunctionalCallsSuppliedFunction (0 ms)
2023-01-11T22:09:45.3190861Z [ RUN      ] ModulesTest.FunctionalWithTorchFunction
2023-01-11T22:09:45.3191274Z [       OK ] ModulesTest.FunctionalWithTorchFunction (0 ms)
2023-01-11T22:09:45.3191690Z [ RUN      ] ModulesTest.FunctionalArgumentBinding
2023-01-11T22:09:45.3192165Z [       OK ] ModulesTest.FunctionalArgumentBinding (0 ms)
2023-01-11T22:09:45.3192714Z [ RUN      ] ModulesTest.BatchNorm1dStateful
2023-01-11T22:09:45.3193216Z [       OK ] ModulesTest.BatchNorm1dStateful (0 ms)
2023-01-11T22:09:45.3193571Z [ RUN      ] ModulesTest.BatchNorm1dStateless
2023-01-11T22:09:45.3193986Z [       OK ] ModulesTest.BatchNorm1dStateless (0 ms)
2023-01-11T22:09:45.3194291Z [ RUN      ] ModulesTest.BatchNorm1d
2023-01-11T22:09:45.3194621Z [       OK ] ModulesTest.BatchNorm1d (0 ms)
2023-01-11T22:09:45.3194984Z [ RUN      ] ModulesTest.BatchNorm2dStateful
2023-01-11T22:09:45.3195304Z [       OK ] ModulesTest.BatchNorm2dStateful (0 ms)
2023-01-11T22:09:45.3195834Z [ RUN      ] ModulesTest.BatchNorm2dStateless
2023-01-11T22:09:45.3196317Z [       OK ] ModulesTest.BatchNorm2dStateless (0 ms)
2023-01-11T22:09:45.3196674Z [ RUN      ] ModulesTest.BatchNorm2d
2023-01-11T22:09:45.3197206Z [       OK ] ModulesTest.BatchNorm2d (0 ms)
2023-01-11T22:09:45.3197674Z [ RUN      ] ModulesTest.BatchNorm3dStateful
2023-01-11T22:09:45.3198015Z [       OK ] ModulesTest.BatchNorm3dStateful (0 ms)
2023-01-11T22:09:45.3198330Z [ RUN      ] ModulesTest.BatchNorm3dStateless
2023-01-11T22:09:45.3198674Z [       OK ] ModulesTest.BatchNorm3dStateless (0 ms)
2023-01-11T22:09:45.3198977Z [ RUN      ] ModulesTest.BatchNorm3d
2023-01-11T22:09:45.3201609Z [       OK ] ModulesTest.BatchNorm3d (0 ms)
2023-01-11T22:09:45.3202210Z [ RUN      ] ModulesTest.InstanceNorm1dStateful
2023-01-11T22:09:45.3202653Z [       OK ] ModulesTest.InstanceNorm1dStateful (0 ms)
2023-01-11T22:09:45.3203009Z [ RUN      ] ModulesTest.InstanceNorm1dStateless
2023-01-11T22:09:45.3203454Z [       OK ] ModulesTest.InstanceNorm1dStateless (0 ms)
2023-01-11T22:09:45.3203970Z [ RUN      ] ModulesTest.InstanceNorm1d
2023-01-11T22:09:45.3204533Z [       OK ] ModulesTest.InstanceNorm1d (0 ms)
2023-01-11T22:09:45.3205120Z [ RUN      ] ModulesTest.InstanceNorm2dStateful
2023-01-11T22:09:45.3205787Z [       OK ] ModulesTest.InstanceNorm2dStateful (0 ms)
2023-01-11T22:09:45.3206165Z [ RUN      ] ModulesTest.InstanceNorm2dStateless
2023-01-11T22:09:45.3206585Z [       OK ] ModulesTest.InstanceNorm2dStateless (0 ms)
2023-01-11T22:09:45.3206980Z [ RUN      ] ModulesTest.InstanceNorm2d
2023-01-11T22:09:45.3208179Z [       OK ] ModulesTest.InstanceNorm2d (0 ms)
2023-01-11T22:09:45.3208804Z [ RUN      ] ModulesTest.InstanceNorm3dStateful
2023-01-11T22:09:45.3209345Z [       OK ] ModulesTest.InstanceNorm3dStateful (0 ms)
2023-01-11T22:09:45.3209696Z [ RUN      ] ModulesTest.InstanceNorm3dStateless
2023-01-11T22:09:45.3210048Z [       OK ] ModulesTest.InstanceNorm3dStateless (0 ms)
2023-01-11T22:09:45.3210370Z [ RUN      ] ModulesTest.InstanceNorm3d
2023-01-11T22:09:45.3214649Z [       OK ] ModulesTest.InstanceNorm3d (0 ms)
2023-01-11T22:09:45.3215212Z [ RUN      ] ModulesTest.L1Loss
2023-01-11T22:09:45.3215965Z [       OK ] ModulesTest.L1Loss (0 ms)
2023-01-11T22:09:45.3216435Z [ RUN      ] ModulesTest.MSELoss
2023-01-11T22:09:45.3217193Z [       OK ] ModulesTest.MSELoss (0 ms)
2023-01-11T22:09:45.3217631Z [ RUN      ] ModulesTest.BCELoss
2023-01-11T22:09:45.3218581Z [       OK ] ModulesTest.BCELoss (0 ms)
2023-01-11T22:09:45.3219011Z [ RUN      ] ModulesTest.KLDivLoss
2023-01-11T22:09:45.3220223Z [W loss.h:57] Warning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release. (function kl_div)
2023-01-11T22:09:45.3220939Z [       OK ] ModulesTest.KLDivLoss (0 ms)
2023-01-11T22:09:45.3221295Z [ RUN      ] ModulesTest.HingeEmbeddingLoss
2023-01-11T22:09:45.3222479Z [       OK ] ModulesTest.HingeEmbeddingLoss (0 ms)
2023-01-11T22:09:45.3223099Z [ RUN      ] ModulesTest.MultiMarginLoss
2023-01-11T22:09:45.3223725Z [       OK ] ModulesTest.MultiMarginLoss (0 ms)
2023-01-11T22:09:45.3224319Z [ RUN      ] ModulesTest.CosineEmbeddingLoss
2023-01-11T22:09:45.3228179Z [       OK ] ModulesTest.CosineEmbeddingLoss (0 ms)
2023-01-11T22:09:45.3228718Z [ RUN      ] ModulesTest.SmoothL1LossDefaultOptions
2023-01-11T22:09:45.3229465Z [       OK ] ModulesTest.SmoothL1LossDefaultOptions (0 ms)
2023-01-11T22:09:45.3230068Z [ RUN      ] ModulesTest.HuberLossDefaultOptions
2023-01-11T22:09:45.3230770Z [       OK ] ModulesTest.HuberLossDefaultOptions (0 ms)
2023-01-11T22:09:45.3231466Z [ RUN      ] ModulesTest.MultiLabelMarginLossDefaultOptions
2023-01-11T22:09:45.3232460Z [       OK ] ModulesTest.MultiLabelMarginLossDefaultOptions (0 ms)
2023-01-11T22:09:45.3233115Z [ RUN      ] ModulesTest.SmoothL1LossNoReduction
2023-01-11T22:09:45.3233828Z [       OK ] ModulesTest.SmoothL1LossNoReduction (0 ms)
2023-01-11T22:09:45.3234459Z [ RUN      ] ModulesTest.HuberLossNoReduction
2023-01-11T22:09:45.3235117Z [       OK ] ModulesTest.HuberLossNoReduction (0 ms)
2023-01-11T22:09:45.3235689Z [ RUN      ] ModulesTest.MultiLabelMarginLossNoReduction
2023-01-11T22:09:45.3236419Z [       OK ] ModulesTest.MultiLabelMarginLossNoReduction (0 ms)
2023-01-11T22:09:45.3237050Z [ RUN      ] ModulesTest.SmoothL1LossBeta
2023-01-11T22:09:45.3237623Z [       OK ] ModulesTest.SmoothL1LossBeta (0 ms)
2023-01-11T22:09:45.3237941Z [ RUN      ] ModulesTest.HuberLossDelta
2023-01-11T22:09:45.3239307Z [       OK ] ModulesTest.HuberLossDelta (0 ms)
2023-01-11T22:09:45.3239680Z [ RUN      ] ModulesTest.TripletMarginLoss
2023-01-11T22:09:45.3242223Z [       OK ] ModulesTest.TripletMarginLoss (0 ms)
2023-01-11T22:09:45.3242790Z [ RUN      ] ModulesTest.TripletMarginWithDistanceLossDefaultParity
2023-01-11T22:09:45.3397095Z [       OK ] ModulesTest.TripletMarginWithDistanceLossDefaultParity (15 ms)
2023-01-11T22:09:45.3397587Z [ RUN      ] ModulesTest.TripletMarginWithDistanceLossFunctionalParity
2023-01-11T22:09:45.3669803Z [       OK ] ModulesTest.TripletMarginWithDistanceLossFunctionalParity (27 ms)
2023-01-11T22:09:45.3670208Z [ RUN      ] ModulesTest.NLLLoss
2023-01-11T22:09:45.3671983Z [       OK ] ModulesTest.NLLLoss (0 ms)
2023-01-11T22:09:45.3672295Z [ RUN      ] ModulesTest.CrossEntropyLoss
2023-01-11T22:09:45.3678946Z [       OK ] ModulesTest.CrossEntropyLoss (0 ms)
2023-01-11T22:09:45.3679269Z [ RUN      ] ModulesTest.CosineSimilarity
2023-01-11T22:09:45.3682446Z [       OK ] ModulesTest.CosineSimilarity (0 ms)
2023-01-11T22:09:45.3682844Z [ RUN      ] ModulesTest.SoftMarginLossDefaultOptions
2023-01-11T22:09:45.3684444Z [       OK ] ModulesTest.SoftMarginLossDefaultOptions (0 ms)
2023-01-11T22:09:45.3684869Z [ RUN      ] ModulesTest.MultiLabelSoftMarginLossDefaultOptions
2023-01-11T22:09:45.3687454Z [       OK ] ModulesTest.MultiLabelSoftMarginLossDefaultOptions (0 ms)
2023-01-11T22:09:45.3687943Z [ RUN      ] ModulesTest.SoftMarginLossNoReduction
2023-01-11T22:09:45.3689427Z [       OK ] ModulesTest.SoftMarginLossNoReduction (0 ms)
2023-01-11T22:09:45.3689999Z [ RUN      ] ModulesTest.MultiLabelSoftMarginLossWeightedNoReduction
2023-01-11T22:09:45.3692478Z [       OK ] ModulesTest.MultiLabelSoftMarginLossWeightedNoReduction (0 ms)
2023-01-11T22:09:45.3692913Z [ RUN      ] ModulesTest.PairwiseDistance
2023-01-11T22:09:45.3694204Z [       OK ] ModulesTest.PairwiseDistance (0 ms)
2023-01-11T22:09:45.3694556Z [ RUN      ] ModulesTest.ELU
2023-01-11T22:09:45.3706933Z [       OK ] ModulesTest.ELU (1 ms)
2023-01-11T22:09:45.3707352Z [ RUN      ] ModulesTest.SELU
2023-01-11T22:09:45.3709581Z [       OK ] ModulesTest.SELU (0 ms)
2023-01-11T22:09:45.3709931Z [ RUN      ] ModulesTest.Hardshrink
2023-01-11T22:09:45.3717452Z [       OK ] ModulesTest.Hardshrink (0 ms)
2023-01-11T22:09:45.3717828Z [ RUN      ] ModulesTest.Hardtanh
2023-01-11T22:09:45.3746499Z [       OK ] ModulesTest.Hardtanh (2 ms)
2023-01-11T22:09:45.3746914Z [ RUN      ] ModulesTest.HardtanhMinValGEMaxVal
2023-01-11T22:09:45.3823351Z [       OK ] ModulesTest.HardtanhMinValGEMaxVal (7 ms)
2023-01-11T22:09:45.3823743Z [ RUN      ] ModulesTest.LeakyReLU
2023-01-11T22:09:45.3834473Z [       OK ] ModulesTest.LeakyReLU (1 ms)
2023-01-11T22:09:45.3834860Z [ RUN      ] ModulesTest.LogSigmoid
2023-01-11T22:09:45.3836177Z [       OK ] ModulesTest.LogSigmoid (0 ms)
2023-01-11T22:09:45.3836486Z [ RUN      ] ModulesTest.Softmax
2023-01-11T22:09:45.3837852Z [       OK ] ModulesTest.Softmax (0 ms)
2023-01-11T22:09:45.3838239Z [ RUN      ] ModulesTest.Softmin
2023-01-11T22:09:45.3839262Z [       OK ] ModulesTest.Softmin (0 ms)
2023-01-11T22:09:45.3839785Z [ RUN      ] ModulesTest.LogSoftmax
2023-01-11T22:09:45.3840685Z [       OK ] ModulesTest.LogSoftmax (0 ms)
2023-01-11T22:09:45.3841321Z [ RUN      ] ModulesTest.AdaptiveLogSoftmaxWithLoss
2023-01-11T22:09:45.3869369Z [       OK ] ModulesTest.AdaptiveLogSoftmaxWithLoss (2 ms)
2023-01-11T22:09:45.3869917Z [ RUN      ] ModulesTest.Softmax2d
2023-01-11T22:09:45.3883065Z [       OK ] ModulesTest.Softmax2d (1 ms)
2023-01-11T22:09:45.3883574Z [ RUN      ] ModulesTest.PReLU
2023-01-11T22:09:45.3887815Z [       OK ] ModulesTest.PReLU (0 ms)
2023-01-11T22:09:45.3888323Z [ RUN      ] ModulesTest.ReLU
2023-01-11T22:09:45.3890397Z [       OK ] ModulesTest.ReLU (0 ms)
2023-01-11T22:09:45.3890898Z [ RUN      ] ModulesTest.ReLU6
2023-01-11T22:09:45.3892820Z [       OK ] ModulesTest.ReLU6 (0 ms)
2023-01-11T22:09:45.3893330Z [ RUN      ] ModulesTest.RReLU
2023-01-11T22:09:45.3928237Z [       OK ] ModulesTest.RReLU (3 ms)
2023-01-11T22:09:45.3928750Z [ RUN      ] ModulesTest.CELU
2023-01-11T22:09:45.3938828Z [       OK ] ModulesTest.CELU (1 ms)
2023-01-11T22:09:45.3939297Z [ RUN      ] ModulesTest.GLU
2023-01-11T22:09:45.3941126Z [       OK ] ModulesTest.GLU (0 ms)
2023-01-11T22:09:45.3941676Z [ RUN      ] ModulesTest.GELU
2023-01-11T22:09:45.3943495Z [       OK ] ModulesTest.GELU (0 ms)
2023-01-11T22:09:45.3944008Z [ RUN      ] ModulesTest.TanhGELU
2023-01-11T22:09:45.3944759Z [       OK ] ModulesTest.TanhGELU (0 ms)
2023-01-11T22:09:45.3945267Z [ RUN      ] ModulesTest.Mish
2023-01-11T22:09:45.3946228Z [       OK ] ModulesTest.Mish (0 ms)
2023-01-11T22:09:45.3946873Z [ RUN      ] ModulesTest.Sigmoid
2023-01-11T22:09:45.3947484Z [       OK ] ModulesTest.Sigmoid (0 ms)
2023-01-11T22:09:45.3948310Z [ RUN      ] ModulesTest.PixelShuffle
2023-01-11T22:09:45.3950119Z [       OK ] ModulesTest.PixelShuffle (0 ms)
2023-01-11T22:09:45.3950764Z [ RUN      ] ModulesTest.PixelUnshuffle
2023-01-11T22:09:45.3952781Z [       OK ] ModulesTest.PixelUnshuffle (0 ms)
2023-01-11T22:09:45.3953362Z [ RUN      ] ModulesTest.Softplus
2023-01-11T22:09:45.3960748Z [       OK ] ModulesTest.Softplus (0 ms)
2023-01-11T22:09:45.3961102Z [ RUN      ] ModulesTest.Softshrink
2023-01-11T22:09:45.3967108Z [       OK ] ModulesTest.Softshrink (0 ms)
2023-01-11T22:09:45.3967518Z [ RUN      ] ModulesTest.Softsign
2023-01-11T22:09:45.3967925Z [       OK ] ModulesTest.Softsign (0 ms)
2023-01-11T22:09:45.3968264Z [ RUN      ] ModulesTest.Tanh
2023-01-11T22:09:45.3968772Z [       OK ] ModulesTest.Tanh (0 ms)
2023-01-11T22:09:45.3969332Z [ RUN      ] ModulesTest.Tanhshrink
2023-01-11T22:09:45.3969776Z [       OK ] ModulesTest.Tanhshrink (0 ms)
2023-01-11T22:09:45.3970108Z [ RUN      ] ModulesTest.Threshold
2023-01-11T22:09:45.3982218Z [       OK ] ModulesTest.Threshold (1 ms)
2023-01-11T22:09:45.3982829Z [ RUN      ] ModulesTest.Upsampling1D
2023-01-11T22:09:45.3985295Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.3986688Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.3988677Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.3990046Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.3992046Z [       OK ] ModulesTest.Upsampling1D (0 ms)
2023-01-11T22:09:45.3992477Z [ RUN      ] ModulesTest.Upsampling2D
2023-01-11T22:09:45.3993410Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.3994614Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.3996615Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.3997704Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.3999447Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.4000814Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.4003072Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.4004287Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.4006359Z [       OK ] ModulesTest.Upsampling2D (1 ms)
2023-01-11T22:09:45.4006913Z [ RUN      ] ModulesTest.Upsampling3D
2023-01-11T22:09:45.4008670Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.4010285Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.4012704Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.4014029Z [W upsampling.h:66] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
2023-01-11T22:09:45.4015301Z [       OK ] ModulesTest.Upsampling3D (0 ms)
2023-01-11T22:09:45.4015798Z [ RUN      ] ModulesTest.CTCLoss
2023-01-11T22:09:45.4018397Z [       OK ] ModulesTest.CTCLoss (0 ms)
2023-01-11T22:09:45.4018935Z [ RUN      ] ModulesTest.PoissonNLLLoss
2023-01-11T22:09:45.4020681Z [       OK ] ModulesTest.PoissonNLLLoss (0 ms)
2023-01-11T22:09:45.4021249Z [ RUN      ] ModulesTest.MarginRankingLoss
2023-01-11T22:09:45.4024084Z [       OK ] ModulesTest.MarginRankingLoss (0 ms)
2023-01-11T22:09:45.4024717Z [ RUN      ] ModulesTest.BCEWithLogitsLoss
2023-01-11T22:09:45.4083125Z [       OK ] ModulesTest.BCEWithLogitsLoss (5 ms)
2023-01-11T22:09:45.4083725Z [ RUN      ] ModulesTest.MultiheadAttention
2023-01-11T22:09:54.6269848Z [       OK ] ModulesTest.MultiheadAttention (9218 ms)
2023-01-11T22:09:54.6270542Z [ RUN      ] ModulesTest.PrettyPrintIdentity
2023-01-11T22:09:54.6271218Z [       OK ] ModulesTest.PrettyPrintIdentity (0 ms)
2023-01-11T22:09:54.6271803Z [ RUN      ] ModulesTest.PrettyPrintFlatten
2023-01-11T22:09:54.6272435Z [       OK ] ModulesTest.PrettyPrintFlatten (0 ms)
2023-01-11T22:09:54.6273042Z [ RUN      ] ModulesTest.PrettyPrintUnflatten
2023-01-11T22:09:54.6273691Z [       OK ] ModulesTest.PrettyPrintUnflatten (0 ms)
2023-01-11T22:09:54.6274172Z [ RUN      ] ModulesTest.ReflectionPad1d
2023-01-11T22:09:54.6274773Z [       OK ] ModulesTest.ReflectionPad1d (0 ms)
2023-01-11T22:09:54.6275199Z [ RUN      ] ModulesTest.ReflectionPad2d
2023-01-11T22:09:54.6275514Z [       OK ] ModulesTest.ReflectionPad2d (0 ms)
2023-01-11T22:09:54.6275814Z [ RUN      ] ModulesTest.ReflectionPad3d
2023-01-11T22:09:54.6277753Z [       OK ] ModulesTest.ReflectionPad3d (0 ms)
2023-01-11T22:09:54.6278097Z [ RUN      ] ModulesTest.ReplicationPad1d
2023-01-11T22:09:54.6279660Z [       OK ] ModulesTest.ReplicationPad1d (0 ms)
2023-01-11T22:09:54.6279989Z [ RUN      ] ModulesTest.ReplicationPad2d
2023-01-11T22:09:54.6281905Z [       OK ] ModulesTest.ReplicationPad2d (0 ms)
2023-01-11T22:09:54.6282233Z [ RUN      ] ModulesTest.ReplicationPad3d
2023-01-11T22:09:54.6285872Z [       OK ] ModulesTest.ReplicationPad3d (0 ms)
2023-01-11T22:09:54.6286403Z [ RUN      ] ModulesTest.ZeroPad2d
2023-01-11T22:09:54.6288252Z [       OK ] ModulesTest.ZeroPad2d (0 ms)
2023-01-11T22:09:54.6288821Z [ RUN      ] ModulesTest.ConstantPad1d
2023-01-11T22:09:54.6289945Z [       OK ] ModulesTest.ConstantPad1d (0 ms)
2023-01-11T22:09:54.6290532Z [ RUN      ] ModulesTest.ConstantPad2d
2023-01-11T22:09:54.6292300Z [       OK ] ModulesTest.ConstantPad2d (0 ms)
2023-01-11T22:09:54.6292862Z [ RUN      ] ModulesTest.ConstantPad3d
2023-01-11T22:09:54.6296394Z [       OK ] ModulesTest.ConstantPad3d (0 ms)
2023-01-11T22:09:54.6296981Z [ RUN      ] ModulesTest.CrossMapLRN2d
2023-01-11T22:09:54.6303153Z [       OK ] ModulesTest.CrossMapLRN2d (0 ms)
2023-01-11T22:09:54.6303687Z [ RUN      ] ModulesTest.RNNCell
2023-01-11T22:09:54.6306107Z [       OK ] ModulesTest.RNNCell (0 ms)
2023-01-11T22:09:54.6306643Z [ RUN      ] ModulesTest.LSTMCell
2023-01-11T22:09:54.6310569Z [       OK ] ModulesTest.LSTMCell (0 ms)
2023-01-11T22:09:54.6311111Z [ RUN      ] ModulesTest.GRUCell
2023-01-11T22:09:54.6314691Z [       OK ] ModulesTest.GRUCell (0 ms)
2023-01-11T22:09:54.6315129Z [ RUN      ] ModulesTest.PrettyPrintLinear
2023-01-11T22:09:54.6315788Z [       OK ] ModulesTest.PrettyPrintLinear (0 ms)
2023-01-11T22:09:54.6316216Z [ RUN      ] ModulesTest.PrettyPrintBilinear
2023-01-11T22:09:54.6316652Z [       OK ] ModulesTest.PrettyPrintBilinear (0 ms)
2023-01-11T22:09:54.6317050Z [ RUN      ] ModulesTest.PrettyPrintConv
2023-01-11T22:09:54.6317461Z [       OK ] ModulesTest.PrettyPrintConv (0 ms)
2023-01-11T22:09:54.6317897Z [ RUN      ] ModulesTest.PrettyPrintConvTranspose
2023-01-11T22:09:54.6322756Z [       OK ] ModulesTest.PrettyPrintConvTranspose (0 ms)
2023-01-11T22:09:54.6323402Z [ RUN      ] ModulesTest.PrettyPrintUpsample
2023-01-11T22:09:54.6323981Z [       OK ] ModulesTest.PrettyPrintUpsample (0 ms)
2023-01-11T22:09:54.6324520Z [ RUN      ] ModulesTest.PrettyPrintFold
2023-01-11T22:09:54.6325233Z [       OK ] ModulesTest.PrettyPrintFold (0 ms)
2023-01-11T22:09:54.6325748Z [ RUN      ] ModulesTest.PrettyPrintUnfold
2023-01-11T22:09:54.6326417Z [       OK ] ModulesTest.PrettyPrintUnfold (0 ms)
2023-01-11T22:09:54.6327037Z [ RUN      ] ModulesTest.PrettyPrintMaxPool
2023-01-11T22:09:54.6327661Z [       OK ] ModulesTest.PrettyPrintMaxPool (0 ms)
2023-01-11T22:09:54.6328278Z [ RUN      ] ModulesTest.PrettyPrintAvgPool
2023-01-11T22:09:54.6328911Z [       OK ] ModulesTest.PrettyPrintAvgPool (0 ms)
2023-01-11T22:09:54.6329775Z [ RUN      ] ModulesTest.PrettyPrinFractionalMaxPool
2023-01-11T22:09:54.6330508Z [       OK ] ModulesTest.PrettyPrinFractionalMaxPool (0 ms)
2023-01-11T22:09:54.6331166Z [ RUN      ] ModulesTest.PrettyPrintLPPool
2023-01-11T22:09:54.6331777Z [       OK ] ModulesTest.PrettyPrintLPPool (0 ms)
2023-01-11T22:09:54.6332464Z [ RUN      ] ModulesTest.PrettyPrintAdaptiveMaxPool
2023-01-11T22:09:54.6333237Z [       OK ] ModulesTest.PrettyPrintAdaptiveMaxPool (0 ms)
2023-01-11T22:09:54.6333857Z [ RUN      ] ModulesTest.PrettyPrintAdaptiveAvgPool
2023-01-11T22:09:54.6334473Z [       OK ] ModulesTest.PrettyPrintAdaptiveAvgPool (0 ms)
2023-01-11T22:09:54.6335065Z [ RUN      ] ModulesTest.PrettyPrintMaxUnpool
2023-01-11T22:09:54.6335633Z [       OK ] ModulesTest.PrettyPrintMaxUnpool (0 ms)
2023-01-11T22:09:54.6336188Z [ RUN      ] ModulesTest.PrettyPrintDropout
2023-01-11T22:09:54.6336744Z [       OK ] ModulesTest.PrettyPrintDropout (0 ms)
2023-01-11T22:09:54.6337307Z [ RUN      ] ModulesTest.PrettyPrintDropout2d
2023-01-11T22:09:54.6337851Z [       OK ] ModulesTest.PrettyPrintDropout2d (0 ms)
2023-01-11T22:09:54.6338431Z [ RUN      ] ModulesTest.PrettyPrintDropout3d
2023-01-11T22:09:54.6339020Z [       OK ] ModulesTest.PrettyPrintDropout3d (0 ms)
2023-01-11T22:09:54.6339607Z [ RUN      ] ModulesTest.PrettyPrintFunctional
2023-01-11T22:09:54.6340258Z [       OK ] ModulesTest.PrettyPrintFunctional (0 ms)
2023-01-11T22:09:54.6340837Z [ RUN      ] ModulesTest.PrettyPrintBatchNorm1d
2023-01-11T22:09:54.6341321Z [       OK ] ModulesTest.PrettyPrintBatchNorm1d (0 ms)
2023-01-11T22:09:54.6341825Z [ RUN      ] ModulesTest.PrettyPrintBatchNorm2d
2023-01-11T22:09:54.6342363Z [       OK ] ModulesTest.PrettyPrintBatchNorm2d (0 ms)
2023-01-11T22:09:54.6342974Z [ RUN      ] ModulesTest.PrettyPrintBatchNorm3d
2023-01-11T22:09:54.6343543Z [       OK ] ModulesTest.PrettyPrintBatchNorm3d (0 ms)
2023-01-11T22:09:54.6344141Z [ RUN      ] ModulesTest.PrettyPrintInstanceNorm1d
2023-01-11T22:09:54.6344736Z [       OK ] ModulesTest.PrettyPrintInstanceNorm1d (0 ms)
2023-01-11T22:09:54.6345384Z [ RUN      ] ModulesTest.PrettyPrintInstanceNorm2d
2023-01-11T22:09:54.6346037Z [       OK ] ModulesTest.PrettyPrintInstanceNorm2d (0 ms)
2023-01-11T22:09:54.6346688Z [ RUN      ] ModulesTest.PrettyPrintInstanceNorm3d
2023-01-11T22:09:54.6347514Z [       OK ] ModulesTest.PrettyPrintInstanceNorm3d (0 ms)
2023-01-11T22:09:54.6348139Z [ RUN      ] ModulesTest.PrettyPrintLayerNorm
2023-01-11T22:09:54.6348745Z [       OK ] ModulesTest.PrettyPrintLayerNorm (0 ms)
2023-01-11T22:09:54.6349327Z [ RUN      ] ModulesTest.PrettyPrintGroupNorm
2023-01-11T22:09:54.6349938Z [       OK ] ModulesTest.PrettyPrintGroupNorm (0 ms)
2023-01-11T22:09:54.6350580Z [ RUN      ] ModulesTest.PrettyPrintLocalResponseNorm
2023-01-11T22:09:54.6351256Z [       OK ] ModulesTest.PrettyPrintLocalResponseNorm (0 ms)
2023-01-11T22:09:54.6351890Z [ RUN      ] ModulesTest.PrettyPrintEmbedding
2023-01-11T22:09:54.6352499Z [       OK ] ModulesTest.PrettyPrintEmbedding (0 ms)
2023-01-11T22:09:54.6353186Z [ RUN      ] ModulesTest.PrettyPrintEmbeddingBag
2023-01-11T22:09:54.6353814Z [       OK ] ModulesTest.PrettyPrintEmbeddingBag (0 ms)
2023-01-11T22:09:54.6354414Z [ RUN      ] ModulesTest.PrettyPrintL1Loss
2023-01-11T22:09:54.6354998Z [       OK ] ModulesTest.PrettyPrintL1Loss (0 ms)
2023-01-11T22:09:54.6355570Z [ RUN      ] ModulesTest.PrettyPrintKLDivLoss
2023-01-11T22:09:54.6356176Z [       OK ] ModulesTest.PrettyPrintKLDivLoss (0 ms)
2023-01-11T22:09:54.6356764Z [ RUN      ] ModulesTest.PrettyPrintMSELoss
2023-01-11T22:09:54.6357397Z [       OK ] ModulesTest.PrettyPrintMSELoss (0 ms)
2023-01-11T22:09:54.6357978Z [ RUN      ] ModulesTest.PrettyPrintBCELoss
2023-01-11T22:09:54.6358569Z [       OK ] ModulesTest.PrettyPrintBCELoss (0 ms)
2023-01-11T22:09:54.6359198Z [ RUN      ] ModulesTest.PrettyPrintHingeEmbeddingLoss
2023-01-11T22:09:54.6359850Z [       OK ] ModulesTest.PrettyPrintHingeEmbeddingLoss (0 ms)
2023-01-11T22:09:54.6360457Z [ RUN      ] ModulesTest.PrettyPrintCosineEmbeddingLoss
2023-01-11T22:09:54.6361139Z [       OK ] ModulesTest.PrettyPrintCosineEmbeddingLoss (0 ms)
2023-01-11T22:09:54.6361829Z [ RUN      ] ModulesTest.PrettyPrintTripletMarginLoss
2023-01-11T22:09:54.6362533Z [       OK ] ModulesTest.PrettyPrintTripletMarginLoss (0 ms)
2023-01-11T22:09:54.6363298Z [ RUN      ] ModulesTest.PrettyPrintTripletMarginWithDistanceLoss
2023-01-11T22:09:54.6364128Z [       OK ] ModulesTest.PrettyPrintTripletMarginWithDistanceLoss (0 ms)
2023-01-11T22:09:54.6364825Z [ RUN      ] ModulesTest.PrettyPrintNLLLoss
2023-01-11T22:09:54.6365411Z [       OK ] ModulesTest.PrettyPrintNLLLoss (0 ms)
2023-01-11T22:09:54.6366044Z [ RUN      ] ModulesTest.PrettyPrinCrossEntropyLoss
2023-01-11T22:09:54.6366704Z [       OK ] ModulesTest.PrettyPrinCrossEntropyLoss (0 ms)
2023-01-11T22:09:54.6367411Z [ RUN      ] ModulesTest.PrettyPrintMultiLabelMarginLoss
2023-01-11T22:09:54.6368139Z [       OK ] ModulesTest.PrettyPrintMultiLabelMarginLoss (0 ms)
2023-01-11T22:09:54.6368882Z [ RUN      ] ModulesTest.PrettyPrintMultiLabelSoftMarginLoss
2023-01-11T22:09:54.6369785Z [       OK ] ModulesTest.PrettyPrintMultiLabelSoftMarginLoss (0 ms)
2023-01-11T22:09:54.6370498Z [ RUN      ] ModulesTest.PrettyPrintSoftMarginLoss
2023-01-11T22:09:54.6371145Z [       OK ] ModulesTest.PrettyPrintSoftMarginLoss (0 ms)
2023-01-11T22:09:54.6371811Z [ RUN      ] ModulesTest.PrettyPrintCosineSimilarity
2023-01-11T22:09:54.6372499Z [       OK ] ModulesTest.PrettyPrintCosineSimilarity (0 ms)
2023-01-11T22:09:54.6373177Z [ RUN      ] ModulesTest.PrettyPrintPairwiseDistance
2023-01-11T22:09:54.6373853Z [       OK ] ModulesTest.PrettyPrintPairwiseDistance (0 ms)
2023-01-11T22:09:54.6374514Z [ RUN      ] ModulesTest.PrettyPrintReflectionPad
2023-01-11T22:09:54.6375180Z [       OK ] ModulesTest.PrettyPrintReflectionPad (0 ms)
2023-01-11T22:09:54.6375797Z [ RUN      ] ModulesTest.PrettyPrintReplicationPad
2023-01-11T22:09:54.6376544Z [       OK ] ModulesTest.PrettyPrintReplicationPad (0 ms)
2023-01-11T22:09:54.6377117Z [ RUN      ] ModulesTest.PrettyPrintZeroPad2d
2023-01-11T22:09:54.6377668Z [       OK ] ModulesTest.PrettyPrintZeroPad2d (0 ms)
2023-01-11T22:09:54.6378243Z [ RUN      ] ModulesTest.PrettyPrintConstantPad
2023-01-11T22:09:54.6378822Z [       OK ] ModulesTest.PrettyPrintConstantPad (0 ms)
2023-01-11T22:09:54.6379386Z [ RUN      ] ModulesTest.PrettyPrintNestedModel
2023-01-11T22:09:54.6379963Z [       OK ] ModulesTest.PrettyPrintNestedModel (0 ms)
2023-01-11T22:09:54.6380501Z [ RUN      ] ModulesTest.PrettyPrintELU
2023-01-11T22:09:54.6381049Z [       OK ] ModulesTest.PrettyPrintELU (0 ms)
2023-01-11T22:09:54.6381572Z [ RUN      ] ModulesTest.PrettyPrintSELU
2023-01-11T22:09:54.6382265Z [       OK ] ModulesTest.PrettyPrintSELU (0 ms)
2023-01-11T22:09:54.6382886Z [ RUN      ] ModulesTest.PrettyPrintGLU
2023-01-11T22:09:54.6383432Z [       OK ] ModulesTest.PrettyPrintGLU (0 ms)
2023-01-11T22:09:54.6384013Z [ RUN      ] ModulesTest.PrettyPrintHardshrink
2023-01-11T22:09:54.6384629Z [       OK ] ModulesTest.PrettyPrintHardshrink (0 ms)
2023-01-11T22:09:54.6385228Z [ RUN      ] ModulesTest.PrettyPrintHardtanh
2023-01-11T22:09:54.6385812Z [       OK ] ModulesTest.PrettyPrintHardtanh (0 ms)
2023-01-11T22:09:54.6386409Z [ RUN      ] ModulesTest.PrettyPrintLeakyReLU
2023-01-11T22:09:54.6387020Z [       OK ] ModulesTest.PrettyPrintLeakyReLU (0 ms)
2023-01-11T22:09:54.6387611Z [ RUN      ] ModulesTest.PrettyPrintLogSigmoid
2023-01-11T22:09:54.6388230Z [       OK ] ModulesTest.PrettyPrintLogSigmoid (0 ms)
2023-01-11T22:09:54.6388823Z [ RUN      ] ModulesTest.PrettyPrintSoftmax
2023-01-11T22:09:54.6389398Z [       OK ] ModulesTest.PrettyPrintSoftmax (0 ms)
2023-01-11T22:09:54.6389967Z [ RUN      ] ModulesTest.PrettyPrintSoftmin
2023-01-11T22:09:54.6390556Z [       OK ] ModulesTest.PrettyPrintSoftmin (0 ms)
2023-01-11T22:09:54.6391146Z [ RUN      ] ModulesTest.PrettyPrintLogSoftmax
2023-01-11T22:09:54.6391751Z [       OK ] ModulesTest.PrettyPrintLogSoftmax (0 ms)
2023-01-11T22:09:54.6392348Z [ RUN      ] ModulesTest.PrettyPrintSoftmax2d
2023-01-11T22:09:54.6392955Z [       OK ] ModulesTest.PrettyPrintSoftmax2d (0 ms)
2023-01-11T22:09:54.6393511Z [ RUN      ] ModulesTest.PrettyPrintPReLU
2023-01-11T22:09:54.6394089Z [       OK ] ModulesTest.PrettyPrintPReLU (0 ms)
2023-01-11T22:09:54.6394648Z [ RUN      ] ModulesTest.PrettyPrintReLU
2023-01-11T22:09:54.6395198Z [       OK ] ModulesTest.PrettyPrintReLU (0 ms)
2023-01-11T22:09:54.6395765Z [ RUN      ] ModulesTest.PrettyPrintReLU6
2023-01-11T22:09:54.6396300Z [       OK ] ModulesTest.PrettyPrintReLU6 (0 ms)
2023-01-11T22:09:54.6396839Z [ RUN      ] ModulesTest.PrettyPrintRReLU
2023-01-11T22:09:54.6397411Z [       OK ] ModulesTest.PrettyPrintRReLU (0 ms)
2023-01-11T22:09:54.6397960Z [ RUN      ] ModulesTest.PrettyPrintCELU
2023-01-11T22:09:54.6398521Z [       OK ] ModulesTest.PrettyPrintCELU (0 ms)
2023-01-11T22:09:54.6399073Z [ RUN      ] ModulesTest.PrettyPrintSigmoid
2023-01-11T22:09:54.6399662Z [       OK ] ModulesTest.PrettyPrintSigmoid (0 ms)
2023-01-11T22:09:54.6400269Z [ RUN      ] ModulesTest.PrettyPrintPixelShuffle
2023-01-11T22:09:54.6400895Z [       OK ] ModulesTest.PrettyPrintPixelShuffle (0 ms)
2023-01-11T22:09:54.6401537Z [ RUN      ] ModulesTest.PrettyPrintPixelUnshuffle
2023-01-11T22:09:54.6402197Z [       OK ] ModulesTest.PrettyPrintPixelUnshuffle (0 ms)
2023-01-11T22:09:54.6402809Z [ RUN      ] ModulesTest.PrettyPrintSoftplus
2023-01-11T22:09:54.6403412Z [       OK ] ModulesTest.PrettyPrintSoftplus (0 ms)
2023-01-11T22:09:54.6404117Z [ RUN      ] ModulesTest.PrettyPrintSoftshrink
2023-01-11T22:09:54.6404736Z [       OK ] ModulesTest.PrettyPrintSoftshrink (0 ms)
2023-01-11T22:09:54.6405314Z [ RUN      ] ModulesTest.PrettyPrintSoftsign
2023-01-11T22:09:54.6405908Z [       OK ] ModulesTest.PrettyPrintSoftsign (0 ms)
2023-01-11T22:09:54.6406475Z [ RUN      ] ModulesTest.PrettyPrintTanh
2023-01-11T22:09:54.6407020Z [       OK ] ModulesTest.PrettyPrintTanh (0 ms)
2023-01-11T22:09:54.6407596Z [ RUN      ] ModulesTest.PrettyPrintTanhshrink
2023-01-11T22:09:54.6408208Z [       OK ] ModulesTest.PrettyPrintTanhshrink (0 ms)
2023-01-11T22:09:54.6408793Z [ RUN      ] ModulesTest.PrettyPrintThreshold
2023-01-11T22:09:54.6409528Z [       OK ] ModulesTest.PrettyPrintThreshold (0 ms)
2023-01-11T22:09:54.6410255Z [ RUN      ] ModulesTest.PrettyPrintCTCLoss
2023-01-11T22:09:54.6410855Z [       OK ] ModulesTest.PrettyPrintCTCLoss (0 ms)
2023-01-11T22:09:54.6411448Z [ RUN      ] ModulesTest.PrettyPrintPoissonNLLLoss
2023-01-11T22:09:54.6412110Z [       OK ] ModulesTest.PrettyPrintPoissonNLLLoss (0 ms)
2023-01-11T22:09:54.6412780Z [ RUN      ] ModulesTest.PrettyPrintMarginRankingLoss
2023-01-11T22:09:54.6413458Z [       OK ] ModulesTest.PrettyPrintMarginRankingLoss (0 ms)
2023-01-11T22:09:54.6414122Z [ RUN      ] ModulesTest.PrettyPrintCrossMapLRN2d
2023-01-11T22:09:54.6414771Z [       OK ] ModulesTest.PrettyPrintCrossMapLRN2d (0 ms)
2023-01-11T22:09:54.6415389Z [ RUN      ] ModulesTest.PrettyPrintAlphaDropout
2023-01-11T22:09:54.6416030Z [       OK ] ModulesTest.PrettyPrintAlphaDropout (0 ms)
2023-01-11T22:09:54.6416704Z [ RUN      ] ModulesTest.PrettyPrintFeatureAlphaDropout
2023-01-11T22:09:54.6417432Z [       OK ] ModulesTest.PrettyPrintFeatureAlphaDropout (0 ms)
2023-01-11T22:09:54.6418121Z [ RUN      ] ModulesTest.PrettyPrintBCEWithLogitsLoss
2023-01-11T22:09:54.6418816Z [       OK ] ModulesTest.PrettyPrintBCEWithLogitsLoss (0 ms)
2023-01-11T22:09:54.6419508Z [ RUN      ] ModulesTest.PrettyPrintMultiheadAttention
2023-01-11T22:09:54.6420196Z [       OK ] ModulesTest.PrettyPrintMultiheadAttention (0 ms)
2023-01-11T22:09:54.6420825Z [ RUN      ] ModulesTest.PrettyPrintRNNCell
2023-01-11T22:09:54.6421413Z [       OK ] ModulesTest.PrettyPrintRNNCell (0 ms)
2023-01-11T22:09:54.6421978Z [ RUN      ] ModulesTest.PrettyPrintLSTMCell
2023-01-11T22:09:54.6422648Z [       OK ] ModulesTest.PrettyPrintLSTMCell (0 ms)
2023-01-11T22:09:54.6423233Z [ RUN      ] ModulesTest.PrettyPrintGRUCell
2023-01-11T22:09:54.6423861Z [       OK ] ModulesTest.PrettyPrintGRUCell (0 ms)
2023-01-11T22:09:54.6424557Z [ RUN      ] ModulesTest.PrettyPrintAdaptiveLogSoftmaxWithLoss
2023-01-11T22:09:54.6425378Z [       OK ] ModulesTest.PrettyPrintAdaptiveLogSoftmaxWithLoss (0 ms)
2023-01-11T22:09:54.6426082Z [----------] 256 tests from ModulesTest (9464 ms total)
2023-01-11T22:09:54.6426352Z 
2023-01-11T22:09:54.6426593Z [----------] 1 test from NestedTest
2023-01-11T22:09:54.6427020Z [ RUN      ] NestedTest.Nested
2023-01-11T22:09:54.6427557Z [W NestedTensorImpl.cpp:179] Warning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (function operator())
2023-01-11T22:09:54.6428213Z [       OK ] NestedTest.Nested (0 ms)
2023-01-11T22:09:54.6428702Z [----------] 1 test from NestedTest (0 ms total)
2023-01-11T22:09:54.6428944Z 
2023-01-11T22:09:54.6429225Z [----------] 10 tests from ParameterDictTest
2023-01-11T22:09:54.6429793Z [ RUN      ] ParameterDictTest.ConstructFromTensor
2023-01-11T22:09:54.6430391Z [       OK ] ParameterDictTest.ConstructFromTensor (0 ms)
2023-01-11T22:09:54.6430959Z [ RUN      ] ParameterDictTest.ConstructFromOrderedDict
2023-01-11T22:09:54.6431898Z [       OK ] ParameterDictTest.ConstructFromOrderedDict (0 ms)
2023-01-11T22:09:54.6432590Z [ RUN      ] ParameterDictTest.InsertAndContains
2023-01-11T22:09:54.6433229Z [       OK ] ParameterDictTest.InsertAndContains (0 ms)
2023-01-11T22:09:54.6433897Z [ RUN      ] ParameterDictTest.InsertAndClear
2023-01-11T22:09:54.6434559Z [       OK ] ParameterDictTest.InsertAndClear (0 ms)
2023-01-11T22:09:54.6435191Z [ RUN      ] ParameterDictTest.InsertAndPop
2023-01-11T22:09:54.6435844Z [       OK ] ParameterDictTest.InsertAndPop (1 ms)
2023-01-11T22:09:54.6436486Z [ RUN      ] ParameterDictTest.SimpleUpdate
2023-01-11T22:09:54.6437133Z [       OK ] ParameterDictTest.SimpleUpdate (1 ms)
2023-01-11T22:09:54.6437809Z [ RUN      ] ParameterDictTest.Keys
2023-01-11T22:09:54.6438386Z [       OK ] ParameterDictTest.Keys (0 ms)
2023-01-11T22:09:54.6438950Z [ RUN      ] ParameterDictTest.Values
2023-01-11T22:09:54.6439687Z [       OK ] ParameterDictTest.Values (0 ms)
2023-01-11T22:09:54.6440243Z [ RUN      ] ParameterDictTest.Get
2023-01-11T22:09:54.6440793Z [       OK ] ParameterDictTest.Get (0 ms)
2023-01-11T22:09:54.6441446Z [ RUN      ] ParameterDictTest.PrettyPrintParameterDict
2023-01-11T22:09:54.6442236Z [       OK ] ParameterDictTest.PrettyPrintParameterDict (0 ms)
2023-01-11T22:09:54.6442987Z [----------] 10 tests from ParameterDictTest (3 ms total)
2023-01-11T22:09:54.6443304Z 
2023-01-11T22:09:54.6443624Z [----------] 8 tests from ParameterListTest
2023-01-11T22:09:54.6444462Z [ RUN      ] ParameterListTest.ConstructsFromSharedPointer
2023-01-11T22:09:54.6445419Z [       OK ] ParameterListTest.ConstructsFromSharedPointer (0 ms)
2023-01-11T22:09:54.6446207Z [ RUN      ] ParameterListTest.isEmpty
2023-01-11T22:09:54.6446852Z [       OK ] ParameterListTest.isEmpty (0 ms)
2023-01-11T22:09:54.6447620Z [ RUN      ] ParameterListTest.PushBackAddsAnElement
2023-01-11T22:09:54.6448470Z [       OK ] ParameterListTest.PushBackAddsAnElement (0 ms)
2023-01-11T22:09:54.6449414Z [ RUN      ] ParameterListTest.ForEachLoop
2023-01-11T22:09:54.6450157Z [       OK ] ParameterListTest.ForEachLoop (0 ms)
2023-01-11T22:09:54.6450898Z [ RUN      ] ParameterListTest.AccessWithAt
2023-01-11T22:09:54.6451486Z [       OK ] ParameterListTest.AccessWithAt (4 ms)
2023-01-11T22:09:54.6452230Z [ RUN      ] ParameterListTest.ExtendPushesParametersFromOtherParameterList
2023-01-11T22:09:54.6453103Z [       OK ] ParameterListTest.ExtendPushesParametersFromOtherParameterList (0 ms)
2023-01-11T22:09:54.6453801Z [ RUN      ] ParameterListTest.PrettyPrintParameterList
2023-01-11T22:09:54.6454425Z [       OK ] ParameterListTest.PrettyPrintParameterList (0 ms)
2023-01-11T22:09:54.6455035Z [ RUN      ] ParameterListTest.IncrementAdd
2023-01-11T22:09:54.6455590Z [       OK ] ParameterListTest.IncrementAdd (0 ms)
2023-01-11T22:09:54.6456167Z [----------] 8 tests from ParameterListTest (5 ms total)
2023-01-11T22:09:54.6456415Z 
2023-01-11T22:09:54.6456673Z [----------] 1 test from NamespaceTests
2023-01-11T22:09:54.6457316Z [ RUN      ] NamespaceTests.NotLeakingSymbolsFromTorchAutogradNamespace
2023-01-11T22:09:54.6458165Z [       OK ] NamespaceTests.NotLeakingSymbolsFromTorchAutogradNamespace (0 ms)
2023-01-11T22:09:54.6458845Z [----------] 1 test from NamespaceTests (0 ms total)
2023-01-11T22:09:54.6459097Z 
2023-01-11T22:09:54.6459349Z [----------] 7 tests from NNUtilsTest
2023-01-11T22:09:54.6459805Z [ RUN      ] NNUtilsTest.ClipGradNorm
2023-01-11T22:09:54.6460321Z [       OK ] NNUtilsTest.ClipGradNorm (2 ms)
2023-01-11T22:09:54.6460868Z [ RUN      ] NNUtilsTest.ClipGradNormErrorIfNonfinite
2023-01-11T22:09:54.8229912Z [       OK ] NNUtilsTest.ClipGradNormErrorIfNonfinite (177 ms)
2023-01-11T22:09:54.8230638Z [ RUN      ] NNUtilsTest.ClipGradValue
2023-01-11T22:09:54.8231236Z [       OK ] NNUtilsTest.ClipGradValue (0 ms)
2023-01-11T22:09:54.8231852Z [ RUN      ] NNUtilsTest.ConvertParameters
2023-01-11T22:09:54.8235863Z [       OK ] NNUtilsTest.ConvertParameters (0 ms)
2023-01-11T22:09:54.8236391Z [ RUN      ] NNUtilsTest.PackSequence
2023-01-11T22:09:54.8622248Z [       OK ] NNUtilsTest.PackSequence (38 ms)
2023-01-11T22:09:54.8622889Z [ RUN      ] NNUtilsTest.PackPaddedSequence
2023-01-11T22:09:54.8785644Z [       OK ] NNUtilsTest.PackPaddedSequence (16 ms)
2023-01-11T22:09:54.8786191Z [ RUN      ] NNUtilsTest.PadSequence
2023-01-11T22:09:54.8841743Z [       OK ] NNUtilsTest.PadSequence (5 ms)
2023-01-11T22:09:54.8842161Z [----------] 7 tests from NNUtilsTest (240 ms total)
2023-01-11T22:09:54.8842337Z 
2023-01-11T22:09:54.8842508Z [----------] 3 tests from PackedSequenceTest
2023-01-11T22:09:54.8842809Z [ RUN      ] PackedSequenceTest.WrongOrder
2023-01-11T22:09:54.8882523Z [       OK ] PackedSequenceTest.WrongOrder (4 ms)
2023-01-11T22:09:54.8882851Z [ RUN      ] PackedSequenceTest.TotalLength
2023-01-11T22:09:54.8957975Z [       OK ] PackedSequenceTest.TotalLength (7 ms)
2023-01-11T22:09:54.8958281Z [ RUN      ] PackedSequenceTest.To
2023-01-11T22:09:54.8959966Z [       OK ] PackedSequenceTest.To (0 ms)
2023-01-11T22:09:54.8960335Z [----------] 3 tests from PackedSequenceTest (11 ms total)
2023-01-11T22:09:54.8960511Z 
2023-01-11T22:09:54.8960645Z [----------] 34 tests from OptimTest
2023-01-11T22:09:54.8960946Z [ RUN      ] OptimTest.OptimizerAccessors
2023-01-11T22:09:54.8986806Z [       OK ] OptimTest.OptimizerAccessors (2 ms)
2023-01-11T22:09:54.8987124Z [ RUN      ] OptimTest.OldInterface
2023-01-11T22:09:54.8988587Z [       OK ] OptimTest.OldInterface (0 ms)
2023-01-11T22:09:54.8988932Z [ RUN      ] OptimTest.XORConvergence_SGD
2023-01-11T22:09:56.2710795Z [       OK ] OptimTest.XORConvergence_SGD (1371 ms)
2023-01-11T22:09:56.2711566Z [ RUN      ] OptimTest.XORConvergence_LBFGS
2023-01-11T22:09:57.2151065Z [       OK ] OptimTest.XORConvergence_LBFGS (943 ms)
2023-01-11T22:09:57.2151841Z [ RUN      ] OptimTest.XORConvergence_Adagrad
2023-01-11T22:09:57.7399839Z [       OK ] OptimTest.XORConvergence_Adagrad (524 ms)
2023-01-11T22:09:57.7400208Z [ RUN      ] OptimTest.XORConvergence_RMSprop
2023-01-11T22:09:58.2588964Z [       OK ] OptimTest.XORConvergence_RMSprop (518 ms)
2023-01-11T22:09:58.2589544Z [ RUN      ] OptimTest.XORConvergence_RMSpropWithMomentum
2023-01-11T22:09:59.7199205Z [       OK ] OptimTest.XORConvergence_RMSpropWithMomentum (1460 ms)
2023-01-11T22:09:59.7200089Z [ RUN      ] OptimTest.XORConvergence_Adam
2023-01-11T22:10:00.2921131Z [       OK ] OptimTest.XORConvergence_Adam (572 ms)
2023-01-11T22:10:00.2922062Z [ RUN      ] OptimTest.XORConvergence_AdamWithAmsgrad
2023-01-11T22:10:00.8667635Z [       OK ] OptimTest.XORConvergence_AdamWithAmsgrad (574 ms)
2023-01-11T22:10:00.8668106Z [ RUN      ] OptimTest.ProducesPyTorchValues_Adam
2023-01-11T22:10:01.0569313Z [       OK ] OptimTest.ProducesPyTorchValues_Adam (190 ms)
2023-01-11T22:10:01.0569850Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamWithWeightDecay
2023-01-11T22:10:01.2529904Z [       OK ] OptimTest.ProducesPyTorchValues_AdamWithWeightDecay (196 ms)
2023-01-11T22:10:01.2530387Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamWithWeightDecayAndAMSGrad
2023-01-11T22:10:01.4556813Z [       OK ] OptimTest.ProducesPyTorchValues_AdamWithWeightDecayAndAMSGrad (202 ms)
2023-01-11T22:10:01.4557528Z [ RUN      ] OptimTest.XORConvergence_AdamW
2023-01-11T22:10:02.0381788Z [       OK ] OptimTest.XORConvergence_AdamW (582 ms)
2023-01-11T22:10:02.0382195Z [ RUN      ] OptimTest.XORConvergence_AdamWWithAmsgrad
2023-01-11T22:10:02.6215951Z [       OK ] OptimTest.XORConvergence_AdamWWithAmsgrad (583 ms)
2023-01-11T22:10:02.6216528Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamW
2023-01-11T22:10:02.8196910Z [       OK ] OptimTest.ProducesPyTorchValues_AdamW (198 ms)
2023-01-11T22:10:02.8197375Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamWWithoutWeightDecay
2023-01-11T22:10:03.0113252Z [       OK ] OptimTest.ProducesPyTorchValues_AdamWWithoutWeightDecay (191 ms)
2023-01-11T22:10:03.0113980Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdamWWithAMSGrad
2023-01-11T22:10:03.2157089Z [       OK ] OptimTest.ProducesPyTorchValues_AdamWWithAMSGrad (204 ms)
2023-01-11T22:10:03.2157621Z [ RUN      ] OptimTest.ProducesPyTorchValues_Adagrad
2023-01-11T22:10:03.3745939Z [       OK ] OptimTest.ProducesPyTorchValues_Adagrad (158 ms)
2023-01-11T22:10:03.3746437Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdagradWithWeightDecay
2023-01-11T22:10:03.5394197Z [       OK ] OptimTest.ProducesPyTorchValues_AdagradWithWeightDecay (164 ms)
2023-01-11T22:10:03.5394674Z [ RUN      ] OptimTest.ProducesPyTorchValues_AdagradWithWeightDecayAndLRDecay
2023-01-11T22:10:03.7045698Z [       OK ] OptimTest.ProducesPyTorchValues_AdagradWithWeightDecayAndLRDecay (165 ms)
2023-01-11T22:10:03.7046135Z [ RUN      ] OptimTest.ProducesPyTorchValues_RMSprop
2023-01-11T22:10:03.8718205Z [       OK ] OptimTest.ProducesPyTorchValues_RMSprop (167 ms)
2023-01-11T22:10:03.8718684Z [ RUN      ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecay
2023-01-11T22:10:04.0462645Z [       OK ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecay (174 ms)
2023-01-11T22:10:04.0463186Z [ RUN      ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecayAndCentered
2023-01-11T22:10:04.2359548Z [       OK ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecayAndCentered (189 ms)
2023-01-11T22:10:04.2360091Z [ RUN      ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecayAndCenteredAndMomentum
2023-01-11T22:10:04.4350485Z [       OK ] OptimTest.ProducesPyTorchValues_RMSpropWithWeightDecayAndCenteredAndMomentum (199 ms)
2023-01-11T22:10:04.4350974Z [ RUN      ] OptimTest.ProducesPyTorchValues_SGD
2023-01-11T22:10:04.5640019Z [       OK ] OptimTest.ProducesPyTorchValues_SGD (128 ms)
2023-01-11T22:10:04.5640607Z [ RUN      ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecay
2023-01-11T22:10:04.7005061Z [       OK ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecay (136 ms)
2023-01-11T22:10:04.7005679Z [ RUN      ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecayAndMomentum
2023-01-11T22:10:04.8548548Z [       OK ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecayAndMomentum (154 ms)
2023-01-11T22:10:04.8549208Z [ RUN      ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecayAndNesterovMomentum
2023-01-11T22:10:05.0145449Z [       OK ] OptimTest.ProducesPyTorchValues_SGDWithWeightDecayAndNesterovMomentum (159 ms)
2023-01-11T22:10:05.0146050Z [ RUN      ] OptimTest.ProducesPyTorchValues_LBFGS
2023-01-11T22:10:05.1609456Z [       OK ] OptimTest.ProducesPyTorchValues_LBFGS (146 ms)
2023-01-11T22:10:05.1610080Z [ RUN      ] OptimTest.ProducesPyTorchValues_LBFGS_with_line_search
2023-01-11T22:10:05.8231009Z [       OK ] OptimTest.ProducesPyTorchValues_LBFGS_with_line_search (662 ms)
2023-01-11T22:10:05.8231565Z [ RUN      ] OptimTest.ZeroGrad
2023-01-11T22:10:05.8231858Z [       OK ] OptimTest.ZeroGrad (0 ms)
2023-01-11T22:10:05.8232183Z [ RUN      ] OptimTest.ExternalVectorOfParameters
2023-01-11T22:10:05.8233402Z [       OK ] OptimTest.ExternalVectorOfParameters (0 ms)
2023-01-11T22:10:05.8234000Z [ RUN      ] OptimTest.AddParameter_LBFGS
2023-01-11T22:10:05.8235084Z [       OK ] OptimTest.AddParameter_LBFGS (0 ms)
2023-01-11T22:10:05.8235647Z [ RUN      ] OptimTest.CheckLRChange_StepLR_Adam
2023-01-11T22:10:05.8236176Z [       OK ] OptimTest.CheckLRChange_StepLR_Adam (0 ms)
2023-01-11T22:10:05.8236503Z [----------] 34 tests from OptimTest (10927 ms total)
2023-01-11T22:10:05.8236655Z 
2023-01-11T22:10:05.8236812Z [----------] 29 tests from OrderedDictTest
2023-01-11T22:10:05.8237175Z [ RUN      ] OrderedDictTest.IsEmptyAfterDefaultConstruction
2023-01-11T22:10:05.8237699Z [       OK ] OrderedDictTest.IsEmptyAfterDefaultConstruction (0 ms)
2023-01-11T22:10:05.8238169Z [ RUN      ] OrderedDictTest.InsertAddsElementsWhenTheyAreYetNotPresent
2023-01-11T22:10:05.8238677Z [       OK ] OrderedDictTest.InsertAddsElementsWhenTheyAreYetNotPresent (0 ms)
2023-01-11T22:10:05.8239144Z [ RUN      ] OrderedDictTest.GetReturnsValuesWhenTheyArePresent
2023-01-11T22:10:05.8239574Z [       OK ] OrderedDictTest.GetReturnsValuesWhenTheyArePresent (0 ms)
2023-01-11T22:10:05.8240034Z [ RUN      ] OrderedDictTest.GetThrowsWhenPassedKeysThatAreNotPresent
2023-01-11T22:10:05.8261785Z [       OK ] OrderedDictTest.GetThrowsWhenPassedKeysThatAreNotPresent (2 ms)
2023-01-11T22:10:05.8262231Z [ RUN      ] OrderedDictTest.CanInitializeFromList
2023-01-11T22:10:05.8262739Z [       OK ] OrderedDictTest.CanInitializeFromList (0 ms)
2023-01-11T22:10:05.8263253Z [ RUN      ] OrderedDictTest.InsertThrowsWhenPassedElementsThatArePresent
2023-01-11T22:10:05.8284348Z [       OK ] OrderedDictTest.InsertThrowsWhenPassedElementsThatArePresent (2 ms)
2023-01-11T22:10:05.8284841Z [ RUN      ] OrderedDictTest.FrontReturnsTheFirstItem
2023-01-11T22:10:05.8285235Z [       OK ] OrderedDictTest.FrontReturnsTheFirstItem (0 ms)
2023-01-11T22:10:05.8285601Z [ RUN      ] OrderedDictTest.FrontThrowsWhenEmpty
2023-01-11T22:10:05.8295244Z [       OK ] OrderedDictTest.FrontThrowsWhenEmpty (1 ms)
2023-01-11T22:10:05.8295605Z [ RUN      ] OrderedDictTest.BackReturnsTheLastItem
2023-01-11T22:10:05.8295982Z [       OK ] OrderedDictTest.BackReturnsTheLastItem (0 ms)
2023-01-11T22:10:05.8296338Z [ RUN      ] OrderedDictTest.BackThrowsWhenEmpty
2023-01-11T22:10:05.8306148Z [       OK ] OrderedDictTest.BackThrowsWhenEmpty (1 ms)
2023-01-11T22:10:05.8306711Z [ RUN      ] OrderedDictTest.FindReturnsPointersToValuesWhenPresent
2023-01-11T22:10:05.8307198Z [       OK ] OrderedDictTest.FindReturnsPointersToValuesWhenPresent (0 ms)
2023-01-11T22:10:05.8307731Z [ RUN      ] OrderedDictTest.FindReturnsNullPointersWhenPasesdKeysThatAreNotPresent
2023-01-11T22:10:05.8308323Z [       OK ] OrderedDictTest.FindReturnsNullPointersWhenPasesdKeysThatAreNotPresent (0 ms)
2023-01-11T22:10:05.8309049Z [ RUN      ] OrderedDictTest.SubscriptOperatorThrowsWhenPassedKeysThatAreNotPresent
2023-01-11T22:10:05.8309995Z [       OK ] OrderedDictTest.SubscriptOperatorThrowsWhenPassedKeysThatAreNotPresent (0 ms)
2023-01-11T22:10:05.8310758Z [ RUN      ] OrderedDictTest.SubscriptOperatorReturnsItemsPositionallyWhenPassedIntegers
2023-01-11T22:10:05.8311387Z [       OK ] OrderedDictTest.SubscriptOperatorReturnsItemsPositionallyWhenPassedIntegers (0 ms)
2023-01-11T22:10:05.8312004Z [ RUN      ] OrderedDictTest.SubscriptOperatorsThrowswhenPassedKeysThatAreNotPresent
2023-01-11T22:10:05.8330952Z [       OK ] OrderedDictTest.SubscriptOperatorsThrowswhenPassedKeysThatAreNotPresent (2 ms)
2023-01-11T22:10:05.8331581Z [ RUN      ] OrderedDictTest.UpdateInsertsAllItemsFromAnotherOrderedDict
2023-01-11T22:10:05.8332238Z [       OK ] OrderedDictTest.UpdateInsertsAllItemsFromAnotherOrderedDict (0 ms)
2023-01-11T22:10:05.8332694Z [ RUN      ] OrderedDictTest.UpdateAlsoChecksForDuplicates
2023-01-11T22:10:05.8343131Z [       OK ] OrderedDictTest.UpdateAlsoChecksForDuplicates (1 ms)
2023-01-11T22:10:05.8343848Z [ RUN      ] OrderedDictTest.CanIterateItems
2023-01-11T22:10:05.8344440Z [       OK ] OrderedDictTest.CanIterateItems (0 ms)
2023-01-11T22:10:05.8345005Z [ RUN      ] OrderedDictTest.EraseWorks
2023-01-11T22:10:05.8345554Z [       OK ] OrderedDictTest.EraseWorks (0 ms)
2023-01-11T22:10:05.8346148Z [ RUN      ] OrderedDictTest.ClearMakesTheDictEmpty
2023-01-11T22:10:05.8346818Z [       OK ] OrderedDictTest.ClearMakesTheDictEmpty (0 ms)
2023-01-11T22:10:05.8347606Z [ RUN      ] OrderedDictTest.CanCopyConstruct
2023-01-11T22:10:05.8348206Z [       OK ] OrderedDictTest.CanCopyConstruct (0 ms)
2023-01-11T22:10:05.8348794Z [ RUN      ] OrderedDictTest.CanCopyAssign
2023-01-11T22:10:05.8349375Z [       OK ] OrderedDictTest.CanCopyAssign (0 ms)
2023-01-11T22:10:05.8349959Z [ RUN      ] OrderedDictTest.CanMoveConstruct
2023-01-11T22:10:05.8350550Z [       OK ] OrderedDictTest.CanMoveConstruct (0 ms)
2023-01-11T22:10:05.8351127Z [ RUN      ] OrderedDictTest.CanMoveAssign
2023-01-11T22:10:05.8351699Z [       OK ] OrderedDictTest.CanMoveAssign (0 ms)
2023-01-11T22:10:05.8352289Z [ RUN      ] OrderedDictTest.CanInsertWithBraces
2023-01-11T22:10:05.8352937Z [       OK ] OrderedDictTest.CanInsertWithBraces (0 ms)
2023-01-11T22:10:05.8353685Z [ RUN      ] OrderedDictTest.ErrorMessagesIncludeTheKeyDescription
2023-01-11T22:10:05.8370155Z [       OK ] OrderedDictTest.ErrorMessagesIncludeTheKeyDescription (2 ms)
2023-01-11T22:10:05.8370895Z [ RUN      ] OrderedDictTest.KeysReturnsAllKeys
2023-01-11T22:10:05.8371511Z [       OK ] OrderedDictTest.KeysReturnsAllKeys (0 ms)
2023-01-11T22:10:05.8372134Z [ RUN      ] OrderedDictTest.ValuesReturnsAllValues
2023-01-11T22:10:05.8372768Z [       OK ] OrderedDictTest.ValuesReturnsAllValues (0 ms)
2023-01-11T22:10:05.8373398Z [ RUN      ] OrderedDictTest.ItemsReturnsAllItems
2023-01-11T22:10:05.8374032Z [       OK ] OrderedDictTest.ItemsReturnsAllItems (0 ms)
2023-01-11T22:10:05.8374654Z [----------] 29 tests from OrderedDictTest (13 ms total)
2023-01-11T22:10:05.8374931Z 
2023-01-11T22:10:05.8375170Z [----------] 13 tests from RNNTest
2023-01-11T22:10:05.8375652Z [ RUN      ] RNNTest.CheckOutputSizes
2023-01-11T22:10:05.8440603Z [       OK ] RNNTest.CheckOutputSizes (6 ms)
2023-01-11T22:10:05.8441163Z [ RUN      ] RNNTest.CheckOutputSizesProj
2023-01-11T22:10:05.8506838Z [       OK ] RNNTest.CheckOutputSizesProj (6 ms)
2023-01-11T22:10:05.8507592Z [ RUN      ] RNNTest.CheckOutputValuesMatchPyTorch
2023-01-11T22:10:05.8512579Z [       OK ] RNNTest.CheckOutputValuesMatchPyTorch (0 ms)
2023-01-11T22:10:05.8513242Z [ RUN      ] RNNTest.EndToEndLSTM
2023-01-11T22:10:07.5615473Z [       OK ] RNNTest.EndToEndLSTM (1710 ms)
2023-01-11T22:10:07.5616238Z [ RUN      ] RNNTest.EndToEndLSTMProj
2023-01-11T22:10:09.2522753Z [       OK ] RNNTest.EndToEndLSTMProj (1690 ms)
2023-01-11T22:10:09.2523412Z [ RUN      ] RNNTest.EndToEndGRU
2023-01-11T22:10:10.7661361Z [       OK ] RNNTest.EndToEndGRU (1513 ms)
2023-01-11T22:10:10.7661936Z [ RUN      ] RNNTest.EndToEndRNNRelu
2023-01-11T22:10:11.5944975Z [       OK ] RNNTest.EndToEndRNNRelu (828 ms)
2023-01-11T22:10:11.5945740Z [ RUN      ] RNNTest.EndToEndRNNTanh
2023-01-11T22:10:12.5261076Z [       OK ] RNNTest.EndToEndRNNTanh (931 ms)
2023-01-11T22:10:12.5261652Z [ RUN      ] RNNTest.PrettyPrintRNNs
2023-01-11T22:10:12.5283371Z [       OK ] RNNTest.PrettyPrintRNNs (2 ms)
2023-01-11T22:10:12.5283974Z [ RUN      ] RNNTest.BidirectionalFlattenParameters
2023-01-11T22:10:12.5390412Z [       OK ] RNNTest.BidirectionalFlattenParameters (10 ms)
2023-01-11T22:10:12.5391041Z [ RUN      ] RNNTest.BidirectionalGRUReverseForward
2023-01-11T22:10:12.5403898Z [       OK ] RNNTest.BidirectionalGRUReverseForward (1 ms)
2023-01-11T22:10:12.5404639Z [ RUN      ] RNNTest.BidirectionalLSTMReverseForward
2023-01-11T22:10:12.5416343Z [       OK ] RNNTest.BidirectionalLSTMReverseForward (1 ms)
2023-01-11T22:10:12.5417054Z [ RUN      ] RNNTest.UsePackedSequenceAsInput
2023-01-11T22:10:12.5434538Z [       OK ] RNNTest.UsePackedSequenceAsInput (1 ms)
2023-01-11T22:10:12.5435442Z [----------] 13 tests from RNNTest (6706 ms total)
2023-01-11T22:10:12.5435717Z 
2023-01-11T22:10:12.5436007Z [----------] 19 tests from SequentialTest
2023-01-11T22:10:12.5436520Z [ RUN      ] SequentialTest.CanContainThings
2023-01-11T22:10:12.5436976Z [       OK ] SequentialTest.CanContainThings (0 ms)
2023-01-11T22:10:12.5437490Z [ RUN      ] SequentialTest.ConstructsFromSharedPointer
2023-01-11T22:10:12.5437952Z [       OK ] SequentialTest.ConstructsFromSharedPointer (0 ms)
2023-01-11T22:10:12.5438489Z [ RUN      ] SequentialTest.ConstructsFromConcreteType
2023-01-11T22:10:12.5438866Z [       OK ] SequentialTest.ConstructsFromConcreteType (0 ms)
2023-01-11T22:10:12.5439248Z [ RUN      ] SequentialTest.ConstructsFromModuleHolder
2023-01-11T22:10:12.5439631Z [       OK ] SequentialTest.ConstructsFromModuleHolder (0 ms)
2023-01-11T22:10:12.5439983Z [ RUN      ] SequentialTest.PushBackAddsAnElement
2023-01-11T22:10:12.5440347Z [       OK ] SequentialTest.PushBackAddsAnElement (0 ms)
2023-01-11T22:10:12.5440674Z [ RUN      ] SequentialTest.AccessWithAt
2023-01-11T22:10:12.5470677Z [       OK ] SequentialTest.AccessWithAt (3 ms)
2023-01-11T22:10:12.5471258Z [ RUN      ] SequentialTest.AccessWithPtr
2023-01-11T22:10:12.5494212Z [       OK ] SequentialTest.AccessWithPtr (2 ms)
2023-01-11T22:10:12.5494994Z [ RUN      ] SequentialTest.CallingForwardOnEmptySequentialIsDisallowed
2023-01-11T22:10:12.5506785Z [       OK ] SequentialTest.CallingForwardOnEmptySequentialIsDisallowed (1 ms)
2023-01-11T22:10:12.5507615Z [ RUN      ] SequentialTest.CallingForwardChainsCorrectly
2023-01-11T22:10:12.5508368Z [       OK ] SequentialTest.CallingForwardChainsCorrectly (0 ms)
2023-01-11T22:10:12.5509196Z [ RUN      ] SequentialTest.CallingForwardWithTheWrongReturnTypeThrows
2023-01-11T22:10:12.5518766Z [       OK ] SequentialTest.CallingForwardWithTheWrongReturnTypeThrows (1 ms)
2023-01-11T22:10:12.5519871Z [ RUN      ] SequentialTest.TheReturnTypeOfForwardDefaultsToTensor
2023-01-11T22:10:12.5520886Z [       OK ] SequentialTest.TheReturnTypeOfForwardDefaultsToTensor (0 ms)
2023-01-11T22:10:12.5521832Z [ RUN      ] SequentialTest.ForwardReturnsTheLastValue
2023-01-11T22:10:12.5522640Z [       OK ] SequentialTest.ForwardReturnsTheLastValue (0 ms)
2023-01-11T22:10:12.5523573Z [ RUN      ] SequentialTest.SanityCheckForHoldingStandardModules
2023-01-11T22:10:12.5525101Z [       OK ] SequentialTest.SanityCheckForHoldingStandardModules (0 ms)
2023-01-11T22:10:12.5526099Z [ RUN      ] SequentialTest.ExtendPushesModulesFromOtherSequential
2023-01-11T22:10:12.5527229Z [       OK ] SequentialTest.ExtendPushesModulesFromOtherSequential (0 ms)
2023-01-11T22:10:12.5527936Z [ RUN      ] SequentialTest.HasReferenceSemantics
2023-01-11T22:10:12.5528571Z [       OK ] SequentialTest.HasReferenceSemantics (0 ms)
2023-01-11T22:10:12.5529330Z [ RUN      ] SequentialTest.IsCloneable
2023-01-11T22:10:12.5531702Z [       OK ] SequentialTest.IsCloneable (0 ms)
2023-01-11T22:10:12.5532280Z [ RUN      ] SequentialTest.RegistersElementsAsSubmodules
2023-01-11T22:10:12.5532984Z [       OK ] SequentialTest.RegistersElementsAsSubmodules (0 ms)
2023-01-11T22:10:12.5533795Z [ RUN      ] SequentialTest.PrettyPrintSequential
2023-01-11T22:10:12.5535346Z [       OK ] SequentialTest.PrettyPrintSequential (0 ms)
2023-01-11T22:10:12.5536119Z [ RUN      ] SequentialTest.ModuleForwardMethodOptionalArg
2023-01-11T22:10:12.5567562Z [       OK ] SequentialTest.ModuleForwardMethodOptionalArg (3 ms)
2023-01-11T22:10:12.5568356Z [----------] 19 tests from SequentialTest (13 ms total)
2023-01-11T22:10:12.5568543Z 
2023-01-11T22:10:12.5568732Z [----------] 11 tests from TransformerTest
2023-01-11T22:10:12.5569411Z [ RUN      ] TransformerTest.TransformerEncoderLayer
2023-01-11T22:10:12.5658255Z [       OK ] TransformerTest.TransformerEncoderLayer (8 ms)
2023-01-11T22:10:12.5659179Z [ RUN      ] TransformerTest.TransformerDecoderLayer
2023-01-11T22:10:12.5738804Z [       OK ] TransformerTest.TransformerDecoderLayer (8 ms)
2023-01-11T22:10:12.5739739Z [ RUN      ] TransformerTest.TransformerDecoderLayer_gelu
2023-01-11T22:10:12.5785390Z [       OK ] TransformerTest.TransformerDecoderLayer_gelu (4 ms)
2023-01-11T22:10:12.5786252Z [ RUN      ] TransformerTest.TransformerEncoder
2023-01-11T22:10:12.5946450Z [       OK ] TransformerTest.TransformerEncoder (16 ms)
2023-01-11T22:10:12.5947443Z [ RUN      ] TransformerTest.PrettyPrintTransformerEncoderLayer
2023-01-11T22:10:12.5949789Z [       OK ] TransformerTest.PrettyPrintTransformerEncoderLayer (0 ms)
2023-01-11T22:10:12.5950303Z [ RUN      ] TransformerTest.PrettyPrintTransformerEncoder
2023-01-11T22:10:12.5958076Z [       OK ] TransformerTest.PrettyPrintTransformerEncoder (0 ms)
2023-01-11T22:10:12.5958699Z [ RUN      ] TransformerTest.PrettyPrintTransformerDecoderLayer
2023-01-11T22:10:12.5959844Z [       OK ] TransformerTest.PrettyPrintTransformerDecoderLayer (0 ms)
2023-01-11T22:10:12.5960433Z [ RUN      ] TransformerTest.TransformerDecoder
2023-01-11T22:10:12.6395122Z [       OK ] TransformerTest.TransformerDecoder (43 ms)
2023-01-11T22:10:12.6395545Z [ RUN      ] TransformerTest.PrettyPrintTransformerDecoder
2023-01-11T22:10:12.6405123Z [       OK ] TransformerTest.PrettyPrintTransformerDecoder (1 ms)
2023-01-11T22:10:12.6405504Z [ RUN      ] TransformerTest.Transformer
2023-01-11T22:10:12.6560518Z [       OK ] TransformerTest.Transformer (15 ms)
2023-01-11T22:10:12.6560893Z [ RUN      ] TransformerTest.TransformerArgsCorrectness
2023-01-11T22:10:12.6620077Z [       OK ] TransformerTest.TransformerArgsCorrectness (5 ms)
2023-01-11T22:10:12.6620481Z [----------] 11 tests from TransformerTest (105 ms total)
2023-01-11T22:10:12.6620657Z 
2023-01-11T22:10:12.6620840Z [----------] 23 tests from SerializeTest
2023-01-11T22:10:12.6621157Z [ RUN      ] SerializeTest.KeysFunc
2023-01-11T22:10:12.6624470Z [       OK ] SerializeTest.KeysFunc (0 ms)
2023-01-11T22:10:12.6624846Z [ RUN      ] SerializeTest.TryReadFunc
2023-01-11T22:10:12.6627230Z [       OK ] SerializeTest.TryReadFunc (0 ms)
2023-01-11T22:10:12.6627526Z [ RUN      ] SerializeTest.Basic
2023-01-11T22:10:12.6629596Z [       OK ] SerializeTest.Basic (0 ms)
2023-01-11T22:10:12.6629888Z [ RUN      ] SerializeTest.MathBits
2023-01-11T22:10:12.6705756Z [       OK ] SerializeTest.MathBits (7 ms)
2023-01-11T22:10:12.6706061Z [ RUN      ] SerializeTest.BasicToFile
2023-01-11T22:10:12.6709235Z [       OK ] SerializeTest.BasicToFile (0 ms)
2023-01-11T22:10:12.6709538Z [ RUN      ] SerializeTest.BasicViaFunc
2023-01-11T22:10:12.6712790Z [       OK ] SerializeTest.BasicViaFunc (0 ms)
2023-01-11T22:10:12.6713075Z [ RUN      ] SerializeTest.Resized
2023-01-11T22:10:12.6714963Z [       OK ] SerializeTest.Resized (0 ms)
2023-01-11T22:10:12.6715248Z [ RUN      ] SerializeTest.Sliced
2023-01-11T22:10:12.6717270Z [       OK ] SerializeTest.Sliced (0 ms)
2023-01-11T22:10:12.6717574Z [ RUN      ] SerializeTest.NonContiguous
2023-01-11T22:10:12.6719662Z [       OK ] SerializeTest.NonContiguous (0 ms)
2023-01-11T22:10:12.6719975Z [ RUN      ] SerializeTest.ErrorOnMissingKey
2023-01-11T22:10:12.6798821Z [       OK ] SerializeTest.ErrorOnMissingKey (7 ms)
2023-01-11T22:10:12.6799120Z [ RUN      ] SerializeTest.XOR
2023-01-11T22:10:12.8542252Z [       OK ] SerializeTest.XOR (174 ms)
2023-01-11T22:10:12.8542763Z [ RUN      ] SerializeTest.Optim
2023-01-11T22:10:12.8563929Z [       OK ] SerializeTest.Optim (2 ms)
2023-01-11T22:10:12.8564231Z [ RUN      ] SerializeTest.Optim_Adagrad
2023-01-11T22:10:12.8593902Z [       OK ] SerializeTest.Optim_Adagrad (2 ms)
2023-01-11T22:10:12.8594233Z [ RUN      ] SerializeTest.Optim_SGD
2023-01-11T22:10:12.8620925Z [       OK ] SerializeTest.Optim_SGD (2 ms)
2023-01-11T22:10:12.8621249Z [ RUN      ] SerializeTest.Optim_Adam
2023-01-11T22:10:12.8655360Z [       OK ] SerializeTest.Optim_Adam (3 ms)
2023-01-11T22:10:12.8655693Z [ RUN      ] SerializeTest.Optim_AdamW
2023-01-11T22:10:12.8689418Z [       OK ] SerializeTest.Optim_AdamW (3 ms)
2023-01-11T22:10:12.8689723Z [ RUN      ] SerializeTest.Optim_RMSprop
2023-01-11T22:10:12.8722419Z [       OK ] SerializeTest.Optim_RMSprop (3 ms)
2023-01-11T22:10:12.8722747Z [ RUN      ] SerializeTest.Optim_LBFGS
2023-01-11T22:10:12.8755586Z [       OK ] SerializeTest.Optim_LBFGS (3 ms)
2023-01-11T22:10:12.8756115Z [ RUN      ] SerializeTest.CanSerializeModulesWithIntermediateModulesWithoutParametersOrBuffers
2023-01-11T22:10:12.8759786Z [       OK ] SerializeTest.CanSerializeModulesWithIntermediateModulesWithoutParametersOrBuffers (0 ms)
2023-01-11T22:10:12.8760287Z [ RUN      ] SerializeTest.VectorOfTensors
2023-01-11T22:10:12.8762224Z [       OK ] SerializeTest.VectorOfTensors (0 ms)
2023-01-11T22:10:12.8762511Z [ RUN      ] SerializeTest.IValue
2023-01-11T22:10:12.8765004Z [       OK ] SerializeTest.IValue (0 ms)
2023-01-11T22:10:12.8765577Z [ RUN      ] SerializeTest.UnserializableSubmoduleIsSkippedWhenSavingModule
2023-01-11T22:10:12.8766596Z [       OK ] SerializeTest.UnserializableSubmoduleIsSkippedWhenSavingModule (0 ms)
2023-01-11T22:10:12.8767145Z [ RUN      ] SerializeTest.UnserializableSubmoduleIsIgnoredWhenLoadingModule
2023-01-11T22:10:12.8772072Z [       OK ] SerializeTest.UnserializableSubmoduleIsIgnoredWhenLoadingModule (0 ms)
2023-01-11T22:10:12.8772766Z [----------] 23 tests from SerializeTest (215 ms total)
2023-01-11T22:10:12.8773057Z 
2023-01-11T22:10:12.8773291Z [----------] 1 test from SpecialTest
2023-01-11T22:10:12.8773617Z [ RUN      ] SpecialTest.special
2023-01-11T22:10:12.8773904Z [       OK ] SpecialTest.special (0 ms)
2023-01-11T22:10:12.8774274Z [----------] 1 test from SpecialTest (0 ms total)
2023-01-11T22:10:12.8774415Z 
2023-01-11T22:10:12.8774560Z [----------] 5 tests from TestStatic
2023-01-11T22:10:12.8774812Z [ RUN      ] TestStatic.AllOf
2023-01-11T22:10:12.8775077Z [       OK ] TestStatic.AllOf (0 ms)
2023-01-11T22:10:12.8775314Z [ RUN      ] TestStatic.AnyOf
2023-01-11T22:10:12.8775579Z [       OK ] TestStatic.AnyOf (0 ms)
2023-01-11T22:10:12.8775862Z [ RUN      ] TestStatic.EnableIfModule
2023-01-11T22:10:12.8776162Z [       OK ] TestStatic.EnableIfModule (0 ms)
2023-01-11T22:10:12.8776618Z [ RUN      ] TestStatic.ReturnTypeOfForward
2023-01-11T22:10:12.8776948Z [       OK ] TestStatic.ReturnTypeOfForward (0 ms)
2023-01-11T22:10:12.8777231Z [ RUN      ] TestStatic.Apply
2023-01-11T22:10:12.8777482Z [       OK ] TestStatic.Apply (0 ms)
2023-01-11T22:10:12.8777776Z [----------] 5 tests from TestStatic (0 ms total)
2023-01-11T22:10:12.8777922Z 
2023-01-11T22:10:12.8778067Z [----------] 45 tests from TensorTest
2023-01-11T22:10:12.8778312Z [ RUN      ] TensorTest.ToDtype
2023-01-11T22:10:12.8782534Z [       OK ] TensorTest.ToDtype (0 ms)
2023-01-11T22:10:12.8782954Z [ RUN      ] TensorTest.ToTensorAndTensorAttributes
2023-01-11T22:10:12.8783322Z [       OK ] TensorTest.ToTensorAndTensorAttributes (0 ms)
2023-01-11T22:10:12.8783792Z [ RUN      ] TensorTest.ToOptionsWithRequiresGrad
2023-01-11T22:10:12.8806739Z [       OK ] TensorTest.ToOptionsWithRequiresGrad (2 ms)
2023-01-11T22:10:12.8807204Z [ RUN      ] TensorTest.ToDoesNotCopyWhenOptionsAreAllTheSame
2023-01-11T22:10:12.8807750Z [       OK ] TensorTest.ToDoesNotCopyWhenOptionsAreAllTheSame (0 ms)
2023-01-11T22:10:12.8808170Z [ RUN      ] TensorTest.AtTensorCtorScalar
2023-01-11T22:10:12.8808603Z [       OK ] TensorTest.AtTensorCtorScalar (0 ms)
2023-01-11T22:10:12.8808917Z [ RUN      ] TensorTest.AtTensorCtorSingleDim
2023-01-11T22:10:12.8810118Z [       OK ] TensorTest.AtTensorCtorSingleDim (0 ms)
2023-01-11T22:10:12.8810546Z [ RUN      ] TensorTest.AtTensorCastRealToComplex
2023-01-11T22:10:12.8810915Z [       OK ] TensorTest.AtTensorCastRealToComplex (0 ms)
2023-01-11T22:10:12.8811316Z [ RUN      ] TensorTest.AtTensorCastComplexToRealErrorChecks
2023-01-11T22:10:12.8862380Z [       OK ] TensorTest.AtTensorCastComplexToRealErrorChecks (5 ms)
2023-01-11T22:10:12.8862871Z [ RUN      ] TensorTest.TorchTensorCtorScalarIntegralType
2023-01-11T22:10:12.8863295Z [       OK ] TensorTest.TorchTensorCtorScalarIntegralType (0 ms)
2023-01-11T22:10:12.8863697Z [ RUN      ] TensorTest.TorchTensorCtorScalarFloatingType
2023-01-11T22:10:12.8864090Z [       OK ] TensorTest.TorchTensorCtorScalarFloatingType (0 ms)
2023-01-11T22:10:12.8864477Z [ RUN      ] TensorTest.TorchTensorCtorScalarBoolType
2023-01-11T22:10:12.8864858Z [       OK ] TensorTest.TorchTensorCtorScalarBoolType (0 ms)
2023-01-11T22:10:12.8865247Z [ RUN      ] TensorTest.TorchTensorCtorSingleDimIntegralType
2023-01-11T22:10:12.8867643Z [       OK ] TensorTest.TorchTensorCtorSingleDimIntegralType (0 ms)
2023-01-11T22:10:12.8868240Z [ RUN      ] TensorTest.TorchTensorCtorSingleDimFloatingType
2023-01-11T22:10:12.8868913Z [       OK ] TensorTest.TorchTensorCtorSingleDimFloatingType (0 ms)
2023-01-11T22:10:12.8869632Z [ RUN      ] TensorTest.TorchTensorCtorSingleDimBoolType
2023-01-11T22:10:12.8870079Z [       OK ] TensorTest.TorchTensorCtorSingleDimBoolType (0 ms)
2023-01-11T22:10:12.8870483Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimIntegralType
2023-01-11T22:10:12.8873313Z [       OK ] TensorTest.TorchTensorCtorMultiDimIntegralType (0 ms)
2023-01-11T22:10:12.8873717Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimFloatingType
2023-01-11T22:10:12.8875989Z [       OK ] TensorTest.TorchTensorCtorMultiDimFloatingType (0 ms)
2023-01-11T22:10:12.8876446Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimBoolType
2023-01-11T22:10:12.8876831Z [       OK ] TensorTest.TorchTensorCtorMultiDimBoolType (0 ms)
2023-01-11T22:10:12.8877234Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimWithOptions
2023-01-11T22:10:12.8877809Z [       OK ] TensorTest.TorchTensorCtorMultiDimWithOptions (0 ms)
2023-01-11T22:10:12.8878269Z [ RUN      ] TensorTest.TorchTensorCtorMultiDimErrorChecks
2023-01-11T22:10:12.8935720Z [       OK ] TensorTest.TorchTensorCtorMultiDimErrorChecks (5 ms)
2023-01-11T22:10:12.8936501Z [ RUN      ] TensorTest.TorchTensorCastRealToComplex
2023-01-11T22:10:12.8937195Z [       OK ] TensorTest.TorchTensorCastRealToComplex (0 ms)
2023-01-11T22:10:12.8937932Z [ RUN      ] TensorTest.TorchTensorCastComplexToRealErrorChecks
2023-01-11T22:10:12.8938631Z [W Copy.cpp:276] Warning: Casting complex values to real discards the imaginary part (function operator())
2023-01-11T22:10:12.8949728Z [       OK ] TensorTest.TorchTensorCastComplexToRealErrorChecks (1 ms)
2023-01-11T22:10:12.8950422Z [ RUN      ] TensorTest.TorchTensorCtorZeroSizedDim
2023-01-11T22:10:12.8951043Z [       OK ] TensorTest.TorchTensorCtorZeroSizedDim (0 ms)
2023-01-11T22:10:12.8951862Z [ RUN      ] TensorTest.TorchTensorCtorWithoutSpecifyingDtype
2023-01-11T22:10:12.8952665Z [       OK ] TensorTest.TorchTensorCtorWithoutSpecifyingDtype (0 ms)
2023-01-11T22:10:12.8953440Z [ RUN      ] TensorTest.TorchTensorCtorWithNonDtypeOptions
2023-01-11T22:10:12.8954188Z [       OK ] TensorTest.TorchTensorCtorWithNonDtypeOptions (0 ms)
2023-01-11T22:10:12.8954773Z [ RUN      ] TensorTest.Arange
2023-01-11T22:10:12.8955240Z [       OK ] TensorTest.Arange (0 ms)
2023-01-11T22:10:12.8955820Z [ RUN      ] TensorTest.PrettyPrintTensorDataContainer
2023-01-11T22:10:12.8956526Z [       OK ] TensorTest.PrettyPrintTensorDataContainer (0 ms)
2023-01-11T22:10:12.8957327Z [ RUN      ] TensorTest.TensorDataContainerCallingAccessorOfWrongType
2023-01-11T22:10:12.9018324Z [       OK ] TensorTest.TensorDataContainerCallingAccessorOfWrongType (6 ms)
2023-01-11T22:10:12.9019010Z [ RUN      ] TensorTest.FromBlob
2023-01-11T22:10:12.9019439Z [       OK ] TensorTest.FromBlob (0 ms)
2023-01-11T22:10:12.9019906Z [ RUN      ] TensorTest.FromBlobUsesDeleter
2023-01-11T22:10:12.9020492Z [       OK ] TensorTest.FromBlobUsesDeleter (0 ms)
2023-01-11T22:10:12.9020874Z [ RUN      ] TensorTest.FromBlobWithStrides
2023-01-11T22:10:12.9021198Z [       OK ] TensorTest.FromBlobWithStrides (0 ms)
2023-01-11T22:10:12.9021475Z [ RUN      ] TensorTest.Item
2023-01-11T22:10:12.9021719Z [       OK ] TensorTest.Item (0 ms)
2023-01-11T22:10:12.9022020Z [ RUN      ] TensorTest.DataPtr
2023-01-11T22:10:12.9022287Z [       OK ] TensorTest.DataPtr (0 ms)
2023-01-11T22:10:12.9022532Z [ RUN      ] TensorTest.Data
2023-01-11T22:10:12.9022856Z [       OK ] TensorTest.Data (0 ms)
2023-01-11T22:10:12.9023133Z [ RUN      ] TensorTest.BackwardAndGrad
2023-01-11T22:10:12.9023431Z [       OK ] TensorTest.BackwardAndGrad (0 ms)
2023-01-11T22:10:12.9023754Z [ RUN      ] TensorTest.BackwardCreatesOnesGrad
2023-01-11T22:10:12.9024098Z [       OK ] TensorTest.BackwardCreatesOnesGrad (0 ms)
2023-01-11T22:10:12.9024431Z [ RUN      ] TensorTest.BackwardNonScalarOutputs
2023-01-11T22:10:12.9052271Z [       OK ] TensorTest.BackwardNonScalarOutputs (3 ms)
2023-01-11T22:10:12.9052782Z [ RUN      ] TensorTest.IsLeaf
2023-01-11T22:10:12.9053255Z [       OK ] TensorTest.IsLeaf (0 ms)
2023-01-11T22:10:12.9053712Z [ RUN      ] TensorTest.OutputNr
2023-01-11T22:10:12.9054117Z [       OK ] TensorTest.OutputNr (0 ms)
2023-01-11T22:10:12.9054379Z [ RUN      ] TensorTest.Version
2023-01-11T22:10:12.9063473Z [       OK ] TensorTest.Version (0 ms)
2023-01-11T22:10:12.9063847Z [ RUN      ] TensorTest.Detach
2023-01-11T22:10:12.9064115Z [       OK ] TensorTest.Detach (0 ms)
2023-01-11T22:10:12.9064398Z [ RUN      ] TensorTest.DetachInplace
2023-01-11T22:10:12.9064713Z [       OK ] TensorTest.DetachInplace (0 ms)
2023-01-11T22:10:12.9064978Z [ RUN      ] TensorTest.SetData
2023-01-11T22:10:12.9065372Z [       OK ] TensorTest.SetData (0 ms)
2023-01-11T22:10:12.9065667Z [ RUN      ] TensorTest.RequiresGradInplace
2023-01-11T22:10:12.9079953Z [       OK ] TensorTest.RequiresGradInplace (2 ms)
2023-01-11T22:10:12.9080308Z [ RUN      ] TensorTest.StdDimension
2023-01-11T22:10:12.9080672Z [       OK ] TensorTest.StdDimension (0 ms)
2023-01-11T22:10:12.9080965Z [ RUN      ] TensorTest.ReshapeAlias
2023-01-11T22:10:12.9083194Z [       OK ] TensorTest.ReshapeAlias (0 ms)
2023-01-11T22:10:12.9083746Z [----------] 45 tests from TensorTest (30 ms total)
2023-01-11T22:10:12.9084033Z 
2023-01-11T22:10:12.9084252Z [----------] 36 tests from TensorIndexingTest
2023-01-11T22:10:12.9084547Z [ RUN      ] TensorIndexingTest.Slice
2023-01-11T22:10:12.9084958Z [       OK ] TensorIndexingTest.Slice (0 ms)
2023-01-11T22:10:12.9085270Z [ RUN      ] TensorIndexingTest.TensorIndex
2023-01-11T22:10:12.9095769Z [       OK ] TensorIndexingTest.TensorIndex (1 ms)
2023-01-11T22:10:12.9096105Z [ RUN      ] TensorIndexingTest.TestNoIndices
2023-01-11T22:10:12.9183520Z [       OK ] TensorIndexingTest.TestNoIndices (8 ms)
2023-01-11T22:10:12.9183976Z [ RUN      ] TensorIndexingTest.TestAdvancedIndexingWithListOfTensor
2023-01-11T22:10:12.9185142Z [       OK ] TensorIndexingTest.TestAdvancedIndexingWithListOfTensor (0 ms)
2023-01-11T22:10:12.9185669Z [ RUN      ] TensorIndexingTest.TestSingleInt
2023-01-11T22:10:12.9186011Z [       OK ] TensorIndexingTest.TestSingleInt (0 ms)
2023-01-11T22:10:12.9186509Z [ RUN      ] TensorIndexingTest.TestMultipleInt
2023-01-11T22:10:12.9186963Z [       OK ] TensorIndexingTest.TestMultipleInt (0 ms)
2023-01-11T22:10:12.9187380Z [ RUN      ] TensorIndexingTest.TestNone
2023-01-11T22:10:12.9187739Z [       OK ] TensorIndexingTest.TestNone (0 ms)
2023-01-11T22:10:12.9188099Z [ RUN      ] TensorIndexingTest.TestStep
2023-01-11T22:10:12.9188488Z [       OK ] TensorIndexingTest.TestStep (0 ms)
2023-01-11T22:10:12.9188886Z [ RUN      ] TensorIndexingTest.TestStepAssignment
2023-01-11T22:10:12.9189250Z [       OK ] TensorIndexingTest.TestStepAssignment (0 ms)
2023-01-11T22:10:12.9189643Z [ RUN      ] TensorIndexingTest.TestBoolIndices
2023-01-11T22:10:12.9190750Z [       OK ] TensorIndexingTest.TestBoolIndices (0 ms)
2023-01-11T22:10:12.9191418Z [ RUN      ] TensorIndexingTest.TestBoolIndicesAccumulate
2023-01-11T22:10:12.9192067Z [       OK ] TensorIndexingTest.TestBoolIndicesAccumulate (0 ms)
2023-01-11T22:10:12.9192653Z [ RUN      ] TensorIndexingTest.TestMultipleBoolIndices
2023-01-11T22:10:12.9193324Z [       OK ] TensorIndexingTest.TestMultipleBoolIndices (0 ms)
2023-01-11T22:10:12.9193794Z [ RUN      ] TensorIndexingTest.TestByteMask
2023-01-11T22:10:12.9194376Z [       OK ] TensorIndexingTest.TestByteMask (0 ms)
2023-01-11T22:10:12.9195015Z [ RUN      ] TensorIndexingTest.TestByteMaskAccumulate
2023-01-11T22:10:12.9195701Z [       OK ] TensorIndexingTest.TestByteMaskAccumulate (0 ms)
2023-01-11T22:10:12.9196378Z [ RUN      ] TensorIndexingTest.TestMultipleByteMask
2023-01-11T22:10:12.9197028Z [       OK ] TensorIndexingTest.TestMultipleByteMask (0 ms)
2023-01-11T22:10:12.9197642Z [ RUN      ] TensorIndexingTest.TestByteMask2d
2023-01-11T22:10:12.9198259Z [       OK ] TensorIndexingTest.TestByteMask2d (0 ms)
2023-01-11T22:10:12.9198842Z [ RUN      ] TensorIndexingTest.TestIntIndices
2023-01-11T22:10:12.9199433Z [       OK ] TensorIndexingTest.TestIntIndices (0 ms)
2023-01-11T22:10:12.9200060Z [ RUN      ] TensorIndexingTest.TestIntIndices2d
2023-01-11T22:10:12.9200649Z [       OK ] TensorIndexingTest.TestIntIndices2d (0 ms)
2023-01-11T22:10:12.9201005Z [ RUN      ] TensorIndexingTest.TestIntIndicesBroadcast
2023-01-11T22:10:12.9201522Z [       OK ] TensorIndexingTest.TestIntIndicesBroadcast (0 ms)
2023-01-11T22:10:12.9201880Z [ RUN      ] TensorIndexingTest.TestEmptyIndex
2023-01-11T22:10:12.9202210Z [       OK ] TensorIndexingTest.TestEmptyIndex (0 ms)
2023-01-11T22:10:12.9202558Z [ RUN      ] TensorIndexingTest.TestEmptyNdimIndex
2023-01-11T22:10:12.9257772Z [       OK ] TensorIndexingTest.TestEmptyNdimIndex (5 ms)
2023-01-11T22:10:12.9258381Z [ RUN      ] TensorIndexingTest.TestEmptyNdimIndexBool
2023-01-11T22:10:12.9278977Z [       OK ] TensorIndexingTest.TestEmptyNdimIndexBool (2 ms)
2023-01-11T22:10:12.9279646Z [ RUN      ] TensorIndexingTest.TestEmptySlice
2023-01-11T22:10:12.9280129Z [       OK ] TensorIndexingTest.TestEmptySlice (0 ms)
2023-01-11T22:10:12.9280800Z [ RUN      ] TensorIndexingTest.TestIndexGetitemCopyBoolsSlices
2023-01-11T22:10:12.9281423Z [       OK ] TensorIndexingTest.TestIndexGetitemCopyBoolsSlices (0 ms)
2023-01-11T22:10:12.9281851Z [ RUN      ] TensorIndexingTest.TestIndexSetitemBoolsSlices
2023-01-11T22:10:12.9369760Z [       OK ] TensorIndexingTest.TestIndexSetitemBoolsSlices (8 ms)
2023-01-11T22:10:12.9370417Z [ RUN      ] TensorIndexingTest.TestIndexScalarWithBoolMask
2023-01-11T22:10:12.9371163Z [       OK ] TensorIndexingTest.TestIndexScalarWithBoolMask (0 ms)
2023-01-11T22:10:12.9371783Z [ RUN      ] TensorIndexingTest.TestSetitemExpansionError
2023-01-11T22:10:12.9488711Z [       OK ] TensorIndexingTest.TestSetitemExpansionError (11 ms)
2023-01-11T22:10:12.9489497Z [ RUN      ] TensorIndexingTest.TestGetitemScalars
2023-01-11T22:10:12.9596911Z [       OK ] TensorIndexingTest.TestGetitemScalars (10 ms)
2023-01-11T22:10:12.9597495Z [ RUN      ] TensorIndexingTest.TestSetitemScalars
2023-01-11T22:10:12.9704482Z [       OK ] TensorIndexingTest.TestSetitemScalars (10 ms)
2023-01-11T22:10:12.9705180Z [ RUN      ] TensorIndexingTest.TestBasicAdvancedCombined
2023-01-11T22:10:12.9706001Z [       OK ] TensorIndexingTest.TestBasicAdvancedCombined (0 ms)
2023-01-11T22:10:12.9706671Z [ RUN      ] TensorIndexingTest.TestIntAssignment
2023-01-11T22:10:12.9707239Z [       OK ] TensorIndexingTest.TestIntAssignment (0 ms)
2023-01-11T22:10:12.9707803Z [ RUN      ] TensorIndexingTest.TestByteTensorAssignment
2023-01-11T22:10:12.9708500Z [       OK ] TensorIndexingTest.TestByteTensorAssignment (0 ms)
2023-01-11T22:10:12.9709098Z [ RUN      ] TensorIndexingTest.TestVariableSlicing
2023-01-11T22:10:12.9709763Z [       OK ] TensorIndexingTest.TestVariableSlicing (0 ms)
2023-01-11T22:10:12.9710244Z [ RUN      ] TensorIndexingTest.TestEllipsisTensor
2023-01-11T22:10:12.9710608Z [       OK ] TensorIndexingTest.TestEllipsisTensor (0 ms)
2023-01-11T22:10:12.9710960Z [ RUN      ] TensorIndexingTest.TestOutOfBoundIndex
2023-01-11T22:10:12.9808205Z [       OK ] TensorIndexingTest.TestOutOfBoundIndex (9 ms)
2023-01-11T22:10:12.9808571Z [ RUN      ] TensorIndexingTest.TestZeroDimIndex
2023-01-11T22:10:12.9829417Z [       OK ] TensorIndexingTest.TestZeroDimIndex (2 ms)
2023-01-11T22:10:12.9829806Z [----------] 36 tests from TensorIndexingTest (74 ms total)
2023-01-11T22:10:12.9829985Z 
2023-01-11T22:10:12.9830136Z [----------] 18 tests from NumpyTests
2023-01-11T22:10:12.9830413Z [ RUN      ] NumpyTests.TestNoneIndex
2023-01-11T22:10:12.9830698Z [       OK ] NumpyTests.TestNoneIndex (0 ms)
2023-01-11T22:10:12.9831008Z [ RUN      ] NumpyTests.TestEmptyFancyIndex
2023-01-11T22:10:12.9882837Z [       OK ] NumpyTests.TestEmptyFancyIndex (5 ms)
2023-01-11T22:10:12.9883228Z [ RUN      ] NumpyTests.TestEllipsisIndex
2023-01-11T22:10:12.9884036Z [       OK ] NumpyTests.TestEllipsisIndex (0 ms)
2023-01-11T22:10:12.9884548Z [ RUN      ] NumpyTests.TestSingleIntIndex
2023-01-11T22:10:12.9906385Z [       OK ] NumpyTests.TestSingleIntIndex (2 ms)
2023-01-11T22:10:12.9906786Z [ RUN      ] NumpyTests.TestSingleBoolIndex
2023-01-11T22:10:12.9907167Z [       OK ] NumpyTests.TestSingleBoolIndex (0 ms)
2023-01-11T22:10:12.9907571Z [ RUN      ] NumpyTests.TestBooleanShapeMismatch
2023-01-11T22:10:13.0117796Z [       OK ] NumpyTests.TestBooleanShapeMismatch (21 ms)
2023-01-11T22:10:13.0118278Z [ RUN      ] NumpyTests.TestBooleanIndexingOnedim
2023-01-11T22:10:13.0118915Z [       OK ] NumpyTests.TestBooleanIndexingOnedim (0 ms)
2023-01-11T22:10:13.0119300Z [ RUN      ] NumpyTests.TestBooleanAssignmentValueMismatch
2023-01-11T22:10:13.0270547Z [       OK ] NumpyTests.TestBooleanAssignmentValueMismatch (15 ms)
2023-01-11T22:10:13.0271031Z [ RUN      ] NumpyTests.TestBooleanIndexingTwodim
2023-01-11T22:10:13.0271586Z [       OK ] NumpyTests.TestBooleanIndexingTwodim (0 ms)
2023-01-11T22:10:13.0272004Z [ RUN      ] NumpyTests.TestBooleanIndexingWeirdness
2023-01-11T22:10:13.0382418Z [       OK ] NumpyTests.TestBooleanIndexingWeirdness (11 ms)
2023-01-11T22:10:13.0382907Z [ RUN      ] NumpyTests.TestBooleanIndexingWeirdnessTensors
2023-01-11T22:10:13.0492268Z [       OK ] NumpyTests.TestBooleanIndexingWeirdnessTensors (10 ms)
2023-01-11T22:10:13.0492674Z [ RUN      ] NumpyTests.TestBooleanIndexingAlldims
2023-01-11T22:10:13.0493105Z [       OK ] NumpyTests.TestBooleanIndexingAlldims (0 ms)
2023-01-11T22:10:13.0493607Z [ RUN      ] NumpyTests.TestBooleanListIndexing
2023-01-11T22:10:13.0494405Z [       OK ] NumpyTests.TestBooleanListIndexing (0 ms)
2023-01-11T22:10:13.0494823Z [ RUN      ] NumpyTests.TestEverythingReturnsViews
2023-01-11T22:10:13.0495192Z [       OK ] NumpyTests.TestEverythingReturnsViews (0 ms)
2023-01-11T22:10:13.0495543Z [ RUN      ] NumpyTests.TestBroaderrorsIndexing
2023-01-11T22:10:13.0710116Z [       OK ] NumpyTests.TestBroaderrorsIndexing (21 ms)
2023-01-11T22:10:13.0710509Z [ RUN      ] NumpyTests.TestTrivialFancyOutOfBounds
2023-01-11T22:10:13.0981678Z [       OK ] NumpyTests.TestTrivialFancyOutOfBounds (27 ms)
2023-01-11T22:10:13.1010306Z [ RUN      ] NumpyTests.TestIndexIsLarger
2023-01-11T22:10:13.1010932Z [       OK ] NumpyTests.TestIndexIsLarger (0 ms)
2023-01-11T22:10:13.1011505Z [ RUN      ] NumpyTests.TestBroadcastSubspace
2023-01-11T22:10:13.1012114Z [       OK ] NumpyTests.TestBroadcastSubspace (0 ms)
2023-01-11T22:10:13.1012711Z [----------] 18 tests from NumpyTests (115 ms total)
2023-01-11T22:10:13.1012983Z 
2023-01-11T22:10:13.1013251Z [----------] 5 tests from TensorOptionsTest
2023-01-11T22:10:13.1013863Z [ RUN      ] TensorOptionsTest.DefaultsToTheRightValues
2023-01-11T22:10:13.1014592Z [       OK ] TensorOptionsTest.DefaultsToTheRightValues (0 ms)
2023-01-11T22:10:13.1015423Z [ RUN      ] TensorOptionsTest.UtilityFunctionsReturnTheRightTensorOptions
2023-01-11T22:10:13.1016391Z [       OK ] TensorOptionsTest.UtilityFunctionsReturnTheRightTensorOptions (0 ms)
2023-01-11T22:10:13.1017232Z [ RUN      ] TensorOptionsTest.ConstructsWellFromCPUTypes
2023-01-11T22:10:13.1017983Z [       OK ] TensorOptionsTest.ConstructsWellFromCPUTypes (0 ms)
2023-01-11T22:10:13.1018717Z [ RUN      ] TensorOptionsTest.ConstructsWellFromCPUTensors
2023-01-11T22:10:13.1019461Z [       OK ] TensorOptionsTest.ConstructsWellFromCPUTensors (0 ms)
2023-01-11T22:10:13.1020223Z [ RUN      ] TensorOptionsTest.ConstructsWellFromVariables
2023-01-11T22:10:13.1020963Z [       OK ] TensorOptionsTest.ConstructsWellFromVariables (0 ms)
2023-01-11T22:10:13.1021891Z [----------] 5 tests from TensorOptionsTest (0 ms total)
2023-01-11T22:10:13.1022178Z 
2023-01-11T22:10:13.1022421Z [----------] 1 test from DeviceTest
2023-01-11T22:10:13.1023048Z [ RUN      ] DeviceTest.ParsesCorrectlyFromString
2023-01-11T22:10:13.1074887Z [       OK ] DeviceTest.ParsesCorrectlyFromString (8 ms)
2023-01-11T22:10:13.1075497Z [----------] 1 test from DeviceTest (8 ms total)
2023-01-11T22:10:13.1075740Z 
2023-01-11T22:10:13.1076010Z [----------] 3 tests from DefaultDtypeTest
2023-01-11T22:10:13.1076587Z [ RUN      ] DefaultDtypeTest.CanSetAndGetDefaultDtype
2023-01-11T22:10:13.1077235Z [       OK ] DefaultDtypeTest.CanSetAndGetDefaultDtype (0 ms)
2023-01-11T22:10:13.1077909Z [ RUN      ] DefaultDtypeTest.NewTensorOptionsHasCorrectDefault
2023-01-11T22:10:13.1078625Z [       OK ] DefaultDtypeTest.NewTensorOptionsHasCorrectDefault (0 ms)
2023-01-11T22:10:13.1079184Z [ RUN      ] DefaultDtypeTest.NewTensorsHaveCorrectDefaultDtype
2023-01-11T22:10:13.1079769Z [       OK ] DefaultDtypeTest.NewTensorsHaveCorrectDefaultDtype (0 ms)
2023-01-11T22:10:13.1080275Z [----------] 3 tests from DefaultDtypeTest (0 ms total)
2023-01-11T22:10:13.1080482Z 
2023-01-11T22:10:13.1080683Z [----------] 1 test from TorchIncludeTest
2023-01-11T22:10:13.1081077Z [ RUN      ] TorchIncludeTest.GetSetNumThreads
2023-01-11T22:10:13.1228953Z [       OK ] TorchIncludeTest.GetSetNumThreads (15 ms)
2023-01-11T22:10:13.1229646Z [----------] 1 test from TorchIncludeTest (15 ms total)
2023-01-11T22:10:13.1229813Z 
2023-01-11T22:10:13.1229976Z [----------] 28 tests from InferenceModeTest
2023-01-11T22:10:13.1230287Z [ RUN      ] InferenceModeTest.TestTLSState
2023-01-11T22:10:13.1230613Z [       OK ] InferenceModeTest.TestTLSState (0 ms)
2023-01-11T22:10:13.1230981Z [ RUN      ] InferenceModeTest.TestInferenceTensorCreation
2023-01-11T22:10:13.1231400Z [       OK ] InferenceModeTest.TestInferenceTensorCreation (0 ms)
2023-01-11T22:10:13.1231797Z [ RUN      ] InferenceModeTest.TestExistingAutogradSession
2023-01-11T22:10:13.1300299Z [       OK ] InferenceModeTest.TestExistingAutogradSession (7 ms)
2023-01-11T22:10:13.1300863Z [ RUN      ] InferenceModeTest.TestInferenceTensorInInferenceModeFunctionalOp
2023-01-11T22:10:13.1301416Z [       OK ] InferenceModeTest.TestInferenceTensorInInferenceModeFunctionalOp (0 ms)
2023-01-11T22:10:13.1301946Z [ RUN      ] InferenceModeTest.TestInferenceTensorInInferenceModeInplaceOp
2023-01-11T22:10:13.1302471Z [       OK ] InferenceModeTest.TestInferenceTensorInInferenceModeInplaceOp (0 ms)
2023-01-11T22:10:13.1303062Z [ RUN      ] InferenceModeTest.TestInferenceTensorInInferenceModeViewOp
2023-01-11T22:10:13.1303552Z [       OK ] InferenceModeTest.TestInferenceTensorInInferenceModeViewOp (0 ms)
2023-01-11T22:10:13.1304067Z [ RUN      ] InferenceModeTest.TestInferenceTensorInNormalModeFunctionalOp
2023-01-11T22:10:13.1304589Z [       OK ] InferenceModeTest.TestInferenceTensorInNormalModeFunctionalOp (0 ms)
2023-01-11T22:10:13.1305094Z [ RUN      ] InferenceModeTest.TestInferenceTensorInNormalModeInplaceOp
2023-01-11T22:10:13.1354096Z [       OK ] InferenceModeTest.TestInferenceTensorInNormalModeInplaceOp (5 ms)
2023-01-11T22:10:13.1354853Z [ RUN      ] InferenceModeTest.TestInferenceTensorInNormalModeViewOp
2023-01-11T22:10:13.1355565Z [       OK ] InferenceModeTest.TestInferenceTensorInNormalModeViewOp (0 ms)
2023-01-11T22:10:13.1356120Z [ RUN      ] InferenceModeTest.TestNormalTensorInplaceOutputInInferenceMode
2023-01-11T22:10:13.1356694Z [       OK ] InferenceModeTest.TestNormalTensorInplaceOutputInInferenceMode (0 ms)
2023-01-11T22:10:13.1357210Z [ RUN      ] InferenceModeTest.TestNormalTensorInplaceOutputInNormalMode
2023-01-11T22:10:13.1357854Z [       OK ] InferenceModeTest.TestNormalTensorInplaceOutputInNormalMode (0 ms)
2023-01-11T22:10:13.1358341Z [ RUN      ] InferenceModeTest.TestNormalTensorViewOutputInInferenceMode
2023-01-11T22:10:13.1358843Z [       OK ] InferenceModeTest.TestNormalTensorViewOutputInInferenceMode (0 ms)
2023-01-11T22:10:13.1359331Z [ RUN      ] InferenceModeTest.TestNormalTensorViewOutputInNormalMode
2023-01-11T22:10:13.1388755Z [       OK ] InferenceModeTest.TestNormalTensorViewOutputInNormalMode (3 ms)
2023-01-11T22:10:13.1389286Z [ RUN      ] InferenceModeTest.TestMixInferenceAndNormalTensorFunctionalOp
2023-01-11T22:10:13.1420480Z [       OK ] InferenceModeTest.TestMixInferenceAndNormalTensorFunctionalOp (3 ms)
2023-01-11T22:10:13.1421250Z [ RUN      ] InferenceModeTest.TestMixInferenceAndNormalTensorInplaceOp
2023-01-11T22:10:13.1502216Z [       OK ] InferenceModeTest.TestMixInferenceAndNormalTensorInplaceOp (8 ms)
2023-01-11T22:10:13.1502848Z [ RUN      ] InferenceModeTest.TestMixInferenceAndNormalTensorViewOp
2023-01-11T22:10:13.1503411Z [       OK ] InferenceModeTest.TestMixInferenceAndNormalTensorViewOp (0 ms)
2023-01-11T22:10:13.1503859Z [ RUN      ] InferenceModeTest.TestHandleDirectViewOnRebase
2023-01-11T22:10:13.1534080Z [       OK ] InferenceModeTest.TestHandleDirectViewOnRebase (3 ms)
2023-01-11T22:10:13.1534498Z [ RUN      ] InferenceModeTest.TestHandleInDirectViewOnRebase
2023-01-11T22:10:13.1556825Z [       OK ] InferenceModeTest.TestHandleInDirectViewOnRebase (2 ms)
2023-01-11T22:10:13.1557480Z [ RUN      ] InferenceModeTest.TestCreationMetaPropagation
2023-01-11T22:10:13.1618425Z [       OK ] InferenceModeTest.TestCreationMetaPropagation (6 ms)
2023-01-11T22:10:13.1618870Z [ RUN      ] InferenceModeTest.TestCreationMetaPropagationInput
2023-01-11T22:10:13.1741055Z [       OK ] InferenceModeTest.TestCreationMetaPropagationInput (12 ms)
2023-01-11T22:10:13.1741501Z [ RUN      ] InferenceModeTest.TestInplaceCopyOnInferenceTensor
2023-01-11T22:10:13.1813198Z [       OK ] InferenceModeTest.TestInplaceCopyOnInferenceTensor (7 ms)
2023-01-11T22:10:13.1813641Z [ RUN      ] InferenceModeTest.TestSetRequiresGradInNormalMode
2023-01-11T22:10:13.1824330Z [       OK ] InferenceModeTest.TestSetRequiresGradInNormalMode (1 ms)
2023-01-11T22:10:13.1824794Z [ RUN      ] InferenceModeTest.TestAccessVersionCounter
2023-01-11T22:10:13.1858016Z [       OK ] InferenceModeTest.TestAccessVersionCounter (3 ms)
2023-01-11T22:10:13.1858573Z [ RUN      ] InferenceModeTest.TestInplaceUpdateInferenceTensorWithNormalTensor
2023-01-11T22:10:13.1929829Z [       OK ] InferenceModeTest.TestInplaceUpdateInferenceTensorWithNormalTensor (7 ms)
2023-01-11T22:10:13.1930694Z [ RUN      ] InferenceModeTest.TestComplexViewInInferenceMode
2023-01-11T22:10:13.1931380Z [       OK ] InferenceModeTest.TestComplexViewInInferenceMode (0 ms)
2023-01-11T22:10:13.1932153Z [ RUN      ] InferenceModeTest.TestComplexViewInNormalMode
2023-01-11T22:10:13.1932891Z [       OK ] InferenceModeTest.TestComplexViewInNormalMode (0 ms)
2023-01-11T22:10:13.1933579Z [ RUN      ] InferenceModeTest.TestCustomFunction
2023-01-11T22:10:13.1934243Z [       OK ] InferenceModeTest.TestCustomFunction (0 ms)
2023-01-11T22:10:13.1935030Z [ RUN      ] InferenceModeTest.TestLegacyAutoNonVariableTypeModeWarning
2023-01-11T22:10:13.1935936Z [       OK ] InferenceModeTest.TestLegacyAutoNonVariableTypeModeWarning (0 ms)
2023-01-11T22:10:13.1936714Z [----------] 28 tests from InferenceModeTest (70 ms total)
2023-01-11T22:10:13.1937001Z 
2023-01-11T22:10:13.1937266Z [----------] 4 tests from GradModeTest
2023-01-11T22:10:13.1937839Z [ RUN      ] GradModeTest.TestRequiresGradFunctionalOp
2023-01-11T22:10:13.1938716Z [       OK ] GradModeTest.TestRequiresGradFunctionalOp (0 ms)
2023-01-11T22:10:13.1939398Z [ RUN      ] GradModeTest.TestRequiresGradInplaceOp
2023-01-11T22:10:13.1940071Z [       OK ] GradModeTest.TestRequiresGradInplaceOp (0 ms)
2023-01-11T22:10:13.1940703Z [ RUN      ] GradModeTest.TestRequiresGradViewOp
2023-01-11T22:10:13.1941333Z [       OK ] GradModeTest.TestRequiresGradViewOp (0 ms)
2023-01-11T22:10:13.1941998Z [ RUN      ] GradModeTest.TestRequiresGradViewOpExiting
2023-01-11T22:10:13.1966679Z [       OK ] GradModeTest.TestRequiresGradViewOpExiting (3 ms)
2023-01-11T22:10:13.1967329Z [----------] 4 tests from GradModeTest (3 ms total)
2023-01-11T22:10:13.1967581Z 
2023-01-11T22:10:13.1967840Z [----------] 3 tests from OperationTest
2023-01-11T22:10:13.1968419Z [ RUN      ] OperationTest.Lerp
2023-01-11T22:10:13.1976106Z [       OK ] OperationTest.Lerp (0 ms)
2023-01-11T22:10:13.1976592Z [ RUN      ] OperationTest.Cross
2023-01-11T22:10:13.2009867Z [       OK ] OperationTest.Cross (3 ms)
2023-01-11T22:10:13.2010402Z [ RUN      ] OperationTest.Linear_out
2023-01-11T22:10:13.2013727Z [       OK ] OperationTest.Linear_out (0 ms)
2023-01-11T22:10:13.2014362Z [----------] 3 tests from OperationTest (4 ms total)
2023-01-11T22:10:13.2014657Z 
2023-01-11T22:10:13.2014990Z [----------] Global test environment tear-down
2023-01-11T22:10:13.2115307Z [==========] 992 tests from 48 test suites ran. (41667 ms total)
2023-01-11T22:10:13.2115720Z [  PASSED  ] 992 tests.
2023-01-11T22:10:13.2955619Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_tensorexpr --gtest_output=xml:test/test-reports/cpp-unittest/test_libtorch/test_tensorexpr.xml
2023-01-11T22:10:13.6643969Z CUDA not available. Disabling CUDA and MultiCUDA tests
2023-01-11T22:10:13.6649234Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:10:13.6649788Z [==========] Running 801 tests from 25 test suites.
2023-01-11T22:10:13.6650254Z [----------] Global test environment set-up.
2023-01-11T22:10:13.6650537Z [----------] 1 test from Approx
2023-01-11T22:10:13.6650778Z [ RUN      ] Approx.log_vml
2023-01-11T22:10:14.8453985Z [       OK ] Approx.log_vml (1180 ms)
2023-01-11T22:10:14.8454588Z [----------] 1 test from Approx (1180 ms total)
2023-01-11T22:10:14.8454853Z 
2023-01-11T22:10:14.8455062Z [----------] 34 tests from ATen
2023-01-11T22:10:14.8455454Z [ RUN      ] ATen._cast_Float
2023-01-11T22:10:14.8456359Z [       OK ] ATen._cast_Float (0 ms)
2023-01-11T22:10:14.8456792Z [ RUN      ] ATen.negInt
2023-01-11T22:10:14.8460866Z [       OK ] ATen.negInt (0 ms)
2023-01-11T22:10:14.8461302Z [ RUN      ] ATen.negFloat
2023-01-11T22:10:14.8465308Z [       OK ] ATen.negFloat (0 ms)
2023-01-11T22:10:14.8465764Z [ RUN      ] ATen.addInt
2023-01-11T22:10:14.8472350Z [       OK ] ATen.addInt (0 ms)
2023-01-11T22:10:14.8472768Z [ RUN      ] ATen.addFloat
2023-01-11T22:10:14.8479530Z [       OK ] ATen.addFloat (0 ms)
2023-01-11T22:10:14.8479965Z [ RUN      ] ATen.subInt
2023-01-11T22:10:14.8486371Z [       OK ] ATen.subInt (0 ms)
2023-01-11T22:10:14.8486811Z [ RUN      ] ATen.subFloat
2023-01-11T22:10:14.8493531Z [       OK ] ATen.subFloat (0 ms)
2023-01-11T22:10:14.8493948Z [ RUN      ] ATen.lerp
2023-01-11T22:10:14.8502038Z [       OK ] ATen.lerp (0 ms)
2023-01-11T22:10:14.8502480Z [ RUN      ] ATen.addcmulInt
2023-01-11T22:10:14.8510584Z [       OK ] ATen.addcmulInt (0 ms)
2023-01-11T22:10:14.8511034Z [ RUN      ] ATen.addcmulFloat
2023-01-11T22:10:14.8519156Z [       OK ] ATen.addcmulFloat (0 ms)
2023-01-11T22:10:14.8519604Z [ RUN      ] ATen.mulInt
2023-01-11T22:10:14.8524285Z [       OK ] ATen.mulInt (0 ms)
2023-01-11T22:10:14.8524713Z [ RUN      ] ATen.mulFloat
2023-01-11T22:10:14.8529719Z [       OK ] ATen.mulFloat (0 ms)
2023-01-11T22:10:14.8530164Z [ RUN      ] ATen.divInt
2023-01-11T22:10:14.8534796Z [       OK ] ATen.divInt (0 ms)
2023-01-11T22:10:14.8535234Z [ RUN      ] ATen.divFloat
2023-01-11T22:10:14.8539996Z [       OK ] ATen.divFloat (0 ms)
2023-01-11T22:10:14.8540417Z [ RUN      ] ATen.maxInt
2023-01-11T22:10:14.8545666Z [       OK ] ATen.maxInt (0 ms)
2023-01-11T22:10:14.8546068Z [ RUN      ] ATen.maxFloat
2023-01-11T22:10:14.8549890Z [       OK ] ATen.maxFloat (0 ms)
2023-01-11T22:10:14.8550364Z [ RUN      ] ATen.minInt
2023-01-11T22:10:14.8554219Z [       OK ] ATen.minInt (0 ms)
2023-01-11T22:10:14.8554899Z [ RUN      ] ATen.minFloat
2023-01-11T22:10:14.8558693Z [       OK ] ATen.minFloat (0 ms)
2023-01-11T22:10:14.8559185Z [ RUN      ] ATen.reluInt
2023-01-11T22:10:14.8562003Z [       OK ] ATen.reluInt (0 ms)
2023-01-11T22:10:14.8562462Z [ RUN      ] ATen.reluFloat
2023-01-11T22:10:14.8565566Z [       OK ] ATen.reluFloat (0 ms)
2023-01-11T22:10:14.8566063Z [ RUN      ] ATen.logFloat
2023-01-11T22:10:14.8569012Z [       OK ] ATen.logFloat (0 ms)
2023-01-11T22:10:14.8569651Z [ RUN      ] ATen.fastLogFloat
2023-01-11T22:10:14.8717218Z [       OK ] ATen.fastLogFloat (14 ms)
2023-01-11T22:10:14.8717828Z [ RUN      ] ATen.fastTanhFloat
2023-01-11T22:10:14.8774630Z [       OK ] ATen.fastTanhFloat (5 ms)
2023-01-11T22:10:14.8775178Z [ RUN      ] ATen.fastSigmoidFloat
2023-01-11T22:10:14.8850645Z [       OK ] ATen.fastSigmoidFloat (7 ms)
2023-01-11T22:10:14.8851131Z [ RUN      ] ATen.log10Float
2023-01-11T22:10:14.8853893Z [       OK ] ATen.log10Float (0 ms)
2023-01-11T22:10:14.8854402Z [ RUN      ] ATen.log2Float
2023-01-11T22:10:14.8857225Z [       OK ] ATen.log2Float (0 ms)
2023-01-11T22:10:14.8857690Z [ RUN      ] ATen.expFloat
2023-01-11T22:10:14.8860537Z [       OK ] ATen.expFloat (0 ms)
2023-01-11T22:10:14.8861024Z [ RUN      ] ATen.erfFloat
2023-01-11T22:10:14.8864041Z [       OK ] ATen.erfFloat (0 ms)
2023-01-11T22:10:14.8864529Z [ RUN      ] ATen.cosFloat
2023-01-11T22:10:14.8867807Z [       OK ] ATen.cosFloat (0 ms)
2023-01-11T22:10:14.8868266Z [ RUN      ] ATen.eqInt
2023-01-11T22:10:14.8872546Z [       OK ] ATen.eqInt (0 ms)
2023-01-11T22:10:14.8873003Z [ RUN      ] ATen.geInt
2023-01-11T22:10:14.8877221Z [       OK ] ATen.geInt (0 ms)
2023-01-11T22:10:14.8877644Z [ RUN      ] ATen.gtInt
2023-01-11T22:10:14.8882142Z [       OK ] ATen.gtInt (0 ms)
2023-01-11T22:10:14.8882584Z [ RUN      ] ATen.leInt
2023-01-11T22:10:14.8887001Z [       OK ] ATen.leInt (0 ms)
2023-01-11T22:10:14.8887437Z [ RUN      ] ATen.ltInt
2023-01-11T22:10:14.8892107Z [       OK ] ATen.ltInt (0 ms)
2023-01-11T22:10:14.8892611Z [----------] 34 tests from ATen (44 ms total)
2023-01-11T22:10:14.8892873Z 
2023-01-11T22:10:14.8893044Z [----------] 26 tests from BoundsInference
2023-01-11T22:10:14.8893317Z [ RUN      ] BoundsInference._1
2023-01-11T22:10:14.8894985Z [       OK ] BoundsInference._1 (0 ms)
2023-01-11T22:10:14.8895467Z [ RUN      ] BoundsInference._2
2023-01-11T22:10:14.8897266Z [       OK ] BoundsInference._2 (0 ms)
2023-01-11T22:10:14.8897574Z [ RUN      ] BoundsInference._3
2023-01-11T22:10:14.8899780Z [       OK ] BoundsInference._3 (0 ms)
2023-01-11T22:10:14.8900081Z [ RUN      ] BoundsInference._4
2023-01-11T22:10:14.8905380Z [       OK ] BoundsInference._4 (0 ms)
2023-01-11T22:10:14.8905681Z [ RUN      ] BoundsInference._5
2023-01-11T22:10:14.8918176Z [       OK ] BoundsInference._5 (1 ms)
2023-01-11T22:10:14.8918455Z [ RUN      ] BoundsInference._6
2023-01-11T22:10:14.8926877Z [       OK ] BoundsInference._6 (0 ms)
2023-01-11T22:10:14.8927162Z [ RUN      ] BoundsInference.Adjacent
2023-01-11T22:10:14.8932445Z [       OK ] BoundsInference.Adjacent (0 ms)
2023-01-11T22:10:14.8932793Z [ RUN      ] BoundsInference.MultipleTopLoopLoad
2023-01-11T22:10:14.8937480Z [       OK ] BoundsInference.MultipleTopLoopLoad (0 ms)
2023-01-11T22:10:14.8938130Z [ RUN      ] BoundsInference.MultipleTopLoopStore
2023-01-11T22:10:14.8942082Z [       OK ] BoundsInference.MultipleTopLoopStore (0 ms)
2023-01-11T22:10:14.8942716Z [ RUN      ] BoundsInference.CacheReads
2023-01-11T22:10:14.8968595Z [       OK ] BoundsInference.CacheReads (2 ms)
2023-01-11T22:10:14.8969256Z [ RUN      ] BoundsInference.Flattened
2023-01-11T22:10:14.8980720Z [       OK ] BoundsInference.Flattened (1 ms)
2023-01-11T22:10:14.8981304Z [ RUN      ] BoundsInference.GetPotentialHazards
2023-01-11T22:10:14.8982018Z [       OK ] BoundsInference.GetPotentialHazards (0 ms)
2023-01-11T22:10:14.8982733Z [ RUN      ] BoundsInference.GetPotentialHazardsLoopNoHazard
2023-01-11T22:10:14.8985788Z [       OK ] BoundsInference.GetPotentialHazardsLoopNoHazard (0 ms)
2023-01-11T22:10:14.8986399Z [ RUN      ] BoundsInference.GetPotentialHazardsLoopCall
2023-01-11T22:10:14.8990274Z [       OK ] BoundsInference.GetPotentialHazardsLoopCall (0 ms)
2023-01-11T22:10:14.8990879Z [ RUN      ] BoundsInference.GetPotentialHazardsLoopSplit
2023-01-11T22:10:14.9001608Z [       OK ] BoundsInference.GetPotentialHazardsLoopSplit (1 ms)
2023-01-11T22:10:14.9002287Z [ RUN      ] BoundsInference.HasConflictingOverlapSameBufferWithPartialOverlap
2023-01-11T22:10:14.9006734Z [       OK ] BoundsInference.HasConflictingOverlapSameBufferWithPartialOverlap (0 ms)
2023-01-11T22:10:14.9007394Z [ RUN      ] BoundsInference.HasConflictingOverlapSameBufferWithFullOverlap
2023-01-11T22:10:14.9008507Z [       OK ] BoundsInference.HasConflictingOverlapSameBufferWithFullOverlap (0 ms)
2023-01-11T22:10:14.9009359Z [ RUN      ] BoundsInference.HasConflictingOverlapSameBufferWithFullOverlapRAW
2023-01-11T22:10:14.9010822Z [       OK ] BoundsInference.HasConflictingOverlapSameBufferWithFullOverlapRAW (0 ms)
2023-01-11T22:10:14.9011509Z [ RUN      ] BoundsInference.HasConflictingOverlapSameBufferNotOverlapping
2023-01-11T22:10:14.9013700Z [       OK ] BoundsInference.HasConflictingOverlapSameBufferNotOverlapping (0 ms)
2023-01-11T22:10:14.9014362Z [ RUN      ] BoundsInference.HasConflictingOverlap2DBufferWithOverlap
2023-01-11T22:10:14.9027697Z [       OK ] BoundsInference.HasConflictingOverlap2DBufferWithOverlap (1 ms)
2023-01-11T22:10:14.9028358Z [ RUN      ] BoundsInference.HasConflictingOverlap2DBufferWithNoOverlap
2023-01-11T22:10:14.9037352Z [       OK ] BoundsInference.HasConflictingOverlap2DBufferWithNoOverlap (0 ms)
2023-01-11T22:10:14.9037854Z [ RUN      ] BoundsInference.HasConflictingOverlapDifferentBuffers
2023-01-11T22:10:14.9040141Z [       OK ] BoundsInference.HasConflictingOverlapDifferentBuffers (0 ms)
2023-01-11T22:10:14.9040622Z [ RUN      ] BoundsInference.HasConflictingOverlapDueToRAWDependence
2023-01-11T22:10:14.9043046Z [       OK ] BoundsInference.HasConflictingOverlapDueToRAWDependence (0 ms)
2023-01-11T22:10:14.9043551Z [ RUN      ] BoundsInference.HasConflictingOverlapDueToWARDependence
2023-01-11T22:10:14.9045543Z [       OK ] BoundsInference.HasConflictingOverlapDueToWARDependence (0 ms)
2023-01-11T22:10:14.9046013Z [ RUN      ] BoundsInference.HasConflictingOverlapWithLoads
2023-01-11T22:10:14.9048097Z [       OK ] BoundsInference.HasConflictingOverlapWithLoads (0 ms)
2023-01-11T22:10:14.9048479Z [ RUN      ] BoundsInference.IsOverlapping
2023-01-11T22:10:14.9069941Z [       OK ] BoundsInference.IsOverlapping (2 ms)
2023-01-11T22:10:14.9070382Z [----------] 26 tests from BoundsInference (17 ms total)
2023-01-11T22:10:14.9070551Z 
2023-01-11T22:10:14.9070673Z [----------] 4 tests from Conv
2023-01-11T22:10:14.9070940Z [ RUN      ] Conv.DepthwiseConv2D
2023-01-11T22:10:15.1630936Z [       OK ] Conv.DepthwiseConv2D (255 ms)
2023-01-11T22:10:15.1631356Z [ RUN      ] Conv.DepthwiseConv2DNoBias
2023-01-11T22:10:15.4160369Z [       OK ] Conv.DepthwiseConv2DNoBias (252 ms)
2023-01-11T22:10:15.4160841Z [ RUN      ] Conv.DepthwiseConv2DDynamicShapes
2023-01-11T22:10:15.6093954Z [       OK ] Conv.DepthwiseConv2DDynamicShapes (193 ms)
2023-01-11T22:10:15.6094372Z [ RUN      ] Conv.Conv2D
2023-01-11T22:10:16.5944082Z [       OK ] Conv.Conv2D (984 ms)
2023-01-11T22:10:16.5944794Z [----------] 4 tests from Conv (1687 ms total)
2023-01-11T22:10:16.5945186Z 
2023-01-11T22:10:16.5945457Z [----------] 28 tests from CppPrinter
2023-01-11T22:10:16.5946083Z [ RUN      ] CppPrinter.IntImm
2023-01-11T22:10:16.5946674Z [       OK ] CppPrinter.IntImm (0 ms)
2023-01-11T22:10:16.5947120Z [ RUN      ] CppPrinter.FloatImm
2023-01-11T22:10:16.5947753Z [       OK ] CppPrinter.FloatImm (0 ms)
2023-01-11T22:10:16.5948249Z [ RUN      ] CppPrinter.FloatImm1
2023-01-11T22:10:16.5948592Z [       OK ] CppPrinter.FloatImm1 (0 ms)
2023-01-11T22:10:16.5948996Z [ RUN      ] CppPrinter.DoubleImm
2023-01-11T22:10:16.5949433Z [       OK ] CppPrinter.DoubleImm (0 ms)
2023-01-11T22:10:16.5949839Z [ RUN      ] CppPrinter.DoubleImm1
2023-01-11T22:10:16.5950172Z [       OK ] CppPrinter.DoubleImm1 (0 ms)
2023-01-11T22:10:16.5950444Z [ RUN      ] CppPrinter.HalfImm
2023-01-11T22:10:16.5950714Z [       OK ] CppPrinter.HalfImm (0 ms)
2023-01-11T22:10:16.5950972Z [ RUN      ] CppPrinter.Add
2023-01-11T22:10:16.5951217Z [       OK ] CppPrinter.Add (0 ms)
2023-01-11T22:10:16.5951482Z [ RUN      ] CppPrinter.AddExpr1
2023-01-11T22:10:16.5951760Z [       OK ] CppPrinter.AddExpr1 (0 ms)
2023-01-11T22:10:16.5952030Z [ RUN      ] CppPrinter.AddExpr2
2023-01-11T22:10:16.5952291Z [       OK ] CppPrinter.AddExpr2 (0 ms)
2023-01-11T22:10:16.5952563Z [ RUN      ] CppPrinter.AddExpr3
2023-01-11T22:10:16.5952837Z [       OK ] CppPrinter.AddExpr3 (0 ms)
2023-01-11T22:10:16.5953081Z [ RUN      ] CppPrinter.Mod
2023-01-11T22:10:16.5953338Z [       OK ] CppPrinter.Mod (0 ms)
2023-01-11T22:10:16.5953603Z [ RUN      ] CppPrinter.ModFloat
2023-01-11T22:10:16.5953866Z [       OK ] CppPrinter.ModFloat (0 ms)
2023-01-11T22:10:16.5954127Z [ RUN      ] CppPrinter.Max
2023-01-11T22:10:16.5954383Z [       OK ] CppPrinter.Max (0 ms)
2023-01-11T22:10:16.5954631Z [ RUN      ] CppPrinter.MaxFloat
2023-01-11T22:10:16.5954905Z [       OK ] CppPrinter.MaxFloat (0 ms)
2023-01-11T22:10:16.5955172Z [ RUN      ] CppPrinter.MaxHalf
2023-01-11T22:10:16.5955430Z [       OK ] CppPrinter.MaxHalf (0 ms)
2023-01-11T22:10:16.5955687Z [ RUN      ] CppPrinter.And
2023-01-11T22:10:16.5955943Z [       OK ] CppPrinter.And (0 ms)
2023-01-11T22:10:16.5956234Z [ RUN      ] CppPrinter.CompareSelect
2023-01-11T22:10:16.5956524Z [       OK ] CppPrinter.CompareSelect (0 ms)
2023-01-11T22:10:16.5956814Z [ RUN      ] CppPrinter.IfThenElse
2023-01-11T22:10:16.5957102Z [       OK ] CppPrinter.IfThenElse (0 ms)
2023-01-11T22:10:16.5957374Z [ RUN      ] CppPrinter.AllocateFree
2023-01-11T22:10:16.5957668Z [       OK ] CppPrinter.AllocateFree (0 ms)
2023-01-11T22:10:16.5961125Z [ RUN      ] CppPrinter.LoadStore
2023-01-11T22:10:16.5961635Z [       OK ] CppPrinter.LoadStore (0 ms)
2023-01-11T22:10:16.5962134Z [ RUN      ] CppPrinter.Var
2023-01-11T22:10:16.5962615Z [       OK ] CppPrinter.Var (0 ms)
2023-01-11T22:10:16.5963053Z [ RUN      ] CppPrinter.Cast
2023-01-11T22:10:16.5963521Z [       OK ] CppPrinter.Cast (0 ms)
2023-01-11T22:10:16.5963830Z [ RUN      ] CppPrinter.BitCast
2023-01-11T22:10:16.5964086Z [       OK ] CppPrinter.BitCast (0 ms)
2023-01-11T22:10:16.5964342Z [ RUN      ] CppPrinter.Let
2023-01-11T22:10:16.5964600Z [       OK ] CppPrinter.Let (0 ms)
2023-01-11T22:10:16.5964838Z [ RUN      ] CppPrinter.For
2023-01-11T22:10:16.5965236Z [       OK ] CppPrinter.For (0 ms)
2023-01-11T22:10:16.5965490Z [ RUN      ] CppPrinter.Cond
2023-01-11T22:10:16.5965750Z [       OK ] CppPrinter.Cond (0 ms)
2023-01-11T22:10:16.5966012Z [ RUN      ] CppPrinter.Intrinsics
2023-01-11T22:10:16.5966296Z [       OK ] CppPrinter.Intrinsics (0 ms)
2023-01-11T22:10:16.5966582Z [ RUN      ] CppPrinter.ExternalCall
2023-01-11T22:10:16.5966866Z [       OK ] CppPrinter.ExternalCall (0 ms)
2023-01-11T22:10:16.5967186Z [----------] 28 tests from CppPrinter (0 ms total)
2023-01-11T22:10:16.5967339Z 
2023-01-11T22:10:16.5967487Z [----------] 8 tests from DynamicShapes
2023-01-11T22:10:16.5967761Z [ RUN      ] DynamicShapes.SimpleGraph
2023-01-11T22:10:16.6820325Z [       OK ] DynamicShapes.SimpleGraph (86 ms)
2023-01-11T22:10:16.6820855Z [ RUN      ] DynamicShapes.GraphWith2InputsSameDims
2023-01-11T22:10:16.7575435Z [       OK ] DynamicShapes.GraphWith2InputsSameDims (75 ms)
2023-01-11T22:10:16.7576128Z [ RUN      ] DynamicShapes.GraphWith2InputsAndBroadcast
2023-01-11T22:10:16.8298317Z [       OK ] DynamicShapes.GraphWith2InputsAndBroadcast (72 ms)
2023-01-11T22:10:16.8298932Z [ RUN      ] DynamicShapes.GraphWithPartiallySymbolicOutput
2023-01-11T22:10:16.8699136Z [       OK ] DynamicShapes.GraphWithPartiallySymbolicOutput (40 ms)
2023-01-11T22:10:16.8699549Z [ RUN      ] DynamicShapes.GraphWithSymbolicStrides
2023-01-11T22:10:17.0377991Z [       OK ] DynamicShapes.GraphWithSymbolicStrides (167 ms)
2023-01-11T22:10:17.0378396Z [ RUN      ] DynamicShapes.GraphWithCatAndBroadcast
2023-01-11T22:10:17.4853737Z [       OK ] DynamicShapes.GraphWithCatAndBroadcast (447 ms)
2023-01-11T22:10:17.4854111Z [ RUN      ] DynamicShapes.GraphFromModel
2023-01-11T22:10:17.8681978Z [       OK ] DynamicShapes.GraphFromModel (382 ms)
2023-01-11T22:10:17.8682412Z [ RUN      ] DynamicShapes.MultiThreadedExecution
2023-01-11T22:10:17.9437795Z [       OK ] DynamicShapes.MultiThreadedExecution (75 ms)
2023-01-11T22:10:17.9438445Z [----------] 8 tests from DynamicShapes (1348 ms total)
2023-01-11T22:10:17.9438716Z 
2023-01-11T22:10:17.9438953Z [----------] 30 tests from Expr
2023-01-11T22:10:17.9439423Z [ RUN      ] Expr.BasicValueTest
2023-01-11T22:10:17.9439894Z [       OK ] Expr.BasicValueTest (0 ms)
2023-01-11T22:10:17.9440304Z [ RUN      ] Expr.BasicValueTest02
2023-01-11T22:10:17.9440812Z [       OK ] Expr.BasicValueTest02 (0 ms)
2023-01-11T22:10:17.9441328Z [ RUN      ] Expr.IsChannelsLastContiguous
2023-01-11T22:10:17.9441868Z [       OK ] Expr.IsChannelsLastContiguous (0 ms)
2023-01-11T22:10:17.9442181Z [ RUN      ] Expr.LetTest01
2023-01-11T22:10:17.9442647Z [       OK ] Expr.LetTest01 (0 ms)
2023-01-11T22:10:17.9443037Z [ RUN      ] Expr.LetTest02
2023-01-11T22:10:17.9443384Z [       OK ] Expr.LetTest02 (0 ms)
2023-01-11T22:10:17.9443657Z [ RUN      ] Expr.LetStmtTest01
2023-01-11T22:10:17.9444110Z [       OK ] Expr.LetStmtTest01 (0 ms)
2023-01-11T22:10:17.9444347Z [ RUN      ] Expr.IntTest
2023-01-11T22:10:17.9444594Z [       OK ] Expr.IntTest (0 ms)
2023-01-11T22:10:17.9444838Z [ RUN      ] Expr.FloatTest
2023-01-11T22:10:17.9445080Z [       OK ] Expr.FloatTest (0 ms)
2023-01-11T22:10:17.9445323Z [ RUN      ] Expr.ByteTest
2023-01-11T22:10:17.9445572Z [       OK ] Expr.ByteTest (0 ms)
2023-01-11T22:10:17.9445800Z [ RUN      ] Expr.CharTest
2023-01-11T22:10:17.9446048Z [       OK ] Expr.CharTest (0 ms)
2023-01-11T22:10:17.9446288Z [ RUN      ] Expr.ShortTest
2023-01-11T22:10:17.9446539Z [       OK ] Expr.ShortTest (0 ms)
2023-01-11T22:10:17.9446769Z [ RUN      ] Expr.LongTest
2023-01-11T22:10:17.9447080Z [       OK ] Expr.LongTest (0 ms)
2023-01-11T22:10:17.9447321Z [ RUN      ] Expr.HalfTest
2023-01-11T22:10:17.9447555Z [       OK ] Expr.HalfTest (0 ms)
2023-01-11T22:10:17.9447810Z [ RUN      ] Expr.DoubleTest
2023-01-11T22:10:17.9448073Z [       OK ] Expr.DoubleTest (0 ms)
2023-01-11T22:10:17.9448313Z [ RUN      ] Expr.VectorAdd01
2023-01-11T22:10:17.9456826Z [       OK ] Expr.VectorAdd01 (1 ms)
2023-01-11T22:10:17.9457334Z [ RUN      ] Expr.CompareSelectEQ
2023-01-11T22:10:17.9493213Z [       OK ] Expr.CompareSelectEQ (3 ms)
2023-01-11T22:10:17.9493520Z [ RUN      ] Expr.CompareSelectDtypes
2023-01-11T22:10:17.9530494Z [       OK ] Expr.CompareSelectDtypes (3 ms)
2023-01-11T22:10:17.9530790Z [ RUN      ] Expr.IntrinsicsDtypes
2023-01-11T22:10:17.9536683Z [       OK ] Expr.IntrinsicsDtypes (0 ms)
2023-01-11T22:10:17.9537012Z [ RUN      ] Expr.Substitute01
2023-01-11T22:10:17.9537302Z [       OK ] Expr.Substitute01 (0 ms)
2023-01-11T22:10:17.9537541Z [ RUN      ] Expr.Math01
2023-01-11T22:10:17.9537783Z [       OK ] Expr.Math01 (0 ms)
2023-01-11T22:10:17.9538069Z [ RUN      ] Expr.UnaryMath01
2023-01-11T22:10:17.9539086Z [       OK ] Expr.UnaryMath01 (0 ms)
2023-01-11T22:10:17.9539354Z [ RUN      ] Expr.BinaryMath01
2023-01-11T22:10:17.9540615Z [       OK ] Expr.BinaryMath01 (0 ms)
2023-01-11T22:10:17.9541073Z [ RUN      ] Expr.LogicalOps01
2023-01-11T22:10:17.9541592Z [       OK ] Expr.LogicalOps01 (0 ms)
2023-01-11T22:10:17.9541906Z [ RUN      ] Expr.LogicalOps02
2023-01-11T22:10:17.9542193Z [       OK ] Expr.LogicalOps02 (0 ms)
2023-01-11T22:10:17.9542533Z [ RUN      ] Expr.LogicalOps03
2023-01-11T22:10:17.9543027Z [       OK ] Expr.LogicalOps03 (0 ms)
2023-01-11T22:10:17.9543489Z [ RUN      ] Expr.BitwiseOps
2023-01-11T22:10:17.9543857Z [       OK ] Expr.BitwiseOps (0 ms)
2023-01-11T22:10:17.9544131Z [ RUN      ] Expr.DynamicShapeAdd
2023-01-11T22:10:17.9546158Z [       OK ] Expr.DynamicShapeAdd (0 ms)
2023-01-11T22:10:17.9546631Z [ RUN      ] Expr.OutOfBounds
2023-01-11T22:10:17.9570348Z [       OK ] Expr.OutOfBounds (2 ms)
2023-01-11T22:10:17.9570844Z [ RUN      ] Expr.OutOfBounds2d
2023-01-11T22:10:17.9578407Z [       OK ] Expr.OutOfBounds2d (0 ms)
2023-01-11T22:10:17.9578970Z [ RUN      ] Expr.OutOfBounds2dFlattenedIndex
2023-01-11T22:10:17.9583628Z [       OK ] Expr.OutOfBounds2dFlattenedIndex (0 ms)
2023-01-11T22:10:17.9584233Z [----------] 30 tests from Expr (14 ms total)
2023-01-11T22:10:17.9584397Z 
2023-01-11T22:10:17.9584552Z [----------] 16 tests from ExternalCall
2023-01-11T22:10:17.9584837Z [ RUN      ] ExternalCall.Conv1d_float
2023-01-11T22:10:17.9869748Z [       OK ] ExternalCall.Conv1d_float (28 ms)
2023-01-11T22:10:17.9870082Z [ RUN      ] ExternalCall.Conv1d_int
2023-01-11T22:10:18.0196694Z [       OK ] ExternalCall.Conv1d_int (32 ms)
2023-01-11T22:10:18.0197438Z [ RUN      ] ExternalCall.Conv1d_nobias_noargs
2023-01-11T22:10:18.0448491Z [       OK ] ExternalCall.Conv1d_nobias_noargs (25 ms)
2023-01-11T22:10:18.0448966Z [ RUN      ] ExternalCall.Conv2d_float
2023-01-11T22:10:18.0769577Z [       OK ] ExternalCall.Conv2d_float (31 ms)
2023-01-11T22:10:18.0770186Z [ RUN      ] ExternalCall.Conv2d_int
2023-01-11T22:10:18.1153459Z [       OK ] ExternalCall.Conv2d_int (38 ms)
2023-01-11T22:10:18.1153954Z [ RUN      ] ExternalCall.Conv2d_nobias_noargs
2023-01-11T22:10:18.1429512Z [       OK ] ExternalCall.Conv2d_nobias_noargs (27 ms)
2023-01-11T22:10:18.1429931Z [ RUN      ] ExternalCall.Addmm_float
2023-01-11T22:10:18.1719843Z [       OK ] ExternalCall.Addmm_float (28 ms)
2023-01-11T22:10:18.1720467Z [ RUN      ] ExternalCall.Embedding
2023-01-11T22:10:18.1969821Z [       OK ] ExternalCall.Embedding (25 ms)
2023-01-11T22:10:18.1970184Z [ RUN      ] ExternalCall.MaxReduction
2023-01-11T22:10:18.2197304Z [       OK ] ExternalCall.MaxReduction (22 ms)
2023-01-11T22:10:18.2197659Z [ RUN      ] ExternalCall.Prepacked_Linear_float
2023-01-11T22:10:18.2535718Z [       OK ] ExternalCall.Prepacked_Linear_float (33 ms)
2023-01-11T22:10:18.2536154Z [ RUN      ] ExternalCall.Prepacked_Conv2d_float
2023-01-11T22:10:18.3016480Z [       OK ] ExternalCall.Prepacked_Conv2d_float (47 ms)
2023-01-11T22:10:18.3017403Z [ RUN      ] ExternalCall.BinaryFloat
2023-01-11T22:10:18.3771231Z [       OK ] ExternalCall.BinaryFloat (75 ms)
2023-01-11T22:10:18.3771654Z [ RUN      ] ExternalCall.UnaryFloat
2023-01-11T22:10:18.4236485Z [       OK ] ExternalCall.UnaryFloat (46 ms)
2023-01-11T22:10:18.4236836Z [ RUN      ] ExternalCall.ComputeInterop
2023-01-11T22:10:19.5129948Z [       OK ] ExternalCall.ComputeInterop (1089 ms)
2023-01-11T22:10:19.5130331Z [ RUN      ] ExternalCall.Inlining
2023-01-11T22:10:19.5862676Z [       OK ] ExternalCall.Inlining (73 ms)
2023-01-11T22:10:19.5863012Z [ RUN      ] ExternalCall.JitCustomFusionOp
2023-01-11T22:10:19.7101000Z [       OK ] ExternalCall.JitCustomFusionOp (123 ms)
2023-01-11T22:10:19.7101386Z [----------] 16 tests from ExternalCall (1751 ms total)
2023-01-11T22:10:19.7101557Z 
2023-01-11T22:10:19.7101703Z [----------] 8 tests from GraphOpt
2023-01-11T22:10:19.7101962Z [ RUN      ] GraphOpt.OptimizeCat
2023-01-11T22:10:19.7427386Z [       OK ] GraphOpt.OptimizeCat (32 ms)
2023-01-11T22:10:19.7427958Z [ RUN      ] GraphOpt.OptimizeCat2
2023-01-11T22:10:19.7782031Z [       OK ] GraphOpt.OptimizeCat2 (35 ms)
2023-01-11T22:10:19.7782335Z [ RUN      ] GraphOpt.OptimizeCat3
2023-01-11T22:10:19.8191590Z [       OK ] GraphOpt.OptimizeCat3 (40 ms)
2023-01-11T22:10:19.8191955Z [ RUN      ] GraphOpt.OptimizeCatWithTypePromotionInUser
2023-01-11T22:10:19.8512123Z [       OK ] GraphOpt.OptimizeCatWithTypePromotionInUser (32 ms)
2023-01-11T22:10:19.8512531Z [ RUN      ] GraphOpt.OptimizeCatWithTypePromotionInCat
2023-01-11T22:10:19.9121521Z [       OK ] GraphOpt.OptimizeCatWithTypePromotionInCat (60 ms)
2023-01-11T22:10:19.9121952Z [ RUN      ] GraphOpt.OptimizeCatNoSingleTensorElementwiseOp
2023-01-11T22:10:19.9493667Z [       OK ] GraphOpt.OptimizeCatNoSingleTensorElementwiseOp (37 ms)
2023-01-11T22:10:19.9494134Z [ RUN      ] GraphOpt.OptimizeCatNoSingleTensorElementwiseOp2
2023-01-11T22:10:19.9893747Z [       OK ] GraphOpt.OptimizeCatNoSingleTensorElementwiseOp2 (39 ms)
2023-01-11T22:10:19.9894422Z [ RUN      ] GraphOpt.AOTGraphPrepPasses
2023-01-11T22:10:19.9895060Z [       OK ] GraphOpt.AOTGraphPrepPasses (0 ms)
2023-01-11T22:10:19.9895493Z [----------] 8 tests from GraphOpt (279 ms total)
2023-01-11T22:10:19.9895880Z 
2023-01-11T22:10:19.9896030Z [----------] 4 tests from IRPrinter
2023-01-11T22:10:19.9896425Z [ RUN      ] IRPrinter.BasicValueTest
2023-01-11T22:10:19.9896736Z [       OK ] IRPrinter.BasicValueTest (0 ms)
2023-01-11T22:10:19.9897134Z [ RUN      ] IRPrinter.BasicValueTest02
2023-01-11T22:10:19.9897604Z [       OK ] IRPrinter.BasicValueTest02 (0 ms)
2023-01-11T22:10:19.9898075Z [ RUN      ] IRPrinter.CastTest
2023-01-11T22:10:19.9898508Z [       OK ] IRPrinter.CastTest (0 ms)
2023-01-11T22:10:19.9899020Z [ RUN      ] IRPrinter.FunctionName
2023-01-11T22:10:19.9899509Z [       OK ] IRPrinter.FunctionName (0 ms)
2023-01-11T22:10:19.9900041Z [----------] 4 tests from IRPrinter (0 ms total)
2023-01-11T22:10:19.9900275Z 
2023-01-11T22:10:19.9900676Z [----------] 8 tests from IRVerifier
2023-01-11T22:10:19.9901203Z [ RUN      ] IRVerifier.BitwiseOps
2023-01-11T22:10:19.9901721Z [       OK ] IRVerifier.BitwiseOps (0 ms)
2023-01-11T22:10:19.9902008Z [ RUN      ] IRVerifier.CompareSelect
2023-01-11T22:10:19.9902307Z [       OK ] IRVerifier.CompareSelect (0 ms)
2023-01-11T22:10:19.9902666Z [ RUN      ] IRVerifier.Ramp
2023-01-11T22:10:19.9902929Z [       OK ] IRVerifier.Ramp (0 ms)
2023-01-11T22:10:19.9903185Z [ RUN      ] IRVerifier.Load
2023-01-11T22:10:19.9903457Z [       OK ] IRVerifier.Load (0 ms)
2023-01-11T22:10:19.9903713Z [ RUN      ] IRVerifier.IfThenElse
2023-01-11T22:10:19.9903999Z [       OK ] IRVerifier.IfThenElse (0 ms)
2023-01-11T22:10:19.9904263Z [ RUN      ] IRVerifier.For
2023-01-11T22:10:19.9904517Z [       OK ] IRVerifier.For (0 ms)
2023-01-11T22:10:19.9904755Z [ RUN      ] IRVerifier.Block
2023-01-11T22:10:19.9905020Z [       OK ] IRVerifier.Block (0 ms)
2023-01-11T22:10:19.9905272Z [ RUN      ] IRVerifier.Store
2023-01-11T22:10:19.9905525Z [       OK ] IRVerifier.Store (0 ms)
2023-01-11T22:10:19.9905819Z [----------] 8 tests from IRVerifier (0 ms total)
2023-01-11T22:10:19.9905968Z 
2023-01-11T22:10:19.9906106Z [----------] 37 tests from Kernel
2023-01-11T22:10:19.9906389Z [ RUN      ] Kernel.ParallelExternalCallBuf
2023-01-11T22:10:20.0468341Z [       OK ] Kernel.ParallelExternalCallBuf (56 ms)
2023-01-11T22:10:20.0468676Z [ RUN      ] Kernel.InliningIntermediates
2023-01-11T22:10:20.1126613Z [       OK ] Kernel.InliningIntermediates (65 ms)
2023-01-11T22:10:20.1127033Z [ RUN      ] Kernel.PreAllocIntermediateBufs
2023-01-11T22:10:20.2231597Z [       OK ] Kernel.PreAllocIntermediateBufs (110 ms)
2023-01-11T22:10:20.2231945Z [ RUN      ] Kernel._1
2023-01-11T22:10:20.2522205Z [       OK ] Kernel._1 (29 ms)
2023-01-11T22:10:20.2522466Z [ RUN      ] Kernel._2
2023-01-11T22:10:20.2831862Z [       OK ] Kernel._2 (30 ms)
2023-01-11T22:10:20.2832126Z [ RUN      ] Kernel._3
2023-01-11T22:10:20.3145901Z [       OK ] Kernel._3 (31 ms)
2023-01-11T22:10:20.3146182Z [ RUN      ] Kernel.Huge
2023-01-11T22:10:20.3389273Z [       OK ] Kernel.Huge (24 ms)
2023-01-11T22:10:20.3389814Z [ RUN      ] Kernel.ParallelStrided
2023-01-11T22:10:20.4586386Z [       OK ] Kernel.ParallelStrided (119 ms)
2023-01-11T22:10:20.4586813Z [ RUN      ] Kernel.CatInputTypesPromotion
2023-01-11T22:10:20.5520315Z [       OK ] Kernel.CatInputTypesPromotion (93 ms)
2023-01-11T22:10:20.5520638Z [ RUN      ] Kernel.ToDType
2023-01-11T22:10:20.5795056Z [       OK ] Kernel.ToDType (27 ms)
2023-01-11T22:10:20.5795388Z [ RUN      ] Kernel.CatAndInlineWithAConstantDim
2023-01-11T22:10:20.6075666Z [       OK ] Kernel.CatAndInlineWithAConstantDim (28 ms)
2023-01-11T22:10:20.6076000Z [ RUN      ] Kernel.CatWithEmptyInputs
2023-01-11T22:10:20.6764075Z [       OK ] Kernel.CatWithEmptyInputs (68 ms)
2023-01-11T22:10:20.6764382Z [ RUN      ] Kernel.CatWoConditionals
2023-01-11T22:10:20.7490797Z [       OK ] Kernel.CatWoConditionals (72 ms)
2023-01-11T22:10:20.7491115Z [ RUN      ] Kernel.OptimizeConditionals
2023-01-11T22:10:20.8437277Z [       OK ] Kernel.OptimizeConditionals (94 ms)
2023-01-11T22:10:20.8437569Z [ RUN      ] Kernel.SumAllAxes
2023-01-11T22:10:20.8924359Z [       OK ] Kernel.SumAllAxes (48 ms)
2023-01-11T22:10:20.8924637Z [ RUN      ] Kernel.SumOneAxis
2023-01-11T22:10:21.2932919Z [       OK ] Kernel.SumOneAxis (400 ms)
2023-01-11T22:10:21.2933243Z [ RUN      ] Kernel.SumMultipleAxes
2023-01-11T22:10:21.7175896Z [       OK ] Kernel.SumMultipleAxes (424 ms)
2023-01-11T22:10:21.7176378Z [ RUN      ] Kernel.Softmax2D
2023-01-11T22:10:22.1546981Z [       OK ] Kernel.Softmax2D (437 ms)
2023-01-11T22:10:22.1547277Z [ RUN      ] Kernel.Softmax3D
2023-01-11T22:10:23.0134107Z [       OK ] Kernel.Softmax3D (858 ms)
2023-01-11T22:10:23.0134428Z [ RUN      ] Kernel.Softmax4D
2023-01-11T22:10:24.1327930Z [       OK ] Kernel.Softmax4D (1119 ms)
2023-01-11T22:10:24.1328237Z [ RUN      ] Kernel.SignTest
2023-01-11T22:10:24.2014095Z [       OK ] Kernel.SignTest (68 ms)
2023-01-11T22:10:24.2014446Z [ RUN      ] Kernel.InlineProducerIntoReduction
2023-01-11T22:10:24.2291507Z [       OK ] Kernel.InlineProducerIntoReduction (27 ms)
2023-01-11T22:10:24.2291872Z [ RUN      ] Kernel.InlineReductionIntoConsumer
2023-01-11T22:10:24.2638125Z [       OK ] Kernel.InlineReductionIntoConsumer (34 ms)
2023-01-11T22:10:24.2638444Z [ RUN      ] Kernel.ConstantTensors
2023-01-11T22:10:24.3028042Z [       OK ] Kernel.ConstantTensors (39 ms)
2023-01-11T22:10:24.3028396Z [ RUN      ] Kernel.ConstantTensorsNonContiguous
2023-01-11T22:10:24.3417522Z [       OK ] Kernel.ConstantTensorsNonContiguous (38 ms)
2023-01-11T22:10:24.3417829Z [ RUN      ] Kernel.RunFast
2023-01-11T22:10:24.3729225Z [       OK ] Kernel.RunFast (31 ms)
2023-01-11T22:10:24.3729547Z [ RUN      ] Kernel.RunWithAllocatedOutputs
2023-01-11T22:10:24.4036820Z [       OK ] Kernel.RunWithAllocatedOutputs (30 ms)
2023-01-11T22:10:24.4037136Z [ RUN      ] Kernel.CodegenInspection
2023-01-11T22:10:24.4424599Z [       OK ] Kernel.CodegenInspection (38 ms)
2023-01-11T22:10:24.4424918Z [ RUN      ] Kernel.CustomLowering
2023-01-11T22:10:24.4660265Z [       OK ] Kernel.CustomLowering (23 ms)
2023-01-11T22:10:24.4660764Z [ RUN      ] Kernel.Vectorize
2023-01-11T22:10:24.4959638Z [       OK ] Kernel.Vectorize (29 ms)
2023-01-11T22:10:24.4959922Z [ RUN      ] Kernel.Strided1dWithinBounds
2023-01-11T22:10:24.5185992Z [       OK ] Kernel.Strided1dWithinBounds (22 ms)
2023-01-11T22:10:24.5186496Z [ RUN      ] Kernel.InputAsOutput
2023-01-11T22:10:24.5521544Z [       OK ] Kernel.InputAsOutput (33 ms)
2023-01-11T22:10:24.5522047Z [ RUN      ] Kernel.ScalarOut
2023-01-11T22:10:24.5722318Z [       OK ] Kernel.ScalarOut (20 ms)
2023-01-11T22:10:24.5722808Z [ RUN      ] Kernel.ScalarTensorOut
2023-01-11T22:10:24.5975773Z [       OK ] Kernel.ScalarTensorOut (25 ms)
2023-01-11T22:10:24.5976338Z [ RUN      ] Kernel.FuseLoopsWithVariableBounds
2023-01-11T22:10:25.0354080Z [       OK ] Kernel.FuseLoopsWithVariableBounds (437 ms)
2023-01-11T22:10:25.0354558Z [ RUN      ] Kernel.FuseLoopsWithVariableConcatDim
2023-01-11T22:10:25.5308255Z [       OK ] Kernel.FuseLoopsWithVariableConcatDim (495 ms)
2023-01-11T22:10:25.5308703Z [ RUN      ] Kernel.DoNotFuseLoopsWithMismatchingVariableDims
2023-01-11T22:10:25.8534301Z [       OK ] Kernel.DoNotFuseLoopsWithMismatchingVariableDims (322 ms)
2023-01-11T22:10:25.8534923Z [----------] 37 tests from Kernel (5863 ms total)
2023-01-11T22:10:25.8535071Z 
2023-01-11T22:10:25.8535229Z [----------] 174 tests from LoopNest
2023-01-11T22:10:25.8535504Z [ RUN      ] LoopNest.ExprSimple01
2023-01-11T22:10:25.8551761Z [       OK ] LoopNest.ExprSimple01 (1 ms)
2023-01-11T22:10:25.8552040Z [ RUN      ] LoopNest.ExprLower01
2023-01-11T22:10:25.8562266Z [       OK ] LoopNest.ExprLower01 (1 ms)
2023-01-11T22:10:25.8562650Z [ RUN      ] LoopNest.ExprSimple02
2023-01-11T22:10:25.8584890Z [       OK ] LoopNest.ExprSimple02 (2 ms)
2023-01-11T22:10:25.8585314Z [ RUN      ] LoopNest.ExprSliceHeadWithLoopOptions
2023-01-11T22:10:25.8587253Z [       OK ] LoopNest.ExprSliceHeadWithLoopOptions (0 ms)
2023-01-11T22:10:25.8587692Z [ RUN      ] LoopNest.ExprSliceTailWithLoopOptions
2023-01-11T22:10:25.8590135Z [       OK ] LoopNest.ExprSliceTailWithLoopOptions (0 ms)
2023-01-11T22:10:25.8590598Z [ RUN      ] LoopNest.ExprSliceHeadWhenFactorEqualsSize
2023-01-11T22:10:25.8603325Z [       OK ] LoopNest.ExprSliceHeadWhenFactorEqualsSize (1 ms)
2023-01-11T22:10:25.8603821Z [ RUN      ] LoopNest.ExprSliceHeadWhenFactorLargerThanSize
2023-01-11T22:10:25.8604390Z [       OK ] LoopNest.ExprSliceHeadWhenFactorLargerThanSize (0 ms)
2023-01-11T22:10:25.8604895Z [ RUN      ] LoopNest.ExprSliceHead
2023-01-11T22:10:25.8605227Z [       OK ] LoopNest.ExprSliceHead (0 ms)
2023-01-11T22:10:25.8605548Z [ RUN      ] LoopNest.ExprSliceHeadWithNonZeroStart
2023-01-11T22:10:25.8607355Z [       OK ] LoopNest.ExprSliceHeadWithNonZeroStart (0 ms)
2023-01-11T22:10:25.8607915Z [ RUN      ] LoopNest.ExprSliceTailWhenFactorEqualsSize
2023-01-11T22:10:25.8608423Z [       OK ] LoopNest.ExprSliceTailWhenFactorEqualsSize (0 ms)
2023-01-11T22:10:25.8608839Z [ RUN      ] LoopNest.ExprSliceTailWhenFactorLargerThanSize
2023-01-11T22:10:25.8609480Z [       OK ] LoopNest.ExprSliceTailWhenFactorLargerThanSize (0 ms)
2023-01-11T22:10:25.8609845Z [ RUN      ] LoopNest.ExprSliceTail
2023-01-11T22:10:25.8610693Z [       OK ] LoopNest.ExprSliceTail (0 ms)
2023-01-11T22:10:25.8610990Z [ RUN      ] LoopNest.ExprSplitAndSlice
2023-01-11T22:10:25.8618285Z [       OK ] LoopNest.ExprSplitAndSlice (0 ms)
2023-01-11T22:10:25.8618589Z [ RUN      ] LoopNest.ExprSliceAndNormalize
2023-01-11T22:10:25.8620870Z [       OK ] LoopNest.ExprSliceAndNormalize (0 ms)
2023-01-11T22:10:25.8621226Z [ RUN      ] LoopNest.ExprSliceWithVariableDimension
2023-01-11T22:10:25.8639038Z [       OK ] LoopNest.ExprSliceWithVariableDimension (1 ms)
2023-01-11T22:10:25.8639372Z [ RUN      ] LoopNest.ExprSplitWithTail
2023-01-11T22:10:25.8647148Z [       OK ] LoopNest.ExprSplitWithTail (0 ms)
2023-01-11T22:10:25.8647471Z [ RUN      ] LoopNest.ExprSplitWithTailNone
2023-01-11T22:10:25.8660522Z [       OK ] LoopNest.ExprSplitWithTailNone (1 ms)
2023-01-11T22:10:25.8660847Z [ RUN      ] LoopNest.ExprSplitWithMask01
2023-01-11T22:10:25.8701349Z [       OK ] LoopNest.ExprSplitWithMask01 (4 ms)
2023-01-11T22:10:25.8701705Z [ RUN      ] LoopNest.ExprSplitWithMaskRepeatedNoMask
2023-01-11T22:10:25.8706810Z [       OK ] LoopNest.ExprSplitWithMaskRepeatedNoMask (0 ms)
2023-01-11T22:10:25.8707379Z [ RUN      ] LoopNest.getLoopAt
2023-01-11T22:10:25.8707798Z [       OK ] LoopNest.getLoopAt (0 ms)
2023-01-11T22:10:25.8708058Z [ RUN      ] LoopNest.TileSimple
2023-01-11T22:10:25.9599151Z [       OK ] LoopNest.TileSimple (89 ms)
2023-01-11T22:10:25.9599628Z [ RUN      ] LoopNest.TileWithTails
2023-01-11T22:10:26.0494729Z [       OK ] LoopNest.TileWithTails (89 ms)
2023-01-11T22:10:26.0495288Z [ RUN      ] LoopNest.TileInMiddle
2023-01-11T22:10:26.1923211Z [       OK ] LoopNest.TileInMiddle (142 ms)
2023-01-11T22:10:26.1923745Z [ RUN      ] LoopNest.SplitWithTailWithLoopOptions
2023-01-11T22:10:26.1924165Z [       OK ] LoopNest.SplitWithTailWithLoopOptions (0 ms)
2023-01-11T22:10:26.1924521Z [ RUN      ] LoopNest.SplitWithMaskWithLoopOptions
2023-01-11T22:10:26.1924890Z [       OK ] LoopNest.SplitWithMaskWithLoopOptions (0 ms)
2023-01-11T22:10:26.1925247Z [ RUN      ] LoopNest.ScheduleBroadcastAddBuffer
2023-01-11T22:10:26.1944575Z [       OK ] LoopNest.ScheduleBroadcastAddBuffer (2 ms)
2023-01-11T22:10:26.1944921Z [ RUN      ] LoopNest.ScheduleFunctionCall01
2023-01-11T22:10:26.2023604Z [       OK ] LoopNest.ScheduleFunctionCall01 (7 ms)
2023-01-11T22:10:26.2024158Z [ RUN      ] LoopNest.ScheduleInlineSimple
2023-01-11T22:10:26.2146565Z [       OK ] LoopNest.ScheduleInlineSimple (12 ms)
2023-01-11T22:10:26.2147057Z [ RUN      ] LoopNest.ScheduleInlineFunc01
2023-01-11T22:10:26.2708337Z [       OK ] LoopNest.ScheduleInlineFunc01 (56 ms)
2023-01-11T22:10:26.2708734Z [ RUN      ] LoopNest.ScheduleInlineRandom
2023-01-11T22:10:26.2712691Z [       OK ] LoopNest.ScheduleInlineRandom (0 ms)
2023-01-11T22:10:26.2713112Z [ RUN      ] LoopNest.ScheduleInlineRandomUnrelated
2023-01-11T22:10:26.2716801Z [       OK ] LoopNest.ScheduleInlineRandomUnrelated (0 ms)
2023-01-11T22:10:26.2717246Z [ RUN      ] LoopNest.ScheduleInlineRandomLowerDimensions
2023-01-11T22:10:26.2720080Z [       OK ] LoopNest.ScheduleInlineRandomLowerDimensions (0 ms)
2023-01-11T22:10:26.2720522Z [ RUN      ] LoopNest.ScheduleInlineIntrinsics
2023-01-11T22:10:26.2809550Z [       OK ] LoopNest.ScheduleInlineIntrinsics (8 ms)
2023-01-11T22:10:26.2809930Z [ RUN      ] LoopNest.ScheduleInlineRandWithIntrinsics
2023-01-11T22:10:26.2812650Z [       OK ] LoopNest.ScheduleInlineRandWithIntrinsics (0 ms)
2023-01-11T22:10:26.2813018Z [ RUN      ] LoopNest.ScheduleSplitAThenInline
2023-01-11T22:10:26.2814611Z [       OK ] LoopNest.ScheduleSplitAThenInline (0 ms)
2023-01-11T22:10:26.2814954Z [ RUN      ] LoopNest.ScheduleSplitBThenInline
2023-01-11T22:10:26.2818829Z [       OK ] LoopNest.ScheduleSplitBThenInline (0 ms)
2023-01-11T22:10:26.2819186Z [ RUN      ] LoopNest.ScheduleSplitTwiceThenInline
2023-01-11T22:10:26.2820495Z [       OK ] LoopNest.ScheduleSplitTwiceThenInline (0 ms)
2023-01-11T22:10:26.2820848Z [ RUN      ] LoopNest.ScheduleInlineThenSplit
2023-01-11T22:10:26.2824513Z [       OK ] LoopNest.ScheduleInlineThenSplit (0 ms)
2023-01-11T22:10:26.2824864Z [ RUN      ] LoopNest.ScheduleSplitInlineThenSplit
2023-01-11T22:10:26.2832374Z [       OK ] LoopNest.ScheduleSplitInlineThenSplit (0 ms)
2023-01-11T22:10:26.2832747Z [ RUN      ] LoopNest.ScheduleSplitInlineSimplify
2023-01-11T22:10:26.2841240Z [       OK ] LoopNest.ScheduleSplitInlineSimplify (0 ms)
2023-01-11T22:10:26.2841593Z [ RUN      ] LoopNest.ScheduleInlineThreeMixedOnce
2023-01-11T22:10:26.2846170Z [       OK ] LoopNest.ScheduleInlineThreeMixedOnce (0 ms)
2023-01-11T22:10:26.2846540Z [ RUN      ] LoopNest.ScheduleInlineThreeMixedTwice
2023-01-11T22:10:26.2851004Z [       OK ] LoopNest.ScheduleInlineThreeMixedTwice (0 ms)
2023-01-11T22:10:26.2851431Z [ RUN      ] LoopNest.ScheduleInlineThreeMixedInner
2023-01-11T22:10:26.2856171Z [       OK ] LoopNest.ScheduleInlineThreeMixedInner (0 ms)
2023-01-11T22:10:26.2856543Z [ RUN      ] LoopNest.ScheduleInlineThreeMixedSplit
2023-01-11T22:10:26.2858701Z [       OK ] LoopNest.ScheduleInlineThreeMixedSplit (0 ms)
2023-01-11T22:10:26.2859069Z [ RUN      ] LoopNest.ScheduleInlineOutputTensors
2023-01-11T22:10:26.2863377Z [       OK ] LoopNest.ScheduleInlineOutputTensors (0 ms)
2023-01-11T22:10:26.2863746Z [ RUN      ] LoopNest.ScheduleInlineWithCompoundIndices
2023-01-11T22:10:26.2884319Z [       OK ] LoopNest.ScheduleInlineWithCompoundIndices (2 ms)
2023-01-11T22:10:26.2884889Z [ RUN      ] LoopNest.ScheduleInlineConsumerIndicesWithCast
2023-01-11T22:10:26.2885879Z [       OK ] LoopNest.ScheduleInlineConsumerIndicesWithCast (0 ms)
2023-01-11T22:10:26.2886468Z [ RUN      ] LoopNest.ScheduleInlineProducerIndicesWithCast
2023-01-11T22:10:26.2886900Z [       OK ] LoopNest.ScheduleInlineProducerIndicesWithCast (0 ms)
2023-01-11T22:10:26.2887255Z [ RUN      ] LoopNest.ScheduleFuserStyle
2023-01-11T22:10:26.2935536Z [       OK ] LoopNest.ScheduleFuserStyle (4 ms)
2023-01-11T22:10:26.2936017Z [ RUN      ] LoopNest.ScheduleFuserThreeArg
2023-01-11T22:10:26.2992775Z [       OK ] LoopNest.ScheduleFuserThreeArg (5 ms)
2023-01-11T22:10:26.2993123Z [ RUN      ] LoopNest.ScheduleDynamicShape2D
2023-01-11T22:10:26.3113932Z [       OK ] LoopNest.ScheduleDynamicShape2D (12 ms)
2023-01-11T22:10:26.3114518Z [ RUN      ] LoopNest.LoopNestComputeAt_1
2023-01-11T22:10:26.3123190Z [       OK ] LoopNest.LoopNestComputeAt_1 (0 ms)
2023-01-11T22:10:26.3123756Z [ RUN      ] LoopNest.LoopNestComputeAt_2
2023-01-11T22:10:26.3621732Z [       OK ] LoopNest.LoopNestComputeAt_2 (49 ms)
2023-01-11T22:10:26.3622284Z [ RUN      ] LoopNest.LoopNestComputeAt_3
2023-01-11T22:10:26.4113833Z [       OK ] LoopNest.LoopNestComputeAt_3 (49 ms)
2023-01-11T22:10:26.4114409Z [ RUN      ] LoopNest.Reduce2dComputeAt
2023-01-11T22:10:26.4936474Z [       OK ] LoopNest.Reduce2dComputeAt (82 ms)
2023-01-11T22:10:26.4937088Z [ RUN      ] LoopNest.LoopNestReorderAxis1
2023-01-11T22:10:26.4939740Z [       OK ] LoopNest.LoopNestReorderAxis1 (0 ms)
2023-01-11T22:10:26.4940375Z [ RUN      ] LoopNest.LoopNestReorderPartialAxes
2023-01-11T22:10:26.4950912Z [       OK ] LoopNest.LoopNestReorderPartialAxes (1 ms)
2023-01-11T22:10:26.4951556Z [ RUN      ] LoopNest.LoopNestReorderInternalAxis
2023-01-11T22:10:26.4961127Z [       OK ] LoopNest.LoopNestReorderInternalAxis (1 ms)
2023-01-11T22:10:26.4961770Z [ RUN      ] LoopNest.LoopNestReorderEnclosingAxis
2023-01-11T22:10:26.4971757Z [       OK ] LoopNest.LoopNestReorderEnclosingAxis (1 ms)
2023-01-11T22:10:26.4972389Z [ RUN      ] LoopNest.LoopNestReorderSameAxis
2023-01-11T22:10:26.4973126Z [       OK ] LoopNest.LoopNestReorderSameAxis (0 ms)
2023-01-11T22:10:26.4973523Z [ RUN      ] LoopNest.LoopNestReorderExtraStatements
2023-01-11T22:10:26.4988075Z [       OK ] LoopNest.LoopNestReorderExtraStatements (1 ms)
2023-01-11T22:10:26.4988509Z [ RUN      ] LoopNest.LoopNestReorderLongStringOfPreOrphans
2023-01-11T22:10:26.5542399Z [       OK ] LoopNest.LoopNestReorderLongStringOfPreOrphans (55 ms)
2023-01-11T22:10:26.5542888Z [ RUN      ] LoopNest.LoopNestReorderLongStringOfPostOrphans
2023-01-11T22:10:26.6102408Z [       OK ] LoopNest.LoopNestReorderLongStringOfPostOrphans (55 ms)
2023-01-11T22:10:26.6102862Z [ RUN      ] LoopNest.LoopNestReorderLongStringFull
2023-01-11T22:10:26.6798498Z [       OK ] LoopNest.LoopNestReorderLongStringFull (69 ms)
2023-01-11T22:10:26.6798975Z [ RUN      ] LoopNest.LoopNestReorderInternalLoopNest
2023-01-11T22:10:26.6913891Z [       OK ] LoopNest.LoopNestReorderInternalLoopNest (11 ms)
2023-01-11T22:10:26.6914240Z [ RUN      ] LoopNest.OuterLoopVectorization
2023-01-11T22:10:26.6916060Z [       OK ] LoopNest.OuterLoopVectorization (0 ms)
2023-01-11T22:10:26.6916408Z [ RUN      ] LoopNest.VectorizeLoopNotNormalized
2023-01-11T22:10:26.6918498Z [       OK ] LoopNest.VectorizeLoopNotNormalized (0 ms)
2023-01-11T22:10:26.6919106Z [ RUN      ] LoopNest.Unroll
2023-01-11T22:10:26.6919581Z [       OK ] LoopNest.Unroll (0 ms)
2023-01-11T22:10:26.6919889Z [ RUN      ] LoopNest.UnrollOuter
2023-01-11T22:10:26.6921947Z [       OK ] LoopNest.UnrollOuter (0 ms)
2023-01-11T22:10:26.6922314Z [ RUN      ] LoopNest.UnrollInner
2023-01-11T22:10:26.6923643Z [       OK ] LoopNest.UnrollInner (0 ms)
2023-01-11T22:10:26.6924117Z [ RUN      ] LoopNest.UnrollMultipleStatements
2023-01-11T22:10:26.6924799Z [       OK ] LoopNest.UnrollMultipleStatements (0 ms)
2023-01-11T22:10:26.6925164Z [ RUN      ] LoopNest.UnrollNonLiteralConstantBounds
2023-01-11T22:10:26.6926783Z [       OK ] LoopNest.UnrollNonLiteralConstantBounds (0 ms)
2023-01-11T22:10:26.6927244Z [ RUN      ] LoopNest.UnrollNonConstantBounds
2023-01-11T22:10:26.6943369Z [       OK ] LoopNest.UnrollNonConstantBounds (1 ms)
2023-01-11T22:10:26.6943805Z [ RUN      ] LoopNest.UnrollByFactorsLessThan2
2023-01-11T22:10:26.6944207Z [       OK ] LoopNest.UnrollByFactorsLessThan2 (0 ms)
2023-01-11T22:10:26.6944565Z [ RUN      ] LoopNest.UnrollByFactorEqualToIters
2023-01-11T22:10:26.6947432Z [       OK ] LoopNest.UnrollByFactorEqualToIters (0 ms)
2023-01-11T22:10:26.6948013Z [ RUN      ] LoopNest.UnrollEmpty
2023-01-11T22:10:26.6948409Z [       OK ] LoopNest.UnrollEmpty (0 ms)
2023-01-11T22:10:26.6948871Z [ RUN      ] LoopNest.NoUnroll
2023-01-11T22:10:26.6949358Z [       OK ] LoopNest.NoUnroll (0 ms)
2023-01-11T22:10:26.6949629Z [ RUN      ] LoopNest.UnrollWithLet
2023-01-11T22:10:26.6950595Z [       OK ] LoopNest.UnrollWithLet (0 ms)
2023-01-11T22:10:26.6951130Z [ RUN      ] LoopNest.IsNormalized
2023-01-11T22:10:26.6951508Z [       OK ] LoopNest.IsNormalized (0 ms)
2023-01-11T22:10:26.6951809Z [ RUN      ] LoopNest.NormalizeStartPositive
2023-01-11T22:10:26.6953384Z [       OK ] LoopNest.NormalizeStartPositive (0 ms)
2023-01-11T22:10:26.6953989Z [ RUN      ] LoopNest.NormalizeStartNegative
2023-01-11T22:10:26.6956379Z [       OK ] LoopNest.NormalizeStartNegative (0 ms)
2023-01-11T22:10:26.6956944Z [ RUN      ] LoopNest.NormalizeStartZero
2023-01-11T22:10:26.6957437Z [       OK ] LoopNest.NormalizeStartZero (0 ms)
2023-01-11T22:10:26.6957760Z [ RUN      ] LoopNest.NormalizeStartVariable
2023-01-11T22:10:26.6960122Z [       OK ] LoopNest.NormalizeStartVariable (0 ms)
2023-01-11T22:10:26.6960465Z [ RUN      ] LoopNest.NormalizeOnNestedOuterLoop
2023-01-11T22:10:26.6962446Z [       OK ] LoopNest.NormalizeOnNestedOuterLoop (0 ms)
2023-01-11T22:10:26.6962795Z [ RUN      ] LoopNest.NormalizeOnNestedInnerLoop
2023-01-11T22:10:26.6964954Z [       OK ] LoopNest.NormalizeOnNestedInnerLoop (0 ms)
2023-01-11T22:10:26.6965311Z [ RUN      ] LoopNest.NormalizeAndSplitWithTail
2023-01-11T22:10:26.6969445Z [       OK ] LoopNest.NormalizeAndSplitWithTail (0 ms)
2023-01-11T22:10:26.6970006Z [ RUN      ] LoopNest.NotNormalizeAndSplitWithTail
2023-01-11T22:10:26.6974932Z [       OK ] LoopNest.NotNormalizeAndSplitWithTail (0 ms)
2023-01-11T22:10:26.6975516Z [ RUN      ] LoopNest.FlattenSimpleLoopNest2D
2023-01-11T22:10:26.6985283Z [       OK ] LoopNest.FlattenSimpleLoopNest2D (1 ms)
2023-01-11T22:10:26.6985840Z [ RUN      ] LoopNest.FlattenSimpleLoopNest3D
2023-01-11T22:10:26.7068438Z [       OK ] LoopNest.FlattenSimpleLoopNest3D (8 ms)
2023-01-11T22:10:26.7069090Z [ RUN      ] LoopNest.FlattenLoopNestAfterNormalize
2023-01-11T22:10:26.7095307Z [       OK ] LoopNest.FlattenLoopNestAfterNormalize (2 ms)
2023-01-11T22:10:26.7095966Z [ RUN      ] LoopNest.FlattenLoopNestWithNonLiteralConstantBounds
2023-01-11T22:10:26.7107552Z [       OK ] LoopNest.FlattenLoopNestWithNonLiteralConstantBounds (1 ms)
2023-01-11T22:10:26.7108256Z [ RUN      ] LoopNest.FlattenImperfectLoopNest
2023-01-11T22:10:26.7108693Z [       OK ] LoopNest.FlattenImperfectLoopNest (0 ms)
2023-01-11T22:10:26.7109062Z [ RUN      ] LoopNest.FlattenReductionLoopNest
2023-01-11T22:10:26.7109479Z [       OK ] LoopNest.FlattenReductionLoopNest (0 ms)
2023-01-11T22:10:26.7109854Z [ RUN      ] LoopNest.FlattenReductionLoopNestFromTensor
2023-01-11T22:10:26.7110335Z [       OK ] LoopNest.FlattenReductionLoopNestFromTensor (0 ms)
2023-01-11T22:10:26.7110715Z [ RUN      ] LoopNest.FlattenIncorrectLoopsAsInput
2023-01-11T22:10:26.7111143Z [       OK ] LoopNest.FlattenIncorrectLoopsAsInput (0 ms)
2023-01-11T22:10:26.7111586Z [ RUN      ] LoopNest.DetectInlineRankMismatch
2023-01-11T22:10:26.7111979Z [       OK ] LoopNest.DetectInlineRankMismatch (0 ms)
2023-01-11T22:10:26.7112296Z [ RUN      ] LoopNest.CacheReadsSimple
2023-01-11T22:10:26.7468267Z [       OK ] LoopNest.CacheReadsSimple (35 ms)
2023-01-11T22:10:26.7468647Z [ RUN      ] LoopNest.CacheReadsOuter
2023-01-11T22:10:26.7856174Z [       OK ] LoopNest.CacheReadsOuter (38 ms)
2023-01-11T22:10:26.7856525Z [ RUN      ] LoopNest.CacheReadsInternal
2023-01-11T22:10:26.8270510Z [       OK ] LoopNest.CacheReadsInternal (41 ms)
2023-01-11T22:10:26.8270885Z [ RUN      ] LoopNest.CacheReadsInner
2023-01-11T22:10:26.8916030Z [       OK ] LoopNest.CacheReadsInner (64 ms)
2023-01-11T22:10:26.8916358Z [ RUN      ] LoopNest.CacheWritesSimple
2023-01-11T22:10:26.9644036Z [       OK ] LoopNest.CacheWritesSimple (72 ms)
2023-01-11T22:10:26.9644380Z [ RUN      ] LoopNest.DeadStoreElimination
2023-01-11T22:10:26.9661198Z [       OK ] LoopNest.DeadStoreElimination (1 ms)
2023-01-11T22:10:26.9661577Z [ RUN      ] LoopNest.DeadStoreEliminationWithIntermediates
2023-01-11T22:10:26.9674644Z [       OK ] LoopNest.DeadStoreEliminationWithIntermediates (1 ms)
2023-01-11T22:10:26.9675043Z [ RUN      ] LoopNest.CompoundTensorSimple
2023-01-11T22:10:26.9687542Z [       OK ] LoopNest.CompoundTensorSimple (1 ms)
2023-01-11T22:10:26.9688105Z [ RUN      ] LoopNest.InlineConstantIndex
2023-01-11T22:10:26.9691576Z [       OK ] LoopNest.InlineConstantIndex (0 ms)
2023-01-11T22:10:26.9692137Z [ RUN      ] LoopNest.CompoundTensorUsed
2023-01-11T22:10:26.9713913Z [       OK ] LoopNest.CompoundTensorUsed (2 ms)
2023-01-11T22:10:26.9714449Z [ RUN      ] LoopNest.InlineFromLoad
2023-01-11T22:10:26.9714980Z [       OK ] LoopNest.InlineFromLoad (0 ms)
2023-01-11T22:10:26.9715465Z [ RUN      ] LoopNest.OptimizeConditionalsSimple
2023-01-11T22:10:26.9716633Z [       OK ] LoopNest.OptimizeConditionalsSimple (0 ms)
2023-01-11T22:10:26.9717288Z [ RUN      ] LoopNest.OptimizeConditionalsNestedConditions
2023-01-11T22:10:26.9719356Z [       OK ] LoopNest.OptimizeConditionalsNestedConditions (0 ms)
2023-01-11T22:10:26.9719964Z [ RUN      ] LoopNest.OptimizeConditionalsMultipleStores
2023-01-11T22:10:26.9722133Z [       OK ] LoopNest.OptimizeConditionalsMultipleStores (0 ms)
2023-01-11T22:10:26.9722797Z [ RUN      ] LoopNest.OptimizeConditionalsMultipleStoresInOneLoop
2023-01-11T22:10:26.9727367Z [       OK ] LoopNest.OptimizeConditionalsMultipleStoresInOneLoop (0 ms)
2023-01-11T22:10:26.9727974Z [ RUN      ] LoopNest.OptimizeConditionalsOuterLoopVar
2023-01-11T22:10:26.9731168Z [       OK ] LoopNest.OptimizeConditionalsOuterLoopVar (0 ms)
2023-01-11T22:10:26.9731811Z [ RUN      ] LoopNest.OptimizeConditionalsCompValuesNotOrdered
2023-01-11T22:10:26.9734635Z [       OK ] LoopNest.OptimizeConditionalsCompValuesNotOrdered (0 ms)
2023-01-11T22:10:26.9735513Z [ RUN      ] LoopNest.OptimizeConditionalsCompValuesNotConstants
2023-01-11T22:10:26.9738016Z [       OK ] LoopNest.OptimizeConditionalsCompValuesNotConstants (0 ms)
2023-01-11T22:10:26.9738641Z [ RUN      ] LoopNest.OptimizeConditionalsInvalidCondition
2023-01-11T22:10:26.9741318Z [       OK ] LoopNest.OptimizeConditionalsInvalidCondition (0 ms)
2023-01-11T22:10:26.9741964Z [ RUN      ] LoopNest.OptimizeConditionalsInvalidCondition2
2023-01-11T22:10:26.9744989Z [       OK ] LoopNest.OptimizeConditionalsInvalidCondition2 (0 ms)
2023-01-11T22:10:26.9745626Z [ RUN      ] LoopNest.OptimizeConditionalsInvalidCondition3
2023-01-11T22:10:26.9748065Z [       OK ] LoopNest.OptimizeConditionalsInvalidCondition3 (0 ms)
2023-01-11T22:10:26.9748817Z [ RUN      ] LoopNest.OptimizeConditionalsInvalidCondition4
2023-01-11T22:10:26.9750827Z [       OK ] LoopNest.OptimizeConditionalsInvalidCondition4 (0 ms)
2023-01-11T22:10:26.9751453Z [ RUN      ] LoopNest.OptimizeConditionalsNotNormalized
2023-01-11T22:10:26.9752468Z [       OK ] LoopNest.OptimizeConditionalsNotNormalized (0 ms)
2023-01-11T22:10:26.9753082Z [ RUN      ] LoopNest.ColReduceSplitTailEvenReorder
2023-01-11T22:10:27.1211791Z [       OK ] LoopNest.ColReduceSplitTailEvenReorder (145 ms)
2023-01-11T22:10:27.1212275Z [ RUN      ] LoopNest.ColReduceSplitTailUnevenReorder
2023-01-11T22:10:27.2367853Z [       OK ] LoopNest.ColReduceSplitTailUnevenReorder (115 ms)
2023-01-11T22:10:27.2368308Z [ RUN      ] LoopNest.ColReduceSplitMaskEvenReorder
2023-01-11T22:10:27.3825616Z [       OK ] LoopNest.ColReduceSplitMaskEvenReorder (145 ms)
2023-01-11T22:10:27.3826028Z [ RUN      ] LoopNest.ColReduceSplitMaskUnevenReorder
2023-01-11T22:10:27.5136099Z [       OK ] LoopNest.ColReduceSplitMaskUnevenReorder (131 ms)
2023-01-11T22:10:27.5136485Z [ RUN      ] LoopNest.ReorderAxisWithMultipleConds
2023-01-11T22:10:27.5138615Z [       OK ] LoopNest.ReorderAxisWithMultipleConds (0 ms)
2023-01-11T22:10:27.5138960Z [ RUN      ] LoopNest.VectorizeUse
2023-01-11T22:10:27.5140082Z [       OK ] LoopNest.VectorizeUse (0 ms)
2023-01-11T22:10:27.5140483Z [ RUN      ] LoopNest.Int64Direct
2023-01-11T22:10:27.5140873Z [       OK ] LoopNest.Int64Direct (0 ms)
2023-01-11T22:10:27.5141252Z [ RUN      ] LoopNest.Int64Compute
2023-01-11T22:10:27.5142293Z [       OK ] LoopNest.Int64Compute (0 ms)
2023-01-11T22:10:27.5142969Z [ RUN      ] LoopNest.DistributeLoopWithAllStmtsAsPivots
2023-01-11T22:10:27.5143708Z [       OK ] LoopNest.DistributeLoopWithAllStmtsAsPivots (0 ms)
2023-01-11T22:10:27.5144384Z [ RUN      ] LoopNest.DistributeLoopWithOneStmtAsPivot
2023-01-11T22:10:27.5144887Z [       OK ] LoopNest.DistributeLoopWithOneStmtAsPivot (0 ms)
2023-01-11T22:10:27.5145281Z [ RUN      ] LoopNest.DistributeLoopWithoutAnyPivot
2023-01-11T22:10:27.5145703Z [       OK ] LoopNest.DistributeLoopWithoutAnyPivot (0 ms)
2023-01-11T22:10:27.5146062Z [ RUN      ] LoopNest.DistributeLoopOverInnerLoops
2023-01-11T22:10:27.5146431Z [       OK ] LoopNest.DistributeLoopOverInnerLoops (0 ms)
2023-01-11T22:10:27.5146878Z [ RUN      ] LoopNest.DistributeLoopAndParentsWithoutAnyPivot
2023-01-11T22:10:27.5147365Z [       OK ] LoopNest.DistributeLoopAndParentsWithoutAnyPivot (0 ms)
2023-01-11T22:10:27.5147729Z [ RUN      ] LoopNest.fuseLoopsSimple
2023-01-11T22:10:27.5150117Z [       OK ] LoopNest.fuseLoopsSimple (0 ms)
2023-01-11T22:10:27.5150540Z [ RUN      ] LoopNest.fuseLoopsMultiple
2023-01-11T22:10:27.5155312Z [       OK ] LoopNest.fuseLoopsMultiple (0 ms)
2023-01-11T22:10:27.5155874Z [ RUN      ] LoopNest.fuseLoopsNested
2023-01-11T22:10:27.5163253Z [       OK ] LoopNest.fuseLoopsNested (0 ms)
2023-01-11T22:10:27.5163860Z [ RUN      ] LoopNest.fuseLoopsNested2D
2023-01-11T22:10:27.5166793Z [       OK ] LoopNest.fuseLoopsNested2D (0 ms)
2023-01-11T22:10:27.5167126Z [ RUN      ] LoopNest.fuseLoopsNested2DInner
2023-01-11T22:10:27.5169502Z [       OK ] LoopNest.fuseLoopsNested2DInner (0 ms)
2023-01-11T22:10:27.5169858Z [ RUN      ] LoopNest.fuseLoopsDifferentStopBounds
2023-01-11T22:10:27.5170230Z [       OK ] LoopNest.fuseLoopsDifferentStopBounds (0 ms)
2023-01-11T22:10:27.5170598Z [ RUN      ] LoopNest.fuseLoopsDifferentStartBounds
2023-01-11T22:10:27.5170957Z [       OK ] LoopNest.fuseLoopsDifferentStartBounds (0 ms)
2023-01-11T22:10:27.5171303Z [ RUN      ] LoopNest.fuseLoopsNotContiguous
2023-01-11T22:10:27.5172603Z [       OK ] LoopNest.fuseLoopsNotContiguous (0 ms)
2023-01-11T22:10:27.5172967Z [ RUN      ] LoopNest.fuseLoopsWithDifferentParents
2023-01-11T22:10:27.5173334Z [       OK ] LoopNest.fuseLoopsWithDifferentParents (0 ms)
2023-01-11T22:10:27.5173695Z [ RUN      ] LoopNest.fuseLoopsWithVariableBounds
2023-01-11T22:10:27.5176836Z [       OK ] LoopNest.fuseLoopsWithVariableBounds (0 ms)
2023-01-11T22:10:27.5177222Z [ RUN      ] LoopNest.fuseLoopsWithExprBounds
2023-01-11T22:10:27.5182716Z [       OK ] LoopNest.fuseLoopsWithExprBounds (0 ms)
2023-01-11T22:10:27.5183146Z [ RUN      ] LoopNest.fuseLoopsWithDifferentExprBounds
2023-01-11T22:10:27.5187958Z [       OK ] LoopNest.fuseLoopsWithDifferentExprBounds (0 ms)
2023-01-11T22:10:27.5188387Z [ RUN      ] LoopNest.fuseLoopsWithNonOverlappingBufferAccesses
2023-01-11T22:10:27.5193027Z [       OK ] LoopNest.fuseLoopsWithNonOverlappingBufferAccesses (0 ms)
2023-01-11T22:10:27.5193498Z [ RUN      ] LoopNest.fuseLoopsWithNonOverlapping2DBufferAccesses
2023-01-11T22:10:27.5202159Z [       OK ] LoopNest.fuseLoopsWithNonOverlapping2DBufferAccesses (0 ms)
2023-01-11T22:10:27.5202551Z [ RUN      ] LoopNest.fuseLoopsWithReductions
2023-01-11T22:10:27.5208451Z [       OK ] LoopNest.fuseLoopsWithReductions (0 ms)
2023-01-11T22:10:27.5208799Z [ RUN      ] LoopNest.fuseLoopsWith2DReductions
2023-01-11T22:10:27.5219367Z [       OK ] LoopNest.fuseLoopsWith2DReductions (1 ms)
2023-01-11T22:10:27.5219731Z [ RUN      ] LoopNest.fuseLoopsWithComplexIndices
2023-01-11T22:10:27.5228826Z [       OK ] LoopNest.fuseLoopsWithComplexIndices (0 ms)
2023-01-11T22:10:27.5229216Z [ RUN      ] LoopNest.fuseLoopsWithMixedLoopVarsAsIndices
2023-01-11T22:10:27.5241281Z [       OK ] LoopNest.fuseLoopsWithMixedLoopVarsAsIndices (1 ms)
2023-01-11T22:10:27.5241651Z [ RUN      ] LoopNest.fuseLoopsWithTranspose
2023-01-11T22:10:27.5247970Z [       OK ] LoopNest.fuseLoopsWithTranspose (0 ms)
2023-01-11T22:10:27.5248324Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies1
2023-01-11T22:10:27.5253635Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies1 (0 ms)
2023-01-11T22:10:27.5254037Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies2
2023-01-11T22:10:27.5258867Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies2 (0 ms)
2023-01-11T22:10:27.5259276Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies3
2023-01-11T22:10:27.5268134Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies3 (0 ms)
2023-01-11T22:10:27.5268533Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies4
2023-01-11T22:10:27.5279595Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies4 (1 ms)
2023-01-11T22:10:27.5279974Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies5
2023-01-11T22:10:27.5285670Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies5 (0 ms)
2023-01-11T22:10:27.5286093Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies6
2023-01-11T22:10:27.5291519Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies6 (0 ms)
2023-01-11T22:10:27.5291990Z [ RUN      ] LoopNest.fuseLoopsThatViolateDependencies7
2023-01-11T22:10:27.5297588Z [       OK ] LoopNest.fuseLoopsThatViolateDependencies7 (0 ms)
2023-01-11T22:10:27.5298172Z [ RUN      ] LoopNest.areLoopsPerfectlyNested
2023-01-11T22:10:27.5298708Z [       OK ] LoopNest.areLoopsPerfectlyNested (0 ms)
2023-01-11T22:10:27.5299254Z [ RUN      ] LoopNest.reorderNestedLoops2D
2023-01-11T22:10:27.5299834Z [       OK ] LoopNest.reorderNestedLoops2D (0 ms)
2023-01-11T22:10:27.5300208Z [ RUN      ] LoopNest.reorderNestedLoops3D
2023-01-11T22:10:27.5300648Z [       OK ] LoopNest.reorderNestedLoops3D (0 ms)
2023-01-11T22:10:27.5301151Z [ RUN      ] LoopNest.reorderNestedLoops4D
2023-01-11T22:10:27.5301586Z [       OK ] LoopNest.reorderNestedLoops4D (0 ms)
2023-01-11T22:10:27.5302070Z [ RUN      ] LoopNest.reorderTrivialPermutation
2023-01-11T22:10:27.5302650Z [       OK ] LoopNest.reorderTrivialPermutation (0 ms)
2023-01-11T22:10:27.5303093Z [ RUN      ] LoopNest.reorderInvalidPermutations
2023-01-11T22:10:27.5303647Z [       OK ] LoopNest.reorderInvalidPermutations (0 ms)
2023-01-11T22:10:27.5304151Z [ RUN      ] LoopNest.reorderInvalidLoopNest
2023-01-11T22:10:27.5304596Z [       OK ] LoopNest.reorderInvalidLoopNest (0 ms)
2023-01-11T22:10:27.5304957Z [ RUN      ] LoopNest.compressBufferSimple
2023-01-11T22:10:27.5305444Z [       OK ] LoopNest.compressBufferSimple (0 ms)
2023-01-11T22:10:27.5305834Z [ RUN      ] LoopNest.compressBufferMultipleDims
2023-01-11T22:10:27.5306189Z [       OK ] LoopNest.compressBufferMultipleDims (0 ms)
2023-01-11T22:10:27.5306531Z [ RUN      ] LoopNest.compressBufferMultipleDims2
2023-01-11T22:10:27.5306887Z [       OK ] LoopNest.compressBufferMultipleDims2 (0 ms)
2023-01-11T22:10:27.5307268Z [ RUN      ] LoopNest.compressBufferDifferentOrderIndices
2023-01-11T22:10:27.5307661Z [       OK ] LoopNest.compressBufferDifferentOrderIndices (0 ms)
2023-01-11T22:10:27.5308039Z [ RUN      ] LoopNest.compressBufferVariableBounds
2023-01-11T22:10:27.5308400Z [       OK ] LoopNest.compressBufferVariableBounds (0 ms)
2023-01-11T22:10:27.5308779Z [ RUN      ] LoopNest.compressBufferNoCommonParentLoops
2023-01-11T22:10:27.5309167Z [       OK ] LoopNest.compressBufferNoCommonParentLoops (0 ms)
2023-01-11T22:10:27.5309534Z [ RUN      ] LoopNest.compressBufferIndicesMixed
2023-01-11T22:10:27.5309886Z [       OK ] LoopNest.compressBufferIndicesMixed (0 ms)
2023-01-11T22:10:27.5310210Z [ RUN      ] LoopNest.compressMultipleBuffers
2023-01-11T22:10:27.5310552Z [       OK ] LoopNest.compressMultipleBuffers (0 ms)
2023-01-11T22:10:27.5310855Z [ RUN      ] LoopNest.sanitizeNames
2023-01-11T22:10:27.5321039Z [       OK ] LoopNest.sanitizeNames (1 ms)
2023-01-11T22:10:27.5321440Z [----------] 174 tests from LoopNest (1678 ms total)
2023-01-11T22:10:27.5321595Z 
2023-01-11T22:10:27.5321747Z [----------] 31 tests from MemDependency
2023-01-11T22:10:27.5322036Z [ RUN      ] MemDependency.BoundOverlap
2023-01-11T22:10:27.5333325Z [       OK ] MemDependency.BoundOverlap (1 ms)
2023-01-11T22:10:27.5333724Z [ RUN      ] MemDependency.BoundComparison
2023-01-11T22:10:27.5342654Z [       OK ] MemDependency.BoundComparison (0 ms)
2023-01-11T22:10:27.5343061Z [ RUN      ] MemDependency.BoundOverlapSymbolic
2023-01-11T22:10:27.5352416Z [       OK ] MemDependency.BoundOverlapSymbolic (0 ms)
2023-01-11T22:10:27.5352923Z [ RUN      ] MemDependency.BoundOverlapMultiDim
2023-01-11T22:10:27.5361760Z [       OK ] MemDependency.BoundOverlapMultiDim (0 ms)
2023-01-11T22:10:27.5362187Z [ RUN      ] MemDependency.BoundSubtract
2023-01-11T22:10:27.5369995Z [       OK ] MemDependency.BoundSubtract (0 ms)
2023-01-11T22:10:27.5370523Z [ RUN      ] MemDependency.BoundSubtractSymbolic
2023-01-11T22:10:27.5394254Z [       OK ] MemDependency.BoundSubtractSymbolic (2 ms)
2023-01-11T22:10:27.5394830Z [ RUN      ] MemDependency.BoundSubtractMultiDim
2023-01-11T22:10:27.5421051Z [       OK ] MemDependency.BoundSubtractMultiDim (2 ms)
2023-01-11T22:10:27.5421666Z [ RUN      ] MemDependency.BoundSubtractMultiDimSymbolic
2023-01-11T22:10:27.5458152Z [       OK ] MemDependency.BoundSubtractMultiDimSymbolic (3 ms)
2023-01-11T22:10:27.5458877Z [ RUN      ] MemDependency.MemDependencyCheckerSimple
2023-01-11T22:10:27.5459685Z [       OK ] MemDependency.MemDependencyCheckerSimple (0 ms)
2023-01-11T22:10:27.5460394Z [ RUN      ] MemDependency.MemDependencyCheckerMultiStmt
2023-01-11T22:10:27.5461039Z [       OK ] MemDependency.MemDependencyCheckerMultiStmt (0 ms)
2023-01-11T22:10:27.5461486Z [ RUN      ] MemDependency.MemDependencyCheckerOverlap
2023-01-11T22:10:27.5461881Z [       OK ] MemDependency.MemDependencyCheckerOverlap (0 ms)
2023-01-11T22:10:27.5462351Z [ RUN      ] MemDependency.MemDependencyCheckerLoop
2023-01-11T22:10:27.5462932Z [       OK ] MemDependency.MemDependencyCheckerLoop (0 ms)
2023-01-11T22:10:27.5463328Z [ RUN      ] MemDependency.MemDependencyCheckerLoopReduce
2023-01-11T22:10:27.5465987Z [       OK ] MemDependency.MemDependencyCheckerLoopReduce (0 ms)
2023-01-11T22:10:27.5466484Z [ RUN      ] MemDependency.MemDependencyCheckerLoopReduceExpanded
2023-01-11T22:10:27.5469062Z [       OK ] MemDependency.MemDependencyCheckerLoopReduceExpanded (0 ms)
2023-01-11T22:10:27.5469531Z [ RUN      ] MemDependency.MemDependencyCheckerInputsOutputs
2023-01-11T22:10:27.5471396Z [       OK ] MemDependency.MemDependencyCheckerInputsOutputs (0 ms)
2023-01-11T22:10:27.5471954Z [ RUN      ] MemDependency.MemDependencyCheckerOutputDoesntDepend
2023-01-11T22:10:27.5472647Z [       OK ] MemDependency.MemDependencyCheckerOutputDoesntDepend (0 ms)
2023-01-11T22:10:27.5473080Z [ RUN      ] MemDependency.MemDependencyCheckerLoopBounds
2023-01-11T22:10:27.5485559Z [       OK ] MemDependency.MemDependencyCheckerLoopBounds (1 ms)
2023-01-11T22:10:27.5486002Z [ RUN      ] MemDependency.MemDependencyCheckerLoopBoundsIndexShift
2023-01-11T22:10:27.5516536Z [       OK ] MemDependency.MemDependencyCheckerLoopBoundsIndexShift (3 ms)
2023-01-11T22:10:27.5517007Z [ RUN      ] MemDependency.MemDependencyCheckerLoopSelfDependency
2023-01-11T22:10:27.5668773Z [       OK ] MemDependency.MemDependencyCheckerLoopSelfDependency (15 ms)
2023-01-11T22:10:27.5669249Z [ RUN      ] MemDependency.MemDependencyCheckerLoopDistinctStrides
2023-01-11T22:10:27.5679003Z [       OK ] MemDependency.MemDependencyCheckerLoopDistinctStrides (1 ms)
2023-01-11T22:10:27.5679458Z [ RUN      ] MemDependency.MemDependencyCheckerLoopBoundsCond
2023-01-11T22:10:27.5698542Z [       OK ] MemDependency.MemDependencyCheckerLoopBoundsCond (1 ms)
2023-01-11T22:10:27.5698973Z [ RUN      ] MemDependency.MemDependencyCheckerIfThenElse
2023-01-11T22:10:27.5711603Z [       OK ] MemDependency.MemDependencyCheckerIfThenElse (1 ms)
2023-01-11T22:10:27.5712097Z [ RUN      ] MemDependency.MemDependencyCheckerCutLoop
2023-01-11T22:10:27.5732595Z [       OK ] MemDependency.MemDependencyCheckerCutLoop (2 ms)
2023-01-11T22:10:27.5733018Z [ RUN      ] MemDependency.MemDependencyCheckerDynamicShapes
2023-01-11T22:10:27.5762883Z [       OK ] MemDependency.MemDependencyCheckerDynamicShapes (3 ms)
2023-01-11T22:10:27.5763289Z [ RUN      ] MemDependency.MemDependencyCheckerMultiDim
2023-01-11T22:10:27.5801517Z [       OK ] MemDependency.MemDependencyCheckerMultiDim (3 ms)
2023-01-11T22:10:27.5801929Z [ RUN      ] MemDependency.MemDependencyCheckerComputeAPI
2023-01-11T22:10:27.5813823Z [       OK ] MemDependency.MemDependencyCheckerComputeAPI (1 ms)
2023-01-11T22:10:27.5814238Z [ RUN      ] MemDependency.MemDependencyCheckerComputeInline
2023-01-11T22:10:27.5822831Z [       OK ] MemDependency.MemDependencyCheckerComputeInline (0 ms)
2023-01-11T22:10:27.5823261Z [ RUN      ] MemDependency.MemDependencyCheckerComputeSplit
2023-01-11T22:10:27.5846847Z [       OK ] MemDependency.MemDependencyCheckerComputeSplit (2 ms)
2023-01-11T22:10:27.5847274Z [ RUN      ] MemDependency.MemDependencyCheckerComputeReorder
2023-01-11T22:10:27.5862702Z [       OK ] MemDependency.MemDependencyCheckerComputeReorder (1 ms)
2023-01-11T22:10:27.5863149Z [ RUN      ] MemDependency.MemDependencyCheckerComputeReduce
2023-01-11T22:10:27.5875187Z [       OK ] MemDependency.MemDependencyCheckerComputeReduce (1 ms)
2023-01-11T22:10:27.5875625Z [ RUN      ] MemDependency.MemDependencyCheckerComputeGEMM
2023-01-11T22:10:27.6054756Z [       OK ] MemDependency.MemDependencyCheckerComputeGEMM (17 ms)
2023-01-11T22:10:27.6055201Z [----------] 31 tests from MemDependency (73 ms total)
2023-01-11T22:10:27.6055365Z 
2023-01-11T22:10:27.6055480Z [----------] 2 tests from Ops
2023-01-11T22:10:27.6055713Z [ RUN      ] Ops.Sum
2023-01-11T22:10:27.6096046Z [       OK ] Ops.Sum (4 ms)
2023-01-11T22:10:27.6096367Z [ RUN      ] Ops.ChannelsLastSum
2023-01-11T22:10:27.7189682Z [       OK ] Ops.ChannelsLastSum (109 ms)
2023-01-11T22:10:27.7190026Z [----------] 2 tests from Ops (113 ms total)
2023-01-11T22:10:27.7190242Z 
2023-01-11T22:10:27.7190448Z [----------] 10 tests from Quantization
2023-01-11T22:10:27.7190841Z [ RUN      ] Quantization.QuantDequantInt8
2023-01-11T22:10:27.7476290Z [       OK ] Quantization.QuantDequantInt8 (28 ms)
2023-01-11T22:10:27.7476802Z [ RUN      ] Quantization.QuantDequantUInt8
2023-01-11T22:10:27.7741241Z [       OK ] Quantization.QuantDequantUInt8 (26 ms)
2023-01-11T22:10:27.7741794Z [ RUN      ] Quantization.QuantDequantUInt8_NLC
2023-01-11T22:10:27.8009601Z [       OK ] Quantization.QuantDequantUInt8_NLC (26 ms)
2023-01-11T22:10:27.8010170Z [ RUN      ] Quantization.QuantAddDequantInt8
2023-01-11T22:10:27.8307908Z [       OK ] Quantization.QuantAddDequantInt8 (29 ms)
2023-01-11T22:10:27.8308373Z [ RUN      ] Quantization.QuantAddDequantUInt8
2023-01-11T22:10:27.8603272Z [       OK ] Quantization.QuantAddDequantUInt8 (29 ms)
2023-01-11T22:10:27.8603752Z [ RUN      ] Quantization.QuantSigmoidDequantUInt8
2023-01-11T22:10:27.8944245Z [       OK ] Quantization.QuantSigmoidDequantUInt8 (34 ms)
2023-01-11T22:10:27.8944663Z [ RUN      ] Quantization.QuantMulDequantUInt8
2023-01-11T22:10:27.9349896Z [       OK ] Quantization.QuantMulDequantUInt8 (40 ms)
2023-01-11T22:10:27.9350385Z [ RUN      ] Quantization.QuantUpsampleNearst2dDequantUInt8
2023-01-11T22:10:27.9816122Z [       OK ] Quantization.QuantUpsampleNearst2dDequantUInt8 (46 ms)
2023-01-11T22:10:27.9816604Z [ RUN      ] Quantization.UpsampleNearst2d
2023-01-11T22:10:28.0117410Z [       OK ] Quantization.UpsampleNearst2d (30 ms)
2023-01-11T22:10:28.0118171Z [ RUN      ] Quantization.QuantCatDequantUInt8
2023-01-11T22:10:28.0728095Z [       OK ] Quantization.QuantCatDequantUInt8 (61 ms)
2023-01-11T22:10:28.0728619Z [----------] 10 tests from Quantization (353 ms total)
2023-01-11T22:10:28.0728842Z 
2023-01-11T22:10:28.0729233Z [----------] 2 tests from BufLiveRange
2023-01-11T22:10:28.0729620Z [ RUN      ] BufLiveRange.SingleRangeLine
2023-01-11T22:10:28.0730280Z [       OK ] BufLiveRange.SingleRangeLine (0 ms)
2023-01-11T22:10:28.0730661Z [ RUN      ] BufLiveRange.MulRangeLine
2023-01-11T22:10:28.0731054Z [       OK ] BufLiveRange.MulRangeLine (0 ms)
2023-01-11T22:10:28.0731469Z [----------] 2 tests from BufLiveRange (0 ms total)
2023-01-11T22:10:28.0731663Z 
2023-01-11T22:10:28.0731859Z [----------] 6 tests from MemPlanning
2023-01-11T22:10:28.0732238Z [ RUN      ] MemPlanning.MemReuseWithTypeCast
2023-01-11T22:10:28.1365203Z [       OK ] MemPlanning.MemReuseWithTypeCast (63 ms)
2023-01-11T22:10:28.1365566Z [ RUN      ] MemPlanning.NoMemReuseForLargerType
2023-01-11T22:10:28.2108521Z [       OK ] MemPlanning.NoMemReuseForLargerType (74 ms)
2023-01-11T22:10:28.2108885Z [ RUN      ] MemPlanning.SameBufSizeMemReuse
2023-01-11T22:10:28.4729534Z [       OK ] MemPlanning.SameBufSizeMemReuse (261 ms)
2023-01-11T22:10:28.4730209Z [ RUN      ] MemPlanning.SameBufSizeMultiMemReuses
2023-01-11T22:10:28.7403294Z [       OK ] MemPlanning.SameBufSizeMultiMemReuses (267 ms)
2023-01-11T22:10:28.7403992Z [ RUN      ] MemPlanning.SameBufSizeMultiMemReusesOfOneBuf
2023-01-11T22:10:29.0827862Z [       OK ] MemPlanning.SameBufSizeMultiMemReusesOfOneBuf (342 ms)
2023-01-11T22:10:29.0828282Z [ RUN      ] MemPlanning.SmallerBufSizeNonMemReuse
2023-01-11T22:10:29.2322301Z [       OK ] MemPlanning.SmallerBufSizeNonMemReuse (149 ms)
2023-01-11T22:10:29.2322885Z [----------] 6 tests from MemPlanning (1159 ms total)
2023-01-11T22:10:29.2323054Z 
2023-01-11T22:10:29.2323239Z [----------] 45 tests from Reductions
2023-01-11T22:10:29.2323673Z [ RUN      ] Reductions.ReduceSum0D_1
2023-01-11T22:10:29.2324263Z [       OK ] Reductions.ReduceSum0D_1 (0 ms)
2023-01-11T22:10:29.2324774Z [ RUN      ] Reductions.ReduceSum0D_2
2023-01-11T22:10:29.2325229Z [       OK ] Reductions.ReduceSum0D_2 (0 ms)
2023-01-11T22:10:29.2325525Z [ RUN      ] Reductions.ReduceSum1D
2023-01-11T22:10:29.2326259Z [       OK ] Reductions.ReduceSum1D (0 ms)
2023-01-11T22:10:29.2326897Z [ RUN      ] Reductions.ReduceSum2D
2023-01-11T22:10:29.2331455Z [       OK ] Reductions.ReduceSum2D (0 ms)
2023-01-11T22:10:29.2331993Z [ RUN      ] Reductions.ReduceSum3D
2023-01-11T22:10:29.2363511Z [       OK ] Reductions.ReduceSum3D (3 ms)
2023-01-11T22:10:29.2364056Z [ RUN      ] Reductions.ReduceSum10D
2023-01-11T22:10:29.9038890Z [       OK ] Reductions.ReduceSum10D (667 ms)
2023-01-11T22:10:29.9039437Z [ RUN      ] Reductions.ReduceProduct
2023-01-11T22:10:29.9043321Z [       OK ] Reductions.ReduceProduct (0 ms)
2023-01-11T22:10:29.9043845Z [ RUN      ] Reductions.ReduceMax
2023-01-11T22:10:29.9049213Z [       OK ] Reductions.ReduceMax (0 ms)
2023-01-11T22:10:29.9049842Z [ RUN      ] Reductions.ReduceMinCustomInitializer
2023-01-11T22:10:29.9051795Z [       OK ] Reductions.ReduceMinCustomInitializer (0 ms)
2023-01-11T22:10:29.9052377Z [ RUN      ] Reductions.ReduceAnyAll
2023-01-11T22:10:29.9074939Z [       OK ] Reductions.ReduceAnyAll (2 ms)
2023-01-11T22:10:29.9075492Z [ RUN      ] Reductions.ReduceMatmul2D
2023-01-11T22:10:29.9085010Z [       OK ] Reductions.ReduceMatmul2D (1 ms)
2023-01-11T22:10:29.9085540Z [ RUN      ] Reductions.ReduceRfactorLike
2023-01-11T22:10:29.9097185Z [       OK ] Reductions.ReduceRfactorLike (1 ms)
2023-01-11T22:10:29.9097753Z [ RUN      ] Reductions.ReduceAsProducer
2023-01-11T22:10:29.9120588Z [       OK ] Reductions.ReduceAsProducer (2 ms)
2023-01-11T22:10:29.9121178Z [ RUN      ] Reductions.ReduceAsConsumer
2023-01-11T22:10:29.9156962Z [       OK ] Reductions.ReduceAsConsumer (3 ms)
2023-01-11T22:10:29.9157526Z [ RUN      ] Reductions.SplitReduceAxis
2023-01-11T22:10:29.9174331Z [       OK ] Reductions.SplitReduceAxis (1 ms)
2023-01-11T22:10:29.9174895Z [ RUN      ] Reductions.SplitNonReduceAxis
2023-01-11T22:10:29.9208628Z [       OK ] Reductions.SplitNonReduceAxis (3 ms)
2023-01-11T22:10:29.9209463Z [ RUN      ] Reductions.ReorderedReductionInitializer
2023-01-11T22:10:29.9248906Z [       OK ] Reductions.ReorderedReductionInitializer (4 ms)
2023-01-11T22:10:29.9249431Z [ RUN      ] Reductions.ReduceRfactor
2023-01-11T22:10:29.9259934Z [       OK ] Reductions.ReduceRfactor (1 ms)
2023-01-11T22:10:29.9260295Z [ RUN      ] Reductions.Reduce3DRfactorInner
2023-01-11T22:10:29.9386769Z [       OK ] Reductions.Reduce3DRfactorInner (12 ms)
2023-01-11T22:10:29.9387234Z [ RUN      ] Reductions.Reduce3DRfactorOuter
2023-01-11T22:10:29.9518104Z [       OK ] Reductions.Reduce3DRfactorOuter (13 ms)
2023-01-11T22:10:29.9518483Z [ RUN      ] Reductions.ReduceRepeatedInternalRfactor
2023-01-11T22:10:30.1247779Z [       OK ] Reductions.ReduceRepeatedInternalRfactor (172 ms)
2023-01-11T22:10:30.1248193Z [ RUN      ] Reductions.ReduceSplitTail
2023-01-11T22:10:30.1645838Z [       OK ] Reductions.ReduceSplitTail (39 ms)
2023-01-11T22:10:30.1646253Z [ RUN      ] Reductions.ReduceSplitNoTail
2023-01-11T22:10:30.2099602Z [       OK ] Reductions.ReduceSplitNoTail (45 ms)
2023-01-11T22:10:30.2100176Z [ RUN      ] Reductions.ReduceOverSplitTail
2023-01-11T22:10:30.2489792Z [       OK ] Reductions.ReduceOverSplitTail (39 ms)
2023-01-11T22:10:30.2490105Z [ RUN      ] Reductions.ReduceSplitMask
2023-01-11T22:10:30.2986474Z [       OK ] Reductions.ReduceSplitMask (49 ms)
2023-01-11T22:10:30.2986806Z [ RUN      ] Reductions.ReduceSplitNoMask
2023-01-11T22:10:30.3449284Z [       OK ] Reductions.ReduceSplitNoMask (46 ms)
2023-01-11T22:10:30.3449620Z [ RUN      ] Reductions.ReduceOverSplitMask
2023-01-11T22:10:30.3855182Z [       OK ] Reductions.ReduceOverSplitMask (40 ms)
2023-01-11T22:10:30.3855535Z [ RUN      ] Reductions.ReduceSplitRfactor
2023-01-11T22:10:30.3897661Z [       OK ] Reductions.ReduceSplitRfactor (4 ms)
2023-01-11T22:10:30.3898006Z [ RUN      ] Reductions.ReduceOverSplitRfactor
2023-01-11T22:10:30.3912250Z [       OK ] Reductions.ReduceOverSplitRfactor (1 ms)
2023-01-11T22:10:30.3912700Z [ RUN      ] Reductions.ReduceInlineReduction
2023-01-11T22:10:30.3913472Z [       OK ] Reductions.ReduceInlineReduction (0 ms)
2023-01-11T22:10:30.3913855Z [ RUN      ] Reductions.ReduceInlineConsumer
2023-01-11T22:10:30.4003164Z [       OK ] Reductions.ReduceInlineConsumer (8 ms)
2023-01-11T22:10:30.4003527Z [ RUN      ] Reductions.ReduceInlineReducerInternal
2023-01-11T22:10:30.4094727Z [       OK ] Reductions.ReduceInlineReducerInternal (9 ms)
2023-01-11T22:10:30.4095139Z [ RUN      ] Reductions.ReductionCacheAccessesOperatorAxis
2023-01-11T22:10:30.4143935Z [       OK ] Reductions.ReductionCacheAccessesOperatorAxis (4 ms)
2023-01-11T22:10:30.4144383Z [ RUN      ] Reductions.ReductionCacheAccessesOuterReduceAxis
2023-01-11T22:10:30.4189149Z [       OK ] Reductions.ReductionCacheAccessesOuterReduceAxis (4 ms)
2023-01-11T22:10:30.4189648Z [ RUN      ] Reductions.ReductionCacheAccessesInnerReduceAxis
2023-01-11T22:10:30.4234457Z [       OK ] Reductions.ReductionCacheAccessesInnerReduceAxis (4 ms)
2023-01-11T22:10:30.4234915Z [ RUN      ] Reductions.ReductionCacheBodyAccess
2023-01-11T22:10:30.4251081Z [       OK ] Reductions.ReductionCacheBodyAccess (1 ms)
2023-01-11T22:10:30.4251517Z [ RUN      ] Reductions.ReductionCacheConsumerAccess
2023-01-11T22:10:30.4270397Z [       OK ] Reductions.ReductionCacheConsumerAccess (1 ms)
2023-01-11T22:10:30.4271019Z [ RUN      ] Reductions.ReductionSplitCacheConsumerAccess
2023-01-11T22:10:30.4292596Z [       OK ] Reductions.ReductionSplitCacheConsumerAccess (2 ms)
2023-01-11T22:10:30.4293064Z [ RUN      ] Reductions.ReductionReorderCacheConsumerAccess
2023-01-11T22:10:30.4312200Z [       OK ] Reductions.ReductionReorderCacheConsumerAccess (1 ms)
2023-01-11T22:10:30.4312646Z [ RUN      ] Reductions.ReductionRfactorCacheTempOuter
2023-01-11T22:10:30.4494279Z [       OK ] Reductions.ReductionRfactorCacheTempOuter (18 ms)
2023-01-11T22:10:30.4494734Z [ RUN      ] Reductions.ReductionRfactorCacheTempInner
2023-01-11T22:10:30.4637767Z [       OK ] Reductions.ReductionRfactorCacheTempInner (14 ms)
2023-01-11T22:10:30.4638196Z [ RUN      ] Reductions.ReductionVectorize
2023-01-11T22:10:30.4650002Z [       OK ] Reductions.ReductionVectorize (1 ms)
2023-01-11T22:10:30.4650434Z [ RUN      ] Reductions.ReductionVectorizeInner
2023-01-11T22:10:30.4658952Z [       OK ] Reductions.ReductionVectorizeInner (0 ms)
2023-01-11T22:10:30.4659318Z [ RUN      ] Reductions.ReductionVectorizeRfactor
2023-01-11T22:10:30.4664641Z [       OK ] Reductions.ReductionVectorizeRfactor (1 ms)
2023-01-11T22:10:30.4664981Z [ RUN      ] Reductions.InitFunction
2023-01-11T22:10:30.4666522Z [       OK ] Reductions.InitFunction (0 ms)
2023-01-11T22:10:30.4666995Z [----------] 45 tests from Reductions (1234 ms total)
2023-01-11T22:10:30.4667154Z 
2023-01-11T22:10:30.4667305Z [----------] 69 tests from Registerizer
2023-01-11T22:10:30.4667596Z [ RUN      ] Registerizer.RegisterizerSimple
2023-01-11T22:10:30.4669464Z [       OK ] Registerizer.RegisterizerSimple (0 ms)
2023-01-11T22:10:30.4669956Z [ RUN      ] Registerizer.RegisterizerLoop
2023-01-11T22:10:30.4670771Z [       OK ] Registerizer.RegisterizerLoop (0 ms)
2023-01-11T22:10:30.4671175Z [ RUN      ] Registerizer.RegisterizerLoopFixedLoad
2023-01-11T22:10:30.4673022Z [       OK ] Registerizer.RegisterizerLoopFixedLoad (0 ms)
2023-01-11T22:10:30.4673396Z [ RUN      ] Registerizer.RegisterizerLoopInternal
2023-01-11T22:10:30.4675224Z [       OK ] Registerizer.RegisterizerLoopInternal (0 ms)
2023-01-11T22:10:30.4675680Z [ RUN      ] Registerizer.RegisterizerLoopInternalLoadOverlap
2023-01-11T22:10:30.4677346Z [       OK ] Registerizer.RegisterizerLoopInternalLoadOverlap (0 ms)
2023-01-11T22:10:30.4677812Z [ RUN      ] Registerizer.RegisterizerLoopInternalRepeated
2023-01-11T22:10:30.4681817Z [       OK ] Registerizer.RegisterizerLoopInternalRepeated (0 ms)
2023-01-11T22:10:30.4682318Z [ RUN      ] Registerizer.RegisterizerLoopInternalRepeatedOverlapLoopVar
2023-01-11T22:10:30.4686120Z [       OK ] Registerizer.RegisterizerLoopInternalRepeatedOverlapLoopVar (0 ms)
2023-01-11T22:10:30.4686667Z [ RUN      ] Registerizer.RegisterizerLoopInternalRepeatedOverlapOther
2023-01-11T22:10:30.4690445Z [       OK ] Registerizer.RegisterizerLoopInternalRepeatedOverlapOther (0 ms)
2023-01-11T22:10:30.4690916Z [ RUN      ] Registerizer.RegisterizerMultiVar
2023-01-11T22:10:30.4715464Z [       OK ] Registerizer.RegisterizerMultiVar (2 ms)
2023-01-11T22:10:30.4715859Z [ RUN      ] Registerizer.RegisterizerVariableLoad
2023-01-11T22:10:30.4718194Z [       OK ] Registerizer.RegisterizerVariableLoad (0 ms)
2023-01-11T22:10:30.4718619Z [ RUN      ] Registerizer.RegisterizerSymbolicIndices
2023-01-11T22:10:30.4720241Z [       OK ] Registerizer.RegisterizerSymbolicIndices (0 ms)
2023-01-11T22:10:30.4720639Z [ RUN      ] Registerizer.RegisterizerMultiLoop
2023-01-11T22:10:30.4723216Z [       OK ] Registerizer.RegisterizerMultiLoop (0 ms)
2023-01-11T22:10:30.4723611Z [ RUN      ] Registerizer.RegisterizerRepeated
2023-01-11T22:10:30.4727021Z [       OK ] Registerizer.RegisterizerRepeated (0 ms)
2023-01-11T22:10:30.4727409Z [ RUN      ] Registerizer.RegisterizerNoLoads
2023-01-11T22:10:30.4728747Z [       OK ] Registerizer.RegisterizerNoLoads (0 ms)
2023-01-11T22:10:30.4729291Z [ RUN      ] Registerizer.RegisterizerNoRepeatedStores
2023-01-11T22:10:30.4731230Z [       OK ] Registerizer.RegisterizerNoRepeatedStores (0 ms)
2023-01-11T22:10:30.4731646Z [ RUN      ] Registerizer.RegisterizerMultiVarOverlap
2023-01-11T22:10:30.4735208Z [       OK ] Registerizer.RegisterizerMultiVarOverlap (0 ms)
2023-01-11T22:10:30.4735598Z [ RUN      ] Registerizer.RegisterizerAllocs
2023-01-11T22:10:30.4738748Z [       OK ] Registerizer.RegisterizerAllocs (0 ms)
2023-01-11T22:10:30.4739245Z [ RUN      ] Registerizer.RegisterizerNoInitializer
2023-01-11T22:10:30.4740363Z [       OK ] Registerizer.RegisterizerNoInitializer (0 ms)
2023-01-11T22:10:30.4740800Z [ RUN      ] Registerizer.RegisterizerNoInitializerLoopVar
2023-01-11T22:10:30.4742464Z [       OK ] Registerizer.RegisterizerNoInitializerLoopVar (0 ms)
2023-01-11T22:10:30.4742936Z [ RUN      ] Registerizer.RegisterizerLoadThenStore
2023-01-11T22:10:30.4744771Z [       OK ] Registerizer.RegisterizerLoadThenStore (0 ms)
2023-01-11T22:10:30.4745129Z [ RUN      ] Registerizer.RegisterizerParallelized
2023-01-11T22:10:30.4774725Z [       OK ] Registerizer.RegisterizerParallelized (2 ms)
2023-01-11T22:10:30.4775100Z [ RUN      ] Registerizer.RegisterizerConditionAfter
2023-01-11T22:10:30.4777090Z [       OK ] Registerizer.RegisterizerConditionAfter (0 ms)
2023-01-11T22:10:30.4777475Z [ RUN      ] Registerizer.RegisterizerConditionBefore
2023-01-11T22:10:30.4779194Z [       OK ] Registerizer.RegisterizerConditionBefore (0 ms)
2023-01-11T22:10:30.4779578Z [ RUN      ] Registerizer.RegisterizerConditionInside
2023-01-11T22:10:30.4781803Z [       OK ] Registerizer.RegisterizerConditionInside (0 ms)
2023-01-11T22:10:30.4782218Z [ RUN      ] Registerizer.RegisterizerConditionInsideOverlap1
2023-01-11T22:10:30.4785209Z [       OK ] Registerizer.RegisterizerConditionInsideOverlap1 (0 ms)
2023-01-11T22:10:30.4785632Z [ RUN      ] Registerizer.RegisterizerConditionInsideOverlap2
2023-01-11T22:10:30.4789379Z [       OK ] Registerizer.RegisterizerConditionInsideOverlap2 (0 ms)
2023-01-11T22:10:30.4789785Z [ RUN      ] Registerizer.RegisterizerConditionHidden
2023-01-11T22:10:30.4791162Z [       OK ] Registerizer.RegisterizerConditionHidden (0 ms)
2023-01-11T22:10:30.4791537Z [ RUN      ] Registerizer.RegisterizerConditionUnhidden
2023-01-11T22:10:30.4793552Z [       OK ] Registerizer.RegisterizerConditionUnhidden (0 ms)
2023-01-11T22:10:30.4793932Z [ RUN      ] Registerizer.RegisterizerCondCondition
2023-01-11T22:10:30.4795888Z [       OK ] Registerizer.RegisterizerCondCondition (0 ms)
2023-01-11T22:10:30.4796271Z [ RUN      ] Registerizer.RegisterizerCondConditionUnhidden
2023-01-11T22:10:30.4798312Z [       OK ] Registerizer.RegisterizerCondConditionUnhidden (0 ms)
2023-01-11T22:10:30.4798712Z [ RUN      ] Registerizer.RegisterizerIfThenElseHidden
2023-01-11T22:10:30.4802713Z [       OK ] Registerizer.RegisterizerIfThenElseHidden (0 ms)
2023-01-11T22:10:30.4803107Z [ RUN      ] Registerizer.RegisterizerIfThenElseUnhidden
2023-01-11T22:10:30.4807626Z [       OK ] Registerizer.RegisterizerIfThenElseUnhidden (0 ms)
2023-01-11T22:10:30.4808120Z [ RUN      ] Registerizer.RegisterizerIfThenElseNested
2023-01-11T22:10:30.4810149Z [       OK ] Registerizer.RegisterizerIfThenElseNested (0 ms)
2023-01-11T22:10:30.4810547Z [ RUN      ] Registerizer.RegisterizerIfThenElseInternal
2023-01-11T22:10:30.4812975Z [       OK ] Registerizer.RegisterizerIfThenElseInternal (0 ms)
2023-01-11T22:10:30.4813369Z [ RUN      ] Registerizer.RegisterizerIfThenElseCondition
2023-01-11T22:10:30.4814893Z [       OK ] Registerizer.RegisterizerIfThenElseCondition (0 ms)
2023-01-11T22:10:30.4815328Z [ RUN      ] Registerizer.RegisterizerIfThenElseConditionUnhidden
2023-01-11T22:10:30.4817383Z [       OK ] Registerizer.RegisterizerIfThenElseConditionUnhidden (0 ms)
2023-01-11T22:10:30.4817805Z [ RUN      ] Registerizer.RegisterizerConditionBranchOnly
2023-01-11T22:10:30.4830092Z [       OK ] Registerizer.RegisterizerConditionBranchOnly (1 ms)
2023-01-11T22:10:30.4830483Z [ RUN      ] Registerizer.RegisterizerCondIfThenElse
2023-01-11T22:10:30.4832562Z [       OK ] Registerizer.RegisterizerCondIfThenElse (0 ms)
2023-01-11T22:10:30.4833141Z [ RUN      ] Registerizer.RegisterizerIfThenElseLoop
2023-01-11T22:10:30.4834126Z [       OK ] Registerizer.RegisterizerIfThenElseLoop (0 ms)
2023-01-11T22:10:30.4834774Z [ RUN      ] Registerizer.RegisterizerIfThenElseLoopCut
2023-01-11T22:10:30.4835937Z [       OK ] Registerizer.RegisterizerIfThenElseLoopCut (0 ms)
2023-01-11T22:10:30.4836535Z [ RUN      ] Registerizer.RegisterizerPartialAfter
2023-01-11T22:10:30.4839089Z [       OK ] Registerizer.RegisterizerPartialAfter (0 ms)
2023-01-11T22:10:30.4839709Z [ RUN      ] Registerizer.RegisterizerPartialBefore
2023-01-11T22:10:30.4842163Z [       OK ] Registerizer.RegisterizerPartialBefore (0 ms)
2023-01-11T22:10:30.4842785Z [ RUN      ] Registerizer.RegisterizerPartialInside
2023-01-11T22:10:30.4846111Z [       OK ] Registerizer.RegisterizerPartialInside (0 ms)
2023-01-11T22:10:30.4846736Z [ RUN      ] Registerizer.RegisterizerPartialCondition
2023-01-11T22:10:30.4850721Z [       OK ] Registerizer.RegisterizerPartialCondition (0 ms)
2023-01-11T22:10:30.4851385Z [ RUN      ] Registerizer.RegisterizerPartialConditionInternalCut
2023-01-11T22:10:30.4852356Z [       OK ] Registerizer.RegisterizerPartialConditionInternalCut (0 ms)
2023-01-11T22:10:30.4853007Z [ RUN      ] Registerizer.RegisterizerPartialConditionInternalStart
2023-01-11T22:10:30.4853927Z [       OK ] Registerizer.RegisterizerPartialConditionInternalStart (0 ms)
2023-01-11T22:10:30.4854548Z [ RUN      ] Registerizer.RegisterizerPartialOverlapsTwo
2023-01-11T22:10:30.4857038Z [       OK ] Registerizer.RegisterizerPartialOverlapsTwo (0 ms)
2023-01-11T22:10:30.4857625Z [ RUN      ] Registerizer.RegisterizerNestedBlocks
2023-01-11T22:10:30.4859086Z [       OK ] Registerizer.RegisterizerNestedBlocks (0 ms)
2023-01-11T22:10:30.4859726Z [ RUN      ] Registerizer.RegisterizerNestedConditions
2023-01-11T22:10:30.4860944Z [       OK ] Registerizer.RegisterizerNestedConditions (0 ms)
2023-01-11T22:10:30.4861578Z [ RUN      ] Registerizer.RegisterizerNestedConditionsUnhidden
2023-01-11T22:10:30.4863246Z [       OK ] Registerizer.RegisterizerNestedConditionsUnhidden (0 ms)
2023-01-11T22:10:30.4863903Z [ RUN      ] Registerizer.RegisterizerNestedConditionsHiddenFirst
2023-01-11T22:10:30.4866185Z [       OK ] Registerizer.RegisterizerNestedConditionsHiddenFirst (0 ms)
2023-01-11T22:10:30.4866833Z [ RUN      ] Registerizer.RegisterizerNestedConditionsHiddenSecond
2023-01-11T22:10:30.4869045Z [       OK ] Registerizer.RegisterizerNestedConditionsHiddenSecond (0 ms)
2023-01-11T22:10:30.4869676Z [ RUN      ] Registerizer.RegisterizerNestedConditionsCut
2023-01-11T22:10:30.4870616Z [       OK ] Registerizer.RegisterizerNestedConditionsCut (0 ms)
2023-01-11T22:10:30.4871285Z [ RUN      ] Registerizer.RegisterizerNestedConditionLoopHidden
2023-01-11T22:10:30.4873357Z [       OK ] Registerizer.RegisterizerNestedConditionLoopHidden (0 ms)
2023-01-11T22:10:30.4874083Z [ RUN      ] Registerizer.RegisterizerNestedConditionThreeDeep
2023-01-11T22:10:30.4879362Z [       OK ] Registerizer.RegisterizerNestedConditionThreeDeep (0 ms)
2023-01-11T22:10:30.4879999Z [ RUN      ] Registerizer.RegisterizerNestedLoopSimple
2023-01-11T22:10:30.4880990Z [       OK ] Registerizer.RegisterizerNestedLoopSimple (0 ms)
2023-01-11T22:10:30.4881380Z [ RUN      ] Registerizer.RegisterizerHiddenAccessYes
2023-01-11T22:10:30.4884291Z [       OK ] Registerizer.RegisterizerHiddenAccessYes (0 ms)
2023-01-11T22:10:30.4884717Z [ RUN      ] Registerizer.RegisterizerHiddenAccessNo
2023-01-11T22:10:30.4887192Z [       OK ] Registerizer.RegisterizerHiddenAccessNo (0 ms)
2023-01-11T22:10:30.4887665Z [ RUN      ] Registerizer.RegisterizerHiddenAccessMultiLoop
2023-01-11T22:10:30.4891367Z [       OK ] Registerizer.RegisterizerHiddenAccessMultiLoop (0 ms)
2023-01-11T22:10:30.4891787Z [ RUN      ] Registerizer.RegisterizerTwoConditionalLoops
2023-01-11T22:10:30.4894288Z [       OK ] Registerizer.RegisterizerTwoConditionalLoops (0 ms)
2023-01-11T22:10:30.4894691Z [ RUN      ] Registerizer.RegisterizerTwoConditionalLoopsCut
2023-01-11T22:10:30.4897712Z [       OK ] Registerizer.RegisterizerTwoConditionalLoopsCut (0 ms)
2023-01-11T22:10:30.4898102Z [ RUN      ] Registerizer.RegisterizerLoopLetVar
2023-01-11T22:10:30.4899659Z [       OK ] Registerizer.RegisterizerLoopLetVar (0 ms)
2023-01-11T22:10:30.4900013Z [ RUN      ] Registerizer.RegisterizerLoopLetVarOuter
2023-01-11T22:10:30.4901868Z [       OK ] Registerizer.RegisterizerLoopLetVarOuter (0 ms)
2023-01-11T22:10:30.4902508Z [ RUN      ] Registerizer.RegisterizerMultiDim
2023-01-11T22:10:30.4904603Z [       OK ] Registerizer.RegisterizerMultiDim (0 ms)
2023-01-11T22:10:30.4905243Z [ RUN      ] Registerizer.RegisterizerMultiDimPartial
2023-01-11T22:10:30.4908466Z [       OK ] Registerizer.RegisterizerMultiDimPartial (0 ms)
2023-01-11T22:10:30.4909142Z [ RUN      ] Registerizer.RegisterizerMultiDimOverlap
2023-01-11T22:10:30.4912535Z [       OK ] Registerizer.RegisterizerMultiDimOverlap (0 ms)
2023-01-11T22:10:30.4913254Z [ RUN      ] Registerizer.RegisterizerMultiDimPartialOverlap
2023-01-11T22:10:30.4916411Z [       OK ] Registerizer.RegisterizerMultiDimPartialOverlap (0 ms)
2023-01-11T22:10:30.4917146Z [ RUN      ] Registerizer.RegisterizerMultiDim3DReduction1
2023-01-11T22:10:30.4920846Z [       OK ] Registerizer.RegisterizerMultiDim3DReduction1 (0 ms)
2023-01-11T22:10:30.4921541Z [ RUN      ] Registerizer.RegisterizerMultiDim3DReduction2
2023-01-11T22:10:30.4925156Z [       OK ] Registerizer.RegisterizerMultiDim3DReduction2 (0 ms)
2023-01-11T22:10:30.4925574Z [----------] 69 tests from Registerizer (25 ms total)
2023-01-11T22:10:30.4925740Z 
2023-01-11T22:10:30.4925883Z [----------] 92 tests from Simplify
2023-01-11T22:10:30.4926156Z [ RUN      ] Simplify.ConstantFoldSimple
2023-01-11T22:10:30.4926471Z [       OK ] Simplify.ConstantFoldSimple (0 ms)
2023-01-11T22:10:30.4926782Z [ RUN      ] Simplify.ConstantFoldTwoLayer
2023-01-11T22:10:30.4927090Z [       OK ] Simplify.ConstantFoldTwoLayer (0 ms)
2023-01-11T22:10:30.4927398Z [ RUN      ] Simplify.ConstantFoldShifts
2023-01-11T22:10:30.4939344Z [       OK ] Simplify.ConstantFoldShifts (1 ms)
2023-01-11T22:10:30.4939768Z [ RUN      ] Simplify.ConstantFoldBitwise
2023-01-11T22:10:30.4940274Z [       OK ] Simplify.ConstantFoldBitwise (0 ms)
2023-01-11T22:10:30.4940828Z [ RUN      ] Simplify.ConstantFoldMultiOp
2023-01-11T22:10:30.4941294Z [       OK ] Simplify.ConstantFoldMultiOp (0 ms)
2023-01-11T22:10:30.4941694Z [ RUN      ] Simplify.ConstantFoldMinMax
2023-01-11T22:10:30.4942335Z [       OK ] Simplify.ConstantFoldMinMax (0 ms)
2023-01-11T22:10:30.4943002Z [ RUN      ] Simplify.ConstantFoldIntrinsics
2023-01-11T22:10:30.4943428Z [       OK ] Simplify.ConstantFoldIntrinsics (0 ms)
2023-01-11T22:10:30.4943990Z [ RUN      ] Simplify.ConstantFoldCastToBool
2023-01-11T22:10:30.4944473Z [       OK ] Simplify.ConstantFoldCastToBool (0 ms)
2023-01-11T22:10:30.4944875Z [ RUN      ] Simplify.ConstantFoldWithVar
2023-01-11T22:10:30.4945228Z [       OK ] Simplify.ConstantFoldWithVar (0 ms)
2023-01-11T22:10:30.4945699Z [ RUN      ] Simplify.ConditionalSelectFoldSimple
2023-01-11T22:10:30.4946060Z [       OK ] Simplify.ConditionalSelectFoldSimple (0 ms)
2023-01-11T22:10:30.4946500Z [ RUN      ] Simplify.ConditionalSelectFoldTwoLayer
2023-01-11T22:10:30.4948759Z [       OK ] Simplify.ConditionalSelectFoldTwoLayer (0 ms)
2023-01-11T22:10:30.4949371Z [ RUN      ] Simplify.ConditionalSelectFoldWithVar
2023-01-11T22:10:30.4951676Z [       OK ] Simplify.ConditionalSelectFoldWithVar (0 ms)
2023-01-11T22:10:30.4951991Z [ RUN      ] Simplify.UnFoldableExpr
2023-01-11T22:10:30.4952299Z [       OK ] Simplify.UnFoldableExpr (0 ms)
2023-01-11T22:10:30.4952577Z [ RUN      ] Simplify.HashSimple
2023-01-11T22:10:30.4952851Z [       OK ] Simplify.HashSimple (0 ms)
2023-01-11T22:10:30.4953122Z [ RUN      ] Simplify.HashEquivalence
2023-01-11T22:10:30.4953417Z [       OK ] Simplify.HashEquivalence (0 ms)
2023-01-11T22:10:30.4953720Z [ RUN      ] Simplify.HashEquivalenceRand
2023-01-11T22:10:30.4954092Z [       OK ] Simplify.HashEquivalenceRand (0 ms)
2023-01-11T22:10:30.4954431Z [ RUN      ] Simplify.HashEquivalenceAfterFolding
2023-01-11T22:10:30.4954791Z [       OK ] Simplify.HashEquivalenceAfterFolding (0 ms)
2023-01-11T22:10:30.4955106Z [ RUN      ] Simplify.HashDifferenceTypes
2023-01-11T22:10:30.4955421Z [       OK ] Simplify.HashDifferenceTypes (0 ms)
2023-01-11T22:10:30.4955727Z [ RUN      ] Simplify.HashLargeExpression
2023-01-11T22:10:30.4964337Z [       OK ] Simplify.HashLargeExpression (1 ms)
2023-01-11T22:10:30.4964856Z [ RUN      ] Simplify.HashForLoopOptions
2023-01-11T22:10:30.4965363Z [       OK ] Simplify.HashForLoopOptions (0 ms)
2023-01-11T22:10:30.4965798Z [ RUN      ] Simplify.SimplifyAdd
2023-01-11T22:10:30.4966302Z [       OK ] Simplify.SimplifyAdd (0 ms)
2023-01-11T22:10:30.4966719Z [ RUN      ] Simplify.SimplifySub
2023-01-11T22:10:30.4967168Z [       OK ] Simplify.SimplifySub (0 ms)
2023-01-11T22:10:30.4967693Z [ RUN      ] Simplify.SimplifyMultiLayer
2023-01-11T22:10:30.4968089Z [       OK ] Simplify.SimplifyMultiLayer (0 ms)
2023-01-11T22:10:30.4968581Z [ RUN      ] Simplify.SimplifyMultiTerm
2023-01-11T22:10:30.4969270Z [       OK ] Simplify.SimplifyMultiTerm (0 ms)
2023-01-11T22:10:30.4969737Z [ RUN      ] Simplify.SimplifyCasts
2023-01-11T22:10:30.4970027Z [       OK ] Simplify.SimplifyCasts (0 ms)
2023-01-11T22:10:30.4970340Z [ RUN      ] Simplify.SimplifyEliminatesNoOps
2023-01-11T22:10:30.4970668Z [       OK ] Simplify.SimplifyEliminatesNoOps (0 ms)
2023-01-11T22:10:30.4970981Z [ RUN      ] Simplify.SimplifyMultiVar
2023-01-11T22:10:30.4971283Z [       OK ] Simplify.SimplifyMultiVar (0 ms)
2023-01-11T22:10:30.4971579Z [ RUN      ] Simplify.SimplifyEliminatesVar
2023-01-11T22:10:30.4980379Z [       OK ] Simplify.SimplifyEliminatesVar (1 ms)
2023-01-11T22:10:30.4980687Z [ RUN      ] Simplify.SimplifyAdds
2023-01-11T22:10:30.4982981Z [       OK ] Simplify.SimplifyAdds (0 ms)
2023-01-11T22:10:30.4983491Z [ RUN      ] Simplify.SimplifyMuls
2023-01-11T22:10:30.4987539Z [       OK ] Simplify.SimplifyMuls (0 ms)
2023-01-11T22:10:30.4988159Z [ RUN      ] Simplify.SimplifySubs
2023-01-11T22:10:30.4994917Z [       OK ] Simplify.SimplifySubs (0 ms)
2023-01-11T22:10:30.4995453Z [ RUN      ] Simplify.SimplifyDiv
2023-01-11T22:10:30.4995871Z [       OK ] Simplify.SimplifyDiv (0 ms)
2023-01-11T22:10:30.4996236Z [ RUN      ] Simplify.SimplifyDivWithLoopContext0
2023-01-11T22:10:30.4996995Z [       OK ] Simplify.SimplifyDivWithLoopContext0 (0 ms)
2023-01-11T22:10:30.4997643Z [ RUN      ] Simplify.SimplifyDivWithLoopContext1
2023-01-11T22:10:30.5000504Z [       OK ] Simplify.SimplifyDivWithLoopContext1 (0 ms)
2023-01-11T22:10:30.5001130Z [ RUN      ] Simplify.SimplifyDivWithLoopContext2
2023-01-11T22:10:30.5003297Z [       OK ] Simplify.SimplifyDivWithLoopContext2 (0 ms)
2023-01-11T22:10:30.5003936Z [ RUN      ] Simplify.SimplifyDivWithLoopContext3
2023-01-11T22:10:30.5004558Z [       OK ] Simplify.SimplifyDivWithLoopContext3 (0 ms)
2023-01-11T22:10:30.5005162Z [ RUN      ] Simplify.SimplifyDivWithLoopContext4
2023-01-11T22:10:30.5008106Z [       OK ] Simplify.SimplifyDivWithLoopContext4 (0 ms)
2023-01-11T22:10:30.5008720Z [ RUN      ] Simplify.SimplifyDivWithLoopContext5
2023-01-11T22:10:30.5011988Z [       OK ] Simplify.SimplifyDivWithLoopContext5 (0 ms)
2023-01-11T22:10:30.5012596Z [ RUN      ] Simplify.SimplifyDivWithLoopContext6
2023-01-11T22:10:30.5015964Z [       OK ] Simplify.SimplifyDivWithLoopContext6 (0 ms)
2023-01-11T22:10:30.5016589Z [ RUN      ] Simplify.SimplifyDivWithLoopContext7
2023-01-11T22:10:30.5017190Z [       OK ] Simplify.SimplifyDivWithLoopContext7 (0 ms)
2023-01-11T22:10:30.5017787Z [ RUN      ] Simplify.SimplifyModWithLoopContext0
2023-01-11T22:10:30.5018767Z [       OK ] Simplify.SimplifyModWithLoopContext0 (0 ms)
2023-01-11T22:10:30.5019377Z [ RUN      ] Simplify.SimplifyModWithLoopContext1
2023-01-11T22:10:30.5022045Z [       OK ] Simplify.SimplifyModWithLoopContext1 (0 ms)
2023-01-11T22:10:30.5022730Z [ RUN      ] Simplify.SimplifyModWithLoopContext2
2023-01-11T22:10:30.5024721Z [       OK ] Simplify.SimplifyModWithLoopContext2 (0 ms)
2023-01-11T22:10:30.5025342Z [ RUN      ] Simplify.SimplifyModWithLoopContext3
2023-01-11T22:10:30.5025944Z [       OK ] Simplify.SimplifyModWithLoopContext3 (0 ms)
2023-01-11T22:10:30.5026542Z [ RUN      ] Simplify.SimplifyModWithLoopContext4
2023-01-11T22:10:30.5029555Z [       OK ] Simplify.SimplifyModWithLoopContext4 (0 ms)
2023-01-11T22:10:30.5030158Z [ RUN      ] Simplify.SimplifyModWithLoopContext5
2023-01-11T22:10:30.5033150Z [       OK ] Simplify.SimplifyModWithLoopContext5 (0 ms)
2023-01-11T22:10:30.5033779Z [ RUN      ] Simplify.SimplifyModWithLoopContext6
2023-01-11T22:10:30.5037286Z [       OK ] Simplify.SimplifyModWithLoopContext6 (0 ms)
2023-01-11T22:10:30.5037886Z [ RUN      ] Simplify.SimplifyModWithLoopContext7
2023-01-11T22:10:30.5038505Z [       OK ] Simplify.SimplifyModWithLoopContext7 (0 ms)
2023-01-11T22:10:30.5039037Z [ RUN      ] Simplify.SimplifyMod
2023-01-11T22:10:30.5042147Z [       OK ] Simplify.SimplifyMod (0 ms)
2023-01-11T22:10:30.5042634Z [ RUN      ] Simplify.SimplifyMultiOp
2023-01-11T22:10:30.5045048Z [       OK ] Simplify.SimplifyMultiOp (0 ms)
2023-01-11T22:10:30.5045562Z [ RUN      ] Simplify.SimplifyManyOps
2023-01-11T22:10:30.5049489Z [       OK ] Simplify.SimplifyManyOps (0 ms)
2023-01-11T22:10:30.5050037Z [ RUN      ] Simplify.SimplifyFactorization
2023-01-11T22:10:30.5055245Z [       OK ] Simplify.SimplifyFactorization (0 ms)
2023-01-11T22:10:30.5055825Z [ RUN      ] Simplify.SimplifyFactorizeUneven
2023-01-11T22:10:30.5056695Z [       OK ] Simplify.SimplifyFactorizeUneven (0 ms)
2023-01-11T22:10:30.5057398Z [ RUN      ] Simplify.SimplifyDeeperTerms
2023-01-11T22:10:30.5058103Z [       OK ] Simplify.SimplifyDeeperTerms (0 ms)
2023-01-11T22:10:30.5058646Z [ RUN      ] Simplify.SimplifyDeeperDifference
2023-01-11T22:10:30.5059615Z [       OK ] Simplify.SimplifyDeeperDifference (0 ms)
2023-01-11T22:10:30.5060217Z [ RUN      ] Simplify.SimplifyFoldComplexDifference
2023-01-11T22:10:30.5061439Z [       OK ] Simplify.SimplifyFoldComplexDifference (0 ms)
2023-01-11T22:10:30.5062008Z [ RUN      ] Simplify.SimplifyIfComponents
2023-01-11T22:10:30.5062791Z [       OK ] Simplify.SimplifyIfComponents (0 ms)
2023-01-11T22:10:30.5063331Z [ RUN      ] Simplify.SimplifyOpaqueTerms
2023-01-11T22:10:30.5064138Z [       OK ] Simplify.SimplifyOpaqueTerms (0 ms)
2023-01-11T22:10:30.5064680Z [ RUN      ] Simplify.SimplifySymbolicMinMax
2023-01-11T22:10:30.5066461Z [       OK ] Simplify.SimplifySymbolicMinMax (0 ms)
2023-01-11T22:10:30.5067005Z [ RUN      ] Simplify.SimplifyNestedMax
2023-01-11T22:10:30.5081872Z [       OK ] Simplify.SimplifyNestedMax (1 ms)
2023-01-11T22:10:30.5082427Z [ RUN      ] Simplify.SimplifyNestedMin
2023-01-11T22:10:30.5096801Z [       OK ] Simplify.SimplifyNestedMin (1 ms)
2023-01-11T22:10:30.5097372Z [ RUN      ] Simplify.SimplifyWontReorderFloat
2023-01-11T22:10:30.5099483Z [       OK ] Simplify.SimplifyWontReorderFloat (0 ms)
2023-01-11T22:10:30.5100091Z [ RUN      ] Simplify.SimplifyRoundModPattern
2023-01-11T22:10:30.5109229Z [       OK ] Simplify.SimplifyRoundModPattern (0 ms)
2023-01-11T22:10:30.5109902Z [ RUN      ] Simplify.SimplifyRoundModPatternFactorization
2023-01-11T22:10:30.5114519Z [       OK ] Simplify.SimplifyRoundModPatternFactorization (0 ms)
2023-01-11T22:10:30.5115225Z [ RUN      ] Simplify.SimplifyRoundModPatternMultivar
2023-01-11T22:10:30.5119459Z [       OK ] Simplify.SimplifyRoundModPatternMultivar (0 ms)
2023-01-11T22:10:30.5120095Z [ RUN      ] Simplify.SimplifyModRoundModPattern
2023-01-11T22:10:30.5125609Z [       OK ] Simplify.SimplifyModRoundModPattern (0 ms)
2023-01-11T22:10:30.5126315Z [ RUN      ] Simplify.SimplifyModRoundModPatternFactorization
2023-01-11T22:10:30.5135575Z [       OK ] Simplify.SimplifyModRoundModPatternFactorization (0 ms)
2023-01-11T22:10:30.5136291Z [ RUN      ] Simplify.SimplifyModRoundModPatternMultivar
2023-01-11T22:10:30.5150725Z [       OK ] Simplify.SimplifyModRoundModPatternMultivar (1 ms)
2023-01-11T22:10:30.5151453Z [ RUN      ] Simplify.SimplifyDivisionScalarFactorization
2023-01-11T22:10:30.5152437Z [       OK ] Simplify.SimplifyDivisionScalarFactorization (0 ms)
2023-01-11T22:10:30.5153063Z [ RUN      ] Simplify.SimplifyConstantBranches
2023-01-11T22:10:30.5153668Z [       OK ] Simplify.SimplifyConstantBranches (0 ms)
2023-01-11T22:10:30.5154226Z [ RUN      ] Simplify.SimplifyConstantCond
2023-01-11T22:10:30.5155291Z [       OK ] Simplify.SimplifyConstantCond (0 ms)
2023-01-11T22:10:30.5155873Z [ RUN      ] Simplify.SimplifyEliminateEmptyCond
2023-01-11T22:10:30.5156485Z [       OK ] Simplify.SimplifyEliminateEmptyCond (0 ms)
2023-01-11T22:10:30.5157098Z [ RUN      ] Simplify.SimplifyConstantComparisons
2023-01-11T22:10:30.5162251Z [       OK ] Simplify.SimplifyConstantComparisons (0 ms)
2023-01-11T22:10:30.5162872Z [ RUN      ] Simplify.SimplifySymbolicComparisons
2023-01-11T22:10:30.5170828Z [       OK ] Simplify.SimplifySymbolicComparisons (0 ms)
2023-01-11T22:10:30.5171345Z [ RUN      ] Simplify.SimplifyEliminateZeroLengthFor
2023-01-11T22:10:30.5171969Z [       OK ] Simplify.SimplifyEliminateZeroLengthFor (0 ms)
2023-01-11T22:10:30.5172316Z [ RUN      ] Simplify.SimplifyOneLoopFor
2023-01-11T22:10:30.5173839Z [       OK ] Simplify.SimplifyOneLoopFor (0 ms)
2023-01-11T22:10:30.5174193Z [ RUN      ] Simplify.SimplifyForWontLoseLoopOptions
2023-01-11T22:10:30.5174623Z [       OK ] Simplify.SimplifyForWontLoseLoopOptions (0 ms)
2023-01-11T22:10:30.5175088Z [ RUN      ] Simplify.SimplifyMultilevelFor
2023-01-11T22:10:30.5175839Z [       OK ] Simplify.SimplifyMultilevelFor (0 ms)
2023-01-11T22:10:30.5176300Z [ RUN      ] Simplify.SimplifyForCleansUp
2023-01-11T22:10:30.5178804Z [       OK ] Simplify.SimplifyForCleansUp (0 ms)
2023-01-11T22:10:30.5179392Z [ RUN      ] Simplify.SimplifyEliminateEmptyFor
2023-01-11T22:10:30.5180493Z [       OK ] Simplify.SimplifyEliminateEmptyFor (0 ms)
2023-01-11T22:10:30.5181213Z [ RUN      ] Simplify.SimplifyFlattenBlock
2023-01-11T22:10:30.5181805Z [       OK ] Simplify.SimplifyFlattenBlock (0 ms)
2023-01-11T22:10:30.5182286Z [ RUN      ] Simplify.SimplifyEliminateZeroLengthAlloc
2023-01-11T22:10:30.5182929Z [       OK ] Simplify.SimplifyEliminateZeroLengthAlloc (0 ms)
2023-01-11T22:10:30.5183274Z [ RUN      ] Simplify.DontSimplifyRand
2023-01-11T22:10:30.5183564Z [       OK ] Simplify.DontSimplifyRand (0 ms)
2023-01-11T22:10:30.5183880Z [ RUN      ] Simplify.SimplifyReorderForCond
2023-01-11T22:10:30.5188503Z [       OK ] Simplify.SimplifyReorderForCond (0 ms)
2023-01-11T22:10:30.5188835Z [ RUN      ] Simplify.SimplifyFuseConditions
2023-01-11T22:10:30.5195185Z [       OK ] Simplify.SimplifyFuseConditions (0 ms)
2023-01-11T22:10:30.5195608Z [ RUN      ] Simplify.SimplifySyncThreads
2023-01-11T22:10:30.5196067Z [       OK ] Simplify.SimplifySyncThreads (0 ms)
2023-01-11T22:10:30.5196550Z [ RUN      ] Simplify.SimplifyRampSubBroadcast
2023-01-11T22:10:30.5197010Z [       OK ] Simplify.SimplifyRampSubBroadcast (0 ms)
2023-01-11T22:10:30.5197373Z [ RUN      ] Simplify.SimplifyBroadcastTermExpander
2023-01-11T22:10:30.5197745Z [       OK ] Simplify.SimplifyBroadcastTermExpander (0 ms)
2023-01-11T22:10:30.5198083Z [ RUN      ] Simplify.CompareSelectLoopBounds
2023-01-11T22:10:30.5294716Z [       OK ] Simplify.CompareSelectLoopBounds (9 ms)
2023-01-11T22:10:30.5295257Z [ RUN      ] Simplify.CompareSelectCondAlwaysInLoopBounds
2023-01-11T22:10:30.5295858Z [       OK ] Simplify.CompareSelectCondAlwaysInLoopBounds (0 ms)
2023-01-11T22:10:30.5296387Z [ RUN      ] Simplify.IfThenCondAlwaysInLoopBounds
2023-01-11T22:10:30.5296858Z [       OK ] Simplify.IfThenCondAlwaysInLoopBounds (0 ms)
2023-01-11T22:10:30.5297238Z [ RUN      ] Simplify.MultiClauseCondAlwaysInLoopBounds
2023-01-11T22:10:30.5299452Z [       OK ] Simplify.MultiClauseCondAlwaysInLoopBounds (0 ms)
2023-01-11T22:10:30.5300115Z [----------] 92 tests from Simplify (37 ms total)
2023-01-11T22:10:30.5300274Z 
2023-01-11T22:10:30.5300423Z [----------] 12 tests from TEFuserPass
2023-01-11T22:10:30.5300701Z [ RUN      ] TEFuserPass.FuserPass_1
2023-01-11T22:10:30.5306324Z [       OK ] TEFuserPass.FuserPass_1 (0 ms)
2023-01-11T22:10:30.5306868Z [ RUN      ] TEFuserPass.FuserPass_2
2023-01-11T22:10:30.5310508Z [       OK ] TEFuserPass.FuserPass_2 (0 ms)
2023-01-11T22:10:30.5311032Z [ RUN      ] TEFuserPass.FuserPass_3
2023-01-11T22:10:30.5313618Z [       OK ] TEFuserPass.FuserPass_3 (0 ms)
2023-01-11T22:10:30.5314186Z [ RUN      ] TEFuserPass.FuserPass_0DimInput
2023-01-11T22:10:30.5316578Z [       OK ] TEFuserPass.FuserPass_0DimInput (0 ms)
2023-01-11T22:10:30.5316958Z [ RUN      ] TEFuserPass.FuserPass_UnfusibleDevice
2023-01-11T22:10:30.5317712Z [       OK ] TEFuserPass.FuserPass_UnfusibleDevice (0 ms)
2023-01-11T22:10:30.5318120Z [ RUN      ] TEFuserPass.FuserPass_UnknownShapes
2023-01-11T22:10:30.5318919Z [       OK ] TEFuserPass.FuserPass_UnknownShapes (0 ms)
2023-01-11T22:10:30.5319330Z [ RUN      ] TEFuserPass.FuserPass_Multidevice
2023-01-11T22:10:30.5326602Z [       OK ] TEFuserPass.FuserPass_Multidevice (0 ms)
2023-01-11T22:10:30.5326990Z [ RUN      ] TEFuserPass.FuserPass_MergeGroups
2023-01-11T22:10:30.5329604Z [       OK ] TEFuserPass.FuserPass_MergeGroups (0 ms)
2023-01-11T22:10:30.5330216Z [ RUN      ] TEFuserPass.FuserPass_IgnoreUnknownShapeAtStart
2023-01-11T22:10:30.5330925Z [       OK ] TEFuserPass.FuserPass_IgnoreUnknownShapeAtStart (0 ms)
2023-01-11T22:10:30.5331296Z [ RUN      ] TEFuserPass.FuserPass_Where
2023-01-11T22:10:30.5334200Z [       OK ] TEFuserPass.FuserPass_Where (0 ms)
2023-01-11T22:10:30.5334703Z [ RUN      ] TEFuserPass.FuserPass_WhereList
2023-01-11T22:10:30.5335659Z [       OK ] TEFuserPass.FuserPass_WhereList (0 ms)
2023-01-11T22:10:30.5335974Z [ RUN      ] TEFuserPass.DynamicShapeFusion
2023-01-11T22:10:30.7942132Z [       OK ] TEFuserPass.DynamicShapeFusion (260 ms)
2023-01-11T22:10:30.7942841Z [----------] 12 tests from TEFuserPass (264 ms total)
2023-01-11T22:10:30.7943019Z 
2023-01-11T22:10:30.7943158Z [----------] 3 tests from Type
2023-01-11T22:10:30.7943407Z [ RUN      ] Type.Test01
2023-01-11T22:10:30.7943641Z [       OK ] Type.Test01 (0 ms)
2023-01-11T22:10:30.7943890Z [ RUN      ] Type.BitCasting
2023-01-11T22:10:30.7944151Z [       OK ] Type.BitCasting (0 ms)
2023-01-11T22:10:30.7944396Z [ RUN      ] Type.Propagation
2023-01-11T22:10:30.7944668Z [       OK ] Type.Propagation (0 ms)
2023-01-11T22:10:30.7944951Z [----------] 3 tests from Type (0 ms total)
2023-01-11T22:10:30.7945100Z 
2023-01-11T22:10:30.7945280Z [----------] 1 test from SpecializationsInCustomPasses
2023-01-11T22:10:30.7945634Z [ RUN      ] SpecializationsInCustomPasses.Basic
2023-01-11T22:10:30.7961017Z [       OK ] SpecializationsInCustomPasses.Basic (1 ms)
2023-01-11T22:10:30.7961415Z [----------] 1 test from SpecializationsInCustomPasses (1 ms total)
2023-01-11T22:10:30.7961605Z 
2023-01-11T22:10:30.7961728Z [----------] 150 tests from LLVM
2023-01-11T22:10:30.7961975Z [ RUN      ] LLVM.ByteImmTest
2023-01-11T22:10:30.8157209Z [       OK ] LLVM.ByteImmTest (19 ms)
2023-01-11T22:10:30.8157505Z [ RUN      ] LLVM.CharImmTest
2023-01-11T22:10:30.8336191Z [       OK ] LLVM.CharImmTest (17 ms)
2023-01-11T22:10:30.8336484Z [ RUN      ] LLVM.ShortImmTest
2023-01-11T22:10:30.8514716Z [       OK ] LLVM.ShortImmTest (17 ms)
2023-01-11T22:10:30.8514996Z [ RUN      ] LLVM.IntImmTest
2023-01-11T22:10:30.8692052Z [       OK ] LLVM.IntImmTest (17 ms)
2023-01-11T22:10:30.8692494Z [ RUN      ] LLVM.LongImmTest
2023-01-11T22:10:30.8872336Z [       OK ] LLVM.LongImmTest (17 ms)
2023-01-11T22:10:30.8872634Z [ RUN      ] LLVM.FloatImmTest
2023-01-11T22:10:30.9052724Z [       OK ] LLVM.FloatImmTest (18 ms)
2023-01-11T22:10:30.9052997Z [ RUN      ] LLVM.DoubleImmTest
2023-01-11T22:10:30.9230584Z [       OK ] LLVM.DoubleImmTest (17 ms)
2023-01-11T22:10:30.9230853Z [ RUN      ] LLVM.HalfImmTest
2023-01-11T22:10:30.9409813Z [       OK ] LLVM.HalfImmTest (17 ms)
2023-01-11T22:10:30.9410070Z [ RUN      ] LLVM.ByteAddTest
2023-01-11T22:10:30.9586420Z [       OK ] LLVM.ByteAddTest (17 ms)
2023-01-11T22:10:30.9586694Z [ RUN      ] LLVM.CharAddTest
2023-01-11T22:10:30.9763604Z [       OK ] LLVM.CharAddTest (17 ms)
2023-01-11T22:10:30.9763875Z [ RUN      ] LLVM.ShortAddTest
2023-01-11T22:10:30.9940940Z [       OK ] LLVM.ShortAddTest (17 ms)
2023-01-11T22:10:30.9941209Z [ RUN      ] LLVM.IntAddTest
2023-01-11T22:10:31.0120198Z [       OK ] LLVM.IntAddTest (17 ms)
2023-01-11T22:10:31.0120473Z [ RUN      ] LLVM.LongAddTest
2023-01-11T22:10:31.0297585Z [       OK ] LLVM.LongAddTest (17 ms)
2023-01-11T22:10:31.0297918Z [ RUN      ] LLVM.FloatAddTest
2023-01-11T22:10:31.0475965Z [       OK ] LLVM.FloatAddTest (17 ms)
2023-01-11T22:10:31.0476296Z [ RUN      ] LLVM.DoubleAddTest
2023-01-11T22:10:31.0654609Z [       OK ] LLVM.DoubleAddTest (17 ms)
2023-01-11T22:10:31.0654918Z [ RUN      ] LLVM.HalfAddTest
2023-01-11T22:10:31.0832443Z [       OK ] LLVM.HalfAddTest (17 ms)
2023-01-11T22:10:31.0832714Z [ RUN      ] LLVM.ByteSubTest
2023-01-11T22:10:31.1010056Z [       OK ] LLVM.ByteSubTest (17 ms)
2023-01-11T22:10:31.1010807Z [ RUN      ] LLVM.CharSubTest
2023-01-11T22:10:31.1189765Z [       OK ] LLVM.CharSubTest (17 ms)
2023-01-11T22:10:31.1190251Z [ RUN      ] LLVM.ShortSubTest
2023-01-11T22:10:31.1366891Z [       OK ] LLVM.ShortSubTest (17 ms)
2023-01-11T22:10:31.1367373Z [ RUN      ] LLVM.IntSubTest
2023-01-11T22:10:31.1544704Z [       OK ] LLVM.IntSubTest (17 ms)
2023-01-11T22:10:31.1545169Z [ RUN      ] LLVM.LongSubTest
2023-01-11T22:10:31.1725453Z [       OK ] LLVM.LongSubTest (18 ms)
2023-01-11T22:10:31.1725945Z [ RUN      ] LLVM.FloatSubTest
2023-01-11T22:10:31.1903105Z [       OK ] LLVM.FloatSubTest (17 ms)
2023-01-11T22:10:31.1903602Z [ RUN      ] LLVM.DoubleSubTest
2023-01-11T22:10:31.2082179Z [       OK ] LLVM.DoubleSubTest (17 ms)
2023-01-11T22:10:31.2082662Z [ RUN      ] LLVM.HalfSubTest
2023-01-11T22:10:31.2260280Z [       OK ] LLVM.HalfSubTest (17 ms)
2023-01-11T22:10:31.2260774Z [ RUN      ] LLVM.ByteMulTest
2023-01-11T22:10:31.2437234Z [       OK ] LLVM.ByteMulTest (17 ms)
2023-01-11T22:10:31.2437704Z [ RUN      ] LLVM.CharMulTest
2023-01-11T22:10:31.2614643Z [       OK ] LLVM.CharMulTest (17 ms)
2023-01-11T22:10:31.2615131Z [ RUN      ] LLVM.ShortMulTest
2023-01-11T22:10:31.2792063Z [       OK ] LLVM.ShortMulTest (17 ms)
2023-01-11T22:10:31.2792531Z [ RUN      ] LLVM.IntMulTest
2023-01-11T22:10:31.2969808Z [       OK ] LLVM.IntMulTest (17 ms)
2023-01-11T22:10:31.2970074Z [ RUN      ] LLVM.LongMulTest
2023-01-11T22:10:31.3151064Z [       OK ] LLVM.LongMulTest (18 ms)
2023-01-11T22:10:31.3151345Z [ RUN      ] LLVM.FloatMulTest
2023-01-11T22:10:31.3328861Z [       OK ] LLVM.FloatMulTest (17 ms)
2023-01-11T22:10:31.3329269Z [ RUN      ] LLVM.DoubleMulTest
2023-01-11T22:10:31.3509933Z [       OK ] LLVM.DoubleMulTest (18 ms)
2023-01-11T22:10:31.3510246Z [ RUN      ] LLVM.HalfMulTest
2023-01-11T22:10:31.3688537Z [       OK ] LLVM.HalfMulTest (17 ms)
2023-01-11T22:10:31.3688807Z [ RUN      ] LLVM.ByteDivTest
2023-01-11T22:10:31.3867093Z [       OK ] LLVM.ByteDivTest (17 ms)
2023-01-11T22:10:31.3867399Z [ RUN      ] LLVM.CharDivTest
2023-01-11T22:10:31.4044063Z [       OK ] LLVM.CharDivTest (17 ms)
2023-01-11T22:10:31.4044602Z [ RUN      ] LLVM.ShortDivTest
2023-01-11T22:10:31.4223126Z [       OK ] LLVM.ShortDivTest (17 ms)
2023-01-11T22:10:31.4223619Z [ RUN      ] LLVM.IntDivTest
2023-01-11T22:10:31.4401999Z [       OK ] LLVM.IntDivTest (17 ms)
2023-01-11T22:10:31.4402497Z [ RUN      ] LLVM.LongDivTest
2023-01-11T22:10:31.4580039Z [       OK ] LLVM.LongDivTest (17 ms)
2023-01-11T22:10:31.4580522Z [ RUN      ] LLVM.FloatDivTest
2023-01-11T22:10:31.4758696Z [       OK ] LLVM.FloatDivTest (17 ms)
2023-01-11T22:10:31.4759222Z [ RUN      ] LLVM.DoubleDivTest
2023-01-11T22:10:31.4938180Z [       OK ] LLVM.DoubleDivTest (17 ms)
2023-01-11T22:10:31.4938665Z [ RUN      ] LLVM.HalfDivTest
2023-01-11T22:10:31.5118125Z [       OK ] LLVM.HalfDivTest (17 ms)
2023-01-11T22:10:31.5118650Z [ RUN      ] LLVM.IntToFloatCastTest
2023-01-11T22:10:31.5296432Z [       OK ] LLVM.IntToFloatCastTest (17 ms)
2023-01-11T22:10:31.5296982Z [ RUN      ] LLVM.FloatToIntCastTest
2023-01-11T22:10:31.5475086Z [       OK ] LLVM.FloatToIntCastTest (17 ms)
2023-01-11T22:10:31.5475645Z [ RUN      ] LLVM.IntToLongCastTest
2023-01-11T22:10:31.5653431Z [       OK ] LLVM.IntToLongCastTest (17 ms)
2023-01-11T22:10:31.5654002Z [ RUN      ] LLVM.ByteToCharCastTest
2023-01-11T22:10:31.5831947Z [       OK ] LLVM.ByteToCharCastTest (17 ms)
2023-01-11T22:10:31.5832486Z [ RUN      ] LLVM.HalfToLongCastTest
2023-01-11T22:10:31.6010543Z [       OK ] LLVM.HalfToLongCastTest (17 ms)
2023-01-11T22:10:31.6011100Z [ RUN      ] LLVM.ByteToDoubleCastTest
2023-01-11T22:10:31.6189935Z [       OK ] LLVM.ByteToDoubleCastTest (17 ms)
2023-01-11T22:10:31.6190515Z [ RUN      ] LLVM.FloatToByteCastTest
2023-01-11T22:10:31.6368247Z [       OK ] LLVM.FloatToByteCastTest (17 ms)
2023-01-11T22:10:31.6368806Z [ RUN      ] LLVM.FloatToCharCastTest
2023-01-11T22:10:31.6546659Z [       OK ] LLVM.FloatToCharCastTest (17 ms)
2023-01-11T22:10:31.6547203Z [ RUN      ] LLVM.ByteToFloatCastTest
2023-01-11T22:10:31.6727407Z [       OK ] LLVM.ByteToFloatCastTest (18 ms)
2023-01-11T22:10:31.6727968Z [ RUN      ] LLVM.CharToFloatCastTest
2023-01-11T22:10:31.6906436Z [       OK ] LLVM.CharToFloatCastTest (17 ms)
2023-01-11T22:10:31.6906928Z [ RUN      ] LLVM.BitCast
2023-01-11T22:10:31.7619201Z [       OK ] LLVM.BitCast (71 ms)
2023-01-11T22:10:31.7619634Z [ RUN      ] LLVM.fastLogFloat
2023-01-11T22:10:31.8402991Z [       OK ] LLVM.fastLogFloat (78 ms)
2023-01-11T22:10:31.8403467Z [ RUN      ] LLVM.LetTest01
2023-01-11T22:10:31.8582274Z [       OK ] LLVM.LetTest01 (17 ms)
2023-01-11T22:10:31.8582812Z [ RUN      ] LLVM.LetTest02
2023-01-11T22:10:31.8760324Z [       OK ] LLVM.LetTest02 (17 ms)
2023-01-11T22:10:31.8760872Z [ RUN      ] LLVM.LetTestMultitype
2023-01-11T22:10:31.8940086Z [       OK ] LLVM.LetTestMultitype (17 ms)
2023-01-11T22:10:31.8940598Z [ RUN      ] LLVM.BufferTest
2023-01-11T22:10:31.9119011Z [       OK ] LLVM.BufferTest (17 ms)
2023-01-11T22:10:31.9119492Z [ RUN      ] LLVM.BlockTest
2023-01-11T22:10:31.9299884Z [       OK ] LLVM.BlockTest (18 ms)
2023-01-11T22:10:31.9300371Z [ RUN      ] LLVM.LoadStoreTest
2023-01-11T22:10:31.9481368Z [       OK ] LLVM.LoadStoreTest (18 ms)
2023-01-11T22:10:31.9481901Z [ RUN      ] LLVM.IfThenElseTest
2023-01-11T22:10:31.9676110Z [       OK ] LLVM.IfThenElseTest (19 ms)
2023-01-11T22:10:31.9676653Z [ RUN      ] LLVM.CondNoFalseBlockTest
2023-01-11T22:10:32.0244105Z [       OK ] LLVM.CondNoFalseBlockTest (56 ms)
2023-01-11T22:10:32.0244614Z [ RUN      ] LLVM.CondTest
2023-01-11T22:10:32.0818213Z [       OK ] LLVM.CondTest (57 ms)
2023-01-11T22:10:32.0818746Z [ RUN      ] LLVM.CondNestedTest
2023-01-11T22:10:32.1627004Z [       OK ] LLVM.CondNestedTest (80 ms)
2023-01-11T22:10:32.1627537Z [ RUN      ] LLVM.DirectVectorization
2023-01-11T22:10:32.1880625Z [       OK ] LLVM.DirectVectorization (25 ms)
2023-01-11T22:10:32.1881182Z [ RUN      ] LLVM.VecLoadStoreTest
2023-01-11T22:10:32.2064017Z [       OK ] LLVM.VecLoadStoreTest (18 ms)
2023-01-11T22:10:32.2064591Z [ RUN      ] LLVM.VecFloat_erfLane4Test
2023-01-11T22:10:32.2250728Z [       OK ] LLVM.VecFloat_erfLane4Test (18 ms)
2023-01-11T22:10:32.2251293Z [ RUN      ] LLVM.VecFloat_erfcLane4Test
2023-01-11T22:10:32.2448112Z [       OK ] LLVM.VecFloat_erfcLane4Test (19 ms)
2023-01-11T22:10:32.2448889Z [ RUN      ] LLVM.VecFloat_acosLane4Test
2023-01-11T22:10:32.2635423Z [       OK ] LLVM.VecFloat_acosLane4Test (18 ms)
2023-01-11T22:10:32.2635982Z [ RUN      ] LLVM.VecFloat_asinLane4Test
2023-01-11T22:10:32.2822290Z [       OK ] LLVM.VecFloat_asinLane4Test (18 ms)
2023-01-11T22:10:32.2822919Z [ RUN      ] LLVM.VecFloat_atanLane4Test
2023-01-11T22:10:32.3009191Z [       OK ] LLVM.VecFloat_atanLane4Test (18 ms)
2023-01-11T22:10:32.3009753Z [ RUN      ] LLVM.VecFloat_coshLane4Test
2023-01-11T22:10:32.3196229Z [       OK ] LLVM.VecFloat_coshLane4Test (18 ms)
2023-01-11T22:10:32.3196787Z [ RUN      ] LLVM.VecFloat_sinhLane4Test
2023-01-11T22:10:32.3383356Z [       OK ] LLVM.VecFloat_sinhLane4Test (18 ms)
2023-01-11T22:10:32.3384102Z [ RUN      ] LLVM.VecFloat_tanhLane4Test
2023-01-11T22:10:32.3571628Z [       OK ] LLVM.VecFloat_tanhLane4Test (18 ms)
2023-01-11T22:10:32.3572196Z [ RUN      ] LLVM.VecFloat_expm1Lane4Test
2023-01-11T22:10:32.3758628Z [       OK ] LLVM.VecFloat_expm1Lane4Test (18 ms)
2023-01-11T22:10:32.3759171Z [ RUN      ] LLVM.VecFloat_lgammaLane4Test
2023-01-11T22:10:32.3946681Z [       OK ] LLVM.VecFloat_lgammaLane4Test (18 ms)
2023-01-11T22:10:32.3947240Z [ RUN      ] LLVM.VecFloat_erfLane8Test
2023-01-11T22:10:32.4133433Z [       OK ] LLVM.VecFloat_erfLane8Test (18 ms)
2023-01-11T22:10:32.4133980Z [ RUN      ] LLVM.VecFloat_erfcLane8Test
2023-01-11T22:10:32.4321386Z [       OK ] LLVM.VecFloat_erfcLane8Test (18 ms)
2023-01-11T22:10:32.4321938Z [ RUN      ] LLVM.VecFloat_acosLane8Test
2023-01-11T22:10:32.4507974Z [       OK ] LLVM.VecFloat_acosLane8Test (18 ms)
2023-01-11T22:10:32.4508556Z [ RUN      ] LLVM.VecFloat_asinLane8Test
2023-01-11T22:10:32.4695351Z [       OK ] LLVM.VecFloat_asinLane8Test (18 ms)
2023-01-11T22:10:32.4695928Z [ RUN      ] LLVM.VecFloat_atanLane8Test
2023-01-11T22:10:32.4882078Z [       OK ] LLVM.VecFloat_atanLane8Test (18 ms)
2023-01-11T22:10:32.4882642Z [ RUN      ] LLVM.VecFloat_coshLane8Test
2023-01-11T22:10:32.5069823Z [       OK ] LLVM.VecFloat_coshLane8Test (18 ms)
2023-01-11T22:10:32.5070383Z [ RUN      ] LLVM.VecFloat_sinhLane8Test
2023-01-11T22:10:32.5257344Z [       OK ] LLVM.VecFloat_sinhLane8Test (18 ms)
2023-01-11T22:10:32.5257903Z [ RUN      ] LLVM.VecFloat_tanhLane8Test
2023-01-11T22:10:32.5444473Z [       OK ] LLVM.VecFloat_tanhLane8Test (18 ms)
2023-01-11T22:10:32.5445103Z [ RUN      ] LLVM.VecFloat_expm1Lane8Test
2023-01-11T22:10:32.5632695Z [       OK ] LLVM.VecFloat_expm1Lane8Test (18 ms)
2023-01-11T22:10:32.5633288Z [ RUN      ] LLVM.VecFloat_lgammaLane8Test
2023-01-11T22:10:32.5819960Z [       OK ] LLVM.VecFloat_lgammaLane8Test (18 ms)
2023-01-11T22:10:32.5820606Z [ RUN      ] LLVM.VecDouble_erfLane2Test
2023-01-11T22:10:32.6007894Z [       OK ] LLVM.VecDouble_erfLane2Test (18 ms)
2023-01-11T22:10:32.6008454Z [ RUN      ] LLVM.VecDouble_erfcLane2Test
2023-01-11T22:10:32.6196008Z [       OK ] LLVM.VecDouble_erfcLane2Test (18 ms)
2023-01-11T22:10:32.6196560Z [ RUN      ] LLVM.VecDouble_acosLane2Test
2023-01-11T22:10:32.6383076Z [       OK ] LLVM.VecDouble_acosLane2Test (18 ms)
2023-01-11T22:10:32.6383646Z [ RUN      ] LLVM.VecDouble_asinLane2Test
2023-01-11T22:10:32.6571664Z [       OK ] LLVM.VecDouble_asinLane2Test (18 ms)
2023-01-11T22:10:32.6572208Z [ RUN      ] LLVM.VecDouble_atanLane2Test
2023-01-11T22:10:32.6758778Z [       OK ] LLVM.VecDouble_atanLane2Test (18 ms)
2023-01-11T22:10:32.6759352Z [ RUN      ] LLVM.VecDouble_coshLane2Test
2023-01-11T22:10:32.6945839Z [       OK ] LLVM.VecDouble_coshLane2Test (18 ms)
2023-01-11T22:10:32.6946586Z [ RUN      ] LLVM.VecDouble_sinhLane2Test
2023-01-11T22:10:32.7133130Z [       OK ] LLVM.VecDouble_sinhLane2Test (18 ms)
2023-01-11T22:10:32.7133695Z [ RUN      ] LLVM.VecDouble_tanhLane2Test
2023-01-11T22:10:32.7320192Z [       OK ] LLVM.VecDouble_tanhLane2Test (18 ms)
2023-01-11T22:10:32.7320751Z [ RUN      ] LLVM.VecDouble_expm1Lane2Test
2023-01-11T22:10:32.7507495Z [       OK ] LLVM.VecDouble_expm1Lane2Test (18 ms)
2023-01-11T22:10:32.7508076Z [ RUN      ] LLVM.VecDouble_lgammaLane2Test
2023-01-11T22:10:32.7695213Z [       OK ] LLVM.VecDouble_lgammaLane2Test (18 ms)
2023-01-11T22:10:32.7695789Z [ RUN      ] LLVM.VecDouble_erfLane4Test
2023-01-11T22:10:32.7883010Z [       OK ] LLVM.VecDouble_erfLane4Test (18 ms)
2023-01-11T22:10:32.7883757Z [ RUN      ] LLVM.VecDouble_erfcLane4Test
2023-01-11T22:10:32.8071103Z [       OK ] LLVM.VecDouble_erfcLane4Test (18 ms)
2023-01-11T22:10:32.8071790Z [ RUN      ] LLVM.VecDouble_acosLane4Test
2023-01-11T22:10:32.8259118Z [       OK ] LLVM.VecDouble_acosLane4Test (18 ms)
2023-01-11T22:10:32.8259677Z [ RUN      ] LLVM.VecDouble_asinLane4Test
2023-01-11T22:10:32.8447908Z [       OK ] LLVM.VecDouble_asinLane4Test (18 ms)
2023-01-11T22:10:32.8448484Z [ RUN      ] LLVM.VecDouble_atanLane4Test
2023-01-11T22:10:32.8635166Z [       OK ] LLVM.VecDouble_atanLane4Test (18 ms)
2023-01-11T22:10:32.8635736Z [ RUN      ] LLVM.VecDouble_coshLane4Test
2023-01-11T22:10:32.8822920Z [       OK ] LLVM.VecDouble_coshLane4Test (18 ms)
2023-01-11T22:10:32.8823496Z [ RUN      ] LLVM.VecDouble_sinhLane4Test
2023-01-11T22:10:32.9010784Z [       OK ] LLVM.VecDouble_sinhLane4Test (18 ms)
2023-01-11T22:10:32.9011433Z [ RUN      ] LLVM.VecDouble_tanhLane4Test
2023-01-11T22:10:32.9198517Z [       OK ] LLVM.VecDouble_tanhLane4Test (18 ms)
2023-01-11T22:10:32.9199114Z [ RUN      ] LLVM.VecDouble_expm1Lane4Test
2023-01-11T22:10:32.9385938Z [       OK ] LLVM.VecDouble_expm1Lane4Test (18 ms)
2023-01-11T22:10:32.9386499Z [ RUN      ] LLVM.VecDouble_lgammaLane4Test
2023-01-11T22:10:32.9572843Z [       OK ] LLVM.VecDouble_lgammaLane4Test (18 ms)
2023-01-11T22:10:32.9573520Z [ RUN      ] LLVM.VectorizerLoadStoreTest
2023-01-11T22:10:32.9757214Z [       OK ] LLVM.VectorizerLoadStoreTest (18 ms)
2023-01-11T22:10:32.9757770Z [ RUN      ] LLVM.VectorizeBitCast
2023-01-11T22:10:32.9961402Z [       OK ] LLVM.VectorizeBitCast (20 ms)
2023-01-11T22:10:33.0160116Z [ RUN      ] LLVM.MemcpyTest
2023-01-11T22:10:33.0160682Z [       OK ] LLVM.MemcpyTest (19 ms)
2023-01-11T22:10:33.0161218Z [ RUN      ] LLVM.BzeroTest
2023-01-11T22:10:33.0354279Z [       OK ] LLVM.BzeroTest (19 ms)
2023-01-11T22:10:33.0354776Z [ RUN      ] LLVM.ElemwiseAdd
2023-01-11T22:10:33.0839392Z [       OK ] LLVM.ElemwiseAdd (48 ms)
2023-01-11T22:10:33.0840001Z [ RUN      ] LLVM.ElemwiseAddFloat
2023-01-11T22:10:33.1304083Z [       OK ] LLVM.ElemwiseAddFloat (46 ms)
2023-01-11T22:10:33.1304706Z [ RUN      ] LLVM.ElemwiseLog10Float
2023-01-11T22:10:33.1529458Z [       OK ] LLVM.ElemwiseLog10Float (22 ms)
2023-01-11T22:10:33.1530028Z [ RUN      ] LLVM.ElemwiseLog1pFloat
2023-01-11T22:10:33.1755286Z [       OK ] LLVM.ElemwiseLog1pFloat (22 ms)
2023-01-11T22:10:33.1755819Z [ RUN      ] LLVM.ElemwiseMaxInt
2023-01-11T22:10:33.2075981Z [       OK ] LLVM.ElemwiseMaxInt (32 ms)
2023-01-11T22:10:33.2076573Z [ RUN      ] LLVM.ElemwiseMinInt
2023-01-11T22:10:33.2396146Z [       OK ] LLVM.ElemwiseMinInt (32 ms)
2023-01-11T22:10:33.2396741Z [ RUN      ] LLVM.ElemwiseMaxFloat
2023-01-11T22:10:33.2679029Z [       OK ] LLVM.ElemwiseMaxFloat (28 ms)
2023-01-11T22:10:33.2679944Z [ RUN      ] LLVM.ElemwiseMaxNaNFloat
2023-01-11T22:10:33.2964195Z [       OK ] LLVM.ElemwiseMaxNaNFloat (28 ms)
2023-01-11T22:10:33.2964838Z [ RUN      ] LLVM.ElemwiseMinFloat
2023-01-11T22:10:33.3248108Z [       OK ] LLVM.ElemwiseMinFloat (28 ms)
2023-01-11T22:10:33.3248747Z [ RUN      ] LLVM.ElemwiseMinNaNFloat
2023-01-11T22:10:33.3531241Z [       OK ] LLVM.ElemwiseMinNaNFloat (28 ms)
2023-01-11T22:10:33.3531846Z [ RUN      ] LLVM.ElemwiseMod
2023-01-11T22:10:33.3800350Z [       OK ] LLVM.ElemwiseMod (26 ms)
2023-01-11T22:10:33.3800882Z [ RUN      ] LLVM.CompareSelectIntEQ
2023-01-11T22:10:33.4132137Z [       OK ] LLVM.CompareSelectIntEQ (33 ms)
2023-01-11T22:10:33.4132795Z [ RUN      ] LLVM.CompareSelectFloatEQ
2023-01-11T22:10:33.4460454Z [       OK ] LLVM.CompareSelectFloatEQ (32 ms)
2023-01-11T22:10:33.4461107Z [ RUN      ] LLVM.CompareSelectByteGT
2023-01-11T22:10:33.4788986Z [       OK ] LLVM.CompareSelectByteGT (32 ms)
2023-01-11T22:10:33.4789637Z [ RUN      ] LLVM.CompareSelectByteGE
2023-01-11T22:10:33.5117503Z [       OK ] LLVM.CompareSelectByteGE (32 ms)
2023-01-11T22:10:33.5118161Z [ RUN      ] LLVM.CompareSelectByteLT
2023-01-11T22:10:33.5445328Z [       OK ] LLVM.CompareSelectByteLT (32 ms)
2023-01-11T22:10:33.5446002Z [ RUN      ] LLVM.CompareSelectByteLE
2023-01-11T22:10:33.5773980Z [       OK ] LLVM.CompareSelectByteLE (32 ms)
2023-01-11T22:10:33.5774588Z [ RUN      ] LLVM.StoreFloat
2023-01-11T22:10:33.5955640Z [       OK ] LLVM.StoreFloat (18 ms)
2023-01-11T22:10:33.5956126Z [ RUN      ] LLVM.SimpleMath01
2023-01-11T22:10:33.6321775Z [       OK ] LLVM.SimpleMath01 (36 ms)
2023-01-11T22:10:33.6322364Z [ RUN      ] LLVM.ComputeMul
2023-01-11T22:10:33.6786548Z [       OK ] LLVM.ComputeMul (46 ms)
2023-01-11T22:10:33.6787135Z [ RUN      ] LLVM.BroadcastAdd
2023-01-11T22:10:33.7891996Z [       OK ] LLVM.BroadcastAdd (110 ms)
2023-01-11T22:10:33.7892553Z [ RUN      ] LLVM.BitwiseOps
2023-01-11T22:10:33.8072568Z [       OK ] LLVM.BitwiseOps (18 ms)
2023-01-11T22:10:33.8073118Z [ RUN      ] LLVM.ArithmeticRightShift
2023-01-11T22:10:33.8250846Z [       OK ] LLVM.ArithmeticRightShift (17 ms)
2023-01-11T22:10:33.8429467Z [ RUN      ] LLVM.LogicalRightShift
2023-01-11T22:10:33.8430025Z [       OK ] LLVM.LogicalRightShift (17 ms)
2023-01-11T22:10:33.8430547Z [ RUN      ] LLVM.DynamicShapeAdd
2023-01-11T22:10:33.9720802Z [       OK ] LLVM.DynamicShapeAdd (129 ms)
2023-01-11T22:10:33.9721371Z [ RUN      ] LLVM.BindDynamicShapeAdd
2023-01-11T22:10:34.1011802Z [       OK ] LLVM.BindDynamicShapeAdd (129 ms)
2023-01-11T22:10:34.1012387Z [ RUN      ] LLVM.TensorDynamicShapeAdd
2023-01-11T22:10:34.2307192Z [       OK ] LLVM.TensorDynamicShapeAdd (129 ms)
2023-01-11T22:10:34.2307735Z [ RUN      ] LLVM.DynamicShape2D
2023-01-11T22:10:34.4471800Z [       OK ] LLVM.DynamicShape2D (216 ms)
2023-01-11T22:10:34.4472304Z [ RUN      ] LLVM.EmptyStmt
2023-01-11T22:10:34.4645057Z [       OK ] LLVM.EmptyStmt (17 ms)
2023-01-11T22:10:34.4645587Z [ RUN      ] LLVM.EliminatedStmt
2023-01-11T22:10:34.4818765Z [       OK ] LLVM.EliminatedStmt (17 ms)
2023-01-11T22:10:34.4819280Z [ RUN      ] LLVM.SimpleReduction
2023-01-11T22:10:34.5638853Z [       OK ] LLVM.SimpleReduction (81 ms)
2023-01-11T22:10:34.5639401Z [ RUN      ] LLVM.RFactorReduction
2023-01-11T22:10:34.5955492Z [       OK ] LLVM.RFactorReduction (31 ms)
2023-01-11T22:10:34.5956040Z [ RUN      ] LLVM.RFactorVectorizedReduction
2023-01-11T22:10:34.6446355Z [       OK ] LLVM.RFactorVectorizedReduction (49 ms)
2023-01-11T22:10:34.6447036Z [ RUN      ] LLVM.SimpleParallelSS
2023-01-11T22:10:34.6683494Z [       OK ] LLVM.SimpleParallelSS (23 ms)
2023-01-11T22:10:34.6684119Z [ RUN      ] LLVM.SimpleParallelSP
2023-01-11T22:10:34.6941255Z [       OK ] LLVM.SimpleParallelSP (25 ms)
2023-01-11T22:10:34.6941743Z [ RUN      ] LLVM.SimpleParallelPS
2023-01-11T22:10:34.7208518Z [       OK ] LLVM.SimpleParallelPS (26 ms)
2023-01-11T22:10:34.7208985Z [ RUN      ] LLVM.SimpleParallelPP
2023-01-11T22:10:34.7474396Z [       OK ] LLVM.SimpleParallelPP (26 ms)
2023-01-11T22:10:34.7474927Z [ RUN      ] LLVM.CompositeParallel
2023-01-11T22:10:37.7785381Z [       OK ] LLVM.CompositeParallel (3030 ms)
2023-01-11T22:10:37.7785783Z [ RUN      ] LLVM.VectorizedGEMM
2023-01-11T22:10:37.8603076Z [       OK ] LLVM.VectorizedGEMM (81 ms)
2023-01-11T22:10:37.8603360Z [ RUN      ] LLVM.CallRaw
2023-01-11T22:10:38.5288047Z [       OK ] LLVM.CallRaw (668 ms)
2023-01-11T22:10:38.5288363Z [ RUN      ] LLVM.CustomTarget
2023-01-11T22:10:38.5519600Z [       OK ] LLVM.CustomTarget (23 ms)
2023-01-11T22:10:38.5519922Z [ RUN      ] LLVM.CodeGenKernelFuncName
2023-01-11T22:10:38.5883273Z [       OK ] LLVM.CodeGenKernelFuncName (36 ms)
2023-01-11T22:10:38.5883861Z [----------] 150 tests from LLVM (7792 ms total)
2023-01-11T22:10:38.5884118Z 
2023-01-11T22:10:38.5884352Z [----------] Global test environment tear-down
2023-01-11T22:10:38.5960740Z [==========] 801 tests from 25 test suites ran. (24923 ms total)
2023-01-11T22:10:38.5961258Z [  PASSED  ] 801 tests.
2023-01-11T22:10:38.5961441Z 
2023-01-11T22:10:38.5961568Z   YOU HAVE 5 DISABLED TESTS
2023-01-11T22:10:38.5961679Z 
2023-01-11T22:10:38.6761436Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *android* ]]
2023-01-11T22:10:38.6761923Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *cuda* ]]
2023-01-11T22:10:38.6762157Z + assert_git_not_dirty
2023-01-11T22:10:38.6762443Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:10:38.6762824Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:10:38.6763773Z ++ git status --porcelain
2023-01-11T22:10:38.7587849Z + git_status=
2023-01-11T22:10:38.7588279Z + [[ -n '' ]]
2023-01-11T22:10:38.7588616Z + test_aot_compilation
2023-01-11T22:10:38.7589106Z + echo 'Testing Ahead of Time compilation'
2023-01-11T22:10:38.7589497Z Testing Ahead of Time compilation
2023-01-11T22:10:38.7590324Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libc10d_cuda_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:10:38.7601239Z + ln -sf /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda_linalg.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so /opt/conda/lib/python3.10/site-packages/torch/lib/libtorchbind_test.so /opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:10:38.7612018Z + TEST_REPORTS_DIR=test/test-reports/cpp-unittest/test_aot_compilation
2023-01-11T22:10:38.7612487Z + mkdir -p test/test-reports/cpp-unittest/test_aot_compilation
2023-01-11T22:10:38.7622870Z + '[' -f /opt/conda/lib/python3.10/site-packages/torch/bin/test_mobile_nnc ']'
2023-01-11T22:10:38.7623412Z + /opt/conda/lib/python3.10/site-packages/torch/bin/test_mobile_nnc --gtest_output=xml:test/test-reports/cpp-unittest/test_aot_compilation/test_mobile_nnc.xml
2023-01-11T22:10:39.0960585Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:10:39.0960944Z [==========] Running 6 tests from 2 test suites.
2023-01-11T22:10:39.0961482Z [----------] Global test environment set-up.
2023-01-11T22:10:39.0961754Z [----------] 4 tests from Function
2023-01-11T22:10:39.0962031Z [ RUN      ] Function.ExecuteSlowMul
2023-01-11T22:10:39.0965095Z [       OK ] Function.ExecuteSlowMul (0 ms)
2023-01-11T22:10:39.0965596Z [ RUN      ] Function.Serialization
2023-01-11T22:10:39.0965879Z [       OK ] Function.Serialization (0 ms)
2023-01-11T22:10:39.0966158Z [ RUN      ] Function.ValidInput
2023-01-11T22:10:39.0966434Z [       OK ] Function.ValidInput (0 ms)
2023-01-11T22:10:39.0966698Z [ RUN      ] Function.InvalidInput
2023-01-11T22:10:39.0975431Z [       OK ] Function.InvalidInput (0 ms)
2023-01-11T22:10:39.0976000Z [----------] 4 tests from Function (1 ms total)
2023-01-11T22:10:39.0976277Z 
2023-01-11T22:10:39.0976671Z [----------] 2 tests from MobileNNCRegistryTest
2023-01-11T22:10:39.0976990Z [ RUN      ] MobileNNCRegistryTest.FindAndRun
2023-01-11T22:10:39.0977335Z [       OK ] MobileNNCRegistryTest.FindAndRun (0 ms)
2023-01-11T22:10:39.0977767Z [ RUN      ] MobileNNCRegistryTest.NoKernel
2023-01-11T22:10:39.0978233Z [       OK ] MobileNNCRegistryTest.NoKernel (0 ms)
2023-01-11T22:10:39.0978829Z [----------] 2 tests from MobileNNCRegistryTest (0 ms total)
2023-01-11T22:10:39.0979156Z 
2023-01-11T22:10:39.0979451Z [----------] Global test environment tear-down
2023-01-11T22:10:39.0979782Z [==========] 6 tests from 2 test suites ran. (1 ms total)
2023-01-11T22:10:39.0980027Z [  PASSED  ] 6 tests.
2023-01-11T22:10:39.0980145Z 
2023-01-11T22:10:39.0980268Z   YOU HAVE 1 DISABLED TEST
2023-01-11T22:10:39.0980389Z 
2023-01-11T22:10:39.1651597Z + '[' -f /opt/conda/lib/python3.10/site-packages/torch/bin/aot_model_compiler_test ']'
2023-01-11T22:10:39.1651942Z + source test/mobile/nnc/test_aot_compile.sh
2023-01-11T22:10:39.1668219Z ++ set -e -o pipefail
2023-01-11T22:10:39.1671672Z +++ python -c 'import site; print(site.getsitepackages()[0])'
2023-01-11T22:10:39.1834522Z ++ TORCH_INSTALL_DIR=/opt/conda/lib/python3.10/site-packages/torch
2023-01-11T22:10:39.1834897Z ++ TORCH_BIN_DIR=/opt/conda/lib/python3.10/site-packages/torch/bin
2023-01-11T22:10:39.1838314Z +++ dirname test/mobile/nnc/test_aot_compile.sh
2023-01-11T22:10:39.1849218Z ++ CURRENT_DIR=test/mobile/nnc
2023-01-11T22:10:39.1849523Z ++ MODEL=aot_test_model.pt
2023-01-11T22:10:39.1849773Z ++ COMPILED_MODEL=aot_test_model.compiled.pt
2023-01-11T22:10:39.1850016Z ++ COMPILED_CODE=aot_test_model.compiled.ll
2023-01-11T22:10:39.1853577Z +++ mktemp -d -t build_XXX
2023-01-11T22:10:39.1896993Z ++ TMP_DIR=/tmp/build_xuu
2023-01-11T22:10:39.1898105Z + test_custom_script_ops
2023-01-11T22:10:39.1898603Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *asan* ]]
2023-01-11T22:10:39.1899063Z + echo 'Testing custom script operators'
2023-01-11T22:10:39.1899400Z Testing custom script operators
2023-01-11T22:10:39.1899766Z + CUSTOM_OP_BUILD=/var/lib/jenkins/workspace/build/custom_test_artifacts/custom-op-build
2023-01-11T22:10:39.1900040Z + pushd test/custom_operator
2023-01-11T22:10:39.1900268Z ~/workspace/test/custom_operator ~/workspace
2023-01-11T22:10:39.1900626Z + cp -a /var/lib/jenkins/workspace/build/custom_test_artifacts/custom-op-build build
2023-01-11T22:10:39.2993535Z + python test_custom_ops.py -v
2023-01-11T22:10:40.5956130Z Test results will be stored in test-reports/python-unittest/test_custom_ops
2023-01-11T22:10:40.5966409Z 
2023-01-11T22:10:40.5966547Z Running tests...
2023-01-11T22:10:40.5967276Z ----------------------------------------------------------------------
2023-01-11T22:10:40.6005522Z   test_calling_custom_op (__main__.TestCustomOperators) ... ok (0.004s)
2023-01-11T22:10:40.6443993Z   test_calling_custom_op_inside_script_module (__main__.TestCustomOperators) ... ok (0.044s)
2023-01-11T22:10:40.6449556Z   test_calling_custom_op_string (__main__.TestCustomOperators) ... ok (0.001s)
2023-01-11T22:10:40.6467190Z   test_calling_custom_op_with_autograd (__main__.TestCustomOperators) ... /opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:197: UserWarning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/autograd/engine.cpp:1134.)
2023-01-11T22:10:40.6468618Z   Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
2023-01-11T22:10:40.6480232Z ok (0.003s)
2023-01-11T22:10:40.6487381Z   test_calling_custom_op_with_autograd_in_nograd_mode (__main__.TestCustomOperators) ... ok (0.001s)
2023-01-11T22:10:40.6490776Z   test_custom_library_is_loaded (__main__.TestCustomOperators) ... ok (0.000s)
2023-01-11T22:10:40.6566756Z   test_saving_and_loading_script_module_with_custom_op (__main__.TestCustomOperators) ... ok (0.007s)
2023-01-11T22:10:40.6567141Z 
2023-01-11T22:10:40.6567583Z ----------------------------------------------------------------------
2023-01-11T22:10:40.6568014Z Ran 7 tests in 0.060s
2023-01-11T22:10:40.6568225Z 
2023-01-11T22:10:40.6568332Z OK
2023-01-11T22:10:40.6568435Z 
2023-01-11T22:10:40.6568523Z Generating XML reports...
2023-01-11T22:10:40.6595602Z Generated XML report: test-reports/python-unittest/test_custom_ops/TEST-TestCustomOperators-20230111221040.xml
2023-01-11T22:10:40.9007491Z + python model.py --export-script-module=model.pt
2023-01-11T22:10:42.2144048Z + build/test_custom_ops ./model.pt
2023-01-11T22:10:42.5681230Z [W engine.cpp:1134] Warning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (function operator())
2023-01-11T22:10:42.6315250Z ok
2023-01-11T22:10:42.7049931Z + popd
2023-01-11T22:10:42.7050295Z ~/workspace
2023-01-11T22:10:42.7050632Z + assert_git_not_dirty
2023-01-11T22:10:42.7051121Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:10:42.7051449Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:10:42.7054616Z ++ git status --porcelain
2023-01-11T22:10:42.7867321Z + git_status=
2023-01-11T22:10:42.7867904Z + [[ -n '' ]]
2023-01-11T22:10:42.7868235Z + test_custom_backend
2023-01-11T22:10:42.7868694Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *asan* ]]
2023-01-11T22:10:42.7868966Z + echo 'Testing custom backends'
2023-01-11T22:10:42.7869167Z Testing custom backends
2023-01-11T22:10:42.7869545Z + CUSTOM_BACKEND_BUILD=/var/lib/jenkins/workspace/build/custom_test_artifacts/custom-backend-build
2023-01-11T22:10:42.7869830Z + pushd test/custom_backend
2023-01-11T22:10:42.7870056Z ~/workspace/test/custom_backend ~/workspace
2023-01-11T22:10:42.7870426Z + cp -a /var/lib/jenkins/workspace/build/custom_test_artifacts/custom-backend-build build
2023-01-11T22:10:42.8816598Z + python test_custom_backend.py -v
2023-01-11T22:10:44.1483164Z Test results will be stored in test-reports/python-unittest/test_custom_backend
2023-01-11T22:10:44.1493245Z 
2023-01-11T22:10:44.1493393Z Running tests...
2023-01-11T22:10:44.1494000Z ----------------------------------------------------------------------
2023-01-11T22:10:44.1498961Z   test_execute (__main__.TestCustomBackend)
2023-01-11T22:10:44.2007328Z Test execution using the custom backend. ... ok (0.051s)
2023-01-11T22:10:44.2012326Z   test_save_load (__main__.TestCustomBackend)
2023-01-11T22:10:44.2177368Z Test that a lowered module can be executed correctly ... ok (0.017s)
2023-01-11T22:10:44.2179599Z 
2023-01-11T22:10:44.2180042Z ----------------------------------------------------------------------
2023-01-11T22:10:44.2180740Z Ran 2 tests in 0.069s
2023-01-11T22:10:44.2180945Z 
2023-01-11T22:10:44.2181025Z OK
2023-01-11T22:10:44.2181121Z 
2023-01-11T22:10:44.2181202Z Generating XML reports...
2023-01-11T22:10:44.2205740Z Generated XML report: test-reports/python-unittest/test_custom_backend/TEST-TestCustomBackend-20230111221044.xml
2023-01-11T22:10:44.4596268Z + python backend.py --export-module-to=model.pt
2023-01-11T22:10:45.8177446Z + build/test_custom_backend ./model.pt
2023-01-11T22:10:46.1857464Z Testing custom_backend
2023-01-11T22:10:46.2591814Z OK
2023-01-11T22:10:46.3315645Z + rm -f ./model.pt
2023-01-11T22:10:46.3346297Z + popd
2023-01-11T22:10:46.3346566Z ~/workspace
2023-01-11T22:10:46.3346838Z + assert_git_not_dirty
2023-01-11T22:10:46.3347419Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:10:46.3348022Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:10:46.3350526Z ++ git status --porcelain
2023-01-11T22:10:46.4165319Z + git_status=
2023-01-11T22:10:46.4165804Z + [[ -n '' ]]
2023-01-11T22:10:46.4166140Z + test_torch_function_benchmark
2023-01-11T22:10:46.4166525Z + echo 'Testing __torch_function__ benchmarks'
2023-01-11T22:10:46.4166753Z Testing __torch_function__ benchmarks
2023-01-11T22:10:46.4167042Z + pushd benchmarks/overrides_benchmark
2023-01-11T22:10:46.4167301Z ~/workspace/benchmarks/overrides_benchmark ~/workspace
2023-01-11T22:10:46.4167586Z + python bench.py -n 1 -m 2
2023-01-11T22:10:47.5461328Z Type tensor had a minimum time of 0.0035762786865234375 us and a standard deviation of 0.052262093959143385 us.
2023-01-11T22:10:47.5461766Z Type SubTensor had a minimum time of 0.0133514404296875 us and a standard deviation of 0.034223241527797654 us.
2023-01-11T22:10:47.5462711Z Type WithTorchFunction had a minimum time of 0.008344650268554688 us and a standard deviation of 0.015678628187743016 us.
2023-01-11T22:10:47.5463140Z Type SubWithTorchFunction had a minimum time of 0.015735626220703125 us and a standard deviation of 0.007417845154122915 us.
2023-01-11T22:10:47.7522826Z + python pyspybench.py Tensor -n 1
2023-01-11T22:10:49.0781520Z + python pyspybench.py SubTensor -n 1
2023-01-11T22:10:50.4069426Z + python pyspybench.py WithTorchFunction -n 1
2023-01-11T22:10:51.7363327Z + python pyspybench.py SubWithTorchFunction -n 1
2023-01-11T22:10:53.0636521Z + popd
2023-01-11T22:10:53.0636764Z ~/workspace
2023-01-11T22:10:53.0636937Z + assert_git_not_dirty
2023-01-11T22:10:53.0637395Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:10:53.0637722Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:10:53.0639338Z ++ git status --porcelain
2023-01-11T22:10:53.1476247Z + git_status=
2023-01-11T22:10:53.1476796Z + [[ -n '' ]]
2023-01-11T22:10:53.1477070Z + test_benchmarks
2023-01-11T22:10:53.1477642Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 == *cuda* ]]
2023-01-11T22:10:53.1477912Z + [[ nogpu_AVX512 != *nogpu* ]]
2023-01-11T22:10:53.1478086Z + test_executorch
2023-01-11T22:10:53.1478343Z + echo 'Testing Executorch op registration'
2023-01-11T22:10:53.1478583Z Testing Executorch op registration
2023-01-11T22:10:53.1478801Z + build/bin/test_edge_op_registration
2023-01-11T22:10:53.4150400Z Note: Google Test filter = *-*_CUDA:*_MultiCUDA
2023-01-11T22:10:53.4150974Z [==========] Running 1 test from 1 test suite.
2023-01-11T22:10:53.4151461Z [----------] Global test environment set-up.
2023-01-11T22:10:53.4151808Z [----------] 1 test from OperatorRegistrationTest
2023-01-11T22:10:53.4152300Z [ RUN      ] OperatorRegistrationTest.Add
2023-01-11T22:10:53.4153056Z [       OK ] OperatorRegistrationTest.Add (0 ms)
2023-01-11T22:10:53.4153699Z [----------] 1 test from OperatorRegistrationTest (0 ms total)
2023-01-11T22:10:53.4153958Z 
2023-01-11T22:10:53.4154127Z [----------] Global test environment tear-down
2023-01-11T22:10:53.4154438Z [==========] 1 test from 1 test suite ran. (0 ms total)
2023-01-11T22:10:53.4154691Z [  PASSED  ] 1 test.
2023-01-11T22:10:53.4746891Z + assert_git_not_dirty
2023-01-11T22:10:53.4747466Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *rocm* ]]
2023-01-11T22:10:53.4747907Z + [[ linux-bionic-cuda11.7-py3.10-gcc7 != *xla* ]]
2023-01-11T22:10:53.4750010Z ++ git status --porcelain
2023-01-11T22:10:53.5565971Z + git_status=
2023-01-11T22:10:53.5566312Z + [[ -n '' ]]
2023-01-11T22:10:53.5689938Z Prepare all required actions
2023-01-11T22:10:53.5690230Z Getting action download info
2023-01-11T22:10:53.8165830Z ##[group]Run ./.github/actions/get-workflow-job-id
2023-01-11T22:10:53.8166035Z with:
2023-01-11T22:10:53.8166355Z   github-token: ***
2023-01-11T22:10:53.8166526Z env:
2023-01-11T22:10:53.8166688Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:10:53.8166976Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:10:53.8167240Z ##[endgroup]
2023-01-11T22:10:53.8191553Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
2023-01-11T22:10:53.8191784Z with:
2023-01-11T22:10:53.8191954Z   shell: bash
2023-01-11T22:10:53.8192117Z   timeout_minutes: 10
2023-01-11T22:10:53.8192302Z   max_attempts: 5
2023-01-11T22:10:53.8192484Z   retry_wait_seconds: 30
2023-01-11T22:10:53.8192889Z   command: set -eux
python3 -m pip install requests==2.26.0
GHA_WORKFLOW_JOB_ID=$(python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}")
echo "job-id=${GHA_WORKFLOW_JOB_ID}" >> "${GITHUB_OUTPUT}"

2023-01-11T22:10:53.8193266Z   polling_interval_seconds: 1
2023-01-11T22:10:53.8193447Z   warning_on_retry: true
2023-01-11T22:10:53.8207712Z   continue_on_error: false
2023-01-11T22:10:53.8207889Z env:
2023-01-11T22:10:53.8208064Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:10:53.8208351Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:10:53.8208735Z   GITHUB_TOKEN: ***
2023-01-11T22:10:53.8208912Z ##[endgroup]
2023-01-11T22:10:54.0673734Z + python3 -m pip install requests==2.26.0
2023-01-11T22:10:54.8283291Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T22:10:54.8599494Z Requirement already satisfied: requests==2.26.0 in /home/ec2-user/.local/lib/python3.7/site-packages (2.26.0)
2023-01-11T22:10:54.8732775Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (2022.12.7)
2023-01-11T22:10:54.8745212Z Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (3.4)
2023-01-11T22:10:54.8764264Z Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (2.0.12)
2023-01-11T22:10:54.8786978Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (1.26.14)
2023-01-11T22:10:55.0995831Z ++ python3 .github/scripts/get_workflow_job_id.py 3896346758 i-06cf3037fd0558aaa
2023-01-11T22:10:58.8604653Z + GHA_WORKFLOW_JOB_ID=10589559762
2023-01-11T22:10:58.8605182Z + echo job-id=10589559762
2023-01-11T22:10:59.0710695Z Command completed after 1 attempt(s).
2023-01-11T22:10:59.0811973Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2023-01-11T22:10:59.0812219Z kill "$MONITOR_SCRIPT_PID"
2023-01-11T22:10:59.1659485Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:10:59.1659715Z env:
2023-01-11T22:10:59.1659893Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:10:59.1660173Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:10:59.1660450Z   MONITOR_SCRIPT_PID: 3314
2023-01-11T22:10:59.1660637Z ##[endgroup]
2023-01-11T22:10:59.1745244Z Prepare all required actions
2023-01-11T22:10:59.1745496Z Getting action download info
2023-01-11T22:10:59.3847187Z Download action repository 'actions/upload-artifact@v3' (SHA:0b7f8abb1508181956e8e162db84b466c27e18ce)
2023-01-11T22:10:59.5467466Z ##[group]Run ./.github/actions/upload-test-artifacts
2023-01-11T22:10:59.5467780Z with:
2023-01-11T22:10:59.5468007Z   file-suffix: test-nogpu_AVX512-1-1-linux.2xlarge_10589559762
2023-01-11T22:10:59.5468232Z env:
2023-01-11T22:10:59.5468396Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:10:59.5468682Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:10:59.5468950Z ##[endgroup]
2023-01-11T22:10:59.5492269Z ##[group]Run # Remove any previous test jsons if they exist
2023-01-11T22:10:59.5492546Z # Remove any previous test jsons if they exist
2023-01-11T22:10:59.5492945Z rm -f test-jsons-*.zip
2023-01-11T22:10:59.5493223Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json'
2023-01-11T22:10:59.5504130Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:10:59.5504384Z env:
2023-01-11T22:10:59.5504562Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:10:59.5504833Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:10:59.5505164Z   FILE_SUFFIX: test-nogpu_AVX512-1-1-linux.2xlarge_10589559762
2023-01-11T22:10:59.5505388Z ##[endgroup]
2023-01-11T22:10:59.5791635Z   adding: test/allowlist_for_publicAPI.json (deflated 78%)
2023-01-11T22:10:59.5828652Z   adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%)
2023-01-11T22:10:59.5843598Z   adding: test/profiler/profiler_utils_mock_events.json (deflated 87%)
2023-01-11T22:10:59.5845146Z   adding: test/.pytorch-slow-tests.json (deflated 77%)
2023-01-11T22:10:59.5849655Z   adding: test/.pytorch-disabled-tests.json (deflated 84%)
2023-01-11T22:10:59.5867318Z ##[group]Run # Remove any previous test reports if they exist
2023-01-11T22:10:59.5867597Z # Remove any previous test reports if they exist
2023-01-11T22:10:59.5867833Z rm -f test-reports-*.zip
2023-01-11T22:10:59.5868100Z zip -r "test-reports-${FILE_SUFFIX}.zip" test -i '*.xml' -i '*.csv'
2023-01-11T22:10:59.5878143Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:10:59.5878369Z env:
2023-01-11T22:10:59.5878546Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:10:59.5878816Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:10:59.5879137Z   FILE_SUFFIX: test-nogpu_AVX512-1-1-linux.2xlarge_10589559762
2023-01-11T22:10:59.5879359Z ##[endgroup]
2023-01-11T22:10:59.6008046Z   adding: test/custom_backend/test-reports/python-unittest/test_custom_backend/TEST-TestCustomBackend-20230111221044.xml (deflated 56%)
2023-01-11T22:10:59.6008712Z   adding: test/custom_operator/test-reports/python-unittest/test_custom_ops/TEST-TestCustomOperators-20230111221040.xml (deflated 65%)
2023-01-11T22:10:59.6025497Z   adding: test/test-reports/python-unittest/dynamo.test_misc/TEST-MiscTests-20230111212233.xml (deflated 90%)
2023-01-11T22:10:59.6030379Z   adding: test/test-reports/python-unittest/dynamo.test_misc/TEST-TestTracer-20230111212233.xml (deflated 39%)
2023-01-11T22:10:59.6039137Z   adding: test/test-reports/python-unittest/dynamo.test_export/TEST-ExportTests-20230111212233.xml (deflated 92%)
2023-01-11T22:10:59.6043086Z   adding: test/test-reports/python-unittest/dynamo.test_optimizations/TEST-NormalizeIRTests-20230111212239.xml (deflated 41%)
2023-01-11T22:10:59.6050869Z   adding: test/test-reports/python-unittest/dynamo.test_optimizations/TEST-TestOptimizations-20230111212239.xml (deflated 78%)
2023-01-11T22:10:59.6061406Z   adding: test/test-reports/python-unittest/dynamo.test_repros/TEST-ReproTests-20230111212241.xml (deflated 87%)
2023-01-11T22:10:59.6065067Z   adding: test/test-reports/python-unittest/dynamo.test_torchxla_integration/TEST-TorchXLAReuseGraphTest-20230111212244.xml (deflated 76%)
2023-01-11T22:10:59.6079211Z   adding: test/test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecNNModuleTests-20230111212248.xml (deflated 91%)
2023-01-11T22:10:59.6093128Z   adding: test/test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecReproTests-20230111212248.xml (deflated 88%)
2023-01-11T22:10:59.6101090Z   adding: test/test-reports/python-unittest/dynamo.test_unspec/TEST-UnspecTests-20230111212248.xml (deflated 75%)
2023-01-11T22:10:59.6105555Z   adding: test/test-reports/python-unittest/profiler.test_profiler_tree/TEST-TestProfilerTree-20230111212258.xml (deflated 82%)
2023-01-11T22:10:59.6110740Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_activation_sparsifier.TestActivationSparsifier-20230111212302.xml (deflated 54%)
2023-01-11T22:10:59.6117913Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_scheduler.TestBaseDataScheduler-20230111212302.xml (deflated 71%)
2023-01-11T22:10:59.6123647Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestBaseDataSparsifier-20230111212302.xml (deflated 65%)
2023-01-11T22:10:59.6130148Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestBaseSparsifier-20230111212302.xml (deflated 82%)
2023-01-11T22:10:59.6134428Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_structured_sparsifier.TestBaseStructuredSparsifier-20230111212302.xml (deflated 85%)
2023-01-11T22:10:59.6146180Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_composability.TestComposability-20230111212302.xml (deflated 74%)
2023-01-11T22:10:59.6152649Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_scheduler.TestCubicScheduler-20230111212302.xml (deflated 58%)
2023-01-11T22:10:59.6155919Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_parametrization.TestFakeSparsity-20230111212302.xml (deflated 73%)
2023-01-11T22:10:59.6160788Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_composability.TestFxComposability-20230111212302.xml (deflated 80%)
2023-01-11T22:10:59.6170989Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestNearlyDiagonalSparsifier-20230111212302.xml (deflated 76%)
2023-01-11T22:10:59.6175279Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestNormDataSparsifiers-20230111212302.xml (deflated 69%)
2023-01-11T22:10:59.6178765Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_data_sparsifier.TestQuantizationUtils-20230111212302.xml (deflated 71%)
2023-01-11T22:10:59.6188461Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_kernels.TestQuantizedSparseKernels-20230111212302.xml (deflated 50%)
2023-01-11T22:10:59.6199970Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_kernels.TestQuantizedSparseLayers-20230111212302.xml (deflated 61%)
2023-01-11T22:10:59.6206561Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_scheduler.TestScheduler-20230111212302.xml (deflated 70%)
2023-01-11T22:10:59.6209794Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsity_utils.TestSparsityUtilFunctions-20230111212302.xml (deflated 78%)
2023-01-11T22:10:59.6215849Z   adding: test/test-reports/python-unittest/test_ao_sparsity/TEST-ao.sparsity.test_sparsifier.TestWeightNormSparsifier-20230111212302.xml (deflated 78%)
2023-01-11T22:10:59.6223772Z   adding: test/test-reports/python-unittest/test_function_schema/TEST-TestFunctionSchema-20230111212316.xml (deflated 82%)
2023-01-11T22:10:59.6228286Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_alias_analysis.TestAliasAnalysis-20230111212322.xml (deflated 66%)
2023-01-11T22:10:59.6231687Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_async.TestAsync-20230111212322.xml (deflated 85%)
2023-01-11T22:10:59.6234805Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_aten_pow.TestAtenPow-20230111212322.xml (deflated 48%)
2023-01-11T22:10:59.6244402Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_autodiff.TestAutodiffJit-20230111212322.xml (deflated 74%)
2023-01-11T22:10:59.6250466Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_autodiff_subgraph_slicing.TestAutodiffSubgraphSlicing-20230111212322.xml (deflated 86%)
2023-01-11T22:10:59.6253755Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_backends.TestBackends-20230111212322.xml (deflated 74%)
2023-01-11T22:10:59.6263339Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_backends.TestBackendsWithCompiler-20230111212322.xml (deflated 59%)
2023-01-11T22:10:59.6270380Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_batch_mm.TestBatchMM-20230111212322.xml (deflated 81%)
2023-01-11T22:10:59.6274678Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_builtins.TestBuiltins-20230111212322.xml (deflated 70%)
2023-01-11T22:10:59.6294864Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_class_type.TestClassType-20230111212322.xml (deflated 83%)
2023-01-11T22:10:59.6297975Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_complex.TestComplex-20230111212322.xml (deflated 84%)
2023-01-11T22:10:59.6301408Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_custom_operators.TestCustomOperators-20230111212322.xml (deflated 83%)
2023-01-11T22:10:59.6304942Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_dce.TestDCE-20230111212322.xml (deflated 56%)
2023-01-11T22:10:59.6316638Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_dataclasses.TestDataclasses-20230111212322.xml (deflated 77%)
2023-01-11T22:10:59.6320903Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_device_analysis.TestDeviceAnalysis-20230111212322.xml (deflated 85%)
2023-01-11T22:10:59.6324589Z   adding: test/test-reports/python-unittest/test_jit/TEST-jit.test_list_dict.TestDict-20230111212322.xml (deflated 82%)
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2023-01-11T22:10:59.9235611Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestDisabledTorchFunction-20230111215849.xml (deflated 41%)
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2023-01-11T22:10:59.9263752Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestNamedTuple-20230111215849.xml (deflated 39%)
2023-01-11T22:10:59.9267294Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestPickle-20230111215849.xml (deflated 39%)
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2023-01-11T22:10:59.9322892Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestTorchFunctionWarning-20230111215849.xml (deflated 42%)
2023-01-11T22:10:59.9325605Z   adding: test/test-reports/python-unittest/test_overrides/TEST-TestWrapTorchFunction-20230111215849.xml (deflated 41%)
2023-01-11T22:10:59.9329247Z   adding: test/test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCSRSampler-20230111215853.xml (deflated 40%)
2023-01-11T22:10:59.9336110Z   adding: test/test-reports/python-unittest/test_torch/TEST-TestBasicVitalSigns-20230111215856.xml (deflated 63%)
2023-01-11T22:10:59.9356484Z   adding: test/test-reports/python-unittest/test_torch/TEST-TestTorch-20230111215856.xml (deflated 93%)
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2023-01-11T22:10:59.9365348Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestConstraints-20230111215900.xml (deflated 56%)
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2023-01-11T22:10:59.9390834Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestFunctors-20230111215900.xml (deflated 70%)
2023-01-11T22:10:59.9395223Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestJit-20230111215900.xml (deflated 73%)
2023-01-11T22:10:59.9398067Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestKL-20230111215900.xml (deflated 82%)
2023-01-11T22:10:59.9406133Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestLazyLogitsInitialization-20230111215900.xml (deflated 58%)
2023-01-11T22:10:59.9409538Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestNumericalStability-20230111215900.xml (deflated 83%)
2023-01-11T22:10:59.9414813Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestRsample-20230111215900.xml (deflated 76%)
2023-01-11T22:10:59.9418355Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestValidation-20230111215900.xml (deflated 67%)
2023-01-11T22:10:59.9429213Z   adding: test/test-reports/python-unittest/nn.test_convolution/TEST-TestConvolutionNN-20230111215941.xml (deflated 84%)
2023-01-11T22:10:59.9434430Z   adding: test/test-reports/python-unittest/nn.test_pooling/TEST-TestAvgPool-20230111215949.xml (deflated 77%)
2023-01-11T22:10:59.9438548Z   adding: test/test-reports/python-unittest/nn.test_pooling/TEST-TestPoolingNN-20230111215949.xml (deflated 80%)
2023-01-11T22:10:59.9475998Z   adding: test/test-reports/python-unittest/test_cpp_api_parity/TEST-TestCppApiParity-20230111215955.xml (deflated 96%)
2023-01-11T22:10:59.9479477Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestCppExtensionAOT-20230111220011.xml (deflated 80%)
2023-01-11T22:10:59.9482318Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestORTTensor-20230111220011.xml (deflated 68%)
2023-01-11T22:10:59.9491529Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestPybindTypeCasters-20230111220011.xml (deflated 41%)
2023-01-11T22:10:59.9494446Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestRNGExtension-20230111220011.xml (deflated 41%)
2023-01-11T22:10:59.9502347Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestTorchLibrary-20230111220011.xml (deflated 42%)
2023-01-11T22:10:59.9506706Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestCppExtensionAOT-20230111220021.xml (deflated 81%)
2023-01-11T22:10:59.9517239Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestORTTensor-20230111220021.xml (deflated 68%)
2023-01-11T22:10:59.9522817Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestPybindTypeCasters-20230111220021.xml (deflated 41%)
2023-01-11T22:10:59.9525449Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestRNGExtension-20230111220021.xml (deflated 41%)
2023-01-11T22:10:59.9529658Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_no_ninja/TEST-TestTorchLibrary-20230111220021.xml (deflated 41%)
2023-01-11T22:10:59.9537851Z   adding: test/test-reports/python-unittest/test_cpp_extensions_open_device_registration/TEST-TestCppExtensionOpenRgistration-20230111220023.xml (deflated 55%)
2023-01-11T22:10:59.9543924Z   adding: test/test-reports/python-unittest/test_dispatch/TEST-TestDispatch-20230111220051.xml (deflated 81%)
2023-01-11T22:10:59.9548105Z   adding: test/test-reports/python-unittest/test_dispatch/TEST-TestPythonDispatcher-20230111220051.xml (deflated 75%)
2023-01-11T22:10:59.9548777Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_gradual_type.AnnotationsTest-20230111220117.xml (deflated 78%)
2023-01-11T22:10:59.9549382Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_cse_pass.TestCSEPass-20230111220117.xml (deflated 85%)
2023-01-11T22:10:59.9549911Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_common_passes.TestCommonPass-20230111220117.xml (deflated 82%)
2023-01-11T22:10:59.9551689Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_fx_const_fold.TestConstFold-20230111220117.xml (deflated 83%)
2023-01-11T22:10:59.9552382Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_fx_param_shape_control_flow.TestConstParamShapeInControlFlow-20230111220117.xml (deflated 82%)
2023-01-11T22:10:59.9553068Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_dce_pass.TestDCE-20230111220117.xml (deflated 82%)
2023-01-11T22:10:59.9556142Z   adding: test/test-reports/python-unittest/test_fx/TEST-TestFX-20230111220117.xml (deflated 87%)
2023-01-11T22:10:59.9556744Z   adding: test/test-reports/python-unittest/test_fx/TEST-TestFXAPIBackwardCompatibility-20230111220117.xml (deflated 63%)
2023-01-11T22:10:59.9558351Z   adding: test/test-reports/python-unittest/test_fx/TEST-TestFunctionalTracing-20230111220117.xml (deflated 94%)
2023-01-11T22:10:59.9558961Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_pass_infra.TestPassManager-20230111220117.xml (deflated 75%)
2023-01-11T22:10:59.9560070Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_subgraph_rewriter.TestSubgraphRewriter-20230111220117.xml (deflated 86%)
2023-01-11T22:10:59.9562294Z   adding: test/test-reports/python-unittest/test_fx/TEST-TestVisionTracing-20230111220117.xml (deflated 88%)
2023-01-11T22:10:59.9563035Z   adding: test/test-reports/python-unittest/test_fx/TEST-fx.test_gradual_type.TypeCheckerTest-20230111220117.xml (deflated 90%)
2023-01-11T22:10:59.9563718Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestEnableDisableCudaFuser-20230111220508.xml (deflated 69%)
2023-01-11T22:10:59.9564309Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-jit.test_fuser_common.TestFuserCommon-20230111220508.xml (deflated 48%)
2023-01-11T22:10:59.9567016Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestCudaFuser-20230111220508.xml (deflated 93%)
2023-01-11T22:10:59.9567609Z   adding: test/test-reports/python-unittest/test_jit_disabled/TEST-TestJitDisabled-20230111220509.xml (deflated 61%)
2023-01-11T22:10:59.9568113Z   adding: test/test-reports/python-unittest/test_mobile_optimizer/TEST-TestOptimizer-20230111220513.xml (deflated 64%)
2023-01-11T22:10:59.9569560Z   adding: test/test-reports/python-unittest/test_multiprocessing/TEST-TestMultiprocessing-20230111220520.xml (deflated 86%)
2023-01-11T22:10:59.9570173Z   adding: test/test-reports/python-unittest/test_multiprocessing_spawn/TEST-ErrorTest-20230111220531.xml (deflated 39%)
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2023-01-11T22:10:59.9571947Z   adding: test/test-reports/python-unittest/test_namedtuple_return_api/TEST-TestNamedTupleAPI-20230111220550.xml (deflated 77%)
2023-01-11T22:10:59.9572493Z   adding: test/test-reports/python-unittest/test_prims/TEST-TestPrimsBasic-20230111220624.xml (deflated 53%)
2023-01-11T22:10:59.9572996Z   adding: test/test-reports/python-unittest/test_show_pickle/TEST-TestShowPickle-20230111220628.xml (deflated 40%)
2023-01-11T22:10:59.9573792Z   adding: test/test-reports/python-unittest/test_tensorexpr/TEST-TestTensorExprFuser-20230111220635.xml (deflated 88%)
2023-01-11T22:10:59.9581183Z   adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-43da9767f75f5d75.xml (deflated 28%)
2023-01-11T22:10:59.9585301Z   adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-c75b30b3898389b7.xml (deflated 28%)
2023-01-11T22:10:59.9588600Z   adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-775c9b995ca41897.xml (deflated 27%)
2023-01-11T22:10:59.9589306Z   adding: test/test-reports/python-pytest/test_ops/test_ops-02e8df424f6ccae0.xml (deflated 28%)
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2023-01-11T22:10:59.9591592Z   adding: test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-d9fcda48e67f6e06.xml (deflated 28%)
2023-01-11T22:10:59.9592088Z   adding: test/test-reports/python-pytest/test_ops_fwd_gradients/test_ops_fwd_gradients-60a240aeadf13b46.xml (deflated 28%)
2023-01-11T22:10:59.9592589Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-b637e26586d3453a.xml (deflated 28%)
2023-01-11T22:10:59.9593083Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-ed1e446199e7e291.xml (deflated 28%)
2023-01-11T22:10:59.9593581Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-3567e610b2a391c3.xml (deflated 28%)
2023-01-11T22:10:59.9594027Z   adding: test/test-reports/python-pytest/test_ops_jit/test_ops_jit-5431f0498166287b.xml (deflated 28%)
2023-01-11T22:10:59.9594477Z   adding: test/test-reports/python-pytest/test_ops_jit/test_ops_jit-096e00faef340b2a.xml (deflated 28%)
2023-01-11T22:10:59.9594919Z   adding: test/test-reports/python-pytest/test_ops_jit/test_ops_jit-256d5705adf79135.xml (deflated 28%)
2023-01-11T22:10:59.9603987Z   adding: test/test-reports/cpp-unittest/test_libtorch/test_jit.xml (deflated 92%)
2023-01-11T22:10:59.9611964Z   adding: test/test-reports/cpp-unittest/test_libtorch/test_lazy.xml (deflated 93%)
2023-01-11T22:10:59.9628652Z   adding: test/test-reports/cpp-unittest/test_libtorch/test_api.xml (deflated 92%)
2023-01-11T22:10:59.9642061Z   adding: test/test-reports/cpp-unittest/test_libtorch/test_tensorexpr.xml (deflated 92%)
2023-01-11T22:10:59.9642572Z   adding: test/test-reports/cpp-unittest/test_aot_compilation/test_mobile_nnc.xml (deflated 75%)
2023-01-11T22:10:59.9664598Z ##[group]Run # Remove any previous test reports if they exist
2023-01-11T22:10:59.9664887Z # Remove any previous test reports if they exist
2023-01-11T22:10:59.9665112Z rm -f usage-log-*.zip
2023-01-11T22:10:59.9665391Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2023-01-11T22:10:59.9665877Z # so check to see if the file exists first
2023-01-11T22:10:59.9666091Z if [ -f 'usage_log.txt' ]; then
2023-01-11T22:10:59.9666336Z     zip "usage-log-${FILE_SUFFIX}.zip" 'usage_log.txt'
2023-01-11T22:10:59.9666550Z fi
2023-01-11T22:10:59.9677209Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:10:59.9677431Z env:
2023-01-11T22:10:59.9677609Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:10:59.9677887Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:10:59.9678210Z   FILE_SUFFIX: test-nogpu_AVX512-1-1-linux.2xlarge_10589559762
2023-01-11T22:10:59.9678431Z ##[endgroup]
2023-01-11T22:11:00.0043234Z   adding: usage_log.txt (deflated 97%)
2023-01-11T22:11:00.0083948Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T22:11:00.0084162Z with:
2023-01-11T22:11:00.0084354Z   s3-prefix: pytorch/pytorch/3896346758/1/artifact

2023-01-11T22:11:00.0084571Z   retention-days: 14
2023-01-11T22:11:00.0084763Z   if-no-files-found: warn
2023-01-11T22:11:00.0084947Z   path: test-jsons-*.zip
2023-01-11T22:11:00.0085128Z   name: artifact
2023-01-11T22:11:00.0085308Z   s3-bucket: gha-artifacts
2023-01-11T22:11:00.0085482Z   region: us-east-1
2023-01-11T22:11:00.0085646Z env:
2023-01-11T22:11:00.0085816Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:00.0086083Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:11:00.0086345Z ##[endgroup]
2023-01-11T22:11:00.3775934Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T22:11:00.3776447Z With the provided path, there will be 1 file uploaded
2023-01-11T22:11:00.3776882Z Uploading to s3 prefix: pytorch/pytorch/3896346758/1/artifact
2023-01-11T22:11:00.3835066Z Starting upload of test-jsons-test-nogpu_AVX512-1-1-linux.2xlarge_10589559762.zip
2023-01-11T22:11:00.5305161Z Finished upload of test-jsons-test-nogpu_AVX512-1-1-linux.2xlarge_10589559762.zip
2023-01-11T22:11:00.5457173Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T22:11:00.5457390Z with:
2023-01-11T22:11:00.5457593Z   s3-prefix: pytorch/pytorch/3896346758/1/artifact

2023-01-11T22:11:00.5457809Z   retention-days: 14
2023-01-11T22:11:00.5458006Z   if-no-files-found: error
2023-01-11T22:11:00.5458212Z   path: test-reports-*.zip
2023-01-11T22:11:00.5458383Z   name: artifact
2023-01-11T22:11:00.5458567Z   s3-bucket: gha-artifacts
2023-01-11T22:11:00.5458756Z   region: us-east-1
2023-01-11T22:11:00.5458909Z env:
2023-01-11T22:11:00.5459087Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:00.5459370Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:11:00.5459618Z ##[endgroup]
2023-01-11T22:11:00.8813063Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T22:11:00.8813639Z With the provided path, there will be 1 file uploaded
2023-01-11T22:11:00.8814162Z Uploading to s3 prefix: pytorch/pytorch/3896346758/1/artifact
2023-01-11T22:11:00.8821550Z Starting upload of test-reports-test-nogpu_AVX512-1-1-linux.2xlarge_10589559762.zip
2023-01-11T22:11:01.0380404Z Finished upload of test-reports-test-nogpu_AVX512-1-1-linux.2xlarge_10589559762.zip
2023-01-11T22:11:01.0499203Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T22:11:01.0499424Z with:
2023-01-11T22:11:01.0499616Z   s3-prefix: pytorch/pytorch/3896346758/1/artifact

2023-01-11T22:11:01.0499839Z   retention-days: 14
2023-01-11T22:11:01.0500036Z   if-no-files-found: ignore
2023-01-11T22:11:01.0500225Z   path: usage-log-*.zip
2023-01-11T22:11:01.0500416Z   name: artifact
2023-01-11T22:11:01.0500599Z   s3-bucket: gha-artifacts
2023-01-11T22:11:01.0500777Z   region: us-east-1
2023-01-11T22:11:01.0500946Z env:
2023-01-11T22:11:01.0501118Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:01.0501385Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:11:01.0501652Z ##[endgroup]
2023-01-11T22:11:01.3792581Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T22:11:01.3793058Z With the provided path, there will be 1 file uploaded
2023-01-11T22:11:01.3793528Z Uploading to s3 prefix: pytorch/pytorch/3896346758/1/artifact
2023-01-11T22:11:01.3800336Z Starting upload of usage-log-test-nogpu_AVX512-1-1-linux.2xlarge_10589559762.zip
2023-01-11T22:11:01.6215049Z Finished upload of usage-log-test-nogpu_AVX512-1-1-linux.2xlarge_10589559762.zip
2023-01-11T22:11:01.6331882Z ##[group]Run # shellcheck disable=SC2156
2023-01-11T22:11:01.6332115Z # shellcheck disable=SC2156
2023-01-11T22:11:01.6332494Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2023-01-11T22:11:01.6343496Z shell: /usr/bin/bash -e {0}
2023-01-11T22:11:01.6343684Z env:
2023-01-11T22:11:01.6343850Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:01.6344142Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:11:01.6344409Z ##[endgroup]
2023-01-11T22:11:03.6506076Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:11:03.6506563Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:11:03.6507089Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:11:03.6507415Z This is free software: you are free to change and redistribute it.
2023-01-11T22:11:03.6507773Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:11:03.6508035Z and "show warranty" for details.
2023-01-11T22:11:03.6508309Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:11:03.6508566Z Type "show configuration" for configuration details.
2023-01-11T22:11:03.6508822Z For bug reporting instructions, please see:
2023-01-11T22:11:03.6509051Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:11:03.6509316Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:11:03.6509780Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:11:03.6509990Z For help, type "help".
2023-01-11T22:11:03.6510229Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:11:03.8372099Z Reading symbols from python...done.
2023-01-11T22:11:04.3806246Z 
2023-01-11T22:11:04.3806701Z warning: core file may not match specified executable file.
2023-01-11T22:11:04.4791180Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:04.4791713Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:04.4792079Z [New LWP 8051]
2023-01-11T22:11:04.4792270Z [New LWP 8055]
2023-01-11T22:11:04.4792439Z [New LWP 8057]
2023-01-11T22:11:04.4792592Z [New LWP 8054]
2023-01-11T22:11:04.4792755Z [New LWP 8056]
2023-01-11T22:11:04.4792919Z [New LWP 8060]
2023-01-11T22:11:04.4793078Z [New LWP 8058]
2023-01-11T22:11:04.4793246Z [New LWP 8059]
2023-01-11T22:11:04.4794053Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:04.4794403Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:04.4794755Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:04.4795102Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:04.4896900Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:04.4897520Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:04.4933692Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:04.4934378Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:04.5321547Z [Thread debugging using libthread_db enabled]
2023-01-11T22:11:04.5322167Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:11:11.8977777Z 51	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
2023-01-11T22:11:11.8978783Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:11:11.8979303Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:11:11.8979647Z Program terminated with signal SIGSEGV, Segmentation fault.
2023-01-11T22:11:11.8979941Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:11:11.8980215Z [Current thread is 1 (Thread 0x7f0970aaf200 (LWP 8051))]
2023-01-11T22:11:11.9080757Z To enable execution of this file add
2023-01-11T22:11:11.9081384Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:11:11.9081682Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:11.9081956Z To completely disable this security protection add
2023-01-11T22:11:11.9082224Z 	set auto-load safe-path /
2023-01-11T22:11:11.9082468Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:11.9082740Z For more information about this security protection see the
2023-01-11T22:11:11.9083101Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:11:11.9083401Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:11:11.9153276Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:11:11.9155892Z #1  0x00007f093914065b in handler_SIGSEGV(int, siginfo_t*, void*) ()
2023-01-11T22:11:11.9156301Z    from /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so
2023-01-11T22:11:11.9165252Z #2  <signal handler called>
2023-01-11T22:11:11.9165624Z #3  __strlen_avx2 () at ../sysdeps/x86_64/multiarch/strlen-avx2.S:65
2023-01-11T22:11:11.9318921Z #4  0x00007f096f56d03b in string_at (ptr=0x0, size=-1) at <artificial>:5564
2023-01-11T22:11:11.9321177Z #5  0x00007f0970964052 in ffi_call_unix64 ()
2023-01-11T22:11:11.9330375Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:11:11.9330686Z #6  0x00007f09709628cd in ffi_call_int ()
2023-01-11T22:11:11.9331001Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:11:11.9338282Z #7  0x00007f096f575879 in _call_function_pointer (argtypecount=2, argcount=2, 
2023-01-11T22:11:11.9338865Z     resmem=0x7ffd26424980, restype=<optimized out>, atypes=<optimized out>, 
2023-01-11T22:11:11.9339234Z     avalues=<optimized out>, pProc=0x7f096f56d002 <string_at>, flags=4357)
2023-01-11T22:11:11.9339645Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:916
2023-01-11T22:11:11.9339882Z #8  _ctypes_callproc ()
2023-01-11T22:11:11.9340190Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:1259
2023-01-11T22:11:11.9340560Z #9  0x00007f096f5753fe in PyCFuncPtr_call () at <artificial>:4201
2023-01-11T22:11:11.9341472Z #10 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:11.9341881Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:11.9375179Z #11 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:11:11.9375584Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9376012Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7f08c996c358, 
2023-01-11T22:11:11.9376397Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9376762Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9377155Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:11.9379950Z #12 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:11.9380243Z     args=0x7f08c996c358, callable=0x7f096f824880, tstate=<optimized out>)
2023-01-11T22:11:11.9380605Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:11.9385134Z #13 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:11.9385442Z     args=0x7f08c996c358, callable=0x7f096f824880)
2023-01-11T22:11:11.9385790Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9389358Z #14 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:11.9389741Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd26424c90, 
2023-01-11T22:11:11.9389981Z     tstate=<optimized out>)
2023-01-11T22:11:11.9390292Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:11.9390528Z #15 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9390833Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:11.9402852Z #16 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:11.9403287Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9403651Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9404005Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9404385Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9409711Z #17 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:11.9410335Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:11.9411043Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:11.9411438Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f096f7ef890, 
2023-01-11T22:11:11.9411823Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f2cbf0, 
2023-01-11T22:11:11.9412213Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9412610Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9412851Z #18 _PyFunction_Vectorcall ()
2023-01-11T22:11:11.9413142Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9419033Z #19 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:11.9419362Z     nargsf=<optimized out>, args=0x7f08c996c1c0, callable=0x7f096f7ef880, 
2023-01-11T22:11:11.9419599Z     tstate=0x1f2cbf0)
2023-01-11T22:11:11.9419898Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9424062Z #20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:11.9424377Z     args=0x7f08c996c1c0, callable=0x7f096f7ef880)
2023-01-11T22:11:11.9424751Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9427296Z #21 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:11.9427695Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd26424e50, 
2023-01-11T22:11:11.9427951Z     tstate=<optimized out>)
2023-01-11T22:11:11.9428270Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:11.9428502Z #22 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9428805Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:11:11.9441915Z #23 0x0000000000543a33 in _PyEval_EvalFrame (
2023-01-11T22:11:11.9442314Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9442683Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9443037Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9443425Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9448479Z #24 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:11:11.9449504Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9449977Z     args=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, con=0x7f08c9a90680, tstate=0x1f2cbf0)
2023-01-11T22:11:11.9450402Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9452322Z #25 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:11.9452809Z     stack=<optimized out>, func=0x7f08c9a90670)
2023-01-11T22:11:11.9453288Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9458306Z #26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:11.9458837Z     args=<optimized out>, callable=0x7f08c9a90670, tstate=0x1f2cbf0)
2023-01-11T22:11:11.9459384Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:11.9459756Z #27 vectorcall_unbound (
2023-01-11T22:11:11.9460162Z     nargs=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:11:11.9460785Z     func=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9461350Z     unbound=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9461711Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9462211Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1629
2023-01-11T22:11:11.9462447Z #28 vectorcall_method ()
2023-01-11T22:11:11.9462823Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1661
2023-01-11T22:11:11.9463459Z #29 0x0000000000543898 in slot_mp_subscript (self=<optimized out>, 
2023-01-11T22:11:11.9463856Z     arg1=<optimized out>)
2023-01-11T22:11:11.9464345Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7258
2023-01-11T22:11:11.9464789Z #30 0x00000000004ef56e in _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9465320Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:2109
2023-01-11T22:11:11.9467518Z #31 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:11.9468066Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9468527Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9468887Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9469270Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9474524Z #32 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:11.9475152Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:11.9475714Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:11.9476115Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f08ca1e8170, 
2023-01-11T22:11:11.9476514Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f2cbf0, 
2023-01-11T22:11:11.9476887Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9477281Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9477517Z #33 _PyFunction_Vectorcall ()
2023-01-11T22:11:11.9477801Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9482888Z #34 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:11.9483222Z     nargsf=<optimized out>, args=0x7f08c9cf2a48, callable=0x7f08ca1e8160, 
2023-01-11T22:11:11.9483460Z     tstate=0x1f2cbf0)
2023-01-11T22:11:11.9483840Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9487937Z #35 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:11.9488227Z     args=0x7f08c9cf2a48, callable=0x7f08ca1e8160)
2023-01-11T22:11:11.9488570Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9491581Z #36 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:11.9491906Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd26425250, 
2023-01-11T22:11:11.9492140Z     tstate=<optimized out>)
2023-01-11T22:11:11.9492434Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:11.9492678Z #37 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9493022Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:11.9493408Z #38 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:11.9493761Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9494124Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9494481Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9494845Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9500190Z #39 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:11.9500951Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:11.9501715Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:11.9502101Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f08cbef9400, 
2023-01-11T22:11:11.9502502Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f2cbf0, 
2023-01-11T22:11:11.9502957Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9503345Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9503570Z #40 _PyFunction_Vectorcall ()
2023-01-11T22:11:11.9503866Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9508749Z #41 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:11.9509205Z     nargsf=<optimized out>, args=0x6ee0f48, callable=0x7f08cbef93f0, 
2023-01-11T22:11:11.9509614Z     tstate=0x1f2cbf0)
2023-01-11T22:11:11.9509934Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9513550Z #42 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x6ee0f48, 
2023-01-11T22:11:11.9514000Z     callable=0x7f08cbef93f0)
2023-01-11T22:11:11.9514445Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9517394Z #43 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:11.9517845Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd26425410, 
2023-01-11T22:11:11.9518265Z     tstate=<optimized out>)
2023-01-11T22:11:11.9518632Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:11.9518909Z #44 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9519205Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:11.9519516Z #45 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:11.9520045Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9520549Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9520902Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9521375Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9526190Z #46 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:11.9526764Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:11.9527481Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:11.9527886Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f08cbef8710, 
2023-01-11T22:11:11.9528286Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f2cbf0, 
2023-01-11T22:11:11.9528655Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9529157Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9529403Z #47 _PyFunction_Vectorcall ()
2023-01-11T22:11:11.9529692Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9535018Z #48 0x00000000004f141c in do_call_core (kwdict=0x7f096f936780, 
2023-01-11T22:11:11.9535500Z     callargs=0x7f08c9bea520, func=0x7f08cbef8700, trace_info=0x7ffd264255d0, 
2023-01-11T22:11:11.9535896Z     tstate=<optimized out>)
2023-01-11T22:11:11.9536190Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:11.9536520Z #49 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9536820Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:11.9537092Z #50 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:11.9537616Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9538136Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9538494Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9538857Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9543833Z #51 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:11.9544396Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:11.9545091Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:11.9545474Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f096f9615b0, 
2023-01-11T22:11:11.9545972Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f2cbf0, 
2023-01-11T22:11:11.9546487Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9547217Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9547479Z #52 _PyFunction_Vectorcall ()
2023-01-11T22:11:11.9547782Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9552944Z #53 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:11.9553418Z     nargsf=<optimized out>, args=0x6e539b8, callable=0x7f096f9615a0, 
2023-01-11T22:11:11.9553824Z     tstate=0x1f2cbf0)
2023-01-11T22:11:11.9554138Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9557808Z #54 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x6e539b8, 
2023-01-11T22:11:11.9558244Z     callable=0x7f096f9615a0)
2023-01-11T22:11:11.9558712Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9561352Z #55 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:11.9561925Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd26425790, 
2023-01-11T22:11:11.9562321Z     tstate=<optimized out>)
2023-01-11T22:11:11.9562640Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:11.9562926Z #56 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9563220Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:11.9563742Z #57 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:11.9564332Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9564786Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9565135Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9565563Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9571047Z #58 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:11.9571665Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:11.9572296Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:11.9572694Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f096f961f40, 
2023-01-11T22:11:11.9573096Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f2cbf0, 
2023-01-11T22:11:11.9573591Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9573988Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9574227Z #59 _PyFunction_Vectorcall ()
2023-01-11T22:11:11.9574526Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9579524Z #60 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:11.9580010Z     nargsf=<optimized out>, args=0x1faa240, callable=0x7f096f961f30, 
2023-01-11T22:11:11.9580381Z     tstate=0x1f2cbf0)
2023-01-11T22:11:11.9580698Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9584419Z #61 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x1faa240, 
2023-01-11T22:11:11.9584880Z     callable=0x7f096f961f30)
2023-01-11T22:11:11.9585344Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9587956Z #62 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:11.9588409Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd26425950, 
2023-01-11T22:11:11.9588833Z     tstate=<optimized out>)
2023-01-11T22:11:11.9589183Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:11.9589459Z #63 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9589764Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:11.9590201Z #64 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:11.9590551Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9590913Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9591271Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9591648Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9597182Z #65 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:11.9597788Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:11.9598423Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:11.9598924Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f096f7e6de0, 
2023-01-11T22:11:11.9599312Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f2cbf0, 
2023-01-11T22:11:11.9599694Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9600080Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9600319Z #66 _PyFunction_Vectorcall ()
2023-01-11T22:11:11.9600604Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9605980Z #67 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:11.9606496Z     nargsf=<optimized out>, args=0x7f096faa8200, callable=0x7f096f7e6dd0, 
2023-01-11T22:11:11.9606851Z     tstate=0x1f2cbf0)
2023-01-11T22:11:11.9607161Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9611158Z #68 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:11.9611441Z     args=0x7f096faa8200, callable=0x7f096f7e6dd0)
2023-01-11T22:11:11.9611765Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9614657Z #69 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:11.9615918Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd26425b10, 
2023-01-11T22:11:11.9616163Z     tstate=<optimized out>)
2023-01-11T22:11:11.9616629Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:11.9617032Z #70 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9617596Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:11.9617849Z #71 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:11.9618154Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9618516Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9618858Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9619235Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:11.9623557Z #72 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:11.9624130Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:11.9624880Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:11.9625279Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f096f7e6d50, 
2023-01-11T22:11:11.9625679Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1f2cbf0, 
2023-01-11T22:11:11.9626062Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9626442Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9626677Z #73 _PyFunction_Vectorcall ()
2023-01-11T22:11:11.9626973Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:11.9631144Z #74 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7f096fa81e40, 
2023-01-11T22:11:11.9631680Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7f096f7e6d40, 
2023-01-11T22:11:11.9632041Z     tstate=0x1f2cbf0)
2023-01-11T22:11:11.9632354Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9634790Z #75 PyObject_Vectorcall (kwnames=0x7f096fa81e40, nargsf=<optimized out>, 
2023-01-11T22:11:11.9635272Z     args=<optimized out>, callable=0x7f096f7e6d40)
2023-01-11T22:11:11.9635775Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:11.9638690Z #76 call_function (kwnames=0x7f096fa81e40, oparg=<optimized out>, 
2023-01-11T22:11:11.9639181Z     pp_stack=<synthetic pointer>, trace_info=0x7ffd26425cd0, 
2023-01-11T22:11:11.9639583Z     tstate=<optimized out>)
2023-01-11T22:11:11.9639896Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:11.9640126Z #77 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:11.9640430Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:11.9641691Z #78 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:11:11.9642166Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9642802Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:11.9643156Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9643611Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:11:11.9643871Z #79 _PyEval_Vector ()
2023-01-11T22:11:11.9644161Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:11.9650356Z #80 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7f096fa44920, 
2023-01-11T22:11:11.9650950Z     globals=globals@entry=0x7f096fa36600, locals=locals@entry=0x7f096fa36600)
2023-01-11T22:11:11.9651572Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:11:11.9652377Z #81 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:11:11.9652974Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:11:11.9654897Z #82 0x00000000005c1d40 in run_mod ()
2023-01-11T22:11:11.9655464Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:11:11.9657435Z #83 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:11:11.9658084Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:11:11.9660010Z #84 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:11:11.9660675Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:11:11.9670337Z #85 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:11:11.9670897Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:11.9671407Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:11:11.9672976Z #86 pymain_run_python (exitcode=0x7ffd26425f30)
2023-01-11T22:11:11.9673579Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:11:11.9673938Z #87 Py_RunMain.localalias ()
2023-01-11T22:11:11.9674252Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:11:11.9689613Z #88 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:11:11.9690022Z     argv=<optimized out>)
2023-01-11T22:11:11.9690470Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:11:11.9695337Z #89 0x00007f096fb13c87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:11:11.9695856Z     argv=0x7ffd26426138, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:11:11.9696214Z     rtld_fini=<optimized out>, stack_end=0x7ffd26426128)
2023-01-11T22:11:11.9696616Z     at ../csu/libc-start.c:310
2023-01-11T22:11:11.9696950Z #90 0x0000000000587ade in _start ()
2023-01-11T22:11:11.9697541Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:11:12.1271130Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:11:12.1271653Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:11:12.1272196Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:11:12.1272729Z This is free software: you are free to change and redistribute it.
2023-01-11T22:11:12.1273195Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:11:12.1273817Z and "show warranty" for details.
2023-01-11T22:11:12.1274291Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:11:12.1274682Z Type "show configuration" for configuration details.
2023-01-11T22:11:12.1275084Z For bug reporting instructions, please see:
2023-01-11T22:11:12.1275480Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:11:12.1275877Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:11:12.1276305Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:11:12.1276674Z For help, type "help".
2023-01-11T22:11:12.1277041Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:11:12.2407814Z Reading symbols from python...done.
2023-01-11T22:11:12.7714498Z 
2023-01-11T22:11:12.7714827Z warning: core file may not match specified executable file.
2023-01-11T22:11:12.8663741Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:12.8664247Z [New LWP 8050]
2023-01-11T22:11:12.8664586Z [New LWP 8064]
2023-01-11T22:11:12.8664835Z [New LWP 8061]
2023-01-11T22:11:12.8665137Z [New LWP 8062]
2023-01-11T22:11:12.8665414Z [New LWP 8067]
2023-01-11T22:11:12.8665707Z [New LWP 8065]
2023-01-11T22:11:12.8665996Z [New LWP 8066]
2023-01-11T22:11:12.8666277Z [New LWP 8063]
2023-01-11T22:11:12.8667220Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:12.8668126Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:12.8669272Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:12.8670135Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:12.8671297Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:12.8682591Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:12.8683620Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:12.8684668Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:12.8685680Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:12.8987190Z [Thread debugging using libthread_db enabled]
2023-01-11T22:11:12.8987888Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:11:19.7339877Z 51	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
2023-01-11T22:11:19.7340728Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:11:19.7341236Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:11:19.7341582Z Program terminated with signal SIGSEGV, Segmentation fault.
2023-01-11T22:11:19.7341873Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:11:19.7342148Z [Current thread is 1 (Thread 0x7fb0efc43200 (LWP 8050))]
2023-01-11T22:11:19.7369430Z To enable execution of this file add
2023-01-11T22:11:19.7369934Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:11:19.7370228Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:19.7370478Z To completely disable this security protection add
2023-01-11T22:11:19.7370745Z 	set auto-load safe-path /
2023-01-11T22:11:19.7370995Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:19.7371447Z For more information about this security protection see the
2023-01-11T22:11:19.7371796Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:11:19.7372102Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:11:19.7413909Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:11:19.7416405Z #1  0x00007fb0b82d465b in handler_SIGSEGV(int, siginfo_t*, void*) ()
2023-01-11T22:11:19.7416895Z    from /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so
2023-01-11T22:11:19.7425573Z #2  <signal handler called>
2023-01-11T22:11:19.7425914Z #3  __strlen_avx2 () at ../sysdeps/x86_64/multiarch/strlen-avx2.S:65
2023-01-11T22:11:19.7581496Z #4  0x00007fb0ee70103b in string_at (ptr=0x0, size=-1) at <artificial>:5564
2023-01-11T22:11:19.7583946Z #5  0x00007fb0efaf8052 in ffi_call_unix64 ()
2023-01-11T22:11:19.7584289Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:11:19.7592617Z #6  0x00007fb0efaf68cd in ffi_call_int ()
2023-01-11T22:11:19.7593014Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:11:19.7600186Z #7  0x00007fb0ee709879 in _call_function_pointer (argtypecount=2, argcount=2, 
2023-01-11T22:11:19.7600607Z     resmem=0x7ffde41f1550, restype=<optimized out>, atypes=<optimized out>, 
2023-01-11T22:11:19.7600906Z     avalues=<optimized out>, pProc=0x7fb0ee701002 <string_at>, flags=4357)
2023-01-11T22:11:19.7601280Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:916
2023-01-11T22:11:19.7601702Z #8  _ctypes_callproc ()
2023-01-11T22:11:19.7602015Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:1259
2023-01-11T22:11:19.7602290Z #9  0x00007fb0ee7093fe in PyCFuncPtr_call () at <artificial>:4201
2023-01-11T22:11:19.7603594Z #10 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:19.7604001Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:19.7608720Z #11 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:11:19.7609360Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7609763Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fb048b00358, 
2023-01-11T22:11:19.7610153Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7610515Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7610939Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:19.7613234Z #12 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:19.7613620Z     args=0x7fb048b00358, callable=0x7fb0ee9b8880, tstate=<optimized out>)
2023-01-11T22:11:19.7613976Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:19.7617828Z #13 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:19.7618364Z     args=0x7fb048b00358, callable=0x7fb0ee9b8880)
2023-01-11T22:11:19.7618767Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7621429Z #14 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:19.7621901Z     pp_stack=<synthetic pointer>, trace_info=0x7ffde41f1860, 
2023-01-11T22:11:19.7622361Z     tstate=<optimized out>)
2023-01-11T22:11:19.7622839Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:19.7623243Z #15 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7623735Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:19.7624166Z #16 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:19.7624730Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7625112Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7625615Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7625996Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7630142Z #17 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:19.7630788Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:19.7631595Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:19.7632192Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fb0ee983890, 
2023-01-11T22:11:19.7632592Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1020bf0, 
2023-01-11T22:11:19.7632978Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7633442Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7633744Z #18 _PyFunction_Vectorcall ()
2023-01-11T22:11:19.7634181Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7638018Z #19 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:19.7638582Z     nargsf=<optimized out>, args=0x7fb048b001c0, callable=0x7fb0ee983880, 
2023-01-11T22:11:19.7638993Z     tstate=0x1020bf0)
2023-01-11T22:11:19.7639514Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7642222Z #20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:19.7642810Z     args=0x7fb048b001c0, callable=0x7fb0ee983880)
2023-01-11T22:11:19.7643339Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7645298Z #21 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:19.7645821Z     pp_stack=<synthetic pointer>, trace_info=0x7ffde41f1a20, 
2023-01-11T22:11:19.7646375Z     tstate=<optimized out>)
2023-01-11T22:11:19.7646923Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:19.7647226Z #22 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7647665Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:11:19.7648238Z #23 0x0000000000543a33 in _PyEval_EvalFrame (
2023-01-11T22:11:19.7648791Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7649332Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7649742Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7650196Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7655113Z #24 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:11:19.7655740Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7656398Z     args=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, con=0x7fb048c28680, tstate=0x1020bf0)
2023-01-11T22:11:19.7656897Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7657935Z #25 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:19.7658498Z     stack=<optimized out>, func=0x7fb048c28670)
2023-01-11T22:11:19.7659024Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7663523Z #26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:19.7664213Z     args=<optimized out>, callable=0x7fb048c28670, tstate=0x1020bf0)
2023-01-11T22:11:19.7664883Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:19.7665393Z #27 vectorcall_unbound (
2023-01-11T22:11:19.7666041Z     nargs=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:11:19.7666492Z     func=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7666941Z     unbound=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7667372Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7667783Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1629
2023-01-11T22:11:19.7668144Z #28 vectorcall_method ()
2023-01-11T22:11:19.7668606Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1661
2023-01-11T22:11:19.7669021Z #29 0x0000000000543898 in slot_mp_subscript (self=<optimized out>, 
2023-01-11T22:11:19.7669371Z     arg1=<optimized out>)
2023-01-11T22:11:19.7669741Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7258
2023-01-11T22:11:19.7670066Z #30 0x00000000004ef56e in _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7670406Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:2109
2023-01-11T22:11:19.7672255Z #31 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:19.7672990Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7673564Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7673940Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7674614Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7678659Z #32 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:19.7679147Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:19.7679660Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:19.7680156Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fb049380170, 
2023-01-11T22:11:19.7680618Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1020bf0, 
2023-01-11T22:11:19.7681016Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7681489Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7681789Z #33 _PyFunction_Vectorcall ()
2023-01-11T22:11:19.7682246Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7686993Z #34 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:19.7687613Z     nargsf=<optimized out>, args=0x7fb048e86a48, callable=0x7fb049380160, 
2023-01-11T22:11:19.7687983Z     tstate=0x1020bf0)
2023-01-11T22:11:19.7688352Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7691619Z #35 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:19.7692147Z     args=0x7fb048e86a48, callable=0x7fb049380160)
2023-01-11T22:11:19.7692855Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7694948Z #36 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:19.7695629Z     pp_stack=<synthetic pointer>, trace_info=0x7ffde41f1e20, 
2023-01-11T22:11:19.7710818Z     tstate=<optimized out>)
2023-01-11T22:11:19.7711393Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:19.7711835Z #37 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7712318Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:19.7712594Z #38 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:19.7712895Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7713381Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7713744Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7714127Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7714429Z #39 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:19.7714891Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:19.7715440Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:19.7715842Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fb04b08d400, 
2023-01-11T22:11:19.7716236Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1020bf0, 
2023-01-11T22:11:19.7716622Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7717010Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7717234Z #40 _PyFunction_Vectorcall ()
2023-01-11T22:11:19.7717544Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7717943Z #41 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:19.7718662Z     nargsf=<optimized out>, args=0x5fd4638, callable=0x7fb04b08d3f0, 
2023-01-11T22:11:19.7718983Z     tstate=0x1020bf0)
2023-01-11T22:11:19.7719293Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7719612Z #42 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x5fd4638, 
2023-01-11T22:11:19.7719919Z     callable=0x7fb04b08d3f0)
2023-01-11T22:11:19.7720420Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7720936Z #43 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:19.7721322Z     pp_stack=<synthetic pointer>, trace_info=0x7ffde41f1fe0, 
2023-01-11T22:11:19.7721540Z     tstate=<optimized out>)
2023-01-11T22:11:19.7721840Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:19.7722084Z #44 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7722374Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:19.7722633Z #45 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:19.7722937Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7723282Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7723638Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7724016Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7726737Z #46 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:19.7727382Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:19.7727979Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:19.7728379Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fb04b08c710, 
2023-01-11T22:11:19.7728782Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1020bf0, 
2023-01-11T22:11:19.7729299Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7729707Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7729947Z #47 _PyFunction_Vectorcall ()
2023-01-11T22:11:19.7730325Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7734567Z #48 0x00000000004f141c in do_call_core (kwdict=0x7fb0eeaca780, 
2023-01-11T22:11:19.7735059Z     callargs=0x7fb048d7e520, func=0x7fb04b08c700, trace_info=0x7ffde41f21a0, 
2023-01-11T22:11:19.7735494Z     tstate=<optimized out>)
2023-01-11T22:11:19.7735847Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:19.7736088Z #49 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7736578Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:19.7736971Z #50 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:19.7737530Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7737926Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7738281Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7738647Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7743104Z #51 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:19.7743744Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:19.7744341Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:19.7744823Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fb0eeaf55b0, 
2023-01-11T22:11:19.7745225Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1020bf0, 
2023-01-11T22:11:19.7745610Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7746008Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7746231Z #52 _PyFunction_Vectorcall ()
2023-01-11T22:11:19.7746532Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7750940Z #53 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:19.7751418Z     nargsf=<optimized out>, args=0x5f47028, callable=0x7fb0eeaf55a0, 
2023-01-11T22:11:19.7751828Z     tstate=0x1020bf0)
2023-01-11T22:11:19.7752161Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7755568Z #54 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x5f47028, 
2023-01-11T22:11:19.7755868Z     callable=0x7fb0eeaf55a0)
2023-01-11T22:11:19.7756195Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7758441Z #55 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:19.7758882Z     pp_stack=<synthetic pointer>, trace_info=0x7ffde41f2360, 
2023-01-11T22:11:19.7759306Z     tstate=<optimized out>)
2023-01-11T22:11:19.7759739Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:19.7760061Z #56 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7760393Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:19.7760844Z #57 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:19.7761316Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7761666Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7762015Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7762392Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7766325Z #58 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:19.7767205Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:19.7767721Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:19.7768126Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fb0eeaf5f40, 
2023-01-11T22:11:19.7768529Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1020bf0, 
2023-01-11T22:11:19.7768902Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7769417Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7769656Z #59 _PyFunction_Vectorcall ()
2023-01-11T22:11:19.7769940Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7774283Z #60 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:19.7774889Z     nargsf=<optimized out>, args=0x109d5a0, callable=0x7fb0eeaf5f30, 
2023-01-11T22:11:19.7775264Z     tstate=0x1020bf0)
2023-01-11T22:11:19.7775562Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7778750Z #61 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x109d5a0, 
2023-01-11T22:11:19.7779194Z     callable=0x7fb0eeaf5f30)
2023-01-11T22:11:19.7779682Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7781731Z #62 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:19.7782331Z     pp_stack=<synthetic pointer>, trace_info=0x7ffde41f2520, 
2023-01-11T22:11:19.7782778Z     tstate=<optimized out>)
2023-01-11T22:11:19.7783205Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:19.7783452Z #63 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7783887Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:19.7784138Z #64 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:19.7784439Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7784793Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7785150Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7785512Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7789662Z #65 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:19.7790224Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:19.7790874Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:19.7791262Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fb0ee97ade0, 
2023-01-11T22:11:19.7791661Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1020bf0, 
2023-01-11T22:11:19.7792047Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7792424Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7792665Z #66 _PyFunction_Vectorcall ()
2023-01-11T22:11:19.7792963Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7797400Z #67 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:19.7797688Z     nargsf=<optimized out>, args=0x7fb0eec3c200, callable=0x7fb0ee97add0, 
2023-01-11T22:11:19.7797926Z     tstate=0x1020bf0)
2023-01-11T22:11:19.7798239Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7801692Z #68 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:19.7802146Z     args=0x7fb0eec3c200, callable=0x7fb0ee97add0)
2023-01-11T22:11:19.7802494Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7804739Z #69 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:19.7805138Z     pp_stack=<synthetic pointer>, trace_info=0x7ffde41f26e0, 
2023-01-11T22:11:19.7805376Z     tstate=<optimized out>)
2023-01-11T22:11:19.7805686Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:19.7805979Z #70 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7806319Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:19.7806818Z #71 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:19.7807189Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7807559Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7807929Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7808336Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:19.7813163Z #72 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:19.7813976Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:19.7814559Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:19.7814950Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fb0ee97ad50, 
2023-01-11T22:11:19.7815353Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1020bf0, 
2023-01-11T22:11:19.7815736Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7816133Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7816375Z #73 _PyFunction_Vectorcall ()
2023-01-11T22:11:19.7816679Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:19.7819587Z #74 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7fb0eec15e40, 
2023-01-11T22:11:19.7820008Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7fb0ee97ad40, 
2023-01-11T22:11:19.7820259Z     tstate=0x1020bf0)
2023-01-11T22:11:19.7820576Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7822766Z #75 PyObject_Vectorcall (kwnames=0x7fb0eec15e40, nargsf=<optimized out>, 
2023-01-11T22:11:19.7823125Z     args=<optimized out>, callable=0x7fb0ee97ad40)
2023-01-11T22:11:19.7823473Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:19.7826060Z #76 call_function (kwnames=0x7fb0eec15e40, oparg=<optimized out>, 
2023-01-11T22:11:19.7826456Z     pp_stack=<synthetic pointer>, trace_info=0x7ffde41f28a0, 
2023-01-11T22:11:19.7826699Z     tstate=<optimized out>)
2023-01-11T22:11:19.7827005Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:19.7827235Z #77 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:19.7827539Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:19.7828798Z #78 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:11:19.7829223Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7829570Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:19.7829927Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7830382Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:11:19.7830653Z #79 _PyEval_Vector ()
2023-01-11T22:11:19.7831060Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:19.7836685Z #80 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7fb0eebd8920, 
2023-01-11T22:11:19.7837081Z     globals=globals@entry=0x7fb0eebca600, locals=locals@entry=0x7fb0eebca600)
2023-01-11T22:11:19.7837428Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:11:19.7838829Z #81 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:11:19.7839232Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:11:19.7841514Z #82 0x00000000005c1d40 in run_mod ()
2023-01-11T22:11:19.7841871Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:11:19.7843925Z #83 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:11:19.7844286Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:11:19.7846408Z #84 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:11:19.7846789Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:11:19.7849187Z #85 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:11:19.7849588Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:19.7849983Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:11:19.7851817Z #86 pymain_run_python (exitcode=0x7ffde41f2b00)
2023-01-11T22:11:19.7852301Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:11:19.7852555Z #87 Py_RunMain.localalias ()
2023-01-11T22:11:19.7852855Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:11:19.7868228Z #88 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:11:19.7868560Z     argv=<optimized out>)
2023-01-11T22:11:19.7869100Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:11:19.7873717Z #89 0x00007fb0eeca7c87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:11:19.7874222Z     argv=0x7ffde41f2d08, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:11:19.7874670Z     rtld_fini=<optimized out>, stack_end=0x7ffde41f2cf8)
2023-01-11T22:11:19.7874928Z     at ../csu/libc-start.c:310
2023-01-11T22:11:19.7875337Z #90 0x0000000000587ade in _start ()
2023-01-11T22:11:19.7875907Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:11:19.9519880Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:11:19.9520409Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:11:19.9520781Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:11:19.9521222Z This is free software: you are free to change and redistribute it.
2023-01-11T22:11:19.9521516Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:11:19.9521774Z and "show warranty" for details.
2023-01-11T22:11:19.9522085Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:11:19.9522328Z Type "show configuration" for configuration details.
2023-01-11T22:11:19.9522586Z For bug reporting instructions, please see:
2023-01-11T22:11:19.9522832Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:11:19.9523090Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:11:19.9523372Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:11:19.9523603Z For help, type "help".
2023-01-11T22:11:19.9523833Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:11:20.0654521Z Reading symbols from python...done.
2023-01-11T22:11:20.6009480Z 
2023-01-11T22:11:20.6009812Z warning: core file may not match specified executable file.
2023-01-11T22:11:20.6969230Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:20.6970083Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:20.6970538Z [New LWP 10536]
2023-01-11T22:11:20.6970809Z [New LWP 10540]
2023-01-11T22:11:20.6971313Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:20.6971954Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:20.6972297Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:20.6972563Z [New LWP 10543]
2023-01-11T22:11:20.6972737Z [New LWP 10541]
2023-01-11T22:11:20.6972895Z [New LWP 10542]
2023-01-11T22:11:20.6973064Z [New LWP 10546]
2023-01-11T22:11:20.6973235Z [New LWP 10544]
2023-01-11T22:11:20.6973389Z [New LWP 10545]
2023-01-11T22:11:20.6973654Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:20.6985922Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:20.6986863Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:20.6987409Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:20.6987932Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:20.7275511Z [Thread debugging using libthread_db enabled]
2023-01-11T22:11:20.7275953Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:11:27.5740427Z 51	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
2023-01-11T22:11:27.5741065Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:11:27.5741585Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:11:27.5741936Z Program terminated with signal SIGSEGV, Segmentation fault.
2023-01-11T22:11:27.5742240Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:11:27.5742503Z [Current thread is 1 (Thread 0x7fe0534ba200 (LWP 10536))]
2023-01-11T22:11:27.5770408Z To enable execution of this file add
2023-01-11T22:11:27.5770880Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:11:27.5771181Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:27.5771453Z To completely disable this security protection add
2023-01-11T22:11:27.5771731Z 	set auto-load safe-path /
2023-01-11T22:11:27.5771967Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:27.5772244Z For more information about this security protection see the
2023-01-11T22:11:27.5772611Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:11:27.5772915Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:11:27.5815562Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:11:27.5819080Z #1  0x00007fe01bb4b65b in handler_SIGSEGV(int, siginfo_t*, void*) ()
2023-01-11T22:11:27.5819848Z    from /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so
2023-01-11T22:11:27.5829200Z #2  <signal handler called>
2023-01-11T22:11:27.5829595Z #3  __strlen_avx2 () at ../sysdeps/x86_64/multiarch/strlen-avx2.S:65
2023-01-11T22:11:27.5977347Z #4  0x00007fe051f7803b in string_at (ptr=0x0, size=-1) at <artificial>:5564
2023-01-11T22:11:27.5979847Z #5  0x00007fe05336f052 in ffi_call_unix64 ()
2023-01-11T22:11:27.5980499Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:11:27.5989470Z #6  0x00007fe05336d8cd in ffi_call_int ()
2023-01-11T22:11:27.5990386Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:11:27.5997961Z #7  0x00007fe051f80879 in _call_function_pointer (argtypecount=2, argcount=2, 
2023-01-11T22:11:27.5998472Z     resmem=0x7ffda6694940, restype=<optimized out>, atypes=<optimized out>, 
2023-01-11T22:11:27.5998914Z     avalues=<optimized out>, pProc=0x7fe051f78002 <string_at>, flags=4357)
2023-01-11T22:11:27.5999498Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:916
2023-01-11T22:11:27.5999844Z #8  _ctypes_callproc ()
2023-01-11T22:11:27.6000360Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:1259
2023-01-11T22:11:27.6000817Z #9  0x00007fe051f803fe in PyCFuncPtr_call () at <artificial>:4201
2023-01-11T22:11:27.6001589Z #10 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:27.6002156Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:27.6007164Z #11 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:11:27.6007743Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6008270Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7fdfac378358, 
2023-01-11T22:11:27.6008659Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6009171Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6009763Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:27.6012411Z #12 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:27.6012948Z     args=0x7fdfac378358, callable=0x7fe052234880, tstate=<optimized out>)
2023-01-11T22:11:27.6013431Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:27.6017266Z #13 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:27.6017721Z     args=0x7fdfac378358, callable=0x7fe052234880)
2023-01-11T22:11:27.6018248Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6021736Z #14 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:27.6022128Z     pp_stack=<synthetic pointer>, trace_info=0x7ffda6694c50, 
2023-01-11T22:11:27.6022384Z     tstate=<optimized out>)
2023-01-11T22:11:27.6022759Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:27.6022992Z #15 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6023308Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:27.6024275Z #16 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:27.6024618Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6024988Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6025349Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6025871Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6030947Z #17 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:27.6031551Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:27.6032285Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:27.6032694Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fe0521f3890, 
2023-01-11T22:11:27.6033079Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2393bf0, 
2023-01-11T22:11:27.6033469Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6033976Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6034222Z #18 _PyFunction_Vectorcall ()
2023-01-11T22:11:27.6034512Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6040053Z #19 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:27.6040579Z     nargsf=<optimized out>, args=0x7fdfac3781c0, callable=0x7fe0521f3880, 
2023-01-11T22:11:27.6040942Z     tstate=0x2393bf0)
2023-01-11T22:11:27.6041252Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6044873Z #20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:27.6045340Z     args=0x7fdfac3781c0, callable=0x7fe0521f3880)
2023-01-11T22:11:27.6045832Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6048222Z #21 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:27.6048679Z     pp_stack=<synthetic pointer>, trace_info=0x7ffda6694e10, 
2023-01-11T22:11:27.6049199Z     tstate=<optimized out>)
2023-01-11T22:11:27.6049743Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:27.6049989Z #22 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6050445Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:11:27.6050885Z #23 0x0000000000543a33 in _PyEval_EvalFrame (
2023-01-11T22:11:27.6051399Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6052033Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6052391Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6052762Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6057682Z #24 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:11:27.6058201Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6058760Z     args=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, con=0x7fdfac49c680, tstate=0x2393bf0)
2023-01-11T22:11:27.6059186Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6060947Z #25 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:27.6061425Z     stack=<optimized out>, func=0x7fdfac49c670)
2023-01-11T22:11:27.6061902Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6067019Z #26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:27.6067630Z     args=<optimized out>, callable=0x7fdfac49c670, tstate=0x2393bf0)
2023-01-11T22:11:27.6068085Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:27.6068500Z #27 vectorcall_unbound (
2023-01-11T22:11:27.6068976Z     nargs=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:11:27.6069359Z     func=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6069706Z     unbound=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6070066Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6070448Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1629
2023-01-11T22:11:27.6070682Z #28 vectorcall_method ()
2023-01-11T22:11:27.6070967Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1661
2023-01-11T22:11:27.6071534Z #29 0x0000000000543898 in slot_mp_subscript (self=<optimized out>, 
2023-01-11T22:11:27.6071938Z     arg1=<optimized out>)
2023-01-11T22:11:27.6072436Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7258
2023-01-11T22:11:27.6072846Z #30 0x00000000004ef56e in _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6073607Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:2109
2023-01-11T22:11:27.6075870Z #31 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:27.6076196Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6076573Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6076928Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6077357Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6082881Z #32 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:27.6083393Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:27.6083840Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:27.6084259Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fdfacbf8170, 
2023-01-11T22:11:27.6084652Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2393bf0, 
2023-01-11T22:11:27.6085035Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6085465Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6085806Z #33 _PyFunction_Vectorcall ()
2023-01-11T22:11:27.6086111Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6091940Z #34 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:27.6092407Z     nargsf=<optimized out>, args=0x7fdfac6fea48, callable=0x7fdfacbf8160, 
2023-01-11T22:11:27.6092834Z     tstate=0x2393bf0)
2023-01-11T22:11:27.6093463Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6097285Z #35 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:27.6097750Z     args=0x7fdfac6fea48, callable=0x7fdfacbf8160)
2023-01-11T22:11:27.6098353Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6101118Z #36 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:27.6101641Z     pp_stack=<synthetic pointer>, trace_info=0x7ffda6695210, 
2023-01-11T22:11:27.6102065Z     tstate=<optimized out>)
2023-01-11T22:11:27.6102653Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:27.6103044Z #37 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6103567Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:27.6104025Z #38 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:27.6104554Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6105202Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6105889Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6106555Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6111422Z #39 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:27.6111986Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:27.6112843Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:27.6113530Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fdfae901400, 
2023-01-11T22:11:27.6114246Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2393bf0, 
2023-01-11T22:11:27.6115175Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6115880Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6116323Z #40 _PyFunction_Vectorcall ()
2023-01-11T22:11:27.6116898Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6121184Z #41 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:27.6121699Z     nargsf=<optimized out>, args=0x7346e38, callable=0x7fdfae9013f0, 
2023-01-11T22:11:27.6122038Z     tstate=0x2393bf0)
2023-01-11T22:11:27.6122664Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6126657Z #42 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7346e38, 
2023-01-11T22:11:27.6127094Z     callable=0x7fdfae9013f0)
2023-01-11T22:11:27.6127650Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6130764Z #43 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:27.6131290Z     pp_stack=<synthetic pointer>, trace_info=0x7ffda66953d0, 
2023-01-11T22:11:27.6131691Z     tstate=<optimized out>)
2023-01-11T22:11:27.6132215Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:27.6132594Z #44 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6133128Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:27.6133580Z #45 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:27.6134246Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6134949Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6135605Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6136251Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6141043Z #46 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:27.6141615Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:27.6142458Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:27.6143230Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fdfae900710, 
2023-01-11T22:11:27.6143999Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2393bf0, 
2023-01-11T22:11:27.6144711Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6145399Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6145784Z #47 _PyFunction_Vectorcall ()
2023-01-11T22:11:27.6146292Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6151006Z #48 0x00000000004f141c in do_call_core (kwdict=0x7fe05233e800, 
2023-01-11T22:11:27.6151533Z     callargs=0x7fdfac5f6520, func=0x7fdfae900700, trace_info=0x7ffda6695590, 
2023-01-11T22:11:27.6151971Z     tstate=<optimized out>)
2023-01-11T22:11:27.6152480Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:27.6152865Z #49 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6153429Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:27.6153880Z #50 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:27.6154397Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6155077Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6155771Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6156531Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6161361Z #51 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:27.6161998Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:27.6162842Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:27.6163440Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fe0523695b0, 
2023-01-11T22:11:27.6164213Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2393bf0, 
2023-01-11T22:11:27.6164913Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6165603Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6166057Z #52 _PyFunction_Vectorcall ()
2023-01-11T22:11:27.6166626Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6171083Z #53 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:27.6171561Z     nargsf=<optimized out>, args=0x72b98a8, callable=0x7fe0523695a0, 
2023-01-11T22:11:27.6171926Z     tstate=0x2393bf0)
2023-01-11T22:11:27.6172552Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6176636Z #54 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x72b98a8, 
2023-01-11T22:11:27.6177077Z     callable=0x7fe0523695a0)
2023-01-11T22:11:27.6177624Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6180551Z #55 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:27.6181044Z     pp_stack=<synthetic pointer>, trace_info=0x7ffda6695750, 
2023-01-11T22:11:27.6181456Z     tstate=<optimized out>)
2023-01-11T22:11:27.6181981Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:27.6182390Z #56 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6182958Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:27.6183419Z #57 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:27.6183967Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6184581Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6185288Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6185996Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6191052Z #58 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:27.6191652Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:27.6192498Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:27.6193142Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fe052369f40, 
2023-01-11T22:11:27.6193897Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2393bf0, 
2023-01-11T22:11:27.6194612Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6195304Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6195772Z #59 _PyFunction_Vectorcall ()
2023-01-11T22:11:27.6196319Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6200672Z #60 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:27.6201290Z     nargsf=<optimized out>, args=0x24105a0, callable=0x7fe052369f30, 
2023-01-11T22:11:27.6201691Z     tstate=0x2393bf0)
2023-01-11T22:11:27.6202300Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6206180Z #61 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x24105a0, 
2023-01-11T22:11:27.6206630Z     callable=0x7fe052369f30)
2023-01-11T22:11:27.6207166Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6210131Z #62 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:27.6210632Z     pp_stack=<synthetic pointer>, trace_info=0x7ffda6695910, 
2023-01-11T22:11:27.6211067Z     tstate=<optimized out>)
2023-01-11T22:11:27.6211566Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:27.6211963Z #63 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6212474Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:27.6212936Z #64 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:27.6213466Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6214103Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6214792Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6215484Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6220586Z #65 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:27.6221191Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:27.6222028Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:27.6222739Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fe0521eade0, 
2023-01-11T22:11:27.6223496Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2393bf0, 
2023-01-11T22:11:27.6224229Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6224915Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6225369Z #66 _PyFunction_Vectorcall ()
2023-01-11T22:11:27.6225972Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6230466Z #67 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:27.6230913Z     nargsf=<optimized out>, args=0x7fe0524b0200, callable=0x7fe0521eadd0, 
2023-01-11T22:11:27.6231290Z     tstate=0x2393bf0)
2023-01-11T22:11:27.6231917Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6235869Z #68 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:27.6236310Z     args=0x7fe0524b0200, callable=0x7fe0521eadd0)
2023-01-11T22:11:27.6236879Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6239625Z #69 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:27.6240120Z     pp_stack=<synthetic pointer>, trace_info=0x7ffda6695ad0, 
2023-01-11T22:11:27.6240511Z     tstate=<optimized out>)
2023-01-11T22:11:27.6240853Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:27.6241215Z #70 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6241571Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:27.6241887Z #71 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:27.6242196Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6242546Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6243024Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6243415Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:27.6248283Z #72 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:27.6248808Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:27.6249469Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:27.6249872Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7fe0521ead50, 
2023-01-11T22:11:27.6250276Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x2393bf0, 
2023-01-11T22:11:27.6250649Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6251052Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6251293Z #73 _PyFunction_Vectorcall ()
2023-01-11T22:11:27.6251583Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:27.6255496Z #74 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7fe052489e40, 
2023-01-11T22:11:27.6256033Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7fe0521ead40, 
2023-01-11T22:11:27.6256452Z     tstate=0x2393bf0)
2023-01-11T22:11:27.6256865Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6258999Z #75 PyObject_Vectorcall (kwnames=0x7fe052489e40, nargsf=<optimized out>, 
2023-01-11T22:11:27.6259473Z     args=<optimized out>, callable=0x7fe0521ead40)
2023-01-11T22:11:27.6260002Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:27.6262903Z #76 call_function (kwnames=0x7fe052489e40, oparg=<optimized out>, 
2023-01-11T22:11:27.6263411Z     pp_stack=<synthetic pointer>, trace_info=0x7ffda6695c90, 
2023-01-11T22:11:27.6263811Z     tstate=<optimized out>)
2023-01-11T22:11:27.6264118Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:27.6264362Z #77 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:27.6264665Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:27.6265625Z #78 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:11:27.6266170Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6266720Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:27.6267076Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6267524Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:11:27.6267791Z #79 _PyEval_Vector ()
2023-01-11T22:11:27.6268087Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:27.6274339Z #80 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7fe05244c920, 
2023-01-11T22:11:27.6274841Z     globals=globals@entry=0x7fe05243e600, locals=locals@entry=0x7fe05243e600)
2023-01-11T22:11:27.6275320Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:11:27.6276634Z #81 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:11:27.6277224Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:11:27.6279233Z #82 0x00000000005c1d40 in run_mod ()
2023-01-11T22:11:27.6279832Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:11:27.6281881Z #83 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:11:27.6282521Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:11:27.6284494Z #84 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:11:27.6285274Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:11:27.6287180Z #85 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:11:27.6287739Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:27.6288267Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:11:27.6290079Z #86 pymain_run_python (exitcode=0x7ffda6695ef0)
2023-01-11T22:11:27.6290689Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:11:27.6291086Z #87 Py_RunMain.localalias ()
2023-01-11T22:11:27.6291406Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:11:27.6306802Z #88 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:11:27.6307178Z     argv=<optimized out>)
2023-01-11T22:11:27.6307726Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:11:27.6312768Z #89 0x00007fe05251ec87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:11:27.6313272Z     argv=0x7ffda66960f8, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:11:27.6313706Z     rtld_fini=<optimized out>, stack_end=0x7ffda66960e8)
2023-01-11T22:11:27.6314134Z     at ../csu/libc-start.c:310
2023-01-11T22:11:27.6314511Z #90 0x0000000000587ade in _start ()
2023-01-11T22:11:27.6315076Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:11:27.7870534Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:11:27.7871898Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:11:27.7872216Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:11:27.7872547Z This is free software: you are free to change and redistribute it.
2023-01-11T22:11:27.7872836Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:11:27.7873097Z and "show warranty" for details.
2023-01-11T22:11:27.7873410Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:11:27.7873657Z Type "show configuration" for configuration details.
2023-01-11T22:11:27.7873916Z For bug reporting instructions, please see:
2023-01-11T22:11:27.7874164Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:11:27.7874435Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:11:27.7874699Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:11:27.7874928Z For help, type "help".
2023-01-11T22:11:27.7875179Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:11:27.8996820Z Reading symbols from python...done.
2023-01-11T22:11:28.4347087Z 
2023-01-11T22:11:28.4347452Z warning: core file may not match specified executable file.
2023-01-11T22:11:28.5239955Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:28.5240611Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:28.5241258Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:28.5241777Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:28.5242041Z [New LWP 10537]
2023-01-11T22:11:28.5242219Z [New LWP 10549]
2023-01-11T22:11:28.5242378Z [New LWP 10550]
2023-01-11T22:11:28.5242546Z [New LWP 10547]
2023-01-11T22:11:28.5242717Z [New LWP 10548]
2023-01-11T22:11:28.5242879Z [New LWP 10552]
2023-01-11T22:11:28.5243153Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:28.5243505Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:28.5243740Z [New LWP 10553]
2023-01-11T22:11:28.5243911Z [New LWP 10551]
2023-01-11T22:11:28.5255901Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:28.5256551Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:28.5257083Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:28.5257627Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:28.5562262Z [Thread debugging using libthread_db enabled]
2023-01-11T22:11:28.5562653Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:11:35.4514680Z 51	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
2023-01-11T22:11:35.4515307Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:11:35.4515837Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:11:35.4516175Z Program terminated with signal SIGSEGV, Segmentation fault.
2023-01-11T22:11:35.4516471Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:11:35.4516745Z [Current thread is 1 (Thread 0x7f2f7fce1200 (LWP 10537))]
2023-01-11T22:11:35.4545458Z To enable execution of this file add
2023-01-11T22:11:35.4546428Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:11:35.4546733Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:35.4547002Z To completely disable this security protection add
2023-01-11T22:11:35.4547258Z 	set auto-load safe-path /
2023-01-11T22:11:35.4547504Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:35.4547780Z For more information about this security protection see the
2023-01-11T22:11:35.4548128Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:11:35.4548427Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:11:35.4591209Z #0  raise (sig=<optimized out>) at ../sysdeps/unix/sysv/linux/raise.c:51
2023-01-11T22:11:35.4593633Z #1  0x00007f2f4837265b in handler_SIGSEGV(int, siginfo_t*, void*) ()
2023-01-11T22:11:35.4594057Z    from /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so
2023-01-11T22:11:35.4603919Z #2  <signal handler called>
2023-01-11T22:11:35.4604344Z #3  __strlen_avx2 () at ../sysdeps/x86_64/multiarch/strlen-avx2.S:65
2023-01-11T22:11:35.4748629Z #4  0x00007f2f7e79f03b in string_at (ptr=0x0, size=-1) at <artificial>:5564
2023-01-11T22:11:35.4750941Z #5  0x00007f2f7fb96052 in ffi_call_unix64 ()
2023-01-11T22:11:35.4751273Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:11:35.4759614Z #6  0x00007f2f7fb948cd in ffi_call_int ()
2023-01-11T22:11:35.4759927Z    from /opt/conda/lib/python3.10/lib-dynload/../../libffi.so.8
2023-01-11T22:11:35.4767543Z #7  0x00007f2f7e7a7879 in _call_function_pointer (argtypecount=2, argcount=2, 
2023-01-11T22:11:35.4768115Z     resmem=0x7fff0d99f6f0, restype=<optimized out>, atypes=<optimized out>, 
2023-01-11T22:11:35.4768501Z     avalues=<optimized out>, pProc=0x7f2f7e79f002 <string_at>, flags=4357)
2023-01-11T22:11:35.4768872Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:916
2023-01-11T22:11:35.4769329Z #8  _ctypes_callproc ()
2023-01-11T22:11:35.4769644Z     at /usr/local/src/conda/python-3.10.8/build-static/stgdict.c:1259
2023-01-11T22:11:35.4769926Z #9  0x00007f2f7e7a73fe in PyCFuncPtr_call () at <artificial>:4201
2023-01-11T22:11:35.4771249Z #10 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:35.4771592Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:35.4777103Z #11 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:11:35.4777500Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4778045Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7f2ed8d38358, 
2023-01-11T22:11:35.4778435Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4778787Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4779194Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:35.4782553Z #12 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:35.4783146Z     args=0x7f2ed8d38358, callable=0x7f2f7ea58880, tstate=<optimized out>)
2023-01-11T22:11:35.4783690Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:35.4787345Z #13 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:35.4787824Z     args=0x7f2ed8d38358, callable=0x7f2f7ea58880)
2023-01-11T22:11:35.4788291Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4791840Z #14 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:35.4792325Z     pp_stack=<synthetic pointer>, trace_info=0x7fff0d99fa00, 
2023-01-11T22:11:35.4792740Z     tstate=<optimized out>)
2023-01-11T22:11:35.4793044Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:35.4793315Z #15 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4793881Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:35.4794232Z #16 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:35.4794759Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4795342Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4795700Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4796074Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.4801280Z #17 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:35.4801787Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:35.4802224Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:35.4802613Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f2f7ea17890, 
2023-01-11T22:11:35.4803012Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x11f8bf0, 
2023-01-11T22:11:35.4803399Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4803787Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.4804020Z #18 _PyFunction_Vectorcall ()
2023-01-11T22:11:35.4804320Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.4810351Z #19 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:35.4810807Z     nargsf=<optimized out>, args=0x7f2ed8d381c0, callable=0x7f2f7ea17880, 
2023-01-11T22:11:35.4811238Z     tstate=0x11f8bf0)
2023-01-11T22:11:35.4811592Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4816474Z #20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:35.4816934Z     args=0x7f2ed8d381c0, callable=0x7f2f7ea17880)
2023-01-11T22:11:35.4817334Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4819826Z #21 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:35.4820381Z     pp_stack=<synthetic pointer>, trace_info=0x7fff0d99fbc0, 
2023-01-11T22:11:35.4820781Z     tstate=<optimized out>)
2023-01-11T22:11:35.4821194Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:35.4821430Z #22 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4821878Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:11:35.4822427Z #23 0x0000000000543a33 in _PyEval_EvalFrame (
2023-01-11T22:11:35.4822882Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4823234Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4823595Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4823978Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.4829334Z #24 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:11:35.4829635Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4830047Z     args=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, con=0x7f2ed8cd8680, tstate=0x11f8bf0)
2023-01-11T22:11:35.4830467Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.4832555Z #25 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:35.4832841Z     stack=<optimized out>, func=0x7f2ed8cd8670)
2023-01-11T22:11:35.4833167Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.4838607Z #26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:35.4838931Z     args=<optimized out>, callable=0x7f2ed8cd8670, tstate=0x11f8bf0)
2023-01-11T22:11:35.4839465Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:35.4839740Z #27 vectorcall_unbound (
2023-01-11T22:11:35.4840285Z     nargs=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:11:35.4840800Z     func=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4841168Z     unbound=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4841531Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4841917Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1629
2023-01-11T22:11:35.4842143Z #28 vectorcall_method ()
2023-01-11T22:11:35.4842444Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:1661
2023-01-11T22:11:35.4843157Z #29 0x0000000000543898 in slot_mp_subscript (self=<optimized out>, 
2023-01-11T22:11:35.4843539Z     arg1=<optimized out>)
2023-01-11T22:11:35.4844067Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7258
2023-01-11T22:11:35.4844552Z #30 0x00000000004ef56e in _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4844924Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:2109
2023-01-11T22:11:35.4847234Z #31 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:35.4847754Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4848378Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4848739Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4849304Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.4854035Z #32 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:35.4854707Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:35.4855191Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:35.4855678Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f2ed93d4170, 
2023-01-11T22:11:35.4856077Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x11f8bf0, 
2023-01-11T22:11:35.4856463Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4856859Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.4857091Z #33 _PyFunction_Vectorcall ()
2023-01-11T22:11:35.4857395Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.4862433Z #34 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:35.4862978Z     nargsf=<optimized out>, args=0x7f2ed8ecaa48, callable=0x7f2ed93d4160, 
2023-01-11T22:11:35.4863406Z     tstate=0x11f8bf0)
2023-01-11T22:11:35.4863729Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4867509Z #35 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:35.4867964Z     args=0x7f2ed8ecaa48, callable=0x7f2ed93d4160)
2023-01-11T22:11:35.4868413Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4871141Z #36 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:35.4871587Z     pp_stack=<synthetic pointer>, trace_info=0x7fff0d99ffc0, 
2023-01-11T22:11:35.4872011Z     tstate=<optimized out>)
2023-01-11T22:11:35.4872435Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:35.4872724Z #37 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4873160Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:35.4873620Z #38 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:35.4874052Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4874405Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4874755Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4875129Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.4880362Z #39 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:35.4880970Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:35.4881653Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:35.4882050Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f2edb121400, 
2023-01-11T22:11:35.4882446Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x11f8bf0, 
2023-01-11T22:11:35.4882816Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4883209Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.4883446Z #40 _PyFunction_Vectorcall ()
2023-01-11T22:11:35.4883729Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.4888410Z #41 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:35.4888768Z     nargsf=<optimized out>, args=0x61ac8d8, callable=0x7f2edb1213f0, 
2023-01-11T22:11:35.4889001Z     tstate=0x11f8bf0)
2023-01-11T22:11:35.4889508Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4893440Z #42 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x61ac8d8, 
2023-01-11T22:11:35.4893886Z     callable=0x7f2edb1213f0)
2023-01-11T22:11:35.4894394Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4897248Z #43 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:35.4897715Z     pp_stack=<synthetic pointer>, trace_info=0x7fff0d9a0180, 
2023-01-11T22:11:35.4897953Z     tstate=<optimized out>)
2023-01-11T22:11:35.4898248Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:35.4898491Z #44 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4898792Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:35.4899410Z #45 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:35.4899769Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4900135Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4900488Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4900851Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.4906676Z #46 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:35.4907310Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:35.4907933Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:35.4908320Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f2edb120710, 
2023-01-11T22:11:35.4908804Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x11f8bf0, 
2023-01-11T22:11:35.4909182Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4909576Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.4909798Z #47 _PyFunction_Vectorcall ()
2023-01-11T22:11:35.4910097Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.4915450Z #48 0x00000000004f141c in do_call_core (kwdict=0x7f2f7eb62800, 
2023-01-11T22:11:35.4915903Z     callargs=0x7f2ed8dbe520, func=0x7f2edb120700, trace_info=0x7fff0d9a0340, 
2023-01-11T22:11:35.4916341Z     tstate=<optimized out>)
2023-01-11T22:11:35.4916737Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:35.4917059Z #49 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4917390Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:35.4917835Z #50 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:35.4918354Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4918703Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4919053Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4919428Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.4924021Z #51 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:35.4924643Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:35.4925197Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:35.4925592Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f2f7eb8d5b0, 
2023-01-11T22:11:35.4925990Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x11f8bf0, 
2023-01-11T22:11:35.4926355Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4926743Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.4927047Z #52 _PyFunction_Vectorcall ()
2023-01-11T22:11:35.4927335Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.4932548Z #53 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:35.4932848Z     nargsf=<optimized out>, args=0x611f348, callable=0x7f2f7eb8d5a0, 
2023-01-11T22:11:35.4933078Z     tstate=0x11f8bf0)
2023-01-11T22:11:35.4933380Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4937253Z #54 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x611f348, 
2023-01-11T22:11:35.4937552Z     callable=0x7f2f7eb8d5a0)
2023-01-11T22:11:35.4937864Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4940661Z #55 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:35.4941118Z     pp_stack=<synthetic pointer>, trace_info=0x7fff0d9a0500, 
2023-01-11T22:11:35.4941542Z     tstate=<optimized out>)
2023-01-11T22:11:35.4941868Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:35.4942110Z #56 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4942521Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:35.4942927Z #57 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:35.4943472Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4943979Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4944433Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4944804Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.4949354Z #58 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:35.4949999Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:35.4950647Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:35.4951031Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f2f7eb8df40, 
2023-01-11T22:11:35.4951426Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x11f8bf0, 
2023-01-11T22:11:35.4951810Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4952189Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.4952423Z #59 _PyFunction_Vectorcall ()
2023-01-11T22:11:35.4952719Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.4957684Z #60 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:35.4958162Z     nargsf=<optimized out>, args=0x12755a0, callable=0x7f2f7eb8df30, 
2023-01-11T22:11:35.4958590Z     tstate=0x11f8bf0)
2023-01-11T22:11:35.4958931Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4962707Z #61 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x12755a0, 
2023-01-11T22:11:35.4962974Z     callable=0x7f2f7eb8df30)
2023-01-11T22:11:35.4963296Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4966160Z #62 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:35.4966481Z     pp_stack=<synthetic pointer>, trace_info=0x7fff0d9a06c0, 
2023-01-11T22:11:35.4966716Z     tstate=<optimized out>)
2023-01-11T22:11:35.4967049Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:35.4967328Z #63 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4967673Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:35.4968079Z #64 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:35.4968527Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4968892Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4969418Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4969791Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.4974751Z #65 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:35.4975206Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:35.4975646Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:35.4976038Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f2f7ea0ede0, 
2023-01-11T22:11:35.4976442Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x11f8bf0, 
2023-01-11T22:11:35.4976830Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4977219Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.4977448Z #66 _PyFunction_Vectorcall ()
2023-01-11T22:11:35.4977750Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.4983352Z #67 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:35.4983649Z     nargsf=<optimized out>, args=0x7f2f7ecd8200, callable=0x7f2f7ea0edd0, 
2023-01-11T22:11:35.4983879Z     tstate=0x11f8bf0)
2023-01-11T22:11:35.4984196Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4988430Z #68 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:35.4988727Z     args=0x7f2f7ecd8200, callable=0x7f2f7ea0edd0)
2023-01-11T22:11:35.4989120Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.4991640Z #69 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:35.4992016Z     pp_stack=<synthetic pointer>, trace_info=0x7fff0d9a0880, 
2023-01-11T22:11:35.4992427Z     tstate=<optimized out>)
2023-01-11T22:11:35.4992974Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:35.4993277Z #70 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.4993770Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:35.4994237Z #71 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:35.4994659Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4995022Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.4995377Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.4995762Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:35.5000130Z #72 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:35.5000750Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:35.5001398Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:35.5001801Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f2f7ea0ed50, 
2023-01-11T22:11:35.5002188Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x11f8bf0, 
2023-01-11T22:11:35.5002574Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.5003071Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.5003314Z #73 _PyFunction_Vectorcall ()
2023-01-11T22:11:35.5003601Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:35.5007093Z #74 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7f2f7ecb1e40, 
2023-01-11T22:11:35.5007620Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7f2f7ea0ed40, 
2023-01-11T22:11:35.5008017Z     tstate=0x11f8bf0)
2023-01-11T22:11:35.5008331Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.5010730Z #75 PyObject_Vectorcall (kwnames=0x7f2f7ecb1e40, nargsf=<optimized out>, 
2023-01-11T22:11:35.5011165Z     args=<optimized out>, callable=0x7f2f7ea0ed40)
2023-01-11T22:11:35.5011781Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:35.5014275Z #76 call_function (kwnames=0x7f2f7ecb1e40, oparg=<optimized out>, 
2023-01-11T22:11:35.5014766Z     pp_stack=<synthetic pointer>, trace_info=0x7fff0d9a0a40, 
2023-01-11T22:11:35.5015142Z     tstate=<optimized out>)
2023-01-11T22:11:35.5015455Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:35.5015695Z #77 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:35.5015999Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:35.5016954Z #78 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:11:35.5017469Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.5018178Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:35.5018522Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.5018979Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:11:35.5019247Z #79 _PyEval_Vector ()
2023-01-11T22:11:35.5019540Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:35.5025565Z #80 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7f2f7ec74920, 
2023-01-11T22:11:35.5025887Z     globals=globals@entry=0x7f2f7ec66600, locals=locals@entry=0x7f2f7ec66600)
2023-01-11T22:11:35.5026244Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:11:35.5027674Z #81 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:11:35.5028230Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:11:35.5030470Z #82 0x00000000005c1d40 in run_mod ()
2023-01-11T22:11:35.5031076Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:11:35.5033031Z #83 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:11:35.5033688Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:11:35.5035623Z #84 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:11:35.5036294Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:11:35.5038368Z #85 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:11:35.5038917Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:35.5039473Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:11:35.5041275Z #86 pymain_run_python (exitcode=0x7fff0d9a0ca0)
2023-01-11T22:11:35.5041735Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:11:35.5042080Z #87 Py_RunMain.localalias ()
2023-01-11T22:11:35.5042399Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:11:35.5057655Z #88 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:11:35.5058023Z     argv=<optimized out>)
2023-01-11T22:11:35.5058611Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:11:35.5063207Z #89 0x00007f2f7ed45c87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:11:35.5063595Z     argv=0x7fff0d9a0ea8, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:11:35.5063874Z     rtld_fini=<optimized out>, stack_end=0x7fff0d9a0e98)
2023-01-11T22:11:35.5064261Z     at ../csu/libc-start.c:310
2023-01-11T22:11:35.5065047Z #90 0x0000000000587ade in _start ()
2023-01-11T22:11:35.5065394Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:11:35.6675616Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:11:35.6676078Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:11:35.6676591Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:11:35.6676990Z This is free software: you are free to change and redistribute it.
2023-01-11T22:11:35.6677305Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:11:35.6677649Z and "show warranty" for details.
2023-01-11T22:11:35.6677939Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:11:35.6678246Z Type "show configuration" for configuration details.
2023-01-11T22:11:35.6678490Z For bug reporting instructions, please see:
2023-01-11T22:11:35.6678729Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:11:35.6678992Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:11:35.6679255Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:11:35.6679478Z For help, type "help".
2023-01-11T22:11:35.6679716Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:11:35.7799251Z Reading symbols from python...done.
2023-01-11T22:11:36.3050562Z 
2023-01-11T22:11:36.3051075Z warning: core file may not match specified executable file.
2023-01-11T22:11:36.3081221Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:36.3081859Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:36.3082185Z [New LWP 23756]
2023-01-11T22:11:36.3082575Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:36.3082985Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:36.3083339Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:36.3083670Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:36.3098979Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:36.3100052Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:36.3101087Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:36.3102115Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:36.3420638Z [Thread debugging using libthread_db enabled]
2023-01-11T22:11:36.3421087Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:11:43.7693101Z 78	../sysdeps/unix/syscall-template.S: No such file or directory.
2023-01-11T22:11:43.7693593Z Core was generated by `/opt/conda/bin/python -bb test_multiprocessing_spawn.py -v --import-slow-tests'.
2023-01-11T22:11:43.7693927Z Program terminated with signal SIGABRT, Aborted.
2023-01-11T22:11:43.7694275Z #0  0x00007f6867403177 in kill () at ../sysdeps/unix/syscall-template.S:78
2023-01-11T22:11:43.7694766Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:11:43.7721855Z To enable execution of this file add
2023-01-11T22:11:43.7722755Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:11:43.7723123Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:43.7723376Z To completely disable this security protection add
2023-01-11T22:11:43.7723649Z 	set auto-load safe-path /
2023-01-11T22:11:43.7723899Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:43.7724161Z For more information about this security protection see the
2023-01-11T22:11:43.7724522Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:11:43.7724816Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:11:43.7769992Z #0  0x00007f6867403177 in kill () at ../sysdeps/unix/syscall-template.S:78
2023-01-11T22:11:43.7785788Z #1  0x00000000004cb0d3 in os_kill_impl (
2023-01-11T22:11:43.7786175Z     module=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7786561Z     signal=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7786918Z     pid=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7787417Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/sys/_iomodule.c:7929
2023-01-11T22:11:43.7791364Z #2  os_kill (module=<optimized out>, args=args@entry=0x7f67bb060358, 
2023-01-11T22:11:43.7791730Z     nargs=<optimized out>)
2023-01-11T22:11:43.7792211Z     at /usr/local/src/conda/python-3.10.8/Modules/codecs.c:3581
2023-01-11T22:11:43.7799892Z #3  0x00000000004fe7d4 in cfunction_vectorcall_FASTCALL (func=0x7f6868345a30, 
2023-01-11T22:11:43.7800210Z     args=0x7f67bb060358, nargsf=<optimized out>, kwnames=<optimized out>)
2023-01-11T22:11:43.7800586Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_bitutils.h:430
2023-01-11T22:11:43.7807997Z #4  0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.7808349Z     nargsf=<optimized out>, args=0x7f67bb060358, callable=0x7f6868345a30, 
2023-01-11T22:11:43.7808586Z     tstate=0x21fcb80)
2023-01-11T22:11:43.7808893Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7812768Z #5  PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.7813070Z     args=0x7f67bb060358, callable=0x7f6868345a30)
2023-01-11T22:11:43.7813419Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7815933Z #6  call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.7816397Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a73bd0, 
2023-01-11T22:11:43.7816801Z     tstate=<optimized out>)
2023-01-11T22:11:43.7817344Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.7817606Z #7  _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.7817932Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:11:43.7818335Z #8  0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.7818878Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7819326Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7819690Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7820055Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.7824555Z #9  _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.7825212Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.7825775Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.7826357Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67c14736e0, 
2023-01-11T22:11:43.7826764Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.7827150Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7827548Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.7827776Z #10 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.7828083Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.7832431Z #11 0x00000000004f141c in do_call_core (kwdict=0x0, callargs=0x7f67bb17bca0, 
2023-01-11T22:11:43.7832936Z     func=0x7f67c14736d0, trace_info=0x7ffee0a73d90, tstate=<optimized out>)
2023-01-11T22:11:43.7833441Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:43.7833687Z #12 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.7833992Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:43.7834234Z #13 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.7834737Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7835300Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7835656Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7836105Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.7840556Z #14 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.7841048Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.7841489Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.7841877Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67c42231d0, 
2023-01-11T22:11:43.7842274Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.7842657Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7843047Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.7843276Z #15 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.7843574Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.7848620Z #16 0x00000000004f141c in do_call_core (kwdict=0x7f67c41e7380, 
2023-01-11T22:11:43.7848981Z     callargs=0x7f67bb04d350, func=0x7f67c42231c0, trace_info=0x7ffee0a73f50, 
2023-01-11T22:11:43.7849408Z     tstate=<optimized out>)
2023-01-11T22:11:43.7849721Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:43.7849955Z #17 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.7850290Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:43.7850980Z #18 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.7851355Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7851791Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7852150Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7852532Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.7857746Z #19 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.7858267Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.7858801Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.7859206Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67c41cf650, 
2023-01-11T22:11:43.7859612Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.7859985Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7860386Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.7860626Z #20 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.7860913Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.7865881Z #21 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.7866238Z     nargsf=<optimized out>, args=0x76ea028, callable=0x7f67c41cf640, 
2023-01-11T22:11:43.7866471Z     tstate=0x21fcb80)
2023-01-11T22:11:43.7866773Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7870404Z #22 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x76ea028, 
2023-01-11T22:11:43.7870748Z     callable=0x7f67c41cf640)
2023-01-11T22:11:43.7871057Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7873661Z #23 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.7874059Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a74110, 
2023-01-11T22:11:43.7874390Z     tstate=<optimized out>)
2023-01-11T22:11:43.7874685Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.7874932Z #24 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.7875238Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:43.7886936Z #25 0x0000000000509dbe in _PyEval_EvalFrame (
2023-01-11T22:11:43.7887358Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7887726Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7888086Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7888455Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.7894442Z #26 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, 
2023-01-11T22:11:43.7894810Z     args=0x7f67c17531d8, locals=0x0, con=0x7f67c41f8050, tstate=0x21fcb80)
2023-01-11T22:11:43.7895143Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.7897970Z #27 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, 
2023-01-11T22:11:43.7898315Z     stack=0x7f67c17531d8, func=0x7f67c41f8040)
2023-01-11T22:11:43.7898640Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.7902992Z #28 _PyObject_VectorcallTstate (kwnames=<optimized out>, 
2023-01-11T22:11:43.7903395Z     nargsf=<optimized out>, args=0x7f67c17531d8, callable=0x7f67c41f8040, 
2023-01-11T22:11:43.7903823Z     tstate=0x21fcb80)
2023-01-11T22:11:43.7904257Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:43.7904501Z #29 method_vectorcall ()
2023-01-11T22:11:43.7904812Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:53
2023-01-11T22:11:43.7909549Z #30 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7f67bb1a0340, 
2023-01-11T22:11:43.7910070Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7f67c10d7a00, 
2023-01-11T22:11:43.7910465Z     tstate=0x21fcb80)
2023-01-11T22:11:43.7910780Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7912938Z #31 PyObject_Vectorcall (kwnames=0x7f67bb1a0340, nargsf=<optimized out>, 
2023-01-11T22:11:43.7913233Z     args=<optimized out>, callable=0x7f67c10d7a00)
2023-01-11T22:11:43.7913680Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7916204Z #32 call_function (kwnames=0x7f67bb1a0340, oparg=<optimized out>, 
2023-01-11T22:11:43.7916572Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a74320, 
2023-01-11T22:11:43.7916829Z     tstate=<optimized out>)
2023-01-11T22:11:43.7917139Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.7917371Z #33 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.7917719Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:43.7918057Z #34 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.7918560Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7919156Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7919516Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7919898Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.7924580Z #35 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.7925200Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.7925864Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.7926365Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67bb04b9b0, 
2023-01-11T22:11:43.7926768Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.7927137Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7927531Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.7927775Z #36 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.7928062Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.7932518Z #37 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.7932875Z     nargsf=<optimized out>, args=0x7f67bb156578, callable=0x7f67bb04b9a0, 
2023-01-11T22:11:43.7933113Z     tstate=0x21fcb80)
2023-01-11T22:11:43.7933411Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7937101Z #38 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.7937451Z     args=0x7f67bb156578, callable=0x7f67bb04b9a0)
2023-01-11T22:11:43.7937777Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7940325Z #39 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.7940698Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a744e0, 
2023-01-11T22:11:43.7940939Z     tstate=<optimized out>)
2023-01-11T22:11:43.7941240Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.7941485Z #40 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.7941789Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:43.7955877Z #41 0x00000000004f706d in _PyEval_EvalFrame (
2023-01-11T22:11:43.7956309Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7956673Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7957033Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7957406Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.7963255Z #42 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:11:43.7963714Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, locals=0x0, con=0x7f67bb04b5c0, 
2023-01-11T22:11:43.7964121Z     tstate=0x21fcb80)
2023-01-11T22:11:43.7964416Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.7967433Z #43 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.7967767Z     stack=<optimized out>, func=0x7f67bb04b5b0)
2023-01-11T22:11:43.7968139Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.7968423Z #44 _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:11:43.7968776Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:142
2023-01-11T22:11:43.7976690Z #45 0x0000000000507af8 in _PyObject_Call_Prepend (kwargs=0x0, 
2023-01-11T22:11:43.7977124Z     kwargs@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7f67c1b53850, 
2023-01-11T22:11:43.7977557Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, obj=<optimized out>, 
2023-01-11T22:11:43.7977981Z     obj@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, callable=0x7f67bb04b5b0, 
2023-01-11T22:11:43.7978398Z     callable@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.7978779Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7979176Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:11:43.7979546Z #46 slot_tp_init ()
2023-01-11T22:11:43.7979833Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7734
2023-01-11T22:11:43.7982682Z #47 0x00000000004f7bdb in type_call (kwds=0x0, args=0x7f67c1b53850, 
2023-01-11T22:11:43.7982924Z     type=<optimized out>)
2023-01-11T22:11:43.7983240Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:43.7983487Z #48 _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:43.7983809Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:215
2023-01-11T22:11:43.7986194Z #49 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:11:43.7986591Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7986994Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7f67bb1563d8, 
2023-01-11T22:11:43.7987380Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.7987752Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.7988147Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:43.7991066Z #50 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.7991448Z     args=0x7f67bb1563d8, callable=0x77a2460, tstate=<optimized out>)
2023-01-11T22:11:43.7991902Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:43.7995872Z #51 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.7996150Z     args=0x7f67bb1563d8, callable=0x77a2460)
2023-01-11T22:11:43.7996503Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.7999892Z #52 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8000384Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a747c0, 
2023-01-11T22:11:43.8000827Z     tstate=<optimized out>)
2023-01-11T22:11:43.8001176Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8001471Z #53 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8001791Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:43.8002230Z #54 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.8002756Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8003281Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8003644Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8004031Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8007942Z #55 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.8008618Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8009405Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.8009820Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67c4220200, 
2023-01-11T22:11:43.8010208Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8010599Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8010997Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8011225Z #56 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.8011526Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8016119Z #57 0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.8016763Z     nargsf=<optimized out>, args=0x7f67bb156098, callable=0x7f67c42201f0, 
2023-01-11T22:11:43.8017190Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8017564Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8020753Z #58 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8021025Z     args=0x7f67bb156098, callable=0x7f67c42201f0)
2023-01-11T22:11:43.8021408Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8023863Z #59 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8024276Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a74980, 
2023-01-11T22:11:43.8024507Z     tstate=<optimized out>)
2023-01-11T22:11:43.8024827Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8025202Z #60 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8025579Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:11:43.8025888Z #61 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.8026279Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8026649Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8026995Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8027380Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8032438Z #62 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.8033038Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8033669Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.8034076Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67c41cf6e0, 
2023-01-11T22:11:43.8034487Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8034856Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8035276Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8035521Z #63 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.8035935Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8039994Z #64 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.8040437Z     nargsf=<optimized out>, args=0x7552ac8, callable=0x7f67c41cf6d0, 
2023-01-11T22:11:43.8040856Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8041280Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8044919Z #65 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7552ac8, 
2023-01-11T22:11:43.8045365Z     callable=0x7f67c41cf6d0)
2023-01-11T22:11:43.8045892Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8048468Z #66 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8048960Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a74b40, 
2023-01-11T22:11:43.8049502Z     tstate=<optimized out>)
2023-01-11T22:11:43.8049809Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8050061Z #67 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8050505Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:43.8050767Z #68 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.8051284Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8051819Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8052277Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8052662Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8056995Z #69 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.8057476Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8057916Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.8058320Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67c423db50, 
2023-01-11T22:11:43.8058706Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8059090Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8059483Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8059710Z #70 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.8060012Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8063459Z #71 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7f68671f5b80, 
2023-01-11T22:11:43.8063887Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7f67c423db40, 
2023-01-11T22:11:43.8064120Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8064438Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8067048Z #72 PyObject_Vectorcall (kwnames=0x7f68671f5b80, nargsf=<optimized out>, 
2023-01-11T22:11:43.8067396Z     args=<optimized out>, callable=0x7f67c423db40)
2023-01-11T22:11:43.8067744Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8070618Z #73 call_function (kwnames=0x7f68671f5b80, oparg=<optimized out>, 
2023-01-11T22:11:43.8070958Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a74d00, 
2023-01-11T22:11:43.8071184Z     tstate=<optimized out>)
2023-01-11T22:11:43.8071498Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8071742Z #74 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8072149Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:43.8072541Z #75 0x0000000000509dbe in _PyEval_EvalFrame (
2023-01-11T22:11:43.8073032Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8073408Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8073754Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8074141Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8079703Z #76 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, 
2023-01-11T22:11:43.8080050Z     args=0x7f67bb289150, locals=0x0, con=0x7f67c1473e30, tstate=0x21fcb80)
2023-01-11T22:11:43.8080398Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8083482Z #77 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, 
2023-01-11T22:11:43.8083803Z     stack=0x7f67bb289150, func=0x7f67c1473e20)
2023-01-11T22:11:43.8097525Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8097875Z #78 _PyObject_VectorcallTstate (kwnames=<optimized out>, 
2023-01-11T22:11:43.8098145Z     nargsf=<optimized out>, args=0x7f67bb289150, callable=0x7f67c1473e20, 
2023-01-11T22:11:43.8098365Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8098669Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:43.8098890Z #79 method_vectorcall ()
2023-01-11T22:11:43.8099185Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:53
2023-01-11T22:11:43.8099583Z #80 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.8099854Z     nargsf=<optimized out>, args=0x7f67bb289158, callable=0x7f67c160d440, 
2023-01-11T22:11:43.8100086Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8100396Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8101016Z #81 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8101336Z     args=0x7f67bb289158, callable=0x7f67c160d440)
2023-01-11T22:11:43.8101677Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8104422Z #82 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8104747Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a74f10, 
2023-01-11T22:11:43.8105167Z     tstate=<optimized out>)
2023-01-11T22:11:43.8105670Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8105967Z #83 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8106439Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:43.8106877Z #84 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.8107408Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8107759Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8108115Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8108497Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8113076Z #85 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.8113674Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8114390Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.8114795Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f686714cd40, 
2023-01-11T22:11:43.8115192Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8115567Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8116049Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8116292Z #86 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.8116580Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8121568Z #87 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.8122126Z     nargsf=<optimized out>, args=0x729ab60, callable=0x7f686714cd30, 
2023-01-11T22:11:43.8122493Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8122839Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8125937Z #88 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x729ab60, 
2023-01-11T22:11:43.8126415Z     callable=0x7f686714cd30)
2023-01-11T22:11:43.8126872Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8129339Z #89 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8129855Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a750d0, 
2023-01-11T22:11:43.8130224Z     tstate=<optimized out>)
2023-01-11T22:11:43.8130535Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8130781Z #90 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8131089Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:43.8132322Z #91 0x0000000000509dbe in _PyEval_EvalFrame (
2023-01-11T22:11:43.8132880Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8133531Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8133889Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8134266Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8139396Z #92 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, 
2023-01-11T22:11:43.8139769Z     args=0x728ccb8, locals=0x0, con=0x7f686714cef0, tstate=0x21fcb80)
2023-01-11T22:11:43.8140121Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8142976Z #93 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, 
2023-01-11T22:11:43.8143328Z     stack=0x728ccb8, func=0x7f686714cee0)
2023-01-11T22:11:43.8143660Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8147940Z #94 _PyObject_VectorcallTstate (kwnames=<optimized out>, 
2023-01-11T22:11:43.8148307Z     nargsf=<optimized out>, args=0x728ccb8, callable=0x7f686714cee0, 
2023-01-11T22:11:43.8148555Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8148872Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:43.8149103Z #95 method_vectorcall ()
2023-01-11T22:11:43.8149415Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:53
2023-01-11T22:11:43.8154541Z #96 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7f67c1c0a290, 
2023-01-11T22:11:43.8154979Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7f67c0f894c0, 
2023-01-11T22:11:43.8155228Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8155540Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8157844Z #97 PyObject_Vectorcall (kwnames=0x7f67c1c0a290, nargsf=<optimized out>, 
2023-01-11T22:11:43.8158131Z     args=<optimized out>, callable=0x7f67c0f894c0)
2023-01-11T22:11:43.8158478Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8161128Z #98 call_function (kwnames=0x7f67c1c0a290, oparg=<optimized out>, 
2023-01-11T22:11:43.8161458Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a752e0, 
2023-01-11T22:11:43.8161728Z     tstate=<optimized out>)
2023-01-11T22:11:43.8162044Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8162290Z #99 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8162607Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:43.8162984Z #100 0x0000000000509dbe in _PyEval_EvalFrame (
2023-01-11T22:11:43.8163427Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8163806Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8164162Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8164545Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8170390Z #101 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, 
2023-01-11T22:11:43.8170792Z     args=0x7f67c17aab08, locals=0x0, con=0x7f67c1470320, tstate=0x21fcb80)
2023-01-11T22:11:43.8171150Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8173915Z #102 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, 
2023-01-11T22:11:43.8174203Z     stack=0x7f67c17aab08, func=0x7f67c1470310)
2023-01-11T22:11:43.8174522Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8178675Z #103 _PyObject_VectorcallTstate (kwnames=<optimized out>, 
2023-01-11T22:11:43.8178984Z     nargsf=<optimized out>, args=0x7f67c17aab08, callable=0x7f67c1470310, 
2023-01-11T22:11:43.8179207Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8179523Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:43.8179769Z #104 method_vectorcall ()
2023-01-11T22:11:43.8180083Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:53
2023-01-11T22:11:43.8185628Z #105 0x00000000004efd83 in _PyObject_VectorcallTstate (
2023-01-11T22:11:43.8185975Z     kwnames=0x7f67c1c22840, nargsf=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8186242Z     callable=0x7f67c14fb6c0, tstate=0x21fcb80)
2023-01-11T22:11:43.8186573Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8188990Z #106 PyObject_Vectorcall (kwnames=0x7f67c1c22840, nargsf=<optimized out>, 
2023-01-11T22:11:43.8189488Z     args=<optimized out>, callable=0x7f67c14fb6c0)
2023-01-11T22:11:43.8190044Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8192390Z #107 call_function (kwnames=0x7f67c1c22840, oparg=<optimized out>, 
2023-01-11T22:11:43.8192692Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a754f0, 
2023-01-11T22:11:43.8192930Z     tstate=<optimized out>)
2023-01-11T22:11:43.8193231Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8193531Z #108 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8193978Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:43.8194583Z #109 0x0000000000509f16 in _PyEval_EvalFrame (
2023-01-11T22:11:43.8195269Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8195637Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8196006Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8196421Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8200732Z #110 _PyEval_Vector (
2023-01-11T22:11:43.8201232Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8201828Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7ffee0a755e0, locals=0x0, con=0x7f67c14703b0, 
2023-01-11T22:11:43.8202147Z     tstate=0x21fcb80, 
2023-01-11T22:11:43.8202433Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8202828Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8206654Z #111 _PyFunction_Vectorcall (
2023-01-11T22:11:43.8207089Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8207510Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, stack=0x7ffee0a755e0, func=0x7f67c14703a0)
2023-01-11T22:11:43.8207957Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8209558Z #112 _PyObject_VectorcallTstate (
2023-01-11T22:11:43.8210176Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, nargsf=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8210654Z     callable=0x7f67c14703a0, tstate=0x21fcb80)
2023-01-11T22:11:43.8210980Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:43.8211225Z #113 method_vectorcall ()
2023-01-11T22:11:43.8211539Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:83
2023-01-11T22:11:43.8217571Z #114 0x00000000004f141c in do_call_core (kwdict=0x7f67c1299780, 
2023-01-11T22:11:43.8218032Z     callargs=0x7f67c1433ca0, func=0x7f67bbae5d00, trace_info=0x7ffee0a75700, 
2023-01-11T22:11:43.8218472Z     tstate=<optimized out>)
2023-01-11T22:11:43.8218845Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:43.8219183Z #115 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8219575Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:43.8220045Z #116 0x00000000004f706d in _PyEval_EvalFrame (
2023-01-11T22:11:43.8220411Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8220885Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8221242Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8221649Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8227166Z #117 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:11:43.8227538Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, locals=0x0, con=0x7f686714d0a0, 
2023-01-11T22:11:43.8227855Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8228150Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8230385Z #118 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8230755Z     stack=<optimized out>, func=0x7f686714d090)
2023-01-11T22:11:43.8231143Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8231553Z #119 _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:11:43.8231914Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:142
2023-01-11T22:11:43.8232237Z #120 0x00000000005084a6 in _PyObject_Call_Prepend ()
2023-01-11T22:11:43.8232542Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:11:43.8235037Z #121 0x00000000005d04d3 in slot_tp_call ()
2023-01-11T22:11:43.8235413Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7494
2023-01-11T22:11:43.8237026Z #122 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:43.8237414Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:43.8241166Z #123 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:11:43.8241600Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8241984Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7f67c14b5970, 
2023-01-11T22:11:43.8242374Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8242742Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8243140Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:43.8246120Z #124 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8246430Z     args=0x7f67c14b5970, callable=0x7f67c14553c0, tstate=<optimized out>)
2023-01-11T22:11:43.8246794Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:43.8250727Z #125 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8251063Z     args=0x7f67c14b5970, callable=0x7f67c14553c0)
2023-01-11T22:11:43.8251405Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8254989Z #126 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8255325Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a75a00, 
2023-01-11T22:11:43.8255563Z     tstate=<optimized out>)
2023-01-11T22:11:43.8255876Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8256123Z #127 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8256442Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:43.8257116Z #128 0x0000000000509f16 in _PyEval_EvalFrame (
2023-01-11T22:11:43.8257480Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8257915Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8258272Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8258659Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8263687Z #129 _PyEval_Vector (
2023-01-11T22:11:43.8264127Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8264890Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7ffee0a75af0, locals=0x0, con=0x7f6867158200, 
2023-01-11T22:11:43.8265208Z     tstate=0x21fcb80, 
2023-01-11T22:11:43.8265485Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8265880Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8268312Z #130 _PyFunction_Vectorcall (
2023-01-11T22:11:43.8268851Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8269412Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, stack=0x7ffee0a75af0, func=0x7f68671581f0)
2023-01-11T22:11:43.8269831Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8272083Z #131 _PyObject_VectorcallTstate (
2023-01-11T22:11:43.8272723Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, nargsf=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8273190Z     callable=0x7f68671581f0, tstate=0x21fcb80)
2023-01-11T22:11:43.8273527Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:43.8273773Z #132 method_vectorcall ()
2023-01-11T22:11:43.8274079Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:83
2023-01-11T22:11:43.8279948Z #133 0x00000000004f141c in do_call_core (kwdict=0x7f67c1299fc0, 
2023-01-11T22:11:43.8280419Z     callargs=0x7f67c142a500, func=0x7f67c13793c0, trace_info=0x7ffee0a75c10, 
2023-01-11T22:11:43.8280876Z     tstate=<optimized out>)
2023-01-11T22:11:43.8281233Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:43.8281600Z #134 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8282120Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:43.8282570Z #135 0x00000000004f706d in _PyEval_EvalFrame (
2023-01-11T22:11:43.8282893Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8283256Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8283705Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8284078Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8288727Z #136 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:11:43.8289530Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, locals=0x0, con=0x7f68671580e0, 
2023-01-11T22:11:43.8289993Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8290296Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8292379Z #137 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8292872Z     stack=<optimized out>, func=0x7f68671580d0)
2023-01-11T22:11:43.8293396Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8293678Z #138 _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:11:43.8294045Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:142
2023-01-11T22:11:43.8294338Z #139 0x00000000005084a6 in _PyObject_Call_Prepend ()
2023-01-11T22:11:43.8294642Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:11:43.8296844Z #140 0x00000000005d04d3 in slot_tp_call ()
2023-01-11T22:11:43.8297158Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7494
2023-01-11T22:11:43.8299086Z #141 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:43.8299482Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:43.8302895Z #142 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:11:43.8303433Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8304353Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7f67c14b5060, 
2023-01-11T22:11:43.8304766Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8305133Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8305563Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:43.8307746Z #143 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8308265Z     args=0x7f67c14b5060, callable=0x7f67c1457c70, tstate=<optimized out>)
2023-01-11T22:11:43.8308764Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:43.8312254Z #144 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8312628Z     args=0x7f67c14b5060, callable=0x7f67c1457c70)
2023-01-11T22:11:43.8312958Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8316106Z #145 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8316572Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a75f10, 
2023-01-11T22:11:43.8316983Z     tstate=<optimized out>)
2023-01-11T22:11:43.8317426Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8317715Z #146 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8318037Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:43.8318337Z #147 0x0000000000509f16 in _PyEval_EvalFrame (
2023-01-11T22:11:43.8318637Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8318998Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8319354Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8319716Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8324335Z #148 _PyEval_Vector (
2023-01-11T22:11:43.8324773Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8325279Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7ffee0a76000, locals=0x0, con=0x7f6867158200, 
2023-01-11T22:11:43.8325588Z     tstate=0x21fcb80, 
2023-01-11T22:11:43.8325874Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8326265Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8328761Z #149 _PyFunction_Vectorcall (
2023-01-11T22:11:43.8329457Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8330042Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, stack=0x7ffee0a76000, func=0x7f68671581f0)
2023-01-11T22:11:43.8330453Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8332703Z #150 _PyObject_VectorcallTstate (
2023-01-11T22:11:43.8333313Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, nargsf=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8333800Z     callable=0x7f68671581f0, tstate=0x21fcb80)
2023-01-11T22:11:43.8334121Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:114
2023-01-11T22:11:43.8334364Z #151 method_vectorcall ()
2023-01-11T22:11:43.8334673Z     at /usr/local/src/conda/python-3.10.8/Programs/_functoolsmodule.c:83
2023-01-11T22:11:43.8340625Z #152 0x00000000004f141c in do_call_core (kwdict=0x7f67c1299f80, 
2023-01-11T22:11:43.8340998Z     callargs=0x7f67c143e4a0, func=0x7f67c9760580, trace_info=0x7ffee0a76120, 
2023-01-11T22:11:43.8341237Z     tstate=<optimized out>)
2023-01-11T22:11:43.8341657Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:43.8341954Z #153 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8342391Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:43.8342805Z #154 0x00000000004f706d in _PyEval_EvalFrame (
2023-01-11T22:11:43.8343108Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8343453Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8343802Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8344179Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8349292Z #155 _PyEval_Vector (kwnames=0x0, 
2023-01-11T22:11:43.8349725Z     argcount=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, locals=0x0, con=0x7f68671580e0, 
2023-01-11T22:11:43.8350036Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8350337Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8352814Z #156 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8353164Z     stack=<optimized out>, func=0x7f68671580d0)
2023-01-11T22:11:43.8353485Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8353811Z #157 _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:11:43.8354184Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:142
2023-01-11T22:11:43.8354659Z #158 0x00000000005084a6 in _PyObject_Call_Prepend ()
2023-01-11T22:11:43.8354973Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:11:43.8357104Z #159 0x00000000005d04d3 in slot_tp_call ()
2023-01-11T22:11:43.8357425Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7494
2023-01-11T22:11:43.8359048Z #160 0x00000000004f7b8b in _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:43.8359457Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:43.8363064Z #161 0x00000000004f37ae in _PyObject_VectorcallTstate (
2023-01-11T22:11:43.8363729Z     kwnames=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8364241Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x728f310, 
2023-01-11T22:11:43.8364607Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8364970Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8365368Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:43.8367682Z #162 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8367996Z     args=0x728f310, callable=0x7f67c1433040, tstate=<optimized out>)
2023-01-11T22:11:43.8368351Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:43.8372430Z #163 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8372711Z     args=0x728f310, callable=0x7f67c1433040)
2023-01-11T22:11:43.8373045Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8376154Z #164 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8376517Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a76420, 
2023-01-11T22:11:43.8376804Z     tstate=<optimized out>)
2023-01-11T22:11:43.8377116Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8377362Z #165 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8377936Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:43.8378311Z #166 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.8378670Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8379104Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8379451Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8379835Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8384883Z #167 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.8385381Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8385805Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.8386202Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67c14947a0, 
2023-01-11T22:11:43.8386600Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8386983Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8387362Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8387604Z #168 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.8387904Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8392810Z #169 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.8393165Z     nargsf=<optimized out>, args=0x715f048, callable=0x7f67c1494790, 
2023-01-11T22:11:43.8393393Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8393708Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8397193Z #170 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8397446Z     args=0x715f048, callable=0x7f67c1494790)
2023-01-11T22:11:43.8397780Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8400238Z #171 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8400580Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a765e0, 
2023-01-11T22:11:43.8400816Z     tstate=<optimized out>)
2023-01-11T22:11:43.8401205Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8401438Z #172 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8401797Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:43.8402385Z #173 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.8402803Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8403173Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8403533Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8403914Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8408765Z #174 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.8409397Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8409903Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.8410302Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f6866fa63c0, 
2023-01-11T22:11:43.8410687Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8411163Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8411557Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8411796Z #175 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.8412081Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8416778Z #176 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.8417128Z     nargsf=<optimized out>, args=0x70fdc08, callable=0x7f6866fa63b0, 
2023-01-11T22:11:43.8417344Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8417653Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8421230Z #177 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8421595Z     args=0x70fdc08, callable=0x7f6866fa63b0)
2023-01-11T22:11:43.8421918Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8424518Z #178 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8424978Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a767a0, 
2023-01-11T22:11:43.8425389Z     tstate=<optimized out>)
2023-01-11T22:11:43.8425863Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8426160Z #179 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8426471Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:43.8426902Z #180 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.8427418Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8427777Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8428117Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8428491Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8432419Z #181 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.8433043Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8433748Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.8434400Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f6866fa5e20, 
2023-01-11T22:11:43.8435121Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8435509Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8435900Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8436142Z #182 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.8436445Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8436730Z #183 0x00000000004f711d in _PyObject_FastCallDictTstate.localalias ()
2023-01-11T22:11:43.8437077Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:153
2023-01-11T22:11:43.8443367Z #184 0x0000000000507af8 in _PyObject_Call_Prepend (kwargs=0x7f67c168b100, 
2023-01-11T22:11:43.8443814Z     kwargs@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=0x7f6868254070, 
2023-01-11T22:11:43.8444228Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, obj=<optimized out>, 
2023-01-11T22:11:43.8444639Z     obj@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, callable=0x7f6866fa5e10, 
2023-01-11T22:11:43.8445048Z     callable@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8445517Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8446024Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:431
2023-01-11T22:11:43.8446253Z #185 slot_tp_init ()
2023-01-11T22:11:43.8446550Z     at /usr/local/src/conda/python-3.10.8/Programs/gcmodule.c:7734
2023-01-11T22:11:43.8448913Z #186 0x00000000004f7bdb in type_call (kwds=0x7f67c168b100, 
2023-01-11T22:11:43.8449485Z     args=0x7f6868254070, type=<optimized out>)
2023-01-11T22:11:43.8449986Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:224
2023-01-11T22:11:43.8450251Z #187 _PyObject_MakeTpCall.localalias ()
2023-01-11T22:11:43.8450557Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:215
2023-01-11T22:11:43.8452920Z #188 0x00000000004f428d in _PyObject_VectorcallTstate (
2023-01-11T22:11:43.8453283Z     kwnames=0x7f67c1c19180, 
2023-01-11T22:11:43.8453840Z     nargsf=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, args=<optimized out>, 
2023-01-11T22:11:43.8454262Z     callable=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8454629Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8455027Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:112
2023-01-11T22:11:43.8457700Z #189 _PyObject_VectorcallTstate (kwnames=0x7f67c1c19180, 
2023-01-11T22:11:43.8458205Z     nargsf=<optimized out>, args=<optimized out>, callable=0x2399b40, 
2023-01-11T22:11:43.8458586Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8458891Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:99
2023-01-11T22:11:43.8461040Z #190 PyObject_Vectorcall (kwnames=0x7f67c1c19180, nargsf=<optimized out>, 
2023-01-11T22:11:43.8461521Z     args=<optimized out>, callable=0x2399b40)
2023-01-11T22:11:43.8462009Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8464371Z #191 call_function (kwnames=0x7f67c1c19180, oparg=<optimized out>, 
2023-01-11T22:11:43.8464860Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a76ac0, 
2023-01-11T22:11:43.8465283Z     tstate=<optimized out>)
2023-01-11T22:11:43.8465710Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8465990Z #192 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8466418Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:43.8466862Z #193 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:43.8467459Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8467826Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8468178Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8468559Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:43.8472678Z #194 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:43.8473301Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:43.8473948Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:43.8474337Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f67c19ea8d0, 
2023-01-11T22:11:43.8474736Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x21fcb80, 
2023-01-11T22:11:43.8475123Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8475511Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8475735Z #195 _PyFunction_Vectorcall ()
2023-01-11T22:11:43.8476117Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:43.8480573Z #196 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:43.8481181Z     nargsf=<optimized out>, args=0x7f6868279ba8, callable=0x7f67c19ea8c0, 
2023-01-11T22:11:43.8481515Z     tstate=0x21fcb80)
2023-01-11T22:11:43.8481831Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8485264Z #197 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:43.8485718Z     args=0x7f6868279ba8, callable=0x7f67c19ea8c0)
2023-01-11T22:11:43.8486145Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:43.8488232Z #198 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:43.8488702Z     pp_stack=<synthetic pointer>, trace_info=0x7ffee0a76c80, 
2023-01-11T22:11:43.8489233Z     tstate=<optimized out>)
2023-01-11T22:11:43.8489567Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:43.8489818Z #199 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:43.8490266Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:43.8490638Z #200 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:11:43.8491144Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8491717Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8492064Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:43.8492517Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:11:43.8492781Z #201 _PyEval_Vector ()
2023-01-11T22:11:43.8493059Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:43.8498327Z #202 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7f68671c7730, 
2023-01-11T22:11:43.8498835Z     globals=globals@entry=0x7f6867306480, locals=locals@entry=0x7f6867306480)
2023-01-11T22:11:43.8499410Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:11:43.8500054Z #203 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:11:43.8500643Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:11:43.8502443Z #204 0x00000000005c1d40 in run_mod ()
2023-01-11T22:11:43.8503088Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:11:43.8515822Z #205 0x000000000045adf2 in pyrun_file (
2023-01-11T22:11:43.8516361Z     fp=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8516913Z     filename=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8517265Z     start=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8517710Z     globals=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8518151Z     locals=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:43.8518539Z     closeit=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, flags=0x7ffee0a76f78)
2023-01-11T22:11:43.8518947Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1208
2023-01-11T22:11:43.8519247Z #206 0x00000000005bc25f in _PyRun_SimpleFileObject.localalias ()
2023-01-11T22:11:43.8519652Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:456
2023-01-11T22:11:43.8520096Z #207 0x00000000005bc063 in _PyRun_AnyFileObject.localalias ()
2023-01-11T22:11:43.8520501Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:90
2023-01-11T22:11:43.8526573Z #208 0x00000000005b8e7d in pymain_run_file_obj (skip_source_first_line=0, 
2023-01-11T22:11:43.8527036Z     filename=0x7f6867381840, program_name=0x7f68672f3a00)
2023-01-11T22:11:43.8527600Z     at /croot/python-split_1669298683653/work/build-static/python.c:357
2023-01-11T22:11:43.8528130Z #209 pymain_run_file (config=0x21e0e50)
2023-01-11T22:11:43.8528506Z     at /croot/python-split_1669298683653/work/build-static/python.c:376
2023-01-11T22:11:43.8530361Z #210 pymain_run_python (exitcode=0x7ffee0a76f70)
2023-01-11T22:11:43.8530938Z     at /croot/python-split_1669298683653/work/build-static/python.c:591
2023-01-11T22:11:43.8531320Z #211 Py_RunMain.localalias ()
2023-01-11T22:11:43.8531633Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:11:43.8545646Z #212 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:11:43.8546024Z     argv=<optimized out>)
2023-01-11T22:11:43.8546568Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:11:43.8551219Z #213 0x00007f68673e5c87 in __libc_start_main (main=0x587be0 <main>, argc=6, 
2023-01-11T22:11:43.8551717Z     argv=0x7ffee0a77178, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:11:43.8552131Z     rtld_fini=<optimized out>, stack_end=0x7ffee0a77168)
2023-01-11T22:11:43.8552403Z     at ../csu/libc-start.c:310
2023-01-11T22:11:43.8552921Z #214 0x0000000000587ade in _start ()
2023-01-11T22:11:43.8553470Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:11:44.0234592Z GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
2023-01-11T22:11:44.0235094Z Copyright (C) 2018 Free Software Foundation, Inc.
2023-01-11T22:11:44.0235632Z License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
2023-01-11T22:11:44.0236211Z This is free software: you are free to change and redistribute it.
2023-01-11T22:11:44.0236688Z There is NO WARRANTY, to the extent permitted by law.  Type "show copying"
2023-01-11T22:11:44.0236946Z and "show warranty" for details.
2023-01-11T22:11:44.0237215Z This GDB was configured as "x86_64-linux-gnu".
2023-01-11T22:11:44.0237477Z Type "show configuration" for configuration details.
2023-01-11T22:11:44.0237729Z For bug reporting instructions, please see:
2023-01-11T22:11:44.0237957Z <http://www.gnu.org/software/gdb/bugs/>.
2023-01-11T22:11:44.0238222Z Find the GDB manual and other documentation resources online at:
2023-01-11T22:11:44.0238496Z <http://www.gnu.org/software/gdb/documentation/>.
2023-01-11T22:11:44.0238706Z For help, type "help".
2023-01-11T22:11:44.0238944Z Type "apropos word" to search for commands related to "word"...
2023-01-11T22:11:44.1370511Z Reading symbols from python...done.
2023-01-11T22:11:44.6663188Z 
2023-01-11T22:11:44.6663716Z warning: core file may not match specified executable file.
2023-01-11T22:11:44.6696445Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:44.6696928Z BFD: warning: /opt/conda/lib/libgomp.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:44.6697288Z [New LWP 23896]
2023-01-11T22:11:44.6697560Z [New LWP 23901]
2023-01-11T22:11:44.6697931Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:44.6698286Z [New LWP 23899]
2023-01-11T22:11:44.6698543Z [New LWP 23898]
2023-01-11T22:11:44.6698824Z [New LWP 23903]
2023-01-11T22:11:44.6699128Z [New LWP 23902]
2023-01-11T22:11:44.6699502Z BFD: warning: /opt/conda/bin/../lib/libstdc++.so.6: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:44.6699849Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:44.6700200Z BFD: warning: /opt/conda/bin/../lib/libgcc_s.so.1: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:44.6700448Z [New LWP 23900]
2023-01-11T22:11:44.6700605Z [New LWP 23904]
2023-01-11T22:11:44.6713180Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:44.6713817Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libgfortran.so.5: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:44.6714577Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
2023-01-11T22:11:44.6715116Z BFD: warning: /opt/conda/lib/python3.10/site-packages/numpy/core/../../../.././libquadmath.so.0: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
2023-01-11T22:11:44.6938885Z [Thread debugging using libthread_db enabled]
2023-01-11T22:11:44.6939271Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
2023-01-11T22:11:51.5233039Z 78	../sysdeps/unix/syscall-template.S: No such file or directory.
2023-01-11T22:11:51.5233608Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
2023-01-11T22:11:51.5234132Z Core was generated by `/opt/conda/bin/python -bb -c from multiprocessing.spawn import spawn_main; spaw'.
2023-01-11T22:11:51.5234473Z Program terminated with signal SIGABRT, Aborted.
2023-01-11T22:11:51.5234809Z #0  0x00007f91fca56177 in kill () at ../sysdeps/unix/syscall-template.S:78
2023-01-11T22:11:51.5235097Z [Current thread is 1 (Thread 0x7f91fd9d4200 (LWP 23896))]
2023-01-11T22:11:51.5273911Z To enable execution of this file add
2023-01-11T22:11:51.5274631Z 	add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit
2023-01-11T22:11:51.5275011Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:51.5275296Z To completely disable this security protection add
2023-01-11T22:11:51.5275568Z 	set auto-load safe-path /
2023-01-11T22:11:51.5275799Z line to your configuration file "/var/lib/jenkins/.gdbinit".
2023-01-11T22:11:51.5276075Z For more information about this security protection see the
2023-01-11T22:11:51.5276437Z "Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
2023-01-11T22:11:51.5276735Z 	info "(gdb)Auto-loading safe path"
2023-01-11T22:11:51.5316157Z #0  0x00007f91fca56177 in kill () at ../sysdeps/unix/syscall-template.S:78
2023-01-11T22:11:51.5318481Z #1  0x00000000004cb0d3 in os_kill_impl (
2023-01-11T22:11:51.5318992Z     module=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5319535Z     signal=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5320085Z     pid=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5320571Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/sys/_iomodule.c:7929
2023-01-11T22:11:51.5324898Z #2  os_kill (module=<optimized out>, args=args@entry=0x7f9156cdabf8, 
2023-01-11T22:11:51.5325379Z     nargs=<optimized out>)
2023-01-11T22:11:51.5325765Z     at /usr/local/src/conda/python-3.10.8/Modules/codecs.c:3581
2023-01-11T22:11:51.5333257Z #3  0x00000000004fe7d4 in cfunction_vectorcall_FASTCALL (func=0x7f91fd9998f0, 
2023-01-11T22:11:51.5333705Z     args=0x7f9156cdabf8, nargsf=<optimized out>, kwnames=<optimized out>)
2023-01-11T22:11:51.5334341Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_bitutils.h:430
2023-01-11T22:11:51.5341673Z #4  0x00000000004f351e in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:51.5342185Z     nargsf=<optimized out>, args=0x7f9156cdabf8, callable=0x7f91fd9998f0, 
2023-01-11T22:11:51.5342633Z     tstate=0x1602d10)
2023-01-11T22:11:51.5342984Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5346615Z #5  PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:51.5347069Z     args=0x7f9156cdabf8, callable=0x7f91fd9998f0)
2023-01-11T22:11:51.5347590Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5350006Z #6  call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:51.5350474Z     pp_stack=<synthetic pointer>, trace_info=0x7ffc9c98dd00, 
2023-01-11T22:11:51.5351101Z     tstate=<optimized out>)
2023-01-11T22:11:51.5351679Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:51.5351980Z #7  _PyEval_EvalFrameDefault ()
2023-01-11T22:11:51.5352460Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4181
2023-01-11T22:11:51.5352938Z #8  0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:51.5353344Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5353709Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5354054Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5354438Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:51.5358886Z #9  _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:51.5359510Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:51.5360123Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:51.5360523Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f9156923890, 
2023-01-11T22:11:51.5360928Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1602d10, 
2023-01-11T22:11:51.5361298Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5361696Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:51.5361938Z #10 _PyFunction_Vectorcall ()
2023-01-11T22:11:51.5362238Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:51.5367165Z #11 0x00000000004f141c in do_call_core (kwdict=0x0, callargs=0x7f91fc74bbe0, 
2023-01-11T22:11:51.5367660Z     func=0x7f9156923880, trace_info=0x7ffc9c98dec0, tstate=<optimized out>)
2023-01-11T22:11:51.5368231Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:51.5368463Z #12 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:51.5368769Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:51.5369503Z #13 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:51.5370209Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5370566Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5370921Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5371308Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:51.5376092Z #14 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:51.5376687Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:51.5377329Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:51.5377731Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f915985a9f0, 
2023-01-11T22:11:51.5378131Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1602d10, 
2023-01-11T22:11:51.5378503Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5378894Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:51.5379132Z #15 _PyFunction_Vectorcall ()
2023-01-11T22:11:51.5379421Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:51.5385041Z #16 0x00000000004f141c in do_call_core (kwdict=0x7f91fc85a7c0, 
2023-01-11T22:11:51.5385356Z     callargs=0x7f915691eac0, func=0x7f915985a9e0, trace_info=0x7ffc9c98e080, 
2023-01-11T22:11:51.5385596Z     tstate=<optimized out>)
2023-01-11T22:11:51.5385905Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5943
2023-01-11T22:11:51.5386154Z #17 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:51.5386461Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4277
2023-01-11T22:11:51.5386910Z #18 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:51.5387295Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5387663Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5388020Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5388387Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:51.5393504Z #19 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:51.5394001Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:51.5394786Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:51.5395185Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f91fc8855b0, 
2023-01-11T22:11:51.5395584Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1602d10, 
2023-01-11T22:11:51.5395968Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5396360Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:51.5396591Z #20 _PyFunction_Vectorcall ()
2023-01-11T22:11:51.5396891Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:51.5402242Z #21 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:51.5402563Z     nargsf=<optimized out>, args=0x6510e28, callable=0x7f91fc8855a0, 
2023-01-11T22:11:51.5402819Z     tstate=0x1602d10)
2023-01-11T22:11:51.5403131Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5407461Z #22 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x6510e28, 
2023-01-11T22:11:51.5407779Z     callable=0x7f91fc8855a0)
2023-01-11T22:11:51.5408102Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5411328Z #23 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:51.5411763Z     pp_stack=<synthetic pointer>, trace_info=0x7ffc9c98e240, 
2023-01-11T22:11:51.5412187Z     tstate=<optimized out>)
2023-01-11T22:11:51.5412740Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:51.5413189Z #24 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:51.5413686Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:51.5414122Z #25 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:51.5414665Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5415310Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5415955Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5416356Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:51.5420673Z #26 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:51.5421267Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:51.5422004Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:51.5422406Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f91fc885f40, 
2023-01-11T22:11:51.5422872Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1602d10, 
2023-01-11T22:11:51.5423249Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5423640Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:51.5423879Z #27 _PyFunction_Vectorcall ()
2023-01-11T22:11:51.5424164Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:51.5429223Z #28 0x00000000004ef101 in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:51.5429721Z     nargsf=<optimized out>, args=0x167f6c0, callable=0x7f91fc885f30, 
2023-01-11T22:11:51.5430103Z     tstate=0x1602d10)
2023-01-11T22:11:51.5430400Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5434059Z #29 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x167f6c0, 
2023-01-11T22:11:51.5434354Z     callable=0x7f91fc885f30)
2023-01-11T22:11:51.5434658Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5437578Z #30 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:51.5437989Z     pp_stack=<synthetic pointer>, trace_info=0x7ffc9c98e400, 
2023-01-11T22:11:51.5438225Z     tstate=<optimized out>)
2023-01-11T22:11:51.5438514Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:51.5438760Z #31 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:51.5439063Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4198
2023-01-11T22:11:51.5439858Z #32 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:51.5440332Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5440999Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5441386Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5441755Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:51.5446983Z #33 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:51.5447703Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:51.5448497Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:51.5449148Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f91fc70ade0, 
2023-01-11T22:11:51.5449650Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1602d10, 
2023-01-11T22:11:51.5450031Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5450421Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:51.5450650Z #34 _PyFunction_Vectorcall ()
2023-01-11T22:11:51.5450950Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:51.5456036Z #35 0x00000000004eecef in _PyObject_VectorcallTstate (kwnames=0x0, 
2023-01-11T22:11:51.5456527Z     nargsf=<optimized out>, args=0x7f91fc9cc200, callable=0x7f91fc70add0, 
2023-01-11T22:11:51.5456950Z     tstate=0x1602d10)
2023-01-11T22:11:51.5457279Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5460848Z #36 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, 
2023-01-11T22:11:51.5461295Z     args=0x7f91fc9cc200, callable=0x7f91fc70add0)
2023-01-11T22:11:51.5461869Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5464480Z #37 call_function (kwnames=0x0, oparg=<optimized out>, 
2023-01-11T22:11:51.5464915Z     pp_stack=<synthetic pointer>, trace_info=0x7ffc9c98e5c0, 
2023-01-11T22:11:51.5465346Z     tstate=<optimized out>)
2023-01-11T22:11:51.5465733Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:51.5465963Z #38 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:51.5466308Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4213
2023-01-11T22:11:51.5466721Z #39 0x00000000004fe5ef in _PyEval_EvalFrame (
2023-01-11T22:11:51.5467279Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5467687Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5468044Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5468421Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5052
2023-01-11T22:11:51.5473041Z #40 _PyEval_Vector (kwnames=<optimized out>, 
2023-01-11T22:11:51.5473610Z     kwnames@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, argcount=<optimized out>, args=<optimized out>, 
2023-01-11T22:11:51.5474341Z     args@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, locals=0x0, 
2023-01-11T22:11:51.5474744Z     locals@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, con=0x7f91fc70ad50, 
2023-01-11T22:11:51.5475144Z     con@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, tstate=0x1602d10, 
2023-01-11T22:11:51.5475513Z     tstate@entry=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5475901Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:51.5476142Z #41 _PyFunction_Vectorcall ()
2023-01-11T22:11:51.5476431Z     at /usr/local/src/conda/python-3.10.8/Include/abstract.h:342
2023-01-11T22:11:51.5480236Z #42 0x00000000004efd83 in _PyObject_VectorcallTstate (kwnames=0x7f91fc9a5d80, 
2023-01-11T22:11:51.5480612Z     nargsf=<optimized out>, args=<optimized out>, callable=0x7f91fc70ad40, 
2023-01-11T22:11:51.5480888Z     tstate=0x1602d10)
2023-01-11T22:11:51.5481273Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5483503Z #43 PyObject_Vectorcall (kwnames=0x7f91fc9a5d80, nargsf=<optimized out>, 
2023-01-11T22:11:51.5483974Z     args=<optimized out>, callable=0x7f91fc70ad40)
2023-01-11T22:11:51.5484555Z     at /usr/local/src/conda/python-3.10.8/Objects/pycore_pyerrors.h:123
2023-01-11T22:11:51.5487067Z #44 call_function (kwnames=0x7f91fc9a5d80, oparg=<optimized out>, 
2023-01-11T22:11:51.5487535Z     pp_stack=<synthetic pointer>, trace_info=0x7ffc9c98e780, 
2023-01-11T22:11:51.5487980Z     tstate=<optimized out>)
2023-01-11T22:11:51.5488523Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5891
2023-01-11T22:11:51.5488912Z #45 _PyEval_EvalFrameDefault ()
2023-01-11T22:11:51.5489335Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:4231
2023-01-11T22:11:51.5490002Z #46 0x0000000000594b72 in _PyEval_EvalFrame (
2023-01-11T22:11:51.5490451Z     throwflag=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5490824Z     f=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>, 
2023-01-11T22:11:51.5491182Z     tstate=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5491639Z     at /croot/python-split_1669298683653/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include/bits/call.c:46
2023-01-11T22:11:51.5491895Z #47 _PyEval_Vector ()
2023-01-11T22:11:51.5492190Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:5065
2023-01-11T22:11:51.5498738Z #48 0x0000000000594ab7 in PyEval_EvalCode (co=co@entry=0x7f91fc968920, 
2023-01-11T22:11:51.5499155Z     globals=globals@entry=0x7f91fc95a5c0, locals=locals@entry=0x7f91fc95a5c0)
2023-01-11T22:11:51.5499501Z     at /usr/local/src/conda/python-3.10.8/Modules/ceval_gil.h:1134
2023-01-11T22:11:51.5501134Z #49 0x00000000005c6e57 in run_eval_code_obj ()
2023-01-11T22:11:51.5501500Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1291
2023-01-11T22:11:51.5503881Z #50 0x00000000005c1d40 in run_mod ()
2023-01-11T22:11:51.5504219Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1312
2023-01-11T22:11:51.5506308Z #51 0x00000000005b9ebb in PyRun_StringFlags.localalias ()
2023-01-11T22:11:51.5506710Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:1183
2023-01-11T22:11:51.5509032Z #52 0x00000000005b9cfb in PyRun_SimpleStringFlags.localalias ()
2023-01-11T22:11:51.5509407Z     at /usr/local/src/conda/python-3.10.8/Objects/clinic/marshal.c.h:503
2023-01-11T22:11:51.5511773Z #53 0x00000000005b8d5c in pymain_run_command (
2023-01-11T22:11:51.5512193Z     command=<error reading variable: dwarf2_find_location_expression: Corrupted DWARF expression.>)
2023-01-11T22:11:51.5512603Z     at /croot/python-split_1669298683653/work/build-static/python.c:252
2023-01-11T22:11:51.5514473Z #54 pymain_run_python (exitcode=0x7ffc9c98e9e0)
2023-01-11T22:11:51.5515063Z     at /croot/python-split_1669298683653/work/build-static/python.c:582
2023-01-11T22:11:51.5515477Z #55 Py_RunMain.localalias ()
2023-01-11T22:11:51.5515789Z     at /croot/python-split_1669298683653/work/build-static/python.c:670
2023-01-11T22:11:51.5531691Z #56 0x0000000000587c29 in Py_BytesMain (argc=<optimized out>, 
2023-01-11T22:11:51.5532005Z     argv=<optimized out>)
2023-01-11T22:11:51.5532322Z     at /croot/python-split_1669298683653/work/build-static/python.c:1090
2023-01-11T22:11:51.5537299Z #57 0x00007f91fca38c87 in __libc_start_main (main=0x587be0 <main>, argc=5, 
2023-01-11T22:11:51.5537662Z     argv=0x7ffc9c98ebe8, init=<optimized out>, fini=<optimized out>, 
2023-01-11T22:11:51.5537973Z     rtld_fini=<optimized out>, stack_end=0x7ffc9c98ebd8)
2023-01-11T22:11:51.5538247Z     at ../csu/libc-start.c:310
2023-01-11T22:11:51.5539321Z #58 0x0000000000587ade in _start ()
2023-01-11T22:11:51.5539625Z     at /usr/local/src/conda/python-3.10.8/Modules/_io/clinic/peg_api.c:880
2023-01-11T22:11:52.3962532Z ##[group]Run set -x
2023-01-11T22:11:52.3962735Z set -x
2023-01-11T22:11:52.3962955Z python3 -m pip install -r requirements.txt
2023-01-11T22:11:52.3963190Z python3 -m pip install boto3==1.19.12
2023-01-11T22:11:52.3963482Z python3 -m tools.stats.print_test_stats --upload-to-s3 --compare-with-s3 test
2023-01-11T22:11:52.3974842Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:11:52.3975049Z env:
2023-01-11T22:11:52.3975224Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:11:52.3975504Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:11:52.3975777Z   AWS_DEFAULT_REGION: us-east-1
2023-01-11T22:11:52.3975959Z   BRANCH: 
2023-01-11T22:11:52.3976131Z   TEST_CONFIG: nogpu_AVX512
2023-01-11T22:11:52.3976312Z   SHARD_NUMBER: 1
2023-01-11T22:11:52.3976534Z   BUILD_ENVIRONMENT: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T22:11:52.3976759Z   PR_NUMBER: 
2023-01-11T22:11:52.3976959Z   PYTORCH_RETRY_TEST_CASES: 1
2023-01-11T22:11:52.3977168Z   PYTORCH_OVERRIDE_FLAKY_SIGNAL: 1
2023-01-11T22:11:52.3977397Z   SHA1: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T22:11:52.3977612Z   TAG: ciflow/trunk/91627
2023-01-11T22:11:52.3977783Z   WORKFLOW_ID: 3896346758
2023-01-11T22:11:52.3978112Z   GITHUB_TOKEN: ***
2023-01-11T22:11:52.3978300Z   GHA_WORKFLOW_JOB_ID: 10589559762
2023-01-11T22:11:52.3978472Z ##[endgroup]
2023-01-11T22:11:52.4002124Z + python3 -m pip install -r requirements.txt
2023-01-11T22:11:52.6045440Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T22:11:52.6320472Z Requirement already satisfied: astunparse in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 2)) (1.6.3)
2023-01-11T22:11:52.6350205Z Requirement already satisfied: expecttest in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 3)) (0.1.4)
2023-01-11T22:11:52.6358046Z Requirement already satisfied: future in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 4)) (0.18.2)
2023-01-11T22:11:52.6366283Z Requirement already satisfied: hypothesis in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 5)) (6.62.0)
2023-01-11T22:11:52.6724962Z Requirement already satisfied: numpy in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 6)) (1.21.6)
2023-01-11T22:11:52.6733907Z Requirement already satisfied: psutil in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 7)) (5.9.1)
2023-01-11T22:11:52.6808378Z Requirement already satisfied: pyyaml in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 8)) (6.0)
2023-01-11T22:11:52.6816347Z Requirement already satisfied: requests in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 9)) (2.26.0)
2023-01-11T22:11:52.7026015Z Requirement already satisfied: setuptools in /usr/lib/python3.7/site-packages (from -r requirements.txt (line 10)) (49.1.3)
2023-01-11T22:11:52.7189335Z Requirement already satisfied: six in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 11)) (1.16.0)
2023-01-11T22:11:52.7197507Z Requirement already satisfied: types-dataclasses in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 12)) (0.6.6)
2023-01-11T22:11:52.7203235Z Requirement already satisfied: typing_extensions in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 13)) (4.4.0)
2023-01-11T22:11:52.7213056Z Requirement already satisfied: sympy in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 14)) (1.10.1)
2023-01-11T22:11:52.7231299Z Requirement already satisfied: filelock in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 15)) (3.9.0)
2023-01-11T22:11:52.7300595Z Requirement already satisfied: networkx in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 16)) (2.6.3)
2023-01-11T22:11:52.7455794Z Requirement already satisfied: jinja2 in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 17)) (3.1.2)
2023-01-11T22:11:52.7479876Z Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from astunparse->-r requirements.txt (line 2)) (0.38.4)
2023-01-11T22:11:52.7495695Z Requirement already satisfied: exceptiongroup>=1.0.0; python_version < "3.11" in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (1.1.0)
2023-01-11T22:11:52.7515414Z Requirement already satisfied: attrs>=19.2.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (22.2.0)
2023-01-11T22:11:52.7760133Z Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (2.4.0)
2023-01-11T22:11:52.7770572Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (2022.12.7)
2023-01-11T22:11:52.7779385Z Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (2.0.12)
2023-01-11T22:11:52.7799535Z Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (3.4)
2023-01-11T22:11:52.7810882Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (1.26.14)
2023-01-11T22:11:52.7959352Z Requirement already satisfied: mpmath>=0.19 in /home/ec2-user/.local/lib/python3.7/site-packages (from sympy->-r requirements.txt (line 14)) (1.2.1)
2023-01-11T22:11:52.8012832Z Requirement already satisfied: MarkupSafe>=2.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from jinja2->-r requirements.txt (line 17)) (2.1.1)
2023-01-11T22:11:52.8553422Z + python3 -m pip install boto3==1.19.12
2023-01-11T22:11:53.0616320Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T22:11:53.0793895Z Requirement already satisfied: boto3==1.19.12 in /home/ec2-user/.local/lib/python3.7/site-packages (1.19.12)
2023-01-11T22:11:53.0842802Z Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (0.5.2)
2023-01-11T22:11:53.0869526Z Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (0.10.0)
2023-01-11T22:11:53.0881827Z Requirement already satisfied: botocore<1.23.0,>=1.22.12 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (1.22.12)
2023-01-11T22:11:53.0929950Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /home/ec2-user/.local/lib/python3.7/site-packages (from botocore<1.23.0,>=1.22.12->boto3==1.19.12) (1.26.14)
2023-01-11T22:11:53.1081002Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from botocore<1.23.0,>=1.22.12->boto3==1.19.12) (2.8.2)
2023-01-11T22:11:53.1100811Z Requirement already satisfied: six>=1.5 in /home/ec2-user/.local/lib/python3.7/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.23.0,>=1.22.12->boto3==1.19.12) (1.16.0)
2023-01-11T22:11:53.2889960Z + python3 -m tools.stats.print_test_stats --upload-to-s3 --compare-with-s3 test
2023-01-11T22:15:10.5449450Z [scribe] Scribe access token not provided, sending report via boto3...
2023-01-11T22:15:10.5450429Z ERROR ENCOUNTERED WHEN UPLOADING TO SCRIBE: {"errorMessage":"2023-01-11T22:14:57.883Z 60f45355-9709-4d86-8456-d9f171b3045f Task timed out after 60.03 seconds"}
2023-01-11T22:15:10.5450800Z 
2023-01-11T22:15:10.5450973Z ----- Historic stats comparison result ------
2023-01-11T22:15:10.5451364Z 
2023-01-11T22:15:10.5451537Z     job: linux-bionic-cuda11.7-py3.10-gcc7
2023-01-11T22:15:10.5451808Z     commit: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T22:15:10.5455018Z 
2023-01-11T22:15:10.5455375Z Commit graph (base is most recent master ancestor with at least one S3 report):
2023-01-11T22:15:10.5455711Z 
2023-01-11T22:15:10.5455815Z     : (master)
2023-01-11T22:15:10.5456102Z     |
2023-01-11T22:15:10.5456418Z     | * 8419ddda87 (HEAD)            total time  3441.34s
2023-01-11T22:15:10.5456689Z     | |
2023-01-11T22:15:10.5456916Z     | : (2 commits)
2023-01-11T22:15:10.5457192Z     |/
2023-01-11T22:15:10.5458665Z     * db2a237763 (base)  11 reports, total time  4966.48s ± 3495.23s
2023-01-11T22:15:10.5459027Z     * 2b0abd4ce3         11 reports, total time  4990.75s ± 3463.36s
2023-01-11T22:15:10.5459353Z     * f7939b21e1         33 reports, total time  3500.97s ± 3537.57s
2023-01-11T22:15:10.5459661Z     * cb3204823e         11 reports, total time  4951.99s ± 3458.09s
2023-01-11T22:15:10.5459975Z     * 6e236553f5         11 reports, total time  4964.39s ± 3513.19s
2023-01-11T22:15:10.5461222Z     * cce577b391         11 reports, total time  4938.35s ± 3358.29s
2023-01-11T22:15:10.5461540Z     * fae821c2f1         11 reports, total time  4751.06s ± 3169.39s
2023-01-11T22:15:10.5461837Z     * 0c3659586d         11 reports, total time  4713.33s ± 3185.77s
2023-01-11T22:15:10.5462144Z     * 122245985a         11 reports, total time  4767.91s ± 3184.40s
2023-01-11T22:15:10.5462521Z     * b797a24259         11 reports, total time  4784.26s ± 3253.10s
2023-01-11T22:15:10.5462879Z     |
2023-01-11T22:15:10.5463017Z     :
2023-01-11T22:15:10.5463112Z 
2023-01-11T22:15:10.5463231Z Removed  (across  859 suites)     0 tests, totaling     0.00s
2023-01-11T22:15:10.5463494Z Modified (across    0 suites)     0 tests, totaling     0.00s
2023-01-11T22:15:10.5463740Z Added    (across  682 suites) 17743 tests, totaling +3441.34s
2023-01-11T22:15:10.6232739Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2023-01-11T22:15:10.6232979Z with:
2023-01-11T22:15:10.6233120Z env:
2023-01-11T22:15:10.6233291Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:15:10.6233571Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:15:10.6233820Z ##[endgroup]
2023-01-11T22:15:10.6246893Z ##[group]Run set -eou pipefail
2023-01-11T22:15:10.6247107Z set -eou pipefail
2023-01-11T22:15:10.6247289Z 
2023-01-11T22:15:10.6247523Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2023-01-11T22:15:10.6247779Z for _ in $(seq 1440); do
2023-01-11T22:15:10.6248002Z     # Break if no ssh session exists anymore
2023-01-11T22:15:10.6248220Z     if [ "$(who)" = "" ]; then
2023-01-11T22:15:10.6248402Z       break
2023-01-11T22:15:10.6248553Z     fi
2023-01-11T22:15:10.6248752Z     echo "."
2023-01-11T22:15:10.6248911Z     sleep 5
2023-01-11T22:15:10.6249305Z done
2023-01-11T22:15:10.6260326Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:15:10.6260544Z env:
2023-01-11T22:15:10.6260725Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:15:10.6260993Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:15:10.6261255Z ##[endgroup]
2023-01-11T22:15:10.6284615Z Holding runner for 2 hours until all ssh sessions have logged out
2023-01-11T22:15:10.6378631Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2023-01-11T22:15:10.6378935Z # ignore expansion of "docker ps -q" since it could be empty
2023-01-11T22:15:10.6379183Z # shellcheck disable=SC2046
2023-01-11T22:15:10.6379406Z docker stop $(docker ps -q) || true
2023-01-11T22:15:10.6379635Z # Prune all of the docker images
2023-01-11T22:15:10.6379835Z docker system prune -af
2023-01-11T22:15:10.6393949Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:15:10.6394317Z env:
2023-01-11T22:15:10.6394601Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:15:10.6395068Z   DOCKER_CONTAINER_ID: ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:15:10.6395489Z ##[endgroup]
2023-01-11T22:15:11.0243476Z ecb438e252c9
2023-01-11T22:15:13.6547321Z Deleted Containers:
2023-01-11T22:15:13.6547864Z ecb438e252c9ff87c91c59eac3830e1515fed1d74edeb61f248247e1289e6ed4
2023-01-11T22:15:13.6548046Z 
2023-01-11T22:15:20.3592039Z Deleted Images:
2023-01-11T22:15:20.3593017Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T22:15:20.3594179Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7@sha256:0da23f4faf0ce20770149c4a783e08eaa91c07112511dc5511c77937c66edb24
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